DEVELOPMENT OF A PROCEDURE FOR VERTICAL STRUCTURE ANALYSIS AND 3D-SINGLE TREE EXTRACTION WITHIN FORESTS BASED ON LIDAR POINT CLOUD

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1 IAPRS Volume XXXVI, Pat 3 / W52, 27 DEVELOPMENT OF A PROCEDURE FOR VERTICAL STRUCTURE ANALYSIS AND 3D-SINGLE TREE EXTRACTION WITHIN FORESTS BASED ON LIDAR POINT CLOUD Yunsheng Wang a,*, Holge Weinacke a, Babaa Koch a a Dept. of Remote Sensing and Landscape Infomation Systems, Univesity of Feibug, Tennenbache St. 4, 7916 Feibug, Gemany (yunsheng.wang, holge.weinacke, babaa.koch)@felis.uni-feibug.de KEY WORDS: Canopy, Vetical stuctue, Tee cown, 3D model, Lida, Point cloud ABSTRACT: A pocedue fo both vetical canopy stuctue analysis and 3D single tee extaction based on Lida aw point cloud is pesented in this pape. The whole study aea is segmented into small study cells by a aste net. Fo each cell, a nomalized point cloud whose point heights epesent the absolute heights of the gound objects is geneated fom the oiginal Lida aw point cloud. The main tee canopy layes and the height anges of the layes ae detected accoding to a statistical analysis of the height distibution pobability of the nomalized aw points. Fo the 3D extaction of individual tees, the nomalized aw points ae esampled into a local voxel space. A seies of hoizontal 2D pojection images at diffeent height levels ae then geneated espect to the voxel space. Tee cown egions ae detected fom the pojection images. Individual tees ae then extacted by means of a pe-ode foest tavesal pocess though all the tee cown egions at diffeent height levels. Finally, 3D tee cown models of the extacted individual tees ae econstucted. 1. INTRUDUCTION The vetical stuctues of stands ae of high inteest in foest management. The knowledge on the vetical stuctue impoves egession models fo estimation of wood volume and biomass. It is also of high inteest fo assessment of the egeneation success and biodivesity aspects. Lida is especially suitable to epoduce the vetical stuctue of foest stand due to its capability of thee-dimensional measuements with high accuacy. The utilization of Lida in foest investigation can be geneally divided into canopy height distibution based and single tee detection based. Seveal appoaches fo Canopy height distibution detection have been achieved in the past few yeas (Naesset, 22; Lim et al, 23; Hogmgen and Jonsson, 24), these eseaches ae mostly concentated with the foest chaacteistics of the top canopy laye. Concen the vetical distibution of canopy layes, eseaches based on lagefootpint Lida data with continuous wavefom have been accomplished by Lefsky et al.(1999) and Hading et al.(21). Andesen et al.(23) has pesented a method fo estimating vetical stuctue of foest though a goup of egession functions based on filed investigations. As fo single tee delineation, the majoity of the existed algoithms ae DSM (Digital Suface Model) based (Hyyppä and Inkinen 1999; Pesson et al. 22; Koch et al. 26). Tees ae delineated accoding to the featues of cowns on the DSM, thus the individual tees in the lowe canopy laye whose cowns ae coveed by the top canopy laye cannot be detected. Beside the detection of individual tees, Pyysalo and Hyyppä (22) has povided a pocess fo econstucting tee cowns, with a pe knowledge of the location and the cown size of single tee, aw points belong to the tee ae extacted, the height of the tee, the height of the cown and the aveage adius of the cown at diffeent heights ae deived. In this pape, we will pesent a pocedue fo both vetical stuctue analysis and single tee extaction within foest aea based on Lida aw point cloud. The majo task of vetical stuctue analysis is to detect the numbe of main canopy layes and the height ange of each canopy laye. A moe detailed study of the spatial featues of canopies is pefomed by the single tee extaction pocess. Individual tees ae detected not only fom the uppe canopy laye but also fom the lowe canopy laye in between the gound and uppe canopy laye. Shapes of individual tee cowns ae then delineated and 3D models of tee cowns ae econstucted. The aw point cloud is the basis of ou analysis. Due to the huge amount of data, it is not possible to analyse all the aw points within a study aea in one step. Theefoe the study aea is segmented into a aste net