Assessing the NDDI Drought Index Using the Google Earth Engine for Peat Fire Risk Monitoring in Siak, Indonesia

Sigit Sutikno; Novreta Darfia; Ilham Hanafiah Wy; Randhi Saily; Sinta Haryati Silviana; Koichi Yamamoto

doi:10.23917/forgeo.16518

Authors

Sigit Sutikno Center for Peatland and Disaster Studies, University of Riau, Pekanbaru, 28293, Riau, Indonesia https://orcid.org/0000-0001-9948-0064
Novreta Darfia Civil Engineering Department, University of Riau, Pekanbaru, 28293, Riau, Indonesia https://orcid.org/0000-0003-1435-1154
Ilham Hanafiah Wy Center for Peatland and Disaster Studies, University of Riau, Pekanbaru, 28293, Riau, Indonesia
Randhi Saily Center for Peatland and Disaster Studies, University of Riau, Pekanbaru, 28293, Riau, Indonesia https://orcid.org/0000-0002-8904-3394
Sinta Haryati Silviana Research Center for Geoinformatics, National Research and Innovation Agency (BRIN), Bandung 40135, West Java, Indonesia https://orcid.org/0000-0003-1970-9186
Koichi Yamamoto Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube city, Yamaguchi, Japan https://orcid.org/0009-0000-0918-1088

DOI:

https://doi.org/10.23917/forgeo.16518

Keywords:

Drought Index, NDDI, Google Earth Engine, Peat Fire, Sentinel 2, Hotspots

Abstract

Peatland ecosystems across tropical regions are prone to severe fires during drought periods, making timely and accurate early drought monitoring vital for fire risk mitigation. This study assesses drought conditions in Siak Regency, Indonesia, employing the Normalized Difference Drought Index (NDDI) derived from Sentinel-2 satellite imagery using the Google Earth Engine (GEE) platform. NDDI, which combines indices of vegetation greenness (NDVI) and moisture (NDWI), was used to map peatland dryness. The research analysed NDDI patterns for each month from 2019 to 2024 and was tested against MODIS and VIIRS fire hotspots both spatially, by overlaying hotspots on classified drought maps, and temporally, using Spearman correlation. The results show that the two approaches produced an informative contrast. Spatially, fire was concentrated strongly in the driest classes: on average 91% of hotspots (85% pooled across all 507 detections made) fell within the severe and extreme drought classes (NDDI > 0.25). A chi-square test confirmed that this distribution was clearly not a question of chance (chi-square = 96.1, p < 0.001). Temporally, however, the month-to-month correlation was weak (mean rho = 0.24), so the surface signal alone does not set fire timing within a particular year. An independent CHIRPS rainfall record confirmed the wet- and dry-year ranking and linked annual rainfall to burnt areas (rho = -0.94, p = 0.005). NDDI is therefore a reliable indicator of areas where peatland is fire-prone, and is best used as a spatial fire-susceptibility layer within a multi-factor early-warning system, being transferable to other tropical peatlands.

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References

Affandy, N. A., Iranata, D., Anwar, N., Maulana, M. A., Prasetyo, D. D., Wardoyo, W., & Sukojo, B. M. (2024). Assessment of Agricultural Drought Using the Normalized Difference Drought Index (NDDI) to Prediction Drought at Corong River Basin. International Journal of Integrated Engineering, 16(1), 378–393. https://doi.org/https://doi.org/10.30880/ijie.2024.16.01.032[Google Scholar] [CrossRef]

Arjasakusuma, S., Kusuma, S. S., Vetrita, Y., Prasasti, I., & Arief, R. (2022). Monthly Burned-Area Mapping using Multi-Sensor Integration of Sentinel-1 and Sentinel-2 and machine learning: Case Study of 2019’s fire events in South Sumatra Province, Indonesia. Remote Sensing Applications: Society and Environment, 27, 100790. https://doi.org/https://doi.org/10.1016/j.rsase.2022.100790[Google Scholar] [CrossRef]

Atwood, E. C., Englhart, S., Lorenz, E., Halle, W., Wiedemann, W., & Siegert, F. (2016). Detection and characterization of low temperature peat fires during the 2015 fire catastrophe in Indonesia using a new high-sensitivity fire monitoring satellite sensor (FireBird). PLoS One, 11(8), e0159410. https://doi.org/https://doi.org/10.1371/journal.pone.0159410[Google Scholar] [CrossRef]

