The Sierra Ancha Experimental Forest Vegetation Mapping Project is a collaborative study between the Rocky Mountain Research Station and the Northern Arizona University School of Informatics, Computing, and Cyber Systems. It employs unmanned aerial vehicle (UAV) technology to create highly detailed, landscape-scale vegetation maps of the Sierra Ancha Experimental Forest that can be compared among many different parameters to historical vegetation datasets dating back to the 1920s. In the past, data collection focused on three main areas of study: vegetation, water yield, and climatology. These datasets formed the foundation in which many pioneering studies were based upon over the last century. Using this new technology to update and expand upon these datasets ensures the continuation of innovative scientific inquiry into the future.
The developing field of drone technology, or unmanned aerial vehicles (UAVs), provides an alternative data source at a lower cost and higher temporal and spatial resolution compared to mannedairborne and satellite images. Here we test and demonstrate the accuracies of UAV multispectral images and Structure-from-Motion(SfM) -derived 3D data in rangeland monitoring and long-term species composition change detection. For this study, we flew a light-weight, fixed-wing UAV (SenseFly, Switzerland) at 90 m altitude aboveground resulting in image spatial resolution of 12 cm and four spectral bands: green (520-580 µm), red (630-690 µm), red edge (720-750 µm), and near-infrared (760-820 µm). The UAV images were also photogrammetrically analyzed via SfM (Pix4D software, Pix4D SA, Lausanne, Switzerland) to create 3D models of topography (10 cm resolution) and vegetation height (12 cm resolution).
Our results indicate that long-term plot-based monitoring can be achieved using UAV-based assessments. The positional accuracies of the UAV-derived map ranged 8-15 cm in the X and Y coordinates. While the ability to accurately identify multiple species is limited by the number of spectral bands available, the very detailed, species-level UAV images over small to mid-sized areas can easily be used in conjunction with freely available, coarse-resolution satellite images to scale-up rangeland monitoring over larger areas. Thus, the unbiased, observational data records from UAV images can act as an invaluable tool for land managers informing targeted land use strategies.
For more information about the Sierra Ancha Experimental Forest and the research conducted there, please visit the Sierra Ancha Experimental Forest webpage.
For more information about the Remote Sensing and Geoinformatics lab, see the NAU lab page. https://sites.google.com/a/nau.edu/remote-sensing-lab/home