30-Year Dynamics of Vegetation Loss in China’s Surface Coal Mines: A Comparative Evaluation of CCDC and LandTrendr Algorithms

Wanxi Liu, Yaling Xu, Huizhen Xie, Han Zhang, Li Guo, Jun Li () and Chengye Zhang
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Wanxi Liu: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Yaling Xu: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Huizhen Xie: Satellite Application Center for Ecology and Environment, MEE, Beijing 100094, China
Han Zhang: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Li Guo: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Jun Li: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Chengye Zhang: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

Sustainability, 2025, vol. 17, issue 20, 1-23

Abstract: Large-scale vegetation loss induced by surface coal mining constitutes a critical driver of regional ecological degradation. However, the applicability of existing change detection methodologies based on remote sensing within complex mining areas under diverse climatic conditions remains systematically unverified. To address this gap and reveal nationwide disturbance patterns, this study systematically evaluates the performance of two algorithms—Continuous Change Detection and Classification (CCDC) and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr)—in identifying vegetation loss across three major climatic zones of China (the humid, semi-humid, and semi-arid zones). Based on the optimal algorithm, the vegetation loss year and loss magnitude across all of China’s surface coal mining areas from 1990 to 2020 were accurately identified, enabling the reconstruction of the comprehensive, nationwide spatio-temporal pattern of mining-induced vegetation loss over the past 30 years. The results show that: (1) CCDC demonstrated superior stability and significantly higher accuracy (OA = 0.82) than LandTrendr (OA = 0.31) in identifying loss years across all zones. (2) The cumulative vegetation loss area reached 1429.68 km 2 , with semi-arid zones accounting for 86.76%. Temporal analysis revealed a continuous expansion of the loss area from 2003 to 2013, followed by a distinct inflection point and decline during 2014–2016 attributable to policy-driven regulations. (3) Further analysis revealed significant variations in the average magnitude of loss across different climatic zones, namely semi-arid (0.11), semi-humid (0.21), and humid (0.25). These findings underscore the imperative for region-specific restoration strategies to ensure effective conservation outcomes. This study provides a systematic quantification and analysis of long-term, nationwide evolution patterns and regional differentiation characteristics of vegetation loss induced by surface coal mining in China, offering critical support for sustainable development decision-making in balancing energy development and ecological conservation.

Keywords: CCDC; LandTrendr; open-pit; vegetation disturbance; algorithm applicability; remote sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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