Efficiency of individual tree detection approaches based on light-weight and low-cost UAS imagery in Australian Savannas

Remote Sensing

Volumn 10, Issue 2, 2018, Pages

  Goldbergs, Grigorijs ^a   Maier, Stefan W ^a   Levick, Shaun R ^a,b   Edwards, Andrew ^a  

^a CHARLES DARWIN UNIVERSITY   (Australia)

^b CSIRO   (Australia)

Author keywords

Biomass; Canopy height; Low cost UAS; Segmentation; Single tree detection; Structure frommotion

Indexed keywords

ANTENNAS; BIOMASS; COST BENEFIT ANALYSIS; COSTS; ECOSYSTEMS; IMAGE SEGMENTATION; OPTICAL RADAR; TREES (MATHEMATICS); UNMANNED AERIAL VEHICLES (UAV);

CANOPY HEIGHTS; INDIVIDUAL TREE DETECTIONS; LIGHT DETECTION AND RANGING; LOW COSTS; THREEDIMENSIONAL (3-D); TREE DETECTIONS; UNMANNED AERIAL SYSTEMS; WATERSHED SEGMENTATION;

FORESTRY;

EID: 85042531840     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10020161     Document Type: Article

References (41)

1. Trumbore, S.; Brando, P.; Hartmann, H. Forest health and global change. Science 2015, 349, 814-818
2. Maltamo, M.; Næsset, E.; Vauhkonen, J. Forestry Applications of Airborne Laser Scanning Concepts and Case Studies; Springer: Berlin/Heidelberg, Germany, 2014; Volume 27
3. Lefsky, M.A.; Cohen, W.B.; Harding, D.J.; Parker, G.G.; Acker, S.A.; Gower, S.T. Lidar remote sensing of above-ground biomass in three biomes. Glob. Ecol. Biogeogr. 2002, 11, 393-399
4. Asner, G.P.; Mascaro, J. Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric. Remote Sens. Environ. 2014, 140, 614-624
5. Goldbergs, G.; Levick, S.R.; Lawes, M.; Edwards, A. Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR. Remote Sens. Environ. 2018, 205, 141-150
6. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79-97
7. Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. 'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 2012, 179, 300-314
8. Torresan, C.; Berton, A.; Carotenuto, F.; Di Gennaro, S.F.; Gioli, B.; Matese, A.; Miglietta, F.; Vagnoli, C.; Zaldei, A.; Wallace, L. Forestry applications of UAVs in Europe: A review. Int. J. Remote Sens. 2017, 38, 2427-2447
9. Tang, L.; Shao, G. Drone remote sensing for forestry research and practices. J. For. Res. 2015, 26, 791-797
10. Paneque-Gálvez, J.; McCall, M.; Napoletano, B.; Wich, S.; Koh, L. Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas. Forests 2014, 5, 1481
11. Beringer, J.; Hutley, L.B.; Abramson, D.; Arndt, S.K.; Briggs, P.; Bristow, M.; Canadell, J.G.; Cernusak, L.A.; Eamus, D.; Edwards, A.C.; et al. Fire in Australian savannas: From leaf to landscape. Glob. Chang. Biol. 2015, 21, 62-81
12. Williams, R.; Hutley, L.B.; Cook, G.D.; Russell-Smith, J.; Edwards, A.; Chen, X. Assessing the carbon sequestration potential of mesic savannas in the Northern Territory, Australia: Approaches, uncertainties and potential impacts of fire. Funct. Plant Biol. 2004, 31, 415-422
13. Russell-Smith, J.; Murphy, B.P.; Meyer, C.P.; Cooka, G.D.; Maier, S.; Edwards, A.C.; Schatz, J.; Brocklehurst, P. Improving estimates of savanna burning emissions for greenhouse accounting in northern Australia: Limitations, challenges, applications. Int. J. Wildland Fire 2009, 18, 1-18
14. Hung, C.; Bryson, M.; Sukkarieh, S. Multi-class predictive template for tree crown detection. ISPRS J. Photogramm. Remote Sens. 2012, 68, 170-183
15. Wallace, L.; Lucieer, A.; Malenovský, Z.; Turner, D.; Vopȩnka, P. Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests 2016, 7, 62
16. Wallace, L.; Lucieer, A.;Watson, C.; Turner, D. Development of a UAV-LiDAR System with Application to Forest Inventory. Remote Sens. 2012, 4, 1519
17. Whiteside, T.G.; Bartolo, R.E. Robust and Repeatable Ruleset Development for Hierarchical Object-Based Monitoring of Revegetation Using High Spatial and Temporal Resolution UAS Data. In Proceedings of the GEOBIA 2016: Solutions and Synergies, Enschede, The Netherlands, 14-16 September 2016
18. TERN. Litchfield Savanna SuperSite. Available online: http://www.tern-supersites.net.au/supersites/lfld (accessed on 30 March 2015)
