جستجوی مقالات مرتبط با کلیدواژه « لندست » در نشریات گروه « منابع طبیعی »

land degradation and desertification in arid areas are the most important environmental challenges in the world. This process due to the lack of precipitation and the occurrence of drought, while the unreasonable exploitation of natural and agricultural areas with increasing demand to provide human food needs, affects various environmental and socio-economic dimensions. So, the continuation of this condition during recent years with the destruction of vegetation and soil, wind and water erosion, soil salinity, soil compaction, and declining groundwater aquifers have significant consequences for the production of agricultural products and biodiversity in an arid region. Since the pattern and dimensions of vegetation changes are the most important factors in detecting land degradation, monitoring the vegetation changes is the best approach to analyzing land degrading and desertification trends in an arid region. Therefore, according to the capabilities of remote sensing data due to the wide coverage and multi-timed,  the use of satellite imagery to monitor vegetation changes by using vegetation index is one of the best methods that developed in recent years. Moreover, concurrent access to high spatial and temporal resolution imageries is one of the important factors that affect the monitoring of vegetation changes. To achieve this goal, It needs to incorporate different satellites with high spatial (e.g., Landsat satellite) and temporal (e.g., MODIS satellite) images. The purpose of this study is the monitoring vegetation changes using daily Landsat simulated images at 30 m Spatial Resolution in three years of wet, normal, and drought in the Nimroze area.

 The study area is located in the north of the Sistan and Baluchistan provinces. Low precipitation (50 mm), high temperature (48 oC), high evaporation (5 m), and 120-day winds are among the specific climatic conditions that characterize this region. In this study, at first, the hydrological drought status of the Hirmand River was investigated. Using the Hydrostats package in R software, the amount of threshold of flood by running the related codes (by running codes such: daily.cv, ann.cv, high. spell, and low. spell) during the statistical period of study (29 years) was calculated. To determine wet, normal, and drought years calculated the length of periods that flood is higher (high. spell. lengths) and lower (low. spell. lengths) than the threshold. To increase the accuracy of monitoring vegetation changes, it needs to incorporate different satellites with high spatial (e.g., Landsat) and temporal (e.g., MODIS) images. To achieve this purpose, in this study, the Enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was evaluated with actual satellite data (OLI, ETM+, TM image). For this purpose at first, pre-processing (geometric, radiometric, and atmospheric correction) was performed on satellite images, and by using the ESTRFM model, simulated daily Landsat images at 30 m spatial resolution for wet, normal, and drought years. In-field operations from different plant communities by GPS were sampled. Comparing filed data with the Normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI), the vegetation index that had the highest correlation with field data was selected. To investigate vegetation changes, using the vegetation index (the vegetation index with high correlation), the map of vegetation for each year was prepared (wet, normal, and drought years). After the classification maps of vegetation, by comparison, approach (cross tab), the map of vegetation changes was extracted.