fistly. Each gid cell is then a study cell, which with a size of 2m*2m = 4m 2 in this case. Futhe analyses ae caied out fo each study cell sepaately. The esults of each study cell can then be assembled fo the whole study aea at the end. The study aea is in Kuenache Wald, the size of this study aea is 2.7km*2.8km, the dataset being used is a fist-last etuned Lida data, the density of aw point cloud is 4~7 points pe m VERTICAL STRUCTURE ANALYSIS 2.1 Nomalized Point cloud To get the absolute object height of the aw points, the influence of teain must be eliminated (Figue 1. Left). A aste DTM (Digital Teain Model) is used fo the nomalization of aw point heights. The DTM is geneated fom the aw point clouds by TeesVis, a softwae fo LIDAR data pocessing developed by Depatment of Remote Sensing and Landscape Infomation Systems (FeLis) (Weinacke et al., 24). Figue 1. Compaison between oiginal Lida aw point cloud and nomalized point cloud; Left: oiginal Lida aw point cloud and the DTM; Right: nomalized point cloud ove a zeo height level suface 419

2 ISPRS Wokshop on Lase Scanning 27 and SilviLase 27, Espoo, Septembe 12-14, 27, Finland As been showed in Figue 1.(Left), aw points ae pojected above the DTM, the height diffeence between a aw point and its coespondent teain is maked as the nomalized height of the point. A nomalized point cloud is then geneated (Figue 1. Right), point heights of the nomalized point cloud epesent the absolute heights of the objects. 2.2 Detection of Canopy Layes Height Distibution Pobability of Nomalized Points: With a statistical pocess of the nomalized points,a height distibution pobability function ф(h) can be deived. Accoding to the physical featue of Lida data, most of the eflected points ae located at canopy layes in the foest aea. Theefoe thee should be an obvious incease of eflected points at each canopy laye. Thus, the poblem of canopy laye detection is then tansfeed to a salient cuve detection based on the height distibution pobability function ф(h). To educe the influence of slight amplitude movements on the function, ф(h) is fistly smoothed with a gaussian function, a smoothed function S(h) is geneated, the second deivative S (h) is then calculated fo the smoothed function S(h). As pesented in figue 2., the magnitude of the second deivative is a useful citeion fo salient cuve detection. With each S (h) =, thee is an inflexion point of function S(h) at h. At the intevals of h whee S (h)<, thee must be salient cuves of function S(h), so the intevals of h ae consideed as height anges of tee canopy layes. Numbe of Points ф(h) S(h) S (h) h (Height, m) Figue 2. Relationship betweenф(h), S(h) and S (h), the anges of canopy layes ae extacted at the height intevals whee S (h) < Attibutes of Canopy Layes: The numbe of canopy layes in each study cell and the height ange of each canopy laye ae the main attibutes deived fom the vetical stuctue analysis. The ange of a canopy laye stats fom the height whee the most apid incease of point amounts occued, the end of the ange is maked at the height whee the shapest decease of point amounts takes place (Figue 3.(a)). As been illustated in Figue 3 (b)., although thee is no diffeence on height distibution of nomalized points between the two cases, the spatial elationship of canopy layes is distinct. In the left case, the canopy layes ovelap, such kind of situation can be consideed as a eal duple laye foest stand, on the contay, the canopy layes in the ight case ae sepaated, this is actually a stand of tees with mixed heights. To detect the eal duple laye stand, futhe studies on the spatial distibution of canopy layes ae needed. This task is accomplished duing the single tee extaction pocess. 3. SINGLE TREE EXTRACTION 3.1 2D Hoizontal Pojection Images As being shown in Figue 4., a local voxel space is defined fo each study cell, all the nomalized points within the study cell will be esampled into the local voxel space. layes ows columns Z Y X (,,) M = 1 1 t x y o 1 (a) (b) Figue 4. (a) Local voxel space with eal wold coodinate system (x,y,z) and voxel coodinate system (ows,columns,layes); (b) Definition of tansfomation matix M Fo the tansfomation of nomalized points fom eal wold coodinate system (x,y,z) to the voxel coodinate system (ows,columns,layes). A tansfomation matix M is defined as figue 4.