Remote Sensing (RS)
Hosein Nesari; Reza Shah-Hosseini; Amirreza Goodarzi; Soheil Sobhan Ardakani; Saeed Farzaneh
Abstract
Extended Abstract
Introduction
Atmospheric aerosols are a colloid of solid particles or liquid droplets suspended in the atmosphere. Their diameter is between 10-2 to 10-3 micrometers. They directly and indirectly affect the global climate by absorbing and scattering solar radiation, and they also ...
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Extended Abstract
Introduction
Atmospheric aerosols are a colloid of solid particles or liquid droplets suspended in the atmosphere. Their diameter is between 10-2 to 10-3 micrometers. They directly and indirectly affect the global climate by absorbing and scattering solar radiation, and they also have a serious impact on human health by emitting harmful substances. In addition, high concentrations of aerosols on a local scale due to natural or human activities have adverse effects on human health, including cancers, pulmonary inflammation, and cardiopulmonary mortality. Monitoring the temporal and spatial variability of high concentrations of aerosols requires regular measurement of their optical properties such as aerosol optical depth (AOD).
Materials & Methods
Algeria is a large country with little knowledge of the spatial and temporal diversity of AOD, and the low spatial resolution of existing products makes it very difficult to predict aerosols (airborne particles) at the local scale, especially in arid southern regions. As a result, AOD recovery with data with higher spatial resolution is crucial for determining air pollution and air quality information. Several AERONET stations have been installed in Algeria. The Tamanrasset_INM station has been selected based on its location and the availability of historical AOD data for the period (2015-2016).
In this study, Landsat-8 / OLI image from tile 192/44 was used for satellite images. To this end, 23 TOA-corrected L1G-level Landsat-8 / OLI cloudless scenes were downloaded from January 2015 to December 2016 in the study area. DN values are converted to TOA reflections using the scaling factor coefficients in the OLI Landsat-8 metadata file. In this study, the minimum monthly reflectance technique was used to recover AOD in this area. As a result, LSR images were used in the recovery process in different months of 2015 and 2016. The process of selecting reference LSRs was initially based on the selection of clear, foggy / cloudless sky images. The selected images were then used to construct artificial images in which each pixel corresponds to the second lowest surface reflection of all selected monthly images to be the LSR pixel for the respective month. The AOD retrieval method developed in this study is based on a LUT, using the 6S radiative transfer model. The advantage of using the 6S model is its ability to estimate direct components and scattering using a limited number of inputs for each spectral band in the entire solar domain. The effect of the viewing angle is limited because Landsat data are usually obtained with a fixed viewing angle. Surface reflectance can be estimated from a pre-calculated LSR database. The accuracy of AOD recovery depends on the use of the appropriate aerosol model. A continental model was selected from the available aerosol models. Other atmospheric parameters such as ozone, carbon dioxide, carbon monoxide and water vapor are considered by default. The AOD values used to make LUT are set as follows: 0.0, 0.05, 0.1, 1.5, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2 and 1.5. The zenith angles of the sun and the sensor range from 0 to 70 degrees with a step of 5 degrees and the range of azimuth angles from 0 to 180 degrees with a step of 12 degrees. Using these parameters, the radiative transfer equation was run in forward to obtain the TOA reflection. Different combinations of input and TOA output parameters are stored in LUT. AOD retrieval is based on a comparison between the TOAs estimated with the model and the observed items using the best fit approach. Using such an approach, the estimated AODs are simulated in accordance with those used in the production of TOAs, using a competency function that minimizes the distance.
Results & Discussion
In this study, the AODs recovered at 550 nm in a 5-by-5-pixel window around the AERONET site were averaged. The considered AERONET values are the average of all measurements taken within ± 30 minutes of image acquisition time. Observation regression results (AOD from Landsat 8 images and AERONET stations) showed that the correlation coefficient is about 84%. This study shows a good fit of the model on the research data and shows the high capability of the model. This study showed a strong recovery of AOD against AERONET data of more than 70% at . The differences can be attributed to a limited number of points or hypotheses related to the aerosol model used in this study. The assumption of using a pre-calculated LSR does not limit the accuracy of this method because we have shown that in arid regions where the change in land cover in different months of the year is small, a pre-calculated LSR image can be representation used the share of surface reflection in the radiative transfer model throughout the month.
Conclusion
In this study, an AOD derived from a high-resolution satellite at an urban scale was produced in the city of Tamanrasset, Algeria. The developed method assumes that the change in land cover is minimal and the temporal change in LSR is not significant. A pre-calculated LSR image is created to show the surface reflection in the retrieval process. Based on the 6S radiative transfer model, an LUT was constructed to simulate the TOA reflection of the built-in LSRs and a set of geometric and atmospheric parameters. The retrieved AODs were compared with the AERONET ground data. The results show that this approach can achieve reasonable accuracy in AOD recovery, which reaches about 70.9% at . In addition, this approach is suitable for estimating AOD in urban areas compared to existing AOD products with low spatial resolution. The results of this study show a 4% improvement compared to the results of Omari et al. (2019). The results of this study showed that ignoring the monthly changes in LSR values leads to good results in AOD recovery.
Remote Sensing (RS)
Somayeh Aslani Katouli; Reza Shah-Hosseini; Hamid Bagheri
Abstract
Extended Abstract
Introduction
A flood is a widespread and dramatic natural disaster that affects the life, infrastructure, economy, and local ecosystems of the world. In this paper, a method for flood detection in urban (and suburban) environments using the intensity and coherence of SAR based on ...
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Extended Abstract
Introduction
A flood is a widespread and dramatic natural disaster that affects the life, infrastructure, economy, and local ecosystems of the world. In this paper, a method for flood detection in urban (and suburban) environments using the intensity and coherence of SAR based on a convolutional neural network is introduced, and from the time series of SAR intensity and coherence to draw flood without obstruction (e.g. Flooded bare soils and short vegetation) are used. Non-cohesive areas blocked by floods (e.g., flooded vegetation) and cohesive areas with flood-blocked areas (e.g., frequently constructed flooded areas) are distinguished.
This method is flexible according to the time period of the data sequences (at least one pair of pre-event and event intensities and one pair of pre-event and in-event coherence are required). The increasing number of SAR missions in orbit that have a fixed viewing scenario with a short retry time increases the chances of seeing a flood event, while also having a good pre-event scene achieved by the same sensor. This makes this method desirable for operational emergency responses.
Materials & Methods
CNN algorithm is a multilayer perceptron that is designed to identify two-dimensional information of images and includes: input layer, convolution layer, sample layer, and output layer. The CNN algorithm has two main processes: collection and sampling.
The convolution process involves the use of a trainable Fx filter, deconvolution of the input image (the first step of image input, input after image convolution, is the feature of each layer called Feature Map), then by adding bx can be hand convolution of the CX layer Found. Sampling process: n pixels are collected from each neighborhood to form a pixel, then weighted with a scalar weight of Wx + 1 and a bx + 1 bias is added, then a map of The Narrow n times feature map properties are generated.
Three images of Sentinel-1A VV polarization, wide width interference (IW), and mode (SLC) data were used in this study. Intensity images were pre-processed with radiometric calibration, noise reduced with a spell-filter (window size 5.5 pixels), and converted from linear units to decibels. Coherent images were obtained with a pair of consecutive images with a window of 7.28 (range _ azimuth). Validation data set due to the lack of other data in two separate sections of ground data in the urban area of GonbadKavous that have been collected to identify homes damaged by floods and terrestrial reality data from gamma image thresholds for output validation were extracted.
Results & Discussion
In this section, the results of the study are qualitatively and quantitatively analyzed. Because the simultaneous display of SAR data over time in the form of RGB compounds is widely used in the qualitative interpretation of land cover and surface dynamics, RGB compounds are used to provide evidence of flood magnitude in terms of intensity and coherence. For both cases, the results of combining intensity and coherence and intensity alone and coherence alone are quantitatively analyzed. Overall accuracy (OA), kappa correlation coefficient, false-positive rate (FPR), precision (e.g., correctly predicted positive patterns out of the total predicted patterns in a positive class), recall (e.g., a fraction of properly classified positive patterns), and an F1 score (ie the harmonic mean between precision and recall). Flood reference and ground data are mentioned and reported based on the reference.
Conclusion
In this paper, a method for mapping floods in urban environments based on SAR intensity and interferometry coherence was introduced. A combination of intensity and coherence extracts flood information in different types of land cover and outlet. This method was tested on the KavousGonbad flood incident obtained by various SAR sensors and the flood maps were confirmed by the flood reference resulting from thresholding and ground harvesting and satisfactory results were shown in this case study. The findings of this experiment show that the shared use of SAR intensity and coherence provides more reliable information than the use of SAR intensity and coherence alone in urban areas with different landscapes. In particular, flood detection in less cohesive / non-cohesive areas (e.g., bare soils, vegetation, vegetated areas) relies heavily on multi-temporality, while multi-temporal coherence provides more comprehensive flood information in areas Create coherence (e.g., mostly built-up areas). However, some flood-specific situations, such as flooded parking lots and flooded dense building blocks, are still challenging in terms of intensity and coherence. Also, since the proposed method is sensor and scene independent, with very frequent and regular observations of SAR missions such as Sentinel-1 and RADARSAT (RCM), there are opportunities to map global floods on a global scale, especially in small countries. Provides income.
Extraction, processing, production and display of geographic data
Heshmat Karami; Zahra Sayadi
Abstract
Extended Abstract
Introduction
Environmental changes are one of the most critical challenges to achieving sustainable development. Wetlands are part of the earth's structure and as one of the important ecosystems consisting of water, vegetation, soil and microorganisms. Monitoring, management and assistance ...
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Extended Abstract
Introduction
Environmental changes are one of the most critical challenges to achieving sustainable development. Wetlands are part of the earth's structure and as one of the important ecosystems consisting of water, vegetation, soil and microorganisms. Monitoring, management and assistance in decision-making and policy-making of surface water changes can be done according to the availability of satellite data. The availability of Landsat data helps a lot in preparing a high-quality map to show the land surface changes. Although remote sensing is superior to traditional methods in terms of time, speed, and cost, these methods require the use of powerful and practical systems that include complex analysis. The use of data and images on the web is a solution that can be used to solve the mentioned problem, which studies can be done with high accuracy and speed without the need for a strong hardware and software system. The Google Earth Engine system creates suitable conditions for processing satellite images for environmental monitoring and analysis. The purpose of this research is to monitor the dynamic changes in the Miangaran wetland sub-basin in the period (2013-2022).
Materials & Methods
Miangaran wetland with an average area of 2500 hectares is located at a distance of one and a half kilometers from Izeh city, in the northeast of Khuzestan province. Time series analysis is one of the most common operations in remote sensing that helps to understand and model seasonal patterns as well as monitor changes. In this research, 421 images from the ee.ImageCollection ("LANDSAT/LC08/C02/T1_L2") data set were used for the period from 2013 to 2022. The construction of a harmonic model was used in this research due to its flexibility in cyclic calculation with simple and repeatable forms. The normalized differential water index is an index for drawing and monitoring content changes in surface waters. Also, the Normalized Difference Vegetation Index (NDVI) is one of the most common remote sensing indices. Harmonic time series of water body and vegetation cover were extracted using NDWI and NDVI indices in Google Earth Engine platform, and Mann-Kendall's non-parametric test was performed using time series data output with XLSTAT extension in Excel software. Finally, global water data was used to confirm and complete the results of time series analysis.
Results, discussion and conclusion
The results of the harmonic time series of the water body showed a decreasing and negative trend and more changes in the sub-basin. Kendall's statistical test confirmed the decreasing and negative trend of the water body. Accordingly, since the calculated p-value (<0.0001) is lower than the alpha significance level (0.05), the null hypothesis should be rejected and its alternative hypothesis, the existence of a trend in the time series, should be accepted. The value of Kendall's tau also confirmed a negative value (-0.245) and a decrease. Due to the negative sen's slope statistic for the water area (-0.002), changes are more in the Miangaran Wetland sub-basin. The results of the Mann-Kendall test for the observed vegetation data showed the absence of a trend in the harmonic time series. Since the calculated p-value (0.064) is higher than the significance level of alpha (0.05), the null hypothesis (absence of trend) cannot be rejected. The risk of rejecting the null hypothesis (while true) is 43.6%. Kendall's tau statistic showed a negative value (-0.060) and a non-significant decrease. Therefore, accepting the null hypothesis (absence of trend) indicates that vegetation changes in the harmonic time series were not significantly different from each other. Also, the negative sen's slope statistic for vegetation (-0.026) indicates more changes in the sub-basin of Miangaran Wetland. By comparing with the results and analysis of other researches, it seems that human intervention and change of land use can be the cause of the lack of trend in the Miangaran Wetland sub-basin. Also, according to the negative value of Man-Kendall's vegetation cover which showed a non-significant decreasing trend, it seems that climate change and drought have also played a major role in the changes under the Miangaran wetland basin. The study of the global water data also showed that the water occurrence in terms of space-time is decreasing and the intensity of the change of water occurrence is critical under the basin of Miangaran wetland. The marginal parts of Miangaran Wetland show seasonal water loss, most of these changes occur during the period. This research confirmed the use of harmonic time series in monitoring wetland dynamic changes. Finally, the allocation of water rights, the establishment of laws and the determination of the limit of the ecological bed, and the use of Google Earth Engine capabilities to monitor environmental changes (use, temperature, precipitation, evaporation, etc.) of the Miangaran Wetland sub-basin were suggested.
