Abstract
Laser scanning data from unmanned aerial vehicles (UAV-LS) offer new opportunities to estimate forest growing stock volume ( V ) exclusively based on the UAV-LS data. We propose a method to measure tree attributes and using these measurements to estimate V without the use of field data for calibration. The method consists of five steps: i) Using UAV-LS data, tree crowns are automatically identified and segmented wall-to-wall. ii) From all detected tree crowns, a sample is taken where diameter at breast height (DBH) can be recorded reliably as determined by visual assessment in the UAV-LS data. iii) Another sample of crowns is taken where tree species were identifiable from UAV image data. iv) DBH and tree species models are fit using the samples and applied to all detected tree crowns. v) Single tree volumes are predicted with existing allometric models using predicted species and DBH, and height directly obtained from UAV-LS. The method was applied to a Riegl-VUX data set with an average density of 1130 points m−2 and 3 cm orthomosaic acquired over an 8.8 ha managed boreal forest. The volumes of the identified trees were aggregated to estimate plot-, stand-, and forest-level volumes which were validated using 58 independently measured field plots. The root-mean-square deviance ( R M S D % ) decreased when increasing the spatial scale from the plot (32.2%) to stand (27.1%) and forest level (3.5%). The accuracy of the UAV-LS estimates varied given forest structure and was highest in open pine stands and lowest in dense birch or spruce stands. On the forest level, the estimates based on UAV-LS data were well within the 95% confidence interval of the intense field survey estimate, and both estimates had a similar precision. While the results are encouraging for further use of UAV-LS in the context of fully airborne forest inventories, future studies should confirm our findings in a variety of forest types and conditions.
Highlights
Forest growing stock volume (V; m3 ha−1) is an essential measure used to characterise forest structure, value and is often used to estimate forest above-ground biomass (AGB; t ha−1)
The method consists of five steps: i) Using unmanned aerial vehicles (UAV-LS) data, tree crowns are automatically identified and segmented wall-to-wall. ii) From all detected tree crowns, a sample is taken where diameter at breast height (DBH) can be recorded reliably as determined by visual assessment in the UAV-LS data. iii) Another sample of crowns is taken where tree species were identifiable from UAV image data. iv) DBH and tree species models are fit using the samples and applied to all detected tree crowns. v) Single tree volumes are predicted with existing allometric models using predicted species and DBH, and height directly obtained from UAV-LS
The proposed method consists of five steps (Figure 3): (1) single tree detection and segmentation, (2) manual selection of a sample of single tree DBH from the UAV-LS point cloud, (3) visual interpretation of a sample of crowns where tree species was identifiable from UAV data, (4) modelling and predicting DBH and tree species on all the detected tree crowns, and (5) prediction of single tree volume using existing species-specific allometric models using predicted DBH and UAV-LS derived height (i.e., 95th percentile of height values)
Summary
Forest growing stock volume (V; m3 ha−1) is an essential measure used to characterise forest structure, value and is often used to estimate forest above-ground biomass (AGB; t ha−1). Maps and estimates of forest V and its changes are central to understand the carbon and water cycles, for assessing the climate change mitigation potential of forests and to quantify ecosystem services [1]. Several earth observation missions, such as Global Ecosystem Dynamics Investigation (GEDI), NASA-ISRO SAR Mission (NISAR), and BIOMASS, aim at mapping forest AGB and will improve our ability to consistently map and estimate forest resources across the globe [2,3]. All operational applications of remote sensing techniques for forest inventory require field observations for calibration of models and their validation [4]. We will use the term ‘direct estimation’ to describe remote-sensing based estimates that are obtained without the use of in-situ field data for fitting or calibrating empirical models
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