 The results of analyzing wet and dry periods showed that, flood volume in dry years compare to normal and wet years decreased 31 and 82 percentages, respectively. To incorporation MODIS and Landsat (OLI, ETM+, TM) Images, using enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), finding indicate that this model improves the accuracy of predicted fine-resolution reflectance and preserves spatial details for heterogeneous landscapes too. So that the mean coefficient of determination (R2) of blue, green, red and near-infrared estimation bands with actual satellite images data is 0.91, 0.89, 0.92 and 0.91 respectively. Also the average Root-Mean-Square Error (RMSE) in four bands obtained 0.01, 0.027, 0.028 and 0.031 successively. Comparing the obtained field data with the Normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI), indicate that SAVI index has the highest correlation (R2= 87) with vegetation of study region. By calculate the regression model (using SAVI and field data) and classify the vegetation maps of wet, normal and drought years, 6 class obtained (class1=0-10%, class2=20-10%, class3=20-30%, class4=40-50%, class5=60-80% and class6=>80%). The results of investigation vegetation changes indicate that during the drought period 70% of study area has less than 10% vegetation (equal to 138176.3 hectares) and during normal and wet years by increasing vegetation, this area decreased by 30 and 48% respectively (equal to 66269.98 and 50559.7 hectares, respectively). According to the results during the study period, the most vegetation changes is relate to conversion of class 1 to class 2 (equivalent to 48.5%). moreover 18 and 27% of vegetation changes relate to class 1 and 2 to class 4 and 5 respectively (equal to 16284.26 and 11471.88 hectares, respectively). Also the finding indicates that the most vegetation changes occurrence in wetland-forest (28%), forest-rangeland (21%) and poor rangeland (19%) land uses respectively. Field study also showed that, the most important plant species that grows in this land-use such as the results of analyzing wet and dry periods showed that flood volume in dry years compare to normal and wet years decreased by 31 and 82 percent, respectively. To incorporation MODIS and Landsat (OLI, ETM+, TM) Images, using enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the finding indicates that this model improves the accuracy of predicted fine-resolution reflectance and preserves spatial details for heterogeneous landscapes too. So that the mean coefficient of determination (R2) of blue, green, red, and near-infrared estimation bands with actual satellite images data is 0.91, 0.89, 0.92, and 0.91 respectively. Also, the average Root-Mean-Square Error (RMSE) in four bands obtained 0.01, 0.027, 0.028, and 0.031 successively. Comparing the obtained field data with the Normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI), indicate that the SAVI index has the highest correlation (R2=87) with the vegetation of the study region. By calculating the regression model (using SAVI and field data) and classifying the vegetation maps of wet, normal, and drought years, 6 classes obtained (class1=0-10%, class2=20-10%, class3=20-30%, class 4=40-50%, class5=60-80% and class6=>80%). The results of the investigation of vegetation changes indicate that during the drought period, 70% of the study area has less than 10% vegetation (equal to 138176.3 hectares) and during normal and wet years by increasing vegetation, this area decreased by 30 and 48% respectively (equal to 66269.98 and 50559.7 hectares, respectively). According to the results during the study period, most vegetation changes are related to the conversion of class 1 to class 2 (equivalent to 48.5%). moreover, 18 and 27% of vegetation changes relate to class 1 and 2 to class 4 and 5 respectively (equal to 16284.26 and 11471.88 hectares, respectively). Also, the finding indicates that the most vegetation changes occur in wetland-forest (28%), forest-rangeland (21%), and poor rangeland (19%) land use respectively. The field study also showed that the most important plant species that grow in this land use such as Aeluropus littoralis, Chenopodiace sp, Tamarix aphylla, Haloxylon aphylum are adaptive to climatic regime in study area.

 In this research for the first time in the Nimroz region of Sistan Vegetation changes were studied using Landsat simulated images during periods of low water, normal, and high water years. Due to low rainfall and harsh climate in the study area, floods in the Helmand River are the only source of water supply required in the study area. The results of analyzing wet and dry periods showed that flood volume in dry years compared to normal and wet years has decreased by 31 and 82, respectively. According to the reduction of flood volume during a drought year, 70% of the study area has poor vegetation and during normal and wet years, providing plants with water needs and increasing vegetation, this area had decreased by 30% and 48%, respectively. According to the results of this study, change in hydrological conditions of the Hirmand River has a significant role in vegetation changes in the study area by using simulated images with high spatial and temporal resolution can improve the accuracy of monitoring vegetation changes to control and management the desertification in Sistan area.

Land surface temperature (LST) and its relationship with land use /cover change are significantly considered in environmental studies. The aim of this study was to retrieve the LST of Bushehr land and explore its spatial relationships with LULC. Landsat images of August of the summer season for three years with fifteen years time intervals were analyzed. The result showed a gradual rising (1.4 °C during 1990–2005 and 2 °C during 2005–2020) of LST during the whole period of study. The mean LST value for three study years was the lowest (30.86 °C) on green vegetation and the highest (49.07 °C) on bare land and built-up areas. The spatial distribution of NDVI and LST reflects an inverse relationship. The best (-0.97) and the least (-0.80) correlation, respectively, whereas a moderate (-0.89) correlation was noticed. This LST-NDVI correlation was strong negative (-0.80) on the vegetation surface. The LST is greatly controlled by land-use characteristics. This study can be used as a reference for land use and environmental planning on coastal land.

