Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China
The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. Thi...
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MDPI AG
2025-05-01
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| author | Xiaodong Zhang Jingyi Zhao Guanzhou Chen Tong Wang Qing Wang Kui Wang Tingxuan Miao |
| author_facet | Xiaodong Zhang Jingyi Zhao Guanzhou Chen Tong Wang Qing Wang Kui Wang Tingxuan Miao |
| author_sort | Xiaodong Zhang |
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| description | The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. This study developed a spatial–temporal adaptive fusion model integrating Landsat 30-m data with MODIS daily observations to generate continuous high-precision dNBR datasets. Using a typical karst fire region in Guizhou and Yunnan, China, as a case study, we validated the method’s effectiveness for fire trace extraction in fragmented landscapes. The proposed fusion technique addresses cloud cover limitations in humid climates by constructing continuous NBR time series, enabling precise fire boundary delineation and severity quantification. We comparatively implemented multiple fusion approaches (FSDAF, STARFM, and STDFA) and evaluated their performance through both spectral (RMSE, AD, and PSNR) and spatial (Edge, LBP, and SSIM) metrics. Key findings include the following: (1) FSDAF outperformed other methods in spectral consistency and spatial adaptation, particularly for heterogeneous mountainous terrain with fragmented vegetation. (2) Comparative experiments demonstrated that pre-calculating vegetation indices before temporal fusion (Strategy I) produced superior results to post-fusion calculation (Strategy II). Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. This study advances fire monitoring in karst regions by significantly improving both the spatio-temporal resolution and accuracy of burn scar detection compared to conventional approaches. The framework provides a viable solution for fire impact assessment in topographically complex landscapes under cloudy conditions. |
| format | Article |
| id | doaj-art-7caaff6ed87d4d82a31f6aac1a610bc1 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7caaff6ed87d4d82a31f6aac1a610bc12025-08-20T02:32:37ZengMDPI AGRemote Sensing2072-42922025-05-011711185210.3390/rs17111852Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in ChinaXiaodong Zhang0Jingyi Zhao1Guanzhou Chen2Tong Wang3Qing Wang4Kui Wang5Tingxuan Miao6School of Geosciences, Yangtze University, Wuhan 430010, ChinaSchool of Geosciences, Yangtze University, Wuhan 430010, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Geosciences, Yangtze University, Wuhan 430010, ChinaHubei United Transportation Investment & Development Co., Ltd., Wuhan 430040, ChinaHubei United Transportation Investment & Development Co., Ltd., Wuhan 430040, ChinaThe surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. This study developed a spatial–temporal adaptive fusion model integrating Landsat 30-m data with MODIS daily observations to generate continuous high-precision dNBR datasets. Using a typical karst fire region in Guizhou and Yunnan, China, as a case study, we validated the method’s effectiveness for fire trace extraction in fragmented landscapes. The proposed fusion technique addresses cloud cover limitations in humid climates by constructing continuous NBR time series, enabling precise fire boundary delineation and severity quantification. We comparatively implemented multiple fusion approaches (FSDAF, STARFM, and STDFA) and evaluated their performance through both spectral (RMSE, AD, and PSNR) and spatial (Edge, LBP, and SSIM) metrics. Key findings include the following: (1) FSDAF outperformed other methods in spectral consistency and spatial adaptation, particularly for heterogeneous mountainous terrain with fragmented vegetation. (2) Comparative experiments demonstrated that pre-calculating vegetation indices before temporal fusion (Strategy I) produced superior results to post-fusion calculation (Strategy II). Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. This study advances fire monitoring in karst regions by significantly improving both the spatio-temporal resolution and accuracy of burn scar detection compared to conventional approaches. The framework provides a viable solution for fire impact assessment in topographically complex landscapes under cloudy conditions.https://www.mdpi.com/2072-4292/17/11/1852karst landscapesflexible spatio-temporal data fusion (FSDAF)spatio-temporal adaptive reflection fusion model (STARFM)spatio-temporal data fusion analysis (STDFA)normalized burn ratio (NBR)fire trace extraction |
| spellingShingle | Xiaodong Zhang Jingyi Zhao Guanzhou Chen Tong Wang Qing Wang Kui Wang Tingxuan Miao Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China Remote Sensing karst landscapes flexible spatio-temporal data fusion (FSDAF) spatio-temporal adaptive reflection fusion model (STARFM) spatio-temporal data fusion analysis (STDFA) normalized burn ratio (NBR) fire trace extraction |
| title | Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China |
| title_full | Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China |
| title_fullStr | Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China |
| title_full_unstemmed | Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China |
| title_short | Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China |
| title_sort | spatio temporal fusion of landsat and modis data for monitoring of high intensity fire traces in karst landscapes a case study in china |
| topic | karst landscapes flexible spatio-temporal data fusion (FSDAF) spatio-temporal adaptive reflection fusion model (STARFM) spatio-temporal data fusion analysis (STDFA) normalized burn ratio (NBR) fire trace extraction |
| url | https://www.mdpi.com/2072-4292/17/11/1852 |
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