Abstract
This study compares methods to estimate stem volume, stem number and basal area from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, spruce dominated forest (Lat. 58°N, Long. 13°E). The stem volume was estimated with five different regression models: one model based on height and density metrics from the ALS data derived from the whole field plot, two models based on similar combinations derived from 0.5 m raster cells, and two models based on canopy volumes from the ALS data. The best result was achieved with a model based on height and density metrics derived from 0.5 m raster cells (Root Mean Square Error or RMSE 37.3%) and the worst with a model based on height and density metrics derived from the whole field plot (RMSE 41.9%). The stem number and the basal area were estimated with: (i) area-based regression models using height and density metrics from the ALS data; and (ii) single tree-based information derived from local maxima in a normalized digital surface model (nDSM) mean filtered with different conditions. The estimates from the regression model were more accurate (RMSE 52.7% for stem number and 21.5% for basal area) than those derived from the nDSM (RMSE 63.4%–91.9% and 57.0%–175.5%, respectively). The accuracy of the estimates from the nDSM varied depending on the filter size and the conditions of the applied filter. This suggests that conditional filtering is useful but sensitive to the conditions.
Highlights
During the last decade, airborne laser scanning (ALS) data have been established as a standard data source for high precision topographic data acquisition and have been used for estimation of forest variables [1]
The most accurate estimate was achieved with a log-log regression model including the vegetation ratio and a measure of the maximum height of the ALS returns derived from 0.5 m raster cells
This study has compared estimation of forest variables from regression models based on measures derived from ALS data in small (0.5 m) raster cells and based on variables derived from the 3D point cloud
Summary
Airborne laser scanning (ALS) data have been established as a standard data source for high precision topographic data acquisition and have been used for estimation of forest variables [1]. For forestry applications, the most commonly used method is to derive measures from the ALS data in raster cells approximately the size of a field plot, 100–200 m2, and use the measures as independent variables in regression models to estimate forest variables such as mean tree height and stem volume [2,3,4]. The estimation of the regression model parameters is based on reference data from one study area [6] Another approach for stem volume or biomass estimation is to use a model based on the structure of the forest by calculating the canopy volumes for different height layers and using those measures as independent variables in a linear regression model [7]. Depending on its sampling design (e.g., angle count sampling, fully callipered sample plot area, stand-based), the spatial unit used to extract the ALS-based measures can vary
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