TY - JOUR
T1 - Quantifying plant-soil-nutrient dynamics in rangelands
T2 - Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland
AU - Sankey, Joel B.
AU - Sankey, Temuulen T.
AU - Li, Junran
AU - Ravi, Sujith
AU - Wang, Guan
AU - Caster, Joshua
AU - Kasprak, Alan
N1 - Funding Information:
This research was funded by the US National Science Foundation (NSF) EAR-1451518 for S. Ravi and EAR-1451489 for J. Li. The authors gratefully acknowledge the contributions of Jon Erz and Andy Lopez (FWS, SNWR), and Scott Collins (Sevilleta LTER, New Mexico, USA) for providing access to field and laboratory facilities and technical guidance. The UAV remote sensing equipment purchased for this study was funded by the Office of the Vice President for Research at Northern Arizona University. Joel Sankey was additionally supported by the U.S. Geological Survey Ecosystems Mission Area. The authors acknowledge Keith Kohl of the U.S. Geological Survey for helpful assistance with RTK-GPS survey logistics and analysis. The authors acknowledge RSE Editor-in-Chief Marie Weiss, three anonymous reviewers, and Seth Munson (USGS) for their helpful comments on previous versions of the manuscript. This manuscript is submitted for publication with the understanding that the US Government is authorized to reproduce and distribute reprints for Governmental purposes. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the US Government.
Funding Information:
This research was funded by the US National Science Foundation (NSF) EAR-1451518 for S. Ravi and EAR-1451489 for J. Li. The authors gratefully acknowledge the contributions of Jon Erz and Andy Lopez (FWS, SNWR), and Scott Collins (Sevilleta LTER, New Mexico, USA) for providing access to field and laboratory facilities and technical guidance. The UAV remote sensing equipment purchased for this study was funded by the Office of the Vice President for Research at Northern Arizona University. Joel Sankey was additionally supported by the U.S. Geological Survey Ecosystems Mission Area. The authors acknowledge Keith Kohl of the U.S. Geological Survey for helpful assistance with RTK-GPS survey logistics and analysis. The authors acknowledge RSE Editor-in-Chief Marie Weiss, three anonymous reviewers, and Seth Munson (USGS) for their helpful comments on previous versions of the manuscript. This manuscript is submitted for publication with the understanding that the US Government is authorized to reproduce and distribute reprints for Governmental purposes. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the US Government.
Publisher Copyright:
© 2020
PY - 2021/2
Y1 - 2021/2
N2 - 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.
AB - 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.
KW - Airborne data
KW - Change detection
KW - Digital elevation model (DEM)
KW - Digital elevation model of difference (DOD)
KW - Drone
KW - Fire
KW - Grass
KW - Hyperspectral
KW - Islands of fertility
KW - Lidar
KW - Machine learning
KW - Nutrient
KW - Photogrammetry
KW - Rangeland
KW - Shrub
KW - Soil
KW - Structure from motion (SFM)
KW - Terrestrial laser scanning
KW - Unmanned aerial system (UAS)
KW - Unmanned aerial vehicle (UAV)
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UR - http://www.scopus.com/inward/citedby.url?scp=85097330177&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2020.112223
DO - 10.1016/j.rse.2020.112223
M3 - Article
AN - SCOPUS:85097330177
SN - 0034-4257
VL - 253
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 112223
ER -