Due to the vast changes it brings about in the land use and land cover, the rapid expansion of cities has had many negative effects on the environmental quality at global level. Examples include air quality, increased temperature, changes in perspective, and the alteration of agricultural lands into barren lands that leads to the loss of biodiversity. Since land use changes happen at extensively, remote sensing technology is a necessary, efficient, and valuable means to monitor changes. In this study, the effect of land use changes on the temporal-spatial patterns of land surface temperature in the urban lands of Ilam city in a 30-year period (1990-2020) was examined using Landsat satellite images and simulation of changes using fuzzy ARTMAP neural network model. Landsat satellite sensors (TM, OLI, ETM) were used to investigate the longitudinal and spatial changes (LST) in Ilam city.  To provide the land use map, the pixel-based classification for all periods (1990-1995-2000-2005-2010-2015-2020) was applied using ENVI and Ecognitio software packs and then estimations were made using NDVI and LST models. The results showed that Ilam city ecosystem has moderate levels of NDVI. The average temperature of residential land use level in June is 35.8 degrees centigrade, and the temperatures of three land uses of horticulture, agriculture, and other uses (uncovered lands) were 32.22, 37.25, and 38.46, respectively, with the main lands with the minimum temperature being green highlands. In city, existence of high rise buildings and building shading, use of materials with less heat absorption such as Isogam, urban green space and furniture, and air pollution lead to lesser absorption of energy. On the other hand, the spatial distribution of NDVI was aligned with LSP values. The results regarding land use areas using fuzzy-neural network show that residential land uses has risen from 19.18 percent in 1990 to 39.35 percent in 2020, which shows the city expansion and development. On the other hand, the horticultural land use has declined from 8.64 in 1990 to 3.49 in 2020, which can be attributed to the development of urban space.

Increasing technology and population in the two recent decades have led to destructing nature and changing its land cover. Remote sensing is one of those new technologies which provide accessibility for managing earth resources. the dynamic information of remote sensing can play an effective role for monitoring and analysis of land use change using digital change detection techniques. Method used for the rate of development of remote sensing, is on the basis geometric correction, spatial and spectral enhancement of available satellite images with regard to the maximum likelihood supervised classification and post classification method. At first we produced land use maps by using the satellite images of landsat. The results of this study are, in addition to the impacts of environmental sensitivity visualization development trend based on the visual display, calculate of the land use changes together in developed area and production of digital map changes, to support land management.

Land cover mapping is important for many planning and management activities. Today, satellite images and remote sensing techniques are extensively used in all sectors including agriculture and natural resources because they provide updated data and high analyzing abilities. In this study, in order to produce land cover map for the northern part of Hamedan province , digital satellite data IRSP6 ( Awifs time series data) were used. First, satellite image geometric correction with a mean square error of less than 0.48 pixels was applied. For image classification, the method of fuzzy classification was used. Finally, the land cover map of the study region was classified into thirteen classes. To assess the classified land cover map precision it was controlled for ground truths with a GPS. Kappa coefficient and overall classification accuracy of fuzzy classificotion were estimated 86 and 88 percent respectively. The results confirmed that the fuzzy clofifier was capable to generate land cover maps and cultivation‎ pattern‎ with high accuracy.