Geographic Data
Foroogh Mohammadi Ravari; Ahmad Mazidi; Zahra Behzadi shahrbabak
Abstract
Extended Abstract
Introduction
Replacing natural vegetation cover with impermeable urban surfaces) stone, cement, metal, etc.) has resulted in increased land surface temperature which is considered to be the most important problem of urban areas. Distinct temperature difference between the city and ...
Read More
Extended Abstract
Introduction
Replacing natural vegetation cover with impermeable urban surfaces) stone, cement, metal, etc.) has resulted in increased land surface temperature which is considered to be the most important problem of urban areas. Distinct temperature difference between the city and the surrounding areas is called heat island (Melkpour et al., 2018). Increased land surface temperature and resulting heat islands in urban areas built without proper preplanning (Khakpour et al., 2016) especially in developing countries such as Iran experiencing a rapid growth rate have resulted in widespread environmental problems. Heat islands mainly occur due to the presence of man-made surfaces which prevent the reflection of sunlight and result in temperature increase. In general, urban heat islands result in increased air and land surface temperature and thermal inversion (Gartland, 2012).
Methodology
The present study applies a statistical-analytical research method based upon statistical data received from meteorological stations and extracted from satellite images. Climatic data recorded from 1976 to 2020 in Yazd Meteorological Station were retrieved from the General Meteorological Department of Yazd Province and used to measure temperature changes. Urban climate studies mainly take advantage of long-term patterns and thus, the present study has applied the common Man-Kendall method to measure the trend of temperature changes in warm season (July, August, and September). Also, satellite images collected by Landsat 4-8 in a 33-year period, including four statistical periods with a time interval of 11 years (the average recorded in July, August and September of 1987, 1998, 2009 and 2020), have been used to extract heat islands of Yazd city in warm seasons. These images collected under clear weather conditions were retrieved from the United States Geological Survey website (http://glovis.usgs.gov/) in the WGS-1984 UTM image system. NDVI index was used to investigate the vegetation cover. Main land uses discussed in the present study included barren lands, urban areas, vegetation cover and roads. Sample land uses were collected from Google Earth and visually interpreted in ArcMap. Maximum likelihood algorithm was used for the classification process. Finally, Land Surface Temperature was extracted from satellite images and compared with air temperature trend using the Mann-Kendall test.
Results & Discussion
Results indicate that due to thicker vegetation cover in summer, there has been a negative relationship between the vegetation cover and land surface temperature. In other words, land surface temperature has increased with decreased vegetation cover and vice versa. Types of land use identified in satellite images collected from Yazd city have showed that the city has experienced a widespread physical expansion during the 33-year statistical period regardless of the season under investigation and thus, built-up urban land use class has expanded significantly. As a result, vegetation cover has experienced a negative trend and decreased. Land surface temperature extracted from thermal images of Yazd city has proved parts of northwest and south of the city to be the core of its heat islands. This is due to the presence of barren lands, lack of evapotranspiration mechanisms, high heat absorption capacity and low conduction capacity. Man-Kendall test has found a significant increasing trend for temperature especially in recent years in which the temperature has increased about 2.3 °C. This is most possibly due to the increasing trend of urban population in recent decades, followed by increased residential structures and resulting heat island phenomenon.
Conclusion
In general, classification of urban land use types in Yazd has shown a significant physical expansion of the city during the statistical period. This physical development has occurred in all directions; beginning from the central and northeast-southeast parts, and moving towards northwest-southwest parts. Maximum NDVI was observed in a strip along the central part of Yazd in which vegetation cover is thicker. Green spaces are also observed in some areas of the city. Color spectrum of the LST map has shown relative changes of the ambient temperature in various parts of the city. High and very high temperature (between 41.5 and 50 °C) show the location of the heat islands on LST maps. Also, areas with a deep red color and a temperature above 50 °C have formed hot clusters formed or strengthened between 2009 and 2020 in the west and southwest parts of the city. Satellite images and related graphs have showed that in 2020, Yazd have witnessed a sharp increase in temperature and a heat island. Temperature data of Yazd Meteorological Station and Man-Kendall test have shown a significant increasing trend (about 2.3°C), especially in recent years. These are related to the urban population growth in recent decades, followed by increased urban structures (residential-commercial) and heat island phenomenon.