Modeling and showing the coverage of the land changes, provides a comprehensive view to researchers in various fields, including environmental and natural resources experts. One of the main methods of environmental studies is to study the land cover/use and vegetation area change. In addition to showing spontaneous changes in nature, changes affected by human activities also fall into this category. Human construction has accelerated these changes in line with its development, especially in recent decades. Today, with the development of space-related sciences and remote sensing in general, and the production of more satellite products, it is possible to display the land use of different areas without the need for field visits and easily. The different behavior of the waves received by the satellite sensor from the various phenomena, known as a spectral signature is the basis for cognition and detection of the uses of the map. Such studies in Qom province have also been considered due to the very urban growth trend and the existence of several different types of climates in the not-so-wide area of this province. Qualitative and quantitative study and display of environmental and peripheral changes in Qom province over a period of about 30 years are one of the main objectives of the present study to help identify the trend of changes in different classes and complications and to model these changes in the future. Also, recognizing the changes in the outlook of Qom province, makes possible the ground for future planning.

In the present study, study times and time steps were selected based on changes in climatic/meteorological parameters. These steps were selected 5 years apart from 1989 to 2019. The study time point was considered to be the end of spring and the beginning of summer. The reason for this was the end of the rainy season in the area. Then the images of various Landsat satellite sensors were taken at specified time steps, and these images were pre-processed, processed, and classified into 11 classes. These 11 classes included; bare land, salty land, sandy land, tree, rock, urban areas, agricultural lands, and 3 different types of range. The results were also presented quantitatively and qualitatively. Based on the available real data, which was obtained visually and by sampling from different classes, the two maximum likelihood and minimum distance classification methods in Qom province were properly evaluated, which of the two, the maximum likelihood method yielded relatively better results considering the whole province with all classes and was used in the final classification. Finally, class changes between time steps were calculated and presented as a change matrix.

The results show that between 2014 and 2019, urban, water, agriculture, and ranges (types 1 and 3) have grown significantly. Also, between the two steps of 2009 to 2014, on average, about 30% of the total rangelands, ie three different types of classified rangelands, have become barren lands. Also, in this step, the main change observed was the largest change of sandy lands to bare lands, the reasons for which need further investigation. An examination of the changes between 2004 and 2009 shows that the negative growth in urban areas is mainly due to the poor quality of Landsat 7 satellite imagery and the similarity of the spectral behavior of salt lands and urban areas. The other negatively growing classes, including lakes, have become saltier lands and rocky areas have become barren, as well as salt lands have become barren and sandy. Examining the changes between 1999 and 2004, it is concluded that the negative changes in the tree class are due to the spectral behavior of vegetation, and this class has become mainly agricultural and rangeland classes. In the lake class, a 4 % change to the salt and rocky class has been detected. Major changes in the bare land class of about 12% have been detected in the rock and sand class. Also, more than 50% of the total area of range classes has been converted to bare land class, which is significant. The study of changes from 1994 to 1999 shows that only 3 classes had positive growth and the rest of the classes have negative growth, most of which was related to the urban class and the main changes were focused on bare lands. Vegetation classes all had negative growth and also due to the spectral similarity of these classes with each other, there was no proper separation between them. 12% of the bare land class has also been turned into a sandy land class. The classification of images and the display of changes from 1989 to 1994 show that sandy soils, range type 1, trees, salt lands, and lakes have grown negatively. In total, about 34% of different types of ranges have become bare lands, which seems reasonable due to the negative change in water areas (lake) and in a way indicates a faster drought. The extent to which other classes change to the bare land class, which includes relatively large numbers, also confirms this in some way.

Considering the geographical location of Qom province and a large area of this province, especially in the eastern half of it, which includes desert lands, including barren, saline, and sandy land classes, the selection of the classes mentioned in this research makes sense. Considering the major coverage of the province, one of the problems in the present study was that almost the majority of the pixels covering the province had a lot of similar spectral behavior and this issue made the classification process difficult.  In general, the classification results related to the images taken in 2019, which is related to the recent time, show positive growth in urban, agricultural, range, and water areas according to the rainfall in early spring 2019 it was logical. Another important point related to this year is the extensive change and conversion of the class of rocky lands into different types of ranges. According to the original image taken from 2019 and the classified images, the error related to the degrees of gray is evident in those images. The software considers the similarity of the degrees of gray and the same spectrum of urban and salt classes as part of a class. These errors are also evident in bare and sandy classes.

