Delineating wetland vegetation species using a UAV-mounted multi-spectral camera and computer-aided classification

Authors

  • Addison Deshambo University of Wisconsin - Whitewater
  • Eleanor C Stewart Lawrence University https://orcid.org/0000-0003-3148-1444
  • Sophie Ulik University of Wisconsin - La Crosse
  • Johanna M Williams Lawrence University

DOI:

https://doi.org/10.17307/wsc.v1i1.366

Keywords:

UAV, multispectral camera, wetland vegetation, classification, stormwater ponds, ArcGIS, MetaShape

Abstract

Agricultural runoff can be harmful to the environment by increasing soil erosion and flooding, while adding excess nutrients such as phosphorus to downstream waterways. Stormwater detention ponds are a common way to mitigate flood risk and improve water quality in urban areas. This concept has since been adapted to protect susceptible areas downstream of agriculture. Wetland vegetation can be planted around these ponds to uptake nutrients and slow water flow. Vegetation mapping is a frequent practice within land management to monitor health and species distribution. This process could be less labor-intensive if paired with the use of aerial imagery and computer-aided classification. Unmanned Aerial Vehicle (UAV) acquired imagery allows for high temporal and spatial resolutions that can map the environment in high detail and accuracy. Here we present a detailed workflow for species-level mapping from data acquisition in the field through image processing and analysis.

References

Abd-Elrahman, A., Britt, K. & Liu, T. (2021). Deep learning classification of high-resolution drone images using the ArcGIS Pro software. UF/IFAS Gulf Coast Research and Education Center, 2021(5), 1-7. https://doi.org/10.32473/edis-fr444-2021

Ahmed, O. S. & Franklin, S. E. (2018). Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data. International Journal of Remote Sensing, 39, 5236-5245. https://doi.org/10.1080/01431161.2017.1363442

Blanchard, S. D., Kelly, M., Kersten, E. & Koy, K. (2011). Terrestrial remotely sensed imagery in support of public health: new avenues of research using object-based image analysis. Remote Sensing 3(11), 2321-2345. https://doi.org/10.3390/rs3112321

Cai, X., Li, E., Xia, Y., Yang, C., Yang, J. & Zhou, R. (2021). Object-based wetland vegetation classification using multi-feature selection of unoccupied aerial vehicle RGB imagery. Remote Sensing, 13(23), 1-21. https://doi.org/10.3390/rs13234910

Chandler, J. H., Ordóñez, C., Rodriguez-Pérez, J. R. & Sanz-Ablanedo, E. (2018). Accuracy of unmanned aerial vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sensing, 10(10). https://doi.org/10.3390/rs10101606

Grüner, C. (2020, April 14). Study of deviation of PPP with RS2 with different observation-times. Emlid Community Forum. https://community.emlid.com/t/study-of-deviation-of-ppp-with-rs2-with-different-observation-times/19185

Heilman, P., McClaran, M. P., McVay, J., Nichols, M, Sankey, T. T. & Swetnam, T. L. (2018). UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sensing in Ecology and Conservation, 4(1), 20-33. https://doi.org/10.1002/rse2.44

Hess, K. M., Kent, D. B., LeBlanc, D. R., McCobb, T. D. & Smith, R. L. (2003). Phosphorus in a ground-water contaminant plume discharging to ashumet pond, cape cod, massachusetts, 1999. United States Geological Survey. https://pubs.usgs.gov/wri/wri024306/

Lu, B. & Yuhong, H. (2017). Species classification using unmanned aerial vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 73-85. https://doi.org/10.1016/j.isprsjprs.2017.03.011

Downloads

Published

2023-10-12

How to Cite

Deshambo, A., Stewart, E. C., Ulik, S., & Williams, J. M. (2023). Delineating wetland vegetation species using a UAV-mounted multi-spectral camera and computer-aided classification. Proceedings of the Wisconsin Space Conference, 1(1). https://doi.org/10.17307/wsc.v1i1.366

Issue

Section

Geosciences