Leyla Karami; Seyed Mohammad Tavakkoli Sabour; Ali Asghar Torahi
Abstract
Extended Abstract
Introduction
Vegetation is considered to be one of the most important elements in all major ecosystems on the Earth. Thus, a proper understanding of vegetation and its growth trends and other environmental factors has always been of particular importance for environmental research. ...
Read More
Extended Abstract
Introduction
Vegetation is considered to be one of the most important elements in all major ecosystems on the Earth. Thus, a proper understanding of vegetation and its growth trends and other environmental factors has always been of particular importance for environmental research. Estimating vegetation phenology parameters (VPPs) requires continuous NDVI data collection over a specific period of time. However, soil moisture, cloud cover, and particulate matter may affect the energy reflected from the vegetation cover and result in noisy images or erroneous data. Vegetation phenology parameters cannot be extracted from raw data due to the presence of random errors. These errors do not follow the phenological process and thus, overestimate or underestimate NDVI and fail to produce accurate results. Smoothing functions and especially the TIMESAT model are used to resolve this issue and eliminate errors in the NDVI time series. There is still no general consensus on which function acts more efficient and accurate in the TIMESAT model especially regarding the highlands. Naturally, each method yields different results in different regions, and thus it is necessary to compare and evaluate different functions used in the TIMESAT model and determine their accuracy in producing a continuous time series. The present study aimed to evaluate the performance of various functions such as asymmetric Gaussian (AG), double-logistic (DL), and Savitzky–Golay (SG) used to extract VPPs especially in mountainous regions.
Materials and Methods
TIMESAT model is a time-series analysis model based on remote sensing (RS) vegetation indices. It includes three functions: Savitzky–Golay, asymmetric Gaussian, and double-logistic, which are used to smooth collected data and identify outliers. Savitzky–Golay is an adaptive-degree polynomial filter (ADPF). The other two functions fit the information using nonlinear functions. These functions use unmodified NDVI data as input to produce modified and smoothed NDVI output. Four wheat farms in cold and warm regions of Khorramabad were used in the present study to investigate plant phenological behaviors and extract VPPs. The northern and eastern parts of Khorramabad have a cold climate, while the southern and western parts have a warm climate. One-year time series (2020) data of MODIS sensor was used in the present study. Using the infrared and near-infrared spectral reflectance values, NDVI was calculated in the Google Earth Engine environment. Errors of the NDVI time series were first corrected and a phenology curve was produced for wheat in both warm and cold farms. Asymmetric Gaussian, double-logistic, and Savitzky–Golay filter functions were also used to adapt the NDVI data. Following the reconstruction of growth curves in the time series of vegetation indices and smoothing the curve, various VPPs such as start of the season (SOS), end of the season (EOS), middle of the season (MOS), length of the growing season (LOS), base limit and value, maximum NDVI, vegetation growth season range, large seasonal integral, and small seasonal integral were extracted.
Results and Discussion
The model indicated that on average, beginning of the wheat growing season (SOS) in the warm regions of Khoramabad coincided with the 31.5th day of the year in the Gregorian calendar, whereas it happened on the 90th day of the year in the cold regions, thus indicating a 1.5-2 month difference between the beginning of the wheat growing season in cold and warm regions. The wheat growing season ended (EOS) on the 163rd day of year in the warm regions and on the 193rd day in the cold regions. In addition, in order to analyze the effect of climate on VPPs such as SOS and EOS, a comparison was made between the parameters obtained from farms in warm and cold regions. On average, the peak of vegetation growth has occurred in late March (Mar. 28, 2020) in farms of warm regions while cold regions experienced the peak of growth on May 20, 2020. In other words, warm regions have experienced peak growth approximately two months earlier than cold regions. Finally, the models were assessed and obtained values were compared with ground-based data collected in field surveys. Validation results showed that with an average RMSE of 2, Savitzky–Golay smoothing model reconstruct data more accurately as compared to asymmetric Gaussian, and double logistic function with an RMSE of 4 and 11, respectively. In other words, Savitzky–Golay estimates SOS and EOS with a higher accuracy and lower errors.
