Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat
Burn-area products from remote sensing provide the backbone for research in fire ecology, management, and modelling. Landsat imagery could be used to create an accurate burn-area map time series at ecologically relevant spatial resolutions. However, the low temporal resolution of Landsat has limited...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/6/1489 |
| _version_ | 1797609202271649792 |
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| author | Aland H. Y. Chan Alejandro Guizar-Coutiño Michelle Kalamandeen David A. Coomes |
| author_facet | Aland H. Y. Chan Alejandro Guizar-Coutiño Michelle Kalamandeen David A. Coomes |
| author_sort | Aland H. Y. Chan |
| collection | DOAJ |
| description | Burn-area products from remote sensing provide the backbone for research in fire ecology, management, and modelling. Landsat imagery could be used to create an accurate burn-area map time series at ecologically relevant spatial resolutions. However, the low temporal resolution of Landsat has limited its development in wet tropical and subtropical regions due to high cloud cover and rapid burn-area revegetation. Here, we describe a 34-year Landsat-based burn-area product for wet, subtropical Hong Kong. We overcame technical obstacles by adopting a new LTS fire burn-area detection pipeline that (1) Automatically uniformized Landsat scenes by weighted histogram matching; (2) Estimated pixel resemblance to burn areas based on a random forest model trained on the number of days between the fire event and the date of burn-area detection; (3) Iteratively merged features created by thresholding burn-area resemblance to generate burn-area polygons with detection dates; and (4) Estimated the burn severity of burn-area pixels using a time-series compatible approach. When validated with government fire records, we found that the LTS fire product carried a low area of omission (11%) compared with existing burn-area products, such as GABAM (49%), MCD64A1 (72%), and FireCCI51 (96%) while effectively controlling commission errors. Temporally, the LTS fire pipeline dated 76.9% of burn-area polygons within two months of the actual fire event. The product represents the first Landsat-based burn-area product in wet tropical and subtropical Asia that covers the entire time series. We believe that burn-area products generated from algorithms like LTS fire will effectively bridge the gap between remote sensing and field-based studies on wet tropical and subtropical fire ecology. |
| first_indexed | 2024-03-11T05:58:13Z |
| format | Article |
| id | doaj.art-5e9447df4b16498598cdf025e5d8cb11 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T05:58:13Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5e9447df4b16498598cdf025e5d8cb112023-11-17T13:37:47ZengMDPI AGRemote Sensing2072-42922023-03-01156148910.3390/rs15061489Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using LandsatAland H. Y. Chan0Alejandro Guizar-Coutiño1Michelle Kalamandeen2David A. Coomes3Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Downing Street, Cambridge CB2 3EA, UKDepartment of Plant Sciences and Conservation Research Institute, University of Cambridge, Downing Street, Cambridge CB2 3EA, UKDepartment of Plant Sciences and Conservation Research Institute, University of Cambridge, Downing Street, Cambridge CB2 3EA, UKDepartment of Plant Sciences and Conservation Research Institute, University of Cambridge, Downing Street, Cambridge CB2 3EA, UKBurn-area products from remote sensing provide the backbone for research in fire ecology, management, and modelling. Landsat imagery could be used to create an accurate burn-area map time series at ecologically relevant spatial resolutions. However, the low temporal resolution of Landsat has limited its development in wet tropical and subtropical regions due to high cloud cover and rapid burn-area revegetation. Here, we describe a 34-year Landsat-based burn-area product for wet, subtropical Hong Kong. We overcame technical obstacles by adopting a new LTS fire burn-area detection pipeline that (1) Automatically uniformized Landsat scenes by weighted histogram matching; (2) Estimated pixel resemblance to burn areas based on a random forest model trained on the number of days between the fire event and the date of burn-area detection; (3) Iteratively merged features created by thresholding burn-area resemblance to generate burn-area polygons with detection dates; and (4) Estimated the burn severity of burn-area pixels using a time-series compatible approach. When validated with government fire records, we found that the LTS fire product carried a low area of omission (11%) compared with existing burn-area products, such as GABAM (49%), MCD64A1 (72%), and FireCCI51 (96%) while effectively controlling commission errors. Temporally, the LTS fire pipeline dated 76.9% of burn-area polygons within two months of the actual fire event. The product represents the first Landsat-based burn-area product in wet tropical and subtropical Asia that covers the entire time series. We believe that burn-area products generated from algorithms like LTS fire will effectively bridge the gap between remote sensing and field-based studies on wet tropical and subtropical fire ecology.https://www.mdpi.com/2072-4292/15/6/1489remote sensingfiresubtropicalrainforestlandsatburn severity |
| spellingShingle | Aland H. Y. Chan Alejandro Guizar-Coutiño Michelle Kalamandeen David A. Coomes Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat Remote Sensing remote sensing fire subtropical rainforest landsat burn severity |
| title | Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat |
| title_full | Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat |
| title_fullStr | Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat |
| title_full_unstemmed | Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat |
| title_short | Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat |
| title_sort | reconstructing 34 years of fire history in the wet subtropical vegetation of hong kong using landsat |
| topic | remote sensing fire subtropical rainforest landsat burn severity |
| url | https://www.mdpi.com/2072-4292/15/6/1489 |
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