Research Article
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Year 2021, Volume: 6 Issue: 1, 20 - 26, 01.02.2021
https://doi.org/10.26833/ijeg.668352

Abstract

References

  • Akbulut Z, Özdemir S, Acar H, Dihkan M & Karslı F (2018). Automatic extraction of building boundaries from high-resolution images with active contour segmentation. International Journal of Engineering and Geosciences, 3(1), 36-42.
  • Beland M, Parker G, Sparrow B, Harding D, Chasmer L, Phinn S, Antonarakis A & Strahler A (2019). On promoting the use of lidar systems in forest ecosystem research. Forest Ecology and Management, 450, 117484. DOI: 10.1016/j.foreco.2019.117484
  • Bentley J L (1975). Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9), 509-517. DOI: 10.1145/361002.361007
  • Chen Y & Zhu X (2013). An integrated GIS tool for automatic forest inventory estimates of Pinus radiata from LiDAR data. GIScience & remote sensing, 50(6), 667-689. DOI: 10.1080/15481603.2013.866783
  • Cramer M (2010). The DGPF-test on digital airborne camera evaluation–overview and test design. Photogrammetrie-Fernerkundung-Geoinformation, 2010(2), 73-82. DOI: 10.1127/1432-8364/2010/0041
  • Dogon-Yaro M A, Kumar P, Rahman A A & Buyuksalih G (2016). Extraction of urban trees from integrated airborne based digital image and LiDAR point cloud datasets-initial results. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 81.
  • Ester M, Kriegel H P, Sander J & Xu X (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, 96(34), 226-231.
  • Gupta A, Byrne J, Moloney D, Watson S & Yin H (2018). Automatic Tree Annotation in LiDAR Data. In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management, 36-41.
  • Haq S M A (2011). Urban green spaces and an integrative approach to sustainable environment. Journal of Environmental Protection, 2(05), 601-608.
  • Hartling S, Sagan V, Sidike P, Maimaitijiang M & Carron J (2019). Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning. Sensors, 19(6), 1284. DOI: 10.3390/s19061284
  • Hyyppä, J, Schardt M, Haggrén H, Koch B, Lohr U, Scherrer H U et al. (2001). HIGH-SCAN: The first European-wide attempt to derive single-tree information from laser scanner data. The Photogrammetric Journal of Finland, 17(2), 58-68.
  • Karsli F, Dihkan M, Acar H & Ozturk A (2016). Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm. Arabian Journal of Geosciences, 9(14), 635. DOI: 10.1007/s12517-016-2664-7
  • Koch B, Heyder U & Weinacker H (2006). Detection of individual tree crowns in airborne lidar data. Photogrammetric Engineering & Remote Sensing, 72(4), 357-363.
  • Lindberg E, Eysn L, Hollaus M, Holmgren J & Pfeifer N (2014). Delineation of tree crowns and tree species classification from full-waveform airborne laser scanning data using 3-D ellipsoidal clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(7), 3174-3181.
  • Liu J, Shen J, Zhao R & Xu S (2013). Extraction of individual tree crowns from airborne LiDAR data in human settlements. Mathematical and Computer Modelling, 58(3-4), 524-535.
  • Liu L, Lim S, Shen X & Yebra M (2019). A hybrid method for segmenting individual trees from airborne lidar data. Computers and Electronics in Agriculture, 163, 104871.
  • Mielcarek M, Stereńczak K & Khosravipour A (2018). Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation and Geoinformation, 71, 132-143.
  • Otsu N (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
  • Pauly M, Keiser R & Gross M (2003). Multi‐scale feature extraction on point‐sampled surfaces. In Computer graphics forum, 22(3), 281-289.
  • Popescu S C (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655.
  • Ramiya A M, Nidamanuri R R & Krishnan R (2019). Individual tree detection from airborne laser scanning data based on supervoxels and local convexity. Remote Sensing Applications: Society and Environment, 15, 100242.
  • Reutebuch S E, McGaughey R J, Andersen H E & Carson W W (2003). Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing, 29(5), 527-535.
  • Rutzinger M, Rottensteiner F & Pfeifer N (2009). A comparison of evaluation techniques for building extraction from airborne laser scanning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1), 11-20.
  • Secord J & Zakhor A (2007). Tree detection in urban regions using aerial LiDAR and image data. IEEE Geoscience and Remote Sensing Letters, 4(2), 196-200.
  • Sevgen S C (2018). Airborne lidar data classification in complex urban area using random forest: A case study of Bergama, Turkey. International Journal of Engineering and Geosciences, 4(1), 45-51. DOI: 10.26833/ijeg.440828
  • Van der Zande D, Hoet W, Jonckheere I, van Aardt J & Coppin P (2006). Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agricultural and Forest Meteorology, 141(2-4), 147-160. DOI: 10.1016/j.agrformet.2006.09.007
  • Véga C, Hamrouni A, El Mokhtari S, Morel J, Bock J, Renaud J P & Durrieu S (2014). PTrees: A point-based approach to forest tree extraction from LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 33, 98-108. DOI: 10.1016/j.jag.2014.05.001
  • Weinmann M (2016). Reconstruction and analysis of 3D scenes. Springer.
  • Weinmann M, Mallet C, Hinz S & Jutzi B (2015). Efficient interpretation of 3D point clouds by assessing feature relevance. AVN–Allg Vermess-Nachr, 10(2015), 308-315.
  • Zhang W, Qi J, Wan P, Wang H, Xie D, Wang X & Yan G (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. DOI: 10.3390/rs8060501
  • Zhen Z, Quackenbush L J & Zhang L (2016). Trends in automatic individual tree crown detection and delineation—Evolution of LiDAR data. Remote Sensing, 8(4), 333. DOI: 10.3390/rs8040333

