scholarly journals ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 585-588
Author(s):  
K. T Chang ◽  
C. Lin ◽  
Y. C. Lin ◽  
J. K. Liu
Keyword(s):  
Forest Canopy ◽  
Accuracy Assessment ◽  
Tree Height ◽  
Experimental Results ◽  
Estimation Methods ◽  
Surface Model ◽  
Individual Tree ◽  
Free Process ◽  
Crown Shape ◽  
Individual Trees

Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.

scholarly journals ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 585-588
Author(s):  
K. T Chang ◽  
C. Lin ◽  
Y. C. Lin ◽  
J. K. Liu
Keyword(s):  
Forest Canopy ◽  
Accuracy Assessment ◽  
Tree Height ◽  
Experimental Results ◽  
Estimation Methods ◽  
Surface Model ◽  
Individual Tree ◽  
Free Process ◽  
Crown Shape ◽  
Individual Trees

Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.

scholarly journals Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

scholarly journals Improvement and Impacts of Forest Canopy Parameters on Noah-MP Land Surface Model from UAV-Based Photogrammetry

2020 ◽  
Vol 12 (24) ◽  
pp. 4120
Author(s):  
Ming Chang ◽  
Shengjie Zhu ◽  
Jiachen Cao ◽  
Bingyin Chen ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Land Surface ◽  
Forest Canopy ◽  
Land Surface Model ◽  
Tree Height ◽  
Research Area ◽  
Underlying Surface ◽  
Surface Model ◽  
Parameter Distribution ◽  
Research Station ◽  
Ecosystem Research

Taking a typical forest’s underlying surface as our research area, in this study, we employed unmanned aerial vehicle (UAV) photogrammetry to explore more accurate canopy parameters including the tree height and canopy radius, which were used to improve the Noah-MP land surface model, which was conducted in the Dinghushan Forest Ecosystem Research Station (CN-Din). While the canopy radius was fitted as a Burr distribution, the canopy height of the CN-Din forest followed a Weibull distribution. Then, the canopy parameter distribution was obtained, and we improved the look-up table values of the Noah-MP land surface model. It was found that the influence on the simulation of the energy fluxes could not be negligible, and the main influence of these canopy parameters was on the latent heat flux, which could decrease up to −11% in the midday while increasing up to 15% in the nighttime. Additionally, this work indicated that the description of the canopy characteristics for the land surface model should be improved to accurately represent the heterogeneity of the underlying surface.

scholarly journals MAESPA: a model to study interactions between water limitation, environmental drivers and vegetation function at tree and stand levels, with an example application to [CO<sub>2</sub>] × drought interactions

2012 ◽  
Vol 5 (4) ◽  
pp. 919-940 ◽  
Author(s):  
R. A. Duursma ◽  
B. E. Medlyn
Keyword(s):  
Forest Canopy ◽  
Spatial Scales ◽  
Root Water Uptake ◽  
Experimental Results ◽  
Environmental Drivers ◽  
Canopy Interception ◽  
Individual Tree ◽  
Water Limitation ◽  
Leaf Physiology ◽  
Potential Applications

Abstract. Process-based models (PBMs) of vegetation function can be used to interpret and integrate experimental results. Water limitation to plant carbon uptake is a highly uncertain process in the context of environmental change, and many experiments have been carried out that study drought limitations to vegetation function at spatial scales from seedlings to entire canopies. What is lacking in the synthesis of these experiments is a quantitative tool incorporating a detailed mechanistic representation of the water balance that can be used to integrate and analyse experimental results at scales of both the whole-plant and the forest canopy. To fill this gap, we developed an individual tree-based model (MAESPA), largely based on combining the well-known MAESTRA and SPA ecosystem models. The model includes a hydraulically-based model of stomatal conductance, root water uptake routines, drainage, infiltration, runoff and canopy interception, as well as detailed radiation interception and leaf physiology routines from the MAESTRA model. The model can be applied both to single plants of arbitrary size and shape, as well as stands of trees. The utility of this model is demonstrated by studying the interaction between elevated [CO2] (eCa) and drought. Based on theory, this interaction is generally expected to be positive, so that plants growing in eCa should be less susceptible to drought. Experimental results, however, are varied. We apply the model to a previously published experiment on droughted cherry, and show that changes in plant parameters due to long-term growth at eCa (acclimation) may strongly affect the outcome of Ca × drought experiments. We discuss potential applications of MAESPA and some of the key uncertainties in process representation.

