Abstract
Structure from Motion (SfM) photogrammetry applied to photographs captured from Unmanned Aerial Vehicle (UAV) platforms is increasingly being utilised for a wide range of applications including structural characterisation of forests. The aim of this study was to undertake a first evaluation of whether SfM from UAVs has potential as a low cost method for forest monitoring within developing countries in the context of Reducing Emissions from Deforestation and forest Degradation (REDD+). The project evaluated SfM horizontal and vertical accuracy for measuring the height of individual trees. Aerial image data were collected for two test sites; Meshaw (Devon, UK) and Dryden (Scotland, UK) using a Quest QPOD fixed wing UAV and DJI Phantom 2 quadcopter UAV, respectively. Comparisons were made between SfM and airborne LiDAR point clouds and surface models at the Meshaw site, while at Dryden, SfM tree heights were compared to ground measured tree heights. Results obtained showed a strong correlation between SfM and LiDAR digital surface models (R2 = 0.89) and canopy height models (R2 = 0.75). However, at Dryden, a poor correlation was observed between SfM tree heights and ground measured heights (R2 = 0.19). The poor results at Dryden were explained by the fact that the forest plot had a closed canopy structure such that SfM failed to generate enough below-canopy ground points. Finally, an evaluation of UAV surveying methods was also undertaken to determine their usefulness and cost-effectiveness for plot-level forest monitoring. The study concluded that although SfM from UAVs performs poorly in closed canopies, it can still provide a low cost solution in those developing countries where forests have sparse canopy cover (<50%) with individual tree crowns and ground surfaces well-captured by SfM photogrammetry. Since more than half of the forest covered areas of the world have canopy cover <50%, we can conclude that SfM has enormous potential for forest mapping in developing countries.
Highlights
A significant decrease in the global deforestation rate has been noted in the last decade, in many developing nations, the deforestation rate still remains very high [1]
When compared to LiDAR, Structure from Motion (SfM) performed well in some areas (Figure 11) while it performed poorly in others (Figure 12). Those areas of poor performance can be attributed to VisualSFM not
Those areas of poor performance can be attributed to VisualSFM not detecting enough feature matches in the images due to poor image coverage
Summary
A significant decrease in the global deforestation rate has been noted in the last decade, in many developing nations, the deforestation rate still remains very high [1]. Reducing greenhouse gas (GHG) emissions from deforestation has been long identified as having the greatest potential for global climate change mitigation [2]. This is the case in developing tropical countries where the largest source of GHG emissions are attributed to land use change from forest loss [3]. In 2005, the United Nations Framework Convention on Climate Change (UNFCCC) initiated a process to investigate how the concept of Reducing Emissions from Deforestation and forest Degradation (REDD) could help combat the challenge of climate change due to GHG emissions in forest-rich developing countries [4]. As initiatives for REDD in tropical countries continue to develop, the need for a forest monitoring system that is both low cost and accurate is imperative, especially for many developing countries where funding for such forest monitoring activities may not always be readily available
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