Abstract
In this study, the potential of using very high resolution Pléiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing alternatives. Height metrics were extracted from image matching of the images acquired from different incidence angles. Spectral derivatives were derived by performing principal component analysis of the spectral bands and lastly, second order textural metrics were extracted from a gray-level co-occurrence matrix, computed with an 11 × 11 pixels moving window. The analysis took place at two Swedish test sites, Krycklan and Remningstorp, containing boreal and hemi-boreal forest. The lowest RMSE was estimated with 1.4 m (7.7%) for Lorey’s mean height, 1.7 m (10%) for airborne laser scanning height percentile 90, 5.1 m2·ha−1 (22%) for basal area, 66 m3·ha−1 (27%) for stem volume, and 26 tons·ha−1 (26%) for above-ground biomass, respectively. It was found that the image-matched height metrics were most important in all models, and that the spectral and textural metrics contained similar information. Nevertheless, the best estimations were obtained when all three explanatory sources were used. To conclude, image-matched height metrics should be prioritised over spectral metrics when estimation of forest attributes is concerned.
Highlights
Accurate information about the forest is essential for making well-founded management decisions.The necessary information has traditionally been obtained from field visits and sample-based forest inventories [1]
The textural metrics had a similar information content compared to the spectral derivatives
This study found the spectral derivatives to be of similar importance to textural metrics, when the textural features were computed from very high resolution (VHR) imagery with 1-m resolution, using an 11 × 11 pixels moving window
Summary
Accurate information about the forest is essential for making well-founded management decisions. The necessary information has traditionally been obtained from field visits and sample-based forest inventories [1]. Field measurements are expensive and time-consuming, and the method is inefficient on larger scales. Remote sensing techniques complement and can further increase the value of the field inventoried data, with large detailed coverages at acceptable costs [2,3,4]. The combined use of remote sensing data and field based measures has been evaluated for estimations of common forest attributes, such as Lorey’s mean height, HL (the tree height is weighted with its basal area, BA), stem diameter, stem volume (VOL), and biomass [5,6,7,8]
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