LISS IV sensor's data from IRS-P6 satellite was used to produce Land use map of eastern region of Isfahan, the studied part of which has an area of 22121 hectares. Its three band data, namely band 2 (Green), band 3 (Red) and band 4 (Near infrared) of LISS-IV sensor images with 5.8 m ground resolution were georeferenced by nearest neighbor method and first-order polynomial model to the DEM map of 1: 25000, where the RMSE was equal to 0.3 pixel. To analyze the satellite data, various image processing methods such as supervised and unsupervised classification methods, principal component analysis, NDVI vegetation index and filtering were applied to the satellite data. Finally, the Land use map was produced with hybrid method. The final map detected 6 Land uses very clearly, which are: Agricultural Lands, barren Lands, disturbed Lands, cultivated Haloxylon amodendron, roads, residential areas and industrial locations. The kappa of Land use map is 0.89 and the overall precision is 0.92. The barren Lands have a very poor natural vegetation and are considered as natural deserts. Disturbed Lands have been formed because of brick kiln activities, and the vegetation cover of these areas has disappeared completely The LISS IV data has a high ability to detect the various studied Land-uses especially to digitize the roads. They can be used to update the 1: 25000 topographic maps, as well.

Pakdasht is located in southern foothills of Alborz mountain. Its area estimated 604 km2. It possesses high agricultural potential. Its close proximity to Tehran (20 km) has turned it into one of the most attractive migrant center. This unprecedented migration rate in turn, has led to the unpleasant urban physical expansion and agricultural destruction.Thus, the major objective of this study is first to investigate the physical characteristics of the region. This is followed by creation of Land use map in order to detect all of the physical changes toward subsequent needed planning management.The research method of this study is followed by production of topographic map of the region along with the corresponding satellite images. As a second step, this map were digited. Through application of GIS, this led to production of elevation maps showing both degree and direction of the elevation of the region. As a further step, relevant processing were done on the satellite images through application of Geomatica software. Superwised classification and maximum likelihood were applied in order to produce Land use map.This study suggests that the northern part of the region essentially is not suitable for human habitation in general and agricultural activities in particular. However, southern part possesses high physical poterntials regarding human habitation. This part has drawned a lot of migrants and therefore needs particular attention regarding management issue. According to the finding, the dominant Land use is idle Lands (50.55). This is followed by agricultural Lands (39.33) and area under construction (9.22). The area of Pakdasht was 430 hectare prior to 1379. This changed into 734 hectare in 1383. This dictates its physical expansion equal to 304 hectare.

The important role of Land use and Land cover in the Lake Urmia basin on the water consumption of this area due to better water management in the basin, has made it necessary to have in-depth knowledge of basic information such as Land use and Land cover. Unfortunately, the available information and statistical sources about LU/LC of basin sometimes are insufficient and contradictory. This study, as one of the important aspects affecting the address of the Urmia Lake issue, has determined the databases that provide Land use maps from satellite images, also it examines the accuracy of these global products and compares them with the map which is created by object oriented method with eCognition software. The results of the overall accuracy assessment of the maps illustate that the Land use maps extracted from the LCtype and GLCF global products are performing well, and Globecover has provided poor results in this regard. There was the best fit in the results of the MODIS product, so that the MODIS product is not only better in pixel dimensions than most products, but also has the longest Land use extraction time in terms of time sequence. The results of this product in the study were in good agreement with the map produced by the object-oriented method, therefore it is recommended to use the MODIS Land use product in studies related to the Urmia Lake basin.

Determining Land capability based on Land suitability, climatic characteristics, and soil physical and chemical properties is considered as a method of achieving sustainable management. In this study, FAO model was used to determine Land capability. To describe the spatial results, Geographical Information System (GIS) was used. Spatial data, as maps, and descriptive data, as database table, were entered into the GIS environment. The results of current Land suitability model for irrigated agriculture and garden Land use showed that 64.5% was in N2 class, 17.86% in N1 class, 17% in S3 class and 0.64% in NR class. For dry farming Land use, 64.5% was in N2 class, 18.86% in S3 class, 17% in S2 class, and 0.64% in NR class. For rangeLand use, 32% was in S3 class, 32.5% in N1 class, and 35.5% in NR class. For forest Land use, 64.5% was in N1 class and 35.5% in NR class. From overlaying the current Land use map with the current Land suitability map, it was found that only 25.5 % of irrigated agriculture Land use, 82% of dry farming Land use and 30.3% of rangeLand use were consistent with their Land suitability map, being exploited properly.

