Abstract Extended
Introduction
Studying land cover changes has a very long history which coincides with the beginning of human life. Following the formation of societies, primitive humans began to change the cover of wasteland to form suitable lands for agriculture and animal husbandry. More than half of the world's population recently lives in cities, urbanization and urbanism is rapidly increasing, and this trend will continue to reach its peak. Due to their extensive coverage, reproducibility, easy-access, high accuracy and reduction in necessary time and expenses, remote sensing data are generally considered a preferred method used to study land cover, vegetation, and their changes. Many researchers have shown an interest in land cover change in different cities of the world. The history of land cover studies dates back to the early nineteenth century and the studies performed by von Thünen (1826). Von Thünen have determined the economic benefits of different land covers based on their distance from the central city and found an optimal distribution for production and land cover in the form of a series of concentric circles. Land cover changes due to human activities are considered to be an important topic in regional and development planning. Since land cover changes and urban development in the study area have not been previously studied, Landsat time series satellite imagery and a combination of Landsat 7 and 8 panchromatic and multispectral bands were used to identify and detect changes in land cover and urban development in the urban areas of Abarkooh from 2000 to 2020.
 
Materials & Methods
Satellite remote sensing data are used in the present study (Landsat 7 and 8 multi-temporal satellite images collected in 2000, 2010 and 2020). 3 images were retrieved from US Geological Survey website and used in the present study. Raw remote sensing images always contain errors in geometry and the measured pixel values. The former category is called geometric errors and the latter is called radiometric errors. Atmospheric corrections were performed for all images used, and stripping in the imagery collected in 2010 image was also corrected. For image enhancement and extraction of more information from the images, false color composites were used (5-4-3 infrared, red and green bands) for Landsat 8 and Landsat 7 (3-4-3 near infrared, red and green bands) images. Using this technique, vegetation is shown in red. Compared to other methods, Gram-Schmidt based pan sharpening method produced higher spatial resolution images of the study area and thus was used to combine the selected images. Maximum likelihood method is considered to have the highest efficiency among various supervised classification methods.
 
Results & Discussion
This method assumes the presence of a normal distribution for all training areas. The accuracy of this classification has to be calculated following the classification. To do so, the kappa coefficient and overall accuracy of each class were calculated in ENVI5.3. The results are shown in the error matrix. Overall accuracy is the average of classification accuracy. The kappa coefficient calculates the accuracy of classification as compared to a completely random classification. Based on the available data, spatial resolution of the images and the information researcher has access to, 5 classes of training data (urban constructed space, roads, barren lands, arable lands, and gardens) have been selected for each image. Results obtained from the maximum likelihood classification method in ENVI5.3 environment were changed into the vector format and then used as a shape file in GIS environment. After compiling the land database, land cover maps and its changes were extracted in three periods and the area of each land cover class was determined. Each of the land cover maps, 5 classes with different colors are determined and shown. To ensure the accuracy of the classification, the accuracy of the classification has been evaluated.
 
Conclusion
The resulting kappa coefficient for 2000 and 2020 equaled 86% and overall accuracy equaled 89%, while for 2010 kappa coefficient equaled 90% and overall accuracy equaled 92%. Thus, the error rate is small and acceptable. Finally, post-classification comparison method was used to investigate the nature of changes. 13 square kilometers of land cover were investigated in the present study. To identify the exact type of land cover changes, categorized images collected in these years were compared. Total area of residential land use showed an increasing trend: a total 4.25 square kilometers in 2000 (32.69 percent of the total area under study) has reached 5.58 square kilometers (42.92 percent) in 2020. Overall area of arable land use did not change much in the period of 2000 to 2010. However, a declining trend was observed in 2020 changing a part of this land use into residential and barren lands. Results of satellite image processing and classification indicate that supervised classification and maximum probability algorithm were close to ground realities and had an acceptable accuracy. In general, results indicate that significant amounts of vegetation and agricultural lands have been converted into urban areas and thus, planning for urban growth in these areas should be in favor of preserving gardens and agricultural lands.