Conclusion
Findings indicate that Savitzky–Golay filter outperformed asymmetric Gaussian and double logistic functions in extracting VPPs in mountainous areas. Accordingly, it is suggested to use Savitzky–Golay in future studies aiming to investigate the phenological behavior of different vegetation covers in other Iranian highlands. The study has also showed that different climatic conditions within the study area affect plant phenological behaviors, which can lead to differences in SOS, peak of growing season, and EOS in different cold and warm regions of the province. Growing season of plants in cold regions of the province has occurred with an approximately two-month delay compared to the warm regions of the province.
Hamid Reza Ghafarian Malamiri; Hadi Zare Khormizi
Abstract
Introduction Investigation of vegetation changes can provide valuable information on global warming, the carbon cycle,water cycle and energy exchange. Satellite imagery timeseriesandremote sensing techniques offers a great deal of information on variations and dynamics of vegetation. Harmonic ANalysis ...
Read More
Introduction Investigation of vegetation changes can provide valuable information on global warming, the carbon cycle,water cycle and energy exchange. Satellite imagery timeseriesandremote sensing techniques offers a great deal of information on variations and dynamics of vegetation. Harmonic ANalysis of Time Series (HANTS) has been effectively used to eliminate missing and outliers in time series of vegetation indices and land surface temperature (LST). However, the algorithm has been less frequently used to detect changes in vegetation and phenology. HANTSalgorithm decomposes periodic phenomena into their components(different sines and cosineswith different amplitudes and phases). The value of phases and amplitudes contains valuable information that can be used to investigate variations and identify different characteristics of vegetation such as growth and phenology. The present study aims to determine changes in each componentof vegetation time series in Iranin the past (1982, 1983, 1984 and 1985) and in recent years (2015, 2016, 2017 and 2018). Materials & Methods A daily NDVI product of AVHRR sensor, with a resolution of 0.05 at 0.05 ° (i.e. AVH13C1) was used in the present study. To obtain reliable harmonic components (amplitude and phase images), a reliable curve has to be fitted on the primary time series data. To do so, first,parameters of HANTS algorithm were determined and then Root Mean Square Error (RMSE) of the curves fitted on data related to four one-year time series in the past year’s category (1982, 1983, 1984 and 1985) and four one-year time series in recent year’s category (2015, 2016, 2017 and 2018) was estimated. This classification (i.e. four one-year time series in the past and recent years) was used for two reasons. First, extraction and comparison of harmonic components in a single time series in the past and recentyears’ categories cannot reflect real changes, as these changes may occur under the influence ofimpermanent dynamics of vegetation, such as dryor wet periods. Second, with four one-year time series in the past category (1982, 1983, 1984 and 1985), and four one-year time series (2015, 2016, 2017 and 2018) in recent years, statistical comparison of the harmonic components through one-way analysis of variance becomes possible. Following the production of reliable harmonic components, variations of the harmonic components in recent years were compared with their variations in the past using difference method, and mean difference of the harmonic components’value in four one-year time seriesin the past and present categories wasdetermined using one-way analysis of variance. Finally, some maps were produced to exhibitthe significance of differenceinmeans. Results & Discussion According to the findings of the present study, mean RMSE of the fitted curves in the four one-year periods ofpresent and past time series were always less than 0.1 unit of NDVI. Moreover, mean RMSEof total area of Iranin the past and present time series were 0.037 and 0.039, respectively. This demonstrates high efficiency of the HANTS algorithm in elimination of missing and outlier data in the daily-NDVI time series ofNOAA-AVHRR. Results indicate thatrange of zero amplitude (the mean value of NDVI or the average vegetation coverage) decreasesin the central, eastern and northeastern regions of Iran atthe 95% probability level (F-value <0.05), whileit increases significantly (F-value <0.05)in the north, northwestern and western regions (especially, the Alborz and Zagros mountains). The meandifferenceof phases value in the four-time series of the past and recent years’categories wassignificant at the 95% probability level (F-value <0.05). Compared to the past time series, first harmonic phase average of total area of Iran in the new time series has decreased by almost 14 degrees. This decrease in the value of the annual and 6-month phases indicates a quicker growth phase and phenological processes of plants compared to past times. Conclusion Results indicated that HANTS algorithm can effectively eliminateand reconstruct outliers in the NDVI time series. Zero harmonic (mean value) represents the overall level of vegetation cover and the firstharmonic phase in a one-year time series determines the starting time of growth in seasonal plants or thosewith agrowth period of6-month or less. Annual Phase indicates the angular starting position of the annual cycles and the 6-month phase inherently indicates the fluctuation and angular position of a half-year or 6-month curve. However, interpreting 6-month amplitude and phases are difficult. As most changes are controlled by the first harmonic phase, the first harmonic phase in a one-year time series contains important information about the beginning of growth and the phenological processes of plants. Therefore, harmonic components of a periodic time series canbeusedto identify and determine changes in vegetation coverage and phenological processes.