A study was conducted to determine the capabilities of the successive numerical Landsat data for assessment and monitoring of Land use. Kashan plain with 7230 km2 of area, which is located in an arid zone of the central part of IRAN, selected as the site of investigation. It seemed to be a region prone to desertification processes. Two sorts of Landsat data: MSS (1976), TM (1998) and the supplementary information such as the soil and topography and Land cover maps were collected.After preprocessing, the images were classified on the base of the field and subsidiary data. For Mss data, the Mss1, Mss2 and Mss3 were merged and showed the best correlation with field samples. In TM data, merging the TM3, Tm4 and Tm5 showed the best correlation. The classification performed by the minimum distance algorithm.Five Land classes were distinguished with the overall precision of 65% and 75% for Mss and TM respectively. Detection of the changes between two maps showed a decrease of the area under range, bare and salt flats as much as 7.4%, 18.3% and 1.5% respectively, and an increase in cultivated and forest Land use by the factor of 0.5% and 20.7% respectively. some referral to reliable documents determined the good agreement between the results and the real occurrence, except for the cultivated Lands, that was significantly under estimated. It is concluded that thematic maps, which have released by image processing in a time series, could be compared and some general and useful results will be expected. Access to the same time field data of image acquisition time can promote the precision.

Background and Objective: High population growth rate has led to excessive use of capacity and double pressure on natural resources, resulting in rapid Land use changes. Therefore, quick and accurate identification of types of Land cover can play an effective role in planning and management. Satellite data because of vast and integrated sight covering with different electromagnetic spectrums and updated images are very suitable for making applicable Land use maps. The aim of this study is preparation of Land use map using ETM+ Landsat (a Case Study in Hendodar Watershed) Method: The Landsat 7 satellite images were used to determine the Land use changes of Hendodar watershed in Markazi province. The GPS was used to determine the position of Land use and Land cover types on the basis of taking test and ground control points on field investigation. Obtained samples were used for supervised classification with four different algorithms including maximum Likelihood, minimum distance, Minimum Mahalanobis Distance and Box Classification. Findings: The optimum index factor (OIF) for the main bands and PCA (principal coordinate analysis) were used to select the optimum combination of three bands in a satellite image to create a color composite, sample set and other operation and classification. Among the algorithms, the maximum likelihood classification algorithm had better results from the types of coverage and Lands use on the images. Discussion and Conclusion: The maximum likelihood classification algorithm with combination of b7, b4, b1 bands with 81. 25% accuracy is the best algorithms of Land use determination and classification comparing with real ground map of the area.

Awareness of different Land cover and human activities in different regions, as a basic information for executing of different programming is an important problem. Hereupon, remote sensing technique due to having special characteristicts will have important.After preprocessing of geometrical, topographical and Atmospherical emending and PCA analyzing and NDVI by applying of stewardship classification, the hight and medium current rangeLand, dense forest- semi dense forest and residential regions were extracted with %88 chriling. Result showed that, the dense forest with about %63.4 and the residential regions about %1.41,were have maximum and minimum area respectively on the extracted Land use map.

