Abstract
BackgroundMapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. With the availability of a wide range of remote sensing imagery of vegetation characteristics from space, development of finer resolution and more accurate maps has advanced in recent years. However, the mapping accuracy relies heavily on the quality of input layers, the algorithm chosen, and the size and quality of inventory samples for calibration and validation.ResultsBy using airborne lidar data as the “truth” and focusing on the mean canopy height (MCH) as a key structural parameter, we test two commonly-used non-parametric techniques of maximum entropy (ME) and random forest (RF) for developing maps over a study site in Central Gabon. Results of mapping show that both approaches have improved accuracy with more input layers in mapping canopy height at 100 m (1-ha) pixels. The bias-corrected spatial models further improve estimates for small and large trees across the tails of height distributions with a trade-off in increasing overall mean squared error that can be readily compensated by increasing the sample size.ConclusionsA significant improvement in tropical forest mapping can be achieved by weighting the number of inventory samples against the choice of image layers and the non-parametric algorithms. Without future satellite observations with better sensitivity to forest biomass, the maps based on existing data will remain slightly biased towards the mean of the distribution and under and over estimating the upper and lower tails of the distribution.
Highlights
Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities
By conducting Monte Carlo cross validations (CV), the root-mean-square error (RMSE) of random forest (RF) method decreases from 6.02 ± 0.10 m to 5.06 ± 0.07 m when using all inputs from three satellite sensors (Table 1)
The overall mean signed deviation (MSD) is small when calculated from all test points, the MSD for large trees (MSD2) reveals a significant underestimation that is consistently lower than the measured mean canopy height (MCH) by
Summary
Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. Unlike conventional passive optical sensors, these active sensors can capture the vertical vegetation structure by either measuring the range of laser light reflected from vegetation elements and the ground [16, 17], or measuring the radar backscatter and phase at a given wavelength and polarization [15, 18,19,20]. Among these new techniques, high resolution (small footprint)
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