Sara Nakhaee Nezhad Fard; Hamid Gholami; Davood Akbari; Matt W. Telfer; Marzieh Rezaee
Abstract
Extended Abstract Introduction Among all Earth’s ecosystems, arid and semi-arid regions (about 30% of the Earth’s land) have experienced significant degradation over the past century due to the intensive land use practices and the increasing effects of droughts and climate changes (Maynard ...
Read More
Extended Abstract Introduction Among all Earth’s ecosystems, arid and semi-arid regions (about 30% of the Earth’s land) have experienced significant degradation over the past century due to the intensive land use practices and the increasing effects of droughts and climate changes (Maynard et al., 2016). Remote sensing is capable of detecting several groups of disturbances and changes, and has been widely used as a toolto identify long-term changes. Recent technological advancements in the methodology of mapping and monitoring land cover changesprovide new opportunities for the utilization of satellite imageries with high temporal frequency. Image fusion technique has been applied in different fields of environmental science, such asmapping crop growth, studying daily pollution of water resources, studying patterns of short-time ecological changes, determining regions with short-term erosion risk, etc. Image fusion algorithms include color combinations in three bands ofRGBimages, statistical and multi-scale methods. The present study seeks toevaluate the efficiency of image fusion algorithms and select the best algorithm for mapping vegetation in SouthKhorasan Province. Materials and Methods Following the pre-processing ofLandsat 8 and MODIS images, six image fusion algorithms, including NNDiffuse, HPF, Brovey, Gram-Schmidt, PC and CN, were studied and evaluated usingdifferent statistical criteria. Three statistical indices, including Root MeanSquare Error(RMSE), Mean Absolute Error(MAE) and Mean Error (MEB)were usedto evaluate the aforementioned algorithms.Then, the best image fusion algorithm was used to merge two different images received from Landsat8 (30m) and MODIS (250m). Finally, two vegetation indices, including NDVI and HVCI, were usedto map vegetation in SouthKhorasan Province. Results and Discussion Results indicate that all six algorithms used in the present research can improvespatial resolution of the merged images. Compared to other 5 algorithms, NNDiffusecan merge thered and NIR bands of Landsat 8 and MODISwith a relatively higher accuracy. Therefore,NDVI extracted from this algorithm has the lowest RMSE and MAE compared to the original Landsat 8images. NDVI obtained from thefusion algorithms used in systematic-random transects of three land uses (including agricultural, urban and pastures) indicate that the index obtained from NNDiffuse algorithm has a better conformitywith the NDVI obtained from the original Landsat 8image. Then,redand NIR bands of Landsat8 and MODIS were combined forsimultaneous mapping of NDVI and HVCI in the case study area. Overall, a great part of SouthKhorasan Province has a vegetation cover of less than 10% and 40-50%, vegetation cover is only limited to small parts of the study area (agricultural land use and gardens). Conclusions Generally, accessing simultaneous satellite images with high spatial resolutions, such as the Landsat series, is considered to be a challenge in vast area. The present study took advantage of different algorithms for image fusion and vegetation mapping in South Khorasan Province. Image fusion techniques, such as integration of Landsat and MODIS images, can be very useful for mapping purposes. Evaluation of 6image fusion techniques indicated thatNNDiffuse algorithm is the most suitable method for mapping vegetation in the study area.
