<![CDATA[Estimasi biomassa dapat dilakukan dengan tiga pendekatan yaitu modeling, pengukuran di lapang dan penginderaan jauh (Lu,2006). Dari tiga pendekatan tersebut, pendekatan penginderaan jauh lebih dipilih karena dapat digunakan untuk kawasan dengan luasan besar. Tujuan penelitian ini adalah menganalisis penutupan lahan melalui citra Landsat, membangun model hubungan antara biomass atas permukaan dengan indeks vegetasi, dan mengetahui estimasi biomassa atas permukaan pada berbagai penutupan lahan di Kabupaten Mamuju Utara.Penelitian ini dilakukan melalui beberapa tahapan yaitu persiapan, pengumpulan data, analisis data (koreksi geometri, interpretasi penutupan lahan, perhitungan indeks vegetasi, pemilihan model, estimasi biomassa Kabupaten Mamuju Utara pada berbagai penutupan lahan). Beberapa indeks vegetasi yang digunakan dalam penelitian ini adalah NDVI (Normalized Difference Vegetation Index), TNDVI (Transformed Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), dan TRVI (Transformed Ratio Vegetation Index). Penutupan lahan di Kabupaten Mamuju Utara berjumlah 12 jenis yang umumnya didominasi oleh hutan, perkebunan dan kebun campuran. Penutupan mangrove, semak belukar, tegalan/ladang, sawah, tubuh air, rawa, tanah terbuka, tambak/empang dan pemukiman relatif lebih sedikit. Indeks vegetasi NDVI memiliki korelasi yang paling tinggi baik dari penutupan vegetasi alami maupun non alami dengan biomassa dengan R sebesar 60%. Jumlah estimasi biomassa atas permukaan di Kabupaten Mamuju Utara pada vegetasi alami: hutan primer 104,1 ton/ha, mangrove 79,0 ton/ha, semak belukar 36,0 ton/ha dan pada vegetasi non alami: perkebunan 66,1 ton/ha, kebun campuran 65,1 ton/ha, tegalan 52,1 ton/ha, sawah 5,6 ton/ha. Jumlah biomassa yang tersimpan bervariasi tergantung pada jenis penutup lahan, tanah dan pengelolaan. Semakin tua umur vegetasi, semakin tinggi kandungan biomassanya.Kata Kunci: Penutup Lahan, Biomassa, Penginderaan Jauh, Indeks Vegetasi ABSTRACTThere are three approaches used in biomass estimation, namely modeling, field measurement, and remote sensing (Lu, 2006). Among the three approaches, remote sensing approach is preferred because it can be applied for large extents areas. This research aims to analyze land cover using Landsat imagery, building a model of relationship between surface biomass using a vegetation index, and determining the surface biomass estimates for the various land cover in North Mamuju. This research was conducted through several stages i.e. preparation, data collection, data analysis (geometry correction, interpretation of land cover, vegetation index calculation, model selection, and biomass estimation of various land cover) in North Mamuju Regency. The vegetation indices used in this study were NDVI (Normalized Difference Vegetation Index), TNDVI (Transformed Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), and TRVI (Transformed Ratio Vegetation Index). Land cover in North Mamuju consisted of 12 types which in general are dominated by forests, plantations, and mixed farms. Meanwhile, the coverage of mangroves, scrub, moor/field, fields, water bodies, wetlands, open land, farms/ponds and settlements relatively small. Correlation between NDVI vegetation index and the land coversâ biomass was the highest, both of the natural and non-natural vegetation covers with R of 60%. The estimated of total biomass on the surface natural vegetation were as follows: primary forest (104,1) ton/ha, mangrove 79,0 ton/ha, shrubs 36,0 ton/ha; and the non-natural vegetation: plantation 66,1 ton/ha, mixed farms 65.1 ton/ha, dry 52,1 ton/ha, rice 5,6 ton/ha. The amount of biomass stored in each land cover varied, depending on the land cover type, soil type, and management. The older of the vegetation, the greater biomass.Keywords: Land Cover, Biomass, Remote Sensing, Vegetation Index]]>