Remote Sensing (RS)
Kolsoom Shokrilahizadeh; Hamed Naghavi; Morteza Ghobadi; Rahim Maleknia
Abstract
Extended Abstract
Introduction:
Urban green spaces constitute a pivotal component of urban ecosystems, offering a plethora of ecological benefits and services to cities. Augmenting these green patches within urban landscapes and establishing interconnected ecological networks therein represent viable ...
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Extended Abstract
Introduction:
Urban green spaces constitute a pivotal component of urban ecosystems, offering a plethora of ecological benefits and services to cities. Augmenting these green patches within urban landscapes and establishing interconnected ecological networks therein represent viable strategies to mitigate the adverse repercussions of inadequate urban development while bolstering urban environment resilience. In the past few decades, the landscape ecology paradigm has introduced innovative methodologies aimed at comprehending the intricacies of urban green space dynamics and how landscape configurations wield influence over the environmental processes within cities. This research, consequently, sets out with the intention of quantitatively assessing and dissecting the transformations transpiring within Khorramabad's urban green spaces. It does so by harnessing remote sensing data and leveraging landscape metrics to gain deeper insights into the urban landscape's evolution.
Materials & Methods:
The focus of this research centers on Khorramabad city, which serves as the capital of Lorestan province and holds the distinction of being the province's largest city in terms of both population and geographical expanse. Municipally zoned into three distinct regions, the study unfolds across two main phases. Initially, the endeavor involved the creation of comprehensive synoptic maps capturing Khorramabad city's green spaces. This process relied on satellite imagery, followed by a subsequent phase of scrutinizing these maps through the application of landscape metrics.
To execute this, satellite images from various sensors—namely TM, ETM+, and OLI on Landsat 5, 7, and 8 satellites—were harnessed for the years 1987, 2003, and 2019, respectively. These images underwent meticulous preprocessing, culminating in their classification using the maximum likelihood method within the ENVI software environment. To validate the accuracy of the resultant maps, an error matrix was employed. In order to model the quantitative alterations and patterns within Khorramabad's urban green spaces, landscape metrics were harnessed. Notably, the Fragstat software facilitated the analysis of selected landscape metrics, which encompassed four key measures: class area (CA), number of patches (NP), percent of landscape (PLAND), and mean Euclidean nearest neighbor distance (ENN-MN).
Results:
The analysis of spatial-temporal changes in Khorramabad city's green spaces reveals an evident declining trend in their overall pattern. The outcomes underscore a substantial reduction both in the quantity of green patches and the area they encompass, dwindling from 703.8 hectares in 1987 to 629.88 hectares in 2019. Additionally, the investigation into landscape metrics' composition and distribution underscores an absence of cohesive dispersion on the city-wide scale. Within Khorramabad city, regions 1 and 3 exhibit inadequate green space composition and distribution. The computed metric for Class Area (CA) reflects a decrease from 195.66 hectares in 1987 to 191.63 hectares in 2003, further diminishing to 170.145 hectares by 2019. Correspondingly, the metric for Number of Patches (NP) indicates the lowest count of patches (33) in 1987, which escalated to 122 patches in 2003, and ultimately reaching 183 patches by 2019. Moreover, Proportion of Landscape (PLAND) data highlights that regions 3 and 2 demonstrate the highest (19.45%) and lowest (7.18%) green area proportions, respectively. Notably, the PLAND metric underwent modification from 229.81 meters in 1987 to 88.47 meters in 2003, further diminishing to 78.65 meters in 2019. The findings underscore deficiencies in Khorramabad city's urban green spaces, indicating a lack of favorable conditions for their development.
Conclusion:
The research conducted an assessment of urban green spaces within the urban areas of Khorramabad, utilizing remote sensing data and landscape metrics. The findings indicated a consistent downward trend in the overall extent of green spaces in Khorramabad city over various years. The distribution of green patches within the city was deemed relatively inappropriate, lacking an optimal arrangement. To enhance the status of green spaces, there is a need to establish continuity between discrete green patches and smaller green areas. This study underscores the significance of prioritizing sustainable management for Khorramabad's urban green space, aiming to prevent its degradation. The study's limitation lies in its reliance on medium-resolution Landsat image data. Overcoming this constraint through the incorporation of high-resolution data holds promise, particularly for fragmented green spaces in urban areas.
Remote Sensing (RS)
Nastaran Nazariani; Asghar Fallah; Hava Hasanvand; Hassan Akbari
Abstract
Extended Abstract
Introduction
The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable ...
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Extended Abstract
Introduction
The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable and environmentally friendly method to quickly identify and investigate the distribution of heavy metals in soil and thus identify suspected contaminated areas (Scheuber & Köhl, 2003:33). Remote sensing is one of the ways that can provide a cost-effective and quick solution to investigate the distribution of heavy metals on a large scale using spectroscopic techniques (Bi et al., 2009:16). Habibi et al. (2023:4) also measured and evaluated the concentration of heavy metals in the aerial parts and soil of the tree species of Bandar Abbas city and also identified the species that has the highest potential for absorbing heavy metals. The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn>Zn>Pb>Cd. (Nikolaevich, 2023:30) they addressed the modeling of heavy metal pollution in Central Russia based on satellite images and machine learning. Al, Fe, and Sb contamination were predicted for 3000 and 12100 grid nodes in an area of 500 km2 for the Central Russian region for 2019 and 2020. Estimating the amount of this pollution requires time and high cost. Considering the traffic on the Aleshtar -Khorramabad highway near Kakareza forests and the effect of heavy metal concentration in the soil and leaves of the oak species which can be caused by natural and human pollution, the accumulation of heavy metals in the species Iranian oak is a serious threat to this forest. Therefore, it is necessary to study and discuss pollutants and their effects on the environmental cycle. In this regard, considering the cost and time-consuming nature of traditional methods and since remote sensing methods are a suitable complement to traditional methods; the aim of the present research is to use remote sensing techniques and spectral analyses to evaluate and model the accumulation of heavy metals in Iranian oak species.
Materials and Methods
The present study is located on the road of Aleshtar -Khorramabad, 20 kilometers northwest of Khorramabad. For this purpose, five transects were created at distances adjacent to the road, 500 and 1000 meters on both sides of the road, and 10 x 10 m sample pieces were planted. Inside the sample plots, 30 soil samples were randomly collected and 30 leaf samples were collected from trees in all directions of the crown. To extract heavy metals from soil samples and plant samples, the acid digestion method was used and the physical characteristics of the soil were measured using standard methods. After preparing the samples, the concentration of Pb, Cu, and zinc heavy metals in soil and leaves was measured and the index of biological concentration of heavy metals from soil to leaves was calculated. Then the relationship between the concentration of heavy elements measured and the reflectance in different bands or band ratios at the corresponding sampling points was obtained. Non-parametric methods and generalized multiple linear regression models were used in order to model quantitative variables and spectral values corresponding to sample parts in satellite data. ArcGIS software was used to implement sample parts on the image, ENVI software was used for image processing, and STATISTICA software was used for modeling.
Results and Discussion
Cu and Pb in Iranian oak leaves had significant differences at different distances at the 0.05 level, but Cu did not have significant differences at different distances at the 0.05 level. Cu and Pb did not have significant differences in different soil intervals at the 0.05 level, but Cu had significant differences in different soil intervals at the 0.05 level. The bioconcentration factor was obtained as (0.2, 0.5, 0.2) mg/kg. The study of modeling of non-parametric methods using Sentinel-2 satellite data showed that the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for the three metals Cu, Pb, and Cu, respectively. The artificial neural network (ANN) algorithm obtained the highest accuracy. Also, according to the results of the random forest algorithm, for the three mentioned metals, PSRI, HMSSI, and PSRI indices are the most important in modeling.
Based on the findings, the concentration values of Cu and zinc were significantly different at different distances, but the Cu values were not significantly different at different distances. In this regard, Mansour concluded in 2014 that there is a significant difference between the concentration of Cu and zinc in the leaves of the species, which can be attributed to traffic density and human activities, and the high amount of zinc metal in this study is the wear of car tires؛ and stated that the concentration of Cu is caused by the production of greenhouse gases and the use of vehicles using Cu gasoline. Based on the findings, the values of Cu and zinc concentrations at different distances did not have significant differences, but the Cu values had significant differences at different distances. Sources of input of Cu element to the soil are urban, industrial, and agricultural waste, fertilizers, and chemicals that add it to the soil through liquid, solid, or mineral fertilizers. These findings are with the results of some researchers including Wu and colleagues (2010:38), Botsou et al. (2016:17) are consistent. Based on the findings obtained from the calculation of the bioconcentration index and their comparison with the classification proposed by Ma et al. (2001:25) for Iranian oak species plants in relation to Cu, zinc, and Cu metals from soil to leaves, it acts as an accumulating plant. In accordance with the results of this research, in the study of Khodakarmi et al. (2009:15), Iranian oak was included in the category of superabsorbent plants in relation to the accumulation of Cu pollutants, which has a high capacity in terms of root absorption. Also, Madejón et al. (2006:25) stated that oak leaves are more resistant than olive leaves. The concentrations of elements in leaves and fruits decrease with time and the risk of toxicity in the food web is reduced. The review and comparison of five algorithms showed that (ANN) the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for three metals, Cu, Zn, and Cu, respectively. Considering the importance of the PSRI synthetic band in increasing the accuracy of modeling with satellite images and the influence of the visible and near-infrared bands, the amount of reflection measured by the spectroscopic method showed that with the increase in the concentration of heavy elements, the amount of reflection in the visible and infrared range decreases (Liu et al., 2011:24).
Conclusion
The results showed that Sentinel-2 images along with artificial intelligence techniques have a relatively good ability to model the level of biological pollution index in the region. In line with the obtained results, it is suggested that the Iranian oak species is used to reduce pollution on highways because it accumulates heavy metals.
Remote Sensing (RS)
Samaneh Bagheri; Mahmoud Soorghali; Hassan Emami
Abstract
Extended Abstract
1-Introduction
Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems ...
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Extended Abstract
1-Introduction
Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems is utilized to evaluate changes and create thematic maps for monitoring diverse plant cover. Today, RS indices are widely used in research projects in specialized fields, such as vegetation health, stress assessment, plant development rate, and plant greenness, to evaluate vegetation health, stress types, and plant illnesses. Hyperspectral imagery, particularly from the red and near-infrared bands in the electromagnetic spectrum (690-740 nm), has been widely used to derive vegetation indices. This project intends to monitor the forest risk regions of a segment of northern Iran's forests in 2020 using a combination of various indices produced by RS data and a geographic information system (GIS). Prisma hyperspectral images were used to assess the health of forests in Northern Iran's Rudsar, Ramsar, and Tonkabon forests, focusing on water stress, insufficient growth, plant pests, diseases, and greenness. Forest areas are divided into five risk-acceptance regions using RS indices, and the data is analyzed using various GIS weighting methods to determine the remaining dangerous forest regions.
2- Methodology
The study utilized twelve plant indices from three categories (greenness, growth, leaf pigments, and leaf surface moisture) and four other individual vegetation indices using various techniques. Based on this, the study selected sixteen forest risk-taking maps from five classes with varying risk-taking potential, weighted the layers using hierarchical analysis, and generated a final map based on the obtained weights. When the average results of combined and individual indices were compared with the classification map, it was discovered that the combined indices were more accurate than the individual indices. Existing composite indices are categorized into three broad groups: plant greenness, leaf pigment, and productivity of water or light usage in the vegetation canopy. The three primary characteristics each possess multiple indices that can be combined to provide crucial insights into forest health.
3- Results and discussion
The study reveals that when combined with appropriate indices, combined indices can provide high accuracy in the risk assessment of forest areas in the north of the country. In contrast, an incorrect combination can result in low-accuracy outcomes. The study found that the combined indices had a 11% error in two high-risk forest areas, while individual indices had a nearly double error of 21%. The use of composite indices significantly reduces the inaccuracy of calculating forest risk regions by 50% and enhances the accuracy of monitoring these areas. Furthermore, when the combined indices were examined independently, the findings revealed that the combination of the VCN and VCNW indices yielded the maximum accuracy. These compounds are highly effective in assessing the health of vegetation, assessing plant stress, and determining plant water content. On the other hand, the combined indexes from RC were less accurate than the previous combination, with the highest accuracy levels being SIPI, NDII, NDWI, and WBI. These synthetic substances are utilized in the fields of plant health and stress assessment. The accuracy of SIPI, NDII, NDWI, WBI1, PRI1, and RGRI is significantly reduced when combined with the NC index. The combination's low accuracy may be due to the NDVI index's limitations, as it is primarily used to detect vegetation presence or absence and does not detect plant health or stress. The study presents the first results from research on plant stress in northern Iranian forests using Prisma hyperspectral data. Hyperspectral data is chosen for its superior spatial, spectral, and radiometric resolution, making it ideal for studying dynamic ecosystems in the current research region. Hyperspectral RS allows for non-destructive monitoring of leaf pigments like chlorophyll, carotenoids, and anthocyanin content, crucial indicators of vegetation health. Therefore, the recommendation is to employ a combination of indices with diverse approaches in hyperspectral images instead of using individual indices for monitoring vegetation usage.
4- Conclusion:
Forest health monitoring is a crucial aspect of forest management programs, and utilizing RS techniques and data can be highly beneficial in this field. The study compared the accuracy of combined indices and individual indices using the classification map, revealing that combined indices were more precise. In addition, the results showed that in almost two high-risk classes of the forest area, the combined indicators have an error of 11% and the individual indicators have an error of almost twice their error, 21%. Therefore, composite indices significantly reduce forest risk area estimation errors by 50% and improve accuracy. Therefore, it's recommended to use a combination of indices with different approaches in hyperspectral images instead of individual indices for monitoring vegetation usage.
