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United States Department of Agriculture

Agricultural Research Service

Research Project: MANAGEMENT TECHNOLOGIES FOR ARID RANGELANDS

Location: Range Management Research

Title: Rangeland monitoring with unmanned aerial vehicles (UAVs)

Authors
item Slaughter, Amalia
item Laliberte, Andrea - NEW MEXICO STATE UNIV
item Rango, Albert
item Maxwell, Connie
item Winters, Craig - NEW MEXICO STATE UNIV

Submitted to: Society for Range Management Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: September 19, 2007
Publication Date: January 26, 2008
Citation: Slaughter, A.L., Laliberte, A., Rango, A., Maxwell, C.J., Winters, C. 2008. Rangeland monitoring with unmanned aerial vehicles (UAVs). In: Society for Range Management Proceedings, Building Bridges: Grasslands to Rangelands, January 26-31, 2008, Louisville, Kentucky. p. 2284. 2008 CDROM.

Technical Abstract: Unmanned aerial vehicles (UAVs) have great potential for rangeland management applications, such as monitoring vegetation change, developing grazing strategies, determining rangeland health, and assessing remediation treatment effectiveness. UAVs have several advantages: they can be deployed quickly and repeatedly; they are less costly and safer than piloted aircraft; they are flexible in terms of flying height and timing of missions; and they can obtain imagery at sub-decimeter resolution. This hyperspatial imagery, imagery with a resolution finer than the object of interest, allows for observation of individual plants, patches, gaps, and patterns over the landscape not previously possible. At the Jornada Experimental Range (JER) in New Mexico, ongoing research is aimed at determining the utility of UAVs for rangeland mapping and monitoring. The UAV was flown at 150 m above ground and acquired imagery at approximately 5 cm ground resolution. The images were orthorectified, mosaicked, and classified using an object-based image analysis technique. Overall classification accuracies were in the high 90% range, and we were able to differentiate multiple soil types and different densities of vegetation. While image acquisition and classification are quite easily accomplished, image rectification and mosaicking are more time consuming due to small image footprints, image distortion and the difficulty of detecting sufficient ground control points. Our efforts are focused on developing and refining a complete and efficient workflow for UAV missions, consisting of flight planning, image acquisition, image rectification and mosaicking, and subsequent image classification.

Last Modified: 9/10/2014
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