Land cover and soil moisture changes have a significant impact on land surface temperature (LST). Therefore, LST can be used to study land cover and desertification changes. Arsanjan County, which is located in the northeast of Fars province, has a relatively good forest and rangeland. Unfortunately, excessive harvesting of the groundwater resources and also reduced precipitation in this area caused to decrease water levels and dried up many wells in this area during recent years. So the area of the farmland and Bakhtegan Lake has decreased in this region during the last decades. However, so far, the condition of the LST and its relationship with land cover changes has not been assessed in Arsanjan County. In this study, spatial-temporal changes of LST and its relationship with vegetation and the water area of Bakhtegan Lake have been studied.

The eleven images related to Level-1 data of Landsat satellite was taken from 2003 to 2018. Since the vegetation situation in the study area is in the best vegetation and water area condition in April and May, so the images related to these months were selected to check the fluctuation of vegetation cover and water level of Bakhtegan Lake. The data pre-processing was performed in three sections: geometric, radiometric and atmospheric correction by ENVI software. The FLAASH algorithm, which is one of the best methods of atmospheric correction, was applied for atmospheric correction. In this study, NDVI was used to estimate the amount of vegetation. The Planck algorithm method was applied to calculate the LST. The change detection process was done using the index differencing method. To classify the LST map and the temporal-spatial changes, the LST difference map was normalized. Then, the normalized image was categorized using the standard deviation parameter in five temperature classes.

In the present study, 11 Landsat images were examined to investigate the spatial-temporal changes in land coverage and LST and the relationship between these two parameters from 2003 to 2018. The NDVI mean value was 0.25 in 2003, which decreased to 0.18 in 2018. On the other hand, the LST mean value had an upward trend as it increased from 29℃ in 2003 to 41.7℃ in 2018. The NDVI mean value was 0.66 in the farmland in 2003, however, its value reached to 0.33 in 2018. In contrast, LST mean value increased in the farmland from 20.9℃ in 2003 to 39.5.5℃ in 2018. Also, the LST mean value in the lake area increased from 20.1℃ in 2003 to 36.5 in 2018. Based on the results, the NDVI mean value in the rangeland and farmland decreased by 0.07 and 0.33, respectively, in 2018. However, due to the positive relationship between NDVI and LST in water-covered areas, the NDVI mean value increased by 0.39 in Bakhtegan Lake area in 2018. In contrast, the LST mean value in the rangeland, farmland and Bakhtegan Lake increased by 12.7℃, 18.6℃ and 16.4℃, respectively, in 2018 compared to 2003. The results indicated a negative relationship between NDVI and LST (R2= 0.862). The LST value decreases by increasing NDVI value in the vegetated area. In contrast, there was a positive correlation between NDVI and LST in salt-marshes and barren areas. According to the results, the highest negative correlation was obtained for the farmland, which was  -0.94. The reason for this high correlation can be related to the high density of vegetation cover in agricultural areas. The low negative correlation between NDVI and LST in the rangeland indicates the low vegetation density in rangeland and forest area. In order to study the area of decrease or increase of LST in the farmland, rangeland and water classes, the LST difference map was classified to five categories including very low temperature, low temperature, medium temperature, high temperature and very high temperature. According to the result of LST classification, the highest area was related to the moderate temperature class in all land covers, so that the highest area of this temperature class was associated with the rangeland by 86733 hectares. Since the vegetation density, especially in the farmland, had a significant decrease in 2018 compared to 2003, the area of high and very high-temperature classes increased in 2018, so that their area reached to 4625 ha and 7192 ha, respectively, in the farmland. Also, since the water area of the lake decreased in 2018 compared to 2003, the area of high and very high-temperature classes in these classes reached to 1824 ha and 3919 ha, respectively.

According to the results, the NDVI mean value in 2018 decreased in the farmland and rangeland and increased in the Bakhtegan Lake area. In contrast, the LST increased in the mentioned areas. The results of the LST classification showed that the highest amount of LST change is related to the moderate temperature class. Since the vegetation density, especially in the agricultural area, had a significant decrease in 2018 compared to 2003, the results showed that the area of high and very high temperatures had a higher increase than low and very low temperatures. Also, since the lake's water level decreased in 2018 compared to 2003, the area of high and very high temperatures in these classes increased. The findings show that there is a negative correlation between vegetation and land surface temperatures.