Remote Sensing (RS)
Mohamad Fathollahzadeh; Mojtaba Yamani; Abolghasem Goorabi; Mehran Maghsoudi; Mernoosh Ghadimi
Abstract
Extended Abstract
Introduction:
The landforms created by tectonic processes are studied by morphotectonics, in other words, morphotectonics is the science of applying geomorphic principles in solving tectonic problems. Quantitative landscape measurements are usually based on the calculation of geomorphic ...
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Extended Abstract
Introduction:
The landforms created by tectonic processes are studied by morphotectonics, in other words, morphotectonics is the science of applying geomorphic principles in solving tectonic problems. Quantitative landscape measurements are usually based on the calculation of geomorphic indices, using topographic maps, satellite images aerial photographs, and field visits. Coastal deltas are part of landforms and landscapes that, due to the proximity of two environments, land, and water, leave visible effects against tectonic activities, such as changing the pattern and location of deltas due to the change in the course of coastal rivers, the formation of unbalanced coastal terraces in parts of the coast, and the emergence of cut beaches in the form of seawalls.
One of the methods of identifying and measuring land changes is using radar remote sensing. The principles of this technique were first described by Graham in 1974 (Pacheco et al., 2006). Interferometry using radar images with an artificial window or SAR is a precise method based on the use of at least two radar images of the same area, which measures the height displacement changes in wide areas and during different time intervals with a significant accuracy of millimeters (Dong et al., 2018).
The coastal areas of northern Iran are of great importance due to the high population density and the ability to grow and develop economically and agriculturally, so monitoring geomorphic changes in the direction of sustainable development of these areas is particularly important.
In this research, the eastern coast of the Caspian Sea from Gomishan to Joibar is investigated in terms of subsidence and uplift using radar remote sensing techniques to determine the active tectonic zones of the coast in terms of temporal and spatial changes.
Materials and Methods:
The Eastern Caspian Plain is the border between the Caspian Sea and West Gorgan and includes the cities of Gomishan, Bandare Turkman, Bandare Gaz, Gulugah, Khazarabad, and Joybar. The absolute height of the Caspian Plain along the coastline is determined according to the sea level, based on the hydrographic data of the Baku station, since 1850, the Caspian sea level has varied between -25.4 and -29.4 (Abdolhi Kakrodi, 2012).
The history of seismic activity in North Alborz shows that cities like Rasht, Lahijan, Amol, and Gorgan, have been destroyed many times due to destructive earthquakes (Aqhanbati, 2013). The Alborz fault is an active fault that is stretched in a clockwise direction in the southern Caspian basin.
In this research, according to the desired goals and radar remote sensing techniques, a series of Sentinel-1 radar images with a suitable time and space difference (maximum 30 days and maximum 150 meters respectively) including 61 images in time from 2014 to 2021 were prepared and processed.
Results:
The results obtained from the SBAS model indicate that the eastern part of the Caspian coast is more affected by the uplift and this trend continues up to Gorgan Bay. The Gorgan city has an uplift between 20 and 40 mm/year, which is reversed towards the coastal area, and subsidence of 10 to 52 mm/year occurs, which decreases as it approaches the coast and reaches 10 mm /year.
Discussion, Conclusion:
According to the results obtained from radar interferometry, the eastern coast of the Caspian Sea is more affected by uplifting. The Gorgan city has an uplift between 20 and 40 mm/year, which is reversed towards the coastal area, and subsidence of 10 to 52 mm/year occurs, which decreases as it approaches the coast and reaches 10 mm/year.
To verify the results obtained, the data of the Gorgan geodynamic station was used, which shows subsidence of about 90 to 100 mm in a 6-year period, which is consistent with the values obtained from radar interferometry Based on comments Shahpasandzadeh (2013) and the reports of Nazari et al (2021), active tectonics caused by the Caspian fault that indicates the horizontal geodynamic displacement diagram of Gorgan, the small area towards the north and east during this time, which is observed in the form of numerous branches with a thrust (reverse) mechanism and a right-slip component with a slope to the south in Golestan province.
Considering that the main feature of the coast of the Caspian Sea is the Surface rivers and the use of groundwater is very little and also the extraction of gas, oil, and mining resources, which is another factor in the occurrence of land subsidence, does not exist in this area, and there isn’t also huge and heavy structure in the study area that affects the subsidence of the surface; so displacement in the study area is the result of active tectonics.
Remote Sensing (RS)
Moslem Darvishi; Reza Shah-Hosseini
Abstract
Extended Abstract IntroductionWith the expansion of the urban limits, some of the lands that were used for gardening years ago have been located within the urban limits. The difference between the value of garden land use and urban land use, such as residential and commercial, encourages gardeners to ...
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Extended Abstract IntroductionWith the expansion of the urban limits, some of the lands that were used for gardening years ago have been located within the urban limits. The difference between the value of garden land use and urban land use, such as residential and commercial, encourages gardeners to change their land use. Urban managers try to prevent this change of use by enforcing strict rules.Assessing the success of such plans requires examining land-use change in the urban over a long periodof time. The main purpose of this study is to detect abandoned urban gardens using Landsat satellite imagery. The second goal is to determine the extent of changes in urban gardens in the study area over the past 30 years. In this study, based on Landsat satellite images in 2018 and 1988 for the northern slope of Alvand Mountain in Hamadan province and the city of Hamadan, the normalized differential index of vegetation (NDVI) along with land surface temperature (LST) in 9 time periods per year was extracted. The results indicated a 4/75 ° C increase in LSTfor the region over 30 years. Also, the inverse relationship of LST with NDVI is confirmed. Based on the separation of urban gardens, a comparison was made between 2018 and 1988, which showed a decrease of 175 hectares of urban gardens in the study area, which is equivalent to a 49% reduction in urban gardens. In the main part of the research, based on the behavioral evaluation of urban gardens, in these two characteristics, a differentiation index for active and abandoned gardens is presented. Examination of the results based on ground truth data including 25 active gardens and 25 abandoned gardens suggested that the proposed method had an overall accuracy of 82% and a Kappa coefficient of 0/64.Materials & MethodsThe study area includes a part of the northern slope of Alvand Mountain, which is limited to the southern part of Hamedan and has a latitude of 34 degrees and 45 minutes to 34 degrees and 48 minutes north and a longitude of 48 degrees and 27 minutes to 48 degrees and 31 minutes east. Ground truth data including 25 active gardens and 25 abandoned gardens were collected as field visits using a Garmin GPSMAP 62s handheld navigator so that coordinates were collected by attending the location of abandoned and active gardens. The satellite data used in this study concern the time series data of Landsat 8 satellite OLI and TIRS sensors for 2018 and Landsat 5 satellite TM sensor for 1988.To achieve the first objective and separate active and abandoned gardens in 2018, the land surface temperature (LST) and the normalized difference vegetation index (NDVI) are calculated and the behavior pattern of these two components is examined during the year for active and abandoned gardens in nine periods according to the proposed method, a final index for separating active and abandoned gardens is presented based on the NDVI behavior pattern throughout the year. The time series of NDVI for each year is evaluated in 9 periods and garden maps are extracted in 1988 and 2018 to achieve the second objective and prepare the maps of 30-year changes in active gardens in the study area. The rate of change of area and the percentage of changes in the class of gardens are obtained by comparing the maps.Results & DiscussionSince this study is conducted mainly to identify abandoned gardens in urban space, two criteria for assessing user accuracy and errors of commission in the abandoned garden class are very important. In other words, in this problem, the number of gardens that are properly divided into the abandoned garden class is important, and the proposed method provides an accuracy of 86%. The most important issue is the number of abandoned gardens that the proposed method has mistakenly labeled as active gardens, which is 14% in this method. Both accuracies provided are evaluated as acceptable. The overall accuracy of the proposed method is estimated at 82%, which is acceptable, indicating the efficiency of the proposed method.ConclusionOne of the problems facing human societies today is the reduction of forests and gardens. Given the important role that trees play in improving the quality of human life, protecting them is one of the inherent duties of rulers. Various factors cause the destruction of trees, one of which is the development of urban areas in the vicinity of forests and gardens. Traditional methods of conserving natural resources and monitoring their changes have failed in practice. For example, in the study area, 49% of the tree-covered areas have declined over the past 30 years. However, the ban on construction in the area has always been emphasized by city managers in the years under study, and the inefficiency of the methods used has been proven by the statistics provided. New methods of monitoring changes based on satellite image processing can be alternatives to traditional methods due to their high accuracy and speed and significant cost reduction. The proposed index is recommended to be evaluated to separate active and abandoned gardens in other areas facing this problem using images with higher spatial resolution. In different cases of threshold limit, the overall accuracy of the proposed method is examined based on the ground truth data of the evaluator. At best, the separation of active and abandoned gardens is associated with an overall accuracy of 82%.
Remote Sensing (RS)
Seyedeh Kosar Hamidi; Asghar Fallah; Nastaran Nazaryani
Abstract
Extended AbstractIntroductionVarious Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro ...
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Extended AbstractIntroductionVarious Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro planning and information generation. Methods using air temperature recorded in weather stations to estimate the relative heat in urban areas are considered to be both time-consuming and costly. On the other hands, data with relatively high spatial resolution are capable of measuring ground surface parameters more efficiently and accurately. Thus, remote sensing technology is now considered to be a solution used to improve previously mentioned methods. Remotely sensed data are now widely used to find the quantitative relationship between patterns of vegetation cover and the elements of climate. Predicting the conditions of vegetation cover is considered to be essential for planners seeking an efficient plan for its exploitation and protection.Materials & MethodsThe present study seeks to investigate the effects of climatic factors on the vegetation trend observed in Frame forest in Mazandaran province using Sentinel 2 images and to determine the most suitable index for this area. Climatic Data collected from the nearest weather station in Farim City have been used to model climate factors (temperature and precipitation). Changes in the height above mean sea level were also considered. Following the pre-processing and processing of the Sentinel 2 images, the corresponding digital values were extracted from the spectral bands and applied as independent variables. ENVI software was used for image processing and STATISTICA and R software were used for modeling. 70% of the resulting data were used for training and the rest were used for testing or evaluating the model. Mean square error, correlation, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the presented models. Models with the highest correlation and the lowest standard error, the mean square error, the Akaike information evaluation criterion and the Bayesian evaluation criterion were selected as the best models for the studied variables.Results & Discussion A correlation coefficient of 0.43 and 0.56 was observed between temperature and precipitation and vegetation indices. AIC and BIC values equaled (565 and 3209) and (739 and 3383) respectively. Differential Vegetation Index (DVI) has proved to be the most effective parameter in relation to both temperature and precipitation factors in the region. Results indicated that differential vegetation index, green normalized difference vegetation index (GNDVI) and green difference vegetation index (GDVI) have a positive correlation with temperature, while there is a negative correlation between temperature and normalized vegetation index. Precipitation is considered to be one of the most important factors affecting vegetation. Results indicate that differential vegetation index, green difference vegetation index, green normalized difference vegetation index, non-linear vegetation index and normalized difference vegetation index have the highest impact on precipitation. In forest ecosystems, changes in climatic factors may affect trees differently. ConclusionCollecting information about the state of vegetation cover in forests is considered to be very important. Thus, the present study has endeavored to investigate the relationship between indices of vegetation cover and climatic variables. To reach this aim, satellite data are used as a suitable and efficient tool for investigating forest ecosystems with a relatively low cost. This provides the possibility of continuously monitoring land surface. Results indicated that climatic factors affect vegetation indices in the study area. Vegetation cover protects and stabilizes the environment and thus, many researchers have tried to investigate the growth and spatial patterns of vegetation cover in different regions. It is also suggested to study the effects of climatic factors on the vegetation cover of the study areas in different geographical directions. In addition, using other climatic factors such as relative humidity, wind speed, evaporation, transpiration, and higher resolution images can increase the accuracy of the study.
Remote Sensing (RS)
Keyvan Mokhtari; Hooshang Asadi Harouni; Mohammad Ali Aliabadi; Somayeh Beiranvand
Abstract
Extended Abstract 1- IntroductionAlteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal ...
