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.

scholarly journals Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests

2020 ◽  
Vol 12 (17) ◽  
pp. 2865
Author(s):  
Kyaw Thu Moe ◽  
Toshiaki Owari ◽  
Naoyuki Furuya ◽  
Takuya Hiroshima ◽  
Junko Morimoto
Keyword(s):  
Forest Management ◽  
Forest Canopy ◽  
3D Structure ◽  
Three Dimensional ◽  
Lidar Data ◽  
Timber Species ◽  
Individual Tree ◽  
Aerial Photogrammetry ◽  
Broadleaf Species ◽  
The Individual

High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.

scholarly journals Stand structure links up canopy processes and forest management

2009 ◽  
Vol 51 (1) ◽  
pp. 40-48
Author(s):  
Toomas Frey
Keyword(s):  
Forest Management ◽  
Stand Structure ◽  
Net Primary Production ◽  
Stand Density ◽  
Tree Height ◽  
Belowground Biomass ◽  
Unit Mass ◽  
Total Biomass ◽  
Individual Tree ◽  
Mean Values

Stand structure links up canopy processes and forest management Above- and belowground biomass and net primary production (Pn) of a maturing Norway spruce (Picea abies (L.) Karst.) forest (80 years old) established on brown soil in central Estonia were 227, 50 and 19.3 Mg ha correspondingly. Stand structure is determined mostly by mean height and stand density, used widely in forestry, but both are difficult to measure with high precision in respect of canopy processes in individual trees. However, trunk form quotient (q2) and proportion of living crown in relation to tree height are useful parameters allowing describe stand structure tree by tree. Based on 7 model trees, leaf unit mass assimilation activity and total biomass respiration per unit mass were determined graphically as mean values for the whole tree growth during 80 years of age. There are still several possible approaches not used carefully enough to integrate experimental work at instrumented towers with actual forestry measurement. Dependence of physiological characteristics on individual tree parameters is the missing link between canopy processes and forest management.

scholarly journals Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 694 ◽  
Author(s):  
Selina Ganz ◽  
Yannek Käber ◽  
Petra Adler
Keyword(s):  
Remote Sensing ◽  
Tree Height ◽  
Field Measurements ◽  
Test Site ◽  
Aerial Images ◽  
Lidar Data ◽  
Individual Tree ◽  
Indirect Measurements ◽  
Remote Sensing Techniques ◽  
Remote Sensing Methods

We contribute to a better understanding of different remote sensing techniques for tree height estimation by comparing several techniques to both direct and indirect field measurements. From these comparisons, factors influencing the accuracy of reliable tree height measurements were identified. Different remote sensing methods were applied on the same test site, varying the factors sensor type, platform, and flight parameters. We implemented light detection and ranging (LiDAR) and photogrammetric aerial images received from unmanned aerial vehicles (UAV), gyrocopter, and aircraft. Field measurements were carried out indirectly using a Vertex clinometer and directly after felling using a tape measure on tree trunks. Indirect measurements resulted in an RMSE of 1.02 m and tend to underestimate tree height with a systematic error of −0.66 m. For the derivation of tree height, the results varied from an RMSE of 0.36 m for UAV-LiDAR data to 2.89 m for photogrammetric data acquired by an aircraft. Measurements derived from LiDAR data resulted in higher tree heights, while measurements from photogrammetric data tended to be lower than field measurements. When absolute orientation was appropriate, measurements from UAV-Camera were as reliable as those from UAV-LiDAR. With low flight altitudes, small camera lens angles, and an accurate orientation, higher accuracies for the estimation of individual tree heights could be achieved. The study showed that remote sensing measurements of tree height can be more accurate than traditional triangulation techniques if the aforementioned conditions are fulfilled.

Estimation of Individual Tree Parameters Using Small-Footprint LiDAR with Different Density in a Coniferous Forest

2012 ◽  
Vol 518-523 ◽  
pp. 5320-5323 ◽  
Author(s):  
Qi Sheng He ◽  
Na Li
Keyword(s):  
Coniferous Forest ◽  
Tree Height ◽  
Gansu Province ◽  
Parameters Estimation ◽  
Western China ◽  
Lidar Data ◽  
Individual Tree ◽  
Crown Diameter ◽  
Crown Width ◽  
Point Density

In this paper, the effects of different LiDAR point density on individual tree parameters including tree height and crown diameter were investigated for the coniferous tree species in the Qilian Mountain area within Gansu province, western China. 10 different density data were acquired in field survey area, with the minimum density of 0.234 points/m2 and the maximum density of 0.6941 points/m2 for per flight. By summing up the different flight data, the different density LIDAR data from 0.234 points/m2 to 5.226 points/m2 for extracting tree height and crown diameter can be analyzed. The result showed that the number of extraction points and the extraction accuracy of tree height and crown width arrived at relative high level in point density of about 2.5 points per m2. When the point density increased, the increased extraction points and the extraction accuracy of tree height and crown width became slow. It means that about 2.5 points per m2 of LiDAR data density may provide relative high accurate individual tree parameters estimation.

Individual Tree Parameters Estimation from Terrestrial Laser Scanner Data

2017 ◽  
Author(s):  
Darius Popovas ◽  
Valentas Mikalauskas ◽  
Dominykas Šlikas ◽  
Simonas Valotka ◽  
Tautvydas Šorys
Keyword(s):  
Forest Management ◽  
Laser Scanner ◽  
Tree Height ◽  
Parameters Estimation ◽  
Carbon Accounting ◽  
Terrestrial Laser Scanner ◽  
Individual Tree ◽  
Diameter At Breast Height ◽  
Crown Diameter ◽  
Breast Height

Tree models and information on the various characteristics of trees and forests are required for forest management, city models, carbon accounting and the management of assets. In order to get precise characteristics and information, tree modelling must be done at individual tree level as it represents the interaction process between trees. For sustainable forest management, more information is needed, however, the traditional methods of investigating forest parameters such as, tree height, diameter at breast height, crown diameter, stem curve and stem mapping or tree location are complex and labour-intensive. Light detection and ranging (LiDAR) has been proposed as a suitable technique for mapping of forest biomass. LiDAR can be operated in airborne configuration (Airborne laser scanning) or in a terrestrial setup. Terrestrial Laser Scanner measures forests from below canopy and offers a much more detailed description of the individual trees. The aim of this study is to derive the essential tree parameters for estimation of biomass from terrestrial LiDAR data. Tree height, diameter at breast height, crown diameter, stem curve and tree locations were extracted from Terrestrial Laser Scanner point clouds.

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%.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

scholarly journals Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

2020 ◽  
Vol 13 (1) ◽  
pp. 77
Author(s):  
Tianyu Hu ◽  
Xiliang Sun ◽  
Yanjun Su ◽  
Hongcan Guan ◽  
Qianhui Sun ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Low Cost ◽  
Canopy Cover ◽  
Tree Height ◽  
Flying Height ◽  
Tree Level ◽  
Forest Site ◽  
Lidar System ◽  
Individual Tree ◽  
Gap Fraction

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

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.

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