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Dasht Yari plain is nearly 580,000 hectares which is under engraving gully erosion and unfortunately the gully development rate is increased in the recent decades. Satellite images may provide quick, extensive, and valuable information for the interpretation of morphometric characterstics of gully erosion expansion due to having attributes such as time series, relatively low cost, large coverage, and finally being capable of digital analysis. Therefore, this research was initiated to use these possible capabilities to find a quick and cost effective method to determine the morphometric characteristics of gullies with use of the Landsat ETM⁺ digital data of Dasht Yari plain in Chabahar county in southeast of Iran. The Landsat 7 data of 2001 and the field data collected from 25 selected gullies from the same area were used as control in this study. After geometric and haze corrections with use of spectral enhancement methods such as linear enhancement and color composites, the images were made ready for visual interpretation and selection field sites for the subsequent field sampling. On the selected 25 gullies, the field data collection including width, length, and height of gullies at 25%, 50%, and 75% cross sections was performed. At the end of the image processing, with use of image interpretation techniques such filtering, fusion and principal component analysis (PCA), morphometric characteristics of the gullies was computed and compared with the field data. Mean comparison and F and t-student tests were used to verify any statistical differences between two set of the data. The results showed that the data set were different at 1 and 5 percent levels. From the image processing methods, the PCA method had the smallest difference with the field collected data. Therefore, we may conclude that PCA method may be used for monitoring the gully expansion in the Dashat Yari plain and similar plains in the southeast of Iran.

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In order to evaluate the capability of ETM+ remotely- sensed data to provide "Forest- shrub land- Rangeland" cover type map in areas near the timberline of northern forests of Iran, the data was analyzed in a portion of nearly 790 ha located in Neka- Zalemroud region. First, ortho-rectification process was implemented to correct the geometric errors of the image, which yielded 0/68 and 0/69 pixels of RMS error toward X and Y axis, respectively. The original multi-spectral bands were fused to the panchromatic band using PANSHARP Statistical module. The ground truth map was prepared using 1 ha field plots in a systematic- random sampling grid. Vegetative form of trees, shrubs and rangelands was recorded as a criterion to allocate the plots. A set of channels including original bands, NDVI and IR/R indices, and first components of PCA was used for classification procedure. Automatic band selection command was used to select the appropriate channel set.. Classification was carried out using ML classifier on both original and fused data sets. It showed 67% of overall accuracy and 0/43 of Kappa coefficient in original data set. Due to the results present presented above, it's concluded that ETM+ data has an intermediate capability to fulfill the spectral variations of 3 form- based classes, in the studied area. Furthermore, applying complementary methods to minimize the background spectral effect is proposed for future studies.

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The natural forest and range stands of Zanjan province are located in mountainous areas. Inventorying and mapping of natural forest and range stands in mountainous areas are difficult and costly. Satellite data are suitable for this purpose. The Landsat ETM+ image data of 2002 are used for classification and mapping of natural forest stands in Zanjan province. For the purpose of data reduction and principal components extraction, the principal components analysis (PCA) was used. Just the scores of the first three PCs (PCA1، PCA2 and PCA3 (that accounted for 76.67 percent of the total variance were considered as new images for future analysis. A raster geographic information system (RGIS) database file was prepared and involved 7 ETM+ bands, 3 principle component analysis, 9 factor analysis and 8 vegetation indexes of image data. The correlation coefficients of 27 image layers and optimum index factors (OIF) of selected images were computed and 12 groups were found suitable for natural forest and range stands. Maximum liklelihood classification (MLC) method was used in this study. In order to test the accuracy of map, kappa index of agreement was calculated. The highest KIP belonged to three λ₃, λ₄, λ₅ Landsat image bands with KIP = 0.86. The highest OIF belonged to three PCA3, FA2 and MIR with value of 233.44 and lower OIF belonged to three λ₄, λ₅, λ₇ with value of 83.63. The overall, user’s and producer’s accuracy rates were 88.45, 73.69 and 70.23 percent respectively. The results of the study show that the Landsat ETM+ image data were appropriate for classification and mapping of natural forest and range stands in Zanjan province.