Automatic extraction of trees by using multiple return properties of the lidar point cloud

Year 2021, Volume: 6 Issue: 1, 20 - 26, 01.02.2021
https://doi.org/10.26833/ijeg.668352

Abstract

Airborne laser scanning has been a valuable tool for forestry applications since it began to be used commercially. Thanks to the high 3D resolution provided by the Light Detection and Ranging (LiDAR) point cloud, it has provided great convenience in complex 3D modeling processes needed for forestry applications such as forest inventory, forest management, determination of carbon stocks and the characterization of biodiversity. LiDAR data provides a new dimension in forestry applications with its high 3D resolution and multiple return characteristics. The extraction of woodland areas from the LiDAR point cloud has great importance for automating the determination of tree heights, species and stand frequency which will be used for generating canopy height models (CHM). In this study, woodland areas in the urban scene were automatically extracted by using the multiple return properties of the LiDAR point cloud. The proposed approach consists of three major steps namely pre-processing, parameter calculation and k-d tree search for trees which were implemented in MATLAB. In the first step, multiple return points have been identified from the LiDAR point cloud, which will be then used to determine possible tree locations. Then, by using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, neighborhood relations among the multi return points which were extracted from the initial point cloud data, were formed and a rule-based filter was applied by taking advantage of neighborhood relations. In addition, the initial point cloud was filtered with the Cloth Simulation Filtering (CSF) algorithm to separate ground and non-ground points where non-ground points used to extract trees. In the second step, non-vegetation points were removed by applying a threshold based on the change of curvature and planarity parameters, which are derived from the filtered non–ground point cloud. In the last step, in order to extract trees, a k-d tree structure was created from the filtered non-ground points to find nearest neighbors of each multi return point within a given diameter in the k-d tree structure. In order to evaluate the accuracy of the approach, the extracted boundaries were compared with the manually digitized woodland boundaries from the true orthophoto of the study area using correctness, completeness and quality metrics.