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Extended Abstract 1- IntroductionAlteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal component processing (PCA) to identify the range of variations in different regions on Aster images. Gomez et al. (2005) visualized the lithological units of Namibian using the PCA algorithm on Aster data.The exposed rock units in Muteh mining area include a series of sedimentary, volcanic, and volcanic-clastic metamorphic rocks that extends from the green schist facies to the border of green schist and amphibolites along the northeast-southwest direction. These units have been repeatedly penetrated by alkaline intrusions, especially acid and granite (Rashidenjad, Omran et al., 2002).In general, the controlling elements of mineralization in Muteh area include structural factors (faults and fractures), alteration, and deformation. Field observations indicate the occurrence of vein mineralization and gold sulfide deposits in mylonite shear zones and fault zones in felsic to mafic metavolcanic host rocks.Gold mineralization is mainly concentrated in highly altered metariolites containing iron and copper sulfides and within fractures as veins and deposits. Alterations in silica, sericite, and carbonation are also observed along with these sediments, which are studied as exploration keys (Moritz et al., 2006).In this area, according to the lithology and distribution of alteration zones and the type of mineralization in Muteh gold mine, gold orogeny-type mineralizations are expected, which can be indirectly identified by recognizing the above alteration.2- Materials and methodsIn this study, Aster satellite images have been used to identify, discover and separate alteration zones in ENVI 5.3 software. Also, Landsat 8 satellite images have been utilized for general investigation and identification of hydrothermal alteration zones and expansion of iron oxide minerals, and Sentinel 2 satellite data due to better spatial and radiometric resolution than the above data has been applied to increase the spatial resolution of these data and the spatial accuracy of the map from the extracted changes.In order to validate between the field observations and spectral analysis, 24 rock samples were taken from the place of alteration, especially siliceous, argillic, and sercitic alteration around Senjedeh and Chah Khatoon deposits. 11 samples were sent to Zarazma laboratory for XRD analysis, and five samples were sent to Zarkavan Alborz Company’s laboratory for chemical analysis of 41 elements by ICP-MS method and gold element by Fire Assay method.3- ResultsConsidering the relationship between alteration zones and metal mineralization, it is very important to know and map these areas in the exploration of these deposits.The results and images show that the methods used in determining and separating the altered areas in Muteh exploratory area are acceptable and the optimal and effective methods in this research, SAM and MF, have been introduced.According to the field observations and surface sampling around Chah Khatoon and Senjedeh mineral deposits, as well as the investigation of changes, it was found that the most important changes in the region are: silicification, kaolinization, sericization, chlorination, alonation, pyrite, carbonation and so forth. This wide range shows the difference in intensity of alteration in different parts of the mineral reserve, which can be attributed to the system of joints, fractures and faults in the region.According to the available evidence, the metariolite rock is highly silicified in the tensile zones or in places with dense seams, and the pyrite particles in the context of these rocks have turned into iron hydroxide.4- DiscussionBy using satellite data processing, various data and information can be identified and extracted. Satellite data processing is done in two ways: visual and digital processing. By combining these two methods, the desired effects can be detected more accurately than the accuracy of satellite images. The visual method consists of preparing images of different color combinations by placing spectral bands in the red, green, and blue channels. Digital satellite image processing methods include band ratio, principal component analysis, least square regression method (Ls-Fit), spectral analysis, spectral angle mapping (SAM), and adaptive MF filter. The selection of the above methods was based on the type of information requested to extract data from images.Aster sensor images have no blue band (spectral range 0.4-0.5 µm) and the color composition of its VNIR bands is a standard RGB (1,2,3) false color composition. In this color combination, vegetation is seen in red. Since the study area is located in a relatively arid environment without vegetation, vegetation cover was avoided in the spectral analysis.The use and processing of Aster satellite data is one of the main features of this sensor; the use of unique spectral reflectance curves of alteration indicator minerals helped to identify and highlight these altered areas as well as finding the potential of areas prone to metal mineralization. Due to the high ability of Sentinel-2A images in identifying gossan and iron oxide ranges, the processing of these data was used to highlight these areas better.5- ConclusionAccording to the agreement of the results of geochemical and XRD studies with the distribution map of the alteration zones identified from the reference spectrum (USGS) and the spectral library (JPL), with the distribution map of lines and structural fractures of Muteh exploratory zone outside the pre-identified areas, new alteration zones were also introduced that require field research to confirm the results of stereo data processing.
Extraction, processing, production and display of geographic data
Masoud Eshghizadeh
Abstract
Extended Abstract Introduction The best and most effective way to control wind erosion is to increase vegetation to cover the land surface. The roughness of the land surface is increased by vegetation. Because it increases the friction that causes a decrease in wind speed on the surface of the ground ...
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Extended Abstract Introduction The best and most effective way to control wind erosion is to increase vegetation to cover the land surface. The roughness of the land surface is increased by vegetation. Because it increases the friction that causes a decrease in wind speed on the surface of the ground and the carrying capacity of sediments by the wind. By determining resistant species and more adapted to dry conditions, it will be possible to establish vegetation in these areas in different non-desertification projects to control and reduce wind erosion.Materials & Methods In this research, in one of the critical centers of wind erosion in Gonabad County in northeastern Iran, investigated the performance of a biological project of non-desertification operations with Haloxylon aphyllum, Haloxylon persicum, Seidlitzia rosmarinus, Nitraria schoberi, Atriplex canescens and annual plants in different intensities of the drought for 2004 to 2021. At first, using the RDI index, drought intensities were determined in March, April, and May in the studied period. In the next step, the maximum, average, and minimum values of NDVI, TDVI, SAVI, and EVI indices were calculated using Landsat satellite images and data processing ENVI 5.3 software in each of the covered areas by desired specie in the studied months. In the final stage, the values of these vegetation indices were compared and analyzed for drought intensities in the areas and months.Results Based on the results, in all the indices, the area covered by Seidlitzia rosmarinus had a better condition than in other areas in the very dry drought intensity and with the highest value of 0.341 in the EVI index. But in the medium and mild drought intensities, the area covered with the Haloxylon sp had a better condition than in other areas and with the highest value of 0.456 in the TDVI index. However, all studied vegetation indicators did not show any significant difference between the planted areas. In March with the very dry condition, vegetation was more dependent on the intensity of dry conditions in February. The severity of the drought in February caused the values of all vegetation indicators in March in the studied areas to be negative, except in the annual species area. In March, the SAVI index, in April TDVI index, and in May TDVI and EVI indices had better ability to distinguish vegetation cover. The results of the Kruskal-Wallis test showed that in March, there was a significant difference between high, medium, and mild dry conditions only for the TDVI index at the level of 5% and the SAVI index at the level of 1%. In April, the NDVI and SAVI indices at the level of 1% and the EVI index at the level of 5% showed a significant difference between the three dry conditions. The results of the Mann-Whitney test showed that in May, only the SAVI index had a significant difference at the level of 1% between the moderate and mild dry conditions.Discussion & Conclusion The results confirmed the ability of vegetation indices obtained from Landsat satellite imagery to monitor the vegetation changes due to the drought. All the indices showed changes in the vegetation in the drought conditions, but no difference was seen between the vegetation areas. The resistance of the species to drought was one of the main reasons that caused there to be no significant difference between the vegetation areas, but the difference between the drought conditions was significant. Due to the adaptation and resistance of desert species to drought conditions, their sensitivity to drought in dry and desert areas is lower than in humid areas. In the condition that February is affected by drought, the cover conditions of annual plant species in the studied area in March were better than in other areas. But in March with very dry or moderate drought conditions, the cover conditions of Seidlitzia rosmarinus species were better coverage than in other areas. Based on the results, in the continuation and occurrence of moderate to high drought in April and May, the area of Seidlitzia rosmarinus showed a better cover than in other areas. In the condition of continued drought in March, annual plants do not have a chance to grow and the species that can use the moisture reserve in the deeper soil will have more opportunity to cover the surface of the ground, which this research showed that among the species in this area, Seidlitzia rosmarinus has more ability. Therefore, the principle of mixed planting and preventing single planting in the desert restoration and non-desertification projects should be emphasized and implemented.
Remote Sensing (RS)
Nazanin Hassanzadeh; Reza Hassanzadeh; Mahdieh Hosseinjanizadeh; Mehdi Honarmand
Abstract
Extended AbstractIntroductionAir pollution is one of the most crucial environmental problem in the glob and its impact on human live and ecosystem is undeniable. The International Agency for Research on Cancer introduced air pollution as one of main causes of cancer. Therefore, by monitoring air pollution ...
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Extended AbstractIntroductionAir pollution is one of the most crucial environmental problem in the glob and its impact on human live and ecosystem is undeniable. The International Agency for Research on Cancer introduced air pollution as one of main causes of cancer. Therefore, by monitoring air pollution would be a necessity in industrialized cities. Air quality index include evaluation of the amount of NO2, SO2, O3, CO and Aerosol in the air. As, ground station has limited ability to assess the amount and distribution of these harmful gases in the urban and rural areas, therefore, remote sensing technology become a popular tool in assisting research to shed light on this subject. The current study evaluates air pollution caused by Khatoonabad Copper Smelting Factory using Sentinel P5 satellite images.Materials & MethodsThis research investigates the air pollution created by Khatoonabad Copper Smelting Factory and determines its impact radius, using Google Earth Engine system and Sentinel P5 satellite images. Khatoonabad Copper Smelting Factory is located in the northwest of Kerman province at the latitude of 29 Degree 59 Minute to 30 Degree 32 Minute and longitude of 54 Degree 52 Minute to 55 Degree 55 Minute. By performing the coding operation in the Google Earth Engine system, the images related to the average air pollution for So2 and No2 in the area of 50 km from the factory and in a period of 30 months from 07/04/2018 to 12/30/2021 were obtained. The amount and distribution of pollutants were examined based on one-day, seven-days, fourteen-days, one-month, two-months, three-months, six-months and twelve-months’ time periods from December 2020 to assess the concentration of pollution in the cold months of the year, also for the same time periods from June 2021 to assess the concentration of pollution in the warm months of the year.In order to map distribution of each pollutants, Natural Break Classification and Hot Spot Analysis methods were performed on the images obtained from Google Earth Engine in GIS. Natural Break Classification method is based on Jenk optimization and classify spatial data based on statistical properties of each input where variances between classes maximize. Hot Spot Analysis methods is a spatial and statistical method that consider spatial autocorrelation among the spatial data to classify the data according to statistical significance of each class. Points that surrounded by high values and they are statistically significant called hot spot and areas that are surrounded by low values and have high negative Z score and low P values ( P value < 0.05) are called cold spot.Results & DiscussionThe results based on an averaged image for the period of 30 months indicated that the amount of So2 from 0.0000987 to 0.000698 (mol/m2) and the amount of No2 from 0.00005854 to 0.00006932 (mol/m2) in the study area that by increasing the distance from the factory, the amount of So2 and No2 decreased. Furthermore, analyzing the average amount of So2 and No2 in different period of daily, weekly, two weeks, and monthly have showed dispersed spatial distributions in warm and cold season of the year. Therefore, Sentinel 5P data in short-term periods such as daily, weekly, two-week and even one-month cannot provide accurate information on the spatial distribution of No2 and So2 in the study area.In the data obtained from the two-month, three-month, six-month and one-year intervals, the amount of sulfur dioxide concentration has less dispersion than the short-term intervals, and as the time interval increases, the images show less dispersion of sulfur dioxide gas in polluted areas. Therefore, the obtained results indicate that Sentinel 5P images with longer time intervals of two months are able to provide more accurate and logical information about the concentration of sulfur dioxide gas in the area. However, in case of nitrogen dioxide, the imaged longer than two weeks can provide accurate information regarding the spatial distribution of this pollutant in the area.Hot spot analysis was also performed on the images obtained in one-day, seven-day, fourteen-day, one-month, two-month, and three-month intervals from June in order to investigate the concentration and dispersion of pollution in the hot days of the year. Then the maps obtained from the hot months were compared with the maps of the same period from the cold months of the year. This comparison showed that in the maps obtained from the short-term intervals related to the hot months of the year, the density of hot spots was more observed in areas prone to the presence of sulfur dioxide gas. For example, the one-day image from December showed a lot of dispersion, while the one-day image from June indicated less dispersion and more density of gases in polluted areas. In addition, in the one-week, two-week and one-month maps from December hot spots and cold spots show much greater dispersion compared to similar maps in the same periods from June. However, by comparing the two-months and three-months hot spot maps of the cold months to the same maps of the hot months of the year, almost similar results were obtained, even more density were observed in the hot spot map of longer periods (more than two months) in winter time. The same trend happened by analyzing nitrogen dioxide in the studied area. ConclusionThe results obtained from the classification of images related to sulfur dioxide gas showed that the concentration of sulfur dioxide gas in the area around the desired factory has the highest concentration value and as the distance from the factory increases, the concentration of sulfur dioxide gas decreases. Also, according to the minimum and maximum concentration of sulfur dioxide in the studied area, it is concluded that more sulfur dioxide is observed in the cold months of the year than in the warm months of the year. However, in the cold months the concentration of sulfur dioxide has a greater range of changes than the hot months of the year.According to the results, the dispersion of sulfur dioxide concentration in short time intervals such as daily, weekly, fortnightly and even one month was very high in these time intervals. As a result, Sentinel 5P images are not able to provide logical and accurate information about the distribution of atmospheric sulfur dioxide concentration in daily, weekly, two-week and one-month intervals. In order to obtain accurate and logical information, images with time intervals longer than one month should be used, and the longer the time interval is, the more reliable the results will be.The results of the hot spot analysis of the images related to sulfur dioxide concentration also indicated a high concentration of sulfur dioxide gas in the area around the factory. According to the obtained results, the activity of the studied factory can be a reason for the increase in the concentration of sulfur dioxide gas in this area, which has affected a radius of about 4 to 6 kilometers and an area of about 10,700 hectares around the factory.The results obtained from the classification of images related to nitrogen dioxide gas show that the concentration of nitrogen dioxide in the area around the factory has a higher limit. According to the minimum and maximum concentration of this gas in the study area, it can be concluded that in the hot months of the year, the concentration of nitrogen dioxide gas is higher than in the cold months of the year. Considering the rapid spread of nitrogen dioxide gas in the atmosphere by the wind due to the high dynamics of this gas (Vîrghileanu et al., 2020), it can be concluded that the images obtained from the time intervals of two weeks of more can provide more information about the concentration of nitrogen dioxide in the atmosphere.The results of the hot spot analysis of the images related to nitrogen dioxide gas showed that in the time intervals of two weeks to two months in the cold months of the year, there are hot spots that indicated the presence of nitrogen dioxide gas in the atmosphere located above the factory. However, in long-term intervals such as three months, six months, one year and thirty months, in the cold and hot months, hot spots are observed towards the northwest and at a distance from the factory.The result of this research can assist environmentalist and researchers in using and interpreting Sentinel 5P data by considering different periods in cold and warm seasons for making informed decisions.
