Extraction of Individual Tree Height Using a Combination of Large-Scale Aerial Photo and LiDAR

2011 ◽  
Vol 268-270 ◽  
pp. 1157-1162
Author(s):  
Ping Wang ◽  
Wen Yi Fan ◽  
Ming Ze Li ◽  
Fang Liu ◽  
Qiong Zhang
Keyword(s):  
Digital Image ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Tree Height ◽  
Surface Model ◽  
Lidar Data ◽  
Individual Tree ◽  
Member Function ◽  
Image Objects ◽  
Object Oriented Approach

With the LiangShui forestry centre as study area, high-density LiDAR data and to synchronously processed high-resolution digital image are taken as data source to extract Individual Tree Height. The LiDAR data of the study area is filtered and classified, using TIN Filter to extract the ground echo points and trees echo points. Then these ground echo points generate Digital Elevation Model (DEM), and these trees echo points generate Digital Surface Model (DSM). Then the DEM and DSM are Taken as a subtraction to obtain Canopy Height Model (CHM), then the object-oriented approach is used to segment air digital image. Through multi-scale and canopy-model which create image objects and class division level, with the nearest neighbor distance and member function, the image objects are classified, and re-segmentation is based on classification results. And the edge is optimized to accurately identify individual tree. The canopy polygon obtained after image segmentation and CHM were superimposed to calculate polygon maximum elevation difference from LiDAR data as a tree height. Associated with the measured height analysis, the accuracy is 92.04%.

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 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 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 Estimating Time Since the Last Stand-Replacing Disturbance (TSD) from Spaceborne Simulated GEDI Data: A Feasibility Study

2020 ◽  
Vol 12 (21) ◽  
pp. 3506
Author(s):  
Nuria Sanchez-Lopez ◽  
Luigi Boschetti ◽  
Andrew T. Hudak ◽  
Steven Hancock ◽  
Laura I. Duncanson
Keyword(s):  
Data Fusion ◽  
Large Scale ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Training Dataset ◽  
Lidar Data ◽  
Landsat 8 ◽  
Image Objects ◽  
Airborne Lidar Data

Stand-level maps of past forest disturbances (expressed as time since disturbance, TSD) are needed to model forest ecosystem processes, but the conventional approaches based on remotely sensed satellite data can only extend as far back as the first available satellite observations. Stand-level analysis of airborne LiDAR data has been demonstrated to accurately estimate long-term TSD (~100 years), but large-scale coverage of airborne LiDAR remains costly. NASA’s spaceborne LiDAR Global Ecosystem Dynamics Investigation (GEDI) instrument, launched in December 2018, is providing billions of measurements of tropical and temperate forest canopies around the globe. GEDI is a spatial sampling instrument and, as such, does not provide wall-to-wall data. GEDI’s lasers illuminate ground footprints, which are separated by ~600 m across-track and ~60 m along-track, so new approaches are needed to generate wall-to-wall maps from the discrete measurements. In this paper, we studied the feasibility of a data fusion approach between GEDI and Landsat for wall-to-wall mapping of TSD. We tested the methodology on a ~52,500-ha area located in central Idaho (USA), where an extensive record of stand-replacing disturbances is available, starting in 1870. GEDI data were simulated over the nominal two-year planned mission lifetime from airborne LiDAR data and used for TSD estimation using a random forest (RF) classifier. Image segmentation was performed on Landsat-8 data, obtaining image-objects representing forest stands needed for the spatial extrapolation of estimated TSD from the discrete GEDI locations. We quantified the influence of (1) the forest stand map delineation, (2) the sample size of the training dataset, and (3) the number of GEDI footprints per stand on the accuracy of estimated TSD. The results show that GEDI-Landsat data fusion would allow for TSD estimation in stands covering ~95% of the study area, having the potential to reconstruct the long-term disturbance history of temperate even-aged forests with accuracy (median root mean square deviation = 22.14 years, median BIAS = 1.70 years, 60.13% of stands classified within 10 years of the reference disturbance date) comparable to the results obtained in the same study area with airborne LiDAR.

