Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya
Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is also very prone to fires, and large are...
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2024-12-01
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| author | Mary C. Henry John K. Maingi |
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| description | Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is also very prone to fires, and large areas of the range burn each year during the dry season. Currently, there are no detailed fire records or burn scar maps to track the burn history. Mapping burn scars using remote sensing is a cost-effective approach to monitor fire activity over time. However, it is not clear whether spectral burn indices developed elsewhere can be directly applied here when Chyulu Hills contains mostly grassland and bushland vegetation. Additionally, burn scars are usually no longer detectable after an intervening rainy season. In this study, we calculated the Differenced Normalized Burn Ratio (dNBR) and two versions of the Relative Differenced Normalized Burn Ratio (RdNBR) using Landsat Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to determine which index, threshold values, instrument, and Sentinel near-infrared (NIR) band work best to map burn scars in Chyulu Hills, Kenya. The results indicate that the Relative Differenced Normalized Burn Ratio from Landsat OLI had the highest accuracy for mapping burn scars while also minimizing false positives (commission error). While mapping burn scars, it became clear that adjusting the threshold value for an index resulted in tradeoffs between false positives and false negatives. While none were perfect, this is an important consideration going forward. Given the length of the Landsat archive, there is potential to expand this work to additional years. |
| format | Article |
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| institution | DOAJ |
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| publishDate | 2024-12-01 |
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| spelling | doaj-art-d88cfe06c8e841739974e8dd18f5b2782025-08-20T02:53:26ZengMDPI AGFire2571-62552024-12-0171247210.3390/fire7120472Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, KenyaMary C. Henry0John K. Maingi1Department of Geography, Miami University, Oxford, OH 45056, USADepartment of Geography, Miami University, Oxford, OH 45056, USAChyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is also very prone to fires, and large areas of the range burn each year during the dry season. Currently, there are no detailed fire records or burn scar maps to track the burn history. Mapping burn scars using remote sensing is a cost-effective approach to monitor fire activity over time. However, it is not clear whether spectral burn indices developed elsewhere can be directly applied here when Chyulu Hills contains mostly grassland and bushland vegetation. Additionally, burn scars are usually no longer detectable after an intervening rainy season. In this study, we calculated the Differenced Normalized Burn Ratio (dNBR) and two versions of the Relative Differenced Normalized Burn Ratio (RdNBR) using Landsat Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to determine which index, threshold values, instrument, and Sentinel near-infrared (NIR) band work best to map burn scars in Chyulu Hills, Kenya. The results indicate that the Relative Differenced Normalized Burn Ratio from Landsat OLI had the highest accuracy for mapping burn scars while also minimizing false positives (commission error). While mapping burn scars, it became clear that adjusting the threshold value for an index resulted in tradeoffs between false positives and false negatives. While none were perfect, this is an important consideration going forward. Given the length of the Landsat archive, there is potential to expand this work to additional years.https://www.mdpi.com/2571-6255/7/12/472East Africatropical dry forestsemiaridgrasslandbushlandburned area |
| spellingShingle | Mary C. Henry John K. Maingi Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya Fire East Africa tropical dry forest semiarid grassland bushland burned area |
| title | Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya |
| title_full | Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya |
| title_fullStr | Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya |
| title_full_unstemmed | Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya |
| title_short | Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya |
| title_sort | evaluating landsat and sentinel 2 derived burn indices to map burn scars in chyulu hills kenya |
| topic | East Africa tropical dry forest semiarid grassland bushland burned area |
| url | https://www.mdpi.com/2571-6255/7/12/472 |
| work_keys_str_mv | AT marychenry evaluatinglandsatandsentinel2derivedburnindicestomapburnscarsinchyuluhillskenya AT johnkmaingi evaluatinglandsatandsentinel2derivedburnindicestomapburnscarsinchyuluhillskenya |