The paper deals with techniques of image classification developed to distinguish gypsiferous soils, using the integration of field observation and remote sensing and more specific Landsat/ETM imagery. A Landsat image was assembled and used in this study. The image was acquired by the ETM/Landsat 7 sensor, which was acquired on August, 2012.Two main data have been used in this research, I) Field and II) Satellite data. The amount of gypsum is different from location to other, may be due to the parent material of some locations of the study area which is rich with gypsum minerals, and there is evidence of Gypsic indopedon horizon. The results indicated that the amount of organic matter decreases with increasing the amount of gypsum. In general, the study area is rich with total lime. These results reflect the effect of decalcification and calcification processes caused the formation of illuvial subsurface (calcic) horizon in some location of the study area.The pH values were around neutral to slightly alkaline due to the effect of calcareous parent material and type of climatic conditions. The low ECe values indicate that the soil was non-saline reflected by low values of ECe. The soil classes of the study area are belonging to Haplogypsids, Haplocalcids, Haplocambids, Calciargids and Haplargids. Two maps were prepared to show the distribution of gypsiferous in the study area, the first one is map which shows the output of supervised classification and maximum- like hood for specific, and the second is the thermal-based classification. Thermal-based map could predict the gypsiferous area in a better way, than the classification based only on spectral properties of non-thermal bands.

Annually, the oak forests of the Zagros Mountains chains in western Iran and northeastern Iraq face recurring challenges posed by forest fires, particularly in the Kurdo–Zagrosian forests in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation conditions. Integrating in situ data, Remote Sensing (RS) data, and Geographical Information Systems (GIS) integration presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces and tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies by isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas crucial for the study region’s fire susceptibility assessment. Results demonstrate a substantial overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

Annually, the oak forests of the Zagros Mountain chains, in western Iran and northeastern Iraq, face recurring challenges posed by forest fires, particularly in the Kurdo-Zagrosian forests situated in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation condition. Integrating in situ data, Remote Sensing (RS) data, alongside the integration of Geographical Information Systems (GIS), presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces from tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input, from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies, isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas that are crucial for fire susceptibility assessment in the study region. Results demonstrate a strong overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

Change in land use and land cover, as one of the most important factors of global-scaled environmental transformations, is considered as heart of the sustainable development debate. It is approved that humans are the main driving force that altering the land cover of the globe. In order to attain substantial development, urban authorities should think of useful tools to monitor how the land is now used to apprise future plan. The apprehension of urban change is crucial for making optimal decision and suitable planning. Satellite remote sensing is known potentially as a powerful way of assessing and mapping land-use/cover change at different spatial and temporal resolution. It also offers lower costs and time than those done using the traditional models. Remote sensing data and techniques are extremely useful because of its flexibility to provide views, repetitive coverage over a single area and real time and near real time data acquisition. land uses change and urban growth in remote sensing compose the analysis of two aerial or satellite image bands of a particular area which is recorded at two different dates. This study aims to a produce urban growth map for the small city of Darbandikhan, in the west of Kurdistan Region-Iraq, using RS-GIS data and techniques integrated with field data from 2003 to 2012. Results revealed that the has town sprawled by 2.7 times, from almost 1.9 km2in 2003 to 5.3 km2 in 2012, mainly to the North and West of the city. During this period population has doubled mainly because of migration from rural to urban area. Developed map shows that the distribution of facilities, except schools, trended to maintain around city center and not well distributed all over the town. It is also resulted that approximately 90% of streets were less than 15m.

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