l. ebadi

In the future, the risk of land subsidence due to water resources shortage crisis and improper water resources management will become more and more dangerous. It is necessary to assess and identify areas susceptible to subsidence risk and take necessary actions to reduce risks related to land subsidence. In this study, first, the risk of land subsidence was identified and evaluated using a radar interferometry method called LiCSBAS. Then, the spatial relationship between the occurrence of land subsidence hazard and effective factors such as ground elevation, slope, slope aspect, lithology, land use, groundwater decline, distance from rivers, distance from faults, topographic moisture index, and arc curvature was investigated using the random forest (RF) model. In the end, the land subsidence hazard sensitivity map was prepared after calibrating the random forest algorithm. The analysis of LiCSBAS interferometric time series data from 2015 to 2022 showed that the center of the Marvdasht-Kharameh plain and adjacent agricultural areas are continuously subsiding and the mean deformation rate map showed a subsidence rate of 11.6 centimeters per year. The results of determining the spatial relationship between subsidence occurrence and effective factors confirmed the positive impact of distance from rivers, urban and agricultural land uses, depth of bedrock (aquifer thickness), groundwater decline, and alluvial and fine-grained formations on this phenomenon. Also, the results of subsidence modeling using the random forest algorithm showed that factors such as bedrock depth, groundwater decline, land use, and geology have the greatest impact on the potential for subsidence occurrence in the study area. Also, based on the results, about 3 to 4 percent of the areas are in the very high and extremely high-risk classes of land subsidence, especially in the center and suburbs of Mervdasht. Therefore, water resources management and control and developing a systematic program to reduce subsidence risk and aquifer recharge conservation in Merudasht-Kharameh Plain is essential.

Aims :

This research aimed to introduce an optimal method for water body extraction from multispectral satellite images.

Methodology : 

The applied research method is practical and based on the case study. In order to the extraction of water from Landsat 8 satellite images, the modified normalized difference water index along with three thresholding methods including the average method, Otsu’s algorithm, and zero-limit thresholding were applied. For evaluation of the results, the Sentinel 2 image was classified using the support vector machine method then the class of water was used as the reference to calculate the
confusion matrix and correlation coefficients.

Findings:

 The results showed that applying the zero-limit thresholding on modified normalized difference water index for water extraction from Landsat 8 satellite images is not suitable as it cannot identify shallow areas near the coastline. In contrast, Otsu’s thresholding method was able to accurately identify water and had good spatial matching with the reference data.

Conclusion :

Modified normalized difference water index method can successfully identify water areas on multispectral images but choosing the right threshold to separate water from land has a great impact on output accuracy. Most of the past research, use zero-limit thresholding, while the low efficiency of this thresholding method was proven in the current research. Otsu’s thresholding method, in addition to increasing the accuracy, does not require user intervention and uses image statistical information, so it is ideal for the automation of the process of water body extraction from satellite images