(b), of which: epesents the aste esolution of the hoizontal suface in the voxel space; t epesents the thickness of each laye in the voxel space; x o, y o ae the coodinates of the local oigin, namely the x, y coodinate in eal wold coodinate system of the uppe-left cone in the study cell; Fo each nomalized point in eal wold P(x,y,z), thee is a coespondent point in voxel space P (ow, column, laye), the elationship between P and P can be defined with function: T [ ow column laye] = Round ( M [ x y z 1] Accoding to the density of aw point cloud and the scale of and t in tansfomation matix M, it is possible that seveal nomalized points ae located within a same voxel. Take all the voxels of a single laye out of the voxel space, the voxels of the laye can be consideed as pixels of an image. The numbe of nomalized points within each voxel can be maked as the gay value of the coespondent pixel in the image. columns T ) ows (a) (b) Figue 3. (a) Points within the anges of the detected canopies with compaison of oiginal point cloud; Left, Nomalized point cloud; Right, Points within detected canopy anges; (b) Two diffeent foest stands with same height distibution pobability density function; Left: Duple laye foest stand; Right: Single laye foest stand with tees of mixed height Figue 5. Nomalized points in local voxel space and two examples of pojection images of two vetical neighbouing layes of the voxel space 42

3 IAPRS Volume XXXVI, Pat 3 / W52, 27 A 2D hoizontal pojection image of the selected laye is then geneated (Figue 5.). The aw points of each individual tee cown will pesent a cluste featue on the hoizontal pojection image and the pesent of the cluste featues is highly elated with the hoizontal esolution and the thickness t of the voxel space. The values of paamete and t in tansfomation matix M ely on the density of the aw point cloud. Accoding to ou expeiments, a hoizontal esolution of.5 mete and a thickness of 2 mete ae the ideal values fo ou dataset. 3.2 Extaction of Individual tees The clustes on the hoizontal pojection image at each laye epesent the distibution of tee cowns in the coespondent height level. Theefoe an individual tee cown should be visible at the same location of seveal vetical neighbouing layes. The basic concept of single tee extaction is to tace the cluste featues on the pojection images fom top to bottom though pojection images at layes of diffeent height levels Tee cown egions on 2D hoizontal pojection image: Potential tee cown egions in each laye ae extacted based on the cluste featues on the coespondent pojection image. A hieachical mophological opening and closing pocess with a goup of pedefined stuctuing elements (Figue 6.) is pefomed. Figue 6. Stuctuing elements used in hieachical mophological pocess It can be pesumed that the amount of aw points should be highe nea the cente of each tee cown. Conside to the pojection image, a highe gay value of a pixel epesents a highe point amount in its coespondent voxel. Thus a highe significance should be assigned to the pixel with highe gay value and a lage neighbouhood of the pixel should be kept. The mophological pocess begins with the bightest pixels on the pojection image, these pixels ae taken as seeds and closed by the lagest stuctuing element, then opened by the smallest stuctuing element, potential tee cown egions ae then extacted based on the bightest pixels. Simila pocess is fulfilled with pixels of othe gay value levels, the lowe gay value the pixels have, the smalle stuctuing element is used fo closing and the bigge stuctuing element is used fo opening. Finally, potential egions fom diffeent gay value levels at same neighbouhoods ae meged (Figue 7.). Levels of gay value ae defined accoding to the histogam of the pojection image at non-zeo gay value aea, of which highest level: α>=8%; middle level: 2% <α<8%; lowest level:α<= 2%. pojection image is used as backgound, seed pixels ae maked as yellow, contous of extacted tee cowns ae maked as ed. Uppe left, Uppe ight, Lowe Left: mophological pocess based on the pixels with highest, middle, lowest gay value level; Lowe ight: final esult of tee cown extaction Pe-ode foest tavesal: In compute science, foest tavesal o moe geneally tee tavesal efes to the pocess of visiting each node in a foest o tee data stuctue