589
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Performance, effectiveness and computational efficiency of powerline extraction methods for quantifying ecosystem structure from light detection and ranging

ORCID Icon &
Article: 2260637 | Received 05 May 2023, Accepted 14 Sep 2023, Published online: 22 Sep 2023

References

  • Almeida, D. R. A. D., E. N. Broadbent, M. P. Ferreira, P. Meli, A. M. A. Zambrano, E. B. Gorgens, A. F. Resende, et al. 2021. “Monitoring Restored Tropical Forest Diversity and Structure Through UAV-Borne Hyperspectral and Lidar Fusion.” Remote Sensing of Environment 264:112582. https://doi.org/10.1016/j.rse.2021.112582.
  • Asner, G. P., C. B. Anderson, R. E. Martin, D. E. Knapp, R. Tupayachi, F. Sinca, and Y. Malhi. 2014. “Landscape-Scale Changes in Forest Structure and Functional Traits Along an Andes-To-Amazon Elevation Gradient.” Biogeosciences 11 (3): 843–20. https://doi.org/10.5194/bg-11-843-2014.
  • ASPRS. 2019. “LAS Specification 1.4-R15.” In ASPRS the Imaging & Geospatial Information Society, 50. MD, USA: American Society for Photogrammetry & Remote Sensing .
  • Assmann, J. J., J. E. Moeslund, U. A. Treier, and S. Normand. 2022. “EcoDes-DK15: High-Resolution Ecological Descriptors of Vegetation and Terrain Derived from Denmark’s National Airborne Laser Scanning Data Set.” Earth System Science Data 14 (2): 823–844. https://doi.org/10.5194/essd-14-823-2022.
  • Awrangjeb, M. 2019. “Extraction of Power Line Pylons and Wires Using Airborne LiDar Data at Different Height Levels.” Remote Sensing 11 (15): 1798. https://doi.org/10.3390/rs11151798.
  • Bello, S. A., Y. Shangshu, C. Wang, J. M. Adam, and J. Li. 2020. “Review: Deep Learning on 3D Point Clouds.” Remote Sensing 12 (11): 1729. https://doi.org/10.3390/rs12111729.
  • Borowiec, M. L., R. B. Dikow, P. B. Frandsen, A. McKeeken, G. Valentini, and A. E. White. 2022. “Deep Learning as a Tool for Ecology and Evolution.” Methods in Ecology and Evolution 13 (8): 1640–1660. https://doi.org/10.1111/2041-210x.13901.
  • Chen, C., A. Jin, B. Yang, R. Ma, S. Sun, Z. Wang, Z. Zong, and F. Zhang. 2022. “DCPLD-Net: A Diffusion Coupled Convolution Neural Network for Real-Time Power Transmission Lines Detection from UAV-Borne LiDar Data.” International Journal of Applied Earth Observation and Geoinformation 112:102960. https://doi.org/10.1016/j.jag.2022.102960.
  • Chen, Y., J. Lin, and X. Liao. 2022. “Early Detection of Tree Encroachment in High Voltage Powerline Corridor Using Growth Model and UAV-Borne LiDar.” International Journal of Applied Earth Observation and Geoinformation 108:102740. https://doi.org/10.1016/j.jag.2022.102740.
  • Deibe, D., M. Amor, and R. Doallo. 2020. “Big Data Geospatial Processing for Massive Aerial LiDar Datasets.” Remote Sensing 12 (4): 719. https://doi.org/10.3390/rs12040719.
  • Eitel, J. U. H., B. Höfle, L. A. Vierling, A. Abellán, G. P. Asner, J. S. Deems, C. L. Glennie, et al. 2016. “Beyond 3-D: The New Spectrum of Lidar Applications for Earth and Ecological Sciences.” Remote Sensing of Environment 186:372–392. https://doi.org/10.1016/j.rse.2016.08.018.
  • Guo, B., X. Huang, F. Zhang, and G. Sohn. 2015. “Classification of Airborne Laser Scanning Data Using JointBoost.” Isprs Journal of Photogrammetry & Remote Sensing 100:71–83. https://doi.org/10.1016/j.isprsjprs.2014.04.015.