scholarly journals Canopy Extraction and Height Estimation of Trees in a Shelter Forest Based on Fusion of an Airborne Multispectral Image and Photogrammetric Point Cloud

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xuewen Wang ◽  
Qingzhan Zhao ◽  
Feng Han ◽  
Jianxin Zhang ◽  
Ping Jiang
Keyword(s):  
Point Cloud ◽  
Object Oriented ◽  
Tree Height ◽  
Multispectral Image ◽  
Support Vector ◽  
Surface Model ◽  
Object Oriented Method ◽  
3D Point Cloud ◽  
Individual Tree ◽  
Height Estimation

To reduce data acquisition cost, this study proposed a novel method of individual tree height estimation and canopy extraction based on fusion of an airborne multispectral image and photogrammetric point cloud. A fixed-wing drone was deployed to acquire the true color and multispectral images of a shelter forest. The Structure-from-Motion (SfM) algorithm was used to reconstruct the 3D point cloud of the canopy. The 3D point cloud was filtered to acquire the ground point cloud and then interpolated to a Digital Elevation Model (DEM) using the Radial Basis Function Neural Network (RBFNN). The DEM was subtracted from the Digital Surface Model (DSM) generated from the original point cloud to get the canopy height model (CHM). The CHM was processed for the crown extraction using local maximum filters and watershed segmentation. Then, object-oriented methods were employed in the combination of 12 bands and CHM for image segmentation. To extract the tree crown, the Support Vector Machine (SVM) algorithm was used. The result of the object-oriented method was vectorized and superimposed on the CHM to estimate the tree height. Experimental results demonstrated that it is efficient to employ point cloud and the proposed approach has great potential in the tree height estimation. The proposed object-oriented method based on fusion of a multispectral image and CHM effectively reduced the oversegmentation and undersegmentation, with an increase in the F -score by 0.12–0.17. Our findings provided a reference for the health and change monitoring of shelter forests as well.

scholarly journals Preliminary assessment of ironwood (Eusideroxylon zwageri Teijsm. & Binnend.) stand on the KPPN Bulian of the District VIII of PT. Wirakarya Sakti, Jambi as seed source candidate

2021 ◽  
Vol 914 (1) ◽  
pp. 012002
Author(s):  
Prastyono ◽  
L Haryjanto ◽  
A I Putri ◽  
T Herawan ◽  
M A Fauzi ◽  
...  
Keyword(s):  
Forest Floor ◽  
Tree Height ◽  
Seed Source ◽  
Preliminary Assessment ◽  
Individual Tree ◽  
Seed Sources ◽  
Timber Tree ◽  
Individual Trees ◽  
Seed Stand ◽  
Eusideroxylon Zwageri

Abstract Ironwood (Eusideroxylon zwageri) is one of Indonesia’s most economically valuable timber tree species and was listed as Vulnerable in 1998 by the IUCN. To support conservation activities and establish E. zwageri’s plantation, good quality planting stocks should be collected from specific seed sources. Currently, there is only one ironwood seed source in Sumatra that has been registered. This study aimed to assess the potential for an ironwood stand on the KPPN Bulian of the District VIII of PT. Wirakarya Sakti is to be proposed as a seed source. The assessment was conducted on July 2020 by a 100% inventory of ironwood trees in the area of 43 ha. Every individual tree and copy of ironwood was measured for its stem diameter and tree height and observed for its health, flowers, fruits, and seedlings in the ground. In total, 1,029 individual trees, copies and seedlings were recorded. Among them, 116 trees were found to have young fruits and seedlings emergence in the forest floor. Generally, the ironwood stand is sound and meets the criteria to be registered as an identified seed stand of ironwood.

