Abstract
LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km.
Highlights
Global warming and climate change attract significant attention in the quantification of the standing above ground biomass (AGB) over the last few decades, to understand its effects on the global carbon cycle and to mitigate the effects of the global warming via the conservation of carbon sinks
The first calibration dataset used in the Random Forest regression contains the canopy height estimates obtained from Geoscience Laser Altimeter System (GLAS) waveforms using the PCA and RF based canopy height estimation model [24]
The best predictors according to their importance are respectively: the roughness, the mean value of the enhanced vegetation index (EVI) time series data, the geology, the mean value of the rainfall, and the slope
Summary
Global warming and climate change attract significant attention in the quantification of the standing above ground biomass (AGB) over the last few decades, to understand its effects on the global carbon cycle and to mitigate the effects of the global warming via the conservation of carbon sinks. Within such constraints, most research studies focus on allometric relations for linking the characteristics of a forest (tree height, diameter at breast height, and wood density) to its biomass (e.g., [1,2,3,4]), either at the tree level, or the plot level (plot aggregate allometries). One of the important variables in the allometric relations that can be estimated from remote sensing techniques is tree height. Several allometries relied on only canopy height for biomass estimation (e.g., [1,3]). Studies have shown that the use of canopy height increases the precision of biomass estimation (e.g., [2,5])
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