  • Guo, Q., Y. Su, T. Hu, H. Guan, S. Jin, J. Zhang, X. Zhao, et al. 2021. “Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective.” IEEE Geoscience and Remote Sensing Magazine 9 (1): 232–257. https://doi.org/10.1109/MGRS.2020.3032713.
  • Guo, Y., H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun. 2021. “Deep Learning for 3D Point Clouds: A Survey.” IEEE Transactions Pattern Analysis Machine Intelligent 43 (12): 4338–4364. https://doi.org/10.1109/TPAMI.2020.3005434.
  • Huang, W., K. Dolan, A. Swatantran, K. Johnson, H. Tang, J. O’Neil-Dunne, R. Dubayah, and G. Hurtt. 2019. “High-Resolution Mapping of Aboveground Biomass for Forest Carbon Monitoring System in the Tri-State Region of Maryland, Pennsylvania and Delaware, USA.” Environmental Research Letters 14 (9): 095002. https://doi.org/10.1088/1748-9326/ab2917.
  • Jung, J., E. Che, M. J. Olsen, and K. C. Shafer. 2020. “Automated and Efficient Powerline Extraction from Laser Scanning Data Using a Voxel-Based Subsampling with Hierarchical Approach.” Isprs Journal of Photogrammetry & Remote Sensing 163:343–361. https://doi.org/10.1016/j.isprsjprs.2020.03.018.
  • Jwa, Y., and G. Sohn. 2012. “A Piecewise Catenary Curve Model Growing for 3D Power Line Reconstruction.” Photogrammetric Engineering & Remote Sensing 78 (12): 1227–1240. https://doi.org/10.14358/pers.78.11.1227.
  • Kim, H. B., and G. Sohn. 2013. “Point-Based Classification of Power Line Corridor Scene Using Random Forests.” Photogrammetric Engineering & Remote Sensing 79 (9): 821–833. https://doi.org/10.14358/pers.79.9.821.
  • Kissling, W. D., A. C. Seijmonsbergen, R. P. B. Foppen, and W. Bouten. 2017. “eEcolidar, eScience Infrastructure for Ecological Applications of LiDar Point Clouds: Reconstructing the 3D Ecosystem Structure for Animals at Regional to Continental Scales.” Research Ideas and Outcomes 3. https://doi.org/10.3897/rio.3.e14939.
  • Kissling, W. D., Y. Shi, Z. Koma, C. Meijer, O. Ku, F. Nattino, A. C. Seijmonsbergen, and M. W. Grootes. 2022. “Laserfarm – a High-Throughput Workflow for Generating Geospatial Data Products of Ecosystem Structure from Airborne Laser Scanning Point Clouds.” Ecological Informatics 72:101836. https://doi.org/10.1016/j.ecoinf.2022.101836.
  • Kissling, W. D., Y. Shi, Z. Koma, C. Meijer, O. Ku, F. Nattino, A. C. Seijmonsbergen, and M. W. Grootes. 2023. “Country-Wide Data of Ecosystem Structure from the Third Dutch Airborne Laser Scanning Survey.” Data in Brief 46:108798. https://doi.org/10.1016/j.dib.2022.108798.
  • LaRue, E. A., F. W. Wagner, S. Fei, J. W. Atkins, R. T. Fahey, C. M. Gough, and B. S. Hardiman. 2020. “Compatibility of Aerial and Terrestrial LiDar for Quantifying Forest Structural Diversity.” Remote Sensing 12 (9): 1407. https://doi.org/10.3390/rs12091407.
  • Li, Y., R. Bu, M. Sun, W. Wei, X. Di, and B. Chen. 2018. “PointCnn: Convolution on χ-Transformed Points.” Advances in Neural Information Processing Systems 31:820–830. https://doi.org/10.48550/arXiv.1801.07791.
  • Li, N., O. Kahler, and N. Pfeifer. 2021. “A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 14:6467–6486. https://doi.org/10.1109/jstars.2021.3091389.