19. Beringer, J.; Hacker, J.; Hutley, L.B.; Leuning, R.; Arndt, S.K.; Amiri, R.; Bannehr, L.; Cernusak, L.A.; Grover, S.; Hensley, C.; et al. SPECIAL-Savanna Patterns of Energy and Carbon Integrated across the Landscape. Bull. Am. Meteorol. Soc. 2011, 92, 1467-1485
20. O'Grady, A.P.; Chen, X.; Eamus, D.; Hutley, L.B. Composition, leaf area index and standing biomass of eucalypt open forests near Darwin in the Northern Territory, Australia. Aust. J. Bot. 2000, 48, 629-638
21. Isenburg, M. LAStools-Efficient LiDAR Processing Software (Version 141017, Unlicensed). Available online: https://rapidlasso.com/lastools/(accessed on 30 May 2015)
22. McGaughey, R.J. FUSION/LDV: Software for LIDAR Data Analysis and Visualization; Version 3.50; US Department of Agriculture, Forest Service, Pacific Northwest Research Station: Seattle, WA, USA, 2015
23. Hirschmüller, H. Semi-global matching-motivation, developments and applications. In Proceedings of the PhotogrammetricWeek 11, Stuttgart, Germany, 9-13 September 2011; pp. 173-184
24. Agisoft. PhotoScan Community Forum Topic: Algorithms Used in Photoscan. Available online: http://www. agisoft.com/forum/index.php?topic=89.msg13780;topicseen#msg13780 (accessed on 27 February 2017)
25. Kaartinen, H.; Hyyppä, J.; Yu, X.; Vastaranta, M.; Hyyppä, H.; Kukko, A.; Holopainen, M.; Heipke, C.; Hirschmugl, M.; Morsdorf, F. An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens. 2012, 4, 950-974
26. Popescu, S.C.;Wynne, R.H.; Nelson, R.F. Estimating plot-level tree heights with lidar: Local filtering with a canopy-height based variable window size. Comput. Electron. Agric. 2002, 37, 71-95
27. Li, W.; Guo, Q.; Jakubowski, M.K.; Kelly, M. A new method for segmenting individual trees from the lidar point cloud. Photogramm. Eng. Remote Sens. 2012, 78, 75-84
28. Goutte, C.; Gaussier, E. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In Proceedings of the 27th European conference on Advances in Information Retrieval Research (ECIR), Santiago de Compostela, Spain, 21-23 March 2005; pp. 345-359
29. Conrad, O.; Bechtel, B.; Bock, M.; Dietrich, H.; Fischer, E.; Gerlitz, L.;Wehberg, J.;Wichmann, V.; Böhner, J. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 2015, 8, 1991-2007
30. QGIS. QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available online: http://www.qgis.org (accessed on 30 May 2015)
31. Agüera-Vega, F.; Carvajal-Ramírez, F.;Martínez-Carricondo, P. Assessment of photogrammetricmapping accuracy based on variation ground control points number using unmanned aerial vehicle.Measurement 2017, 98, 221-227
32. Goldstein, E.B.; Oliver, A.R.; de Vries, E.; Moore, L.J.; Jass, T. Ground control point requirements for structure-from-motion derived topography in low-slope coastal environments. PeerJ PrePrints 2015, 3, e1444v1441
33. Russell-Smith, J.; Murphy, B.; Edwards, A.; Meyer, C.P. Carbon Accounting and Savanna Fire Management; CSIRO Publishing: Clayton, Australia, 2015
34. Ferraz, A.; Bretar, F.; Jacquemoud, S.; Gonçalves, G.; Pereira, L.; Tomé, M.; Soares, P. 3-D mapping of a multi-layered Mediterranean forest using ALS data. Remote Sens. Environ. 2012, 121, 210-223
35. Reitberger, J.; Schnörr, C.; Krzystek, P.; Stilla, U. 3D segmentation of single trees exploiting full waveform LIDAR data. ISPRS J. Photogramm. Remote Sens. 2009, 64, 561-574
36. Duncanson, L.I.; Cook, B.D.; Hurtt, G.C.; Dubayah, R.O. An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sens. Environ. 2014, 154, 378-386
37. Edson, C.;Wing, M.G. Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements. Remote Sens. 2011, 3, 2494-2528
38. Turner, R.S. An Airborne Lidar Canopy Segmentation Approach for Estimating Above-Ground Biomass in Coastal Eucalypt Forests. Ph.D. Thesis, University of New South Wales, Sydney, Australia, 2006
39. James, M.R.; Robson, S. Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surf. Process. Landf. 2014, 39, 1413-1420
40. Luhmann, T.; Fraser, C.; Maas, H.-G. Sensor modelling and camera calibration for close-range photogrammetry. ISPRS J. Photogramm. Remote Sens. 2016, 115, 37-46
41. Bosak, K. Secrets of UAV Photomapping. Pteryx: Poland, 2011. Available online: http://www.academia. edu/download/32814759/pteryx-mapping-secrets.pdf (accessed on 30 September 2014)