Assessing Land Cover Changes and CO2 Emissions
in Tropical Forests, 1998-2016: A Case Study
of the Sungai Wain Protection Forest

Askar, Askar; Nuthammachot, Narissara; Sayektiningsih, Tri; Hermudananto, Hermudananto

doi:10.15244/pjoes/94845

Cited by 1 publication

(1 citation statement)

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“…The observation of forest changes can be effectively obtained with the advantages of remote sensing techniques. For instance, Landsat multitemporal data was used to assess the land cover changes and CO 2 emissions in the tropical forest [2], or to extract construction land information [3]. In addition, the integration of remote sensing and a geographical information system (GIS) provides a useful tool to develop a forest plan.…”

Section: Introductionmentioning

confidence: 99%

Combination of SAR Polarimetric Parameters for Estimating Tropical Forest Aboveground Biomass

Tuong¹,

Tani²,

Wang³

et al. 2020

Pol. J. Environ. Stud.

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There is a demand for better information on forest biomass in tropical regions for use in carbon accounting. This needs robust above-ground biomass (AGB) estimation in different forest types. Our study sought to improve biomass estimation by selecting the best regression models based on observations of the contribution of radar signals to AGB in five forest types in Vietnam. Data from PALSAR and PALSAR-2, which covered the forest area, were used to extract 16 polarimetric radar (PolSAR) parameters in 2007 and 2016. This study was designed as a comparative experiment of four regression models: linear, polynomial, support vector machine (SVR) and random forest. First, the contribution of PolSAR data to AGB estimation was evaluated using two approaches: the sample data from all forest types, and the five individual forest types (rich, medium, poor, restoration and bamboo forest). Second, we examined the improvement of AGB prediction by selecting the important variables and assessing the best models for different forest types. The results showed an improvement in the value of R-squared and RMSE using the five individual forest types compared to the combined forest types. In particular, using a multivariate model, RMSE values were enhanced by 9-18% for the rich forest, and by 80-85% for the remaining forest types in all models. SVM provided the best performance for medium and poor forest (RMSE of 8.27 tons ha -1 and 12.38 tons ha -1 , respectively), random forest for bamboo (RMSE of 23.18 tons ha -1 ), and the polynomial regression for the restoration forest (RMSE *of 10.11 tons ha -1 ). Further research is required to derive a more robust AGB estimation model for the rich forest.

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Section: Introductionmentioning

confidence: 99%