Fatemeh Firouzi; Taghi Tavosi; Peyman Mahmoudi
Abstract
Extended Abstract Introduction With recent advances in satellite remote sensing productions in past few decades, several indices have been provided for the study of vegetation dynamics, and especially for the assessment of drought impacts. Among these, two vegetation indices -Normalized Difference Vegetation ...
Read More
Extended Abstract Introduction With recent advances in satellite remote sensing productions in past few decades, several indices have been provided for the study of vegetation dynamics, and especially for the assessment of drought impacts. Among these, two vegetation indices -Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) - have gained the attention of various researchers. Therefore, the present study aims to investigate the reaction of these two vegetation indices (i.e. NDVI and EVI) to dry and wet years in a dry plain in Iran (i.e. Sistan plain in eastern Iran). Materials & Methods To assess the sensitivity of these indices to dry and wet years, two different databases were required. First, NDVI and EVI image base received from Terra satellite (MODIS sensor) for April, May and June 2000-2014, and downloaded from EOS website. Second, daily data base of Zabol synoptic meteorological station (for a statistical period of 30-years 1985-2014) received from Iran Meteorological Organization. After data acquisition, separate vegetation dynamics maps (for April, May and June) were produced for the study area based on the information derived through processing of MODIS sensor images (Terra satellite) using NDVI and EVI. Effective drought index (EDI) was used to determine the frequency of dry and wet years in Sistan plain. Results & Discussion Mapping of vegetation dynamics based on images received from MODIS sensor (Terra satellite) for a 15-year statistical period (2000 to 2014: April, May, and June) indicated that NDVI and EVI had significant differences in exhibiting the dynamics of vegetation in the study area. These differences were obvious in areas with average amount of vegetation (0.4-0.5 in both NDVI and EVI) and also in areas with sparse dispersed vegetation (0.3-0.4 in both NDVI and EVI). In average levels of vegetation, total area of vegetation calculated by EVI is much higher than what is calculated by NDVI, while in sparse and dispersed vegetation, total area of vegetation calculated by NDVI is almost higher than EVI. Subsequently by selection of a dry (2010-2011) and a wet year (2005-2006), we compared changes in total area of vegetation (average and sparse) calculated by NDVI and EVI. Regarding the response of these two indices to dry and wet years, it was concluded that NDVI shows a better and more logical response during droughts, while EVI provides better results in wet years. However, it should be noted that the mean annual precipitation of Sistan plain is so low (59 mm per year) and its evapotranspiration is so high (4800 mm per year) that precipitation does not play a significant role in vegetation dynamics of this plain. Therefore, water flow in Helmand River, which is the lifeblood of this desert, is much more important than this limited precipitation in Sistan plain; hence, we can conclude that meteorological drought monitoring indices cannot reflect the relationship between drought and vegetation dynamics in Sistan plain, and this makes it difficult to compare NDVI and EVI in the region. Conclusion In general, it can be concluded that NDVI is a more suitable index for dynamics of vegetation in plains such as Sistan, whose life depends not on precipitation but on water running in the river. Because of the computational nature of EVI, it responds better in areas with dense vegetation. According to the vegetation type obtained from MODIS sensor images and field visits, NDVI is a better index for these types of plains.
mohammad Rezaei; Elham Ghasemifar; Chenour Mohammadi
Abstract
Extended Abstract Introduction Vegetation plays an important role in the cycle of energy, carbon, hydrology and bio-geochemistry. The climate and vegetation have a mutual effect on each other. For example, the surface vegetation affects atmospheric patterns by affecting the surface albedo (which ...
