Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

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.