Remote Sensing (RS)
Mahdiyeh Fathi; Reza Shah-Hosseini
Abstract
Extended AbstractIntroductionRice is an important crop and the main food of more than half of the world’s population, which needs water and heat to grow. Thus, mapping and monitoring rice fields with efficient means such as remote sensing technology is necessary for food security and the lack of ...
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Extended AbstractIntroductionRice is an important crop and the main food of more than half of the world’s population, which needs water and heat to grow. Thus, mapping and monitoring rice fields with efficient means such as remote sensing technology is necessary for food security and the lack of water sources. The phenology extracted from the time series of vegetation indices is used for monitoring and mapping the area under rice cultivation. In addition to the phenological curve, the LST time series map, which is calculated from Landsat 8 images and is related to the phenomenon of evaporation and transpiration of irrigated crops, can cause the separation of rice cultivation from rainfed crops, summer crops, water, etc. Therefore, in this study, the effect of the LST time series map is investigated map for improving the accuracy of rice field identification.Materials & MethodsSince the planting to harvest period of rice is from May to October, in this study, the time-series maps of LST and NDVI for the 3rd of April, 21st of May, 6th of John, 22nd of John, 8th of July, 24th of July, 9th of August, 12th of October, and 28th of October have been calculated after download the Landsat-8 time-series in 2020 The ground truth map of the study area has been obtained from the US Department of Agriculture. To identify rice fields and calculate the LST and NDVI using the Landsat-8 images, initial pre-processing including radiometric and geometric corrections has been applied to these images first. After initial corrections and the calculation of NDVI and LST maps, to identify rice fields in the study area, machine learning algorithms such as Support Vector Machine, K-Nearest Neighborhood, Multilayer Perspective, Logistic Regression, and Decision Tree, have been proposed. Results & DiscussionThe results of the proposed method at the state of California showed that using the time series map of Land surface temperature (LST) with the time-series map of Normalized Difference vegetation Index, improved the results of identifying rice fields (the average Overall Accuracy= + 3/572% and the average kappa coefficient= +7/112%). Visual results showed that some cultivation such as tomato, corn, cucumber, fallow, and water were removed from the rice final map when using the LST time-series map with the NDVI time-series map. According to the numerical results, the Support Vector Machine algorithm (Overall Accuracy 94/28 and Kappa Coefficient 88/29), the Multilayer Perceptron algorithm (Overall Accuracy 94/26 and Kappa Coefficient 88/21), and the K-Nearest Neighborhood algorithm (Overall Accuracy 93/71 and Kappa Coefficient 87/08) showed the highest Overall Accuracy and Kappa Coefficient compared to the Logistic Regression algorithm (overall accuracy 91/96 and kappa coefficient 83/54) and the Decision Tree algorithm (Overall Accuracy 91/34 and Kappa Coefficient 81/97), respectively.ConclusionAlthough, many methods have been proposed to identify rice fields from satellite images. But, the similarity of rice class with other classes is one of the main challenges related to rice identification. In this research, the effect of LST time series maps to improve the identification accuracy of rice fields in Landsat-8 time-series images was investigated. In this study, the effect of the time series map of land surface temperature index extracted from Landsat-8 images on improving the accuracy of identifying rice fields from other rice fields due to the evapotranspiration process using machine learning algorithms was investigated. The results showed the effectiveness of the proposed index in improving the identification accuracy of rice fields. One of the reasons for improving the accuracy of identifying rice fields is to extract the characteristics of the thermal growing season from the Earth's surface temperature time series (LST) maps along with the rice phenology curve. The results showed that due to the flooding of rice fields when using the NDVI time series map, water class and fields summer crops were identified as rice class. But, water and summer crops classes were removed from the rice final map using a land surface temperature time-series map with the extraction of thermal growth season characteristics. Therefore, the results showed that there was a direct relationship between LST time-series maps and rice cultivation.
Remote Sensing (RS)
Raheleh Ostadhashemi; Khosrow Mirakhorlou; Jamshid Yarahmadi; Mohamad Reza Najibzadeh
Abstract
Extended AbstractIntroductionNowadays, natural resources are exploited for the purpose of economical development in developing countries. Expansion of agricultural lands, supply of charcoal and fuel wood and wood production play an important role in forest degradation which affects biodiversity, ...
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Extended AbstractIntroductionNowadays, natural resources are exploited for the purpose of economical development in developing countries. Expansion of agricultural lands, supply of charcoal and fuel wood and wood production play an important role in forest degradation which affects biodiversity, soil conservation, the quantity and quality of water and the global climate conducted to the importance of forest conservation and reforestation. Therefore quantitative assessment of forests is required for conservation programs and forest monitoring is defined as a tool for sustainable forest management.Today, remote sensing techniques and satellite images can widely provide functional information in environmental studies. In this work, sentinel-2 satellite images with high spatial, temporal and spectral resolution were applied to determine the area, distribution and density of the Arasbaran forestsas well as other land use classes in the area.Materials and MethodsArasbaran area is located in Qaradagh mountainous region inthe north of East Azerbaijan province between 38°25′ 59 " N- 39°20′ 7.7 " N latitude and 46°09′ 18 " E- 47°16′ 5.3 " E longitude which covers an area of 551211 hectares and the deciduous forests of this area are known as the 11th Biosphere Reserved in Iran. The altitude varies from ca. 256 m tomore than 2000 m. the importance of the area is in having a rich flora (about 1334plant species) and unique vegetation among the vegetation of the country.For the first time, the Sentinel-2 images with a combination of high spatial and temporal resolution were used to classify the land use of the area.The best band combination was found for bands 2, 3, 6, 12 and NDVI index. Land use classification included dense, semi -dense, sparse and very sparse forests as well as rangeland, agriculture, residential area-bare soil, garden and water was implemented using 9 different algorithms in a pilot area to find the best algorithm. 280 training sample points were collected from all different land use classes in the area.Consequently, supervised classification technique and Maximum Likelihood algorithm with the Kappa coefficient of 0.886 and anoverall accuracy of 89.6% was identified as the best classification method for the Arasbaran area.Accuracy assessment of the final map was done using ground control points and Google earth images with a total accuracy of 95%.Finally creating an error matrix with 880 ground reference test pixels revealed the accuracy indices.ResultsThe final land use map of the Arasbaran area based onthe Supervisedclassification technique and Maximum Likelihood algorithm was created.Based on the results, the accuracy assessment of the final map showed that the Kappa coefficient and the overall accuracy of the classified map were 0.88 and 89.8% respectively.The forest distribution and canopy cover density map were extracted from the land use area map. The total area of forests with a canopy cover of more than 5%, obtained 131019 ha consisting of 39% dense forest, 36% semi -dense forest, 17% sparse forest and 8% very sparse forest. In addition, the largest type of land use accounted for rangeland with 270000, forest with 131019, agriculture with 101974, residential area-bare soil with 30028, garden with 15434 and water with 2756 hectares respectively. Based on the error matrix table and correct classified points as well as total ground control points, the highest user’s and producer’s accuracy belonged to the densed forest class as well as the lowest user’s accuracy and lowest producer’s accuracy belonged to garden and agriculture classes respectively.ConclusionThe results conducted supervised pixel-based image classification based on the Maximum Likelihood algorithms an acceptable method. It can be because of well -distributed training sample points, the high spatial resolution of sentinel-2 images or Environmental heterogeneity of the area. According to the results, dense forests declined(from 56910 to 50628 ha)however semi -dense and sparse forests have increased (from 35280 to 47930 ha)with respect tothe last forest survey project in the Arasbaran area in 2003.In addition, the results revealed an overlap between agriculture and garden as well as rangeland and residential area-bare soil classes because of multi culture of crops and fruit trees together as well as dried or low vegetation cover of rangelands in the area. These results can provide useful information for decision- making and sustainable forest management for reducing forest degradation and it seems to be an important next step to manage these forests based on conservation policies.
Remote Sensing (RS)
Heshmat Karami; Zahra Sayadi
Abstract
Extended AbstractIntroductionCoral reefs are one of the most diverse and ecologically important areas in the world. However, with increasing ocean temperatures, many coral reefs are severely threatened by bleaching events. When the water is too warm, corals expel the algae that live in their tissues, ...
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Extended AbstractIntroductionCoral reefs are one of the most diverse and ecologically important areas in the world. However, with increasing ocean temperatures, many coral reefs are severely threatened by bleaching events. When the water is too warm, corals expel the algae that live in their tissues, causing the coral to turn completely white. When a coral bleaches, it is not dead, and corals can survive a bleaching event, but they are more stressed and at risk of dying. Today, in order to predict and identify areas at risk of coral bleaching, data based on satellite remote sensing are used. In this research, using 35-year data trends, the sea surface temperature in 2022 was predicted using ArcGIS Pro tools for the Persian Gulf area and possible areas exposed to thermal stress leading to coral bleaching were identified.Materials & Methods In order to predict the bleaching of corals, the research data archive of the American National Center for Atmospheric Research (NCAR) has been used. In this analysis, the harmonic method was used to fit the trend line. A harmonic trendline is a periodically repeating curved line that is best used to describe data that follows a cyclical pattern. For anomaly analysis parameters, the average monthly temperature in each location was compared with the overall average temperature to identify anomalies. There are three mathematical methods for calculating anomaly values with the Anomaly function, in this research, the method of difference From mean was used. At the end, the dimension value or band index was extracted, in which a certain statistic is obtained for each pixel in a multi-dimensional or multi-band raster, and the final map of coral bleaching prediction was prepared, and then using the data and global maps of the National Oceanic Administration NOAA , it was evaluated.Results, discussion and conclusionThe preliminary results showed that the sea surface temperature has changed in the Persian Gulf. The range has experienced higher average temperatures since 1996, which could put the area at risk of coral bleaching. The minimum average temperature in the studied time period is 298.758 degrees Kelvin in 1991 and the maximum average temperature in 1399 is 300.737 degrees Kelvin. The parameters that were chosen for multidimensional data trend analysis include water surface temperature variable (SST) and time dimension. The obtained trend map (1980-2015) indicated that the northwestern regions of the Persian Gulf and a part of its south are more exposed to prolonged heat. In this study, frequency parameter 2 was used in the harmonic model, which uses the combination of the first-order linear harmonic curve and the second-order harmonic curve to fit the data. The accuracy of data trend fitting by harmonic regression function provided statistical parameters, R2=0.78 and RMSE=0.5. The value of R2 indicates that the observed value of sea surface temperature (SST) was predicted by the harmonic regression model by 78% and the rest remains undefined. This value of the determination coefficient confirmed the accuracy of the trend map. Another statistical parameter is the root mean square error, the lower the value, the better the fit. In the obtained results, the mean of this error is 0.5, which shows that the harmonic regression model can accurately predict the data. In this study, forecast data was analyzed to find locations where water temperatures remain warm for extended periods of time. In this context, first, anomalies in the data were calculated, anomaly or anomaly is the deviation of an observed value from its average value, and in the analysis, it shows areas that have a temperature higher than the average. As a result of this step, the anomalies in the data were calculated and the areas with higher temperature than the average were identified. In the predicted annual time frame (2022), the north-west and a part of the south of the Persian Gulf region will face a longer period of high temperature. To evaluate the accuracy of the results obtained from the analysis and the method used in predicting sea surface temperature and identifying anomalies (2022-09-03), they were compared with the maps of Nova organization on the same date and were confirmed. It is suggested that responsible organizations use methods based on remote sensing and trend analysis to assess the situation and prepare a risk map of coral reefs.
Remote Sensing (RS)
Narges Arab; Abdolrassoul Salmanmahiny; Alireza Mikaeili Tabrizi; Thomas Houet
Abstract
Extended Abstract Introduction:The Land surface temperature is one of the most important factors in controlling the biological, chemical and physical processes of the earth.Land surface temperature data provide information about the spatial and temporal changes of the Land's surface on a global ...