Read More
Extended Abstract Introduction Vegetation plays an important role in the cycle of energy, carbon, hydrology and bio-geochemistry. The climate and vegetation have a mutual effect on each other. For example, the surface vegetation affects atmospheric patterns by affecting the surface albedo (which determines the amount of radiation available for global warming, low atmosphere and evaporation as well). Therfore, the long-term study of the effect of the remot linking patterns on the varibility of vegetation is essential. So far, no study has been done on the effect of remote linking patterns on the varibility of vegetions.Therefore, the main objective of this study is to detect the vegetation changes in the month of May in Iran in relation to the remote linking patterns of the North Atlantic Oscillation. In this regard, remote linking patterns, such as El Nino have a significant effect on the surface climate with their periodic oscillations (Glantz, 1991). Many studies have been carried out in relation to the remote linking patterns and climatic elements on regional scale, but the role of remote linking patterns in the vegetation changes is a new topic which has been brought up lately (Wang et al., 2004). The normalized difference vegetation index (NDVI) obtained from the remote sensing satellite data is widely used to examine the vegetation features. Vicent Serrano et al. (2004) identified the positive and negative trends between NDVI and NAO in the Northern and Southern parts of Iberian Peninsula, respectively, by investigating the relation of NDVI, the North Atlantic Oscillation index (NAO) and the precipitation. Gouveia et al (2008) extracted the NAO correlation in the winter with vegetation activity in the spring and summer seasons by the combination of NDVI and luminosity temperature. Cook et al. (2004), Stockli and Vidale (2004), Sarkar and Kafatos (2004), Mennis, (2001), Erasmiet et al., (2009) also showed that there was a relationship between the remote linking patterns and vegetation in different parts of the world. Lu et al. (2012), showed that the vegetation impressibility in china in El Nino phase is greater than that of La Nino phase. Materials & Methods In order to investigate the relationship between the North Atlantic Oscillation and vegetation changes in the month of May in Iran, the normalized vegetation index products of MODIS sensor (MOD13A3) were used during the statistical period of 2001-2014. By applying the NDVI 0.2 threshold on the average long-term map of the vegetation index for the month of May in Iran, the area with larger and equal vegetation of the desired threshold was separated. Then, due to the severity and weakness of the NDVI values, the aforementioned area was divided into 3 areas based on the values of NDVI in order to assess the sensitivity of each area with regard to the remote linking patterns of the North Atlantic Oscillation which, helps identify the relationship between each vegetation category (namely, thinned, medium and dense vegetation) and the North Atlantic Oscillation index. Results & Discussion Due to the existence of vegetation-free deserts in Iran, an area susceptible to vegetation was first separated based on the threshold of at least 0.2 of the NDVI values. This region has about 38.2% of the country’s total area. Due to the high spatial variations in the NDVI values, the area was divided into 3 classes of thinned, medium and dense vegetation based on 0.2 to 0.5, 0.5 to 0.7 and higher than 0.7 ranges. It was assumed that the area with thinned and dense vegetation had the highest and lowest sensitivity respectively, with regard to the changes of the remote linking patterns. The positive and negative phases of the North Atlantic Oscillation (NAO) have significant effects on the climate of Iran. For example, the amount of vegetation, precipitation and humidity advection in many parts of the West, Northwest, and Northeast of Iran in the February 2010 (as a negative phase), were much higher than that in the February 2014 (as a positive phase). A 14-year time series was prepared from the NDVI values of the May for 18363 points in Iran and, each point was calculated with the variations in the values of the NAO index of January to May in a Pearson correlation coefficient matrix (assuming that the NAO changes in January influence the vegetation of May in Iran). The results showed that the positive and negative correlation values in terms of spatiality can be observed in all regions without a regular spatial pattern however, the maps showed that negative correlation values have covered a wider range of Iran in January and February. This indicates that, in the positive phase of the pattern, the higher values of sea level pressure in the Azore region, coinciding with the poor moisture transfer and precipitation systems, have caused less vegetation in a few months later (May) in Iran. Conclusion Given the highest coefficient of determination obtained in February(0.77) in East Azerbaijan province, the vegetation values of May can be estimated for the index points located in the Northwest and western provinces using the state of NAO in the months of winter.
Ali Ahmadabadi; Amanollah Fathnia; Saeed Rajaei
Abstract
Abstract[1]
Vegetation cover has a high relationship with climatic conditions. Identification of the seasonal variation of plant growth to determine the response of ecosystems to climate change in seasonal and inter-annual time scales is decisive.To present a prediction model, 7 climatic elements including ...
