Applied Ecology and Environmental Sciences
ISSN (Print): 2328-3912 ISSN (Online): 2328-3920 Website: Editor-in-chief: Alejandro González Medina
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Applied Ecology and Environmental Sciences. 2020, 8(6), 387-395
DOI: 10.12691/aees-8-6-9
Open AccessArticle

Smart Tools for Wetland Management: UAV Data and Artificial Intelligence Technique for Change Detection of Phragmites Australis in the Bear River Migratory Bird Refuge

Bushra Zaman1, 2, and Mac McKee1, 2

1Utah Water Research Laboratory, 1600 Canyon Rd, Logan, Utah 84321, USA

2Department of Civil and Environmental Engineering, Utah State University, 0160 Old Main Hill, Logan, Utah 84322-0160 USA

Pub. Date: September 15, 2020

Cite this paper:
Bushra Zaman and Mac McKee. Smart Tools for Wetland Management: UAV Data and Artificial Intelligence Technique for Change Detection of Phragmites Australis in the Bear River Migratory Bird Refuge. Applied Ecology and Environmental Sciences. 2020; 8(6):387-395. doi: 10.12691/aees-8-6-9


Unmanned Aerial Vehicle (UAV) data and artificial intelligence (AI) techniques have often been separately used for wetland management applications. Existence of native or invasive weed species which are a threat to the biodiversity of the wetland ecosystem is a common problem. High resolution multispectral image data from UAV and AI analysis together may prove to be a very robust combination for solving some of the wetland management problems. AggieAir, a UAV platform developed at Utah State University (USU), was used to acquire very high-resolution, multispectral aerial images of the study area in the summers of 2010 and 2011. The AggieAir data and an AI model have been used to classify and detect the weed P. australis in the Bear River Migratory Bird Refuge (BRMBR) wetland ecology. The images were classified based on the reflectance values in red, green and NIR bands using the multiclass relevance vector machine (MCRVM). The total P. australis cover was calculated and results indicated a decrease of 6.6% in total P. australis cover between June and September 2010 but an increase of 43.85% between June 2010 to July 2011. This study provided useful information about the extent and spread of P. australis P. australis which is a priority as regards to invasive plant treatment and control in the BRMBR. The results are easy to interpret and can contribute to management advising. Given the high spatial and temporal resolution of the UAV and excellent performance of the MCRVM model, we propose their further use for wetland management applications.

wetland management water management relevance vector machine unmanned aerial vehicles classification P. australis P. australis modeling artificial intelligence

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


[1]  Szantoi, Z., Escobedo, F. J., Abd-Elrahman, A., Pearlstine, L., Dewitt, B., and Smith, S., "Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features," Environmental Monitoring and Assessment, 187 (5). 262. 2015.
[2]  Pierce, J., Diaz-Barrios, M., Pinzon, J., Ustin, S. L., Shih, P., Tournois, S., Zarco-Tejada, P. J., Vanderbilt, V. C., and Perry, G. L., “Using support vector machines to automatically extract open water signatures from POLDER multi-angle data over boreal regions”, in IEEE International Geoscience and Remote Sensing Symposium. 2349-2350. 2002.
[3]  Goetz, S. J., "Remote sensing of riparian buffers: past progress and future prospects," JAWRA Journal of the American Water Resources Association, 42 (1).133-43. 2006.
[4]  Zhang, C., and Kovacs, J. M., "The application of small unmanned aerial systems for precision agriculture: a review," Precision agriculture, 13 (6). 693-712. 2012.
[5]  Laliberte, A. S., and Rango, A., "Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery," in IEEE Transactions on Geoscience and Remote Sensing, 47 (3). 761-70. 2009.
[6]  Chabot, D., and Bird, D. M., "Small unmanned aircraft: precise and convenient new tools for surveying wetlands," Journal of Unmanned Vehicle Systems, 1 (01).15-24. 2013.
[7]  Ozesmi, S. L., and Bauer, M. E., “Satellite remote sensing of wetlands,” Wetlands ecology and management, 10 (5). 381-402. 2002.
[8]  MacAlister, C., and Mahaxay, M., “Mapping wetlands in the Lower Mekong Basin for wetland resource and conservation management using Landsat ETM images and field survey data,” Journal of Environmental Management, 90 (7). 2130-7. 2009.
[9]  Ozesmi, S. L., "Satellite remote sensing of wetlands and a comparison of classification techniques." 2001.
[10]  Kettenring, K. M., Mock, K. E., Zaman, B., and McKee, M., “Life on the edge: reproductive mode and rate of invasive Phragmites australis patch expansion,” Biological Invasions, 18 (9). 2475-95. 2016.
[11]  Zaman, B., Jensen, A. M., and McKee, M. 2011, “Use of high-resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle to quantify the spread of an invasive wetlands species”, in IEEE International Geoscience and Remote Sensing Symposium. 803-806. 2011.
[12]  Tipping, M. E., "Sparse Bayesian learning and the relevance vector machine," Journal of machine learning research, 1. 211-44. 2001.
[13]  Thayananthan, A., Navaratnam, R., Stenger, B., Torr, P. H., and Cipolla, R., “Multivariate relevance vector machines for tracking”, in European conference on computer vision, 124-128, 7-13 May 2006.
[14]  Zhang, H., and Malik, J., "Selecting shape features using multi-class relevance vector machine," Technical Rep. No. UCB/EECS-2005, 6. 2005.
[15]  Camps-Valls, G., Marsheva, T. V. B., and Zhou, D., "Semi-supervised graph-based hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, 45 (10). 3044-54. 2007.
[16]  Khalil, A., Almasri, M. N., McKee, M., and Kaluarachchi, J. J., "Applicability of statistical learning algorithms in groundwater quality modeling," Water Resources Research, 41 (5). 2005.
[17]  Zaman, B., Jensen, A., Clemens, S. R., and McKee, M., “Retrieval of spectral reflectance of high resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle,” Photogrammetric Engineering & Remote Sensing, 80 (12). 1139-50. 2014.
[18]  Thayananthan, A.; Navaratnam, R.; Stenger, B.; Torr, P. H.; Cipolla, R., "Pose estimation and tracking using multivariate regression," Pattern Recognition Letters, 29 (9). 1302-10. 2008.
[19]  Jensen, A. M., Chen, Y., McKee, M., Hardy, T., and Barfuss, S. L., “AggieAir—a low-cost autonomous multispectral remote sensing platform: new developments and applications”, in IEEE International Geoscience and Remote Sensing Symposium. IV-995-IV-998. 2009.
[20]  Chao, H., Cao, Y., and Chen, Y., "Autopilots for small unmanned aerial vehicles: a survey," International Journal of Control, Automation and Systems, 8 (1). 36-44. 2010.