Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands

Abstract

High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R 2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86-87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015-2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool.

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

“…The higher accuracies obtained using the low-cost UAV-RGB sensor revealed the potential of this solution. Ni et al [93] (R 2 = 0.87), Guerra-Hernández et al [94] (R 2 = 0.81), Guerra-Hernández et al [95] (R 2 = 0.96) and Lin et al [96] (R 2 = 0.92) studies presented a strong correlation in the height parameter estimation, using a UAV-RGB sensor. Guerra-Hernández et al [97] have a more recent study that also performed height estimations with an RGB sensor, obtaining a good correlation (r: 0.61-0.69).…”

Use of Unmanned Aerial Vehicles (UAVs) for Updating Farmland Cadastral Data in Areas Subject to Landslides

“…Guerra-Hernández et al [6] tested the applicability of this technology to the stock-taking of forests. In Japan, UAV-based remote sensing was successfully used to monitor real-time wheat growth status, and to map within-field spatial variations of wheat yield for smallholder wheat growers [7].…”

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

“…The 3D surface models in this study were important for the classification of the vegetation height of R. pseudoacacia, grass, and forest pine trees. The height of the black locust trees in the analyzed plantation ranged between 0.2 and 3.0 m. This height classification approach is common for tree analysis [53][54][55]57,58,60]. However, a limitation of current UAS images is that depending on stand density only the top of the tree or stump shoot is detectable [138].…”

“…Today, black locust appears in many European countries, especially in Hungary, Northern Germany, Western Poland, Czech Republic, Southern Slovakia, and Eastern Austria [6]. In Germany, a change in analysis [18] and object-based image analysis (OBIA) [70], primarily including structure from motion (SfM) point clouds [53][54][55]57,58,60]. Nevertheless, OBIA has not yet been used to analyze the spreading of black locust in short rotation coppices.…”