Object-Based Reconstruction of Mining-Associated Water Body Lifecycles from Multisource Satellite Time Series

Mining-associated water bodies are typically mapped as independent annual patches, obscuring their formation, transformation and continuity over time. Changes in visible water therefore need to be distinguished from changes in water body identity and surface state. Here, we develop an object-based, interval-aware framework to reconstruct water body lifecycles across seven domains in the Huainan and Huaibei coalfields, China, separating persistent identity from geometry and surface state. The framework integrates Landsat, Sentinel-2, PlanetScope and RapidEye imagery with contextual evidence to interpret changes and constrain event timing. Across these domains, the screened visible water area increased by 853.0 ha between the 2015–2017 and 2022–2024 epochs, comprising 1758.4 ha of gain and 905.4 ha of loss. The case histories documented continuity under photovoltaic cover and engineered subdivision, as well as terrestrial surface conversion with an unresolved termination status. In the examined cases, the changes in area and component overlap did not uniquely identify lifecycle events without temporal and spatial context evidence. Dense image sequences documented the early development and expansion of individual water bodies and bounded coalescence events. An independent blinded review reproduced 94.4% (17/18) of identity-relation decisions and all endpoint decisions in the original review sample. By distinguishing object continuity from visible water change, the framework complements area-based monitoring by providing evidence of water body lifecycle processes.

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Publication Details

Journal
Remote Sensing
Published
2026-10-14
DOI
https://doi.org/10.3390/rs18203524
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
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article

Object-Based Reconstruction of Mining-Associated Water Body Lifecycles from Multisource Satellite Time Series

Yuhan Huang, Guangming Zhao, Jucai CHANG, Shihao Hu et al.
Remote Sensing
Remote-Sensing Image Classification
article

Object-Based Reconstruction of Mining-Associated Water Body Lifecycles from Multisource Satellite Time Series

Yuhan Huang, Guangming Zhao, Jucai CHANG, Shihao Hu, Xiao Chen
article en

Abstract

Mining-associated water bodies are typically mapped as independent annual patches, obscuring their formation, transformation and continuity over time. Changes in visible water therefore need to be distinguished from changes in water body identity and surface state. Here, we develop an object-based, interval-aware framework to reconstruct water body lifecycles across seven domains in the Huainan and Huaibei coalfields, China, separating persistent identity from geometry and surface state. The framework integrates Landsat, Sentinel-2, PlanetScope and RapidEye imagery with contextual evidence to interpret changes and constrain event timing. Across these domains, the screened visible water area increased by 853.0 ha between the 2015–2017 and 2022–2024 epochs, comprising 1758.4 ha of gain and 905.4 ha of loss. The case histories documented continuity under photovoltaic cover and engineered subdivision, as well as terrestrial surface conversion with an unresolved termination status. In the examined cases, the changes in area and component overlap did not uniquely identify lifecycle events without temporal and spatial context evidence. Dense image sequences documented the early development and expansion of individual water bodies and bounded coalescence events. An independent blinded review reproduced 94.4% (17/18) of identity-relation decisions and all endpoint decisions in the original review sample. By distinguishing object continuity from visible water change, the framework complements area-based monitoring by providing evidence of water body lifecycle processes.

Remote SensingVol. 18(20)
University of Technology Sydney (AU), Anhui University of Science and Technology (CN), Huaibei Mining (China) (CN)
National Natural Science Foundation of China
Clean water and sanitation
Openalex Percentile: Top 14%
Remote-Sensing Image Classification
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