Considering the increasing importance of forest ecosystems in climate change mitigation projects, reliable and cost-effective methods are required to estimate the aboveground biomass (AGB). Common  methods used to estimate the aboveground biomass (AGB) include in-situ measurement, the biomass calculation using aalometric equations and using remote sensing techniques. Remote sensing has been widely used to estimate the biomass of forests in recent decades.The used statistical modeling method is one of the most important factors to use remotely-sensed data for estimation of the aboveground biomass. A large number of researches have been carried out about using the modeling methods. However, these studies face the following different challenges: 1) no modeling method has been recommended as the best method 2) the performence of these modeling methods is affected by forest type, the forest structure, and the present disturbance intensity 3) the performance evaluation and the comparion of the results of these methods were done by using goodness-of-fit test and cross-validation methods. The purpose of this study is to considering the role of choosing statistical modeling methods to estimate remotely-sensed aboveground biomass, the current study was conducted to investigate nine statistical modeling methods including linear regression (LR), generalized additive model (GAM), random forest (RF), support vector machine (SVM), boosted regression tree (BRT), k-nearest neighbor (kNN), cubist regression (CR), Gaussian process model (GPR), multivariate adaptive regression spline (MARS) using bootstrap process and 1000-repeated 10-fold cross-validation approach to estimate the aboveground biomass of Zagros forests using Landsat 8 images.

 The cuurent study was conducted in Kermanshah forests which is mostly dominated by oak species trees (Quercus spp.) and is located in western Iran on the Zagros Mountains. Zagros forests are generally sparse and open  and comprise approximately 20% of Iran’s area and 40% forest regions of Iran. In order to conduct this study, two forest regions with different levels of human disturbances were chosen; SarfiruzAbad region with highly degraded (HD) forests, and Gahvareh forest region with minor degradation (MD). Geographical coordinates of SarfiruzAbad and Gahvareh regions are 33º57′-34º04′N / 47º03′-47º17′E & 34º21′- 34º24′N / 46º16′-46º23′ E respectively. The Leaf area index (LAI) map derived from the Landsat images based on a global model was used to collect field-based sample plots  in both regions of the study. Both regions were divided into three  low, moderate and high  Leaf area index (LAI) strata, and the locations of the sample plots were located by using a systematic inventory at the intersections of a 200m×200 m grid in each stratum. 124 georeferenced square plots of field-based sample plots (63 plots in Gahvareh region and 61 plots in SarfiruzAbad region) with 30m×30m dimensions the same size as a Landsat 8 image’s pixel were collected. Allometric equation developed for oak tree in Zagros forests was used to calculate the amount of  the aboveground biomass of each individual tree or sprout-clump. The allometric equation used in this study uses  two vertical tree crown diameters to estimate the amount of the biomass of each individual tree or sprout-clump. The sum of the amount of the biomass  of each individual tree in sample plot was used to calculate the amount of the biomass plot in sample plot level at a ton per hectare. Our study regions were located in a frame of Landsat 8 images (path/row:167/36). A cloud-free Landsat image relating to 19th Mordad 1394 (10th August 2015) relating to the time when the tree canopies are completely closed and near to the date of land inventory was downloaded from earthexplorer.usgs.gov site. Based on the previous studies, the pre-processing of the used image comprising the radiometric and topographic corrections was done.using C method. To estimate the aboveground biomass in the study areas by using remote sensing, 38 spectral variables including band values, simple band ratios, vegetation indices and common linear transformations like tasseled cap and principle component analysis  were extracted from the used Landsat 8 image. Generally, the efficiency of nine different statistical modeling methods including parametric methods (Linear Regression, LR), semiparametric (Generalized Additive Model, GAM), and nonparametric Random Forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Boosted regression trees (BRT), multivariate additive regression splines, cubist regression (CR), and Gaussian processes regression/model) were compared in order to estimate aboveground biomass. To assess the models, two common quality statistics: (i) determination coefficient and (2) root mean square error via 10 fold cross validation repeated 1000 times approach were calculated. This number of repeats helps to ensure an acceptable assessment of robustness of the results.

