Monitoring height and greenness of non-woody floodplain vegetation with UAV time series

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 141, Issue , 2018, Pages 112-123

  van Iersel, Wimala ^a   Straatsma, Menno ^a   Addink, Elisabeth ^a   Middelkoop, Hans ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

Consumer grade camera vegetation index; DSM; Multitemporal aerial photography; River floodplains; UAV; Vegetation height

Indexed keywords

AERIAL PHOTOGRAPHY; ANTENNAS; BANKS (BODIES OF WATER); BIODIVERSITY; CAMERAS; FLOODS; HYSTERESIS; REMOTE SENSING; SURVEYS; UNMANNED AERIAL VEHICLES (UAV); VEGETATION MAPPING;

CONSUMER-GRADE CAMERA VEGETATION INDEX; DIGITAL SURFACE MODELS; DSM; FLOOD PLAINS; MULTI-TEMPORAL; MULTITEMPORAL AERIAL PHOTOGRAPHY; RIVER FLOODPLAIN; UNMANNED AIRBORNE VEHICLES; VEGETATION HEIGHT; VEGETATION INDEX;

RIVERS;

AERIAL PHOTOGRAPHY; AIRBORNE SENSOR; AIRBORNE SURVEY; BIODIVERSITY; DATA SET; FLOODPLAIN; GRASS; GRASSLAND; GREENSPACE; GROWING SEASON; HERB; INDEX METHOD; NDVI; SATELLITE IMAGERY; SATELLITE SENSOR; SEASONAL VARIATION; SPATIAL RESOLUTION; SPATIOTEMPORAL ANALYSIS; UNMANNED VEHICLE; VEGETATION COVER; VEGETATION TYPE;

NETHERLANDS; RHINE RIVER; WAAL RIVER;

EID: 85046345274     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2018.04.011     Document Type: Article

References (52)

