Publisher

APTKLHIAPTKLHI

Journal Indonesian Journal of Forestry ResearchIndonesian Journal of Forestry Research Abstract

Indonesia khususnya Kota Palangka Raya yang terletak di Kalimantan Tengah memiliki kawasan hutan dengan luas sekitar 241.736,25 hektar. Kawasan hutan tersebut mempunyai peranan penting bagi kelangsungan hidup manusia. Namun setiap tahunnya, kebakaran hutan selalu terjadi dan menimbulkan kerusakan hutan, berdampak pada kondisi lingkungan seperti kesehatan vegetasi dan kualitas udara. Penelitian ini bertujuan untuk mengidentifikasi kawasan kebakaran hutan dan dampaknya berdasarkan kesehatan vegetasi dan perubahan kualitas udara dengan menggunakan teknologi penginderaan jauh. Metode penginderaan jauh yang digunakan dalam makalah ini adalah indeks spektral kebakaran Normalized Burn Ratio 2 (NBR2) untuk identifikasi area terbakar, Enhanced Vegetation Index (EVI) untuk mengidentifikasi kesehatan vegetasi, dan PM2.5 untuk analisis kualitas udara. Data citra satelit Landsat-8 digunakan sebagai data primer untuk mengekstraksi area terbakar dan dampaknya. Area terbakar yang dihasilkan NBR2 menunjukkan akurasi keseluruhan yang tinggi yaitu 82,229%. Studi tersebut menunjukkan bahwa kebakaran hutan berdampak pada polusi udara dengan peningkatan besar dalam paparan PM2.5 setelah kebakaran hutan, dan penurunan kesehatan vegetasi, yang terjadi di sekitar area yang diidentifikasi sebagai area yang terbakar. Dengan hasil tersebut, identifikasi kawasan kebakaran hutan dan dampaknya dapat dipantau secara terus menerus menggunakan data penginderaan jauh guna meminimalkan dampak kebakaran hutan.

Conclusion

Forest fires in 2019 severely degraded vegetation health and increased PM2.5 levels, classifying air quality as not healthy.These findings highlight the significant environmental and public health impacts of the 2019 fire event.

Future Research

First, perform a five‑year EVI time‑series analysis to quantify peat swamp forest recovery after fires. The analysis will reveal a clear vegetation‑health threshold that can trigger early fire‑impact alerts. Second, build a spatial model that combines fire‑occurrence data with detailed local land‑clearing practices. Include climatic variables such as pronounced dry peat layers to improve high‑risk period forecasting accuracy. Third, develop a hybrid remote‑sensing framework fusing Landsat‑8 and MODIS observations to refine NBR2 detection performance. The fusion will systematically correct water‑related misclassifications commonly seen in wet peatlands. Assessing these methods will provide policymakers with precise tools for targeting mitigation and prevention in Palangka Raya. Integrating vegetation dynamics, human activity patterns, and advanced sensor fusion directly addresses knowledge gaps highlighted by this study. The resulting strategy will support the construction of evidence‑based fire‑prevention plans specifically adapted to local environmental conditions. Overall, this comprehensive approach will enhance peat swamp forest resilience and ultimately safeguard community health and wellbeing.

References

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Pages	12
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