EXTRACTING INDIVIDUAL TREES AND LIDAR METRICS USING A WEBLIDAR FOREST INVENTORY APPLICATION. PART 3: THE 3D CLUSTERTREE TOOL 1
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Carlos A. Silva , Andrew T. Hudak , Nicolas L. Crookston , Carine K. Silva , Veraldo Liesenberg
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UDSA- Forest Service – Rocky Mountain Research Station –RMRS, 1221 South Main Street. Moscow, ID 83843, USA. E-mail:
[email protected] 2 Luiz de Queiroz College of Agriculture - ESALQ, University of São Paulo (USP), Av. Pádua Dias, 11, Piracicaba, SP - Brazil, 13418-050. 3 Institute of Geosciences, University of Campinas (Unicamp), R. Pandia Calogeras, 51, PO Box 6152, Campinas, SP, Brazil, 13083-970.
ABSTRACT The purpose of this study is to present a Web-LiDAR Forest Inventory Application: The 3D ClusterTree Tool. With the tool, we propose an innovative way to visualize and process LiDAR data on the web. We intend to extract individual trees and metrics that can be used for further regression analysis in support of forest management. The 3D ClusterTree tool was developed by the USDA Forest Service - Rock Mountain Research Station (RMRS) laboratory, and it is freely available on the web. The main objectives of this tool are the online visualization of a given LiDAR dataset and the extraction of metrics for individual trees. The LiDAR-derived metrics can be extracted from height, intensity and/or canopy coverage attributes over a projected crown area and volume. Such information is of great interest for ecological studies. We show here how to extract individual tree LiDAR metrics from the webapplication. As a study area we select a longleaf pine forest in Georgia, USA. However, it can be used for other forest types and we encourage potential users to test the tool broadly. Keywords: Remote sensing, web application, forestry engineering, LiDAR metrics.
INTRODUCTION Light Detection and Ranging (LiDAR) remote sensing has been widely used for mapping purposes and forestry applications (EVANS et al., 2009, YAO et al. 2013). LiDAR-derived metrics enable prediction of forest attributes such as basal area, stem volume, above ground biomass and carbon content that can be estimated at both stand and tree levels (LEFSKY et al., 2002; NÆSSET 1997, 2002, 2004a, 2004b, 2007; NÆSSET et al, 2001; HUDAK et al. 2006, 2012; MORSDOF et al., 2004; REITBERGER et al., 2009; FERRAZ et al., 2012; LI et al., 2012; and YAO et al., 2013). Forest inventory at the single tree level has been of great interest for a proper management and decision making activities. Several approaches based on the use of LiDAR data have been achieved. The detection and delineation of tree crowns is highly desirable to forest managers (Jakubowski et al. 2013). Such information is important for ecological studies, demographic and growth modeling, wildlife habitat management, and also allows more precise prediction of aboveground biomass.
The local maxima and inverse watershed segmentation algorithms have been two basic methods applied to the detection of individual trees and their crown delineation (Popescu et al., 2002 and Goerndt et al., 2010). On the other hand, a 3D point cloud segmentation and clustering is also suggested for the detection of single trees (MORSDOF et al., 2004; REITBERGER et al., 2009; FERRAZ et al., 2012; LI et al., 2012 and YAO et al., 2013). There are few specific software available to process LiDAR data efficiently in terms of individual tree detection and extraction. Programming skills are usually envisage in order to create algorithms to perform these tasks. Yet, it is hard to find freely available routines and/or codes. Therefore, we present a simple, free and online tool to visualize and process small LiDAR datasets. The main objective was to extract individual trees and to generate LiDAR derived metrics at individual tree level.