1. Aasen, H., Burkart, A., Bolten, A., Bareth, G., Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS J. Photogramm. Remote Sens. 108 (2015), 245–259.
2. Agisoft, 2014. PhotoScan Professional 1.1 User Manual. Agisoft, St Petersburg.
3. Antero, K., Anttoni, J., Hyyppä J., 2016. The Current State of the Art in UAS-based Laser Scanning. < https://www.gim-international.com/content/article/the-current-state-of-the-art-in-uas-based-laser-scanning> (last accessed March 17, 2017).
4. ASPRS, 2013. LAS Specification Version 1.4 -R13. < http://www.asprs.org/wp-content/uploads/2010/12/LAS_1_4_r13.pdf> (accessed 22 March, 2017).
5. Baltsavias, E.P., A comparison between photogrammetry and laser scanning. ISPRS J. Photogramm. Remote Sens. 54:2-3 (1999), 83–94.
6. Baptist, M.J., Penning, W.E., Duel, H., Smits, A.J.M., Geerling, G.W., van der Lee, G.E.M., van Alphen, J.S.L., Assessment of the effects of cyclic floodplain rejuvenation on flood levels and biodiversity along the Rhine river. River Res. Appl. 20:3 (2004), 285–297.
7. Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., Bareth, G., Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens. 6:11 (2014), 10395–10412.
8. Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L., Bareth, G., Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39 (2015), 79–87.
9. Cihlar, J., Ly, H., Xiao, Q., Land cover classification with AVHRR multichannel composites in northern environments. Remote Sens. Environ. 58:1 (1996), 36–51.
10. Cobby, D.M., Mason, D.C., Davenport, I.J., Image processing of airborne scanning laser altimetry data for improved river flood modelling. ISPRS J. Photogramm. Remote Sens. 56:2 (2001), 121–138.
11. Dandois, J.P., Ellis, E.C., High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 136 (2013), 259–276.
12. Davies, A.B., Asner, G.P., Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends Ecol. Evol. 29:12 (2014), 681–691.
13. Dieck, J., Ruhser, J., Hoy, E., Robinson, L., 2015. General Classification Handbook for Floodplain Vegetation in Large River Systems (ver. 2.0, November 2015). Tech. Rep. Book 2, U.S. Geological Survey Techniques and Methods.
14. Džubáková K., Molnar, P., Schindler, K., Trizna, M., Monitoring of riparian vegetation response to flood disturbances using terrestrial photography. Hydrol. Earth Syst. Sci. 19:1 (2015), 195–208.
15. Eco Logical Australia, 2015. Vegetation of the Barwon-Darling and Condamine-Balonne Floodplain Systems of New South Wales Mapping and Survey of Plant Community Types. Tech. rep., Eco Logical Australia.
16. Edwards, D., A broad-scale structural classification of vegetation for practical purposes. Bothalia 14:4 (1983), 705–712.
17. Fraser, R.H., Olthof, I., Lantz, T.C., Schmitt, C., UAV photogrammetry for mapping vegetation in the low-arctic. Arct. Sci. 102:June (2016), 1–51.
18. Fu, B., Burgher, I., Riparian vegetation NDVI dynamics and its relationship with climate, surface water and groundwater. J. Arid Environ. 113 (2015), 59–68.
19. Geerling, G.W., Labrador-Garcia, M., Clevers, J.G.P.W., Ragas, A.M.J., Smits, A.J.M., Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data. Int. J. Remote Sens. 28:19 (2007), 4263–4284.
20. Göthe, E., Timmermann, A., Baattrup-pedersen, A., Januschke, K., Structural and functional responses of floodplain vegetation to stream ecosystem restoration. Hydrobiologia 769 (2016), 79–92.
21. Holman, F.H., Riche, A.B., Michalski, A., Castle, M., Wooster, M.J., Hawkesford, M.J., High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens., 8(12), 2016.
22. Houkes, G., 2008. Toelichting ecotopenkartering Rijntakken-Oost 2005. Tech. rep. Rijkswaterstaat, Data-ICT-Dienst, Delft.
23. Huising, E.J., Gomes Pereira, L.M., Errors and accuracy estimates of laser data acquired by various laser scanning systems for topographic applications. ISPRS J. Photogramm. Remote Sens. 53:5 (1998), 245–261.
24. Jensen, J.R., Remote Sensing of the Environment: An Earth Resource Perspective. second ed., 2007, Pearson Prentice Hall.
25. Knotters, M., Brus, D.J., Purposive versus random sampling for map validation: a case study on ecotope maps of floodplains in the Netherlands. Ecohydrology 6:3 (2013), 425–434.
26. Kouwen, N.N., Li, R.-M., 1980. Biomechanics of vegetative channel linings. J. Hydraul. Div. 106 (ASCE 15464).
27. LAStools, 2016. Efficient LiDAR Processing Software (version 160221, academic) obtained from < http://rapidlasso.com/LAStools>.
28. Lee, J.K., Roig, L.C., Jenter, H.L., Visser, H.M., Drag coefficients for modeling flow through emergent vegetation in the Florida Everglades. Ecol. Eng. 22:4-5 (2004), 237–248.
