UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy?

International Journal of Applied Earth Observation and Geoinformation

Volumn 75, Issue , 2019, Pages 151-162

  Prošek, Jiří ^a   Šímová, Petra ^a  

^a CZECH UNIVERSITY OF LIFE SCIENCES PRAGUE   (Czech Republic)

Author keywords

Fine spatial resolution; Multispectral; Normalized digital surface model (nDSM); Species classification; Structure from motion (SfM); Tetracam Micro MCA6; Unmanned aerial systems

Indexed keywords

ACCURACY ASSESSMENT; AERIAL SURVEY; LIDAR; MULTISPECTRAL IMAGE; SHRUBLAND; SPATIAL RESOLUTION; UNMANNED VEHICLE; VEGETATION CLASSIFICATION; VEGETATION MAPPING;

CZECH REPUBLIC;

EID: 85062823060     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2018.10.009     Document Type: Article

References (44)

1. Agisoft, Agisoft PhotoScan User Manual, Version 1.3. professional edition, 2017.
2. Ahmed, O.S., Shemrock, A., Chabot, D., Dillon, C., Williams, G., Wasson, R., Franklin, S.E., Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle. Int. J. Remote Sens. 38 (2017), 2037–2052, 10.1080/01431161.2017.1294781.
3. Alonzo, M., Bookhagen, B., Roberts, D.A., Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sens. Environ. 148 (2014), 70–83, 10.1016/J.RSE.;1;2014.03.018.
4. Anderson, K., Gaston, K.J., Lightweight unmanned aerial vehicles will revolutionize spatial ecology Lightweight unmanned aerial vehic revolutionize spatial ecology. Front. Ecol. Environ. 11 (2013), 138–146, 10.1890/120150.
5. Bazzichetto, M., Malavasi, M., Barták, V., Acosta, A.T.R., Moudrý, V., Carranza, M.L., Modeling plant invasion on Mediterranean coastal landscapes: an integrative approach using remotely sensed data. Landsc. Urban Plan. 171 (2018), 98–106, 10.1016/J.LANDURBPLAN.;1;2017.11.006.
6. Blaschke, T., Lang, S., Hay, G., Object-Based Image Analysis. 2008, Springer.
7. Blaschke, T., Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65 (2010), 2–16, 10.1016/j.isprsjprs.2009.06.004.
8. Bork, E.W., Su, J.G., Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: a meta analysis. Remote Sens. Environ. 111 (2007), 11–24, 10.1016/j.rse.2007.03.011.
9. Chignell, S.M., Luizza, M.W., Skach, S., Young, N.E., Evangelista, P.H., An integrative modeling approach to mapping wetlands and riparian areas in a heterogeneous Rocky Mountain watershed. Remote Sens. Ecol. Conserv. 4 (2018), 150–165, 10.1002/rse2.63.
10. Clinton, N., Holt, A., Scarborough, J., Yan, L., Gong, P., Accuracy assessment measures for object-based image segmentation goodness. Photogramm. Eng. Remote Sens. 76 (2010), 289–299, 10.14358/PERS.76.3.289.
11. Colomina, I., Molina, P., Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 92 (2014), 79–97, 10.1016/J.ISPRSJPRS.;1;2014.02.013.
12. Cunliffe, A.M., Brazier, R.E., Anderson, K., Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens. Environ. 183 (2016), 129–143, 10.1016/j.rse.2016.05.019.
13. Díaz-Varela, R.A., Calvo Iglesias, S., Cillero Castro, C., Díaz Varela, E.R., Sub-metric analisis of vegetation structure in bog-heathland mosaics using very high resolution rpas imagery. Ecol. Indic. 89 (2018), 861–873, 10.1016/j.ecolind.2017.11.068.
14. 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, 10.1016/j.rse.2013.04.005.
15. Diaz-Varela, R.A., Zarco-Tejada, P.J., Angileri, V., Loudjani, P., Automatic identification of agricultural terraces through object- oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. J. Environ. Manage. 134 (2014), 117–126, 10.1016/j.jenvman.2014.01.006.
16. Drǎguţ, L., Tiede, D., Levick, S.R., ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci. 24 (2010), 859–871, 10.1080/13658810903174803.
17. Drǎguţ, L., Csillik, O., Eisank, C., Tiede, D., Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J. Photogramm. Remote Sens. 88 (2014), 119–127, 10.1016/j.isprsjprs.2013.11.018.
18. Fisher, J.R.B., Acosta, E.A., Dennedy-Frank, P.J., Kroeger, T., Boucher, T.M., Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality. Remote Sens. Ecol. Conserv. 4 (2018), 137–149, 10.1002/rse2.61.
19. Fonstad, M.A., Dietrich, J.T., Courville, B.C., Jensen, J.L., Carbonneau, P.E., Topographic structure from motion: a new development in photogrammetric measurement. Earth Surf. Process. Landforms 38 (2013), 421–430, 10.1002/esp.3366.
20. Hartfield, K.A., Landau, K.I., van Leeuwen, W.J.D., Fusion of high resolution aerial multispectral and lidar data: land cover in the context of urban mosquito habitat. Remote Sens. 3 (2011), 2364–2383, 10.3390/rs3112364.
21. Holmgren, J., Persson, A., Söderman, U., Species identification of individual trees by combining high resolution LiDAR data with multi-Spectral images. Int. J. Remote Sens. 29 (2008), 1537–1552, 10.1080/01431160701736471.
