3D segmentation of non-forest trees for biomass assessment using LiDAR data

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1 3D segmentation of non-forest s for biomass assessment using LiDAR data Matthias RENTSC 1, Josef REITBERGER 1, Alfons KRISMANN and Peter KRZYSTEK 1 1 Department of Geoinformatics Munich University of Applied Sciences Karlstraße Munich Tel.: +49 (0) {matthias.rentsch, josef.reitberger, peter.krzystek}@hm.edu Institute for Landscape- and Plant-Ecology University of ohenheim Aug.-v.-artmann Str Stuttgart Tel.: +49 (0) a_krismann@uni-hohenheim.de Keywords: full waveform, non-forest, 3D segmentation, vegetation volume, biomass Summary: The main goal of the BMU (German federal ministry for the environment, nature conservation and nuclear safety) funded project LiDAR based biomass assessment is the precise and nationwide detection and calculation of the biomass potential coming from wood cuttings. For this purpose, first and last pulse LiDAR data serve as data basis and are available all over the country by 013. A major issue of our study is to model the relationship between the vegetation volume derived from adequate LiDAR measurements and the above-ground biomass (AG for welldefined grove types. The mandatory field calibrations are performed in advance for selected reference groves (e.g. single s, stands, hedges). For this purpose, full waveform LiDAR data were acquired in August 009 in leaf-on condition. Furthermore, the groves are captured in April 010 by a precise and full 3D TLS registration in leaf-off situation. The paper is reporting about results from a novel 3D segmentation adopted for non-forest s and the follow-on vegetation volume calculation. Furthermore, the focus is on the estimation of the AGB from the diameter at breast height (DB) and the height. Finally, conversion factors are derived which relate the AGB to the LiDAR derived volume calculations. 1 Introduction One of the main intensions of the Erneuerbare Energien Gesetz (EEG) is the enhancement of renewable energy resources for the electric power supply up to at least 30% until 00. Beside biomass coming from short-rotation wood and forest fuel, the incorporation of wood cuttings from landscaping is getting more and more attractive. owever, an intensive use of this primary energy source is not yet realized due to technical and logistic reasons. A comprehensive estimation of above-ground biomass based on first and last pulse data (density pts/m ) was carried out in Norway s boreal forests, leading to R =0.88 for AGB (Næsset and Gobakken, 008). An ongoing nationwide project, LASER-WOOD (ollaus et al., 010) aims to assess the growing stock and AGB potential for the Austrian forests using LiDAR and forest inventory data. The study of (van Aardt et al., 008) is dedicated to evaluate the potential of an object-oriented approach for forest type classification (deciduous-coniferous, deciduous-coniferous-mixed) as well as volume and biomass estimation in the Virginia Piedmont, U.S.A. based on small-footprint, multiple return LiDAR data. The classifications yield an overall accuracy up to 89%, and the estimates considering the volume and biomass estimations exhibit differences of no more than 5.5% with respect to field surveys. All in all, many methods described in the literature were more or less applied to closed forest stands. In contrast, non-forest stands (e.g. open woodland groves, urban s) are characterized by heterogeneous structures containing various species within a small area, complex shapes, and high fractions of bushy material in many cases which suppress the penetration of laser pulses. In addition, the standard methods for segmentation which lead to reasonable good results for forest stands are less effective in case of non-forest conditions. These factors make it even difficult to correctly assess the biomass for open woodland groves (Straub, 010). This thesis (Straub, 010) is also reporting about some studies in Germany, which are characterized by high uncertainties in terms of the amount of biomass. One reason is that the validation is performed only with a small number of