29. Liang, T., Yang, S., Feng, Q., Liu, B., Zhang, R., Huang, X., Xie, H., Multi-factor modeling of above-ground biomass in alpine grassland: a case study in the Three-River Headwaters Region, China. Remote Sens. Environ. 186 (2016), 164–172.
30. LLC LDP, 2017. Remote Sensing Cameras. Obtained from < https://www.maxmax.com/maincamerapage/remote-sensing> (accessed 18 December, 2017).
31. Lucieer, A., de Jong, S.M., Turner, D., Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Prog. Phys. Geogr. 38:1 (2014), 97–116.
32. Makaske, B., Maas, G.J., Van Den Brink, C., Wolfert, H.P., The influence of floodplain vegetation succession on hydraulic roughness: is ecosystem rehabilitation in dutch embanked floodplains compatible with flood safety standards?. Ambio 40:4 (2011), 370–376.
33. Malambo, L., Popescu, S.C., Murray, S.C., Putman, E., Pugh, N.A., Horne, D.W., Richardson, G., Sheridan, R., Rooney, W.L., Avant, R., Vidrine, M., McCutchen, B., Baltensperger, D., Bishop, M., Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. Int. J. Appl. Earth Obs. Geoinf. 64:August 2017 (2018), 31–42.
34. Montgomery, D.C., Peck, E.A., Introduction to Linear Regression Analysis. 1992, Wiley.
35. Müller, H., Rufin, P., Griffiths, P., Barros Siqueira, A.J., Hostert, P., Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape. Remote Sens. Environ. 156 (2015), 490–499.
36. Nijland, W., de Jong, R., de Jong, S.M., Wulder, M.A., Bater, C.W., Coops, N.C., Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agric. For. Meteorol. 184 (2014), 98–106.
37. Perktold, J., Seabold, S., Taylor, J., 2017. Statsmodels's Documentation Obtained From: < http://www.statsmodels.org/stable/index.html> (accessed 19 December, 2017).
38. Peters, B., Kurstjens, G., van Diermen, J., 2011. Rijn in beeld Natuurontwikkeling langs de grote rivieren Deel 1 De Waal. Tech. rep., Bureau Drift/Kurstjens Ecologisch Adviesbureau.
39. Pfeifer, N., Gorte, B., Elberink, O., Influences of vegetation on laser altimetry analysis and correction approaches. Int. Arch. Photogramm. Sens. Spat. Inf. Sci. XXXVI-8/W2 (2004), 283–287.
40. Possoch, M., Bieker, S., Hoffmeister, D., Bolten, A., Schellberg, J., Bareth, G., Conservation, R., Group, C.S., Conservation, R., Ecology, P., Model, C.S., Multi-temporal crop surface models combined with the RGB vegetation index from UAV-based images for forage monitoring in grassland. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 2016, 991–998.
41. Rasmussen, J., Ntakos, G., Nielsen, J., Svensgaard, J., Poulsen, R.N., Christensen, S., Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?. Eur. J. Agron. 74 (2016), 75–92.
42. Schindler, S., Sebesvari, Z., Damm, C., Euller, K., Mauerhofer, V., Schneidergruber, A., Biró M., Essl, F., Kanka, R., Lauwaars, S.G., Schulz-Zunkel, C., van der Sluis, T., Kropik, M., Gasso, V., Krug, A., Pusch, M.T., Zulka, K.P., Lazowski, W., Hainz-Renetzeder, C., Henle, K., Wrbka, T., Multifunctionality of floodplain landscapes: relating management options to ecosystem services. Landsc. Ecol. 29:2 (2014), 229–244.
43. Straatsma, M.W., Baptist, M.J., Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing. Remote Sens. Environ. 112:3 (2008), 1062–1080.
44. Straatsma, M.W., Bloecker, A.M., Lenders, H.J.R., Leuven, R.S.E.W., Kleinhans, M.G., Biodiversity recovery following delta-wide measures for flood risk reduction. Sci. Adv. 3:11 (2017), 1–10.
45. Straatsma, M.W., Middelkoop, H., Extracting structural characteristics of herbaceous floodplain vegetation under leaf-off conditions using airborne laser scanner data. Int. J. Remote Sens. 28:11 (2007), 2447–2467.
46. Tucker, C.J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:2 (1979), 127–150.
47. Verrelst, J., Geerling, G.W., Sykora, K.V., Clevers, J.G.P.W., Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data. Int. J. Appl. Earth Obs. Geoinf. 11:1 (2009), 83–94.
48. Vierling, K.T., Vierling, L.A., Gould, W.A., Martinuzzi, S., Clawges, R.M., Lidar: shedding new light on habitat characterization and modeling. Front. Ecol. Environ. 6:2 (2008), 90–98.
49. Wang, F.M., Huang, J.F., Tang, Y.L., Wang, X.Z., New vegetation index and its application in estimating leaf area index of rice. Rice Sci. 14:3 (2007), 195–203.
50. Weidner, U., Förstner, W., Towards automatic building extraction from high-resolution digital elevation models. ISPRS J. Photogramm. Remote Sens. 50:4 (1995), 38–49.
51. Willkomm, M., Bolten, A., Bareth, G., America, S., 2016. Non-destructive monitoring of rice by hyperspectral in-field spectrometry and UAV-based remote sensing: case study of field- grown rice in North Rhine-Westphalia, Germany. In: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 1071–1077.
52. Zarco-Tejada, P.J., Diaz-Varela, R., Angileri, V., Loudjani, P., Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur. J. Agron. 55 (2014), 89–99.