 The measuredstatistical characteristics of the field sample plots showed that the mean aboveground biomass of SarfiruzAbad and Gahvareh regions were 12.6 ton/ha and 20.5 ton/ha respectively. ANOVA indicated significant differences between modelling methods (treatment effect: p< 0.001) for both R2 and RMSPE calculated in 1000-time repeats using 10-fold cross- validation.The Cubist modeling method with the mean determination coefficient of 0.61 outperformed other methods in SarfiruzAbad region.These resultsfor Gahvareh region showed better efficiency of linear regression (LR), generalized additive model (GAM), and k-nearest neighbor (KNN) with the mean determination coeffieient of 0.87.The multiple comparisons of different models by using Tukey test concerning RMSE showed that in SarfiruzAbad region, cubist method  with the mean of RMSE 3.3 ton/ha and kNN and RF methods with the mean of RMSE 5.8 ton/ha had a significant difference in comparison to the other methods. Totally, the results of the research revealed the suitable efficiency of Landsat 8 image for AGB estimation in Zagros forests. The acceptable results are due to the low AGB in our study regions that did not reached the saturation point as one of challenges of using optical images like Landsat. The other results of this research is the assessment of the effiecieny of modeling method in order to increase the accuracy of the estimation of remotely-sensed aboveground biomass.Unlike the results of the previous studies, linear regression yielded better results compared to nonparametric methods that can be due to the presence of the linear relationship between aboveground biomass and spectral variables derived from Landsat images. Among the used various spectral variables, red, near infrared, and  shortwave infrared 1 and 2  band ratios were selected as the final variable in most modeling methods.

In this study, we evaluated the effieincy of different statistical modeling methods to estimate AGB in Zagros forests by using Landsat images. The biomass estimations were compared by using nine parametric, semi-parametric, and non-parametric methods and using 1000-repeated 10-fold cross-validation. The results illustrated the acceptable potentiality of Landsat images for cost-efficient AGB estimating in Zagros oak forests. The accuracy of AGB estimation in Gahvareh region with low-degraded forest stands was higher than SarfiruzAbad region with highly degraded stands.

The object of this research is evaluated of flood productivity on vegetation cover changes. For this purpose, 5 linear transects with a length of 50 m were installed before flooding in the year 2006. In each of these transects, two plots 3×3 were identified at a height of 50 m at the beginning and 1% in length, and within each of them a plot of 1×1 was laid out for uniform harvesting at equal distances, their coordinates using specific GPS and feature the percentages of canopy cover, forage production, bare soil percentage, density and litter before starting flood spreading in 2003 were estimated. In addition, the vegetation characteristic changes were measured in the same place after the flood propagation in the year 2019. Landsat satellite images for a period of 16 years (2003-2019) was also used to study vegetation changes before and after flood propagation. For this purpose, after applying pre-processing on the images, the vegetation index was adjusted according to the soil adjusted vegetation index (SAVI) and soil water index (SWI) over five different time periods. Field results showed that with the application of flood propagation application, the amount of forage production increased from 68 to kg/ha, the percentage of canopy cover increased from 5.9 to 31.4%, as well as the amount of litter from 2 to 16.8% and bare soil decreased from 92.1 to 51.8%. Statistical analysis of vegetation characteristics before and after flood propagation revealed a significant difference between these parameters (p<0.01). In addition, the results showed significant changes in the SAVI and SWI indices over the period. The SAVI and SWI indices increased from 0.027 and 0.5 in the year 2003 to 0.49 and 1 in the year 2019, respectively, and the trend of changes in these indices showed high correlation (R² = 0.65) in flood propagation area. The results of this study showed that flooding productivity by providing soil moisture in the study area improved germination and plant growth conditions, which resulted in the establishment and improvement of vegetation cover in the floodplain.

The areas of natural resources and vegetation in the Taft and Mehriz townships in recent decades have undergone changes due to its close proximity to the capital of Yazd province. The purpose of this study is to assess the extent and direction and prediction of land and vegetation changes in these two cities. In this study, Landsat 5 (1998, 2004 and 2008) and Landsat 8 (2017) satellite images in the period from May to June was used. Modeling land use/land cover changes were carried out based on supervised classification. The process of changes was analyzed using land change modeling and perceptron neural network method. The results showed that 3% (558.8 ha) of land and vegetation cover of Taft to Bayer and 1.3% (209.9 ha) were added to the urban lands and reduced from the lands of gardens by 4.3% (559.2 ha), this is the highest rating. The amount of 2.8% (678.8 ha) of land and vegetation in Mehriz Bayer and 1.7% (184 ha) has been changed to urban residential land. In terms of urban expansion, Mehriz has had the highest amount of reducing vegetation. The results show that land use and land cover changes in Taft city where more than Mehriz city so that most of these changes were related to gardens, but in terms of area of land use and land use conversion in Mehriz city has the highest value More intense.