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Extended Abstract Introduction:The Land surface temperature is one of the most important factors in controlling the biological, chemical and physical processes of the earth.Land surface temperature data provide information about the spatial and temporal changes of the Land's surface on a global scale.Land surface temperature is used as the main parameter in many studies, including energy stock estimation, humidity and evapotranspiration, climate change, urban heat islands and environmental studies. Therefore, it is necessary to measure the temperature of the Land surface in order to plan its use. In general, LST investigation is important and necessary to deal with interdisciplinary issues in earth sciences, urban climatology, environmental changes and human-environment interactions. LST can provide important information about surface physical properties as well as climate, which plays a vital role in many environmental processes. In such a situation, LST maps, which are prepared from satellite images, are a desirable option because they provide a permanent data collection.Materials and methods: Many algorithms have been used by researchers to estimate LST using satellite images, especially thermal bands. In this research, Split window and Mono window algorithms are used from Landsat 8 satellite images to obtain land surface temperature (LST) in Mashhad city.The purpose of this study was to investigate the spatial distribution of the Land surface temperature and also to determine an accurate method for preparing the Land surface temperature map.In the present study, using the Split window algorithm, the land surface temperature (LST) data was used from the TIRS sensor in Landsat 8. Also, in addition to TIRS, OLI sensor data are also needed to estimate LST when using the Split window algorithm. In the first stage, the OLI bands of Landsat 8, bands 3, 4 and 5 are layered on top of each other and the NDVI image is produced using bands 4 and 5. The FVC image is obtained using the NDVI image. FVC is calculated by considering the fraction of vegetation in the area. The split window algorithm uses the FVC image to generate the land surface emissivity (LSE) image. The LSE image measures the internal characteristics of the Land surface, which shows the ability to convert thermal energy into radiant energy. Estimating land surface emissivity (LSE) requires soil and vegetation emissivity for bands 10 and 11. LSE images from bands 10 and 11 are obtained separately and then the average and difference of LSE are calculated. The NDVI image is classified into soil and vegetation and is obtained separately for soil and vegetation. Landsat 8 has two TIRS bands. TB, or brightness temperature, is estimated for bands 10 and 11. The thermal calibration process is done by converting thermal digital numerical values (DN) of thermal bands 10 and 11 of the TIRS meter to spectral radiance of the atmosphere (TOA) and then to TB. Finally, LST is estimated using SW, TB, average LSE, LSE difference and water vapor constant.Results and discussion: The results showed that the temperature of the Land surface calculated by the Mono-window and Split-window method compared to the air temperature calculated in the desired weather station showed a difference of 5.1 and 1.7 degrees Celsius respectively. Therefore, it can be said that the Split window method has higher accuracy and the temperature obtained from it is more consistent with the actual temperature.The regression analysis between the results obtained from these two algorithms for LST shows the value of R2 equal to 0.96, as shown in Figure 8.The close correlation between the LST retrieved using the Split window algorithm, with the LST retrieved from the Mono window algorithm, shows that they can be transferred with a small accuracy error.The difference in LST estimation from Mono-window and Split-window algorithms can be attributed to the spectral bands and atmospheric water vapor content used in LST retrieval. The SW algorithm uses two spectral bands (band 10 and 11) with wavelengths of approximately 11 and 12 μm, while the Mono-window algorithm uses one spectral band (band 10) with a wavelength of approximately 11.5 μm to retrieve LST. In addition, the SW Split window algorithm uses the water vapor content of the atmosphere, which represents the true value of the prevailing conditions at the site. On the other hand, the water vapor content of the atmosphere is not used in the Mono window algorithm. The water vapor content of the atmosphere is a sensitive parameter that affects the climate and the temperature of the Land surface. Since two spectral bands are used in the SW algorithm to determine the emission rate and brightness temperature, and these values are used together with the atmospheric water vapor content in LST retrieval, the SW algorithm is able to record the conditions in the region more accurately. and provide better results compared to the Mono window algorithm.Conclusion: The results showed that the air temperature calculated by the Mono window and Split window method compared to the air temperature calculated in the desired weather station shows a difference of 1.7 and 5.1 degrees centigrade on average, respectively. Therefore, it can be said that the Split window method has a higher accuracy and the obtained temperature is more consistent with the actual temperature. The calculated LST values can differ by up to 5 degrees Celsius with the observed air temperature measurement at the station. In the parts covered with greenery, there are low LST values, while in the southeast with barren lands, non-cultivable lands and urban areas, there are high LST values. The results of this research can provide planners and experts with useful information about the temperature status of different regions where the possibility of building weather stations is impossible, and identifying regions with the potential to create thermal islands and its relationship with land use. and provide protection of natural resources.
Remote Sensing (RS)
Nastaran Nazariani; Asghar Fallah
Abstract
Extended Abstract Introduction Estimation of forest habitat characteristics is a necessary issue in order to collect information for sustainable forest management (Ahmadi et al., 2020). Data collection methods require a lot of time and money. Therefore, it is always tried to use ...
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Extended Abstract Introduction Estimation of forest habitat characteristics is a necessary issue in order to collect information for sustainable forest management (Ahmadi et al., 2020). Data collection methods require a lot of time and money. Therefore, it is always tried to use complementary methods, with lower costs and acceptable accuracy, using the achievements obtained in various scientific fields (Sivanpillai et al., 2006). Sentinel 2 is a new generation optical satellite for Earth monitoring developed by the European Space Agency with new spectral capabilities, wide coverage and good spatial and temporal resolution for data continuity and enhanced Landsat and Spot missions (Wang et al., 2017). When the size of the population is not very large, the application of each of the simple random, classification and systematic methods leads to a more or less similar result. But when the size of the community increases, these methods are associated with problems such as: preparing a sampling framework, high cost of surveying sample units with high dispersion and preparing a sampling plan from units far from each other (Zubair, 2007). The cluster method is one of the recommended methods for large areas in which instead of one sample plot, several sample plots are harvested in one part of the study area (Yim et al., 2015). Among the researches done on the mentioned subjects are the research of Kleinn (1994), Ismaili et al. (1396), Behera et al. (2021), Sibanda et al. (2021), Praticò et al. (2021), Nazariani et al. (1400) and Dabija et al. (2021). Although studies on estimating quantitative forest characteristics using distance measurement data and nonparametric algorithms in Zagros forests may have been done extensively, the effect of main and artificial bands to estimate canopy characteristics and density (number Per hectare) using Sentinel 2 images in the forests of Watershed Orfi Olad Ghobad Koohdasht with the aim of selecting the optimal cluster design to save time and money to achieve forest inventory has not been reported, so in this study, we tried to investigate this issue. Materials and methods In order to conduct the present study, a part of the Zagros forests located 35 km north of Koohdasht city, named Watershed Olad Ghobad was selected. Sampling points were determined in a regular-random manner using a grid with dimensions of 600 × 500 meters. Then, at each sampling point, 16 different cluster sampling designs with four circular and square subplots were designed and implemented. The radius of the circular subplots was 15 meters, the diameter of the square sample was 37 meters and the distance between the subplots was 60 meters. Then, the information on the characteristics of the number per hectare and canopy of trees including the number, of two large and small canopy diameters per sample was measured. In this study, Sentinel 2 sensor images related to August 6, 2021, equivalent to summer 1400, were used at the L1C correction level. This level of correction is geometrically error-free due to the reference ground and because their reflection is at the upper level of the atmosphere. In the present study, four bands (2-blue band, 3-green band, 4-red band, and 8-near-infrared band) of this sensor with a resolution of 10 meters were used. In general, Sentinel 2 image preprocessing operations involve radiometric and geometric correction. The image processing also includes various operations such as grading, texture analysis, band integration, and fabrication of plant features (Naghavi, 2014). In addition to the main bands, artificial bands were created by applying appropriate processing, which was used in the modeling process. Spectral values equivalent to ground plots were extracted from the main and artificial bands and used as an independent variable in the models. In order to evaluate and fit the regression models, 25% of the data were randomly selected (Lu et al, 2004) and excluded from the evaluation data set. The validity of statistical models was evaluated using the coefficient of determination of the mean squared error squared, bias, mean squared error, and squared percentage. In total, ArcGIS software was used to implement the sample parts on the image, ENVI software was used for image processing and STATISTICA software was used for modeling.ResultsIn this method, during data validation, the results showed the characteristic of number per hectare of cluster 16 and the characteristic of canopy cover of cluster 15 with a coefficient of explanation (0.66) and (0.59), respectively, it has the highest accuracy. The results obtained from the application of the nearest neighbor algorithm with four criteria of Euclidean distance, Euclidean square, Manhattan, and Chapichev showed that for the number of characteristics per hectare, the Euclidean distance criterion with cluster 16 and for the canopy characteristic of the Euclidean distance criterion with cluster three, respectively (R2 = 0.59 and RMSE=5.70%) and (R2 = 0.62 and RMSE= 12.30%). The accuracy and efficiency of the support vector machine algorithm are influenced by the type of kernel used. The results of different kernels by considering different cluster sampling designs in the backup vector machine method showed for the characteristic number of linear kernel trees and 13 cluster sampling designs with an explanation coefficient of 0.72 and for the canopy characteristic. The linear kernel and the cluster sampling design of seven with a coefficient of determination of 0.65 have the best results. Evaluation of the artificial neural network model showed that the MLP algorithm is more suitable than the RBF algorithm in estimating the studied characteristics with its high accuracy and average squared percentage. Based on this, among the 16 designs used with the MLP algorithm, they showed the most suitable results for the number of characteristics per hectare of cluster six with a coefficient of reflection of 0.86 and for the canopy characteristic of cluster 10 with a coefficient of reflection of 0.76, respectively. Based on the values of the coefficient of explanation and the lowest squared percentage of the mean squares of error, the most appropriate model was selected from the four types of algorithms studied in modeling and the results showed both characteristics of the artificial neural network model respectively (with MLP algorithms MLP 80-20-1 and MLP 80-11-1) presented optimal results with explanation coefficients of 0.86 and 0.76.Discussion and conclusionThe modeling results with four studied algorithms for the canopy characteristic showed that the artificial neural network model algorithm with a cluster sampling design of 10 with an explanation coefficient of 0.76 was the most suitable method. The results are consistent with the study (Yim et al., 2015;) and show the superiority of using cluster sampling, nonparametric modeling of the artificial neural networks and Sentinel 2 images in the structure of the forest ecosystem. Yim et al. (2015) acknowledged that in natural environments, the correlation between sub-plots and habitat conditions in terms of their shape and size should be more sensitive to forest structure. According to the study of Sivanpillai et al. (2006) in poorer masses, due to the presence of more gaps in the canopy, absorption and distribution occur. In contrast, Dabija et al. (2021) compared support vector machine and stochastic forest algorithms for canopy mapping using Sentinel-2 and Landsat 8 satellite imagery to evaluate regional and spatial classification and development in three different regions. Catalonia, Poland, and Romania paid. The results showed that Sentinel-2 satellite images were better than Landsat 8 data inaccuracy (8-10%) in land cover classification and radial-based support vector algorithm than in random forest with accuracy (6-7%). Function. Nazariani et al. (1400) also had the stochastic forest algorithm as the most suitable model for estimating the canopy characteristic, which is not consistent with the results of the present study. The reason for the difference can be found in the type of algorithm obtained and the accuracy achieved.
Remote Sensing (RS)
Mahsa Jahanbakhsh; Ali Esmaeily
Abstract
Extended AbstractIntroduction In recent years, we have seen the importance and high demand of lithium (Li) due to its many applications, for example in the production of rechargeable lithium batteries, which are mainly related to the global markets of electric vehicle manufacturing ...
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Extended AbstractIntroduction In recent years, we have seen the importance and high demand of lithium (Li) due to its many applications, for example in the production of rechargeable lithium batteries, which are mainly related to the global markets of electric vehicle manufacturing to achieve a healthy environment and more suitable transportation. Due to this high demand, the identification of new lithium reserves is very important and the investigation of its identification and zoning methods has been the focus of many researchers, and the use of remote sensing data and image processing techniques in the detection of lithium due to cost reduction of earth exploration has increased, greatly.In this research, using modern methods, a general and intelligent approach was presented, so that with the least time and cost, after selecting the bands of the desired satellite images and zoning the area of Degh Ptergan, in Zirkoh city, South Khorasan province, as a possible area for the existence of lithium reserves, modeling was done by the supervised machine learning method, and the relative importance of the variables was determined using the trained model.Also, the relative importance of the variables was determined by the trained model, and the ability of each of the remote sensing techniques to achieve this goal has been challenged.Materials& Methods Here, 13 bands of Sentinel-2 images and the region of 12 known lithium mines around the world were used as lithium presence areas, so that, by going through steps, suitable data for modeling were produced. In this way, by using the boundaries of these mines, samples were produced that can be used as input for modeling algorithms. The maximum entropy algorithm was used to model the distribution of lithium samples. Since the correlation between the input variables reduces the performance of the model and makes it difficult to interpret the results of the modeling, first, the correlation between the input variables was calculated and those with a high correlation were discarded. So that, 16 variables were used as input in the maximum entropy algorithm and finally a suitable model was obtained with the AUC (Area Under the Curve) criterion of 0.706 and by it, the study area of Degh Patregan, located in the province South Khorasan, Iran was zoned and two possible areas containing lithium resources were identified.To determine the relative importance and contribution of the input variables in the prediction map of lithium minerals, the Jacknife method was implemented. According to this method, the variables B10, B06/B08, B06/B07 and B01/B10 have a high relative importance, which shows that they have more information than the other variables. Then classic remote sensing techniques including color composition, band ratio, principal component analysis and SAM was done to zone the study area, too. The results of maximum entropy modeling were compared with these techniques and the high ability of the maximum entropy algorithm was determined.Results & Discussion According to the results and prediction maps related to the classical methods, it showed that although some of these methods approximately identified the areas specified by the maximum entropy algorithm, but they had problems that is emphasized on the development of more suitable remote sensing algorithms to describe the changes associated with lithium minerals. The maximum entropy algorithm with its unique options is a powerful tool for extracting the features of satellite images and expresses their hidden information more clearly. The accuracy of this method was compared with classical techniques and it was able to provide a more appropriate classification with a low noise and with a Kappa coefficient of 0.8775 and an overall accuracy of 0.9435, and identified two areas with the possibility of the presence of lithium minerals in the study area.Conclusion & SuggestionsIn the present research, the study area of Degh Patergan, located in South Khorasan province, Iran, was zoned, whereby two possible areas containing lithium resources were identified and the ability of classical remote sensing methods and maximum entropy algorithm was challenged. The method discussed in the research may be used as a cost-effective and technological solution with priority over field mapping for mineral exploration in remote border areas with difficult access, also an automatic approach with the maximum entropy algorithm was presented for the exploration of different mineral resources, which can be used for other exploration as well. Therefore, it is suggested to be used in different areas and to explore different sources.
Remote Sensing (RS)
Heshmat Karami; Hadi Abdolazimi
Abstract
Extended AbstractIntroductionWetlands are considered valuable resources of the environment. Despite the importance of wetlands, they are currently threatened by intensive water harvesting for irrigation, industrial development, deforestation, construction of dam reservoirs, and changing rainfall patterns. ...
