Considerations for achieving cross-platform point cloud data fusion across different dryland ecosystem structural states

Frontiers in Plant Science

Volumn 8, Issue , 2018, Pages

  Swetnam, Tyson L ^a   Gillan, Jeffrey K ^a   Sankey, Temuulen T ^b   McClaran, Mitchel P ^a   Nichols, Mary H ^c   Heilman, Philip ^c   McVay, Jason ^b  

^a UNIVERSITY OF ARIZONA   (United States)

^b NORTHERN ARIZONA UNIVERSITY   (United States)

^c AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

Lidar; Structure from motion; SUAS; Terrestrial laser scanning

Indexed keywords

EID: 85041309915     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2017.02144     Document Type: Article

References (70)

1. AgiSoft (2017). Agisoft PhotoScan. Professional Edn. AgiSoft LLC, St. Petersburg, Russia.
2. Anderson, K., and Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 11, 138–146. doi: 10.1890/120150
3. Brekenfeld, D. J., and Robinett, D. (1997). Soil and Range Resource Inventory of the Santa Rita Experimental Range. Tucson, AZ: Natural Resources Conservation Service. Available online at: https://cals.arizona.edu/srer/maps/BR1997.pdf
4. Boettiger, C. (2015). An introduction to docker for reproducible research. ACM SIGOPS Operat. Syst. Rev. 49, 71–79. doi: 10.1145/2723872.2723882
5. Boulton, G., Rawlins, M., Vallance, P., and Walport, M. (2011). Science as a public enterprise: the case for open data. Lancet 377, 1633–1635. doi: 10.1016/S0140-6736(11)60647-8
6. Butler, D. (2014). Many eyes on earth. Nature 505, 143–144. doi: 10.1038/505143a
7. Carrivick, J. L., Smith, M. W., and Quincey, D. J. (2016). Structure fromMotion in the Geosciences. Hoboken, NJ: John Wiley &Sons.
8. Cruzan, M. B., Weinstein, B. G., Grasty, M. R., Kohrn, B. F., Hendrickson, E. C., Arredondo, T. M., et al. (2016). Small unmanned aerial vehicles (micro-UAVs, Drones) in plant ecology. Appl. Plant Sci. 4:1600041. doi: 10.3732/apps. 1600041
9. Cunliffe, A. M., Brazier, R. E., and Anderson,. K. (2016). Ultra-Fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens. Environ. 183, 129–143. doi: 10.1016/j.rse.2016.05.019
10. Dandois, J. P., and Ellis, E. C. (2010). Remote sensing of vegetation structure using computer vision. Remote Sens. 2, 1157–1176. doi: 10.3390/rs2041157
11. Dandios, J. P., and Ellis, E. C. (2013). High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 136, 259–276. doi: 10.1016/j.rse.2013.04.005
12. Docker Development Team (2017). Version 17.03. Available online at: https://www.docker.com/
13. Everest Innovation Technology (2017). Altizure for DJI App. Kowloon: Everest Innovation Technology. Available online at: https://next.altizure.com/
14. Exelis Visual Information Solutions (2010). ENVI v. 5.3. Boulder, CO. FFmpeg Developers (2016). ffmpeg Tool (Version be1d324) [Software]. Available online at: http://ffmpeg.org/
15. Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., and Carbonneau, P. E. (2013). Topographic structure from motion: a new development in photogrammetric measurement. Earth Surf. Processes Landforms 38, 421–430. doi: 10.1002/esp.3366
16. Gillan, J. K., McClaran, M. P., Swetnam, T. L., Heilman, P., and Turner, R. (2017). “Estimating forage utilization with unmanned aerial imagery,” in Poster Presented at Research Insights in Semi-arid Ecosystems (RISE) (Tucson, AZ). Available online at: http://www.tucson.ars.ag.gov/rise/Posters/GillanPoster.pdf
17. Girardeau-Montaut, D. (2011). CloudCompare-Open Source Project. Grenoble: OpenSource Project. Available online at: http://www.cloudcompare.org/
18. Glennie, C. L., Carter, W. E., Shrestha, R. L., and Dietrich, W. E. (2013). Geodetic imaging with airborne LiDAR: the earth’s surface revealed. Rep. Prog. Phys. 76:086801. doi: 10.1088/0034-4885/76/8/086801