References

  • Akbulut Z, Özdemir S, Acar H, Dihkan M & Karslı F (2018). Automatic extraction of building boundaries from high-resolution images with active contour segmentation. International Journal of Engineering and Geosciences, 3(1), 36-42.
  • Beland M, Parker G, Sparrow B, Harding D, Chasmer L, Phinn S, Antonarakis A & Strahler A (2019). On promoting the use of lidar systems in forest ecosystem research. Forest Ecology and Management, 450, 117484. DOI: 10.1016/j.foreco.2019.117484
  • Bentley J L (1975). Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9), 509-517. DOI: 10.1145/361002.361007
  • Chen Y & Zhu X (2013). An integrated GIS tool for automatic forest inventory estimates of Pinus radiata from LiDAR data. GIScience & remote sensing, 50(6), 667-689. DOI: 10.1080/15481603.2013.866783
  • Cramer M (2010). The DGPF-test on digital airborne camera evaluation–overview and test design. Photogrammetrie-Fernerkundung-Geoinformation, 2010(2), 73-82. DOI: 10.1127/1432-8364/2010/0041
  • Dogon-Yaro M A, Kumar P, Rahman A A & Buyuksalih G (2016). Extraction of urban trees from integrated airborne based digital image and LiDAR point cloud datasets-initial results. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 81.
  • Ester M, Kriegel H P, Sander J & Xu X (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, 96(34), 226-231.
  • Gupta A, Byrne J, Moloney D, Watson S & Yin H (2018). Automatic Tree Annotation in LiDAR Data. In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management, 36-41.
  • Haq S M A (2011). Urban green spaces and an integrative approach to sustainable environment. Journal of Environmental Protection, 2(05), 601-608.
  • Hartling S, Sagan V, Sidike P, Maimaitijiang M & Carron J (2019). Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning. Sensors, 19(6), 1284. DOI: 10.3390/s19061284
  • Hyyppä, J, Schardt M, Haggrén H, Koch B, Lohr U, Scherrer H U et al. (2001). HIGH-SCAN: The first European-wide attempt to derive single-tree information from laser scanner data. The Photogrammetric Journal of Finland, 17(2), 58-68.
  • Karsli F, Dihkan M, Acar H & Ozturk A (2016). Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm. Arabian Journal of Geosciences, 9(14), 635. DOI: 10.1007/s12517-016-2664-7
  • Koch B, Heyder U & Weinacker H (2006). Detection of individual tree crowns in airborne lidar data. Photogrammetric Engineering & Remote Sensing, 72(4), 357-363.
  • Lindberg E, Eysn L, Hollaus M, Holmgren J & Pfeifer N (2014). Delineation of tree crowns and tree species classification from full-waveform airborne laser scanning data using 3-D ellipsoidal clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(7), 3174-3181.
  • Liu J, Shen J, Zhao R & Xu S (2013). Extraction of individual tree crowns from airborne LiDAR data in human settlements. Mathematical and Computer Modelling, 58(3-4), 524-535.
  • Liu L, Lim S, Shen X & Yebra M (2019). A hybrid method for segmenting individual trees from airborne lidar data. Computers and Electronics in Agriculture, 163, 104871.
  • Mielcarek M, Stereńczak K & Khosravipour A (2018). Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation and Geoinformation, 71, 132-143.
  • Otsu N (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
  • Pauly M, Keiser R & Gross M (2003). Multi‐scale feature extraction on point‐sampled surfaces. In Computer graphics forum, 22(3), 281-289.
  • Popescu S C (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655.
  • Ramiya A M, Nidamanuri R R & Krishnan R (2019). Individual tree detection from airborne laser scanning data based on supervoxels and local convexity. Remote Sensing Applications: Society and Environment, 15, 100242.
  • Reutebuch S E, McGaughey R J, Andersen H E & Carson W W (2003). Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing, 29(5), 527-535.
  • Rutzinger M, Rottensteiner F & Pfeifer N (2009). A comparison of evaluation techniques for building extraction from airborne laser scanning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1), 11-20.
  • Secord J & Zakhor A (2007). Tree detection in urban regions using aerial LiDAR and image data. IEEE Geoscience and Remote Sensing Letters, 4(2), 196-200.
  • Sevgen S C (2018). Airborne lidar data classification in complex urban area using random forest: A case study of Bergama, Turkey. International Journal of Engineering and Geosciences, 4(1), 45-51. DOI: 10.26833/ijeg.440828
  • Van der Zande D, Hoet W, Jonckheere I, van Aardt J & Coppin P (2006). Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agricultural and Forest Meteorology, 141(2-4), 147-160. DOI: 10.1016/j.agrformet.2006.09.007
  • Véga C, Hamrouni A, El Mokhtari S, Morel J, Bock J, Renaud J P & Durrieu S (2014). PTrees: A point-based approach to forest tree extraction from LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 33, 98-108. DOI: 10.1016/j.jag.2014.05.001
  • Weinmann M (2016). Reconstruction and analysis of 3D scenes. Springer.
  • Weinmann M, Mallet C, Hinz S & Jutzi B (2015). Efficient interpretation of 3D point clouds by assessing feature relevance. AVN–Allg Vermess-Nachr, 10(2015), 308-315.
  • Zhang W, Qi J, Wan P, Wang H, Xie D, Wang X & Yan G (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. DOI: 10.3390/rs8060501
  • Zhen Z, Quackenbush L J & Zhang L (2016). Trends in automatic individual tree crown detection and delineation—Evolution of LiDAR data. Remote Sensing, 8(4), 333. DOI: 10.3390/rs8040333
There are 31 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Samed Özdemir 0000-0001-7217-899X

