Extraction, processing, production and display of geographic data
Hossein Asakereh; Somayeh Taheri Alam; Nosrat Farhadi
Abstract
Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological ...
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Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological phenomena, primarily vegetation, which reflects an intricate pattern of changes in climatic elements, particularly temperature, and precipitation. Although the substantial role of climatic elements on the density and geographical distribution of vegetation has been confirmed, it is arduous to estimate the relationship between climate changes and vegetation due to the complexity of the mechanism of different characteristics of climatic elements (such as the amount, type, intensity, season, continuity, etc.), feedback processes, and also the response time of the vegetation to climatic changes.
Materials and Methods
In the current research, the gridded data of the Normalized Difference Vegetation Index (NDVI), a product of the MODIS terra, was used from 2001 through 2016. The data were extracted from a GIOVANNI website. In the present study, Iran's vegetation density classes were determined based on quantitative methods, and the geographical distribution of two-half parts of the understudy periods was compared.
Results and Discussion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. The finding revealed that the NDVI has a direct relationship with the precipitation. Accordingly, the northern, northwestern, and western regions, as wet regions in Iran and comprise proper soil, included high NDVI.
Dividing NDVI data into two 8-year periods revealed that in the first 8 - year, despite the high amount of precipitation, the NDVI was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Conclusion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. Our finding showed that the spatial distribution of NDVI has a direct relationship with the precipitation. Comparing two - half of understudy data showed despite the high amount of precipitation, the NDVI in the first half was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
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%.
Arash Karimi Zarchi; Reza Shahhoseini
Abstract
Extended Abstract Introduction Heat island phenomenon occurs when the land surface temperature and the air temperature in urban areas are higher than that of the surrounding areas. This temperature difference is shown as the urban heat islands on thermal maps. Information obtained from the urban heat ...
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Extended Abstract Introduction Heat island phenomenon occurs when the land surface temperature and the air temperature in urban areas are higher than that of the surrounding areas. This temperature difference is shown as the urban heat islands on thermal maps. Information obtained from the urban heat islands can be a useful source in urban planning applications. The availability of reliable information about the urban heat islands plays an important role in predicting and preventing the occurrence of many heating risks in urban areas. One of the common methods of calculating heat islands intensity in urban areas is the use of two temperature sensors installed in the city and around it. Given the limited temperaturemeasuring stations, there is no accurate estimate of the urban heat islands. With the introduction of Remote Sensing technology into the space arena, and with the help of satellite images processing, a precise map can be produced for the land surface temperature, i.e. a precise estimation of the urban heat islands is obtained by calculating the pixels temperature difference at the urban areas and around them. Therefore, one of the important issues in such studies is to detect the urban and non-urban pixels and to separate them from each other. Materials&Methods The most important reason for the occurrence of the heat island phenomenon is the change in land use from rural to urban, which is well exhibited in the urban cover index maps.In this paper, in order to measurethe intensity of surface urban heat islands, a method based on generating the urban percentage map was proposed by combining the Land Surface Temperature (LST) map, the Normalized Difference Built-up Index (NDBI) map and the Normalized Difference Vegetation Index (NDVI) map.Considering the relationship between the land surface temperature and the land cover type, it can be said that the relationship between the land surface temperature and the urban percentage map follows a linear function which can be fitted to the land surface temperature graph in terms of land cover type. Finally, the Urban Heat Island Intensity (UHII) map was calculatedfrom the slope of the fitted line.In order to evaluate the strengths and weaknesses of the proposed method, a classification-based method was used to separate the urban and non-urban pixels and to calculate the urban heat island intensity. The proposed method was implemented on the Landsat-7 ETM + satellite data in the city of Rasht and on the Landsat-8 OLI / TIR satellite data in the city of Langroud. Results&Discussion The results of the classification-based method indicated a large difference between the maximum and the minimum temperature of the urban areas, which led to a high-temperature changein all land cover typesin the study area. Therefore, the use of the average temperature of each class to calculate the heat island intensity is not a suitable method and the accuracy of the heat islands maps is not high and they cannot be used in applications that require high precision.Although, this problem can be solved by increasing the number of classes, increasing the number of classes requires more training data and a sensor with higher spatial resolution. By contrast, the results indicated that the proposed method (based on the urban percentage map) had a high accuracy for calculating the urban heat island intensity which was similar for both study areas. Also, fitting a linear function to the values of land surface temperature and the urban percentage map led to decreasing the effect of suspicious pixels (noisy pixels) on the overall accuracy of the estimation of the urban heat island intensity. Meanwhile, the results obtained on two datasets indicated that this method did not require any training data or any other background information about the study area and it can be applied for many satellite images having thermal band with any spatial resolution. However, because of the ineffectiveness of urban cover indicators in desert areas, the heat islands intensity in these regions was underestimated. Conclusion In applications that do not require high accuracy in calculating the urban heat island intensity, and there are high spatial resolution satellite imagery and sufficient training data in a region, the use of a classification-based approach seems to be suitable. Since the collection of such data and information is costly, a new method based on the urban percentage map was proposed in this paper by fitting a line to the LST parameter diagram in terms of the NDBI index for measuring the heat island intensity. The results indicated the higher efficiency and accuracy of the proposed method compared to the conventional classification-based methods for calculating the urban heat island intensity.