2 M.Rentsch, J.Reitberger, A.Krismann and P.Krzystek random tests coming from destructive sampling. In (Velázques-Martí et al., 010), a prediction model was developed which allows the biomass estimation of a bushy Mediterranean forest area near Valencia/Spain consisting of 5 species using a LiDAR derived canopy height model (CM). If this model can be transferred to the conditions in Germany was not yet examined. An investigation with emphasis on the estimation of urban green volume is presented by (echt et al., 008). Our paper is focused on the results concerning the initial calibration efforts for two sample reference groves. (1) The volume determination after a 3D Normalized Cut segmentation for first+last pulse (F+L) and full waveform (FWF) airborne LiDAR data. () Determination of a conversion factor to relate the estimated AGB derived from allometric equations to the calculated vegetation volume. Methods The main objective of the field calibrations is the determination of correlation factors which allow the appropriate conversion of the vegetation volume [m 3 ] of non-forest s derived by LiDAR methods into the unit of totally dry above-ground biomass in form of wood chips, the bulk cubic meter [BCM]. As a first step, the simplified assumption is established by considering a linear correlation between vegetation volume and the corresponding above-ground biomass. erein, the biomass is estimated on behalf of allometric equations using DB and height as input parameters (Zianis et al., 005), and by applying a common factor of.43 to transform solid cubic meter into BCM. In recent studies concerning the volume-biomass-conversion, the vegetation volume is in many cases approximated by simple geometric elements like cuboids, leading to high uncertainties for the volume determination (e.g. 3% (Cremer, 007)). Thus, the primary option of our work is to refine the calculation of the vegetation volume by means of a precise triangulated surface model using the 3D Alpha-Shape algorithm, and taking advantage of the 3D Normalized Cut segmentation applied to the F+L and FWF airborne LiDAR data..1 3-D Normalized Cut segmentation Single s are found by a novel 3D segmentation technique which is based on Normalized Cut segmentation known from image analysis (Reitberger et al., 009). The key idea of the new approach is to feature the vegetation area in a voxel representation (figure 1a). The Normalized Cut segmentation applied in the voxel structure of a region of interest (ROI) is based on a graph G. The two disjoint segments A and B of the graph are found by maximizing the similarity of the segment members and minimizing the similarity between the segments A and B (figure 1b) solving the cost function NCut( Cut( Assoc( V ) Cut( Assoc( B, V ) (1) with Cut( as the total sum of weights between the segments A and B and w ij i j B Assoc( V ) as the sum of the weights of all edges ending in the segment A. w ij i j V The weights w ij specify the similarity between the voxels and are a function of the LiDAR point distribution and features calculated from the pulse width and the intensity of the single decomposed reflections. A minimum solution for (1) is found by means of a corresponding generalized eigenvalue problem.

3 M.Rentsch, J.Reitberger, A.Krismann and P.Krzystek 3 (a) Fig. 1: Single segmentation using Normalized Cut. (a) Subdivision of ROI into a voxel structure and division of voxels into two segments and (b) Segmentation results with the reference s as black lines. It turned out that the new segmentation technique is able to improve the detection rate in lower forest areas significantly. Furthermore, the approach can split single s which do not appear in the CM as isolated local maxima. Note that the approach is not dependent from full waveform LiDAR data. It can also successfully be applied to conventional LiDAR data just providing 3D point coordinates like first+last pulse. (b). Derivation of stem volume Since the stem (resp. vegetation) volume cannot be directly measured from ALS data, an indirect calculation is conducted according to equation () using measurable segment features (table I). V stem A V 7 A V () The coefficients b 0 b 8 in () are estimated in a least squares adjustment whereby V stem is the known stem volume of reference s. In the case of the normalized cut segments the points are separated from possible existing stem reflections at first in order to calculate the parameters V and. According to (Reitberger et al., 007) these points are all the laser hits above the base height h base. Finally, the 3D Alpha-Shape triangulation was performed by making use of the Computational Geometry Algorithms Library (CGAL). Segment feature A V Definition eight of Area of Volume of eight of Watershed Normalized Cut segments segments Difference between highest laser reflection within segment and interpolated DTM height Area of Watershed polygon of laser points in segment Maximum D convex hull Crown surface by using - 3D Alpha-Shapes Difference between - highest and lowest point Tab. 1: Measureable segment features 3 Material For the entire project, various calibration sites are distributed over six federal states of Germany (mostly in Baden-Württemberg). The two calibration areas which are concerned in our investigations are located in the Bavarian Forest in southeastern Germany (Arnbruck and Regen). For this two areas, a sum of 13 reference groves were chosen by visual

