A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time series
Topographic effects in mountainous forested regions disrupt the spectral consistency of remote sensing imagery, hindering accurate forest disturbance detection in Landsat time series acquired over wide time intervals (WTW-LTSs). This study evaluates the necessity of topographic correction for improv...
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002158 |
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| author | Yating Li Shuai Li Xiao Xu Zhenzi Wu Hui Fan |
| author_facet | Yating Li Shuai Li Xiao Xu Zhenzi Wu Hui Fan |
| author_sort | Yating Li |
| collection | DOAJ |
| description | Topographic effects in mountainous forested regions disrupt the spectral consistency of remote sensing imagery, hindering accurate forest disturbance detection in Landsat time series acquired over wide time intervals (WTW-LTSs). This study evaluates the necessity of topographic correction for improving forest disturbance detection and proposes a novel post-processing topographic correction framework using the sun-canopy-sensor with C corrections (SCS + C) model. The framework simulates spectral reflectance distortions from illumination variations in uncorrected WTW-LTSs before change detection and employs post-processing to remove the resulting topographic artifacts from detected disturbances. Applied in Yunnan Province, China, the results show that (1) the post-processing framework effectively distinguishes topographic artifacts caused by intra-annual variations, achieving a high accuracy of 81.65 %; (2) by removing topographic artifacts, the post-processing framework significantly enhances forest disturbance detection, improving overall accuracy (OA), user’s accuracy (UA), and producer’s accuracy (PA) by 0.38 %–0.51 %, 1.08 %–1.83 %, and 0.18 %–2.18 %, respectively; (3) the pre-processing framework introduces uncertainties, reducing OA and PA by 0.1 % and 1.93 %–2.99 %, leading to the omission of 14.15 %–16.77 % of disturbances and the false detection of 10.03 %–14.57 % of new disturbances. These findings underscore the importance of eliminating topographic effects in WTW-LTSs for accurate forest disturbance detection. The proposed post-processing framework significantly improves accuracy, particularly in complex terrains, contributing to more reliable disturbance maps. This advancement provides valuable insights for ecological monitoring and supports sustainable forest management for mountainous regions. |
| format | Article |
| id | doaj-art-e6d8afc62e134443844867689203a5c4 |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-e6d8afc62e134443844867689203a5c42025-08-20T02:58:30ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910456810.1016/j.jag.2025.104568A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time seriesYating Li0Shuai Li1Xiao Xu2Zhenzi Wu3Hui Fan4Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China; Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, China; State Key Laboratory of Regional and Urban Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China; Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China; Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China; Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China; Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, China; Corresponding author at: Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Yunnan University, No. 2 North Cuihu Road, Kunming, Yunnan, China 650091.Topographic effects in mountainous forested regions disrupt the spectral consistency of remote sensing imagery, hindering accurate forest disturbance detection in Landsat time series acquired over wide time intervals (WTW-LTSs). This study evaluates the necessity of topographic correction for improving forest disturbance detection and proposes a novel post-processing topographic correction framework using the sun-canopy-sensor with C corrections (SCS + C) model. The framework simulates spectral reflectance distortions from illumination variations in uncorrected WTW-LTSs before change detection and employs post-processing to remove the resulting topographic artifacts from detected disturbances. Applied in Yunnan Province, China, the results show that (1) the post-processing framework effectively distinguishes topographic artifacts caused by intra-annual variations, achieving a high accuracy of 81.65 %; (2) by removing topographic artifacts, the post-processing framework significantly enhances forest disturbance detection, improving overall accuracy (OA), user’s accuracy (UA), and producer’s accuracy (PA) by 0.38 %–0.51 %, 1.08 %–1.83 %, and 0.18 %–2.18 %, respectively; (3) the pre-processing framework introduces uncertainties, reducing OA and PA by 0.1 % and 1.93 %–2.99 %, leading to the omission of 14.15 %–16.77 % of disturbances and the false detection of 10.03 %–14.57 % of new disturbances. These findings underscore the importance of eliminating topographic effects in WTW-LTSs for accurate forest disturbance detection. The proposed post-processing framework significantly improves accuracy, particularly in complex terrains, contributing to more reliable disturbance maps. This advancement provides valuable insights for ecological monitoring and supports sustainable forest management for mountainous regions.http://www.sciencedirect.com/science/article/pii/S1569843225002158Topographic correctionPre- and post-processing frameworkTopographic artifactsForest disturbanceMountainous regions |
| spellingShingle | Yating Li Shuai Li Xiao Xu Zhenzi Wu Hui Fan A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time series International Journal of Applied Earth Observations and Geoinformation Topographic correction Pre- and post-processing framework Topographic artifacts Forest disturbance Mountainous regions |
| title | A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time series |
| title_full | A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time series |
| title_fullStr | A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time series |
| title_full_unstemmed | A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time series |
| title_short | A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time series |
| title_sort | novel topographic correction framework for detecting forest disturbance from annual wide time window landsat time series |
| topic | Topographic correction Pre- and post-processing framework Topographic artifacts Forest disturbance Mountainous regions |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002158 |
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