systematically. In ou case, tee cown egions on the layes at diffeent height levels can be consideed as nodes at diffeent levels of a foest data stuctue, cown egions on the top laye ae the pime oot nodes of the foest. A pe-ode foest tavesal pocess is fulfilled to visit all the cown egions of the foest. Individual tees ae extacted duing the foest tavesal pocess by gouping the vetical neighbouing cown egions fom layes at diffeent height levels. The main pocedues of single tee extaction is illustated in figue 8., a eal case of single tee extaction is demonstated in figue 9.. Fo each oot node, namely the top egion of each cown, the conditions of the existence of a child node, namely a vetical neighbouing cown egion in next laye, ae listed as follows: Ai/A >Ca (1) Ai/Ac >Ca (2) D < Min(R, Rc) (3) whee Ai = intesection aea of oot node and child node A = aea of oot node; Ac is the aea of child node Ca = constant citeia in inteval [.5, 1.], fo which.8 is an ideal value in ou study case D = distance between cente points of oot node and child node R = aveage adius of oot node Rc = the aveage adius of the child node All the thee conditions ae sufficient condition, two egions can be consideed as neighbouing egions no matte which condition is fulfilled. if(foest!empty) get a Root node fom Top laye seach Child node in Next laye espect to condition (1), (2) o (3) find Child? no yes Top laye = Next laye; Root = Child; save a single tee; delete all tee nodes fom foest; Figue 8. Main pocess of single tee extaction; Figue 7. Hieachical mophological pocess fo tee cown egion extaction fom pojection image; A 421

4 ISPRS Wokshop on Lase Scanning 27 and SilviLase 27, Espoo, Septembe 12-14, 27, Finland analysis. Afte the analysis fo all the study cells, the distibution of duple canopy layes in the whole study aea can be mapped (Figue 11.) Figue 9. Demonstation of single tee extaction duing a peode foest tavesal pocess; Left: tee cown egions in diffeent layes; Right: Result of single tee extaction, each tee is maked with a diffeent colou, the oot note of each tee is filled up with the same colou as the coesponding tee D models of tee cowns: Each detected tee cown is descibed by an aay of 2D tee cown egions in diffeent layes at diffeent height level. Since the layes in voxel space have cetain thickness, 3D pisms can be constucted fo the 2D cown egions in diffeent layes with the thickness of layes as the height of the pisms. A goup of 3D pisms at diffeent height levels ae then deived fo each individual cown, and a pismatic 3D tee cown model can be econstucted by a combination of all the tee cown pisms (Figue 1.). (,,) (2,,) (,2,) (,2,) (2,,) (,,) Figue 1. VRML(Vitual Reality Modeling Language) pismatic model of individual tee cowns within a single study cell with compaison of nomalized point cloud; Left: Nomalized aw points in a single study cell; Middle & Right: VRML models of individual tee cowns in the study cell visualized fom diffeent view diections, individual tee cowns ae maked with diffeent colous. The 3D models have the same vetical esolution with the local voxel space Featues of study cell and individual tees: 1. Fo an individual tee the following paametes ae available: Height of the tee; Height ange of the cown; Diametes of the cown at diffeent height levels; The lagest diamete of the tee cown and its coespondent height; Volume of the cown. 2. Fo a study cell, beside the numbe of individual tees, the numbe of cown layes is examined accoding to the spatial distibution of the extacted cowns. If thee is an ovelapping between individual cowns at diffeent height levels, the study cell is consideed as duple layeed. This is a supplemental pocess fo the study cells in which moe than one canopy layes have been detected accoding to the vetical stuctue Figue 11. Map of duple canopy laye stands in one of the study aea (Kuenache Wald); Study cells with two canopy layes ae maked as ed; Study cells with single canopy laye ae maked as yellow; Fo 3D single tee extaction, a compehensive efeence data set based on field measuements is still missing. Coase evaluations ae based on ocula inspections of the aw data cloud points, aeial photogaphs and field infomation. 