ESTIMATION OF FOREST FIRE AREAS IN PALANGKA RAYA, CENTRAL KALIMANTAN, INDONESIA USING NBR2 AND ITS IMPACT ON ENVIRONMENT
DOI:
https://doi.org/10.59465/ijfr.2025.12.1.1-12Keywords:
Air quality, burned area, remote sensing, spectral indices, vegetation healthAbstract
Indonesia, particularly Palangka Raya City in Central Kalimantan, boasts approximately 241,736.25 hectares of forested areas crucial for human survival. Despite their significance, these areas are plagued by annual forest fires that lead to damage and adverse effects on the environment, including vegetation health and air quality. This research sought to pinpoint the extent of forest fire occurrences and their repercussions by analyzing changes in vegetation health and air quality through remote sensing technology. The study employed various remote sensing techniques, such as the Normalized Burn Ratio 2 (NBR2) for detecting burned areas, the Enhanced Vegetation Index (EVI) for assessing vegetation health, and PM2.5 for analyzing air quality. Utilizing Landsat-8 satellite imagery data as the primary source, the research successfully identified burned areas with an impressive overall accuracy of 82.229% using the NBR2 index. The findings revealed a direct correlation between forest fires and increased air pollution, particularly in PM2.5 levels, as well as a decline in vegetation health in the vicinity of the burned areas. These results highlight the importance of continuous monitoring of forest fire occurrences and their impact through remote sensing data to mitigate their adverse effects.
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References
Baumgartner, R. J. (2019). Sustainable Development Goals and the Forest Sector—A Complex Relationship. Forests, 10(2), 152. doi://10.3390/f10020152.
Berlanga-Robles, C. A., & Ruiz-Luna, A. (2020). Assessing seasonal and long-term mangrove canopy variations in Sinaloa, northwest Mexico, based on time series of enhanced vegetation index (EVI) data. Wetlands Ecology and Management, 28(2), 229–249. doi://10.1007/S11273-020-09709-0.
Cahyono, B. E., Fibyana, V., Nugroho, A. T., & Subekti, A. (2021). Mapping and analysis burned area based on LANDSAT 8 OLI/TIRS and hotspots data in palangkaraya of central kalimantan province - Indonesia. Journal of Physics: Conference Series, 1825(1). doi://10.1088/1742-6596/1825/1/012087.
Chen, C. C., Wang, Y. R., Yeh, H. Y., Lin, T. H., Huang, C. S., & Wu, C. F. (2021). Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach. Environmental Pollution (Barking,Essex : 1987), 291. doi://10.1016/J. ENVPOL.2021.118159.
Chuvieco, E., Martín, M. P., & Palacios, A. (2002). Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23(23), 5103–5110. doi://10.1080/01431160210153129.
Datta, R. (2021). To extinguish or not to extinguish: The role of forest fire in nature and soil resilience. Journal of King Saud University
- Science, 33(6), 101539. doi://10.1016/J. JKSUS.2021.101539.
Souza, A. A. d., Galvão, L. S., Korting, T. S., & Almeida, C. A. (2021). On a Data-Driven Approach for Detecting Disturbance in the Brazilian Savannas Using Time Series of Vegetation Indices. Remote Sensing, 13(24), 4959. doi://10.3390/rs13244959.
EOS. (2019). NDVI FAQs: Frequently Asked Questions About The Index. https://eos. com/blog/ndvi-faq-all-you-need-to-knowabout-
ndvi/
Gholamrezaie, H., Hasanlou, M., Amani, M., & Mirmazloumi, S. M. (2022). Automatic Mapping of Burned Areas Using Landsat 8 Time-Series Images in Google Earth Engine: A Case Study from Iran. Remote Sensing,14(24), 6376. doi://10.3390/rs14246376.
Goldstein, J. E., Graham, L., Ansori, S., Vetrita, Y., Thomas, A., Applegate, G., Vayda, A. P., Saharjo, B. H., & Cochrane, M. A. (2020). Beyond slash-and-burn: The roles of human activities, altered hydrology and fuels in peat fires in Central Kalimantan, Indonesia.