Introduction High-resolution satellite imagery data is widely utilized for Land use/Land Cover (LULC) mapping. Analyzing the patterns of LULC and the data derived from changes in Land use caters to the increasing societal demands, improving convenience, and fostering a deeper comprehension of the interaction between human activities and environmental factors. Although numerous studies have focused on remote sensing for LULC‎ mapping, there is a pressing need to improve the quality of LULC maps to achieve sustainable Land management, especially in light of recent advancements made. This study was carried out in an area covering approximately 8000 hectares, characterized by diverse conditions in LULC, geomorphology and pedology. The objective was to investigate the potential for achieving maximum differentiation and accurate mapping of Land features related to LULC. Additionally, the study assessed the impact of various spectral indices on enhancing the results from the classification of Landsat 8 imagery, while also evaluating the efficacy of support vector machine (SVM) and maximum likelihood algorithms in producing maps with satisfactory accuracy and precision.   Materials and Methods As an initial step, LULC features were identified through fieldwork, and their geographic coordinates were recorded using GPS. These features included various types of LULC, soil surface characteristics, and Landform types. Following the fieldwork, 12 types of LULC units were identified. Subsequently, the LULC pattern in the study area was classified using the RGB+NIR+SWIR1 bands of Landsat 8, employing both SVM and maximum likelihood classifiers. To assess the impact of various spectral indices on improving the accuracy of the LULC maps, a set of vegetation indices (NDVI, SAVI, LAI, EVI, and EVI2), bare soil indices (BSI, BSI3, MNDSI, NBLI, DBSI, and MBI), and integrated indices (TLIVI, ATLIVI, and LST), and digital elevation model of study area were successively incorporated into the classification algorithms. Finally, the outcomes from the two classification algorithms were compared, taking into account the influence of the applied indexes. The classification process continued with the selected classifier and indices until reaching the maximum overall accuracy and kappa coefficient.   Results and Discussion Field observations revealed that the study area could be categorized into 12 primary LULC units, including irrigated farms, flow farming, dry farming, traditional gardens (with no evident order observed among planted trees), modern gardens (featuring regular rows where soil reflectance is visible between tree rows), grassLands, degraded grassLands, highLand pastures (covered by Astragalus spp., dominantly), lowLand pastures (covered by halophyte plants), salt domes (with no or very poor vegetation), outwash areas (River channel with many waterways), and resistant areas. The results of image classification indicated that the performance of the SVM algorithm across different band combinations is superior to that of the maximum likelihood method. Using SVM resulted in an increase in overall accuracy and Kappa coefficient by 3-8% and 0.03-0.08, respectively. For the map generated using RGB+NIR+SWIR1 bands and employing SVM, overall accuracy and Kappa coefficient were determined to be 76.6% and 0.72, respectively. Among the vegetation indices used in the SVM algorithm, LAI had the most significant impact, increasing the classification accuracy by 2.64%. Among the soil indices, BSI and MBI indices demonstrated the best performance; with BSI increasing the classification accuracy by 1.95% and MBI by 1.64%. Among the integrated indices, LST and ALTIVI enhanced the classification accuracy by 2.75% and 2.35%, respectively. It should be noted that the inclusion of the digital elevation model did not significantly improve the classification accuracy when using the support vector machine algorithm; in fact, it led to a decrease in accuracy when applied to the maximum likelihood classification. The probable reason for this issue is the different nature of DEM data compared to the other input data, as well as the limitations of parametric statistical approaches to effectively integrating data from diverse sources. Finally, the classification process was executed using the three visible bands, NIR, and SWIR1, in conjunction with selected indices (LAI, BSI, MBI, LST, and ALTIVI). Results indicated that using these spectral indices significantly improved classification accuracy, particularly for the DF, DGL, MG, O, and IF Land cover/use classes. The calculated accuracies for these classes increased by 11.62%, 18.57%, 20.06%, 29.39%, and 33.19% respectively. Consequently, the accuracy of the classification and the Kappa coefficient (using support vector machine algorithm) increased to 85.24% and 0.82, respectively.   Conclusion In this research, we aimed to accurately map various Land use/Land covers by utilizing Landsat 8 imagery and incorporating three group of spectral indexes. Despite spectral interferences and overlaps among various phenomena related to LULC, the utilization of different spectral indices resulted in significant differentiation among LULC classes. Finally, considering the limitations of modelling in ENVI software, it is recommended to investigate the effectiveness of other models for classification in more specialized software, such as R.

Land use/cover maps resulting of satellite images play an important role in assessing the Land use/ Land cover at regional and national levels. Over the last years, many applications of neural network classifiers for Land use classification have been reported in the literature, but afew studies have assessed their comparison. In this study, firstly, geometric correction was performed on ETM+ data.Then, with field surveyings, the various Land cover classes were defined and training areas were selected. The main Objective of this study is to compare four artificial neural network methods for Land cover classification in Doiraj, Mehran and Sarableh region of Ilam province with various climatic conditions. In this study, we have used four artificial neural networks methods of Fuzzy Artmap, multi-layer perceptron, Kohonen and radial basis function. The results obtained of accuracy assessment of classified images showed that fuzzy Artmap classification algorithm with the overall accuracy 94.84 and kappa coefficient 0.93% have the highest accuracy than other methods. Accuracy overall difference in this approach than multi-layer percepteron method was 11.44 and Kappa coefficient 0.18, Compared to kohonen’s 17.30 and 0.23% and rather than radial basis function 31.01 and 0.36%, respectively. In this study, the highest accuracy was related to fuzzy Artmap artificial neural network. Therefore, this study proves the efficiency and capability of fuzzy Artmap neural network algorithm in classification of remote sensing images.