scholarly journals INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 337-343 ◽  
Author(s):  
A. Moradi ◽  
M. Satari ◽  
M. Momeni
Keyword(s):  
Urban Forest ◽  
Accuracy Assessment ◽  
Airborne Lidar ◽  
Light Detection And Ranging ◽  
Individual Tree ◽  
Point Density ◽  
Peak Points ◽  
3D Information ◽  
Individual Trees ◽  
Very High

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.

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

2021 ◽  
Vol 13 (18) ◽  
pp. 3655
Author(s):  
André Almeida ◽  
Fabio Gonçalves ◽  
Gilson Silva ◽  
Adriano Mendonça ◽  
Maria Gonzaga ◽  
...  
Keyword(s):  
Point Cloud ◽  
Digital Terrain Model ◽  
Tree Height ◽  
Northeastern Brazil ◽  
Maximum Height ◽  
Individual Tree ◽  
Terrain Model ◽  
Aerial Photogrammetry ◽  
Individual Trees

Digital aerial photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAV) have been increasingly used for forest inventory and monitoring. In this study, we evaluated the potential of UAV photogrammetry data to detect individual trees, estimate their heights (ht), and monitor the initial silvicultural quality of 1.5-year-old Eucalyptus sp. stand in northeastern Brazil. DAP estimates were compared with accurate tree locations obtained with real time kinematic (RTK) positioning and direct height measurements obtained in the field. In addition, we assessed the quality of a DAP-UAV digital terrain model (DTM) derived using an alternative ground classification approach and investigated its performance in the retrieval of individual tree attributes. The DTM built for the stand presented an RMSE of 0.099 m relative to the RTK measurements, showing no bias. The normalized 3D point cloud enabled the identification of over 95% of the stand trees and the estimation of their heights with an RMSE of 0.36 m (11%). However, ht was systematically underestimated, with a bias of 0.22 m (6.7%). A linear regression model, was fitted to estimate tree height from a maximum height metric derived from the point cloud reduced the RMSE by 20%. An assessment of uniformity indices calculated from both field and DAP heights showed no statistical difference. The results suggest that products derived from DAP-UAV may be used to generate accurate DTMs in young Eucalyptus sp. stands, detect individual trees, estimate ht, and determine stand uniformity with the same level of accuracy obtained in traditional forest inventories.

scholarly journals Improvement and Impacts of Forest Canopy Parameters on Noah-MP Land Surface Model from UAV-Based Photogrammetry

2020 ◽  
Author(s):  
Ming Chang ◽  
Shengjie Zhu ◽  
Jiachen Cao ◽  
Bingyin Chen ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Land Surface ◽  
Forest Canopy ◽  
Land Surface Model ◽  
Tree Height ◽  
Research Area ◽  
Underlying Surface ◽  
Surface Model ◽  
Research Station ◽  
Ecosystem Research ◽  
Aerial Vehicle

Taking a typical forest underlying surface as the research area, this study employed the unmanned aerial vehicle (UAV) photogrammetry to explore more accurate canopy parameters including tree height and canopy radius, which were used to improve the Noah-MP land surface model conducted in Dinghushan Forest Ecosystem Research Station (CN-Din). While the canopy radius was fitted as a Burr distribution, the canopy height of CN-Din forest followed a Weibull distribution. The replacement of the parameters by these observed UAV would result in the Noah-MP model. It was found that the influence on the simulation of the energy fluxes could not be negligible, and the main influence of these canopy parameters was on the latent heat flux which could decrease up to -11% in the midday while increase up to 15% in the nighttime. Additionally, this work indicated that the description of the canopy characteristics for the land surface model should be improved to accurately deliver the heterogeneity for the underlying surface.

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