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Of the applications of remote sensing and satellite images in natural resources is distinguishing and detection of changes in land surface. The image classification using Maximum Likelihood (MLC) is one the prevalent method which is used in a study of the application of TM and ETM+ satellite images to detect sediment deposition on an implemented floodwater spreading scheme. In order to implement the research, field sampling and checking were done using transect networking method by selection of 30 sample points in floodwater spreading area as well as another 30 control points in the study area. The results of the study are shown that detection of sediment deposition using MLC method by application of LANDSAT TM and ETM+ can lead to increase the precision of change detection up to 82 percent. Furthermore, the results also show that the trend and changes due to sediment deposition on water spreading area can be precisely detected. Considering the present and potential applicability of the applied method in distinguishing changes due to sediment deposition on land surface which is absorbed on 450 hectares of water spreading area in this research study, it can be pointed out that the use of this method in larger area could be tend to increase the precision of change detection and to decrease the required time.

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The aim of this study was to detect change of the forest area in the north of Iran between 1994 and 2001. The study area was covered by a 1:25000 topographic map (about 15000 ha) in Babol forests. The forest map of 1994 was extracted from 1:25000 topographic digital map. Landsat 7 ETM+ image dated July 30, 2001 was analyzed to produce the forest map for the end of the period. Since the evaluation of the image quality illustrated it less than ±1DN in the ETM 2, 4, 5, the rectification of the stripping distortion was ignored. There were also duplicate scan lines and sweep distortions in all the spectral bands. Orthorectification was implemented using ephemeris data and digital elevation model. Several spectral transformations such as rationing, PCA, Tasseled cap and image fusion (using Color space transformation and Spectral response method) were performed on the ETM+ data. The sample ground-truth map was prepared using GPS in 3% of the study area. In order to classify the image, hybrid classification method (digital and visual), using original and synthetic bands, was employed. At first the image was classified using maximum likelihood classifier. The most accurate map (overall accuracy and kappa coefficient equal to 94.56% and 0.89, respectively) was converted to the vector format and then it was edited on the basis of various color composites, fused images and other ancillary data. The obtained map showed overall accuracy and kappa coefficient equal to 96.39% and 0.927, respectively. The comparison of the classified map with the forest map of 1994, illustrated that 751 ha of forest area (equal to 8.2% of the previous forest area), were decreased. This includes a 417 ha increase (mostly reforested areas) and a 1168 ha decrease over the study period. The findings indicate the high potential of ETM+ data in forest mapping and change detection over the whole extent of the northern forest of Iran.

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In order to manage the rangeland resources, remote sensing data is able to provide a sensible role of different cases in flora community such as biomass. The study area in SefidAb subbasin of the Lar Dam basin is located in central Alborz, where the climatic condition is semihumid and near to moderate. For the assessment of the sattelite data and their capability in estimation of the range production, Landsat-TM data with different bands was used. In this research, the field data was collected using random-systematic method in 20 sampling units of 200 plots. For geographic coordinates of the sampling units and related pixels in digital data, GPS and also existing benchmark data of the nearest points were used. Then correlation between ground data and vegetation index from different band combination was investigated and the reasonble vegetation indices were obtained. Finally, the best models were extracted for this purpose, which showed sensible relation between the field data and vegetation index. Therefor, it is possible to estimate range production using Landsat TM data related to ground control.

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Monitoring Land Use and Land Cover Changes have a significant role in environmental programming and management. Satellite data is an essential tool for detecting and analyzing environmental changes. Many change detection techniques have been developed which have advantages or disadvantages. Change Vector Analysis (CVA) technique is one such a method. This method is based on radiometric changes between two dates of satellite imagery. Main advantage of this method is that it provides direction and magnitude image of change. The aim of this study was to describe change vector analysis technique and it applies to detect land cover change in Isfahan area during an 11-year period. The data used for this study were two images Landsat: TM 05 June 1987 and 03 June 1998. Correction radiometric was not carried out because of the similar sensor and acquisition time of the remote sensing data. After geometric correction, the study area was selected from Landsat images. Change vector technique was applied to analyze magnitude and direction of change. The change map showed Kappa and overall accuracy coefficient of 63.19% and 74.4%, respectively. The results showed that the changed land cover was 3340 ha during this period. Overall, the results show that 1325 hectares (especially agricultural lands) have been converted into urban areas, agricultural areas were increased up to1385 hectares, and 435 hectares of agricultural areas were converted to other land use over the period of study. This study showed that CVA is a robust approach for detecting and characterizing radiometric change in multi-spectral remote sensing data sets.

