Seeing trees from space: above-ground biomass estimates of intact and degraded montane rainforests from high-resolution optical imagery

Abstract

Accurately quantifying the above-ground carbon stock of tropical rainforest trees is the core component of "Reduction of Emissions from Deforestation and Forest Degradation-plus" (REDD+) projects and is important for evaluating the effects of anthropogenic global change. We used high-resolution optical imagery (IKONOS-2) to identify individual tree crowns in intact and degraded rainforests in the mountains of Northern Borneo, comparing our results with 50 ground-based plots dispersed in intact and degraded forests, within which all stems > 10 cm in diameter were measured and identified to species or genus. We used the dimensions of tree crowns detected in the imagery to estimate above-ground biomasses (AGBs) of individual trees and plots. To this purpose, preprocessed IKONOS imagery was segmented using a watershed algorithm; stem diameter values were then estimated from the cross-sectional crown areas of these trees using regression relationships obtained from ground-based measurements. Finally, we calculated the biomass of each tree (AGBT, in kg), and the AGB of plots by summation (AGBP, in Mg ha -1 ). Remotely sensed estimates of mean AGBT were similar to ground-based estimates in intact and degraded forests, even though small trees could not be detected from space-borne sensors. The intact and degraded forests not only had different AGB but were also dissimilar in biodiversity. A tree-centric approach to carbon mapping based on high-resolution optical imagery, could be a cheap alternative to airborne laser-scanning.

Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest

“…The discretization of the plots according to the negative or positive nature of the β-diversity scores has been reported before, also relating biomass mean values to certain classes [10]. The previous study and other related [11,45] had found a clear link between community taxonomic diversity and carbon stock through patches with human disturbances.…”

“…Tree community composition is a key indicator of forest degradation [10,11], because it is one of the last recovering variables after a disturbance [12,13], and consequently it is an excelent measure of diversity loss. Crown-level assessment of species composition has been increasingly successful for boreal and temperate forests [14][15][16], but its applicability remains challenging for the most complex tropical forests.…”

“…Compositional ordination has been widely used along with remote sensing methods because, contrary to field mapping, it proffers the potential to extrapolate the floristic gradient over space, from the plots to the surrounding area in a continuous way [22,28,32]. In some studies, it is just an intermediated step to ulterior purposes like analyze topics on vegetation ecology [33,34], predict the pertinence probability of each pixel to certain habitat types [27] or classify intact and degraded forest with multispectral imagery [10]. Some other studies have focused specifically on modeling compositional ordination, in order to propose a predictive map of the floristic gradient, which can directly give us an intuitive access to dissimilarity patterns, very difficult to discern from categorical maps [11].…”

How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review

“…In Borneo, above-ground biomass was calculated (Root mean square error (RMSE) = 20.3 tonnes.ha −1 ) by relating stem diameter to crown area. The crown was delineated by segmenting IKONOS images (1 to 4 m-resolution), with a segmentation accuracy of 39% for degraded forests [90]. Good results were also obtained by mixing different types of data, giving both spectral and structural information (optical and aerial LiDAR [91], optical and radar satellite with data saturation beyond 300 Mg.ha −1 [92]).…”

“…Very high-resolution optical data provide better results and opportunities for forest monitoring. They allow the detection of small disturbances (SPOT, WorldView-2, GeoEye-1) [75,153], the boundaries of tree crown on which the biomass depends (SPOT, WorldView-2) [90,154,155], the identification of tree species (WorldView-3) [156,157] or the measurement of biomass in combination with airborne LiDAR measurements (Planet Dove) [158,159]. These data are, however, not free of charge and are still dependent on the very numerous clouds in tropical regions, preventing regular monitoring via satellite data.…”