WEB-LIDAR FOREST INVENTORY: 3D CLUSTERTREE APPLICATION 3D ClusterTree is a free web application to process and to visualize LiDAR data. The main objectives of this tool are: (i) to detect individual trees; and (ii) to extract metrics for each detected tree in the provided LiDAR dataset. The Web-LiDAR forest inventory application: 3D CusterTree Tool is an online platform and it can be accessed freely at: http://forest.moscowfsl.wsu.edu:3838/LiDAR3DclusterTree/. Moreover, the web application performs analysis over small datasets (up to 30Mb). The requested input file is a “.LAS” file that needs to have their heights normalized. The Web-LiDAR application has three major tab panels. The first one is a short presentation of the tools (i.e. “Welcome panel”), the second is the main page of the application (i.e. “Application panel”) where the users can visualize and process their own LiDAR datasets, and the last one (“About panel”) is the tab panel that describes the web-LiDAR application and presents a tutorial in both pdf and a youtube link to help the users understand the tool better. Some information of the project, authors and tutorials are also presented. Fig. 2 shows the main page of the web application organized in five displays: (1) Settings menu; (2) Summary of the LiDAR metrics; (3) Canopy height model profile; (4) Top viewer; (5) Canopy height model histrogram; and (6) interactive 3D viewer of the LiDAR point cloud. In the settings menu the user can upload a new LiDAR data or play it with the provided sample data. Fig. 1 shoes the flowchart of the proposed tool and the algorithms behind of the web-application. The requested inputs are the LiDAR data itself, a prior definition of the height threshold (m), and expected number of trees and alpha per ha. The processing chain consists basically in a subset of the LiDAR dataset by height threshold followed by the k-men’s clustering classification and LiDAR metrics calculation. Finally, the outputs are the classified LiDAR data, LiDAR metrics at every single detected tree and profiles. The generated LiDAR metrics at tree level are divided in six major groups: (i) tree location, (ii) tree crown width, (iii) height and intensity, (iv) volume, (v) canopy projected area, and (vi) canopy density (Fig. 3A). The processing chain uses only returns above the pre-defined height threshold (Fig. 3B). The alpha parameter has a strong influence on the alpha shape 3D that is a convex hull geometric structure (Fig. 3C). The tree volume is calculated from the alpha shape 3D and the canopy projected area is a measure from the canopy boundary created for each single detected tree (Fig. 3D).
Figure 1. Flowchart of the Web-LiDAR algorithm for individual tree detection and generation of the LiDAR derived metrics.
Figure 2. Main display of the Web-LiDAR forest inventory application: 3D ClusterTree tool. (1) Settings menu; (2) Summary of the LiDAR metrics; (3) Canopy height model profile; (4) Top viewer of the point cloud; (5) Histogram and box-plot; and 6) Interactive 3D viewer of the LiDAR point cloud.
Figure 1. List of the LiDAR derived metrics for each individual tree.
LIDAR DATA EXAMPLE The study area is located in Georgia, 14 miles west of Camilla, GA (Fig. 4). The climate is characterized by hot, humid summers and generally mild to cool winters (CAMILA, 2014). The vegetation is characterized predominantly by the longleaf pine forest (Pinus palustris Mill; Figs. 4A, B) that has an open canopy structure (up to 50% canopy cover). As field inventory data we use a circular sample plot with a diameter of 39,89m (equivalent to 0.5ha). A total of 32 trees were identified on it.
Figure 4. Pictures representing the longleaf pine forest plantation (Photo credits to Heezin Lee, 2010).
The LiDAR data was collected using an Optech GEMINI Airborne Laser Terrain Mapper (ALTM) serial number 06SEN195 mounted in a twin-engine Cessna Skymaster (Tail Number N337P). The survey parameters are given in Table 1 below. Table 1. LiDAR survey parameters Parameters Values Scan Frequency 45 Hz Scan Angle +/- 20 deg Scan Cutoff +/- 4.0 deg Scan Offset 0 deg System PRF 125 kHz Swath Width 344.64 m Flying Altitude 600m AGL Cross Track Resolution 0.522 m Down Track Resolution 0.75 m Points per square meter 5.06
RESULTS OF THE LIDAR DATA PROCESSING Individual tree LiDAR metrics calculated from Web-LiDAR The following threshold values were used in the outcome of the Fig. 5: 32 trees, height threshold of 1.37m and 0.25 for the alpha value. Fig. 5A displays the trees locations on the canopy height model (CHM) with their crown delineation. Figs. 5B to 5E shows the density histogram of the maximum height (Hmax), volume of the canopy (m³), canopy projected area on the ground (m²) and canopy projected radius (m) for the 32 detected trees. The LiDAR derived metrics for each single detected tree have been used for forest management purposes. Morsdof et al., 2004 used the k-mean’s cluster analysis to point cloud segmentation in a boreal type forest stands. Tree positions, tree heights, and crown diameters were derived from the segmented clusters and compared with field measurements for the management of a wild fires. Reitberger et al., 2009 used 3Dnormalized cut segmentation for single tree extraction and afterwards the alpha shape 3D algorithm to calculate tree volume. Then, estimated stem volume as measured in the field from an allometric equation regressed against tree volume calculated from the alpha shape 3D. Ferraz et al., 2012 used a mean shift algorithm with 3D point cloud clustering. Yao et al. (2013) combined mean shift clustering with normalized cut segmentation. The authors found good results for individual tree extraction, biomass and stem diameter estimation.