Water as one of the most basic needs of our present life and the extent of our use in drinking, agriculture, industry, economic, social, and political-security politics make us to identify with minimal cost savings and time characteristics of the watersheds, rivers and water levels by various methods, including the use of satellite imagery. The purpose of this research was to evaluate the methods of detecting zones, water levels and rivers with indicators; Normalized difference vegetation index, Enhanced vegetation index, Soli  adjusted  vegetation index, Normalized difference water index, Modified normalized difference water index, Automated water extraction index, Automated water extraction index and Unsupervised IsoClusterc and supervised Maximum likelihood classification methods to identification waters basin and the Optimum factor index for identifying the quality of water in terms of salinity, as well as determination infiltrate tabs water entering the larger zones in the part of the basins of the Karun river, Jarahi-Zohreh in the southern province of Khuzestan, with Landsat-8 satellite Land Earth Observations sensor. The results of the study showed that the automatic indicators of the extraction of water in shadow and urban areas are more effective than other indicators because of the consideration of short-range infrared wavelengths in water identification. With the results of the Supervised classification method, they were Maximum likelihood to the Kappa coefficient of the same 94% and the same performance. The results of the Optimum factor Index indicator for the detection of salinity water and the determination infiltrate tab water Show the most useful information and remove duplicate image banding data the Landsat-8 satellite Earth Observation Sensor was 79.10% for the color combination RGB of 651.

Vegetation is one of the criteria indicating the production potential of the land. So that the quality and quantity of vegetation in each region is a criterion for determining its production potential. In this research, vegetation indices of Landsat 8 digital data were used to estimate vegetation cover and biomass in the protected area of the central Alborz in June 2016. To study the correlation between vegetation cover and biomass with satellite data, 27 samples were obtained randomly in the region. Linear regression was used to determine the relationship between cover percentage and biomass values with remotely sensed vegetation indices. The results showed that among selected vegetation indexes,  Enhanced vegetation index (EVI) and Visible atmospherically resistant index (VAR) had the highest correlation coefficient with vegetation percentage 0.53 and 0.52, respectively. Therefore, these vegetation indices are appropriate for estimating vegetation cover at a 5% significance level. And in calibration, the correlation coefficient for the wet weight of vegetation and the indices of Global environment monitoring index (GEMI), Simple ratio (VR) and Enhanced vegetation index (EVI) were 0.43, 0.41 and 0.41, respectively even though their estimations were unsuccessful in the validation stage. According to the results of this research, it is recommended that the indicators for the estimation of the quality and quantity of vegetation should be used in which the atmosphere and soil impacts are considered.

Lithological studies and geological unit's mapping are generally applicable to the many fields of natural resources management. Satellite remote sensing images have been widely used for separating of geological units which can generate accurate results, as well as time and cost saving. This research aims at comparing the performance of different enhancement techniques in order to separate four geological units in the study area of Taft watershed, Yazd. In this study, Landsat 8 satellite images (OLI sensor) were used, analyzed and classified. Geological maps were also up to dated using google earth images. A comparison of the results of the satellites classified images and base geological maps, indicates the Kappa coefficient and overall accuracy of the classified supervised maps of the ration of b7/b5 have the highest values of 0.78 and 86.7%, respectively. To verify the accuracy of the comparison results, QQ plots were used to compare the percentage of areas. Comparing the percentage of areas, the oblique angle of the b7/b5 bands ratio was lower (i.e. better) than the other different ratio combinations. Finally, the values of accuracy assessment for the reflection of lithological units in the observation and estimation range of supervised classification map of the ratio bands of the b7/b5 in the geological units Ks, gd, Kt-1, and Qal were 0.993, 0.980, 0.948, and 0.985, respectively, indicates that the b7/b5 band ratio separate four different geological units of the study area more precisely, and easily than the other classification methods.

