Chaokui Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Chaokui Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Chuping Fu
School of Architecture and Design, Hunan University of Science and Technology, Xiangtan 411201, China
1 shared publication - Hanwu Yu
School of Architecture and Design, Hunan University of Science and Technology, Xiangtan 411201, China
1 shared publication - Jia Fan
School of Architecture and Art, Central South University, Changsha 410083, China
1 shared publication - Ling Jiang
Anhui Province Key Laboratory of Realistic Geographic Environment, Chuzhou University, Chuzhou 239000, China
1 shared publication - Ping Zhang
School of Architecture and Design, Hunan University of Science and Technology, Xiangtan 411201, China
1 shared publication - Ting Li
School of Architecture and Design, Hunan University of Science and Technology, Xiangtan 411201, China
1 shared publication
Latest Publications
- Open Access
- 30 October 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (21) , 3736
OEI-GTCD: An Object-Based Ecological Index-Driven Framework for Detecting Long-Term Disturbance–Recovery Trajectories in Mining Areas
Ecological changes in mining areas are characterized by long-term dynamics, temporal stages, and spatial heterogeneity. Existing monitoring methods often face challenges in balancing multidimensional ecological condition characterization with spatially consistent analysis and have limited capability to identify complex disturbance–recovery trajectories. To address these issues, an Object-based Ecological Index-driven Framework (OEI-GTCD) is proposed to monitor ecological disturbance and recovery trajectories in mining areas. The framework uses the Mining-area Ecological Index (MEI) to characterize multidimensional ecological conditions and conducts analysis based on spatially consistent object units. On this basis, a Globally Optimized Time-series Change Detection method (GTCD) is proposed to improve the identification of complex ecological disturbance–recovery trajectories by integrating trend-change detection, abrupt-change detection, and segmentation optimization. Landsat imagery from 2000 to 2024 was used to characterize the spatiotemporal patterns of ecological disturbance and recovery across the Daye Mining Area, China. The findings suggest that MEI captures multiple dimensions of ecological conditions, while object-scale analysis characterizes ecological changes through overall object-level trajectories, supporting their spatial interpretation. OEI-GTCD achieved disturbance and recovery detection accuracies of 88.4% and 84.8%, respectively, representing improvements of 6.0 and 5.2 percentage points over OEI-LandTrendr. Ablation analyses further highlighted the contributions of abrupt-change detection and the improved segmentation criterion to event identification, as well as the role of local breakpoint refinement in temporal localization. From 2000 to 2024, stable and continuously improving ecological states together accounted for 82.07% of the area represented by the non-water objects used in the ecological analysis, while disturbance and recovery processes exhibited pronounced spatiotemporal heterogeneity. Overall, OEI-GTCD provides an integrated approach for monitoring ecological disturbance and recovery and characterizing their spatiotemporal patterns in mining areas.
- 30 October 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (21) , 3736
Open AccessOEI-GTCD: An Object-Based Ecological Index-Driven Framework for Detecting Long-Term Disturbance–Recovery Trajectories in Mining Areas
Ecological changes in mining areas are characterized by long-term dynamics, temporal stages, and spatial heterogeneity. Existing monitoring methods often face challenges in balancing multidimensional ecological condition characterization with spatially consistent analysis and have limited capability to identify complex disturbance–recovery trajectories. To address these issues, an Object-based Ecological Index-driven Framework (OEI-GTCD) is proposed to monitor ecological disturbance and recovery trajectories in mining areas. The framework uses the Mining-area Ecological Index (MEI) to characterize multidimensional ecological conditions and conducts analysis based on spatially consistent object units. On this basis, a Globally Optimized Time-series Change Detection method (GTCD) is proposed to improve the identification of complex ecological disturbance–recovery trajectories by integrating trend-change detection, abrupt-change detection, and segmentation optimization. Landsat imagery from 2000 to 2024 was used to characterize the spatiotemporal patterns of ecological disturbance and recovery across the Daye Mining Area, China. The findings suggest that MEI captures multiple dimensions of ecological conditions, while object-scale analysis characterizes ecological changes through overall object-level trajectories, supporting their spatial interpretation. OEI-GTCD achieved disturbance and recovery detection accuracies of 88.4% and 84.8%, respectively, representing improvements of 6.0 and 5.2 percentage points over OEI-LandTrendr. Ablation analyses further highlighted the contributions of abrupt-change detection and the improved segmentation criterion to event identification, as well as the role of local breakpoint refinement in temporal localization. From 2000 to 2024, stable and continuously improving ecological states together accounted for 82.07% of the area represented by the non-water objects used in the ecological analysis, while disturbance and recovery processes exhibited pronounced spatiotemporal heterogeneity. Overall, OEI-GTCD provides an integrated approach for monitoring ecological disturbance and recovery and characterizing their spatiotemporal patterns in mining areas.