Identifying forest fire risk areas using climatological and satellite indices for monitoring peatland fire
Management of the groundwater table is critical in peatland management. We examined the groundwater level (GWL) and remote sensing data of vegetation indices in Peninsular Malaysia during the northeast monsoon in the months of January, February, and March 2020. The GWL obtained from the Internet of...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Jabatan Perhutanan Semenanjung Malaysia
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/108254/ |
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| Summary: | Management of the groundwater table is critical in peatland management. We examined the groundwater level (GWL) and remote sensing data of vegetation indices in Peninsular Malaysia during the northeast monsoon in the months of January, February, and March 2020. The GWL obtained from the Internet of Things (IoT) system was analysed and profiled based on the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images, normalised difference vegetation index (NDVI), and enhanced vegetation index (EVI). The IoT system is owned by Universiti Putra Malaysia, the Selangor State Forestry Department and National Institute of Information and Communication Technology (NICT). The MODIS high-temperature events were collected as hotspot pixels from satellite images and linked to historical forest fire data. Assessing relationships among GWL, NDVI, and EVI revealed their strong correlations. The NDVI measured at 1-km radius provides a quantifiable early warning that GWL had dropped. Observations of GWL revealed that rainfall fluctuated similarly. Rainfall increased in March, resulting in a higher GWL level, which is undoubtedly useful in monitoring the frequency of fire. The use of hotspot pixels in the study is useful for natural resource managers because it demonstrated a relationship between historical fire occurrences in Klang Valley. Given that forest and natural resource managers have access to all of the climatological assessment and GWL of the IoT data from this study, it can have a significant impact on fire management planning in the peat swamp forest. |
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