In the present age, one of the most important challenges is soil erosion and consequently land degradation. One of the reasons of soil erosion in the source areas of dust is the low quality of nourishing the soil at the base of growth and development of vegetation. Lime is one of the main factors of decreasing the quality of nourishing the soil. Soil’s lime measuring by laboratory method is time consuming and expensive, thus developing the non-destructive and fast methods like the satellite and VNIR spectrometry data is necessary. In this study 29 intact soil samples have been collected on the same day of Landsat 8 satellite’s overpass from two sources. The spectroscopy has been done on these samples in three modes: IMS, IDS, and SMD. The surface and mixed samples lime have been measured in the laboratory. The soil index and PLSR methods have been used for processing data. The results obtained from PLSR method for SMD mode were R2=0.30 and RMSe=1.84 and for IDS and IMS modes were R2=0.13, 0.08 and RMSe=0.85, 0.87 respectively. The results of the RI index for SMD, IDS, and IMS were R2=0.56, 0.29, 0.19 and RMSe=1.41, 0.75, 0.80 respectively, that the results for SMD mode were acceptable. The results of image in PLSR method were R2=0.84 and RMSe=0.34. But the results related to using RI, DI, and NDI indices (R2=0.28, 0.08, 0.31 and RMSe=0.75, 0.86, 0.74, respectively), were unacceptable and weaker than PLSR method. Based on these results the lime map has been produced by using PLSR method.

Soil is a non-renewable and dynamic resource that the improper land use and management, is susceptible to degradation. Soil salinity one of the important land degradation factors in arid and semi-arid region. Identification and mapping of saline soils often due to temporal and spatial variability, and the need for sampling and laboratory work is difficult. In recent years remote sensing technology, due to the ability to identify phenomena has always been of interest to specialists. Information that obtained from satellite images contributed greatly to the study of various phenomena and can be very helpful in detecting phenomena changes. Case study, Aji Chay river delta was selected that is located in the west of Tabriz city and east of Urmia lake, because this region's importance in terms of agricultural and ecological. In this study used ground data and OLI sensor images from Landsat satellite during 2015, and field sampling was taken at the 5 and 6 August 2015. This data used for study and evaluation, soil salinity indexes. With a significance level analysis and measure study between ground data and output of models, best salinity index selected and extraction soil salinity map. In this index, SIT index has the highest correlation (97%) and presented as best index for salinity study in this region that salinity maps extraction from this index. SI2 index with 52% correlation has lowest correlate between salinity index and ground data. In the study region most areas included very saline class with 42% of the total area.

The present study aims to improve the assessment, soil salinity accuracy, using of OLI Landsat satellite image dated to 26 May 2015 and topographic parameters in the eastern region of Isfahan. Ground data were collected in date second half May by using the random sampling method from 29 sampling sites from the depth of 0-30cm. Spectrum indexes include OLI sensor bands, salinity indexes SI1, SI2 and SI3, soil indexes SBI and SCI, and vegetation indexes NDVI and RVI were obtained from processing referenced ground images. Also, topographic feature including slope percent, orientation of slope and elevation from sea level were extracted from Digital Elevation Model (DEM), then to estimate the salinity linear regression the stepwise method model was used. From analysis, regression indicated that on 99% statistical level, there is a meaningful correlation between EC of topsoil samples, elevation from sea level and band 4 (R2=0.68). The salinity map acquired from the regression model indicates the salinity is very high in the region so that more than 70% of the area has a salt concentration of more than 60 ds/m. Finally, the reliability of the model was evaluated using 20 percent of the samples. Results indicated that the model can estimate soil salinity by an RMSE=6.54 ds/m. According to the research results, Conclusion They are using elevation from sea level parameter with satellite data is Beneficial in the soil salinity research and causes improve the accuracy of the assessment.

Albedo is determined in new and old method based on SEBAL algorithm. In the new method that has been used since 2004, the extracting albedo was based on the experimental models. To determine the albedo of the object to the surface in large-scale and with consideration of the varied topography at the surface, the measurement of object's albedo was impossible, thus they should use remote sensing methods. In this research, the surface albedo was determined by use of ETM+ and MODIS images and tries to compare the obtained results from these two devices in various land uses. To determine the abode, we used the following programs, ERDAS® 9.1 and ArcGIS® 10.1. The result of study shows the average albedo in agricultural land extracted from ETM+ and MODIS images are 0.186 and 0.344, respectively, that the lowest, between all land use categories the highest albedo belonged to watercourse land use with the 0.242. Furthermore, the minimum albedo in follow agriculture in the ETM+ and MODIS image was 0.088, 0.274 and 0.374, 0.464 relatively. By determining albedo in different land use, we can determine the difference between net received energy and use it as one of the factors in determining the evapotranspiration with using the METRIC or SEBAL remote sensing algorithm.