4 M.Rentsch, J.Reitberger, A.Krismann and P.Krzystek 4 inspection comprising four hedges with s, one hedge, one high grove, five single s and two forest islands. All of these groves were captured by full waveform LiDAR data (10 pts/m ) in August 009 by the Riegl LMS-Q560 scanner in leaf-on condition. Furthermore, the areas were covered by routine first+last pulse ALS surveys on behalf of the Bavarian Survey Administration in April 008 and October 009. The acquisitions were performed under leaf-off conditions with a mean point density of 1- pts/m. In addition, seven of the groves are measured by a precise and full 3D TLS registration in April 010 using the Riegl LMS-Z390 in leaf-off situation. Two groves located near the city of Regen are described in more detail. The first reference grove of type Forest Island consists of around 00 s and various bushes. It covers an area of approximate 0.4 ha. Tree species are mostly oak (47%), birch (9%) and aspen (6%). The second grove of type edge with Trees comprises about 90 s, with mostly aspen (80%), on an area of around 0.16 ha. 4 Results The 3D segmentation was applied to the F+L and the FWF LiDAR data using a voxel size of 1.0 m (F+L) and 0.5 m (FWF). Figures to 5 display the polygon outlines of the resulting 3D segments as colored solid lines and, in addition, the locations of all reference s as colored dots. Fig. : Outline polygons (solid colored lines) of F+L 3D segments and locations of reference s (colored dots) for grove type Forest Island. Fig. 3: Outline polygons (solid colored lines) of FWF 3D segments and locations of reference s (colored dots) for grove type Forest Island.

5 M.Rentsch, J.Reitberger, A.Krismann and P.Krzystek 5 Apparently, the 3D segmentation of FWF data reveals more segments compared to the F+L situation. Moreover, in case of grove type Forest Island, it can be seen that the segments of the outer zone are considerably larger than these of the inner region. In contrast, for grove type edge with Trees, more or less similar segment sizes are derived, especially for FWF. This can be explained by the rather consistent structure of this grove type. Fig. 4: Outline polygons (solid colored lines) of F+L 3D segments and locations of reference s (colored dots) for grove type edge with Trees. Fig. 5: Outline polygons (solid colored lines) of FWF 3D segments and locations of reference s (colored dots) for grove type edge with Trees. The triangulated surfaces using the 3D Alpha-Shape algorithm based on FWF 3D segments are shown in figure 6. Fig. 6: Tree surface triangulation using 3D alpha shapes for grove type Forest Island (FWF).

6 M.Rentsch, J.Reitberger, A.Krismann and P.Krzystek 6 The estimated stem volume was regressed against the summed stem volume based on allometric equations of the reference s within a segment. The results are presented in figures 7 and 8. In addition, the residuals after the regression are highlighted in figures to 5 as colored bold solid lines. In this case, the colors indicate residuals below 1-sigma (green), between 1- and -sigma (yellow) and above -sigma standard deviation (red). The magenta colored lines mark outliers, and the light grey lines denote segments without reference s caused by overhanging branches of some of the outer s. Finally, table II resumes the results for the calculated stem volume, AGB and biomass conversion factors. Fig. 7: Stem volume regression of 3D segments for grove Forest Island (left:f+l, right: FWF). Fig. 8: Stem volume regression of 3D segments for grove edge with Trees (left:f+l, right: FWF). Reference grove Stem volume [m 3 ] Above-ground biomass [BCM] Biomass conv. factor Forest Island (F+L) edge with Trees (F+L) Forest Island (FWF) edge with Trees (FWF) Table : Results for stem (resp. vegetation) volume, AGB and biomass conversion factors 5 Discussion The regression analysis exhibits different results for the two reference groves. The coefficients of determination R =0.77 (F+L) and R =0.6 (FWF) for the type Forest Island reveal a higher discrepancy between the stem volume estimations derived from LiDAR data and the reference s. Probably, due to several inclined s within this grove type, the s are often not included in the segments with the corresponding stem positions. The difference of R between F+L and FWF can be explained by an adjusting effect due to the larger segments in case of F+L. Moreover, the laser penetration

