On promoting the use of lidar systems in forest ecosystem research

Béland, Martin; Parker, Geoffrey G.; Sparrow, Ben; Harding, David J.; Chasmer, L.; Phinn, Stuart; Antonarakis, Alexander S.; Strahler, Alan H.

doi:10.1016/j.foreco.2019.117484

Cited by 135 publications

(120 citation statements)

References 61 publications

(52 reference statements)

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“…As an active remote sensing technology with rapid development, UAV-LiDAR has distinct advantages compared with traditional LiDAR platforms (satellite, aerial, and terrestrial), such as easy operability, lightweight, financial viability, and flexibility in acquisition and sensor integration [48,49]. Most importantly, UAV laser scanning systems provide higher point density accompanying with lower speeds and altitudes than airborne laser scanning [50]. Detailed characteristics can be extracted from the denser point cloud [51,52].…”

Section: The Feasibility Of Uav-lidar Data For Afw Predictionmentioning

confidence: 99%

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Tang

et al. 2020

Remote Sensing

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Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R2 = 0.96, RMSE = 1.27 kg m−2) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R2 = 0.97, RMSE = 1.33 kg m−2). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production.

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Section: The Feasibility Of Uav-lidar Data For Afw Predictionmentioning

confidence: 99%

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Tang

et al. 2020

Remote Sensing

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“…Using the transmission angle of the laser-beam origin and the distance, three-dimensional (3-D) coordinates of points on the object(s) that reflected the laser can be calculated, relative to the scanner location. Increasingly, laser scanning is being performed from multiple complementary aerial and terrestrial platforms to collect structural data from forests at variable resolutions and perspectives (Beland et al 2019; Lindberg and Holmgren 2017) ( Figure 2).…”

Section: Remote Sensing Of Forests and The Role Of Laser Scanningmentioning

confidence: 99%

Assessing wood properties in standing timber with laser scanning

Pyörälä¹

2020

Diss. For.

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Managed forests play crucial roles in ongoing climatic and environmental changes. Among other things, wood is capable of sinking and storing carbon in both standing timber and wood products. To promote these positive effects, more precise planning is required that will ensure sustainable forest management and maximal deposition of harvested wood for long-term applications. Information on wood properties plays a key role; i.e. the wood properties can impact the carbon stocks in forests and the suitability of wood for structural timber. With respect to the theoretical background of wood formation, stem, crown, and branching constitute potential inputs (i.e. wood quality indicators) to allometric wood property, tree biomass, and wood quality models. Due to the complex nature of wood formation, measurements of wood quality indicators that could predict wood properties along the relevant directions of variation have previously been elusive in forest inventories. However, developments in laser scanning from aerial and terrestrial platforms support more complex mapping and modeling regimes based on dense three-dimensional point clouds. The aim here was to determine how wood properties could be estimated in remotesensing-aided forest inventories. For this purpose, methods for characterizing select wood quality indicators in standing timber, using airborne and terrestrial laser scanning (ALS and TLS, respectively) were developed and evaluated in managed boreal Scots pine (Pinus sylvestris L.) forests. Firstly, the accuracies of wood quality indicators resolved from TLS point clouds were assessed. Secondly, the results were compared with x-ray tomographic references from sawmills. Thirdly, the accuracies of tree-specific crown features delineated from the ALS data in predictive modeling of the wood quality indicators were evaluated. The results showed that the quality and density of point clouds significantly impacted the accuracies of the extracted wood quality indicators. In the assessment of wood properties, TLS should be considered as a tool for retrieving as dense stem and branching data as possible from carefully selected sample trees. Accurately retrieved morphological data could be applied to allometric wood property models. The models should use tree traits predictable with aerial remote sensing (e.g. tree height, crown dimensions) to enable extrapolations. As an outlook, terrestrial and aerial remote sensing can play an important role in filling in the knowledge gaps regarding the behavior of wood properties over different spatial and temporal extents. Further interdisciplinary cooperation will be needed to fully facilitate the use of remote sensing and spatially transferable wood property models that could become useful in tackling the challenges associated with changing climate, silviculture, and demand for wood.

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“…Remote sensing technologies have been evolving and can be used as tools to aid foresters in obtaining necessary parameters to improve management decision-making. In the last decades, one of the main studied technologies is the laser scanner, namely LiDAR (Light Detection and Ranging) [4,5]. LiDAR sensors work by tracking the emission and return of laser pulses (light emitted energy), determining the sensor-target distance based on the return time lag.…”

Section: Introductionmentioning

confidence: 99%

“…This technique demonstrates the potential to improve estimates of forest parameters (e.g., diameter at 1.3 m above ground (dbh), volume, and biomass) since it extends spatial analysis beyond the x-and y-axes, generating three-dimensional information [6]. Since the '80s, LiDAR has been used to estimate forest parameters [5,[7][8][9][10][11][12]. Aerial platforms are often used (named Airborne Laser Scanner-ALS) for the benefit of covering larger areas to provide spatialized estimates (or "wall-to-wall" estimates) [2].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, this approach has limited ability in terms of offering a direct measurement of stem density, height, and dbh distribution. Moreover, the estimates are spatialized in tiles, often the same size as the sample plots, where accuracy could be affected by several factors such as missing variances on a smaller scale [5]. Whereas in the ITD approach, the automatic detection of the treetops seems not to be trivial and several studies have developed methods for performing this task [3,[27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches

et al. 2020

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Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( r y y ^ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( r y y ^ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance.

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On promoting the use of lidar systems in forest ecosystem research

Abstract: Alan (2019) On promoting the use of lidar systems in forest ecosystem research. Forest Ecology and Management, 450 (117484). pp. 1-9.

Cited by 135 publications

References 61 publications

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Assessing wood properties in standing timber with laser scanning

Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches

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