  • Limberger, F. A., and M. M. Oliveira. 2015. “Real-Time Detection of Planar Regions in Unorganized Point Clouds.” Pattern Recognition 48 (6): 2043–2053. https://doi.org/10.1016/j.patcog.2014.12.020.
  • Liu, K., Z. Gao, F. Lin, and B. M. Chen. 2023. “FG-Net: A Fast and Accurate Framework for Large-Scale LiDar Point Cloud Understanding.” IEEE Transactions on Cybernetics 53 (1): 553–564. https://doi.org/10.1109/TCYB.2022.3159815.
  • Matikainen, L., M. Lehtomäki, E. Ahokas, J. Hyyppä, M. Karjalainen, A. Jaakkola, A. Kukko, and T. Heinonen. 2016. “Remote Sensing Methods for Power Line Corridor Surveys.” Isprs Journal of Photogrammetry & Remote Sensing 119:10–31. https://doi.org/10.1016/j.isprsjprs.2016.04.011.
  • McLaughlin, R. A. 2006. “Extracting Transmission Lines from Airborne LiDar Data.” IEEE Geoscience & Remote Sensing Letters 3 (2): 222–226. https://doi.org/10.1109/lgrs.2005.863390.
  • Morsy, S., A. Shaker, and A. El-Rabbany. 2022. “Classification of Multispectral Airborne LiDar Data Using Geometric and Radiometric Information.” Geomatics 2 (3): 370–389. https://doi.org/10.3390/geomatics2030021.
  • Moudrý, V., A. F. Cord, L. Gábor, G. Vaglio Laurin, V. Barták, K. Gdulová, M. Malavasi, et al. 2023. “Vegetation Structure Derived from Airborne Laser Scanning to Assess Species Distribution and Habitat Suitability: The Way Forward.” Diversity & Distributions 29 (1): 39–50. https://doi.org/10.1111/ddi.13644.
  • Nardinocchi, C., M. Balsi, and S. Esposito. 2020. “Fully Automatic Point Cloud Analysis for Powerline Corridor Mapping.” IEEE Transactions on Geoscience & Remote Sensing 58 (12): 8637–8648. https://doi.org/10.1109/tgrs.2020.2989470.
  • Otcenasova, A., M. Hoger, and J. Altus. 2014. “Possible Use of Airborne LiDar for Monitoring of Power Lines in Slovak Republic.” Paper presented at the Proceedings of the 2014 15th International Scientific Conference on Electric Power Engineering (EPE), Brno-Bystrc, Czech Republic, May 12-14 2014.
  • Peng, S., X. Xiaohuan, C. Wang, P. Dong, P. Wang, and S. Nie. 2019. “Systematic Comparison of Power Corridor Classification Methods from ALS Point Clouds.” Remote Sensing 11 (17). https://doi.org/10.3390/rs11171961.
  • Potapov, P., L. Xinyuan, A. Hernandez-Serna, A. Tyukavina, M. C. Hansen, A. Kommareddy, A. Pickens, et al. 2021. “Mapping Global Forest Canopy Height Through Integration of GEDI and Landsat Data.” Remote Sensing of Environment 253:112165. https://doi.org/10.1016/j.rse.2020.112165.
  • Qi, C. R., H. Su, M. Kaichun, and L. J. Guibas. 2017. “PointNet: Deep Learning on Point Sets for 3d Classification and Segmentation.” Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.48550/arXiv.1612.00593.
  • Qi, C. R., L. Yi, S. Hao, and L. J. Guibas. 2017. “Pointnet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.” Advances in Neural Information Processing Systems 30. https://doi.org/10.48550/arXiv.1706.02413.
  • Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and Prabhat. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204. https://doi.org/10.1038/s41586-019-0912-1.
  • Roussel, J.-R., A. Achim, and D. Auty. 2021. “Classification of High-Voltage Power Line Structures in Low Density ALS Data Acquired Over Broad Non-Urban Areas.” Peer Journal Computer Science 7:e672. https://doi.org/10.7717/peerj-cs.672.
