IDENTIFICATION OF PARTIAL CANOPIES USING FIRST AND LAST RETURN LIDAR DATA INTRODUCTION

Transcription

1 IDENTIFICATION OF PARTIAL CANOPIES USING FIRST AND LAST RETURN LIDAR DATA Rafael Loos 1, Olaf Niemann, Fabio Visintini Hyperspectral/LiDAR Research Group University of Victoria Victoria, BC, Canada 1 Corresponding author rats@uvic.ca ABSTRACT LiDAR (Light Detection and Ranging) is currently being used to extract the biophysical characteristics of forests. LiDAR can provide extensive information about tree canopies; pulses reflected back to the sensor can represent understory vegetation as well as partial tree canopies below the dominant trees. Canopy structure can yield valuable clues regarding the biodiversity, and processes affecting the ecology of forest stands. In addition structural information can provide insight into other processes such as fire behaviour and the distribution of fuels.. The objective of this paper is to provide an algorithm to identify and delineate partial tree crowns underneath dominant canopies. The study area is located in the Greater Victoria Water District, west of Victoria, British Columbia, Canada. Nine plots were chosen to represent the study area. A complete census was conducted in the summer of 2005 to provide information about height of living crown (HLC), tree height, diameter at breast height (DBH), and tree dominance (based on the criteria: suppressed, intermediate, co-dominant and dominant). Using an algorithm previously developed by the Hyperspectral and LiDAR Research Group (Department of Geography, University of Victoria), treetops and canopies of the dominant trees must first be identified and delineated. Then, using the HLC as a threshold for identifying canopy height, it is possible to remove the LiDAR points representing the dominant tree crowns from the dataset. With this newly developed algorithm, it is possible to identify and delineate understory tree canopies. INTRODUCTION Airborne LiDAR (Light Detection and Ranging) is an accurate tool for extracting 3-dimensional attributes from a particular area. Its development started in the 1970s with a focus on bathymetric and hydrological applications (Wehr et. al, 1999). However, during the 1980s, with the development of more precise GPS systems, LiDAR started to gain more importance. Studies at Stuttgart University demonstrated the potential of the system at large scales for acquiring Digital Terrain Models (DTMs) (Ackermann, 1999). LiDAR is an active sensor that obtains height information using pulses of laser light as a source of energy (Kraus and Pfeifer, 1998). The system records the total time that an emitted pulse takes to travel from the sensor to an object close to the surface and back to the sensor. This time is used to calculate the distance from the sensor to the object and therefore identify its altitude (Wehr and Lohr, 1999). Horizontal and vertical information are acquired at the same time by using an on-board differential global positioning system (dgps), which communicates with a ground station and an inertial measurement unit (IMU) (Lim et al., 2001). The IMU records the roll, pitch and heading of the aircraft to determine its orientation, while the dgps records the airplane s location (Wehr and Lohr, 1999). After the data acquisition, the position and orientation values gathered by the dgps and IMU are used to georeference the ranging measurements. LiDAR produces a 3D dataset of elevation points X (easting), Y (northing) and Z (elevation) for the ground surface and objects. The point density will vary depending on flying height, airborne speed, field of view (FOV) and frequency of the pulses (Axelsson, 1999). Airborne LiDAR can be used to derive information regarding forest biophysical characteristics, providing information on topography, tree heights, stem density, crown dimensions and gap structure (Popescu et al., 2002). These parameters enhance forest management and, more specifically, ecosystem management, fire management, and harvesting management. A number of different LiDAR and vegetation-based variables affect the quality of the information that is derived. Fewer points will reach the understory vegetation, or the terrain below, in areas where there is a dense canopy cover. Takahashi et al. (2006) tested the penetration rates of two species of cypress (Sugi cypress and Hinoki cypress) with low canopy openness. Fewer gaps in the tree canopies affect the degree to which LiDAR pulses able to penetrate the tree structure. This results in a less accurate DTM and also less precise information about understory vegetation.

