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Remote sensing provides necessary and sufficient facilities to extract and update land use/land cover (LULC) maps for planners and managers. In this work, we presented a case study of LULC change as a consequence of human population and activities, in the Lighvan watershed, using multi temporal satellite images analysis. In order to perform the research, four images of Landsat satellite include MSS 1976, TM 1989, ETM+ 2000 and 2010 have been used. Supervised classification technique 10 English Abstracts by using maximum likelihood method with the aid of ground truth data is carried out. Normalized difference vegetation Index (NDVI) and post classification comparison, were evaluated to detect environmental changes over the period from 1976 to 2010. NDVI results highlighted significant reduction of dense vegetation cover, over time. This reduction in vegetation cover density is mainly due to the increasing extent of urban areas, the building of roads, non managed rainfed farming, and the conversion of dense pastures to weak. Analysis of population trend changes in Lighvan and Sefideh Khan villages revealed that population changes is matched with the LULC changes and immigration of villagers between 1979 to 1986 has been played an important role in the LULC changes of study watershed.

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Landuse maps of Khalkhal County using Landsat and IRS imagery by considering geometric and radiometric corrections based on supervised classification with Maximum Likelihood algorithm for 1987, 2002 and 2008 were produced. The accuracy of the produced maps using overall accuracy and Kappa statistic were calculated and results of comparison for the maps of 1987 with 2002 show that, dry farming land has increased from 18.37 to 25.22% and irrigated farming has also increased from 5.77 to 7.30%. On the other hand, forest area has decreased from 2 to 0.38% and rangelands have also reduced from 38.44 to 31.61%. Moreover, the results of map comparison from 2002 and 2008 show that, rangelands and residential areas with 0.23 and 0.06% have increased respectively, and dry farming with 1.58% has the most decreased areas. Statistical analyses in the level of 1 and 5% showed that the rock on the 1988 landuse map were 89 and 91%, and meadow 62 and 65% as the lowest and highest significance. Results of significance for the landuse map of 2005 were 91 and 94% for dry farming, and 67 and 69% for forest as the lowest and highest and for the landuse map of 2008 significance were 86 and 89% for rock, and 67 and 69% for forest as the lowest and highest. By considering accuracy assessment and the significance of the results for the produced maps, the results were acceptable.

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Abstract

Detecting land use, land cover changes and recognizing effective factors is necessary to prevent land use changes and better management. The aim of this study was detecting changes of Arasbaran forest cover in two periods of 12 years, modeling and predicting forest cover destruction in this region. At first, the multi temporal Landsat 5 images in 1990, ETM+ Landsat 7 in 2002 and OLI Landsat 8 in 2014 were provided and were classified in two categories including high dense forest, low dense forest. Forest changes were detected in three periods, 1990-2002, 2002-2014, and 1990-2014, also changes in forest cover were estimated in different classes of variables influencing changes. Forest area changes in the study period were modeled by logistic regression models and Geomod. In order to compare the performance of these two models in predicting land uses status by preparing maps in 2014 and validating by real map of that year. Results showed that in the period of 24 years, 992 and 1592 hectares of high and low dense forests were degraded during 1990-2014, respectively. The results of decreasing forest cover modeling showed that variables such as distances from roads and residential, elevation and slope has a direct relation with forest degradation. However, there is an inverse relation between forest degradation and distance from forest variables. The validation result of forest cover maps which is predicted in 2014 show total accuracy and kappa coefficient is 96.8 and 0.9342, for logistic regression map and 96.4 and 0.9269 for Geomod map respectively. These results indicated that model had a good performance in predicting of land use changes. Finally, using the logistic regression and Geomod, forest cover changes predicted for 2025. The result of predicting showed that the forest cover will degradeted 3.9% in the next 10 years.

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The present resarch was performed to investigate the changes of Ilam oak forests following the occurance of tree declines and Identifying and zoning of Dieback oak stands. To study the status of Ilam forests, before the tree decline, the satellite images of Landsat 7 ETM 2001 was used. In order to identify the declined oak forest stands, classification of declined areas in terms of  topography, size and location of damaged forests in the province by training sample method in geographic information systems, satellite images of Landsat 8 in 2013 were used. The results from comparison of the satellite images of 2001 and 2013 show that the area of Ilam forests decreased about 26073 hectares (from 542252 to 516179 ha) which about 12847 ha is belongs to decline stands and about 13226 ha is belongs to construction of towns, roads, and etc. The most amount and percentage of tree decline was found in moderate class of forest density, climates of cold semi-arid, cold Mediterranean and sub-humid ultracold, in elevations of 800 to 1200 m, low slopes 0-15% and south west aspects. Also the most amount and percentage of tree decline was found in the cities of Ilam, darrehshahr and Badreh.

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Earth surface temperature is one of the important criteria in regional regional planning. Today, the increase in temperature of some densely populated urban areas compared to the surrounding rural areas has created a phenomenon known as the urban heat island and has caused many problems. Urban heat island is a surface of the city that is significantly warmer than the surrounding rural areas. For this purpose, first, 8 satellite images of the warm period of the city of Shiraz, during the period from 1985 to 2020, using the data of sensors (TM) Landsat 4 and 5, (ETM+) Landsat 7, (OLI) /TIRS) was collected and extracted by Landsat 8. After the necessary pre-processing, normalized difference vegetation indices (NDVI), land surface temperature (LST) and city thermal area dispersion index with ecological assessment (UTFVI) were calculated. Based on the results of image processing, the places with heat islands, how the temperature changes in the city, the relationship between the changes in the surface temperature and the surface cover were investigated and analyzed in order to identify and analyze the urban heat islands of Shiraz. Changes in the time scale of temperature patterns in Shiraz showed that from 1985 to 2020, about 12.76 square kilometers have been added to the area of ​​the fourth temperature floor. The results of calculating the NDVI index during the studied time period, the vegetation area has decreased from 22.28 square kilometers in 1985 to 17.54 square kilometers in 2020 due to the change of urban uses, which can be the reason for the shape Catching and increasing heat islands in the mentioned areas. The UTFVI index showed that it is very very bad (very hot temperature class) mainly in the western parts of Shiraz from the northwest to the southwest (including parts of region 9 and 10), the southeastern regions of region 7 and the northern regions. Zone 1 is concentrated.