Abstract
In this study we compared the accuracy of low-pulse airborne laser scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted of seven dual-polarized (HH/HV or VH/VV) Stripmap mode images from all seasons of the year. We were especially interested in distinguishing between the tree species. The dependent variables estimated included mean volume, basal area, mean height, mean diameter and tree species-specific mean volumes. Selection of best possible feature set was based on a genetic algorithm (GA). The nonparametric k-nearest neighbour (k-NN) algorithm was applied to the estimation. The research material consisted of 124 circular plots measured at tree level and located in the vicinity of Espoo, Finland. There are large variations in the elevation and forest structure in the study area, making it demanding for image interpretation. The best feature set contained 12 features, nine of them originating from the ALS data and three from the TerraSAR-X data. The relative RMSEs for the best performing feature set were 34.7% (mean volume), 28.1% (basal area), 14.3% (mean height), 21.4% (mean diameter), 99.9% (mean volume of Scots pine), 61.6% (mean volume of Norway spruce) and 91.6% (mean volume of deciduous tree species). The combined feature set outperformed an ALS-based feature set marginally; in fact, the latter was better in the case of species-specific volumes. Features from TerraSAR-X alone performed poorly. However, due to favorable temporal resolution, satellite-borne radar imaging is a promising data source for updating large-area forest inventories based on low-pulse ALS.
Highlights
The biggest advances in forest inventory technology in recent years have been in applications based on airborne laser scanning (ALS)
The objective of the study was to compare the accuracy of low-pulse ALS, high-resolution noninterferometric TerraSAR-X radar data and their combined feature set in the estimation of forest variables at the plot level
The results show that the ALS-based features performed far better than the TerraSAR-X -based features
Summary
The biggest advances in forest inventory technology in recent years have been in applications based on airborne laser scanning (ALS). The two main approaches in deriving forest information from small-footprint ALS data have been those based on laser canopy height distribution (area-based method, [1]) and individual tree detection [2]. ALS is as accurate as traditional ocular field measurements in estimating the stand mean volume (V) at plot level with area-based inventory methods (e.g., [3,4]) or via single-tree characteristics (e.g., [5,6,7]). Tree-level estimation is computationally heavier; in large-area inventories the area-based approach can, at least currently, be considered more feasible. Other remotely sensed data will still be needed, especially when updated information is required e.g.,several times per year. Of special interest are inexpensive images with favourable temporal resolution that can be utilized in multiphase sampling and change detection in addition to the ALS measurements
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