Bruno, A. G., Moore, D. P., Harrison, J. J., Graham, A., & Chipperfield, M. P. (2025). Hydrological drivers of hydrogen cyanide wildfire emissions from Indonesian peat fires during the 2015, 2019, and 2023 El Niño events. EGUsphere, 2025, 1–33. https://doi.org/10.5194/egusphere-2025-5109[Google Scholar] [CrossRef]

Burdun, I., Bechtold, M., Aurela, M., De Lannoy, G., Desai, A. R., Humphreys, E., Kareksela, S., Komisarenko, V., Liimatainen, M., Marttila, H., Minkkinen, K., Nilsson, M. B., Ojanen, P., Salko, S.-S., Tuittila, E.-S., Uuemaa, E., & Rautiainen, M. (2023). Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands. Remote Sensing of Environment, 296, 113736. https://doi.org/https://doi.org/10.1016/j.rse.2023.113736[Google Scholar] [CrossRef]

Chandrasekar, K., Srikanth, P., Chakraborty, A., Choudhary, K., & Ramana, K. V. (2024). Response of crop water indices to soil wetness and vegetation water content. Advances in Space Research, 73(2), 1316–1330. https://doi.org/https://doi.org/10.1016/j.asr.2022.11.019[Google Scholar] [CrossRef]

Cheng, Y.-S., Lu, J.-R., & Yeh, H.-F. (2024). Multi-Index Drought Analysis in Choushui River Alluvial Fan, Taiwan. Environments, 11(11), 233. https://doi.org/https://doi.org/10.3390/environments11110233[Google Scholar] [CrossRef]

Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., & Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25–36. https://doi.org/https://doi.org/10.1016/j.rse.2011.11.026[Google Scholar] [CrossRef]

Firdaus, R. A., Hermawan, E., & Kamilah, N. (2024). IMPLEMENTASI METODE NORMALIZE DIFFERENCE DROUGHT INDEX (NDDI) TERHADAP PEMANTAUAN PRODUKTIVITAS PERTANIAN TANAMAN PADI (STUDI KASUS: KECAMATAN JONGGOL TAHUN 2019-2022). INFOTECH Journal, 10(1), 147–160. https://doi.org/https://doi.org/10.31949/infotech.v10i1.9794[Google Scholar] [CrossRef]

Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/https://doi.org/10.1016/S0034-4257(96)00067-3[Google Scholar] [CrossRef]

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/https://doi.org/10.1016/j.rse.2017.06.031[Google Scholar] [CrossRef]

Graham, A. M., Spracklen, D. V, McQuaid, J. B., Smith, T. E. L., Nurrahmawati, H., Ayona, D., Mulawarman, H., Adam, C., Papargyropoulou, E., & Rigby, R. (2024). Updated smoke exposure estimate for Indonesian peatland fires using a network of low‐cost PM2. 5 sensors and a regional air quality model. GeoHealth, 8(11), e2024GH001125. https://doi.org/https://doi.org/10.1029/2024GH001125[Google Scholar] [CrossRef]

Grosvenor, M. J., Ardiyani, V., Wooster, M. J., Gillott, S., Green, D. C., Lestari, P., & Suri, W. (2024). Catastrophic impact of extreme 2019 Indonesian peatland fires on urban air quality and health. Communications Earth & Environment, 5(1), 649. https://doi.org/10.1038/s43247-024-01813-w[Google Scholar] [CrossRef]

Gu, Y., Brown, J. F., Verdin, J. P., & Wardlow, B. (2007). A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters, 34(6). https://doi.org/https://doi.org/10.1029/2006GL029127[Google Scholar] [CrossRef]

Hajek, P., Mörsdorf, M., Kovach, K. R., Greinwald, K., Rose, L., Nock, C. A., & Scherer-Lorenzen, M. (2024). Quantifying the influence of tree species richness on community drought resistance using drone-derived NDVI and ground-based measures of Plant Area Index and leaf chlorophyll in a young tree diversity experiment. European Journal of Forest Research, 143(1), 141–155. https://doi.org/https://doi.org/10.1007/s10342-023-01615-3[Google Scholar] [CrossRef]

Hamdi, S., Rizal, S., Shibata, T., Darmawan, A., Irfan, M., & Sulaiman, A. (2025). The dispersion of smoke haze from peatland fires over South Sumatra during the moderate El Niño of 2023. Natural Hazards, 121(1), 1095–1116. https://doi.org/https://doi.org/10.1007/s11069-024-06857-x[Google Scholar] [CrossRef]

