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

REMOTE SENSING OF ENVIRONMENT

卷:253

Article

Sankey, Joel B.¹  Sankey, Temuulen T.²  Li, Junran³  Ravi, Sujith⁴  Wang, Guan³  Caster, Joshua¹  Kasprak, Alan^1,5,6 

[1] US Geol Survey, Southwest Biol Sci Ctr, Grand Canyon Monitoring & Res Ctr, Flagstaff, AZ 86004 USA

[2] No Arizona Univ, Sch Informat Comp & Cyber Syst, 1295 S Knoles Driver, Flagstaff, AZ 86011 USA

[3] Univ Tulsa, Dept Geosci, Tulsa, OK 74104 USA

[4] Temple Univ, Dept Earth & Environm Sci, 1901 N 13th St, Philadelphia, PA 19122 USA

[5] Ft Lewis Coll, Dept Geosci, Durango, CO 81301 USA

[6] Ft Lewis Coll, Four Corners Water Ctr, Durango, CO 81301 USA

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

DOI  :  10.1016/j.rse.2020.112223

来源: Elsevier

【 摘 要 】

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.

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