Read More
Abstract[1]
Vegetation cover has a high relationship with climatic conditions. Identification of the seasonal variation of plant growth to determine the response of ecosystems to climate change in seasonal and inter-annual time scales is decisive.To present a prediction model, 7 climatic elements including precipitation, temperature and relative humidity (maximum, average and minimum) for a 20 year period (1987-2006) were converted into spatial data in 141 synoptic and climatological stations. The combination of maximum monthly NDVI values from NOAA-AVHRR images was extracted in the same period. Then climatic elements and NDVI entered the multivariate linear regression as independent variable and dependent variable respectively. The results showed that the highest correlation coefficient between climatic elements and the amount of NDVI was 0.82 and happens in May that is the peak of greenery. The least correlation in winter is due to the lack of sufficient tree growth. Taking into account the random error, the annual correlation coefficient of the model amount with computational mode is more than 93/0. In total, the computational value of May and June for 2004 and 2005 is close to the correlation coefficient of the model, but in the winter months, the correlation coefficient decreases due to lack of greenness.In 2006, there was less prediction due to more severe dryness in the late spring (June). In winter, the role of temperature control is more than rainfall and relative humidity, but with increasing temperature and decreasing precipitation and relative humidity, the role of precipitation and relative humidity becomes positive and temperature becomes negative from the beginning of May. In the autumn, the role of precipitation decreases and the temperature is increased.
[1] - به دلیل کیفیت نامناسب متن چکیده مبسوط انگلیسیِ ارائه شده توسط نویسنده مسئول مقاله، نشریه به ناچار اقدام به ترجمه مجدد متن چکیده فارسی و انتشار آن به جای چکیده مبسوط انگلیسی نموده است.
Saharnaz Shekoohizadegan; Hassan Khosravi; Hossein Azarnivand; Gholamreza Zehtabian; Behzad Raygani
Abstract
Abstract
Desertification means land degradation in arid, semi-arid and dry sub-humid regions in result of climate variability and human-activity. Desertification is the third major challenge for international community in twenty-first century after the two challenges of climate change and scarcity of ...
Read More
Abstract
Desertification means land degradation in arid, semi-arid and dry sub-humid regions in result of climate variability and human-activity. Desertification is the third major challenge for international community in twenty-first century after the two challenges of climate change and scarcity of fresh water.This phenomenon has been raised as one of the most striking aspect of environmental degradation and destruction of natural resources in the world.Desertification, byaffecting vegetation cover, water and soil, is a serious factor threatening national parks in arid and semi-arid regions including Iran.Executive actions related to desertification control must be based on the recognition of the current state of desertification and its intensity.The aim of this study was to evaluate and monitor desertification by usingvegetation indices (NDVI and EVI) extracted from MODIS satellite imagery and classification of desertification by using fuzzy logic.
Materials and Methods
The study area covers an area with about 47,244 hectares, which has been named as Bamou National Park.The height distribution of Bamou National Park shows that most of the area is locatedbetween 1700 and 1900 meters altitude and a maximum height of the study area is 2700 meters above the sea level.The average annual rainfall in the main station area representing the Shiraz station is 392.9 mm with a mean annual temperature of 17.9°C.Based on Domarten developed method, Bamou National Park has a semi-arid climate and is cold with winter rains.
In this research, to monitor and evaluate desertification in Shiraz Bamou national park, the annual changes in vegetation cover were studied during the period of 2000 - 2014. On the other hand, this paper tries to monitor desertification changes using long term-time series analysis of satellite data and vegetationcover indices (EVI & NDVI).Therefore, in this study, profile and map of annual changes were prepared on IDRISI Selva and then analyzed using the MOD13A1product, MODIS sensor, Terra satellite and Aqua system. Finally, using fuzzy logic, profile and desertification intensity map were prepared for 2000-2014. According to the climatic conditions of the region and based on expert opinion, the value of fuzzy classes index changes, the software IDRIDIselva and Arc GIS 10.2 severity of desertification on each indicator based on fuzzy logic was prepared.