2 The horizontal and vertical structure of tree canopies are important parameters that influence the micro- and macroclimate of a forest. Both parameters influence the amount of solar energy that passes through the canopy, the quantity of precipitation intercepted by vegetation before hitting the ground, and air movement. All these factors regulate humidity and temperature inside the forest, creating different habitats for organisms (i.e., fungus, worms, insects and plants) (Jennings et al., 1999). Outlining the canopies and differentiating high vegetation from understory vegetation in a specific area enhances understanding of the forest s heterogeneity. This paper discusses an approach developed to identify and map, not only individual crowns in the dominant and subdominant canopies, but also those individuals occurring in the more subordinate subcanopy. The method used employs first and last return LiDAR data and is wholly automated. STUDY AREA The study area is located in the Greater Victoria Water District, situated west from Greater Victoria city, B.C., Canada. Established in 1942, it includes two main protected watersheds: Sooke and Goldstream. These watersheds represent more than 11,000 hectares in total area. Logging was allowed in the area up to 15 years ago. However, it has since been prohibited due to concerns regarding water quality and ecosystem health. The area presents a variety of terrain types, stand ages and structures, making it a perfect study area for testing the different modules of the software s ability to identify trees and their canopies. The most common tree species assemblage in this valley is Douglas-fir (Pseudotsuga menziesii (Mirb) Franco) western hemlock (Tsuga heterophylla (Raf.)). Other less common tree species in the area, such as Western Red Cedar, White Pine and Alder. LiDAR DATA LiDAR data were collected in 2004 by TERRA Remote Sensing Limited. The data were acquired using pulse frequency of 50KHz at a flying height of 800 m.a.g.l., resulting is an average posting density of 4 points per square metre. Data were cleaned to remove noise from spurious heights resulting from atmospheric interference and other influences. Subsequently the data were classified to identify the vegetation hits from those reaching the ground. Given the density of point collection and the large number of points, the dataset was divided into 1Km X 1Km tiles. The resulting LiDAR file structure contains specific information on northing and easting coordinates in UTM (Universal Transverse Mercator), elevation in meters, class identifier number, LiDAR intensity. The variables focused for this project were only the UTM coordinates (northing and easting), the elevation in meters and the class identifier number. METHODS Since Douglas-fir is the dominant tree species in the study area, special attention will be given to show how the algorithm detects its apex and delineates its crown. To develop the algorithm it is important to understand the 3- dimensional structure of the tree, understand how the LiDAR pulses interact with the tree, and how the algorithm will be able to recognize the tree crown through the point collection. The LiDAR pulses that fall inside the crown region interact with all the tree components (needles, branches, bark, etc.). Pulses that reflect back to the LiDAR sensor represent understory or crown points. The algorithm must be able to recognize crown points that represent the area where the tree is located. Douglas-fir crowns are not perfectly shaped like a cone. There are usually many branches that stick up towards the top creating what are called here "pseudo-small apexes". These can trick the algorithm, making it stop the search of crown edges. Consequently, it may locate the apex in a wrong place. Preparation of LiDAR Data Surfaces The first step in the individual tree identification is to create different files for only ground and vegetation. A simple routine was developed search the files and separate the data based on class (that is ground vs. vegetation). Having the ground points in separate files simplifies the gridding of the data. For this project, the ground points were gridded in a Bare Earth Digital Elevation Model (DEM) with the highest resolution possible of 2 meters. At least one ground point was found inside each grid cell. The DEM (figure 1) that represents the relief of the study area was created by gridding the data with the specific spacing (2x2m). All of the LiDAR elevation points (Z) that are inside each 2x2m grid cell are summed and then averaged. The new elevation

3 value for each cell will be attributed with the respective median value of the easting (X) and median value of the northing (Y) coordinates. Figure 2 shows how the gridding process works. As described earlier, all the classified vegetation points were separated into a single file. This vegetation points represent the LiDAR pulses reflected from any vegetated surface, such as understory vegetation or trees. Many reflected returns acquired by the sensor come from the same tree but represent hits from different parts of the tree including returns from the lower branches, the stem or the crown top. One of the main goals of this project is to leave the vegetation points as intact as raw data, that is, not to impose a gridded structure. Furthermore, no filters were applied to smooth the dataset. The result was to retain all of the original information in the LiDAR data files. For visualization, a Digital Surface Model (DSM) (figure 1) can be created from the vegetation file. This DSM represents the vegetation in the area but with the influence of the topography. In other words, each vegetation Z value represents the height of the tree plus the elevation of the terrain above sea level. Figure 1. Bare Earth Digital Elevation Model (DEM), Digital Surface Model (DSM) and Canopy Height Model (CHM) respectively. With the gridded ground surface and the vegetation raw points it is possible to create a Canopy Height Model (CHM). The CHM output (figure 1) represents the actual tree height, normalized for the effects of terrain. All the vegetation points that fall within a gridded DEM cell are subtracted from the elevation height of that cell. The subtracted file contains all the original vegetation points but now without the topography. It is still a raw point file, not subjected to any sort of gridding or interpolation. With the subtracted file it is possible to start the individual tree detection, as the treetops and canopies are more distinguishable. Partial Canopies Delineation and Treetops Detection Algorithm For identifying partial tree canopies underneath the dominant and co-dominant canopies it is first necessary follow the steps to identify the dominant and co-dominant trees, as outlined by Loos and Niemann (2006), where the tallest tree in the dataset is located and the highest value is considered as the treetop. From that treetop location, a search is performed clockwise (figure 2) at every ten degrees to find the edges of the tree.