19. Hampton, S. E., Anderson, S. S., Bagby, S. C., Gries, C., Han, X., Hart, E. M., et al. (2015). The tao of open science for ecology. Ecosphere 6, 1–13. doi: 10.1890/ES14-00402.1
20. Harpold, A. A., Marshall, J. A., Lyon, S. W., Barnhart, T. B., Fisher, B. A., Donovan, M., et al. (2015). Laser vision: lidar as a transformative tool to advance critical zone science. Hydrol. Earth Syst. Sci. Discuss. 12, 1017–1058. doi: 10.5194/hessd-12-1017-2015
21. Heidemann, H. K. (2012). Lidar Base Specification. Techniques and Methods.
22. Heilman, P., Nichols, M. H., Goodrich, D. C., Miller, S. N., and Guertin, D. P. (2008). Geographic information systems database, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resour. Res. 44:W05S11. doi: 10.1029/2006WR005777
23. Higgins, M. A., Asner, G. P., Martin, R. E., Knapp, D. E., Anderson, C., Kennedy-Bowdoin, T., et al. (2014). Linking Imaging Spectroscopy and LiDAR with Floristic Composition and Forest Structure in Panama. Remote Sens. Environ. 154, (Suppl. C), 358–367. doi: 10.1016/j.rse.2013.09.032
24. Hobu Inc. (2017). PDAL: Point cloud Data Abstraction Library. Release 1.5. Copywrite (c) 2017. Available online at: http://www.pdal.io
25. Huenneke, L. F., Clason, D., and Muldavin, E. (2001). Spatial heterogeneity in chihuahuan desert vegetation: implications for sampling methods in semi-arid ecosystems. J. Arid Environ. 47, 257–270. doi: 10.1006/jare.2000.0678
26. Isenburg, M. (2014). LAStools - Efficient LiDAR Processing Software (version 141017, unlicensed). Available online at: http://rapidlasso.com/LAStools
27. James, M. R., and Robson, S. (2014). Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surf. Processes Landforms 39, 1413–1420. doi: 10.1002/esp.3609
28. James, M. R., Robson, S., and Smith, M. W. (2017). 3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: precision maps for ground control and directly georeferenced surveys. Earth Surf. Processes Landforms 42, 1769–1788. doi: 10.1002/esp.4125
29. Kachamba, D. J., Ørka, H. O., Næsset, E., Eid, T., and Gobakken, T. (2017). Influence of plot size on efficiency of biomass estimates in inventories of dry tropical forests assisted by photogrammetric data from an unmanned aircraft system. Remote Sens. 9:610. doi: 10.3390/rs9060610
30. Kato, A., Obanawa, H., Hayakawa, Y., Watanabe, M., Yamaguchi, Y., and Enoki, T. (2015). “Fusion between UAV-SFM and terrestrial laser scanner for field validation of satellite remote sensing,” in 2015 IEEE International Geoscience and Remote Sensing Symposium(IGARSS) (Milan).
31. Keefer, T. O., Moran, M. S., and Paige, G. B. (2008). Long-term meteorological and soil hydrology database, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resour. Res. 44:W05S07. doi: 10.1029/2006WR005702
32. Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and their Consequences. London: SAGE Publishing.
33. Lague, D., Brodu, N., and Leroux, J. (2013). Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the rangitikei canyon (N-Z). ISPRS J. Photogramm. Remote Sens. 82, 10–26. doi: 10.1016/j.isprsjprs.2013.04.009
34. Lane, S. N., Westaway, R. M., and Murray Hicks, D. (2003). Estimation of erosion and deposition volumes in a large, gravel-bed, braided river using synoptic remote sensing. Earth Surf. Processes Landforms 28, 249–271. doi: 10.1002/esp.483
35. Levin, S. A. (1992). The problem of pattern and scale in ecology: the robert H. MacArthur Award Lecture. Ecology 73, 1943–1967. doi: 10.2307/1941447
36. Lucieer, A., Robinson, S., Turner, D., Harwin, S., and Kelcey, J. (2012). “Using a micro-UAV for ultra-high resolution multi-sensor observations of Antarctic moss beds,” in ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B1 (Melbourne), 429–433.