Discussion and results
Based on the results of EVI & NDVI, vegetation destruction and desertification intensity have been more in the north west of the study area. The reason for this destruction and desertification is the construction of the new city of Sadra in part of the North West and the west of this park. It can be said that, this degradation is a new form of desertification entitled anthropogenic desertification.As a result of the construction of Sadra city in the western area of the park, it is practically impossible to protect this area.The results show that EVI is more sensitive than NDVI for monitoring parameters such as canopy cover, leaf area index, canopy structure, phenology, and stress plants. The EVI index due to greater sensitivity to changes in areas with high biomass (vegetation growth season) and mitigating the effects of atmospheric conditions on vegetation index values is more applicable to monitor vegetation changes than NDVI.This paper introduces fuzzy logicas one of the methods for classifying the severity of desertification. Fuzzy logic can be used to determine the boundaries of class and privilege of desertification indicators and explain the process. Fuzzy sets, or classes of fuzzy are no sharply defined boundaries and membership or non- membership of a place in particular.The severity of desertification in the form of fuzzy maps based on each available indicator provided the values between 0 and 1 as the classes of desertificationon the map.It can be concluded that for better management of desertification it is necessary to prioritize areas affected by desertification according to its severity.As a result, we can say that accurate desertification classification can be helped to manage this phenomenon. In fact, it is a set of unpleasant consequences that human environment brings. Hence, monitoring and evaluation of the severity of desertification and mapping always isone of the most important management andplanning tools to achieve sustainable development in the field of natural resources.
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 ...
Read More
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.
Saeed Khezri; Ja'far Maleki
Volume 20, Issue 79 , November 2011, , Pages 61-65
Abstract
In this research, the method of extraction and identification of hot and cold water springs using thermal images of TM and ETM+ is investigated. The process is as follows. After taking images from the TM and + ETM sensors on different dates and implementing geometric and radiometric corrections on the ...
Read More
In this research, the method of extraction and identification of hot and cold water springs using thermal images of TM and ETM+ is investigated. The process is as follows. After taking images from the TM and + ETM sensors on different dates and implementing geometric and radiometric corrections on the images, surface temperature (LST) for the images is extracted using existing relations. Of course, this requires NDVI and emission extraction for the studied areas due to the certain effect of vegetation on surface temperature. This was carried out in TM and + ETM images using bands 3 and 4 . After the extraction of surface temperature by using limits and surface profiles in different directions and by using high pass filters, and finally matching vector layers related to the location of the springs on available images prepared by GPS during the fieldwork, the ability of TM and ETM+ images to identify the springs was evaluated. The results show that the use of images of cooler seasons has a higher priority and importance in comparison with the warmer seasons for the identification and extraction of thermal points and thermal anomalies of the surface of the earth on the images. The reverse is true for the identification of cold springs. Furthermore, the size and temperature of springs regarding their geographical location can be effective factors in identifying and separating these resources.
Majid Danesh; Hosseinali Bahrami; Seyyed Kazem Alavipanah; Aliakbar Nowruzi
Volume 17, Issue 67 , October 2008, , Pages 26-34
Abstract
Soil texture and lime can be considered as amongst the most important soil characteristics, which are considered in many agricultural and environmental projects. Today, with the scientific progress and the advent of remote sensing technology, the possibility of exploiting this technology in soil science ...
Read More
Soil texture and lime can be considered as amongst the most important soil characteristics, which are considered in many agricultural and environmental projects. Today, with the scientific progress and the advent of remote sensing technology, the possibility of exploiting this technology in soil science has also been provided. In this study, for the analysis of soil texture and lime in the Pol Dokhtar area, the four-spectral data for September 7, 2007 prepared by IRS-P6 satellite with LISS III sensors were used. Geometric corrections and processes including UNC, SLED, NDVI, PCA, were performed on the main image. Finally, using randomized sampling method and based on PMU, FCC image of the region, 95 points were selected and samples were taken from two depths of 0-5 and 20-5 cm. Finally, using multiple regression, it was found that the lime and clay of samples at the first depth had a significant relationship with the near infrared band with modified R2 =0.73, and in the green band it was 0.72, and also at the second depth, with a red band of 0.54 and a green band of 0.48, of which all relationships were statistically significant at 1% statistical level. Consequently it was found that clay and lime have a significant effect on the spectral reflection from the soil surface in the region, and it is possible to study them in the region using satellite data and auxiliary data (incidental information).
