ANALISIS MODEL ESTIMASI TINGGI MUKA AIR TANAH MENGGUNAKAN INDEK KEKERINGAN
Abstract
The Ground Water Level plays an important role in determining the greenhouse gas emission and, in turn, in regulating global climate system. Information on existing water levels is still using field measurements. The purpose of this study was to evaluate the best approximation model for estimating water level using drought index. This study utilizes Landsat 8 data to calculate Normalized Difference Water Index and Visible and Shortwave infrared Drought Index for 3 months (March, April and June 2016). The best estimation model is selected by the Akaike Information Criteria correction method and validated using K-Fold cross-validation. The results of this study indicate that the estimation of water level is affected by both drought indices with the TMA (mm) equation= -439,47 – 1639,7 * NDWI_Maret – 640,23 * NDWI_April + 477 * VSDI_Maret. Estimated water level began to detect hotspots ranging from 64,35 ± 36,9 6 cm (27 - 101 cm). The critical point for KHG Sei Jangkang - Sei Liong is 27 cm, thus the water level depth should be maintained less than that to avoid fire in peatlands.
ABSTRAK
Tinggi muka air tanah lahan gambut atau secara teknis dikenal dengan kedalaman muka air tanah memegang peran penting dalam menentukan emisi gas rumah kaca dan mengatur sistem iklim global. Informasi tentang tinggi muka air yang ada saat ini masih menggunakan hasil pengukuran lapangan. Tujuan penelitian ini adalah mengevaluasi model aproksimasi terbaik untuk estimasi tinggi muka air dengan menggunakan indeks kekeringan. Penelitian ini memanfaatkan data Landsat 8 untuk menghitung Normalized Difference Water Index dan Visible and Shortwave infrared Drought Index selama 3 bulan (Maret, April dan Juni 2016). Model estimasi terbaik dipilih dengan metode koreksi Kriteria Informasi Akaike dan divalidasi menggunakan validasi silang K-Fold. Hasil penelitian ini menunjukkan bahwa estimasi tinggi muka air dipengaruhi oleh kedua indeks kekeringan tersebut dengan persamaan TMA (mm) = - 439,47 – 1639,7 * NDWI_Maret – 640,23 * NDWI_April + 477 * VSDI_Maret. Estimasi tinggi muka air mulai terdeteksi adanya hotspot berkisar antara 64,35±36,9 6 cm (27 – 101 cm). Titik kritis untuk KHG Sei Jangkang – Sei Liong adalah 27 cm, dengan demikian kedalaman tinggi muka air harus dipertahankan kurang dari itu untuk menghindari terjadinya kebakaran di lahan gambut.
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Beal D.J., (2007). Information Criteria Methods in SAS for Multiple Linear Regression Models. The 15th Annual South East SAS Users Group (SESUG) Conference in Hilton Head, SC on November 4–6, 2007. South Carolina (US).
Burnham, K.P., & Anderson, D.R., (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. Colorado (US): Springer.
Ceccato, P., Flasse, S., Tarantola, S., Jacquemond, S., & Gregoire, J.M., (2001). Detecting Vegetation Water Content Using Reflectance in the Optical Domain. Remote Sensing of Environment, 77: 22–33.
Chen, D., Huang, J., & Jackson, T.J., (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. Tesis. Fakultas Pertanian Institut Pertanian Bogor. Bogor.
Gao, B-C., (1996). NDWI—A Normalized Difference Water Index For Remote Sensing Of Vegetation Liquid Water From Space. Int. Remote Sensing of Environment, 58: 257-266.
Gu, Y., Hunt, E., Wardlow, B., Basara,
J.B., Brown, J.F., & Verdin, J.P., (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., & Kovács, Ferenc., (2015). Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary. Environmental Geography 8 (3–4), 11–20
Guo, Q., Pu, R., Li, J., & Cheng, J., (2017). A Weighted Normalized Difference Water Index for Water Extraction Using Landsat Imagery. Remote Sensing, 38 (19), 5430-5445.
Jackson, T.J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P., & Hunt, E.R., (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.
Ma’rufah, U., Hidayat, R. & Prasasti, I., (2008). Analysis Of Relationship Between Meteorological And Agricultural Drought Using Standardized Precipitation Index And Vegetation Health Index. IOP Conference Series: Earth and Environmental Science, 54
Maki, M., Ishiahra, M., & Tamura, M., (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.
Pemerintah Republik Indonesia, (2014). Peraturan Pemerintah Republik Indonesia Nomor 71 Tahun 2014 tentang Perlindungan dan Pengelolaan Ekosistem Gambut. Jakarta (ID): Sektretariat Negara.
Pemerintah Republik Indonesia, (2017). Peraturan Menteri Lingkungan Hidup dan Kehutanan Nomor P.15 Tahun 2017 tentang Tata Cara Pengukuran Muka Air Tanah di Titik Pengamatan Ekosistem Gambut. Jakarta (ID): Sektretariat Negara.
Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S., (2016). A New Approach for Detection of Surface Water Changes Based on Principal Component Analysis of Multitemporal Normalized Difference Water Index. Coastal Research, 32 (2): pp. 443 – 451.
Taufik, M., (2010). Analisis Perilaku Indeks Kekeringan di Wilayah Rentan Kebakaran, Sumatra Selatan. Agromet, 24 (2): 9-17.
Taufik, M., & Setiawan, B.I., (2012). Interpretationof Soil Water Content into Dryness Index; Implication for Forest Fire Management. Manajemen Hutan Tropika, 18 (1): 31-38.
Taufik, M., Setiawan, B.I., Prasetyo, L.B., & Pandjaitan, N.H., (2011). Development of Fire Danger Index at SBA Wood Industries, South Sumatra. Penelitian Hutan Tanaman, 8 (4): 215– 223.
Thenkabail, P.S., (2015). Remote Sensing of Water Resources, Disasters, and Urban Studies. Remote Sensing Handbook (3). CRC Press. New York.
Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W., & Moore III, B., (2005). Mapping Paddy Rice Agriculture in Southern China Using Multi-Temporal MODIS Images. Remote Sensing of Environment, 95 (4): 480–492.
Zarco-Tejada, P.J., Rueda, C.A., & Ustin, S.L., (2003). Water Content Estimation in Vegetation with MODIS Reflectance Data and Model Inversion Methods. Remote Sensing of Environment, 85 (1): 109–124.
Zargar, A, Sadiq, R, Naser, B., & Khan F.I., (2011). A Review Of Drought Indices. Environ. Rev., 19: 333–349.
Zhang, N., Hong, Y., Qin, Q., & Liu, L., (2013a). VSDI: A Visible And Shortwave Infrared Drought Index For Monitoring Soil and Vegetation Moisture Based On Optical Remote Sensing. Remote Sensing, 34 (13):
-4609.
Zhang, N., Hong, Y., Qin, Q., & Zhu, L., (2013b). Evaluation of the Visible and Shortwave Infrared Drought Index in China. Disaster Risk Sci., 4 (2): 68–76.
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