Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes

Corte, Ana Paula Dalla; Souza, Deivison Venicio; Rex, Franciel Eduardo; Sanquetta, Carlos Roberto; Mohan, Midhun; Silva, Carlos Alberto; Zambrano, Angélica M. Almeyda; Prata, Gabriel Atticciati; Almeida, Danilo Roberti Alves de; Trautenmüller, Jonathan William; Klauberg, Carine; Moraes, Aníbal de; Sanquetta, Mateus Niroh Inoue; Wilkinson, Benjamin; Broadbent, Eben N.

doi:10.1016/j.compag.2020.105815

Cited by 85 publications

(53 citation statements)

References 89 publications

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“…All the remaining surface points were then interpolated by triangulated irregular networks. Many studies have generated CHM from UAVLS data with various spatial resolutions, ranging 0.1 to 1 m [10,16,49,50]. Yin and Wang (2019) have recommended that spatial resolution should be finer than one fourth of the crown diameter to correctly delineate crown boundaries and characterize the crown shapes.…”

Section: Uavls Data Preprocessingmentioning

confidence: 99%

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

et al. 2020

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Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

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Section: Uavls Data Preprocessingmentioning

confidence: 99%

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

et al. 2020

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“…On the other hand, the seed dispersing UAVs come in a variety of sizes, which depend on state of the technology and the investment in the platform, and correspond to the legal regulations for the operating jurisdiction. With recent scientific advances, UAVs are increasingly being employed for forestry purposes, such as biometrics evaluation, edaphic condition estimation, canopy structural modeling, forest health monitoring and habitat assessments [57,[69][70][71][72][73], and these applications are transferable to UAVsSS systems. An important step in the UAVsSS is to identify the site of the operation, for direct seeding using UAVs.…”

Section: Forest Regeneration With Unmanned Aerial Vehicle-supported Seed Sowing (Uavsss)mentioning

confidence: 99%

UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

Mohan

Richardson²,

Gopan³

et al. 2021

Remote Sensing

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Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and flooding, minimizing the risks of zoonotic disease outbreaks, and providing ecosystem services and livelihood to the indigenous people, in addition to sequestering carbon dioxide for mitigating climate change. Consequently, it is important to explore new methods and technologies that are aiming to upscale and fast-track afforestation and reforestation (A/R) endeavors, given that many of the current tree planting strategies are not cost effective over large landscapes, and suffer from constraints associated with time, energy, manpower, and nursery-based seedling production. UAV (unmanned aerial vehicle)-supported seed sowing (UAVsSS) can promote rapid A/R in a safe, cost-effective, fast and environmentally friendly manner, if performed correctly, even in otherwise unsafe and/or inaccessible terrains, supplementing the overall manual planting efforts globally. In this study, we reviewed the recent literature on UAVsSS, to analyze the current status of the technology. Primary UAVsSS applications were found to be in areas of post-wildfire reforestation, mangrove restoration, forest restoration after degradation, weed eradication, and desert greening. Nonetheless, low survival rates of the seeds, future forest diversity, weather limitations, financial constraints, and seed-firing accuracy concerns were determined as major challenges to operationalization. Based on our literature survey and qualitative analysis, twelve recommendations—ranging from the need for publishing germination results to linking UAVsSS operations with carbon offset markets—are provided for the advancement of UAVsSS applications.

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“…Wu et al [88] applied CHM-based models for individual tree detection and delineation first, and achieved an accuracy of 0.77-0.83 for canopy cover estimation based on the tree segmentation. Dalla Corte et al [92] performed individual tree detection with a local maximum filter algorithm in R package "lidR" from rasterized CHM generated from UAV-LS point clouds. Based on the individual tree detection and segmentation results, Dalla Corte et al [92] applied machine learning methods, including Support Vector Regression, Random Forest, Artificial Neural Networks and Extreme Gradient Boosting, for DBH estimation (i.e., with a RMSE of 15%), height (i.e., with a RMSE of 9%) and stand volume (i.e., with a RMSE of 29%) at a single tree scale.…”

Section: Unmanned Aerial Vehicle Laser Scanning (Uav-ls)mentioning

confidence: 99%

“…Dalla Corte et al [92] performed individual tree detection with a local maximum filter algorithm in R package "lidR" from rasterized CHM generated from UAV-LS point clouds. Based on the individual tree detection and segmentation results, Dalla Corte et al [92] applied machine learning methods, including Support Vector Regression, Random Forest, Artificial Neural Networks and Extreme Gradient Boosting, for DBH estimation (i.e., with a RMSE of 15%), height (i.e., with a RMSE of 9%) and stand volume (i.e., with a RMSE of 29%) at a single tree scale. Dalla Corte et al [93] estimated DBH and tree height based on individual tree detection and segmentation outputs using voxel-based methods, resulting in an RMSE of 11.3% for DBH and 7.9% for tree height.…”

Section: Unmanned Aerial Vehicle Laser Scanning (Uav-ls)mentioning

confidence: 99%

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

Wang

et al. 2021

Forests

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Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

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Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes

Cited by 85 publications

References 89 publications

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

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