Comparative analysis of burn area between google earth engine and manual digitization using the NBR algorithm
DOI:
https://doi.org/10.61511/rstde.v3i1.2026.3100Keywords:
burn saverity, peatland fires, remote sensingAbstract
Background: Indonesia, as the third-largest tropical forest country in the world, is experiencing significant forest degradation driven by illegal logging, land-use conversion, and recurrent wildfires. Peatland ecosystems, particularly in Kubu Raya District, West Kalimantan, are highly susceptible to fire due to their organic-rich composition and seasonal desiccation. This study aims to assess the spatial distribution and severity of forest and land fires in Kubu Raya from 2019 to 2023 using remote sensing and geographic information system (GIS) techniques. Methods: Hotspot data from the Fire Information for Resource Management System (FIRMS) MODIS were analyzed to determine fire occurrences, while Sentinel-2 imagery was utilized to calculate the Normalized Burn Ratio (NBR) index for burn severity estimation. Image analysis was conducted using both manual digitization and the Google Earth Engine (GEE) platform to compare accuracy, efficiency, and spatial representation of burned-area detection. Findings: The findings indicated that 2023 recorded the largest burned area, covering 832,188.98 ha, predominantly within peatland zones. Accuracy assessment demonstrated that the GEE-based method achieved higher reliability, with overall accuracy and kappa statistic values of 86% and 74%, respectively, outperforming the manual approach. The spatial distribution of fire hotspots revealed that peat-dominated areas were more vulnerable to large-scale fires due to their hydrological characteristics. Conclusion: The results highlight that GEE provides a rapid, consistent, and accurate technique for burn area detection and fire severity analysis. Integrating cloud-based remote sensing with conventional GIS enhances monitoring capabilities for sustainable peatland management. Novelty/Originality of this article: The novelty of this research lies in its comparative accuracy evaluation between automated and manual burn area mapping. This study provides new methodological insights for fire monitoring across Indonesia’s tropical peatlands, demonstrating the advantages of cloud-based platforms for large-scale environmental assessments.
References
Aflahah, E., Hidayati, R., Hidayat, R., & Alfahmi, F. (2019). Estimation of hotspots as indicators of forest fires in Kalimantan based on climatic factors. JPSL, 9(2), 405–418. https://doi.org/10.29244/jpsl.9.2.405-418
Antonius. (2016). Upaya konservasi ekosistem hutan rawa gambut. Jurnal Piper, 12(23), 136-146. http://doi.org/10.51826/piper.v12i23.31
Arisanty, D., Sari, N., & Setiawan, R. (2022). Utilizing Sentinel-2 Data for Mapping Burned Areas in Wetland Regions: A Case Study in Indonesia. International Journal of Environmental Research and Public Health, 19(15), 9321. http://doi.org/10.1155/2022/7936392
Asyrowi, H., Saharjo, B. H., & Putra, E. I. (2021). Pola Persebaran Hotspot di Taman Hutan Raya Raden Soerjo. Jurnal Penelitian Hutan dan Konservasi Alam, 18(2), 151–165. https://doi.org/10.20886/jphka.2021.18.2.151-165
Austin, K. G., Schwantes, A., Gu, Y., & Kasibhatla, P. S. (2019). What causes deforestation in Indonesia?. Environmental Research Letters, 14(2), 024007. https://doi.org/10.1088/1748-9326/aaf6db.
Bousquet, E., Mialon, A., Rodriguez-Fernandez, N., Mermoz, S., & Kerr, Y. (2022). Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing. Biogeosciences, 19(13), 3317-3336. https://doi.org/10.5194/bg-19-3317-2022
Evans, D. C., Williamson, J. M., Kacaribu, F., Irawan, D., Suardiwerianto, Y., Hidayat, M. F., Laurén, A., Page, S. E. (2019). Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra, Indonesia. Geoderma, 338, 410-421. https://doi.org/10.1016/j.geoderma.2018.12.028.
Fanin, T., & van der Werf, G. R. (2017). Precipitation–fire linkages in Indonesia (1997–2015). Biogeosciences, 14, 3995–4008. https://doi.org/10.5194/bg-14-3995-2017.
Field, R. D., van der Werf, G. R., Fanin, T., Fetzer, E. J., Fuller, R., Jethva, H., Levy, R. C., Livesey, N. J., Luo, M., Torres, O., & Worden, H. M. (2016). Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño–induced drought. Proceedings of the National Academy of Sciences, 113(33), 9204-9209. https://doi.org/10.1073/pnas.1524888113.
Gaveau, D. L. A., Descals, A., Salim, M. A., Sheil, D., & Sloan, S. (2021). Refined burned‐area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning. Earth System Science Data, 13, 5353–5368. https://doi.org/10.5194/essd-13-5353-2021.
Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178, 31-41. https://doi.org/10.1016/j.rse.2016.02.054.
Horton, A. J., Virkki, V., Lounela, A., Miettinen, J., Alibakhshi, S., Kummu, M. (2021). Identifying key drivers of peatland fires across Kalimantan’s tropical peatlands. Earth and Space Science, 8(10), 1-17. https://doi.org/10.1029/2021EA001873 .
Jawad, A., Nurdjali, B., Widiastuti, T. (2015). Zonasi daerah rawan kebakaran hutan dan lahan di Kubu Raya Provinsi Kalimantan Barat. Jurnal Hutan Lestari, 3(1), 88-97. https://doi.org/10.26418/jhl.v3i1.9244.