Hayasaka, H. (2023). Peatland fire weather conditions in Sumatra, Indonesia. Climate, 11(5), 92. https://doi.org/10.3390/cli11050092[Google Scholar] [CrossRef]

Irsyad, F., Sari, N., Putri, A. E., & Filipović, V. (2025). Application of the Normalized Difference Drought Index (NDDI) for Monitoring Agricultural Drought in Tropical Environments. In Land (Vol. 14, Issue 12, p. 2431). https://doi.org/10.3390/land14122431[Google Scholar] [CrossRef]

Islam, M. M., & Ahamed, T. (2023). Development of a near-infrared band derived water indices algorithm for rapid flash flood inundation mapping from sentinel-2 remote sensing datasets. Asia-Pacific Journal of Regional Science, 7(2), 615–640. https://doi.org/10.1007/s41685-023-00288-5[Google Scholar] [CrossRef]

KHAMPEERA, A., YONGCHALERMCHAI, C., & TECHATO, K. (2018). Drought monitoring using drought indices and GIS techniques in Kuan Kreng peat swamp, Southern Thailand. Walailak Journal of Science and Technology (WJST), 15(5), 357–370. https://doi.org/10.48048/wjst.2018.2723[Google Scholar] [CrossRef]

Kiely, L., Spracklen, D. V, Arnold, S. R., Papargyropoulou, E., Conibear, L., Wiedinmyer, C., Knote, C., & Adrianto, H. A. (2021). Assessing costs of Indonesian fires and the benefits of restoring peatland. Nature Communications, 12(1), 7044. https://doi.org/10.1038/s41467-021-27353-x[Google Scholar] [CrossRef]

Mezbahuddin, S., Nikonovas, T., Spessa, A., Grant, R. F., Imron, M. A., Doerr, S. H., & Clay, G. D. (2023). Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology. Scientific Reports, 13(1), 619. https://doi.org/10.1038/s41598-022-27075-0[Google Scholar] [CrossRef]

Page, S. E., Siegert, F., Rieley, J. O., Boehm, H.-D. V, Jaya, A., & Limin, S. (2002). The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature, 420(6911), 61–65. https://doi.org/https://doi.org/10.1038/nature01131[Google Scholar] [CrossRef]

Patil, P. P., Jagtap, M. P., Khatri, N., Madan, H., Vadduri, A. A., & Patodia, T. (2024). Exploration and advancement of NDDI leveraging NDVI and NDWI in Indian semi-arid regions: A remote sensing-based study. Case Studies in Chemical and Environmental Engineering, 9, 100573. https://doi.org/10.1016/j.cscee.2023.100573[Google Scholar] [CrossRef]

Peng, Y., & Gong, H. (2025). Analysis of Spatiotemporal Changes in NDVI-Derived Vegetation Index and Its Influencing Factors in Kunming City (2000 to 2020). Forests, 16(12), 1781. https://doi.org/https://doi.org/10.3390/f16121781[Google Scholar] [CrossRef]

Pratama, H., Sutikno, S., & Yusa, M. (2020). Modeling of groundwater level fluctuation in the tropical peatland area of Riau, Indonesia. IOP Conference Series: Materials Science and Engineering, 796(1), 12037. https://doi.org/10.1088/1757-899X/796/1/012037[Google Scholar] [CrossRef]

Purnomo, H., Puspitaloka, D., Okarda, B., Andrianto, A., Qomar, N., Sutikno, S., Muhammad, A., Basuki, I., Jalil, A., Yesi, Prasetyo, P., Tarsono, Zulkardi, Kusumadewi, S. D., Komarudin, H., Dermawan, A., & Brady, M. A. (2024). Community-based fire prevention and peatland restoration in Indonesia: A participatory action research approach. Environmental Development, 50, 100971. https://doi.org/https://doi.org/10.1016/j.envdev.2024.100971[Google Scholar] [CrossRef]

Putra, E. I., Cochrane, M. A., Saharjo, B. H., Graham, L., Thomas, A., Applegate, G., Saad, A., Setianto, E., Sutikno, S., & Prayitno, A. (2019). Developing better understanding on tropical peat fire occurrences and dynamics. IOP Conference Series: Earth and Environmental Science, 394(1), 12044. https://doi.org/https://doi.org/10.1088/1755-1315/394/1/012044[Google Scholar] [CrossRef]