37. Luscombe, D. J., Anderson, K., Gatis, N., Wetherelt, A., Grand-Clement, E., and Brazier, R. E. (2014). What does airborne LiDAR really measure in upland ecosystems? Ecohydrology 8, 584–594. doi: 10.1002/eco.1527
38. McCabe, M. F., Houborg, R., and Lucieer, A. (2016). “High-resolution sensing for precision agriculture: from earth-observing satellites to unmanned aerial vehicles,” in Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII (Edinburgh).
39. McClaran, M. P., Browning, D. M., and Huang, C. (2010). “Temporal dynamics and spatial variability in desert grassland vegetation,” in Repeat Photography: Methods and Applications in the Natural Sciences, eds R. H. Webb, D. E. Boyer, and R. M. Turner (Washington, DC: Island Press), 145–166.
40. McClaran, M. P., Ffolliott, P. F., and Edminster, C. B. (2003). “Santa rita experimental range−100 years (1903 to 2003) of accomplishments and contributions,” in Conference Proceedings (Tucson, AZ).
41. McClaran, M. P., and Wei, H. (2014). Recent drought phase in a 73-year record at two spatial scales: implications for livestock production on rangelands in the Southwestern United States. Agric. For. Meteorol. 197, 40–51. doi: 10.1016/j.agrformet.2014.06.004
42. Merkel, D. (2014). Docker: lightweight Linux containers for consistent development and deployment. Linux J. 2014:239.
43. Midgley, N. G., and Tonkin, T. N. (2017). Reconstruction of former glacier surface topography from archive oblique aerial images. Geomorphology 282, 18–26. doi: 10.1016/j.geomorph.2017.01.008
44. Mlambo, R., Woodhouse, I. H., Gerard, F., and Anderson, K. (2017). Structure from Motion (SfM) photogrammetry with drone data: a low cost method for monitoring greenhouse gas emissions from forests in developing countries. Forests 8:68. doi: 10.3390/f8030068
45. Moran, S. M., Emmerich, W. E., Goodrich, D. C., Heilman, P., Holifield Collins, C. D., Keefer, T. O., et al. (2008). Preface to special section on Fifty Years of Research and Data Collection: U.S. Department of Agriculture Walnut Gulch Experimental Watershed. Water Resour. Res. 44:W05SW01. doi: 10.1029/2007WR006083
46. Nouwakpo, S. K., Weltz, M. A., and McGwire, K. (2015). Assessing the performance of structure-from-motion photogrammetry and terrestrial lidar for reconstructing soil surface microtopography of naturally vegetated plots. Earth Surf. Processes Landforms 41, 308–322. doi: 10.1002/esp.3787
47. Osterkamp, W. R. (2008). Geology, soils, and geomorphology of the walnut gulch experimental watershed, tombstone, Arizona. J. Arizona Nevada Acad. Sci. 40, 136–154. doi: 10.2181/1533-6085-40.2.136
48. Pelletier, J. D., Nichols, M. H., and Nearing, M. A. (2016). The influence of holocene vegetation changes on topography and erosion rates: a case study at walnut gulch experimental watershed, Arizona. Earth Surf. Dyn. Discuss. 4, 471–488. doi: 10.5194/esurf-4-471-2016
49. Pelletier, J. D., and Orem, C. A. (2014). How do sediment yields from post-wildfire debris-laden flows depend on terrain slope, soil burn severity class, and drainage basin area? Insights from airborne-LiDAR change detection. Earth Surf. Processes Landforms 39, 1822–1832. doi: 10.1002/esp.3570
50. Quantum GIS Development Team (2017). Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. Available online at: https://www.qgis.org
51. Riegl (2017). Reigl VZ-400 Data Sheet. Available online at: http://www.riegl.com/uploads/tx_pxpriegldownloads/10_DataSheet_VZ-400_2017-06-14.pdf
52. Safriel, U., Adeel, Z., Niemeijer, D., Puigdefabregas, J., White, R., Lal, R., et al. (2006). “Dryland systems,” in Ecosystems and Human Well-being. Current State and Trends, Vol. 1, (Washington, DC: Island Press), 625–656.