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Extended AbstractIntroductionWetlands are considered valuable resources of the environment. Despite the importance of wetlands, they are currently threatened by intensive water harvesting for irrigation, industrial development, deforestation, construction of dam reservoirs, and changing rainfall patterns. Monitoring can determine the changes in the location, extent, and quality of the wetland and therefore plays an important role in the maintenance and protection of the wetland. Ecosystem monitoring with remote sensing methods offers the advantage of difference, frequent and uniform coverage of large areas. The study of effective parameters or up-to-date maps that show spatial and temporal changes in the sub-basin of Horul Azim Wetland is not available. Therefore, considering that currently, this wetland is struggling with various problems to continue its survival, the purpose of this research is to use Google Earth Engine and satellite data to study the process of wetland changes.Materials & MethodsThis study was done on the platform of Google Earth Engine open source system. In this study, the data of water area, vegetation cover, precipitation, evaporation, and surface temperature were coded in the Google Earth Engine system in a standard way and their time series was obtained. Also, the NASA GRACE data analysis tool (DAT) was used for time series of groundwater levels. In this research, the Mann-Kendall test and Spearman's correlation were used in order to evaluate the changes in different parameters. In this research, the period from 2000 to 2022 was considered to investigate the trend of the data according to the available time range of the data. Finally, to check the fact that the changes in the zones were affected by floods, the data of the Global Surface Water of Water Occurrence (GSWE) probe was used.Results, discussion, and conclusion The results of the analysis graph of the water area data trend showed that from 2007 to 2019 the water area trend is increasing, with 2007 being the minimum year and 2019 being the maximum year, and the reason for this was the 90% water withdrawal of the Hor al-Azim wetland in the Iranian part. Also, the reason for the increase in the water area in 2017 is heavy rains that lead to floods and overflowing of the Karkheh dam in the sub-basin of the Hor al-Azim wetland. In 2017 and 2020, 2021, the water area shows a significant increase, which is due to the change in climatic behavior and the occurrence of floods in these years. Finally, the trend of the blue zone will be downward until July 2022. The results of a careful analysis of the data trend by the Mann-Kendall test showed that the trend of the available time period was observed. Kendall's tau value also confirms the increasing trend. It seems that the increasing trend of the water area in the years 2019 to 2021 in this study using the Google Earth Engine system is the result of the floods of the last few years, that Considering only this parameter and these data leads to errors in the study and investigation of the condition of Hor-al Azim wetland. No significant trend was observed in the time series of vegetation cover, but according to the positive Mann-Kendall vegetation cover statistic, one of the causes of the non-significant decrease in the groundwater level could be the increase of pastures and agricultural lands. Kendall's tau value for the surface temperature also showed a negative value (-0.24). According to this result and the sensitivity of the evaporation parameter to temperature, we can point to the role of this parameter in reducing evaporation in the sub-basin of the Hor al-Azim wetland. The northwest and southeast regions have the highest temperature up to a part of the central region of the sub-basin. The western part, which includes the border of the Hor al-Azim wetland, has the lowest temperature, and most of the central part has the lowest temperature, one of the causes of which can be the presence of vegetation and the development of agricultural lands. The time series graph of precipitation showed that the parameter of precipitation in the years 2017 to 2020 had an upward trend, which led to recent floods in the studied area. The results of the Mann-Kendall test for the general trend of evaporation and transpiration parameters, ground surface temperature, and precipitation in the sub-basin of the Hor al-Azim wetland did not show a significant trend. Using the Global Surface Water Explorer (GSWE) data, the occurrence of water, the intensity of water changes, and the seasonal change of water on the wetland were studied for the period of 1984-2021. The study of this dataset confirmed the human interference (creating the Karkheh Dam and draining its lake) and the occurrence and effects of the flood on the sub-basin of the Hor-al Azim wetland. The results of Spearman's correlation test also showed that climate changes such as changes in precipitation patterns and human activities can become factors that affect the surface of the water body of Hor al-Azim Wetland. The results of this research can be used in the management of Hor al-Azim wetland and wetlands with similar conditions.
Geographic Information System (GIS)
Sakine Koohi; Asghar Azizian
Abstract
Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts ...
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Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts of the world in different spatial resolutions. DEMs can be classified into two categories of high (small pixel size) and low (large pixel size) resolution DEMs. Several studies have addressed the vertical accuracy of different digital elevation datasets especially in countries lacking access to high quality ground-based data. Despite the widespread application of these products, vertical accuracy of these datasets in different land uses has not been addressed in Iran and most engineering studies use 1:1000 and 1:2000 topographic maps which are very expensive and time-consuming to obtain. The present study seeks to assess vertical accuracy of different resolution DEM datasets used to estimate elevation in various land uses in two Iranian provinces of Qazvin (urban, agricultural lands, garden, and forest, mountainous areas, plains, and rivers) and Mazandaran (urban, agricultural, forest/mountain, plains, and rivers). Materials & MethodsASTER and SRTM DEMs with a resolution of 30-meter and SRTM DEM with a resolution of 90 m resolution were collected in the present study to investigate their vertical accuracy in various land uses of Qazvin and Mazandaran provinces. Several topographic maps and GPS based datasets of the study areas were also investigated for a better assessment of these DEM datasets. Finally, common statistical measures such as standard deviation (SD), mean absolute difference (MAD) and root mean square error (RMSE) were used to compare remotely sensed DEMs with ground-based observations. Results & DiscussionFindings indicated that 30m SRTM DEMs showed a better agreement with ground-based observations in both study areas. RMSE of this dataset in Qazvin and Mazandaran provinces equaled 3.8m and 5.8 m, respectively. Results also indicated that in 30m SRTM DEM, 87% of points in Qazvin and 79.7% of points in Mazandaran provinces showed a lower than 5m mean absolute difference (MAD), while in the case of 30m ASTER DEM 79% of points in Qazvin and 53% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). For 90m STRM DEM, around 29% of points in Qazvin and 74% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). Although 90m SRTM DEM did not work efficiently in Qazvin province, its result in Mazandaran province was almost as efficient as 30m SRTM dataset. Assessing the vertical accuracy of different elevation datasets in different land uses indicated that 30m SRTM showed an acceptable result in most land uses except for mountainous areas and forests. This was mainly due to forest canopies blocking the radio waves penetrating such areas and low density of points generated by STRM sensors. Moreover, 30m ASTER did not show an acceptable result in most land uses except for plains in Qazvin along with urban and agricultural land uses in Mazandaran. Despite having a lower resolution, 90m SRTM worked better than 30m ASTER. However, 90m SRTM showed considerable errors in mountainous, urban and forest land uses, and therefore it shall not be used in such areas. ConclusionResults indicated that 30m STRM DEM is a valuable resource which makes elevation estimation for areas lacking ground-based information possible. Moreover, the type of land cover has a significant effect on the vertical accuracy of elevation datasets and thus, increased vegetation results in decreased accuracy of DEM datasets. Therefore depending on the land cover type in the study area, ground control points can be used along with remotely sensed DEMs to decrease errors.
Remote Sensing (RS)
Fateme Amjadipour; Hamid Dehghani; Mojtaba Behzad Fallahpour
Abstract
Extended AbstractIntroductionThe complexity of interpreting SAR radar images makes target recognition difficult despite many studies performed in this regard. Various factors including material and dimensions of the target, radar frequency, polarization, target shape, and vision geometry affect the response ...
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Extended AbstractIntroductionThe complexity of interpreting SAR radar images makes target recognition difficult despite many studies performed in this regard. Various factors including material and dimensions of the target, radar frequency, polarization, target shape, and vision geometry affect the response received from SAR radar. Investigating these characteristics facilitate target recognition.Synthetic Aperture Radar sensors are widely used in both airborne and space-borne systems. Space-borne systems equipped with Synthetic Aperture Radar sensors are side-looking and because of their nature as a radar, many parameters such as vision geometry will affect their ability (or disability) in seeing the target and change the resulting images. Therefore, it is very important to study the effect of this parameter to identify the target and interpret these images. The visibility geometry includes incidence angle, look angle, and the direction of the imaging. Materials & MethodsThe present study investigates visibility geometry in revision images and ascending and descending scenes. To reach this aim, a single scene captured by Sentinel-1 from a residential area is examined in different images with different directions, incidence angles, and imaging time. Results indicate that incidence angle changed slightly (4 degrees) and thus, left a negligible effect on the image. Moreover, there was a 5-day time interval between the captured images and therefore, this factor had the least effect on Synthetic Aperture Radar images. Unlike optical images, the direction of imaging had the greatest effect on SAR images. For an instance, a single ramp behaves differently in two images captured from different directions. Therefore, direction of imaging and its effects on seeing (or not seeing) the target are analyzed in ascending and descending images. Results & DiscussionThe effect of vision geometry on radar images has been rarely investigated in similar studies, and the present paper has taken a step forward in this regard. Fallahpour et al., (2016) have simulated the effect of incidence angle, which is a parameter of visibility geometry and the shape of the targets in SAR images. Shapes such as cones, cylinders, and cubes were used in this simulation representing real buildings, niches, tree trunks, etc. which are very common in SAR images. Moreover, behavioral pattern of the aforementioned geometric shapes were simulated at different landing angles (30, 40, 45, 50, and 60 degrees) from the viewpoint of SAR imaging systems to reach a more comprehensive result.Then, various studies investigating the effects of incidence angle and direction on radar images have been reviewed. Some of these studies have dealt with the effect of these parameters on the classification of radar images. Dumitru et al. have examined the effects of resolution, pixel spacing, patch size, path direction, and incidence angle on the classification of TerraSAR-X images. To reach this aim, they have selected an optimal TerraSAR-X product and then specified the number of classes. They have finally investigated the effects of incidence angle and path direction on the classification results. Results indicated that images captured in ascending direction were 80% better than the descending images. Moreover, images captured from an incidence angle near the upper wing showed better results. ConclusionThe present study has investigated the effect of usually neglected parameter of visibility geometry on SAR images. Images were captured by Sentinel-1 in both ascending and descending directions. Following speckle noise reduction and geometric correction, incidence angle and its effects on the detected changes were investigated. The slight 4-degree changes of this parameter have not caused the resulting changes. Moreover, there was a 5 day time interval between these two images and thus, time could not be an effective parameter too. Results indicate that detected changes in the residential area were due to a change in the direction of imaging. Changes of this parameter can result in seeing (or not seeing) the target, and therefore, it is very important to investigate the effects of this parameter and correct it.
Geographic Data
Keyvan Mohammadzdeh; Sayyed Ahmad Hosseini; Mehdi Samadi; Ilia Laaliniyat; Masoud Rahimi
Abstract
Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms ...
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Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms affect the flow in water bodies, sediment transport, soil production, and climate at a local and regional scale. Identification and classification of landforms are among the most important purposes of geomorphological maps and also a fundamental step in the process of producing such maps. Geomorphologists have always been interested in achieving a proper and accurate classification of landforms in which their morphometric properties and construction processes are clearly indicated. The present study has attempted to develop a new method and identify the relationship between morphometry of landforms and surface processes using a multi-scale and object-based analysis. Extraction and classification of landforms are especially important in mountainous areas, which are considered to be dynamic due to their special physical and climatic conditions. These areas are often remote and sometimes unknown. Mountainous topography has also made them difficult to access. However, they are of great importance due to their impact on the macro-regional system. Because of this significant importance, Maku County was selected as the study area.
Materials and methods
Maku County is located in northwestern Iran (West Azerbaijan Province) which borders Qarasu River and Turkey in the north, Aras River and the Republic of Azerbaijan in the east, Turkey in the west, and Shut County in the south. This County is located between 44° 17' and 44° 52' east longitude and 39° 8' and 39° 46' north latitude. The present study takes advantage of satellite images (sentinel-2A) with a spatial resolution of 10 m, derivatives of DEM layer (slope, maximum curvature, and minimum curvature, profile and plan curvature) and object-based methods to identify and extract landforms of the study area precisely.
Discussion and results
The present study applies various functions and capabilities of OBIA techniques to extract landforms precisely. These functions include texture features (GLCM), average bands in the image, geometric information (shape, compression, density, and asymmetry), brightness index, terrain roughness index (TRI), maximum and minimum curvature, texture, and etc. The image segmentation scale was first optimized in the present study using ESP tools and objects of the image were created on three levels (9, 17, and 27 scales). In the next step, sample landforms were introduced, membership weights were calculated and defined for the classes in accordance with the fuzzy functions, and finally, 14 types of landforms were extracted using object-oriented analysis.
Conclusion
Fuzzy method includes boundary conditions, defines membership function, and constantly considers landform changes in class definition. Thus, it seems to be ideal for the purpose of the present study. The present study used two types of data (data derived from satellite imagery and DEM layer) along with OBIA approach to extract landforms. Classification of landforms based on fuzzy theory makes it possible to collect more comprehensive information from the earth's surface. Results indicate that fuzzy object-based method has classified landforms with an accuracy of 87% and a kappa index of 85%. Considering the resolution of the images applied in the present study, all features were extracted with an acceptable accuracy except for debris. This can be attributed to the fact that debris is usually accumulated in a small area on steep mountainsides, and thus remains hidden from satellites in nadir images. OBIA approach shows a high efficiency because it can combine spectral characteristics of various types of data (i.e. images and DEM data) and their derivatives while analyzing the shape of the segment, and size, texture and spatial distribution of segments based on their class and other neighboring segments.
Aerial photography
Zahra Azizi; Mojdeh Miraki
Abstract
Extended Abstract
Introduction
Advances in computer vision and the development of remote sensing instruments have made indirect measurement of tree features possible. Individual tree crown delineation is an important step towards information collection and mapping trees in an urban area. This information ...