Mohammad Mahdi Khoshgoftar; Mehdi Akhoondzadeh Hanzaei; Iman Khosravi
Abstract
Introduction
Drought is a critical climate condition affecting many places on Earth. Drought severity is often measured using a combination of different variables including rainfall, temperature, humidity, wind, soil moisture, and steam flow. During the last decades, Iran has suffered from drought conditions ...
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Introduction
Drought is a critical climate condition affecting many places on Earth. Drought severity is often measured using a combination of different variables including rainfall, temperature, humidity, wind, soil moisture, and steam flow. During the last decades, Iran has suffered from drought conditions and it may suffer more in future. The frequent occurrence of drought in Iran is mainly due to lack of sufficient precipitation and improper water management system. Drought is often categorized into three types: meteorological, agricultural, and hydrological. There are various methods for measuring and quantifying drought severity. The most commonly used ones are Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI). Remotely sensed data can also be used for monitoring drought condition. The most widely used ones are Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Condition Index (VCI), Temperature Vegetation Index (TVX) and NDVI deviation Index (DEV). Neural Network (NN) and Autoregressive Integrated Moving Average (ARIMA) are two of the most widely applied methods for modeling and monitoring drought severity indices.
In this paper, monthly time series data (2000 to 2014) of three remotely sensed indices (i.e., NDVI, VCI, and TVX) and one meteorological index (i.e., SPI) were applied for modeling drought severity. In addition, the NN and ARIMA were developed for modeling these indices.
Materials & Methods
Data used in this paper were the time series of NDVI, VCI, TVX, and SPI. The study area in this paper was Arak, center of Markazi province. It has cold and wet winters with warm and dry summers. ARIMA and NN were employed for modeling indices.
ARIMA model is generally derived from three basic time series models: Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA). These basic models are used with static time series, i.e., they have constant mean and covariance in relation to time.
Usually, NN method has three layers. The first layer or the input layer introduces data to network. Input data is processed in the second layer or the hidden layer. Finally, the output layer produces the results of the input data. In this paper, single hidden layer feed forward network, which is the most widely utilized NN form, was employed for modeling indices.
Results & Discussion
After implementing NN and ARIMA models on the time series data, the performance of the models was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The RMSE obtained by NN and used for modeling NDVI, VCI, TVX, and SPI indices of Arak were 0.1944, 0.2191, 0.1295, and 0.2990, respectively. In addition, RMSE obtained from ARMIA, and used for modeling these indices were 0.0770, 37.2318, 0.2658, and 1.3370. In another experiment, the correlation between remotely sensed indices and SPI was studied. Among the remotely sensed indices, TVX shows the most powerful correlation with SPI.
Conclusion
In the present study, drought condition in the central region of Markazi province was studied during the 2000 to 2014 period. We used the time series of remotely sensed data (such as LST and NDVI) and meteorological data (such as SPI). Then TVX, VCI, and DEV indices were extracted from NDVI and LST data. NN and ARIMA were applied for modeling time series data. Based on the findings, it is concluded that NN is more successful and efficient than ARIMA for this study area. In addition, TVX, which is built based on NDVI and LST, had the most powerful correlation with SPI. This issue implies that both vegetation index and temperature index had an important role in modeling and monitoring drought condition.
Fatemeh Mohammadyari; Hamidreza Pourkhabaz; Morteza Tavakoli; Hossein Aghdar
Abstract
Knowledge of qualitative and quantitative characteristics of changes are extremely important in environmental planning, land use planning and sustainable development. Currently, using vegetation maps is one of the key factors in data production for macro and micro planning. In this research, information ...
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Knowledge of qualitative and quantitative characteristics of changes are extremely important in environmental planning, land use planning and sustainable development. Currently, using vegetation maps is one of the key factors in data production for macro and micro planning. In this research, information of Landsat ETM + and OLI sensors were used to display the temporal and spatial changes of vegetation in Behbahan city in 1999 and 2013 and the value of NDVI index was calculated for two years. In order to evaluate the quality changes of vegetation, the numerical values of the index were classified into 4 classes of different lush green vegetation including land with excellent, very good, good, and poor coverage. Then, the changes were determined using CROSSTAB. The results showed that the qualitative and quantitative changes in vegetation for the study area have been extensive over 14 years, so that, the area of lands with excellent, very good and poor coverage has increased and the area of landswith good coverage, has decreased. The greatest increase in areashas occurred in lands with excellent coverage, so that, it has increased from 5069.76 hectares (ha) in 1999 to 7735.5 ha in 2013. Also, the highestdecrease in areas has occurred in lands with good coverage thathas reached from 34061.4 ha to 27434.43 ha. Finally, the regression equation was obtained to show better relationship between the two parameters of vegetation and temperature. The results confirmed the point that the areas covered with vegetation have lower temperature and vegetation has cooling effects on the surrounding. Therefore, the degradationof the region’s vegetationwill be followed by the warming of the city and many other environmental consequences.