53. Sankey, T., Donager, J., McVay, J., and Sankey, J. B. (2017a). UAV Lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens. Environ. 195, 30–43. doi: 10.1016/j.rse.2017.04.007
54. Sankey, T. T., McVay, J., Swetnam, T. L., McClaran, M. P., Heilman, P., and Nichols, M. (2017b). UAVhyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sens. Ecol. Conserv. 1–14. doi: 10.1002/rse2.44. [Epub ahead of print].
55. Scott, R. (2010a). “AmeriFlux radiological and meteorological data for santa rita mesquite savanna site,” Carbon Dioxide Information Analysis Center (CDIAC) Datasets (Tucson, AZ).
56. Scott, R. L. (2010b). Using watershed water balance to evaluate the accuracy of eddy covariance evaporation measurements for three semiarid ecosystems. Agric. For. Meteorol. 150, 219–225. doi: 10.1016/j.agrformet.2009.11.002
57. Scott, R. L., Huxman, T. E., Cable, W. L., and Emmerich, W. E. (2006). Partitioning of evapotranspiration and its relation to carbon dioxide exchange in a chihuahuan desert shrubland. Hydrol. Processes 20, 3227–3243. doi: 10.1002/hyp.6329
58. Skirvin, S., Kidwell, M., Biedenbender, S., Henley, J. P., King, D., Collins, C. H., et al. (2008). Vegetation data, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resour. Res. 44:W05S08. doi: 10.1029/2006WR 005724
59. Stewart, C. A., Cockerill, T. M., Foster, I., Hancock, D., Merchant, N., Skidmore, E., et al. (2015). “Jetstream: a self-provisioned, scalable science and engineering cloud environment,” in Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure (St. Louis, MI: ACM), 1–8. doi: 10.1145/2792745.2792774
60. Stoker, J. M., Brock, J. C., Soulard, C. E., Ries, K. G., Sugarbaker, L. J., Newton, W. E., et al. (2016). USGS Lidar Science Strategy—Mapping the Technology to the Science: U.S. Geological Survey Open-File Report 2015–1209, 33.
61. Trimble (2012) Trimble R10 GNSS Dat Sheet. Trimble Navigation Limited 10355 Westmoor Dr Westminster CO80021 USA. Available online at: https://solutions.seilerinst.com/Portals/1/Trimble/TrimbleR10_DS_1012_DataSheetLR.pdf
62. Trimble (2013) Trimble S6 Robotic Total Station Data Sheet. Trimble Navigation Limited 10355 Westmoor Dr Westminster CO 80021 USA. Available online at: http://trl.trimble.com/docushare/dsweb/Get/Document-208580/022543-098L_TrimbleS6_DS_0613_LR.pdf
63. Turner, M. G. (1989). Landscape Ecology: the effect of pattern on process. Annu. Rev. Ecol. Syst. 20, 171–197. doi: 10.1146/annurev.es.20.110189.001131
64. Turner, M. G., O’Neill, R. V., Gardner, R. H., and Milne,. B. T. (1989). Effects of changing spatial scale on the analysis of landscape pattern. Landsc. Ecol. 3, 153–162.
65. Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., and Vopěnka, P. (2016). Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure frommotion (SfM) point clouds. Forests 7:62. doi: 10.3390/f7030062
66. Walls, R. (2017). “CyVerse data commons,” in Plant and Animal Genome XXV Conference. Plant and Animal Genome, 2017 (San Diego, CA).
67. Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., and Reynolds, J. M. (2012). ‘Structure-from-motion’ photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179, 300–314. doi: 10.1016/j.geomorph.2012.08.021
68. Woodcock, C. E., and Strahler, A. H. (1987). The factor of scale in remote sensing. Remote Sens. Environ. 21, 311–332. doi: 10.1016/0034-4257(87)90015-0
69. Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., et al. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sens. 8:501. doi: 10.3390/rs8060501
70. Zimmerman, R., Doan, D., Leung, L., Mason, J., Parsons, N., and Shahid, K. (2017). “Commissioning the world’s largest satellite constellation,” in Conference on Small Satellites. Available online at: http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=3669&context=smallsat