Abstract

Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland <i>Dipterocarp</i> forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland <i>Dipterocarp</i> forest.

Highlights

  • IntroductionThey provide important resources for medicine, foods, habitat for animal and play an essential role in carbon sequestration (Abd Latif et al, 2011)

  • 1.1 Aboveground Biomass and carbon stocks in relation to Climate ChangeForests are crucial for human life

  • This paper presents a model development from fusion of CHM derived from Light Detection and Ranging (LiDAR) and WorldView-3 imagery using the multiple linear re gression (MLR)-based methodology to estimates the forest biomass from remote sensing technology

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Summary

Introduction

They provide important resources for medicine, foods, habitat for animal and play an essential role in carbon sequestration (Abd Latif et al, 2011). In year 1992, the United Nation Framework Convention on Climate Change (UNFCC) formed a framework to decrease the GHG conveyed at the United Nations Conference on Environment and Development (UNCED) held in Rio de Janeiro (UNFCC, 2015). In 2007, the Bali Climate Change Conference under United Nations had adopted Kyoto protocol by setting rightfully binding obligation for reduction of GHG gases emission (UNFCC, 1998). During this conference, an important agreement was reached for the developing countries to initiate actions in reducing emissions from

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