Abstract
Tropical forests are a key component of the global carbon cycle, and mapping their carbon density is essential for understanding human influences on climate and for ecosystem-service-based payments for forest protection. Discrete-return airborne laser scanning (ALS) is increasingly recognised as a high-quality technology for mapping tropical forest carbon, because it generates 3D point clouds of forest structure from which aboveground carbon density (ACD) can be estimated. Area-based models are state of the art when it comes to estimating ACD from ALS data, but discard tree-level information contained within the ALS point cloud. This paper compares area-based and tree-centric models for estimating ACD in lowland old-growth forests in Sabah, Malaysia. These forests are challenging to map because of their immense height. We compare the performance of (a) an area-based model developed by Asner and Mascaro (2014), and used primarily in the neotropics hitherto, with (b) a tree-centric approach that uses a new algorithm (itcSegment) to locate trees within the ALS canopy height model, measures their heights and crown widths, and calculates biomass from these dimensions. We find that Asner and Mascaro's model needed regional calibration, reflecting the distinctive structure of Southeast Asian forests. We also discover that forest basal area is closely related to canopy gap fraction measured by ALS, and use this finding to refine Asner and Mascaro's model. Finally, we show that our tree-centric approach is less accurate at estimating ACD than the best-performing area-based model (RMSE 18% vs 13%). Tree-centric modelling is appealing because it is based on summing the biomass of individual trees, but until algorithms can detect understory trees reliably and estimate biomass from crown dimensions precisely, areas-based modelling will remain the method of choice.
Highlights
Forests are an important component of the global carbon cycle, and their future management is key to international efforts to abate climate change
Their generalised model parameterised mostly from Neotropical datasets (Eq (3)) gave biased results when applied to the Sepilok data, even when local basal area (BA)-top-of-canopy height (TCH) and wood density (WD)-TCH sub-models were used, so locally parameterised power-law functions were developed
Basal area was better modelled as a function of gap fraction rather than top-of-canopy height
Summary
Forests are an important component of the global carbon cycle, and their future management is key to international efforts to abate climate change. During the 1990s, about 89,000 km of tropical forests were lost to agriculture each year, and a further 24,000 km were degraded (Nabuurs et al, 2007). Estimates of deforestation rates vary, but somewhere in the region of 230 million hectares of forest were lost per year between 2000 and 2012 (Hansen et al, 2013). D.A. Coomes et al / Remote Sensing of Environment 194 (2017) 77–88 unless 500 million ha of degraded tropical forests are protected, and land unsuitable for agriculture is afforested (Houghton et al, 2015). Forest protection can offset emissions over the 40 years, buying time for humanity to reduce its dependency on fossil fuels (Houghton et al, 2015)
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