To use remote sensing data effectively, one must understand the spectral characteristics of the particular features under investigations. To study the relationship between soil salinity and soil reflectance, four Multispectral Scanners (MSS) and seven Thematic Mappers (TM) bands over the Ardakan playa located on the Central Iranian Desert margin were selected. In this study soil map, soil salinity observations, an interpolated soil salinity ...ap and 13 images in all, including 7 TM, 4 MSS and 2 Normalized Difference Vegetation Indices (NDVI) images were used. After smoothing the imagery using a 3 x 3 kernel, and delineating the bare soil from vegetated areas, the correlation coefficients between soil salinity (Electrical Conductivity) and related Digital Number (DN) values from TM and MSS bands on different soil types, such as gypsiferons and saline soils, were calculated. The results obtained demonstrate the trend of the correlation coefficients between soil salinity and the related DN values of MSS and TM bands. Based on the results obtained, we may conclude that the presence of gypsum in soil plays a significant role in lowering the correlation coefficients between soil salinity and surface reflectance -Further studies are required to draw more general conclusions. From the results obtained we may also conclude that the behavior of band 6 of TM on gypsiferous soil is completely different from that on saline soil and, therefore, we may generally conclude that LANDSAT TM imagery with six reflective bands, in one hand one hand, and the TM thermal band as complementary information to the TM reflective bands, on the other hand, contained some useful information that may play an important role in soil salinity studies and also the detection of gypsiferous soils in desert areas.

Electromagnetic radiation reflected from ground features is scattered by atmospheric particles and molecules in its way to satellite sensors. This causes some degree of haze in satellite imagery produced. Scattering and its consequent produced haze in imageries vary with time and weather condition. Consequently, image analysis becomes difficult. It is sometimes necessary to omit or at least reduce atmospheric effects on images in hand. This article studies application of an atmospheric correction model on LANDSAT TM image of an area in north mountainous part of Iran. The result of atmospheric correction was evaluated via comparing the product of maximum likelihood classification on corrected and on non corrected images. The results show that applying this method considerably reduces the haze effect on corrected image and produces higher classification accuracy.

In case the performance of irrigation and drainage systems could be monitored by using satellite data, which are taken in short intervals, the problems concerning these systems could be corrected. Roodasht region which is located in the lower part of the Zayanderood River Basin was considered as a pilot plain. The basin is struggling with salinity and waterlogging which started by construction of the Zayanderood Dam and consequently, doubling the share of irrigation water of the area. For this purpose the satellite images of LANDSAT MSS and TM of 1976 through 1990 were used. Modifications was performed after field works, reviewing the available reports and maps from the area, and then, the satellite data were classified. Temporal analysis of the satellite images showed that by doubling the irrigation water share during 14 years, soils with severe and no salinity risk were decreased by 5 and 16%, respectively, while 20% was added to the land with moderately salinity risk. During this time the area of waterlogged lands has been doubled. The images of 1990 showed that new waterlogged lands were developed in the vicinity of the drainage and irrigation canals.

In order to evaluate the potentiality of satellite data in classification of Saline Soils a thematic mapper subscene of Hoze-Soltan area in Qom province, Iran dated september 1991 was analysed. The satellite image was evaluated qualitatively. Geometric registration of image data was implemented using ground control points which were taken with DGPS. The RMS error was calculated to be about a half-pixel (0.55m). Different image enhancement techniques such as Band-Ratioing and PCA had been determined. 52 samples were taken random and systematically from topsoil and subsoiL The salinity parameters such as Electrical Conductivity (Ec) pH and soil texture of the soil samples were determined in the labratory. By specifying sample site on the geocoded satellite image, digital number (DN) relevant to the samples were extracted. Relationship between extracted DN and measured soil parameters were studied. According to the results, correlation coefficient for TM4 and TM3/TM4 was higher than other origin and synthetic bands. Although, the highest obtained coefficient (r=0.54) is significant at the 1% level, but it is not sufficient in order to use in classification of saline soils.