Román, M. O., Justice, C., Paynter, I., Boucher, P. B., Devadiga, S., Endsley, A., Erb, A., Friedl, M., Gao, H., Giglio, L., Gray, J. M., Hall, D., Hulley, G., Kimball, J., Knyazikhin, Y., Lyapustin, A., Myneni, R. B., Noojipady, P., Pu, J., … Wolfe, R. (2024). Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products. Remote Sensing of Environment, 302, 113963. https://doi.org/https://doi.org/10.1016/j.rse.2023.113963[Google Scholar] [CrossRef]

Rufin, P., Rabe, A., Nill, L., & Hostert, P. (2021). GEE TIMESERIES EXPLORER FOR QGIS – INSTANT ACCESS TO PETABYTES OF EARTH OBSERVATION DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-4/W2-, 155–158. https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-155-2021[Google Scholar] [CrossRef]

Salas-Martínez, F., Valdés-Rodríguez, O. A., Palacios-Wassenaar, O. M., Márquez-Grajales, A., & Rodríguez-Hernández, L. D. (2023). Methodological estimation to quantify drought intensity based on the NDDI index with Landsat 8 multispectral images in the central zone of the Gulf of Mexico. Frontiers in Earth Science, 11, 1027483. https://doi.org/https://doi.org/10.3389/feart.2023.1027483[Google Scholar] [CrossRef]

Sánchez Alcalde, G., & Escorihuela, M. J. (2025). Remote Sensing Standardized Soil Moisture Index for Drought Monitoring: A Case Study in the Ebro Basin. Remote Sensing, 17(23), 3916. https://doi.org/https://doi.org/10.3390/rs17233916[Google Scholar] [CrossRef]

Sandhyavitri, A., Perdana, M. A., Sutikno, S., & Widodo, F. H. (2018). The roles of weather modification technology in mitigation of the peat fires during a period of dry season in Bengkalis, Indonesia. IOP Conference Series: Materials Science and Engineering, 309(1), 12016. https://doi.org/10.1088/1757-899X/309/1/012016[Google Scholar] [CrossRef]

Sirin, A., & Medvedeva, M. (2022). Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires. In Remote Sensing (Vol. 14, Issue 1, p. 194). https://doi.org/10.3390/rs14010194[Google Scholar] [CrossRef]

Suharnoto, Y., Taufik, M., Setiawan, B. I., Buchori, D., & Dewantara, B. (2022). Development of Spatial Peatland Fire Danger Index Using Coupled SWAT-MODFLOW Model. In Sustainability (Vol. 14, Issue 13, p. 7632). https://doi.org/10.3390/su14137632[Google Scholar] [CrossRef]

Sukma, E. W., & Sutikno, S. (2025). Spatio-Temporal Analysis of Meteorological Drought Index and Peat Fires Using Google Earth Engine (GEE) in Pelalawan Regency, Riau. International Journal of Peatland Research and Innovation, 1(1), 44–55. [Google Scholar]

Suseno, B., Brunel, G., Wijayanto, H., Sadik, K., Afendi, F. M., & Tisseyre, B. (2025). Reconstructing satellite temporal series data under cloudy conditions: Application in predicting rice growth phases. Smart Agricultural Technology, 12, 101378. https://doi.org/https://doi.org/10.1016/j.atech.2025.101378[Google Scholar] [CrossRef]

Sutikno, S., Afdeni, S., Rinaldi, & Handayani, Y. L. (2020). Analysis of tropical peatland fire risk using drought standardized precipitation index method and TRMM rainfall data. AIP Conference Proceedings, 2255(1), 70018. https://doi.org/10.1063/5.0013880[Google Scholar] [CrossRef]

Sutikno, S., Amalia, I. R., Sandhyavitri, A., Syahza, A., Widodo, H., & Seto, T. H. (2020). Application of weather modification technology for peatlands fires mitigation in Riau, Indonesia. AIP Conference Proceedings, 2227(1), 30007. https://doi.org/https://doi.org/10.1063/5.0002137[Google Scholar] [CrossRef]

Sutikno, S., Rinaldi, M. Y., Nasrul, B., Yesi, C., Prayitno, A., Putra, A., & Ardi, M. G. (2023). Water Management for Integrated Peatland Restoration in Pulau Tebing Tinggi PHU, Riau. Vulnerability and Transformation of Indonesian Peatlands, 161. https://doi.org/https://doi.org/10.1007/978-981-99-0906-3_9[Google Scholar] [CrossRef]

