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Abstract
We have investigated for forest plantations in Chile the stand-level retrieval of canopy height (CH) and growing stock volume (GSV) using Airborne Laser Scanner (ALS), ALOS PALSAR and Landsat. In a two-stage up-scaling approach, ensemble regression tree models (randomForest) were used to relate a suite of ALS canopy structure indices to stand-level in situ measurements of CH and GSV for 319 stands. The retrieval of CH and GSV with ALS yielded high accuracies with R2s of 0.93 and 0.81, respectively. A second set of randomForest models was developed using multi-temporal ALOS PALSAR intensities and repeat-pass coherences in two polarizations as well as Landsat data as predictor and stand-level ALS based estimates of CH and GSV as response variables. At three test sites, the retrieval of CH and GSV with PALSAR/Landsat reached promising accuracies with R2s in the range of 0.7 to 0.85. We show that the combined use of multi-temporal PALSAR intensity, coherence and Landsat yields higher retrieval accuracies than the retrieval with any of the datasets alone. Potential limitations for the large-area application of the fusion approach included (1) the low sensitivity of ALS first/last return data to forest horizontal structure, affecting the retrieval of GSV in less managed types of forest, and (2) the dense ALS sampling required to achieve high retrieval accuracies at larger scale.
Highlights
We considered the use of Landsat optical data, which is available globally and free of cost, as in several studies it was shown that a retrieval of forest biophysical parameters based on the fusion of Synthetic Aperture Radar (SAR) and optical data yielded higher retrieval accuracies than that based on either SAR or optical data alone [17,32,39,40,41,42]
For the stand-level retrieval of canopy height (CH) and growing stock volume (GSV) by means of the Airborne Laser Scanner (ALS) data, randomForest was applied using the default values for the number of regression trees that are grown (500), the number of randomly selected predictors that are considered at each node as well as the percentage of bootstrap samples (OOB) that are used for each added regression tree to obtain an estimate of the retrieval error (33%)
The RMSEr represented the root mean square error (RMSE) divided by the average GSV and CH in the in situ dataset and the bias was calculated from the difference between the average GSV and CH in the in situ dataset and the ALS predictions, respectively
Summary
A number of investigators have assessed the feasibility of using passive optical imagery to spatially extend point-based estimates of biophysical parameters derived from Lidar to wall-to-wall maps. The synergy of spaceborne large-footprint Lidar (ICESAT GLAS) and medium resolution optical data, primarily from the Moderate Resolution Imaging Spectrometer (MODIS), has been exploited to map canopy height and biomass at regional to global scales [9,10,11,12,13,14]. At a forest site in western Oregon, USA, Hudak et al [16] tested different spatial and aspatial methods for the extrapolation of small-footprint airborne Lidar estimates of canopy height by means of Landsat data
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