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Abstract:The development of reliable methods for the estimation of crown architecture parameters is a key issue for the quantitative evaluation of tree crop adaptation to environment conditions and/or growing system. In the present work, we developed and tested the performance of a method based on low-cost unmanned aerial vehicle (UAV) imagery for the estimation of olive crown parameters (tree height and crown diameter) in the framework of olive tree breeding programs, both on discontinuous and continuous canopy cropping systems. The workflow involved the image acquisition with consumer-grade cameras on board a UAV and orthomosaic and digital surface model generation using structure-from-motion image reconstruction (without ground point information). Finally, geographical information system analyses and object-based classification were used for the calculation of tree parameters. Results showed a high agreement between remote sensing estimation and field measurements of crown parameters. This was observed both at the individual tree/hedgerow level (relative RMSE from 6% to 20%, depending on the particular case) and also when average values for different genotypes were considered for OPEN ACCESS Remote Sens. 2015, 7 4214 phenotyping purposes (relative RMSE from 3% to 16%), pointing out the interest and applicability of these data and techniques in the selection scheme of breeding programs.

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.

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Aim of study: The study aims to analyse the potential use of lowcost unmanned aerial vehicle (UAV) imagery for the estimation of Pinus pinea L. variables at the individual tree level (position, tree height and crown diameter).Area of study: This study was conducted under the PINEA project focused on 16 ha of umbrella pine afforestation (Portugal) subjected to different treatments.Material and methods: The workflow involved: a) image acquisition with consumer-grade cameras on board an UAV; b) orthomosaic and digital surface model (DSM) generation using structure-from-motion (SfM) image reconstruction; and c) automatic individual tree segmentation by using a mixed pixel-and region-based algorithm.Main results: The results of individual tree segmentation (position, height and crown diameter) were validated using field measurements from 3 inventory plots in the study area. All the trees of the plots were correctly detected. The RMSE values for the predicted heights and crown widths were 0.45 m and 0.63 m, respectively.Research highlights: The results demonstrate that tree variables can be automatically extracted from high resolution imagery. We highlight the use of UAV as a fast, reliable and cost-effective technique for small scale applications.

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Solar radiation is affected by absorption and emission phenomena during its downward trajectory from the Sun to the Earth's surface and during the upward trajectory detected by satellite sensors. This leads to distortion of the ground radiometric properties (reflectance) recorded by satellite images, used in this study to estimate aboveground forest biomass (AGB). Atmospherically-corrected remote sensing data can be used to estimate AGB on a global scale and with moderate effort. The objective of this study was to evaluate four atmospheric correction algorithms (for surface reflectance), ATCOR2 (Atmospheric Correction for Flat Terrain), COST (Cosine of the Sun Zenith Angle), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) and 6S (Second Simulation of Satellite Signal in the Solar), and one radiometric correction algorithm (for reflectance at the sensor) ToA (Apparent Reflectance at the Top of Atmosphere) to estimate AGB in temperate forest in the northeast of the state of Durango, Mexico. The AGB was estimated from Landsat 5 TM imagery and ancillary information from a digital elevation model (DEM) using the non-parametric multivariate adaptive regression splines (MARS) technique. Field reference data for the model training were collected by systematic sampling of 99 permanent forest growth and soil research sites (SPIFyS) established during the winter of 2011. The following predictor variables were identified in the MARS model: Band 7, Band 5, slope (β), Wetness Index (WI), NDVI and MSAVI2. After cross-validation, 6S was found to be the optimal model for estimating AGB (R 2 = 0.71 and RMSE = 33.5 Mg¨ha´1; 37.61% of the average stand biomass). We conclude that atmospheric and radiometric correction of satellite images can be used along with non-parametric techniques to estimate AGB with acceptable accuracy.