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Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA's mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia's tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests. [ABSTRACT FROM AUTHOR]

Monitoring ecological restoration has been historically dependent on traditional inventory methods based on detailed information obtained from field plots. New paradigms are now needed to successfully achieve restoration as a large‐scale, long‐lasting transformative process. Fortunately, advances in technology now allow for unprecedented shifts in the way restoration has been planned, implemented, and monitored. Here, we describe our vision on how the use of new technologies by a new generation of restoration ecologists may revolutionize restoration monitoring in the coming years. The success of the many ambitious restoration programs planned for the coming decade will rely on effective monitoring, which is an essential component of adaptive management and accountability. The development of new remote sensing approaches and their application to a restoration context open new avenues for expanding our capacity to assess restoration performance over unprecedented spatial and temporal scales. A new generation of scientists, which have a background in remote sensing but are getting more and more involved with restoration, will certainly play a key role for making large‐scale restoration monitoring a viable human endeavor in the coming decade—the United Nations' decade on ecosystem restoration. [ABSTRACT FROM AUTHOR]

Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of – 0.17%, – 0.12% and – 0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume. [ABSTRACT FROM AUTHOR]

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry efficiency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random effects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coefficient (GC). We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random effect variable, and canopy height, crown volume, and crown projected area as fixed effects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus—and similar forest plantations for carbon dynamics and forest product planning. [ABSTRACT FROM AUTHOR]

Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 square field plots sized 50 × 50 m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross-validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (±sd—standard deviation) predicted AGB stock at the landscape level was 229.10 (±232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd ± 177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R2 with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain. [ABSTRACT FROM AUTHOR]

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments. [ABSTRACT FROM AUTHOR]

Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying "problematic" segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe. [ABSTRACT FROM AUTHOR]