Nur Febrianti, Kukuh Murtilaksono, Baba Barus


Disasters of forest and land fires are increasingly concerned. The nature of peat soil which is easy to lose water and high organic matter content causes peat soils to be very sensitive to fire. Therefore it is necessary to know indicators for early warning of fires on peatlands. The purpose of this study is to determine the critical groundwater level (GWL) as an indicator of peatland fires on the Jangkang River - Sungai Liong. Determination of the critical point of peatland fires as a fire early warning is done by calculating the difference from the value of the undefined TMA with a range of possible errors. The TMA value is obtained from the estimation of several methods, namely data on the physical properties of the soil, the drought index, and a combination of both. The TMA estimation of the physical properties of the soil has a range of fires at depths of 74.3 - 107 cm. In estimating TMA using a drought index, potential fires occur in TMA ranging from 27 - 101 cm. While the combined estimates of the physical properties of the soil and the drought index ranged from 66.8 - 98.8 cm the occurrence of fires on peatland. The results of this study show that the estimated TMA from a combination of field data and drought index provides fairly good accuracy. Thus TMA can be an early warning indicator of the danger of peatland fires. This TMA estimation can give faster results and pretty good accuracy. But this estimation model for TMA does not necessarily apply directly to other research locations. The critical point of peat soil water depth ranges from 27 to 74 cm. The depth of the peatland surface should be maintained less than the critical point, if not then the potential for peatland fires will increase.


critical point, peatland fire, remote sensing

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Ceccato, P., S. Flasse, S. Tarantola, S. Jacquemond, & J.M. Gregoire. (2001). Detecting Vegetation Water Content Using Reflectance in the Optical Domain. Remote Sensing of Environment, 77: 22–33.

Chen, D., J. Huang, & T.J. Jackson. (2005). Vegetation Water Content Estimation for Corn and Soybeans Using Spectral Indices Derived from MODIS Near- and Short-Wave Infrared Bands. Remote Sensing of Environment, 98 (2–3): 225–236.

Edi, H. (2017). Identifikasi Potensi Bahaya Subsidence di Kesatuan Hidrologi Gambut Sungai Jangkang – Sungai Liong Pulau Bengkalis. (Thesis), IPB (Bogor Agricultural University), Bogor.

Febrianti, N., K. Murtilaksono, & B. Barus (2018). Model Estimasi Tinggu Muka Air Tanah Lahan Gambut Menggunakan Indeks Kekeringan. Inderaja, 15 (1): 25-36.

Gao, B-C. (1996). NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. Remote Sensing of Environment, 58: 257-266.

Gu, Y., E. Hunt, B. Wardlow, J.B. Basara, J.F. Brown, & J.P. Verdin. (2008). Evaluation of MODIS NDVI and NDWI for Vegetation Drought Monitoring Using Oklahoma Mesonet Soil Moisture Data. Geophysical Research Letters, 35 (5): L22401.

Gulácsi, A. & F. In Hungary. Environmental Kovács. (2015). Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images Geography, 8 (3–4), 11–20.

Guttler F., D. Ienco, M. Teisseire, J. Nin, & P. Poncelet. (2014). Towards the Use of Sequential Patterns for Detection and Characterization of Natural and Agricultural Areas. In: Laurent A., Strauss O., Bouchon-Meunier B., Yager R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, Springer (442), Cham.

Hazaymeh, K. & Q.K. Hassan. (2016). A Remote Sensing-Based Agricultural Drought Indicator and Its Implementation Over Semi-Arid Region, Jordan. AIMS Environmental Science, 3(4): 604-630.

Https:// Akses (2017)

Jackson, T.J., D. Chen, M. Cosh, F. Li, M. Anderson, C. Walthall, P. Doriaswamy, & E.R. Hunt. (2004). Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans. Remote Sensing of Environment, 92 (4): 475−482.

Khoiriyah, Y.M., & I.S. Sitanggang. (2014). A Spatial Decision Tree Based On Topological Relationships for Classifying Hotspot Occurences in Bengkalis Riau Indonesia. International Conference on Advanced Computer Science and Information Systems (ICACSIS), 268-272.

Maki, M., M. Ishiahra, & M. Tamura. (2004). Estimation of Leaf Water Status to Monitor the Risk of Forest Fires by Using Remotely Sensed Imagery. Remote Sensing of Environment, 90 (4): 441−450.