Figure 5. Outcomes of the Individual tree LiDAR metrics. A) Single tree location and their crown on the canopy height model (CHM). Histograms for the maximum tree height (B), tree volumes (C), projected area of the tree canopy, and projected radius of the canopy (D). LiDAR data visualization The previous data inspection and visualization is a very important task in the LiDAR data processing chain. This simple step can help users to better explore their data and to remove noise (Fig. 6). The majority of the points in this example were found in the interval between 10 and 20 meters. Both overstory and understory (shrubs and small trees) can be visualized and partitioned (Figs. 6A and B). Fig. 6C show a tree top visualization whereas Figs. 6D and E show height maximum, minimum, mean, median and standard deviation statistics. Beyond the 2D graphics, the Web-LiDAR tools also provides a 3D visualization of the LiDAR dataset. Fig. 7A shows the result of the k-mean’s clustering classification. This 3D graphic allows the user to judge whether the classification performance was good or not. Fig. 7B shows the alpha shape 3D created for each single tree. We used for this procedure the Web-LiDAR forest inventory application: AlphaShape3D tool also developed by the USDA Forest Service - Rock Mountain Research Station (RMRS) laboratory. It can be accessed at: http://forest.moscowfsl.wsu.edu:3838/LiDARAlphaShape3D/. Additionally, the individual tree LiDAR metrics shown in this paper can also provide information to create artificial and 3D forest landscapes (Figs. 7C, D, and E for forest visualization. However, a predefined argument for each single tree is necessary. This can be performed by creating a list of species and looking for the best geometric form that represents that species. The 3D representation can depict single trees not only by species identification, but also by their height, crown area and volume.
Figure 6. Summary of the Web-LiDAR application.
Figure 7. 3D representation of the LiDAR dataset. Point cloud classification by k-mean’s clustering (A), Alpha Shape 3D (B). 3D forest landscape created by the open source RLiDAR-Tree software developed by the Forest Service - Rock Mountain Research Station (RMRS) laboratory (C, D and E).
FINAL REMARKS
The Web-LiDAR forest inventory application: 3D ClusterTree tool can be a useful tool to detect individual trees and to generate LiDAR-derived metrics. The graphics generated by the tool provide a good visualization and allow users to explore their own LiDAR datasets. The LiDAR derived metrics at single tree level can be used for regression modeling to predict aboveground biomass, carbon content, and other attributes. This web- application was developed to support lidar-based forest inventory and management of longleaf pine forests, but users are encouraged to test the tools in other forest types.
ACKNOWLEDGEMENTS We thank the US Forest Service International Department for supporting an exchange program with the USDA-FS Forestry Sciences Laboratory at Moscow, Idaho, where Web –LiDAR application was developed. This study was also supported by Department of Defense Strategic Environmental Research and Development Program (SERDP): Patterns and processes: monitoring and understanding plant diversity in frequently burned longleaf pine landscapes. J. O'Brien, PI; R. Mitchell, A. Hudak, L. Dyer, Co-PIs. Field data were provided by R. Mitchell, and LiDAR data were collected by NCALM, Department of Electrical and Computer Engineering University of Florida, USA.
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