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To investigate land use changes, Qale Shahrokh basin (15098.1 ha area) was selected. Satellite images of Landsat sensors (MSS, TM and ETM+) were used. After improvement and different enhancement analysis of images such as FCC, PCA, the study area was checked using GPS and topographic maps (1:50000) and other information. Land use units were determined using classified random sampling method. Maps accuracy was assessed after performing different classifications. Final land use maps of 1354, 1369, 1381 years were produced using a hybrid method with fine accuracy. Trend of land use changes was investigated during the study periods. Results showed that during the first period (1354), most area of land use was rangeland with sparse vegetation cover (%41.6) and least area was irrigated farming (1.5%). Also, during the second period (1354-1369) most area of land use was rangeland with sparse vegetation cover (%43.4) and least area was irrigated farming (4.1%). During the third period (1369-1381), the maximum area of land use was dry farming (%35.6) and minimum of area was irrigated farming (7%). Maximum land use change was related to rangeland with medium vegetation cover. They were changed into dry farming and rangeland with sparse vegetation cover during 1354 to 1369. During 1369 to 1381, maximum land use changes occurred on poor rangeland with sparse vegetation cover and rangeland with medium vegetation cover was changed into irrigated and dry farming.

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Remote sensing has been considered as an appropriate tool for temporal monitoring of some natural phenomena. Ardestan Region is prone to land degradation and masked by sand sheets, sand dunes, clay flats, desert pavement and different kinds of salt crust due to dry climate. To study the trends of land degradation in last three decades, four satellite data sets of Landsat MSS, Landsat TM, Landsat ETM+ and IRS acquired in 1976, 1990, 2001 and 2008, respectively were analyzed. The time series analysis revealed that the bare clayflats have decreased and clayflats with vegetation cover have expanded over 32 years. During this period, the areas which are covered by gravel have decreased 13 percent and both the area covered by salt crusts and aeolians have extended 2 percent. Puffy grounds have developed by 2001 but their magnitudes have decreased between 2001 and 2008 as they have been masked by the moving sand ripples. Reduction of 13 percent of sand sheets between 1990 and 2008 indicates that soil conservation practices have efficiently controlled land degradation and desertification in the area.

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This research was carried out to determine spatial distribution of heavy metals concentration in soil particle size classes using Landsat ETM+ reflectance in Southern Isfahan city in the vicinity of Bama mine. To fulfill this goal, 100 compound soil surface samples were collected randomly from the area. The samples were air dried and soil particle size classes 250-500, 125-250, 75-125, 50-75 and <50 μm were determined using appropriate sieves after dispersion of the bulk samples of soil using ultrasonic apparatus. Total Zn, Pb and Cd concentrations were measured using Atomic Absorption Spectrophotometer after wet digestion of samples in acid nitric. The results indicated significant negative correlation coefficients between heavy metals concentrations of soil particle size classes and soil spectral reflectance in the visible, near infrared and panchromatic bands of Landsat ETM+ satellite. Stepwise multiple regression models were used for estimating heavy metals concentration in soil particle classes through satellite data. Furthermore, spatial distributions of heavy metals were mapped using stepwise multiple regression equations. Results also showed heavy metals concentrations in all soil particle size classes were maximum close to the mines and decreased by increasing the distance from these sources.

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The current study aims to find the best methods of using remote sensing and supervised classification algorithms in long-term salinity monitoring of salinity changes in the Atabieh area with an area of 5000 hectares in the west of Khuzestan province. The procedure is based on the separation of different levels of saline soils utilizing information obtained from Landsat 7 and 8 satellite images (2001 to 2015) along with salinity data taken from the study area, and salinity indices including SI1, SI2, SI3, NDSI, IPVI, and VSSI. The results show the expansion of the saline zone trend in the soils of the study area, among which, soils with EC of more than 16 dS m-1 (very saline) have the highest frequency. The area of saline soils has increased significantly over the past 15 years, with a saline land area increasing by more than 90%. The percentage of salinity class is low (S1). According to this study, the only significant index in soil salinity at a 95% confidence level is the SI3 index, which has been able to have a good estimate of the increasing changes in soils in the region. The results of the supervised classification showed that the support vector machine (with an overall accuracy of 95.78 and a kappa coefficient of 0.89) is more accurate. After the vector machine method, the methods of minimum distance, maximum likelihood, and distance of Mahalanobis have the highest accuracy, respectively. Based on salinity maps obtained in years in 2001, 2005, 2010, and 2015, it can be said that the salinity rate in the whole of the study area was progressing and at the same time the salinity area in the middle and high classes increased decreased and on the other hand, the salinity area in the high class in 2001 gradually increased and distributed in 2015 throughout the region.