Journal of Radar and Optical Remote Sensing
Volume:5 Issue: 1, Winter 2022

Subsidence is the earth’s surface movement towards down relative to a datum such as sea level. The main reason of subsidence in Iran is groundwater overuse which if not managed correctly, it causes irreparable damages. Therefore, the first step in solving this problem is identification of subsidence areas and estimating the rate which will have a significant role in controlling this phenomenon. One of the most suitable methods of identification of subsidence is using Interferometric Synthetic Aperture Radar (InSAR)technique. This method is superior to other detection T in terms of cost, precision, extent of the study area and time and it can provide an accurate estimate of the area. In this research, zone of the Rafsanjan plain has been investigated between 2006 and 2010. In order to calculate subsidence rate, SAR data related to the ASAR sensor in C-band and ALOS PALSAR in L-band were used. Generalized linear models in C-band and L-band with values of 0.91 and 0.89 and RMSE coefficient of 0.37 and 0.61 represented a strong linear relationship. Also the relationship between subsidence and the changes inpiezometric levels (groundwater extraction) in the study area showed that for each 4.7 centimeters groundwater level decrease, there has been 1 centimeter subsidence.

Remote sensing is a useful technology as a superior to other methods thanks tofeatures like vast and integrated view of the area, repeatability, accessibility and highprecision of information, and saving in time. Vegetation index is extensively usednowadays in different continental, regional, and areal scales. Due to excessive use ofnatural resources, area of landscapes has been changing day to day and updating of themaps is a costly and time-consuming task. Thus, many of the well-developed countriestake benefit of satellite data at different levels. The analysed factors included 1-preparation of vegetation and land use maps of North Khuzestan; 2- assessment ofbiological potential in agriculture development of the studied area using WLC andweighted overlay technique.Based on the acquired results and performed computations, it was demonstrated thevariations in the pasture and agriculture soil during the years from 2003 to 2014 were19 percent and a significant reduction is observed in this part of land use. The changesbetween the years 2014 to 2016 were equal to approximately 11 percent according tothe computations. This value is remarkably high and indicates intensity of changesduring the recent years.

Investigation and analysis of land use changes was done using remote sensing and GIS techniques with supervised classification methods. The selected images from the years 2000 and 2022 were taken by the Landsat satellite. Necessary pre-processing of the images was done and then the best band combination was selected. The best band combinations of 2000 and 2022 were selected as 245 and 467, respectively, using the OIF index. The area changes from 2000 to 2022, in the support vector machine method, the uses of dense pasture, poor pasture, agriculture, residential, forest have had area changes of 9580.53, 34267.49, 237.2, 1603.41, 26527.57 hectares. Therefore, the use of dense pasture and forest has decreased by 5.87% and 16.25%, and other uses have increased. The area changes from 2000 to 2022, in the neural network method, the uses of dense pasture, poor pasture, agriculture, residential, forest have had area changes of 6021.05, 33869.57, 360.79, 1492.16, 29701.47 hectares. Therefore, the use of dense pasture and forest has decreased by 3.69% and 18.20%, and the use of poor pasture has increased by 20.75%, agriculture by 0.22%, and residential by 0.91%. In the assessment of classification accuracy, kappa coefficient and overall accuracy in the support vector machine method in 2000 were 0.84 and 0.87 and in 2022, 0.86 and 0.88 were obtained. Kappa coefficient and overall accuracy were obtained in 2000, 0.94 and 0.95 and in 2022, 0.96 and 0.97 in the neural network method. Therefore, the neural network method has higher accuracy.

According to UNESCO, subsidence is the collapse or subsidence of the earth's surface that occurs on a large scale for a variety of reasons.Land subsidence is the gradual subsidence or sudden subsidence of the earth's surface. Land subsidence due to the movement of materials underground is often caused by the removal of water, oil, natural gas or mineral resources from the ground by pumping, breaking parts or mining activities. As a result, deep cracks are created in the ground, well pipes are tilted, buildings are destroyed or cracked, and part of the well pipes are pushed out of the ground.In this article, using ENVISAT ASAR radar images, C band is related to the years 2006-2009 using ENVI5.3.1 software, SARSCAPE plugin. After performing PHASE TO DISPLACEMENT CONVERSION AND GEOCODING, we obtained the subsidence map. The subsidence obtained from the maps was between -18 to +12 related to agricultural plains, river banks, mountain falls and deposits due to rainfall and soil dissolution due to geological type. In the plains, subsidence has probably occurred due to the drop in groundwater. Consecutive droughts and uncontrolled groundwater abstraction and lack of management in abstraction in the future will be witnessed by severe crises in various regions.

The land subsidence phenomenon as a geomorphic hazard has reached to a critical level in some parts of central Iran recently. The gradual and sudden land subsidence is influenced by natural and artificial parameters. The gradual land subsidence has a slow pace mainly due to the overextraction of underground water or oil resources. The continued land subsidence can, in the long period, deliver irreversible damages to buildings, roads, bridges and pipelines. The application of remote sensing technology in earth survey is very common due to the wide coverage of satellite imagery, the timeliness of images and its lower required budgets versus ground-based methods. One of the applications of remote sensing is to control the movement of the ground surface due to various factors such as earthquakes, landslides and land subsidences. In this research, two satellite images of SENTINEL1 from 2015 to 2017 and DEM of the mentioned region have been used. Special software was used for processing images by forming an interferogram and performing filtration, consequently the land subsidence was determined at this time interval. Investigations confirmed that the excessive use of underground water resources has led ro substantial amounts of land subsidence in Yazd province. Interferograms resulting from the processing of radar images showed the occurrence of land subsidence within the neighbourhood of gas distribution and transmission pipelines in specific at an average of 8 to 10 cm around several cities, in specific Ardakan and Meybod