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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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One of the major challenges in water resources management is the operation of trans boundary watershed. This has been experienced in case of Helmand River between Iran and Afghanistan since the last century. For such a situation, application of a conceptual rainfall-runoff models that can simulate management scenarios is a relevant tool. The SWAT model can be a relevant option in this regard. However, the required hydro-climatic data for them is a serious obstacle. Especially, this problem gets exacerbated in the case of Afghanistan with poor infrastructures. So, application of this type of model would be more problematic. This paper aims to investigate capabilities of SWAT for the simulation of rainfall-runoff processes in such a data-scarce region and the upper catchment of Helmand River is used as the case study. For this purpose, discharge data of Dehraut station from 1969 to 1979 along with some metrological data were prepared and used to calibrate and validate the simulations. The results were acceptable and the coefficients of determinations (R2) during calibration and validation periods were 0.76 and 0.70, respectively. Notably, with respect to snowy condition of the basin, the elevation band option of the snow module of model had a significant effect on the results, especially in the base flows. Moreover, two Landsat satellite images during February 1973 and 1977 when the basin was partly covered with snow was prepared and compared with the SWAT outputs. Similarly, the results showed good performance of the model such that R2 were 0.87 and 0.82, respectively.

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Conventional methods for estimating evapotranspiration are based on point measurement and suitable for local areas, therefore, cannot be generalized for larger areas or watershed basins. The remote sensing technology is capable of using satellite images and meteorological data to estimate evapotranspiration in a wider area. In this study, estimates of evapotranspiration (ET) by SEBAL and METRIC models based on Landsat 7 ETM+ sensor were compared against ET measured by lysimeter on seven satellites passing time over Shahrekord plain located in Karun basin. The results showed that the lowest indices of NRMSE, MAE and MBE (respectively, 0.317, 1.503 and -0.973 mm per day) and the maximum of d index (0.768) belonged to SEBAL. These indices were 0.420, 2.120, 2.023 and 0.646 for METRIC, respectively. The results showed that the SEBAL was more accurate than METRIC model for estimating ET under Shahrekord plain conditions. As long as the possibility of getting complete hourly meteorological data be provided, or some modifications on METRIC model were done, SEBAL show closer results to reality, and therefore is recommended.
 

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This study was designed to investigate the possibility of using the surface energy balance algorithm for land (SEBAL) and mapping evapotranspiration at high resolution with internalized calibration (METRIC) models to estimate evapotranspiration (ET) in Shahrekord  plain (Chaharmahal va Bakhtiari province, Iran). Two sets of Landsat ETM+ data dated June 30^th and August 21^st, 1999 were provided to estimate and compare reference evapotranspiration (alfalfa) at regional scale using Landsat ETM+ data to ET estimations by five mathematical methods (experimental and combined) known as standardized Penman-Monteith by American Society of Civil Engineers (ASCE-stPM), Penman-Monteith (F56PM), Blaney-Cridle (F24BC), Hargreaves-Samani (HS) and evaporation pan (F24P). Results showed that ET at cold anchor pixel for SEBAL were 6.97 and 6.77 millimeters per day and for METRIC were 10.27 and 9.31 millimeters per day, on days when the satellite passed over. Hargreaves-Samani ET values, as the suitable mathematical model for the studied area, were 8.0 and 7.5 millimeters per day, respectively, on two satellite passes. Results showed that, in the first pass all statistical indices for SEBAL were less than the second pass, maybe due to higher air temperature and wind speed. On the other way, statistical indices in METRIC on the alternate pass, however, showed higher values over the corresponding values in SEBAL. ET values on two satellite passes for anchor pixels were 5.65 and 5.93 mm/day in SEBAL, and 5.22 and 6.65 mm/day in METRIC, respectively. ET values on the same days of satellite overpass for Hargreaves – Samani (HS) were 8.0 and 7.5 mm/day. Consequently, based on the results, both RS-ET models were comparable to empirical models such as (HS). Generally, the results showed that SEBAL had higher accuracy than METRIC, presumably due to lack of accurate weather data (hourly data), so SEBAL is recommended in similar conditions. Generally, the results showed that SEBAL had higher accuracy in comparison to HS and lysimeters data than METRIC, so SEBAL is recommended in similar conditions.
 
 
 

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Land surface temperature (LST) is used as one of the key sources to study land surface processes such as evapotranspiration, development of indexes, air temperature modeling and climate change. Remote sensing data offer the possibility of estimating LST all over the world with high temporal and spatial resolution. Landsat-8, which has two thermal infrared channels, provides an opportunity for the retrieval of LST using the split- window method. The main objective of this research was to analyze the LST of land use/land cover types of the central part of Isfahan Province using the split- window algorithm. The obtained results demonstrated that the "other" class which had been mainly covered with bare lands exhibited the highest LST (50.9°C). Impervious surfaces including residential areas, roads and industries had the LST of 45°C. The lowest temperature was observed in the "water" class, which was followed by vegetation. Vegetation recorded a mean LST of 42.3°C. R² was 0.63 when regression was carried out on LST and air temperature.
 

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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.

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