22. Husson, E., Reese, H., Ecke, F., Combining spectral data and a DSM from UAS-images for improved classification of non-submerged aquatic vegetation. Remote Sens., 9, 2017, 247, 10.3390/rs9030247.
23. Kalacska, M., Chmura, G.L., Lucanus, O., Bérubé, D., Arroyo-Mora, J.P., Structure from motion will revolutionize analyses of tidal wetland landscapes. Remote Sens. Environ. 199 (2017), 14–24, 10.1016/j.rse.2017.06.023.
24. Klouček, T., Lagner, O., Šímová, P., How does data accuracy influence the reliability of digital viewshed models? A case study with wind turbines. Appl. Geogr. 64 (2015), 46–54, 10.1016/j.apgeog.2015.09.005.
25. Komárek, J., Klouček, T., Prošek, J., The potential of Unmanned Aerial Systems: a tool towards precision classification of hard-to-distinguish vegetation types?. Int. J. Appl. Earth Obs. Geoinf. 71 (2018), 9–19, 10.1016/j.jag.2018.05.003.
26. Laba, M., Tsai, F., Ogurcak, D., Smith, S., Richmond, M.E., Field determination of optimal dates for the discrimination of invasive wetland plant species using derivative spectral analysis. Photogramm. Eng. Remote Sens. 71 (2005), 603–611, 10.14358/PERS.71.5.603.
27. Liu, J., Li, P., Wang, X., A new segmentation method for very high resolution imagery using spectral and morphological information. ISPRS J. Photogramm. Remote Sens. 101 (2015), 145–162, 10.1016/j.isprsjprs.2014.11.009.
28. Lu, B., He, Y., Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS J. Photogramm. Remote Sens. 128 (2017), 73–85, 10.1016/J.ISPRSJPRS.;1;2017.03.011.
29. Lu, D., Weng, Q., A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28:5 (2007), 823–870, 10.1080/01431160600746456.
30. Müllerová, J., Brůna, J., Bartaloš, T., Dvořák, P., Vítková, M., Pyšek, P., Timing is important: unmanned aircraft vs. satellite imagery in plant invasion monitoring. Front. Plant Sci., 8, 2017, 887, 10.3389/fpls.2017.00887.
31. Mairota, P., Cafarelli, B., Labadessa, R., Lovergine, F., Tarantino, C., Lucas, R.M., Nagendra, H., Didham, R.K., Very high resolution earth observation features for monitoring plant and animal community structure across multiple spatial scales in protected areas. Int. J. Appl. Earth Obs. Geoinf. 37 (2015), 100–105, 10.1016/j.jag.2014.09.015.
32. Moudrý, V., Šímová, P., Influence of positional accuracy, sample size and scale on modelling species distributions: a review. Int. J. Geogr. Inf. Sci., 26, 2012, 10.1080/13658816.2012.721553.
33. Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyyppä, J., Saari, H., Pölönen, I., Imai, N.N., Tommaselli, A.M.G., Individual tree detection and classification with UAV-Based photogrammetric point clouds and hyperspectral imaging. Remote Sens., 9, 2017, 10.3390/rs9030185.
34. Niethammer, U., James, M.R., Rothmund, S., Travelletti, J., Joswig, M., UAV-based remote sensing of the Super-Sauze landslide: evaluation and results. Eng. Geol. 128 (2012), 2–11, 10.1016/j.enggeo.2011.03.012.
35. Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., Wulder, M.A., Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148 (2014), 42–57, 10.1016/J.RSE.;1;2014.02.015.
36. Peña, J.M., Torres-Sánchez, J., Isabel De Castro, A., Kelly, M., López-Granados, F., Weed mapping in early-Season maize fields using object-Based analysis of unmanned aerial vehicle (UAV) images. PLoS One, 8, 2013, 10.1371/journal.pone.0077151.
37. Rampi, L.P., Knight, J.F., Pelletier, K.C., Wetland mapping in the upper midwest United States. Photogramm. Eng. Remote Sens. 80 (2014), 439–448, 10.14358/PERS.80.5.439.
38. Rapinel, S., Hubert-Moy, L., Clément, B., Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping. Int. J. Appl. Earth Obs. Geoinf. 37 (2015), 56–64, 10.1016/j.jag.2014.09.002.
39. Sankey, T., Donager, J., McVay, J., Sankey, J.B., UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens. Environ. 195 (2017), 30–43, 10.1016/J.RSE.;1;2017.04.007.
40. Sankey, T., McVay, J., Swetnam, T.L., McClaran, M.P., Heilman, P., Nichols, M., UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sens. Ecol. Conserv., 2017, 1–14, 10.1002/rse2.44.
41. Schmidt, J., Fassnacht, F.E., Neff, C., Lausch, A., Kleinschmit, B., Förster, M., Schmidtlein, S., Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status. Int. J. Appl. Earth Obs. Geoinf. 60 (2017), 61–71, 10.1016/J.JAG.;1;2017.04.005.
42. Šímová, P., Moudrý, V., Komárek, J., Hrach, K., Fortin, M.-J., Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data. Ecography (Cop.), 2018, 10.1111/ecog.03724.
43. Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., Vopěnka, P., Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests, 7, 2016, 62, 10.3390/f7030062.
44. Wang, R., Gamon, J.A., Cavender-Bares, J., Townsend, P.A., Zygielbaum, A.I., The spatial sensitivity of the spectral diversity-biodiversity relationship: an experimental test in a prairie grassland. Ecol. Appl. 28 (2018), 541–556, 10.1002/eap.1669.