Evaluation of nonlinear equations for predicting diameter from tree height

2012 ◽  
Vol 42 (4) ◽  
pp. 789-806 ◽  
Author(s):  
Huiquan Bi ◽  
Julian C. Fox ◽  
Yun Li ◽  
Yuancai Lei ◽  
Yong Pang
Keyword(s):  
Prediction Accuracy ◽  
Laser Scanning ◽  
Large Scale ◽  
Inverse Function ◽  
Tree Height ◽  
Predictive Performance ◽  
Systematic Evaluation ◽  
Large Area ◽  
Individual Tree ◽  
Breast Height

With the emergence and advancement of airborne laser scanning technology over the past decade, individual tree height can be easily measured over a large area of forests with a comparable degree of accuracy to conventional ground-based methods. In laser scanning based large-scale forest inventories, the need to predict diameter from remotely sensed tree height calls for a systematic evaluation of equation forms as the first step towards a well-developed approach to developing diameter–height equations. This study evaluated more than 30 height–diameter equations in the forest biometrics literature to select candidates for deriving equation forms for diameter–height equations. The evaluation was based on four criteria: (i) the height–diameter function is inversable; (ii) the inverse function is continuous and monotonically increasing over a specified working range of total tree height; (iii) diameter at breast height is equal to zero when tree height equals breast height in the inverse function; and preferably, (iv) the inverse function has an inflection point that is consistent with biological expectations. A total of 12 candidate equation forms were derived, which included five two-parameter and seven three-parameter equations. The estimation properties and predictive performance of these 12 equation forms were further evaluated and compared through repeated sampling and fitting using data from 3581 trees destructively sampled for taper measurements from Pinus radiata D. Don plantations across New South Wales, Australia. Three equation forms, including the constrained Richards, Weibull, and the combined power and exponential function, displayed superior prediction accuracy and estimation properties and so were recommended as the primary equation forms for developing diameter–height equations. The remaining equation forms were marred by either lower prediction accuracy or poorer estimation properties or both. The three recommended equation forms should only serve as basic deterministic specifications upon which other tree and stand variables should be incorporated as predictors to further improve their predictive performance.

scholarly journals ESTIMATION OF AERODYNAMIC ROUGHNESS AND ZERO PLANE DISPLACEMENT USING MEDIUM DENSITY OF AIRBORNE LIDAR DATA

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 217-224
Author(s):  
M. R. Mohd Salleh ◽  
M. Z. Abdul Rahman ◽  
M. A. Abu Bakar ◽  
A. W. Rasib ◽  
H. Omar
Keyword(s):  
Structural Parameters ◽  
Ground Surface ◽  
Airborne Lidar ◽  
Frontal Area ◽  
Surface Model ◽  
Lidar Data ◽  
Individual Tree ◽  
Aerodynamic Roughness ◽  
Plane Displacement ◽  
Airborne Lidar Data

This paper presents a framework to estimate aerodynamic roughness over specific height (<i>zo/H</i>) and zero plane displacement (<i>d/H</i>) over various landscapes in Kelantan State using airborne LiDAR data. The study begins with the filtering of airborne LiDAR, which produced ground and non-ground points. The ground points were used to generate digital terrain model (DTM) while the non-ground points were used for digital surface model (DSM) generation. Canopy height model (CHM) was generated by subtracting DTM from DSM. Individual trees in the study area were delineated by applying the Inverse Watershed segmentation method on the CHM. Forest structural parameters including tree height, height to crown base (HCB) and diameter at breast height (DBH) were estimated using existing allometric equations. The airborne LiDAR data was divided into smaller areas, which correspond to the size of the <i>zo/H</i> and <i>d/H</i> maps i.e. 50 m and 100 m. For each area individual tree were reconstructed based on the tree properties, which accounts overlapping between crowns and trunks. The individual tree models were used to estimate individual tree frontal area and the total frontal area over a specific ground surface. Finally, three roughness models were used to estimate <i>zo/H</i> and <i>d/H</i> for different wind directions, which were assumed from North/South and East/West directions. The results were shows good agreements with previous studies that based on the wind tunnel experiments.