Sutikno, S., Yusa, M., Rinaldi, R., Muhammad, A., Saputra, H., Wardani, K. S., & Yamamoto, K. (2026). Hydrological modeling of small coastal peat island in degraded peatlands of Bengkalis Island, Riau Province. Journal of Degraded and Mining Lands Management, 13(1), 9163–9175. https://doi.org/10.15243/jdmlm.2026.131.9163[Google Scholar] [CrossRef]

Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2020.04.001[Google Scholar] [CrossRef]

Taufik, M., Haikal, M., Widyastuti, M. T., Arif, C., & Santikayasa, I. P. (2023). The impact of rewetting peatland on fire hazard in riau, Indonesia. Sustainability, 15(3), 2169. https://doi.org/https://doi.org/10.3390/su15032169[Google Scholar] [CrossRef]

Taufik, M., Torfs, P. J. J. F., Uijlenhoet, R., Jones, P. D., Murdiyarso, D., & Van Lanen, H. A. J. (2017). Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nature Climate Change, 7(6), 428–431. https://doi.org/10.1038/nclimate3280[Google Scholar] [CrossRef]

Thottolil, R., & Kumar, U. (2018). Cloud Computing for Big Geospatial Data Analysis with Google Earth Engine–Urban Research Applications. Lake 2022:-13th Biennial Lake Symposium, 66. [Google Scholar]

Varghese, D., Radulovic, M., Stojkovic, S., & Crnojevic, V. (2021). Reviewing the potential of Sentinel-2 in assessing the drought. Remote Sens., 13, 3355. https://doi.org/https://doi.org/10.3390/rs13173355[Google Scholar] [CrossRef]

Vicente-Serrano, S. M., Beguería, S., Lorenzo-Lacruz, J., Camarero, J. J., López-Moreno, J. I., Azorin-Molina, C., Revuelto, J., Morán-Tejeda, E., & Sanchez-Lorenzo, A. (2012). Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interactions, 16(10), 1–27. https://doi.org/https://doi.org/10.1175/2012EI000434.1[Google Scholar] [CrossRef]

Vukadinović, L., Galić, V., Mazur, M., Jambrović, A., & Šimić, D. (2025). Genome-Wide Association Study of Chlorophyll Fluorescence and Hyperspectral Indices in Drought-Stressed Young Plants in Maize. Genes, 16(9), 1068. https://doi.org/10.3390/genes16091068[Google Scholar] [CrossRef]

Wahyunto, Ritung, S., & Subagjo, H. (2003). Peta luas sebaran lahan gambut dan kandungan karbon di Pulau Sumatera 1990–2002 [Maps of peatland distribution and carbon content in Sumatera]. Wetlands International – Indonesia Programme & Wildlife Habitat Canada, Bogor.

Wee, S. J., Park, E., Alcantara, E., & Lee, J. S. H. (2023). Exploring Multi-Driver Influences on Indonesia’s Biomass Fire Patterns from 2002 to 2019 through Geographically Weighted Regression. Journal of Geovisualization and Spatial Analysis, 8(1), 4. https://doi.org/10.1007/s41651-023-00166-w[Google Scholar] [CrossRef]

Wilhite, D. A., & Glantz, M. H. (1985). Understanding: the Drought Phenomenon: The Role of Definitions. Water International, 10(3), 111–120. https://doi.org/10.1080/02508068508686328[Google Scholar] [CrossRef]

Wu, J., Lin, L., Zhang, C., Li, T., Cheng, X., & Nan, F. (2023). Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 16–31. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2022.12.017[Google Scholar] [CrossRef]

Yokelson, R. J., Saharjo, B. H., Stockwell, C. E., Putra, E. I., Jayarathne, T., Akbar, A., Albar, I., Blake, D. R., Graham, L. L. B., Kurniawan, A., Meinardi, S., Ningrum, D., Nurhayati, A. D., Saad, A., Sakuntaladewi, N., Setianto, E., Simpson, I. J., Stone, E. A., Sutikno, S., … Cochrane, M. A. (2022). Tropical peat fire emissions: 2019 field measurements in Sumatra and Borneo and synthesis with previous studies. Atmos. Chem. Phys., 22(15), 10173–10194. https://doi.org/10.5194/acp-22-10173-2022[Google Scholar] [CrossRef]

Yusa, M., Sandyavitri, A., & Sutikno, S. (2019). Application of electrical resistivity test to estimate carbon storage of tropical peat deposit (Case study of Bengkalis island). MATEC Web of Conferences, 276, 5004. https://doi.org/10.1051/matecconf/201927605004[Google Scholar] [CrossRef]