Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches

Abstract

Southeast Asia is the epicentre of world palm oil production. Plantations in Malaysia have increased 150% in area within the last decade, mostly at the expense of tropical forests. Maps of the aboveground carbon density (ACD) of vegetation generated by remote sensing technologies, such as airborne LiDAR, are vital for quantifying the effects of land use change for greenhouse gas emissions, and many papers have developed methods for mapping forests. However, nobody has yet mapped oil palm ACD from LiDAR. The development of carbon prediction models would open doors to remote monitoring of plantations as part of efforts to make the industry more environmentally sustainable. This paper compares the performance of tree-centric and area-based approaches to mapping ACD in oil palm plantations. We find that an area-based approach gave more accurate estimates of carbon density than tree-centric methods and that the most accurate estimation model includes LiDAR measurements of top-of-canopy height and canopy cover. We show that tree crown segmentation is sensitive to crown density, resulting in less accurate tree density and ACD predictions, but argue that tree-centric approach can nevertheless be useful for monitoring purposes, providing a method to detect, extract and count oil palm trees automatically from images.

A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment

“…The vegetation varies greatly both among and within sites in terms of species and succession leading to difficulties fitting a model that can account for variation present. The most successful VWF applications are conducted on homogenous plots or plots with very few dominant species (Alonzo et al 2018, Popescu andWynne 2004), but consistent with this study, troubles are documented when trying to utilize this method in diverse plots (Coomes et al 2017) and in areas with very dense vegetation (Nunes et al 2017).…”

“…This algorithm also leverages height to variably shift search extents for crown edges dependent upon local maxima. Known as variable window filtering (VWF), this process adjusts expectations for crown extent based upon heights of detected crowns to mitigate problems of under-segmentation for small trees and shrubs and over-segmentation for taller trees that possibly exhibit multiple maxima (Nunes et al 2017). To fit a model to be used for VWF, height and crown data were used from both grass-and tree-dominated sites.…”

UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

“…To date, LiDAR-derived metrics have been used to predict attributes such as AGB in forests and crops [7,14,16,[31][32][33][34][35]. Given the lack of fast and non-destructive alternatives, the capacity to estimate AGB using UAV-derived LiDAR metrics is a critical outcome for dense bioenergy crops such as A. donax.…”

Modeling Potential C, N, H Content in Aboveground Biomass with Spectral Data from Sentinel 2a

“…Indirect estimation of C and forest variability is done with different methods and technologies [36] from different variables, either height [37,38] or biomass, which was the methodology used in our investigation.…”