scholarly journals SECTION-BASED TREE SPECIES IDENTIFICATION USING AIRBORNE LIDAR POINT CLOUD

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1001-1007 ◽  
Author(s):  
C. Yao ◽  
X. Zhang ◽  
H. Liu
Keyword(s):  
Species Identification ◽  
Tree Species ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Forest Community ◽  
Surface Model ◽  
Lidar Data ◽  
Palm Tree ◽  
Crown Height ◽  
Individual Tree

The application of LiDAR data in forestry initially focused on mapping forest community, particularly and primarily intended for largescale forest management and planning. Then with the smaller footprint and higher sampling density LiDAR data available, detecting individual tree overstory, estimating crowns parameters and identifying tree species are demonstrated practicable. This paper proposes a section-based protocol of tree species identification taking palm tree as an example. Section-based method is to detect objects through certain profile among different direction, basically along X-axis or Y-axis. And this method improve the utilization of spatial information to generate accurate results. Firstly, separate the tree points from manmade-object points by decision-tree-based rules, and create Crown Height Mode (CHM) by subtracting the Digital Terrain Model (DTM) from the digital surface model (DSM). Then calculate and extract key points to locate individual trees, thus estimate specific tree parameters related to species information, such as crown height, crown radius, and cross point etc. Finally, with parameters we are able to identify certain tree species. Comparing to species information measured on ground, the portion correctly identified trees on all plots could reach up to 90.65&amp;thinsp;%. The identification result in this research demonstrate the ability to distinguish palm tree using LiDAR point cloud. Furthermore, with more prior knowledge, section-based method enable the process to classify trees into different classes.

scholarly journals A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes

2014 ◽  
Vol 7 (6) ◽  
pp. 8565-8647 ◽  
Author(s):  
K. Naudts ◽  
J. Ryder ◽  
M. J. McGrath ◽  
J. Otto ◽  
Y. Chen ◽  
...  
Keyword(s):  
Forest Management ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Model ◽  
Basal Area ◽  
Tree Height ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Validation Data ◽  
Mitigate Climate Change

Abstract. Since 70% of global forests are managed and forests impact the global carbon cycle and the energy exchange with the overlying atmosphere, forest management has the potential to mitigate climate change. Yet, none of the land surface models used in Earth system models, and therefore none of today's predictions of future climate, account for the interactions between climate and forest management. We addressed this gap in modelling capability by developing and parametrizing a version of the land surface model ORCHIDEE to simulate the biogeochemical and biophysical effects of forest management. The most significant changes between the new branch called ORCHIDEE-CAN (SVN r2290) and the trunk version of ORCHIDEE (SVN r2243) are the allometric-based allocation of carbon to leaf, root, wood, fruit and reserve pools; the transmittance, absorbance and reflectance of radiation within the canopy; and the vertical discretisation of the energy budget calculations. In addition, conceptual changes towards a~better process representation occurred for the interaction of radiation with snow, the hydraulic architecture of plants, the representation of forest management and a~numerical solution for the photosynthesis formalism of Farquhar, von Caemmerer and Berry. For consistency reasons, these changes were extensively linked throughout the code. Parametrization was revisited after introducing twelve new parameter sets that represent specific tree species or genera rather than a group of unrelated species, as is the case in widely used plant functional types. Performance of the new model was compared against the trunk and validated against independent spatially explicit data for basal area, tree height, canopy strucure, GPP, albedo and evapotranspiration over Europe. For all tested variables ORCHIDEE-CAN outperformed the trunk regarding its ability to reproduce large-scale spatial patterns as well as their inter-annual variability over Europe. Depending on the data stream, ORCHIDEE-CAN had a 67 to 92% chance to reproduce the spatial and temporal variability of the validation data.