4 Figure 2. Birds-eye view of canopy edge detection and imaginary cylinder produced by the newly delineated canopy. After delineating the dominant tree, all the points that fall within an imaginary cylinder (figure 2) with the top as the delineated canopy, are used to detect partial crowns underneath the dominant canopy. For this approach, the algorithm uses the Height of Living Crown (HLC) to determine the initial search height for other subordinate crowns. All the LiDAR points inside the imaginary cylinder, that are above the HLC, are removed temporarily from the dataset while the algorithm continues. The same algorithm used to identify the treetops and delineate their canopies is used again, but now with the remaining LiDAR points. When all the crowns below the dominant tree are delineated, the code looks for the next tallest tree in the dataset and all the steps repeated. To test the accuracy of the algorithm, each identified tree were compared against field data. UTM coordinates and tree heights were used to judge whether the tree exists in the proper location in the field. All the tree heights for the dominant and co-dominant trees were collected in the field with a LASER rangefinder but only a few intermediate trees were surveyed. A simple regression model, using the surveyed tree heights and Diameter of Breast Height (DBH), was created to predict the heights for the remaining intermediate trees. RESULTS This section presents the results from two old growth plots number 5 and 8. These two plots show fairly accurate GPS coordinates for the trees surveyed in the field. Plot number 5 is characterized by a variety of different species and tree heights. This variety in height results in many large gaps between trees and canopies in the area. The main dominant, and co-dominant, trees were Douglas-fir, while Western red cedar was the predominant species for the subcanopy or intermediate layer. In the entire plot only 16 trees were classified as intermediate. Of these 16 intermediate trees, only 7 were identified as alive and located below dominant or co-dominant trees. Table 1 compares the data collected in the field (UTM coordinates and tree heights) with the data extracted by the algorithm.

5 Table 1. Comparison between field data and data extracted by the algorithm for plot number 5. Tree ID Field Coord. Code Coord. Obs. Residual X Residual Y Easting 445, , Treetop of tree #153 was Northing 5,383, ,383,656.0 observed being broken in Height m 8.06 m the field Easting 445, , Northing 5,383, , Height m m Easting 445, , Northing 5,383, ,383, Height m m Easting 445, , Northing 5,383, ,383, Height m 22.89m Easting 445, , Treetop of tree #180 was Northing 5,383, ,383,664.5 observed being broken in Height m m the field Easting 445, , Northing 5,383, ,383, Height m m Easting 445, , Treetop of tree #183 was Northing 5,383, ,383,667.0 observed being broken in Height m m the field RMSE 1.19 For plot number 5 the linear regression used for estimating the intermediate tree height produced a R 2 of 0.95 and regression equation of: y = x , where y is the estimated height and x the tree DBH DBH (m) Figure 3. DBH x Tree heights for plot number 5. The height was used, in addition to the UTM coordinates, to validate the accuracy of the algorithm. Using the UTM coordinates, residuals could be calculated for each tree in each direction (x and y), and with these a Root Mean Square Error (RMSE) was calculated. For plot number 5, the RMSE is Figure 4 shows the dominant tree canopies, the field location for the each intermediate tree, and the predicted location of each tree by the algorithm.

6 Figure 4. Plot number 5 Dominant canopies, tree location and location predicted by the algorithm. Plot number 8, another old growth site, was used to assess the algorithm accuracy. Seven intermediate trees were located below dominant trees, but the algorithm identified only 4 of them. Table 2 shows the results for plot number 8. Table 2. Comparison between field data and data extracted by the algorithm for plot number 8. Tree ID Field Coord. Code Coord. Residual X Residual Y Easting 446, , Northing 5,381, ,381,436.0 Height m m Easting 446, , Northing 5,381, ,381,463.5 Height m m Easting 446, , Northing 5,381, ,381,432.0 Height m m Easting 446, , Northing 5,381, ,381,429.5 Height m m RMSE 1.51 For estimating the intermediate tree heights in plot number 8 a linear regression was used and produced a R 2 of 0.91 and regression equation of: y = 54.14x , where y is the estimated height and x the tree DBH.

