Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pleiades Data

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 Pleiades, 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 R-2 = 0.71 for stem volume, R-2 = 0.87 for height, and R-2 = 0.60 for CSA. In addition, our results showed a high linear relation between spectral data from the UAV and the satellite (R-2 = 0.94). Overall, the accuracy of the results between UAV and Pleiades was reasonable and showed that the used methods are feasible for extrapolation of extracted data from UAV to larger areas.