UAV-Based Canopy Height Modeling under Leaf-on and Leaf-off Conditions for Determining Tree Height and Crown Diameter (Case study: Hyrcanian mixed Forest)

2021 ◽  
Author(s):  
Vahid Nasiri ◽  
Ali.A. Darvishsefat ◽  
Hossein Arefi ◽  
Marc Pierrot-Deseilligny ◽  
Manochehr Namiranian ◽  
...  
Keyword(s):  
Mixed Forest ◽  
Digital Terrain Model ◽  
Local Maximum ◽  
Tree Height ◽  
Field Measurements ◽  
Canopy Height ◽  
Aerial Images ◽  
Surface Model ◽  
Individual Tree ◽  
Crown Diameter

Tree height and crown diameter are two common individual tree attributes that can be estimated from Unmanned Aerial Vehicles (UAVs) images thanks to photogrammetry and structure from motion. This research investigates the potential of low-cost UAV aerial images to estimate tree height and crown diameter. Two successful flights were carried out in two different seasons corresponding to leaf-off and leaf-on conditions to generate Digital Terrain Model (DTM) and Digital Surface Model (DSM), which were further employed in calculation of a Canopy Height Model (CHM). The CHM was used to estimate tree height using low pass and local maximum filters, and crown diameter was estimated based on an Invert Watershed Segmentation (IWS) algorithm. UAV-based tree height and crown diameter estimates were validated against field measurements and resulted in 3.22 m (10.1%) and 0.81 m (7.02%) RMSE, respectively. The results showed high agreement between our estimates and field measurements, with R2=0.808 for tree height and R2=0.923 for crown diameter. Generally, the accuracy of the results was considered acceptable and confirmed the usefulness of this approach for estimating tree heights and crown diameter.

scholarly journals Comparing Individual Tree Height Information Derived from Field Surveys, LiDAR and UAV-DAP for High-Value Timber Species in Northern Japan

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 223 ◽  
Author(s):  
Kyaw Moe ◽  
Toshiaki Owari ◽  
Naoyuki Furuya ◽  
Takuya Hiroshima
Keyword(s):  
Forest Management ◽  
Field Survey ◽  
Tree Height ◽  
Spatial Position ◽  
Individual Species ◽  
Lidar Data ◽  
Timber Species ◽  
Individual Tree ◽  
Broadleaf Species ◽  
Northern Japan

High-value timber species such as monarch birch (Betula maximowicziana Regel), castor aralia (Kalopanax septemlobus (Thunb.) Koidz), and Japanese oak (Quercus crispula Blume) play important ecological and economic roles in forest management in the cool temperate mixed forests in northern Japan. The accurate measurement of their tree height is necessary for both practical management and scientific reasons such as estimation of biomass and site index. In this study, we investigated the similarity of individual tree heights derived from conventional field survey, digital aerial photographs derived from unmanned aerial vehicle (UAV-DAP) data and light detection and ranging (LiDAR) data. We aimed to assess the applicability of UAV-DAP in obtaining individual tree height information for large-sized high-value broadleaf species. The spatial position, tree height, and diameter at breast height (DBH) were measured in the field for 178 trees of high-value broadleaf species. In addition, we manually derived individual tree height information from UAV-DAP and LiDAR data with the aid of spatial position data and high resolution orthophotographs. Tree heights from three different sources were cross-compared statistically through paired sample t-test, correlation coefficient, and height-diameter model. We found that UAV-DAP derived tree heights were highly correlated with LiDAR tree height and field measured tree height. The performance of individual tree height measurement using traditional field survey is likely to be influenced by individual species. Overall mean height difference between LiDAR and UAV-DAP derived tree height indicates that UAV-DAP could underestimate individual tree height for target high-value timber species. The height-diameter models revealed that tree height derived from LiDAR and UAV-DAP could be better explained by DBH with lower prediction errors than field measured tree height. We confirmed the applicability of UAV-DAP data for obtaining the individual tree height of large-size high-value broadleaf species with comparable accuracy to LiDAR and field survey. The result of this study will be useful for the species-specific forest management of economically high-value timber species.

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