Abstract
With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous.
Highlights
Forest companies commonly utilize lidar-based forest information, estimated primarily using area-based methods [1,2,3]
The aim of this study is to compare how different methods for colouring image point cloud data affects the accuracy of tree species-specific proportions and stem volume estimations using standard image products
The point cloud, digital surface model (DSM) and lidar height values were transformed from height above mean sea level to height above ground level by subtracting the height of the ground provided by the national digital terrain model (DTM)
Summary
Forest companies commonly utilize lidar-based forest information, estimated primarily using area-based methods [1,2,3] In boreal forest, these methods deliver stand level estimation accuracies in terms of relative root mean square errors (RMSEs) typically in the range of 2.5–13.6% for basal area-weighted mean tree height, 5.9–15.8% for basal area-weighted mean stem diameter and 8.4–16.6% for mean stem volume [3,4]. This generally outperforms traditional sources for forest management data, such as subjective field inventory. The corresponding figures for stand level accuracies were 28% for pine, 32% for spruce and
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