Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation
Dead fuel moisture content (DFMC) is essential for assessing wildfire danger, fire behavior, and fuel consumption. Several process-based models have been proposed to estimate DFMC. Previous studies have employed process-based models to estimate DFMC, solely relying on meteorological data obtained fr...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2324556 |
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| author | Chunquan Fan Binbin He Jianpeng Yin Rui Chen Hongguo Zhang |
| author_facet | Chunquan Fan Binbin He Jianpeng Yin Rui Chen Hongguo Zhang |
| author_sort | Chunquan Fan |
| collection | DOAJ |
| description | Dead fuel moisture content (DFMC) is essential for assessing wildfire danger, fire behavior, and fuel consumption. Several process-based models have been proposed to estimate DFMC. Previous studies have employed process-based models to estimate DFMC, solely relying on meteorological data obtained from meteorological stations. Satellite data can offer higher spatial resolution compared to meteorological data, with the potential to enhance the process-based DFMC estimates. Within this content, we aimed to improve the DFMC estimates by consideration of geostationary meteorological satellite-derived key variable (relative humility, RH) into the Fuel Stick Moisture Model (FSMM). The RH was derived from Himawari-8 geostationary satellite data, and other variables required by FSMM were obtained from Global Forecast System (GFS). As comparison, an equilibrium moisture content (EMC) model, Simard, and random forest regression were also used for the DFMC estimates. DFMC field measurement from the southwest China validate the DFMC from these three models. Results show that the DFMC estimated from the FSMM and Himawari-8 derived RH reached to a reasonable accuracy (R2 = 0.73, RMSE = 3.60%, MAE = 2.69%). The comparison between FSMM and the other two models also confirmed the superior performance of the process-based model. A wildfire case over this region also confirmed that the DFMC continuous decreasing trends until the fire outbreak, highlighting the applicability of our approach in contributing to fire risk assessment. |
| format | Article |
| id | doaj-art-ff51509c65e84d6ebf2adac79b33fcff |
| institution | OA Journals |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-ff51509c65e84d6ebf2adac79b33fcff2025-08-20T01:59:30ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2024.2324556Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimationChunquan Fan0Binbin HeJianpeng Yin1Rui Chen2Hongguo Zhang3School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaDead fuel moisture content (DFMC) is essential for assessing wildfire danger, fire behavior, and fuel consumption. Several process-based models have been proposed to estimate DFMC. Previous studies have employed process-based models to estimate DFMC, solely relying on meteorological data obtained from meteorological stations. Satellite data can offer higher spatial resolution compared to meteorological data, with the potential to enhance the process-based DFMC estimates. Within this content, we aimed to improve the DFMC estimates by consideration of geostationary meteorological satellite-derived key variable (relative humility, RH) into the Fuel Stick Moisture Model (FSMM). The RH was derived from Himawari-8 geostationary satellite data, and other variables required by FSMM were obtained from Global Forecast System (GFS). As comparison, an equilibrium moisture content (EMC) model, Simard, and random forest regression were also used for the DFMC estimates. DFMC field measurement from the southwest China validate the DFMC from these three models. Results show that the DFMC estimated from the FSMM and Himawari-8 derived RH reached to a reasonable accuracy (R2 = 0.73, RMSE = 3.60%, MAE = 2.69%). The comparison between FSMM and the other two models also confirmed the superior performance of the process-based model. A wildfire case over this region also confirmed that the DFMC continuous decreasing trends until the fire outbreak, highlighting the applicability of our approach in contributing to fire risk assessment.https://www.tandfonline.com/doi/10.1080/15481603.2024.2324556Dead fuel moisture contentHimawari-8process-based modelsensitivity analysiswildfire |
| spellingShingle | Chunquan Fan Binbin He Jianpeng Yin Rui Chen Hongguo Zhang Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation GIScience & Remote Sensing Dead fuel moisture content Himawari-8 process-based model sensitivity analysis wildfire |
| title | Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation |
| title_full | Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation |
| title_fullStr | Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation |
| title_full_unstemmed | Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation |
| title_short | Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation |
| title_sort | process based and geostationary meteorological satellite enhanced dead fuel moisture content estimation |
| topic | Dead fuel moisture content Himawari-8 process-based model sensitivity analysis wildfire |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2024.2324556 |
| work_keys_str_mv | AT chunquanfan processbasedandgeostationarymeteorologicalsatelliteenhanceddeadfuelmoisturecontentestimation AT binbinhe processbasedandgeostationarymeteorologicalsatelliteenhanceddeadfuelmoisturecontentestimation AT jianpengyin processbasedandgeostationarymeteorologicalsatelliteenhanceddeadfuelmoisturecontentestimation AT ruichen processbasedandgeostationarymeteorologicalsatelliteenhanceddeadfuelmoisturecontentestimation AT hongguozhang processbasedandgeostationarymeteorologicalsatelliteenhanceddeadfuelmoisturecontentestimation |