Julianto, F. D., Putri, D. P. D., & Safi’i, H. H. (2020). Analisis Perubahan Vegetasi dengan Data Sentinel-2 menggunakan Google Earth Engine (Studi Kasus Provinsi Daerah Istimewa Yogyakarta). Jurnal Penginderaan Jauh Indonesia, 2(2), 13–18. https://journal.its.ac.id/index.php/jpji/article/view/262/100
Kettridge, N., Turetsky, M. R., Sherwood, J. H., Thompson, D. K., Miller, C. A., Benscoter, B. W., Flannigan, M. D., Wotton, B. M & Waddington, J. M. (2015). Moderate drop in water table increases peatland vulnerability to post-fire regime shift. Scientific Reports, 5(8063). https://doi.org/10.1038/srep08063
Key, C. H. & Benson, N. C. (2006). Landscape Assessment: Ground measure of severity, the Composite Burn Index; and remote sensing of severity, the Normalized Burn Ratio. In: FIREMON: Fire Effects Monitoring and Inventory System. USDA Forest Service, Rocky Mountain Research Station.
Kiely, L., Spracklen, D. V., Wiedinmyer, C., Conibear, L., Reddington, C. L., Archer-Nicholls, S., Lowe, D., Arnold, S. R., Knote, C., Khan, M. F., Latif, M. T., Kuwata, M., Budisulistiorini, S. H., & Syaufina, L. (2019). New estimate of particulate emissions from Indonesian peat fires in 2015. Atmospheric Chemistry and Physics, 19, 11105–11121. https://doi.org/0.5194/acp-19-11105-2019
Kementerian Lingkungan Hidup dan Kehutanan. (2022). Status Lingkungan Hidup Indonesia 2022. Jakarta: KLHK.
Pusat Pemanfaatan Penginderaan Jauh Lembaga Penerbangan dan Antariksa Nasional [PPPJL]. (2015). Pedoman Pemanfaatan Data Landsat 8 untuk Deteksi Daerah Terbakar (Burned Area). Jakarta: Lembaga Penerbangan dan Antariksa Nasional.
Putri, I. (2020). KLHK Sebut Tren Deforestasi Indonesia Cenderung Stabil. Detiknews.com. https://news.detik.com/berita/d-4988578/klhk-sebut-tren-deforestasi-indonesia-cenderung-stabil?utm_source=chatgpt.com
Ministry of Environment and Forestry, Republic of Indonesia-KLHK. (2022). The State of Indonesia’s Forests 2022: Towards FOLU Net Sink 2030. Jakarta: Ministry of Environment and Forestry of the Republic of Indonesia.
Muhayah, R., & Asysyfa. (2021). Biaya revegetasi gambut berdasarkan karakteristik spesifik lahan gambut. Jurnal Hutan Tropis, 9(2), 454-463. https://doi.org/10.20527/jht.v9i2.11297.
Muin, A. & Rakuasa, H. (2023). Pemetaan kerentanan kebakaran hutan di Pulau Buru, Provinsi Maluku berdasarkan fire hotspot. Jurnal Sains dan Teknologi, 2(4), 675-683. https://doi.org/10.55123/insologi.v2i4.2256
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. https://doi.org/10.1111/j.1365-2486.2010.02279.x.
Page, S. E., & Hooijer, A. (2016). In the line of fire: The peatlands of Southeast Asia. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1696), 20150176. https://doi.org/10.1098/rstb.2015.0176.
Saharjo, B.H., Ramadhania, D. (2019). Hubungan antara hotspot dan kebakaran terhadap timbulnya penyakit infeksi saluran pernapasan akut (ISPA) di Kabupaten Kubu Raya, Kalimantan Barat. Jurnal Silvikultur Tropika, 10(3), 133-139. https://doi.org/10.29244/jsilvik.10.3.133-139
Schmidt, J. I., Berman, M., & Waigl, C. F. (2024). Avoid getting burned: lessons from the McKinley wildfire in rural Alaska, USA. International Journal of Wildland Fire, 33(11), 1-14. https://doi.org/10.1071/WF24014
Simanjuntak, K. P., & Khaira, U. (2021). Clustering of fire hotspots in Jambi Province using the Agglomerative Hierarchical Clustering algorithm. MALCOM, 1(1), 7–16. https://doi.org/10.57152/malcom.v1i1.6
Shofiana, D. A., & Sitanggang, I. S. (2020). Confidence analysis of hotspots as indicators of peat forest fires. Journal of Physics: Conference Series, 1751, 1–10. https://doi.org/10.1088/1742-6596/1751/1/012015
Suwarsono, Rokhmatullah, & Waryono, T. (2013). Development of a model for identifying burned areas using MODIS imagery in Kalimantan. Indonesian Journal of Remote Sensing, 10(2), 93-112. https://doi.org/10.30536/inderaja.v10i2.3278
Turetsky, M.R., Benscoter,B., Page, S., Rein, G., & van der Werf GR, Watts A. (2015). Global vulnerability of peatlands to fire and carbon loss. Nature Geoscience, 8(1), 11-14. https://doi.org/10.1038/ngeo2325
Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. Journal Catena, 160, 134-140. https://doi.org/10.1016/j.catena.2017.09.010
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2026 Hanum Resti Saputri
This work is licensed under a Creative Commons Attribution 4.0 International License.
