Burned Area Mapping Using ΔBAI-Otsu from Landsat 8 Imagery in Bukit Anak Dara East Lombok
DOI:
10.29303/jbt.v25i4b.10836Published:
2025-12-11Downloads
Abstract
Forest and land fires are recurrent in Indonesian tropical mountain savannas and threaten biodiversity, carbon stocks, and local livelihoods, yet spatially explicit burned-area information is still limited. This study aimed to evaluate the performance of the Burn Area Index (BAI) from Landsat 8 OLI–TIRS imagery for mapping the 2024 fire in Bukit Anak Dara, East Lombok. Burned and unburned pixels were classified by applying a two-class Otsu threshold to the ΔBAI histogram for the full scene extent. The resulting burned-area map was validated against high-resolution polygons obtained from visual interpretation of Sentinel-2A imagery and against fire hotspots from the SiPongi+ system. Compared with Sentinel-2A polygons, the ΔBAI–Otsu method produced a burned-area estimate of 275.49 ha versus 318.87 ha from the reference and achieved an overall accuracy of 0.97, precision of 0.94, recall of 0.81, and an F1-score of 0.87. Validation against hotspot data yielded lower performance (overall accuracy 0.87, precision 0.40, recall 0.41, F1-score 0.41), reflecting conceptual and spatial-scale differences between point-based active-fire detections and patch-based burned-area mapping. Burned pixels were concentrated on west–northwest facing slopes dominated by dry savanna, highlighting the role of topography and fuel characteristics in fire spread. Overall, the results therefore indicate that the ΔBAI–Otsu approach is a rapid, transparent, and reproducible tool for post-fire burned-area mapping in tropical mountain ecosystems and has strong potential for routine operational monitoring.
Keywords:
Burn area index Burned area mapping Landsat 8 OLI–TIRS Otsu Threshold Tropical Mountain SavannaReferences
Badan Nasional Penanggulangan Bencana (2023). Data Bencana Indonesia 2023. Website: https://bpbd.kepriprov.go.id/files/buku-data-bencana-indonesia-tahun-2023.pdf (terakhir diakses pada tanggal 4 januari 2025
Carreiras, J., Quegan, S., Tansey, K., & Page, S. (2020). Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia. Environmental Research Letters, 15. https://doi.org/10.1088/1748-9326/ab7765.
Chuvieco, E., Martín, M., & 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, 5103 - 5110. https://doi.org/10.1080/01431160210153129.
Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., & Oliva, P. (2020). Satellite Remote Sensing Contributions to Wildland Fire Science and Management. Current Forestry Reports, 6, 81-96. https://doi.org/10.1007/s40725-020-00116-5.
Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: Principles and practices (3rd ed.). CRC Press.
Foody, G. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111630.
Howe, A., Parks, S., Harvey, B., Saberi, S., Lutz, J., & Yocom, L. (2022). Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America. Remote. Sens., 14, 5249. https://doi.org/10.3390/rs14205249.
Kurbanov, E., Vorobev, O., Lezhnin, S., Sha, J., Wang, J., Li, X., Cole, J., Dergunov, D., & Wang, Y. (2022). Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review. Remote. Sens., 14, 4714. https://doi.org/10.3390/rs14194714.
Liu, S., Zheng, Y., Dalponte, M., & Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. European Journal of Remote Sensing, 53(1), 104–112. https://doi.org/10.1080/22797254.2020.1738900
Morante-Carballo, F., Bravo-Montero, L., Carrión-Mero, P., Velástegui-Montoya, A., & Berrezueta, E. (2022). Forest Fire Assessment Using Remote Sensing to Support the Development of an Action Plan Proposal in Ecuador. Remote. Sens., 14, 1783. https://doi.org/10.3390/rs14081783.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076
Prasetyo, A. R., Valentino, N., & Hambali, M. R. (2024). Analisis Komparatif Tutupan Mangrove Menggunakan Citra Landsat 9 dan Sentinel 2A di Desa Labuan Tereng Kabupaten Lombok Barat. Jurnal Sains Teknologi & Lingkungan, 10(2), 187-200. https://doi.org/10.29303/jstl.v10i2.606
Prasetyo, A. R., Valentino, N., & Hadi, M. A. (2023). Identifikasi Sebaran Spasial dan Kerapatan Mangrove Gili Lawang menggunakan Citra Landsat 9 OLI-2/TIRS-2: Identification Gili Lawang Mangrove Spatial Distribution and Density with Landsat 9 OLI-2/TIRS-2 Imagery. Jurnal Sains Teknologi & Lingkungan, 9(2), 215-225. https://doi.org/10.29303/jstl.v9i2.450
Purnomo, H., Okarda, B., Shantiko, B., Achdiawan, R., Dermawan, A., Kartodihardjo, H., & Dewayani, A. (2019). Forest and Land Fires, Toxic Haze and Local Politics in Indonesia. International Forestry Review, 21, 486 - 500. https://doi.org/10.1505/146554819827906799.
Rokhmatuloh, A., Indratmoko, S., Riyanto, I., Margatama, L., & Arief, R. (2022). Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data. Applied Sciences. https://doi.org/10.3390/app122311922.
Sutomo, & Van Etten, E. (2023). Fire Impacts and Dynamics of Seasonally Dry Tropical Forest of East Java, Indonesia. Forests. https://doi.org/10.3390/f14010106.
Szpakowski, D., & Jensen, J. (2019). A Review of the Applications of Remote Sensing in Fire Ecology. Remote. Sens., 11, 2638. https://doi.org/10.3390/rs11222638.
Székely, B., & Baur, J. (2023). Detecting Forest Fires by Using Remotely Sensed Data in Riau, Indonesia. https://doi.org/10.5194/egusphere-egu23-7744
Sirin, A., & Medvedeva, M. (2022). Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires. Remote. Sens., 14, 194. https://doi.org/10.3390/rs14010194.
U.S. Geological Survey. (2023). Landsat Collection 2 Level-2 Science Product Guide. U.S. Geological Survey. https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-product-guide
Vetrita, Y., Albar, I., Santoso, I., Prasasti, I., Kartika, T., Usman, A., Tosiani, A., Haryanto, D., , E., Famurianty, E., Ulfa, K., & Purwanto, J. (2024). Monthly mapping of Indonesia’s burned areas: implementation, history, techniques, and future directions. International Journal of Remote Sensing, 46, 636 - 660. https://doi.org/10.1080/01431161.2024.2421942.
Weng, L., Luo, R., Huang, M., Wang, C., & Chen, L. (2022, December). Wildfire Detection and Burned Area Estimation Based on Multi-source Spatial Data. In 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta) (pp. 1502-1507). IEEE. https://doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00217.
Zidane, I., Lhissou, R., Ismaili, M., Manyari, Y., Bouli, A., & Mabrouki, M. (2021). Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images. International Journal on Advanced Science, Engineering and Information Technology, 11, 72-83. https://doi.org/10.18517/ijaseit.11.1.10342.
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Copyright (c) 2025 Andrie Ridzki Prasetyo, Niechi Valentino, Rato Firdaus Silamon, Muhamad Husni Idris, Sitti Latifah, Irwan Mahakam Lesmono Aji, Roni Putra Pratama
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