Remote Sensing-Based Assessment of Soil and Water Pollution in Deep Excavation Scenario

Abstract

Soil and water resource mismanagement can negatively intensify global poverty and jeopardize ecology. Soil can be contaminated by heavy metals, organic chemicals like biological pathogens, pesticides, micro plastics and nano particles. Contamination decreases the soil’s capability to yield food thereby affecting food production by means of pollution and disease. Moreover, soil contaminants move into rivers damaging the water quality. Numerous technologies have been established to tackle water and soil remediation, such as deep excavation technique where transportation of contaminated soils or water is done to remote unpopulated sites. Recent development in Remote Sensing and geographic information processing techniques have led to exciting new opportunities for investigating and closely monitoring environmental factors that influence key land and soil management approaches. Hence, this paper uses the Deep Excavation and Remote Sensing based Assessment Model (DERSAM) to assess the soil and water pollution in contaminated areas. The data are taken from the Europe soil contamination map to classify the contaminated and non-contaminated areas. This data obtained from the high-resolution satellite of Remote Sensing (RS) technique and Geographical Information System (GIS) with the parallel expansion of a fully assimilated geospatial database system that provides monitoring and feedback at suitable spatial scales. Thus, such data can be utilized for long-term environmental management and monitoring of remediation and rehabilitation of excavation areas. The numerical outcomes show that the recommended DERSAM model increases the land use and land cover change prediction by 90.2%, the classification ratio by 98.2%, the pollution reduction ratio by 96.6%, the soil contamination detection ratio by 95.9%, and the overall performance by 97.2% compared to other existing approaches.