Abstract
Modern forestry poses new challenges that space technologies can solve thanks to the advent of unmanned aerial vehicles (UAVs). This study proposes a methodology to extract tree-level characteristics using UAVs in a spatially distributed area of pine trees on a regular basis. Analysis included different vegetation indices estimated with a high-resolution orthomosaic. Statistically reliable results were found through a three-phase workflow consisting of image acquisition, canopy analysis, and validation with field measurements. Of the 117 trees in the field, 112 (95%) were detected by the algorithm, while height, area, and crown diameter were underestimated by 1.78 m, 7.58 m2, and 1.21 m, respectively. Individual tree attributes obtained from the UAV, such as total height (H) and the crown diameter (CD), made it possible to generate good allometric equations to infer the basal diameter (BD) and diameter at breast height (DBH), with R2 of 0.76 and 0.79, respectively. Multispectral indices were useful as tree vigor parameters, although the normalized-difference vegetation index (NDVI) was highlighted as the best proxy to monitor the phytosanitary condition of the orchard. Spatial variation in individual tree productivity suggests the differential management of ramets. The consistency of the results allows for its application in the field, including the complementation of spectral information that can be generated; the increase in accuracy and efficiency poses a path to modern inventories. However, the limitation for its application in forests of more complex structures is identified; therefore, further research is recommended.
Highlights
The world’s forests offer a great diversity of goods and services that have led the scientific community to continuously look for new modern and efficient management schemes [1,2]
Under the hypothesis that unmanned aerial vehicles (UAVs) use significantly improves tree-level estimates regarding direct field measurements, this study proposes a method to estimate tree counts, total height, diameter, and crown area, including basal-diameter (BD) and diameter-at-breast-height (DBH) inferences on the individual-tree level in a pilot area in the north of Mexico
The major goal of this research was to extract tree-level attributes from a UAV
Summary
The world’s forests offer a great diversity of goods and services that have led the scientific community to continuously look for new modern and efficient management schemes [1,2]. The accurate and automated estimation of tree attributes has been a research trend in recent years. According to a recent study [6], such technologies represent a more efficient means of obtaining detailed information of individual trees, such as total height, diameter at breast height, basal area, tree count, crown delimitation, and volume estimation. Gollob, et al [7] documented a methodology for diameter estimation, diameter-at-breast-height (DBH) measurement, and tree detection in forest inventory. Et al [8] conducted an approach for the estimation of forest standing volume, obtaining a 95% confidence interval for the total volume. Puliti, et al [9] measured tree attributes to estimate volume without the use of field data
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