Abstract
The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. With the availability of very high-resolution satellites, such as Pléiades, it is now possible to estimate tree parameters at the individual level with high fidelity. Despite innovative advantages on high-precision satellites, data acquisition is not yet available to the public at a reasonable cost. Unmanned aerial vehicles (UAVs) have the practical advantage of data acquisition at a higher spatial resolution than that of satellites. This study is divided into two main parts: (1) we describe the estimation of basic tree attributes, such as tree height, crown diameter, diameter at breast height (DBH), and stem volume derived from UAV data based on structure from motion (SfM) algorithms; and (2) we consider the extrapolation of the UAV data to a larger area, using correlation between satellite and UAV observations as an economically viable approach. Results have shown that UAVs can be used to predict tree characteristics with high accuracy (i.e., crown projection, stem volume, cross-sectional area (CSA), and height). We observed a significant relation between extracted data from UAV and ground data with R2 = 0.71 for stem volume, R2 = 0.87 for height, and R2 = 0.60 for CSA. In addition, our results showed a high linear relation between spectral data from the UAV and the satellite (R2 = 0.94). Overall, the accuracy of the results between UAV and Pléiades was reasonable and showed that the used methods are feasible for extrapolation of extracted data from UAV to larger areas.
Highlights
Technological advances in unmanned aerial vehicles (UAVs) have made it feasible to obtain high-resolution imagery and three-dimensional (3D) data for assessing tree attributes and forest monitoring
We mainly focused on the potential of the UAV platform deployed with an RGB camera to act as an accurate, alternative field measurement technique
Our results showed that there is a significant correlation between the cross-sectional area (CSA) and stem volume and spectral/textural values derived from the UAV
Summary
Technological advances in unmanned aerial vehicles (UAVs) have made it feasible to obtain high-resolution imagery and three-dimensional (3D) data for assessing tree attributes and forest monitoring. Methods of data acquisition with remotely-sensed aerial or satellite data at high spatial resolution have partially replaced conventional methods of field measurement for forest inventory purposes [1,2,3,4]. Modern techniques of remote sensing can provide accurate estimations of tree height and crown area characteristics at the individual level using a series of algorithms [5]. The individual tree identification (IDS) algorithm allows for the estimation of crown diameter [6], and the smoothing of the canopy height model (CHM) using local maxima techniques [7] can provide estimates of individual tree heights
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