BIOMASS ESTIMATION MODEL FOR MANGROVE FOREST USING MEDIUM-RESOLUTION IMAGERIES IN BSN CO LTD CONCESSION AREA, WEST KALIMANTAN

Authors

  • Sendi Yusandi Directorate of Biodiversity Conservation, Ministry of Environment and Forestry, Manggala Wanabhakti
  • I Nengah Surati Jaya Remote Sensing and GIS Laboratory, Faculty of Forestry, Bogor Agricultural University Bogor
  • Fairus Mulia PT Kandelia Alam. Jalan Supadio Pontianak

DOI:

https://doi.org/10.30536/j.ijreses.2018.v15.a2683

Keywords:

mangrove forests, biomass, model, score, NDVI

Abstract

Mangrove forest is one of the forest ecosystem types that have the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as on carbon sequestration. However, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods are very costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e. by using Landsat 8 and SPOT 5. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI of Landsat 8 and SPOT 5 have considerably high correlation coefficients with the standing biomass with a value of higher than 0.7071. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass for Landsat 8 is B=0.00023404 e(20 NDVI) with the R² value of 77.1% and B=0.36+25.5 NDVI² with the R² value of 49.9% for SPOT 5. In general, the concession area of Bina Silva Nusa (BSN) Group (PT Kandelia Alam and PT Bina Ovivipari Semesta) have the potential of biomass ranging from 45 to 100 ton per ha.

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Published

2025-11-25

Issue

Vol. 15 No. 1 (2018)

Section

Articles

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Copyright (c) 2018 Author (S)

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