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

Joel B. Sankey, Temuulen T. Sankey, Junran Li, Sujith Ravi, Guan Wang, Joshua Caster, Alan Kasprak

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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 tn0020 smaller extents. 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 fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Among them, creosote bush and black grama, the most important native species in the rangeland, 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 1474 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.

Original languageEnglish (US)
Article number112223
JournalRemote Sensing of Environment
Volume253
DOIs
StatePublished - Feb 2021

Keywords

  • Airborne data
  • Change detection
  • Digital elevation model (DEM)
  • Digital elevation model of difference (DOD)
  • Drone
  • Fire
  • Grass
  • Hyperspectral
  • Islands of fertility
  • Lidar
  • Machine learning
  • Nutrient
  • Photogrammetry
  • Rangeland
  • Shrub
  • Soil
  • Structure from motion (SFM)
  • Terrestrial laser scanning
  • Unmanned aerial system (UAS)
  • Unmanned aerial vehicle (UAV)

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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