Zeynep Akbulut 0000-0001-9801-1506

Fevzi Karslı 0000-0002-0411-3315

Hayrettin Acar 0000-0002-2954-7734

Publication Date February 1, 2021
Published in Issue Year 2021 Volume: 6 Issue: 1

Cite

APA Özdemir, S., Akbulut, Z., Karslı, F., Acar, H. (2021). Automatic extraction of trees by using multiple return properties of the lidar point cloud. International Journal of Engineering and Geosciences, 6(1), 20-26. https://doi.org/10.26833/ijeg.668352
AMA Özdemir S, Akbulut Z, Karslı F, Acar H. Automatic extraction of trees by using multiple return properties of the lidar point cloud. IJEG. February 2021;6(1):20-26. doi:10.26833/ijeg.668352
Chicago Özdemir, Samed, Zeynep Akbulut, Fevzi Karslı, and Hayrettin Acar. “Automatic Extraction of Trees by Using Multiple Return Properties of the Lidar Point Cloud”. International Journal of Engineering and Geosciences 6, no. 1 (February 2021): 20-26. https://doi.org/10.26833/ijeg.668352.
EndNote Özdemir S, Akbulut Z, Karslı F, Acar H (February 1, 2021) Automatic extraction of trees by using multiple return properties of the lidar point cloud. International Journal of Engineering and Geosciences 6 1 20–26.
IEEE S. Özdemir, Z. Akbulut, F. Karslı, and H. Acar, “Automatic extraction of trees by using multiple return properties of the lidar point cloud”, IJEG, vol. 6, no. 1, pp. 20–26, 2021, doi: 10.26833/ijeg.668352.
ISNAD Özdemir, Samed et al. “Automatic Extraction of Trees by Using Multiple Return Properties of the Lidar Point Cloud”. International Journal of Engineering and Geosciences 6/1 (February 2021), 20-26. https://doi.org/10.26833/ijeg.668352.
JAMA Özdemir S, Akbulut Z, Karslı F, Acar H. Automatic extraction of trees by using multiple return properties of the lidar point cloud. IJEG. 2021;6:20–26.
MLA Özdemir, Samed et al. “Automatic Extraction of Trees by Using Multiple Return Properties of the Lidar Point Cloud”. International Journal of Engineering and Geosciences, vol. 6, no. 1, 2021, pp. 20-26, doi:10.26833/ijeg.668352.
Vancouver Özdemir S, Akbulut Z, Karslı F, Acar H. Automatic extraction of trees by using multiple return properties of the lidar point cloud. IJEG. 2021;6(1):20-6.

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