  • Roussel, J.-R., D. Auty, N. C. Coops, P. Tompalski, T. R. H. Goodbody, A. S. Meador, J.-F. Bourdon, F. de Boissieu, and A. Achim. 2020. “lidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data.” Remote Sensing of Environment 251:112061. https://doi.org/10.1016/j.rse.2020.112061.
  • Simpson, J. E., T. E. L. Smith, and M. J. Wooster. 2017. “Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDar-Derived DTMs.” Remote Sensing 9 (11): 1101. https://doi.org/10.3390/rs9111101.
  • Sohn, G., Y. Jwa, and H. B. Kim. 2012. “Automatic Powerline Scene Classification and Reconstruction Using Airborne Lidar Data.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 13 (167172): 28. https://doi.org/10.5194/isprsannals-I-3-167-2012.
  • Tang, H., L. Ma, A. Lister, J. O’Neill-Dunne, J. Lu, R. L. Lamb, R. Dubayah, and G. Hurtt. 2021. “High-Resolution Forest Carbon Mapping for Climate Mitigation Baselines Over the RGGI Region, USA.” Environmental Research Letters 16 (3): 035011. https://doi.org/10.1088/1748-9326/abd2ef.
  • Thomas, H., C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas. 2019. “Kpconv: Flexible and Deformable Convolution for Point Clouds.” Paper presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, South Korea.
  • Valbuena, R., B. O’Connor, F. Zellweger, W. Simonson, P. Vihervaara, M. Maltamo, C. A. Silva, et al. 2020. “Standardizing Ecosystem Morphological Traits from 3D Information Sources.” Trends in Ecology & Evolution 35 (8): 656–667. https://doi.org/10.1016/j.tree.2020.03.006.
  • Varney, N., V. K. Asari, and Q. Graehling. 2020. “DALES: A Large-Scale Aerial LiDar Data Set for Semantic Segmentation.” Paper presented at the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Seattle, WA, USA.
  • Wang, R., J. Peethambaran, and D. Chen. 2018. “LiDar Point Clouds to 3-D Urban Models: A Review.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 11 (2): 606–627. https://doi.org/10.1109/jstars.2017.2781132.
  • Weinmann, M., B. Jutzi, S. Hinz, and C. Mallet. 2015. “Semantic Point Cloud Interpretation Based on Optimal Neighborhoods, Relevant Features and Efficient Classifiers.” Isprs Journal of Photogrammetry & Remote Sensing 105:286–304. https://doi.org/10.1016/j.isprsjprs.2015.01.016.
  • Wen, C., X. Li, X. Yao, L. Peng, and T. Chi. 2021. “Airborne LiDar Point Cloud Classification with Global-Local Graph Attention Convolution Neural Network.” Isprs Journal of Photogrammetry & Remote Sensing 173:181–194. https://doi.org/10.1016/j.isprsjprs.2021.01.007.
  • Wulder, M. A., J. C. White, R. F. Nelson, E. Næsset, H. O. Ørka, N. C. Coops, T. Hilker, C. W. Bater, and T. Gobakken. 2012. “Lidar Sampling for Large-Area Forest Characterization: A Review.” Remote Sensing of Environment 121:196–209. https://doi.org/10.1016/j.rse.2012.02.001.
  • Yang, J., and Z. Kang. 2018. “Voxel-Based Extraction of Transmission Lines from Airborne LiDar Point Cloud Data.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 11 (10): 3892–3904. https://doi.org/10.1109/JSTARS.2018.2869542.
  • Zhao, P., H. Guan, D. Li, Y. Yu, H. Wang, K. Gao, J. Marcato Junior, and L. Jonathan. 2021. “Airborne Multispectral LiDar Point Cloud Classification with a Feature Reasoning-Based Graph Convolution Network.” International Journal of Applied Earth Observation and Geoinformation 105:102634. https://doi.org/10.1016/j.jag.2021.102634.
  • Zhu, L., and J. Hyyppä. 2014. “Fully-Automated Power Line Extraction from Airborne Laser Scanning Point Clouds in Forest Areas.” Remote Sensing 6 (11): 11267–11282. https://doi.org/10.3390/rs61111267.