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Extended Abstract
Introduction
Advances in computer vision and the development of remote sensing instruments have made indirect measurement of tree features possible. Individual tree crown delineation is an important step towards information collection and mapping trees in an urban area. This information is then used to help planners design strategies for optimization of urban ecosystem services and adapt to climate changes. Common methods of Individual tree crown delineation (ITCD) were based on very high-resolution satellite or Light Detection and Ranging (LiDAR) data. However, satellite data are usually covered by clouds and thus, cannot be appropriate for the measurement of individual trees. Aerial Laser scanning is also relatively expensive. Remote sensing with unmanned aerial vehicle (UAV) captures low altitude imagery and thus, is potentially capable of mapping complex urban vegetation. Automatic delineation of trees with UAV data makes collection of detailed information from trees in large geographic and urban regions possible. Therefore, a multirotor UAV equipped with a high-resolution RGB camera was used in the present study to obtain aerial images and delineate individual trees.
Materials & Methods
The present study has compared the performance of Inverse watershed segmentation (IWS) and region growing (RG) algorithms using point clouds derived from Structure from Motion (SfM) algorithm and UAV imagery captured with the aim of tree delineation in Fateh urban forest located in Karaj. Region growing (RG) is used to separate regions and distinguish objects in an image. It starts at the initial seed points and determines whether the neighboring pixels should be added to the growing region. If the neighboring pixels are sufficiently similar to the seed pixel, they are labeled as belonging to the seed pixel. To implement the algorithm, the window size and the growing threshold were set for all resolutions. In order to obtain the most appropriate size for the search window, we examined different window sizes with a growing threshold of 0.5 for each resolution. Individual trees delineation was performed for each CHM resolution in the three different sites using "itcSegment" package of R software. Watershed segmentation algorithm is also similar to RG algorithm. The only difference is that it sets the growing seeds at the local minima. In other words, the local maxima in this algorithm change into local minima and vice versa. Inverse Watershed Segmentation (IWS) method was implemented in ArcGIS 10.3 because of its capability in delineation of distinct tree entities. In the summer of 2018, three sites with different structures including a mixed uneven-aged dense stand (site 1), a mixed uneven-aged sparse stand (site 2), and a homogeneous even-aged dense stand (site 3) were surveyed and photographed, and a 3D point cloud was extracted from the images. Then, the performance of algorithms was tested using a series of different canopy height models (CHM) with spatial resolutions of 25, 50, 75, 100, and 120 cm. To generate these models, digital surface model (DSM) was subtracted from digital terrain model (DTM). Results of individual tree delineation were validated using data collected in field observation of the aforementioned sites.
Results & Discussion
Results indicated that both RG and IWS algorithms yielded their best performance in the dense homogeneous structure. Moreover, the number of segments resulting from CHMs with low resolution was often much more than the actual number of trees. This was due to the occurrence of several peaks within an individual tree crown especially in low resolution images. With an F-score of 0.88, homogeneous even-aged dense stand (site 3) showed the highest overall accuracy in RG algorithm with a pixel size of 75 cm. Generally, results indicated that RG is an appropriate approach for individual tree delineation due to its flexibility in delineation of varying crown sizes. Furthermore, this method does not assume a circular shape for tree crowns and thus, is capable of detecting and segmenting irregular crowns. Generally, delineation of trees in urban forests using CHMs obtained from UAV-captured aerial imagery was highly accurate in homogeneous sites, while such models lacked efficiency in heterogeneous sites.
Geographic Data
Shahin Jafari; Saeid Hamzeh; Hadi Abdolazimi; Sara Attarchi
Abstract
Extended AbstractIntroductionHuman activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential ...
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Extended AbstractIntroductionHuman activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential for taking measures and making decisions based on the goals of sustainable water and soil resources management. Over the past decade, many researchers around the world have been attracted to remote sensing and especially satellite remote sensing and used this technology to detect such changes over time. The present study has used Landsat (monitoring the area of water body), TRMM (monitoring rainfall), MODIS (monitoring vegetation and evapotranspiration), Grace (monitoring groundwater) satellite images available in Google Earth Engine to study last two decades changes (from 2000 to 2019) in Maharloo wetland, Goshnegan catchment and their surroundings. Materials & MethodsMaharloo wetland is located in Fars province and Goshnegan catchment (426 square kilometers). The present study has used Landsat 7 and 8 images to extract the area of water body, TRMM images to obtain precipitation values, MODIS products to calculate NDVI and evapotranspiration, and data received from Grace to extract changes in groundwater level. These satellite images were available in Google Earth Engine. Mann-Kendall test was also used to assess the overall trend of the aforementioned factors. Results & DiscussionThe automated water extraction index was used in the present study to identify and estimate the area covered by water bodies in the study area. The largest area belonged to 2006 (216.76 square kilometers) and the smallest belonged to 2018 (66 square kilometers). In 2000 (the beginning of the reference period), an area of 216.52 square kilometers was covered by this wetland which is close to what was observed in 2006. In 2018, this has reduced to 66 square kilometers. Thus, there is about 150.72 square kilometers (69.54 percent) difference between these two years. In 2009, the total area has reduced to 66.67 square kilometers. A numerical comparison between 2000 and 2019 also indicates a reduction of 91.17 square kilometers (42% decrease) in the total area covered by this wetland. Also, a 53.72 square kilometers (29.60%) difference was observed between the average area covered by the water body in the first and second ten years. Since calculated p-value value (< 0.00001) is less than the alpha level (0.05), so a significant trend was observed in the average annual data of the area covered by this wetland. Kendall's tau also indicated declining trend of the collected data. Groundwater level was calculated using data received from Grace Satellite to investigate the role of groundwater level in reducing the area covered by the water body. Results indicated that since 2008, groundwater level have always showed a negative value (a decreasing trend). For an instance, a groundwater level of -10.86 cm in 2019 indicates a decrease in the water level in the study area. As the calculated p-value (< 0.0001) is less than the alpha level (0.05), so a significant decreasing trend was observed in the groundwater level. Results of Mann-Kendall test (-0.6) also indicated that changes in water bodies, vegetation, rainfall and groundwater level had a decreasing, increasing, increasing and decreasing trend, respectively. No significant trend was observed in evapotranspiration. It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. ConclusionWetlands provide many ecological services including water treatment, natural hazard prevention, soil and water protection, and coastline management (Amani et al., 2019). Therefore, understanding the importance of wetlands and their management need to be seriously considered by relevant organizations in different countries of the world, and Iran is no exception. Satellite data and remote sensing methods and techniques are considered to be one of the most important and cost-effective methods of monitoring wetlands. The present study used satellite data collected by Landsat, MODIS, Grace, and TRMM to monitor water bodies, vegetation, groundwater level, and rainfall in Goshnegan catchment in which Maharloo wetland is located. The results of Mann-Kendall test showed a decreasing annual trend for changes in the average area of this wetland. This decreasing trend is considered to be a serious threat to human settlements around the wetland which can intensify over time. It will also affect the thermal islands of Shiraz and Sarvestan in near future. Obviously, management of agricultural and forest land uses with the aim of stopping their increasing trend can improve water balance in catchment areas. A 132.2 ha (approximately 36.16%) difference was observed between the average vegetation cover in this catchment area over the first and second ten years (233.4 vs. 365.6 ha). It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. Due to the proximity of this wetland to the city of Shiraz and its importance as an ecological and tourist attraction, it is suggested that related authorities (Department of Environment and Water Organization) demarcate lake bed and riparian zone with the help of remote sensing researchers to improve the management of this wetland and prevent it from drying up. Also, it is suggested that the Organization of Agriculture Jihad review and improve water consumption methods and cultivation patterns in the areas surrounding this wetland.
Spatial planning with regard to military defense
Mahshad Bagheri; Amir Ansari; Azadeh Kazemi; Mahmoud Bayat; Sahar Heidari Masteali
Abstract
Extended Abstract
Introduction
Proper distribution of urban green space is one of the most important issues in urban planning and especially in management of urban green space. In other words, the physical expansion of cities destroys surrounding natural environments and arable lands. It also results ...
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Extended Abstract
Introduction
Proper distribution of urban green space is one of the most important issues in urban planning and especially in management of urban green space. In other words, the physical expansion of cities destroys surrounding natural environments and arable lands. It also results in fundamental changes in ecological structure and functionof urban landscape, along with gradual changesin spatial structure and patterns of this landscape (Wang et al., 2008). Since ecosystem processes depends on its structure, landscape metrics have been accepted as a very useful tool for expressing the structure of urban green space and its human-causedchanges (Hessburg et al., 2013).There has always been discussion onacceptable per capita green space or changes in green space over time and place. Iranian cities are no exception in this regard, thougheven a city enjoying a high ratio of green space per capita may still lack enough green space per capita in some districts. This suggests the necessity of investigating various measures and avoiding studies limited to per capita green space and urban forestry. (Botequilha and Ahren, 2002). If as an ecological structure,green space is proportional to populationcomposition and distribution, ecological performance and land use type of an urban area, it can have important ecological functions. Since most studies on urban green space have primarily focused onfinding a proper location, calculating appropriate per capita green space and introducing suitable species for green space, investigatingthe spatial distribution of urban green spaceseems to be of great importance. Therefore, the present study seeks to investigate the spatial pattern and distribution of public green space in Khomein using a landscape approach.
Materials and methods
Study area
The study area, Khomein, is bounded by agricultural lands and gardens in its northeast, west, and partly in its south. Only the main area of urban texture is located on barren lands (Abbasi et al., 1986). The study area includes four districts of Khomeinin which the pattern of green space distribution isinvestigated.
Methods
Sentinel-2 images were used in the present study. Satellite images were processed and then, their geographical effects were extracted inthe first step of classification. Different indices were defined for each patch of the image and using supervised method, images were classified into four classes of agricultural lands, barren lands, urban parks and residential areas in accordance with the training data. Visual method was used to improve classification results. In this method, classification results are matched with the imagesand possible errors are rectified. Google Earth was used to evaluate the accuracy of results obtained from classification of satellite images. In the next step,the base map of the present study was produced and then, the layer containing urban parkswas integrated with the layers prepared for four districts of Khomein. It should be noted that the present study focuses on urban parks prepared by the municipality for public use and does not include other urban green spaceareas such as the green belt or private gardens, etc.
To study the spatial distribution of green space, measures of land cover were calculated and analyzed in each of the four districts. Geographic Information System (GIS) and Landscape Measurement Analysis Program (FRAGSTATS) were among the tools used to calculate and measure landusein the present study. Landscape metrics used in the present study included:
Landscape Shape Index (LSI) which measures the area of the largest patch in a class divided by the total area of that landscape (multiplied by 100 to convert to percent)
Euclidean Nearest Neighbor distance (ENN) which is the average distance between patches in a class. Meter is used as the standard unit of measurement for this index.
Perimeter /Area Ratio (PARA) which is the ratio of the perimeter of the patch (m) to its area (m2). This measure lacks a specific unit and for PARA> 0 it is without a limit.
Number of Patches (NP) equals the number of patchesof the corresponding patch type (class).
Shape Index:sum of patches’ perimeter divided by the square root of the area of the patch (ha) for each class (class surface) or the entire patch (land surface). This index iscalculated for circle standard (polygon), or square standard (grid) and divided by the number of patches.
Largest Patch Index (LPI) which measures the area of the largest patch in a class divided by the total area of the landscape (multiplied by 100 to convert to percent)
Mean Patch Size (MPS) which measures the average size of a patchin the landscape.
Results and Discussion
District 3 ranked highest and district 1 ranked lowest in ENN indexindicating that urban green space patches in this district were closer together, while green space patches in the third district were limited and far apart from each other. Regarding LPI index, the second district ranked thehighest and the third district ranked the lowest indicating that the largest urban parks in this districtwere much smaller than other districts. Other district had a relatively acceptable statusin this respect. In MPS index, district 2 with 697 patches ranked highest and district 1 with 564 patches ranked lowest indicating that average green space patches in district 1 were smaller. This was also confirmed by maps prepared based on other metrics.Regarding the LSI index, district 1 ranked highest and district 2 ranked lowest, while districts 3 and 4 had a similar status in this measure. The first district had the highest number of patches (NP), while the third district had the lowest NP. The highestPARA ratio was observed in District 1, and the lowestin District 4, while districts 3 and 2 ranked near the middle. In Landscape shape index which increases with the heterogeneity of patches,district 1 (with 13.12) ranked highest and District 3(6.64) ranked lowestwhiledistricts 2 and 4 ranked near the middle.This indicates the heterogeneous shape of green space patches in district 1, while showing that patches of green space in district 3 are very simple and homogeneous.Finally it should be noted that calculating landscape metrics for the four districts ofKhomein indicated a very low per capita green space in this city and also absence of a proper and equitable spatial distribution.
Conclusions
Calculatinglandscape metrics in the four districts of Khomeinindicated thatcompared to other districts, district 1, located in the southern part of the city, has a more desirable status in indices such as PARA, LSI, NP, and ENN. At the same time, district 3, located in the southeastern part of the city, has the least appropriate status regarding these metrics indicating the necessity of a comprehensive analysis of green space areas in this district in near future. Urban managers and planners need to focus on this district and its green space, and if possible find appropriate sites for future green space areas in this district.Although the status of districts 2 and 4, located in the west and north of the cityrespectively, were not very desirable, theyranked higher than districts 3in NP, LPI, and MPS. Using GIS in combination with satellite imagery, and land use metrics provided an innovative way to study the gradual spatial changes in urban green space. Results of landscape metrics analysis indicated an unbalanced distribution of land use in the four urban districts in this study.
