Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Abstract

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

“…Thereafter, it is of pivotal importance to compare different remote sensed tree biomasses through statistic indexes that facilitate comparison between datasets or models with different scales, as rRMSE and adjR 2 . Guerra-Hernandez et al [57] in P. pinea plantation gained good results in the estimation of Wa SfM in comparison to measured Wa (0.85 < adjR 2 < 0.87 and 11.44% < rRMSE < 12.59% in two different years and model approaches) while Guerra-Hernandez et al [56], in a Eucalyptus plantation, got slightly worse performances (R 2 = 0.43 and rRMSE = 20.31%). However, the current work presents lower correlation values (except in one case) and lower rRMSE (Table 6) with respect to the literature references, mainly due to orchard characteristics (uneven-aged and irregularly spaced) and fine pruning evaluation purposes with respect to growth monitoring.…”

“…Nevertheless, there have already been recent studies that specifically derived individual biomass and V SfM at tree level with ITC segmentation. Among these, important references in the Mediterranean environment were represented mainly by Guerra-Hernandez et al [57] and Guerra-Hernandez et al [56], who used a fixed-wing UAV equipped with an RGB camera to evaluate (i) Wa SfM in Pinus pinea regular forest plantation (10 x m regular spaced, open canopy, fairly flat terrain, no understory) and (ii) V SfM in Eucalyptus regular forest plantation (3.7 × 2.5 m regularly spaced, steep terrain). Comparing our results with the aforementioned studies, it worth noting that the RMSE could only be compared with Guerra-Hernandez et al [57], who used the same unit (kg) and reported a value of 87.46 and 117.80 kg for 2015 and 2017, respectively.…”

Individual Tree Detection and Qualitative Inventory of a Eucalyptus sp. Stand Using UAV Photogrammetry Data

“…The accuracy of ht estimated in our study was similar to that found by Hentz et al [37] while estimating tree heights of a young Eucalyptus plantation using DAP-UAV data (RMSE = 0.44 m). In a 7-year-old Eucalyptus plantation, Guerra-Hernández et al [25] also observed an underestimation of individual ht values, with an RMSE and bias of 2.84 m and 2.67 m, respectively. Our results were also similar to those observed by Krause et al [44] (RMSE = 0.49 m or 2.78% and bias = 0.365 m or 2.21%), who estimated individual ht using DAP-UAV and a DTM obtained by RTK in a wild pine stand in northeastern Berlin, Germany.…”

“…Besides the lower costs, DAP-UAV products present similarities to LiDAR data and are generated at a higher point density in a shorter time interval [22,23]. Some studies indicate that the performance of UAV photogrammetry data is adequate for estimating the characteristics of Eucalyptus stands [24,25], thus making them a feasible alternative to forest monitoring [26,27].…”

Metrics of Growth Habit Derived from the 3D Tree Point Cloud Used for Species Determination—A New Approach in Botanical Taxonomy Tested on Dragon Tree Group Example

“…Stationary laser scanning to produce high-precision 3D point clouds is particularly useful for tree stem modeling [45]. In forestry, air/space-borne laser scanning and digital aerial photogrammetry are often used for stock volume estimation [25,46]. Furthermore, different LiDAR scanning techniques are applied to develop the methods of tree leaf area estimation [20,47].…”