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Understanding Surface Water Dynamics in Post-Mining Area Through Multi-Source Remote Sensing and Spatial Regression Analysis.

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Titel: Understanding Surface Water Dynamics in Post-Mining Area Through Multi-Source Remote Sensing and Spatial Regression Analysis.
Autoren: Buczyńska, Anna, Głąbicki, Dariusz, Kopeć, Anna, Modlińska, Paulina
Quelle: Remote Sensing; Sep2025, Vol. 17 Issue 18, p3218, 28p
Schlagwörter: REMOTE sensing, HYDROLOGY, ENVIRONMENTAL history, RESTORATION ecology, ABANDONED mined lands reclamation, GEOGRAPHIC spatial analysis, HUMIDITY, COPPER mining
Geografische Kategorien: POLAND
Abstract: Highlights: What are the main findings? An increase in water content of vegetation and minor changes in soil moisture were noted between 2015 and 2024. The extent of water in the Wartowice flotation reservoir increased significantly over the last 10 years. What is the implication of the main finding? Past copper mining was not the main driving factor of the observed changes in surface water according to local and global regression models. Remote sensing enabled detection of surface water changes over a post-mining area. Despite successful land reclamation efforts, post-mining areas are still prone to secondary effects of mineral extraction. These effects include surface deformations, damage to infrastructure and buildings, and periodic or permanent changes to surface water resources. This study focused on analyzing a former copper mine in southwest Poland in terms of surface water changes, which may be caused by the restoration of groundwater conditions in the region after mine closure. The main objective of the study was to detect areas with statistically significant changes in surface water between 2015 and 2024, as well as to identify the main factors influencing the observed changes. The methodology integrated open remote sensing datasets from Landsat and Sentinel-1 missions for deriving spectral indices—Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Moisture Index (NDMI), as well as Surface Soil Moisture index (SSM); spatial statistics methods, including Emerging Hot Spot analysis; and regression models—Random Forest Regression (RFR) and Geographically Weighted Regression (GWR). The results obtained indicated a general increase in vegetation water content, a reduction in the extent of surface water, and minor soil moisture changes during the analyzed period. The Emerging Hot Spot analysis revealed a number of new hot spots, indicating regions with statistically significant increases in surface water content in the study area. Out of the investigated regression models, global regression (RFR) outperformed local (GWR) models, with R2 ranging between 74.7% and 87.3% for the studied dependent variables. The most important factors in terms of influence were the distance from groundwater wells, surface topography, vegetation conditions and distance from active mining areas, while surface geology conditions and permeability had the least importance in the regression models. Overall, this study offers a comprehensive framework for integrating multi-source data to support the analysis of environmental changes in post-mining regions. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
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Abstract:Highlights: What are the main findings? An increase in water content of vegetation and minor changes in soil moisture were noted between 2015 and 2024. The extent of water in the Wartowice flotation reservoir increased significantly over the last 10 years. What is the implication of the main finding? Past copper mining was not the main driving factor of the observed changes in surface water according to local and global regression models. Remote sensing enabled detection of surface water changes over a post-mining area. Despite successful land reclamation efforts, post-mining areas are still prone to secondary effects of mineral extraction. These effects include surface deformations, damage to infrastructure and buildings, and periodic or permanent changes to surface water resources. This study focused on analyzing a former copper mine in southwest Poland in terms of surface water changes, which may be caused by the restoration of groundwater conditions in the region after mine closure. The main objective of the study was to detect areas with statistically significant changes in surface water between 2015 and 2024, as well as to identify the main factors influencing the observed changes. The methodology integrated open remote sensing datasets from Landsat and Sentinel-1 missions for deriving spectral indices—Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Moisture Index (NDMI), as well as Surface Soil Moisture index (SSM); spatial statistics methods, including Emerging Hot Spot analysis; and regression models—Random Forest Regression (RFR) and Geographically Weighted Regression (GWR). The results obtained indicated a general increase in vegetation water content, a reduction in the extent of surface water, and minor soil moisture changes during the analyzed period. The Emerging Hot Spot analysis revealed a number of new hot spots, indicating regions with statistically significant increases in surface water content in the study area. Out of the investigated regression models, global regression (RFR) outperformed local (GWR) models, with R2 ranging between 74.7% and 87.3% for the studied dependent variables. The most important factors in terms of influence were the distance from groundwater wells, surface topography, vegetation conditions and distance from active mining areas, while surface geology conditions and permeability had the least importance in the regression models. Overall, this study offers a comprehensive framework for integrating multi-source data to support the analysis of environmental changes in post-mining regions. [ABSTRACT FROM AUTHOR]
ISSN:20724292
DOI:10.3390/rs17183218