7 M.Rentsch, J.Reitberger, A.Krismann and P.Krzystek 7 is different for F+L (leaf-off) and FWF (leaf-on), causing an inappropriate base height h base for FWF data. In contrast, a high coherence with R =0.95 (F+L) and R =0.91 (FWF) is resulting from the regression analysis for reference grove type edge with Trees. In this case, the dominant species is aspen, and furthermore, the structure of this grove type is more homogeneous than for Forest Island. Finally, the biomass conversion factor in table II is in the same order of magnitude for both grove types showing that the volume calculations are reasonable. 6 Conclusions and outlook It can be resumed, that our approach shows promising results in terms of accuracy and additional structural information. This is a significant improvement with respect to the common approaches used so far in which the vegetation volume is simply approximated by the product of mean height and base area, i.e. the green volume. The first directive for the next future will be the refinement of the 3D segmentation algorithm to fully exploit the capabilities of airborne LiDAR data. In this context, we have to account for the special conditions of non-forest s with respect to forest s, e.g. more complex structures, higher vegetation density and divergent shapes. Finally, the approach has to be validated including much more sample reference groves to derive significant outcomes. owever, there is a strong need for destructive sampling of selected reference groves to calibrate the AGB estimations. Acknowledgement This research has been funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) under the reference 03KB037A. References CREMER, T., 007: Mobilisierung und wirtschaftliche Nutzung von Rohholz aus Wald und Landschaft zur Energieerzeugung. Final report (in German), DBU, Osnabrück, Germany. ECT, R., MEINEL, G. & BUCROITNER, M.F., 008: Estimation of urban green volume based on single-pulse LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, Vol. 46 (11): OLLAUS, M., EYSN, L., SCADAUER, K., JOCEM, A., PETRINI, F. & MAIER, B., 010: LASER-WOOD: Estimation of the above ground biomass based on laser scanning and forest inventory data. 11. Österreichischer Klimatag, Vienna, Austria, p. NÆSSET, E. & GOBAKKEN, T., 008: Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment, Vol. 11 (6): REITBERGER, J., KRZYSTEK, P. & STILL U., 007: Combined segmentation and stem detection using full waveform LiDAR data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36 (part 3/W5): REITBERGER, J., SCNÖRR, C., KRZYSTEK, P. & STILL U., 009: 3D segmentation of single s exploiting full waveform LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 64 (6): STRAUB, C., 010: Acquisition of the bioenergy potential and the availability in the forest and nonforest land using new remote sensing techniques. PhD thesis (in German), University of Freiburg, Germany. VAN AARDT, J.A.N., WYNNE, R.. & SCRIVANI, J.A., 008: Lidar-based mapping of forest volume and biomass by taxonomic group using structurally homogeneous segments. Photogrammetric Engineering & Remote Sensing, Vol. 74 (8): VELÁZQUES-MARTÍ, B., FERNÁNDEZ-GONZÁLEZ, E., ESTORNELL, J. & RUIZ, L.A., 010: Dendrometric and dasometric analysis of the bushy biomass in Mediterranean forests. Forest Ecology and Management, Vol. 59 (5): ZIANIS, D., MUUKKONEN, P., MÄKIPÄÄ, R. & MENCUCCINI, M., 005: Biomass and stem volume equations for species in Europe. Silva Fennica Monographs, Vol. 4, 63 p.