Meingast K.M., M.J. Falkowski, E.S. Kane, L.R. Potvin, B.W. Benscoter, A.M.S. Smith, L.L. Bourgeau-Chavez, & M.E. Miller. (2014). Spectral Detection Of Near-Surface Moisture Content And Water-Table Position In Northern Peatland Ecosystems. Remote Sensing of Environment, 152: 536 –546.

Putra, E.I., & H. Hayasaka. (2011). The Effect of Precipitation Pattern of Dry Season on Peat Fire Occurrence in Mega Rice Project Area, Central Kalimantan, Indonesia. Tropics 19(4): 145-156.

Ramadhan, M.M., I.S. Sitanggang, & L.P. Anzani. (2017). Classification Model for Hotspot Sequences as Indicator for Peatland Fires Using Data Mining Approach. Science and Technology, 3 (2): 588-597.

Sadeghi, M., E. Babaeian, M, Tuller, & S.B. Jones. (2017). The Optical Trapezoid Model: A Novel Approach to Remote Sensing of Soil Moisture Applied to Sentinel-2 and Landsat-8 Observations. Remote Sensing of Environment, 198: 52–68.

Sekretariat Negara. 2014. Peraturan Pemerintah Republik Indonesia Nomor 71 Tahun 2014 Tentang Perlindungan dan Pengelolaan Ekosistem Gambut. Jakarta (ID).

Syaufina L., B.H. Saharjo, & T. Tiryana. (2004). The Estimation of Greenhouse Gases Emission of Peat Fire. Working Paper, No. 04. Environmental Research Center, Bogor Agricultural University. Bogor.

Takeuchi W., T. Hirano, N. Anggraini, & O. Roswintiarti. (2010). Estimation of Ground Water Table at Forested Peatland in Kalimantan Using Drought Index Towards Wildfire Control. Prosiding ACRS, 1-5 November 2010. Hanoi (VN).

Taufik M., B.I. Setiawan, L.B. Prasetyo, N.H. Pandjaitan, & Soewarso. (2010). Peluang untuk Mengurangi Bahaya Kabakaran di HTI Lahan Basah: Model Pendekatan Pengelolaan Air. Hidrosfir Indonesia, 5(2): 55 – 62.

Taufik M., B.I. Setiawan, L.B. Prasetyo, N.H. Pandjaitan, & Soewarso. (2011). Development of Fire Danger Index at SBA Wood Industries, South Sumatera. Penelitian Hutan Tanaman, 8 (4): 215 – 223.

Taufik M., & B.I. Setiawan. (2012). Interpretation of Soil Water Content Into Dryness Index; Implication for Forest Fire Management. Manajemen Hutan Tropika, 18 (1): 31-38.

Taufik M. (2010). Analisis Perilaku Indeks Kekeringan di Wilayah Rentan Kebakaran, Sumatera Selatan. Agromet, 24 (2): 9-17.

Thenkabail, P.S. (2015). Remote Sensing of Water Resources, Disasters, and Urban Studies. Remote Sensing Handbook, (3). CRC Press. New York.

Xiao, X., S. Boles, J. Liu, , D. Zhuang, S. Frolking, C. Li, W. Salas, & B. Moore III. (2005). Mapping Paddy Rice Agriculture in Southern China Using Multi-Temporal MODIS Images. Remote Sensing of Environment, 95 (4): 480–492.

Zarco-Tejada, PJ., C.A. Rueda, & S.L. Ustin. (2003). Water Content Estimation in Vegetation with MODIS Reflectance Data and Model Inversion Methods. Remote Sensing of Environment, 85 (1): 109–124.

Zargar, A, R. Sadiq, B. Naser, & F.I. Khan. (2011). A Review of Drought Indices. Environmental Reviews, 19: 333–349.

Zhang, N., Y. Hong, Q. Qin, & L. Liu. (2013a). VSDI: A Visible and Shortwave Infrared Drought Index for Monitoring Soil and Vegetation Moisture Based on Optical Remote Sensing. Remote Sensing, 34 (13): 4585-4609.

Zhang, N., Y. Hong, Q. Qin, & L. Zhu. (2013b). Evaluation of the Visible and Shortwave Infrared Drought Index in China. Disaster Risk Sci., 4 (2): 68–76.


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