4. CONCLUSION The statistical method seems to be efficient and eliable in detecting the existence and height ange of canopy layes due to the fist visual checks. The dawback is that it is incapable in distinguishing a eal duple canopy laye stuctue and a single laye stuctue with tees of mixed heights. The existence of duple canopy laye stuctue is examined duing the single tee extaction pocess accoding to the spatial distibution of canopy layes. Fo the single tee extaction, the advantage of ou algoithm is that not only the individual tees whose cowns ae at the top canopy laye, but also the lowe tees whose cowns ae coveed by the top canopy laye ae extacted. The cown contous extacted fom diffeent height level of an individual tee cown will povide a highe appoximation between the 3D cown model and the eality. The main poblems still faced ae due to the tees with big cowns and the conjunct neighbouing tees. Fo big tee cown with moe than one cown peak, the cown might be split into moe than one individual cowns. Tees whose cowns ae tightly conjunct might be consideed as a single tee. Anothe disadvantage of cuent algoithm is that the segmentation of study cells might split the tees along the bode of the cells. The main influence factos on the quality of the 3D single tee extaction algoithm ae the density of the aw point cloud and the stand situation of the foest. Highe point density will impove the accuacy of tee extaction. Bette esult can be expected fo a lowe canopy closue foest stand. Despite the mentioned challenges, the outcomes of ecent single tee extaction algoithm ae encouaging. Futhe studies will concentate on the impovement of the single tee detection and modelling pocess, utilization of full wavefom data could be helpful fo the achievement of bette tee cown models, and an enlagement of study cell size o a substitution of the aste gid by a moving window fo study cell segmentation would educe the ove split of the tees along the bode of each cell. 422

5 IAPRS Volume XXXVI, Pat 3 / W52, 27 REFERENCES Andesen, H.E., Foste, J.R., Reutebuch, S.E. (23). Estimating foest stuctue paametes on Fot Lewis Militay Resevation using aibone lase scanne (LIDAR) data. Poceedings of the 2nd Intenational Pecision Foesty Symposium. Seattle, Washington. Univesity of Washington, College of FoestResouces, Hading, D., Lefsky, M., Pake, G., Blai, J. (21). Lase altimete canopy height pofiles: Methods and validation fo closed-canopy, boadleaf foests. Remote Sensing of The Envionment 76, Holmgen, J. and Jonsson, T. (24). Lage scale aibone lasescanning of foest esouces in Sweden. In:Thies, M., Koch, B, Spiecke, H.and Weinacke, H.(eds.). Lase scannes fo foest and landscape assessment. Poceedings of the ISPRS woking goup VIII/2. Feibug, Gemany, Octobe, Intenational Achives of Photogammety, Remote Sensing and Spatial Infomation Sciences. Volume XXXVI,Pat 8/W2, Hyyppä, J. and Inkinen, M. (1999). Detecting and estimating attibutes fo single tees using lase scanne. The Photogammetic Jounal of Finland 16, Koch, B., Heyde, U., Weinacke, H.(26) Detection of individual tee cowns in aibone lida data. Photogammetic engineeing and emote sensing 72:4, popeties and canopy stuctue of foests of Douglas-fi and westen hemlock. Remote Sensing of Envionment, 7, Lim. K, Teitz, P., Baldwin, K., Moison, I. and Geen J. (23). Lida emote sensing of biophysical popeties of toleant nothen hadwood foests. Canadian Jounal of Remote Sensing 29, Næsset, E. (22). Pedicting foest stand chaacteistics with aibone scanning lase using a pactical two-stage pocedue and field data. Remote Sensing of Envionment 8, Pesson, Å., Holmgen, J. and Södeman, U. (22). Detecting and measuing individual tees using an aibone lase scanne. Photogametic engineeing & Remote Sensing 68, Pyysalo, U., Hyyppä, H. (22). Reconstucting tee cowns fom lase scanne data fo featue extaction. Commission III Symposion Gaz, Weinacke, H., Koch, B., Weinacke. R.(24). TREESVIS- A softwae system fo simultaneous 3D-Real-Time visualization of DTM, DSM, Lase ow data, Multi-spectal data, simple tee and building models. Poceedings of the ISPRS woking goup on Lase-Scannes fo Foest and Landscape Assessment, 3-6 Octobe, Feibug, Gemany, The Intenational Achives of Photogammety, Remote Sensing and Spatial Infomation Sciences, vol XXXVI pat 8/W2, ISSN , 9-95 Lefsky,M., Cohen,W., Acke,S., Pake, G., Spies, T., & Hading, D. (1999). Lida emote sensing of biophysical 423