Singapore Journal of Tropical Geography, 41(2),190–208. doi://10.1111/sjtg.12319.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis 6th ed (J. Hair, Ed.; 6th ed., Vol. 1). Pearson Prentice Hall.
Hislop, S., Jones, S., Soto-Berelov, M., Skidmore, A., Haywood, A., & Nguyen, T. H. (2018). Using Landsat Spectral Indices in Time-Series to
Assess Wildfire Disturbance and Recovery. Remote Sensing, 10(3), 460. doi://10.3390/rs10030460.
Huang, Liu, Zhu, Atzberger, & Liu. (2019). The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method. Remote Sensing, 11(23), 2725. doi://10.3390/rs11232725.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. doi://10.1016/S0034-4257(02)00096-2.
Lutes. Duncan C., Keane, R. E., Caratti, J., Key, C. H., Benson, N., Sutherland, S., & Gangi, L. J. (2006). FIREMON: Fire effects monitoring and
inventory system. Department of Agriculture, Forest Service, Rocky Mountain Research Station.
Martin, D., Tomida, M., & Meacham, B. (2016). Environmental impact of fire. Fire Science Reviews, 5(1), 1–21. doi://10.1186/S40038-016-0014-1.
Nurfindarti, E., Perencanaan, B., Daerah, P., Serang, K., Sudirman, J. J., Kota, K., Baru, S., & Banten, P. (2019). Strategy and Roadmap for
Achieving Sustainable Development Goals in Serang City. Jurnal Bina Praja, 11(2), 219–235. doi://10.21787/JBP.11.2019.219-235.
Page, S. E., Rieley, J. O., & Banks, C. J. (2011). Global and regional importance of the tropical peatland carbon pool. Global Change
Biology, 17(2), 798–818.doi://10.1111/J.1365-2486.2010.02279.X.
Pérez-Cabello, F., Montorio, R., & Alves, D. B. (2021). Remote sensing techniques to assess post-fire vegetation recovery. Current Opinion
in Environmental Science & Health, 21, 100251. doi://10.1016/J.COESH.2021.100251.
Pujana, A. M. (2020). Identifikasi Burned Area Menggunakan Citra Satelit Landsat 8 Dengan Metode Normalized Difference Vegetation Index (NDVI) dan Normalized Burn Ratio (NBR) (Studi Kasus : Kota Palangka Raya, Kalimantan Tengah) [Thesis]. Institut Teknologi Nasional Malang.
Saputra, A. D., Setiabudidaya, D., Setyawan, D., Khakim, M. Y. N., & Iskandar, I. (2017). Burnscar analysis using normalized burning ratio
(NBR) index during 2015 forest fire at Merang-Kepahyang peat forest, South Sumatra, Indonesia. 100001. doi://10.1063/1.4987107.
Somashekar, R. K., Ravikumar, P., Mohan Kumar, C. N., Prakash, K. L., & Nagaraja, B. C.(2009). Burnt area mapping of Bandipur National Park, India using IRS 1C/1D LISS III data. Journal of the Indian Society of Remote Sensing, 37(1), 37–50. doi://10.1007/S12524-009-0010-1/METRICS.
Somvanshi, S. S., Vashisht, A., Chandra, U., & Kaushik, G. (2019). Delhi Air Pollution Modeling Using Remote Sensing Technique. Handbook of Environmental Materials Management, 1–27. doi://10.1007/978-3-319-58538-3_174-1.
Sukojo, B. M., & Aini, N. (2018). Analisa Perbandingan Berdasarkan Identifikasi Area Kebakaran Dengan Menggunakan Citra Landsat-8 Dan Citra Modis (Studi Kasus :Kawasan Gunung Bromo). Geoid, 13(2), 174. doi://10.12962/j24423998.v13i2.3665.
Sunar, F., & Özkan, C. (2001). Forest fire analysis with remote sensing data. International Journal of Remote Sensing, 22(12), 2265–2277.
doi://10.1080/01431160118510.

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