7 DBH (m) Figure 5. DBH x Tree heights for plot 8. Figure 6 shows the dominant tree canopies, the field location for the each intermediate tree, and the predicted location of each tree by the algorithm for plot number 8. Figure 6. Plot number 8 Dominant canopies, tree location and location predicted by the algorithm. DISCUSSION AND CONCLUSION Of nine plots surveyed, only 3 had sufficient number of LiDAR points for the algorithm to operate successfully, and identify, crowns below the subdominant canopy. These three plots represent old growth stands where tree age ranges from years, and heights between 55-65m. Unfortunately, only two old growth plots could be used for

8 assessing the algorithm accuracy. These plots, as described in detail earlier, were plot number 5 and 8. The third plot suffered from poor GPS reception so that an accurate absolute positioning of the individual stems was not possible. These old growth plots, with older and taller trees, have a large number and size of gaps. Usually, the taller trees are subject to strong winds that break their branches and treetops. Larger and more numerous gaps makes it possible for LiDAR pulses to reach deep into the canopy penetrating to objects closer to the ground surface. In other words, the density of points found in between the canopy and ground surface increases, and enhances, the chance of identifying LiDAR points that represent partial canopies of trees that grow underneath dominant ones. The best way of testing the algorithm was to identify trees classified as intermediate trees in the plots. Intermediate trees are classified as those that have their canopy covered by one or more taller trees (dominant or co-dominant trees). These intermediate trees are typically half the size of dominant trees. Because of that, these trees have the potential to be identified by the algorithm, as they have enough height not be confused as any other small object close to ground such as: bushes, rocks or fallen trees. Unfortunately, the number of intermediate trees underneath dominant or codominant trees found in the field was not very high. Only a few trees could be used to test the performance of the algorithm. The Root Mean Square Error (RMSE) of location for the field measured and corresponding modeled tree was used as a metric to assess the output accuracy. UTM coordinates acquired in the field and coordinates found by the algorithm were used. Higher RMSE values may have been caused by two main factors: tree inclination and insufficient LiDAR points to define the treetop. In plot number 8, not all intermediate trees were identified by the algorithm since not enough points penetrated the first layer of vegetation. The code could not recognize the few existing points between the dominant canopy and the ground as an intermediate tree. Tree positions were acquired using a LASER rangefinder mounted on a tripod and the coordinates represent the base of the tree in the study area. All trees exhibit some inclination that may be the result of terrain slope, tree species, wind, type of soil, surrounding trees (competition for light), diseases, etc. Because of this inclination, the treetop coordinate predicted by the algorithm, using the LiDAR data, may not represent the coordinate of the base picked by the LASER instrument. Also, the treetop might not be centered on the crown, depending on the growth of its branches. There are many factors that determine how well LiDAR points are distributed between a canopy and ground surface such as physical characteristics of the trees (gaps, tree species, etc.) as well as the characteristics of the LiDAR system, such as the number of points per m 2. With the data collected during the summers of 2004 and 2005, the algorithm shows potential to identify trees wholly or partially covered by other more dominant crowns, but it still needs further refinement and testing. This is a preliminary study to explore the issues encountered in the field data collection and the development of the algorithm. More extensive and precise field data needs to be collected, especially for areas containing numerous trees underneath dominant ones. Further studies with the algorithm will also focus in heterogeneous areas, where Douglas-fir is not the dominant tree species. REFERENCES Ackermann, F., Airborne laser scanning present status and future expectations. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 54, Number 2, July 1999, pp (4) Axelsson, P., Processing of laser scanner data algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 54, Number 2, July 1999, pp (10) Jennings, S.B., Brown, N.D. and Sheil, D., Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures. Forestry, Volume 72, pp (1). Kraus, K. and Pfeifer, N., Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 53, pp (4). Lim, K., Treitz, P., Groot, A. and St-Onge, B., Estimation of Individual Tree Heights Using LiDAR Remote Sensing. Proceedings of the Twenty-Third Annual Canadian Symposium on Remote Sensing, Quebec, QC, August 20-24, 2001, (1). Loos, R., Niemann, O., Identification of Individual Trees And Canopy Shapes using LiDAR Data for Fire Management. Geoscience and Remote Sensing Symposium, IGARSS IEEE International Conference, Popescu, S.C., Wynne, R.H. and Nelson, R.F., Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37(1-3), pp Takahashi, T., Yamamoto, K., Miyachi, Y., Senda, Y., Tsuzuku, M., The penetration rate of laser pulses transmitted from a small-footprint airborne LiDAR: a case study in closed canopy, middle-aged pure sugi (Cryptomeria japonica D. Don) and hinoki cypress (Chamaecyparis obtuse Sieb. Et Zucc.) stands in Japan.

9 Journal of Forest Research, Volume 11, Number 2, pp Wehr, A., Lohr, U. and Baltsavias, E., Theme issue on airborne laser scanning. ISPRS Journal of Photogrammetry & Remote Sensing, Volume 54, pp Wehr, A. and Lohr, U., Airborne laser scanning an introduction and overview. ISPRS Journal of Photogrammetry & Remote Sensing, Volumes 54, pp