Empirical models are important tools for relating field-measured biophysical variables to remotely sensed data. Regression analysis has been a popular empirical method of linking these two types of data to estimate variables such as biomass, percent vegetation canopy cover, and bare soil. This study was conducted in a semi-arid rangeland ecosystem of Qazvin province, Iran. This paper presents the development of a regression model for predicting rangeland biophysical variables using the original image data of LANDSAT TM nonthermal bands. The biophysical variables of interest within the rangeland ecosystem were percent vegetation canopy cover, bare soil extent, and stone and gravel which their correlations were analyzed in relation to LANDSAT TM original data. The results of applying stepwise multiple regression showed that there is a significant correlation between LANDSAT TM band 2 reflectance values and biophysical variables. The developed models were applied to LANDSAT TM band 2 and relevant maps were generated. We concluded that such problems as an inexact location of field samples on the image, small size of samples, vegetation heterogeneity may significantly affect the modeling of real rangeland LANDSAT TM data relationships. 

This research involves land cover changes of Osmancik Basin. The basin, is located in the northern part of Turkey, between 40o 48’ – 41o 40’ north latitudes and 34o 41’ – 35o 04’ east longitudes. The aim of this study is to research the permanent effects of human activities on natural land cover change. For this purpose, satellite images which enclose 1987 - 2010 periods are used as data. Erdas Imagine 10.0 software was used to analyse these images by using data images (LANDSAT TM) processing techniques. Land cover change nomenclature is made due to Corine (Coordination of Information on the Environment) Class 2 (Urban fabric, Forest, Shrub and/or herbaceous vegetation association, Heterogeneous areas, Inlands wetlands) and Class 3 (Rice fields). All images were independently classified using the supervised classification method of maximum likelihood algorithm. As a result, a period of over 20 years deforestation was calculated 62 % from 66663, 1 to 25266, 2 hectares between 1987 and 2010. In the same period, urban fabric changing was observed 190 % from 1949, 8 to 5671, 8 hectares. On the contrary, Inland wetlands have increased from 429, 4 to 1641, 6 hectares because of the built up new big dam named Obruk.

West of Iran has rich natural recourse such as forest and rangeland by have spatial weather. This recourse has been high changes in last decade by population growth and unusual uses. Marivan area is one of the cities that are in this location. In addition, Marivan has Zarivar Lake with different uses such as tourist attractions and spatial ecosystem. Aim of this study is the investigation of land use change detection in Marivan city by using TM and ETM+ sensors of LANDSAT satellite during 16 years (1989-2005) by Post classification method and ENVI4.7 Software. Results showed that during the period study 24.11 present of the land use has been changed. The most changes related to agriculture and forest land use (49.45 and 38.29 present). These changes have the reduction trend. The reason of agriculture land use reduction is given up dry land farming. On the other hand, residential land use increased during period study. Zarivar lake area was not important changes during study and this suggested establishment of lake area during period study.

A number of approaches for optimal satellite data band selection for information extraction from LANDSAT data have been proposed. In this study, an evaluation has been carried out based on LANDSAT TM data concerning different land cover types of the four areas in different deserts in Iran. For selection of optimal band combination for different land cover/ land use types, the methods of Correction Matrix, Optimum Index Factor (OIF), Principal Component Anaysis (PCA) and two dimensional Feature Space analysis (FS) have been used.The obtained results show that selection of the most suitable bands not only depends on the climatic conditions and geographical region, but also land use, land cover types and date of satellite data. It may be concluded that to use LANDSAT satellite data effectively, the optimal band based on some important features such as, vegetated area and bare lands under the investigation should be selected.The obtained results show that TM thermal band contains information complementary to the TM reflective bands and it may be concluded that in determining the nature of an extensive material, spatial resolution might have less importance than the spectral resolution. Therefore it is essential to evaluate the information content of the TM thermal band for computer aided digital classification.