EGU21-749
https://doi.org/10.5194/egusphere-egu21-749
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland

Temuulen Sankey1, Joel Sankey2, Junran Li3, Sujith Ravi4, Guan Wang3, Joshua Caster2, and Alan Kasprak2,5
Temuulen Sankey et al.
  • 1School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, 86001, United States of America (temuulen.sankey@nau.edu)
  • 2U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, Flagstaff, AZ 86001, USA
  • 3Department of Geosciences, The University of Tulsa, Tulsa, OK 74104, USA
  • 4Department of Earth and Environmental Science, Temple University, Philadelphia, PA 19122, USA
  • 5Department of Geosciences, Fort Lewis College, Durango CO 81301, USA

Rangelands cover 70% of the world’s land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands over smaller extents than typically imaged with satellite and manned airborne platforms. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR (light detection and ranging) fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Creosote bush (Larrea tridentata) and black grama (Bouteloua eriopoda) are the most important native species in the rangeland and have the highest producer’s accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1,474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.

How to cite: Sankey, T., Sankey, J., Li, J., Ravi, S., Wang, G., Caster, J., and Kasprak, A.: Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-749, https://doi.org/10.5194/egusphere-egu21-749, 2021.

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