Forest structure modeling with combined airborne hyperspectral and LiDAR data

The interest in the joint use of remote sensing data from multiple sensors has been remarkably increased for environmental applications. This is because a combined use is supposed to improve the results of e.g. forest modeling tasks compared to single-data use. To explore the ability of combined airborne 2D and 3D information to describe the forest structure in local level, we employed various height/intensity metrics from Light Detection and Ranging (LiDAR) data and original reflectance, indices, and linear transformations of airborne hyperspectral HyMap data to build spatial models of stem density, above ground total biomass, and biomass of coniferous species in a temperate forest site in Germany. The study area was stratified into coniferous, deciduous and mixed strata using the plot information from forest inventory data. Combinations of data sources were tested, and an evolutionary Genetic Algorithm (GA) was used to tailor the numerous predictor variables to final parsimonious sets. Most Similar Neighbor (MSN) approach based on variance-weighted canonical correlations were used to make simultaneous single-Nearest Neighbor (NN) models of the attributes, where NN was searched either within the whole geographical domain or within the restricted forest strata. Results were evaluated by leave-one-out cross validations on 1000 bootstrap resample data. They showed that the LiDAR height metrics (descriptive statistics and percentiles) provided the most effective information amongst the entire data source combinations, while the HyMap metrics contributed only slightly to describe the variation beyond those explained by ALS data. Furthermore, restricted NN search improved the performance and returned approximately unbiased models of all the responses. The GA-screened HyMap predictors corresponded well to the atmospheric windows in visual and NIR domains, as well as to the mean reflectance curve of Scots Pine across the study area. It is concluded that GA-screened models featuring 9–12 predictors containing LiDAR height metrics and few HyMap original channels can be suggested for timely-efficient, unbiased modeling of area-based forest structural attributes.