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Abstract The purpose of this study is to develop an unmanned aerial vehicle (UAV)‐based remote sensing method that can estimate vegetation indicators in arid and semiarid rangelands. This method was used to quantify six rangeland indicators (canopy size, bare soil gap size, plant height, scaled height, vegetation cover, and bare soil cover) in a semiarid grass–shrub ecosystem. The drone‐based estimates were validated with field measurements by using the standard transect methods (gap intercept, drop disk, and line‐point intercept methods) in the spring and summer of 2017. The drone‐based estimates showed strong agreements with in situ measurements in cases where deciduous vegetation (mesquite) had leaves withR2for bare soil gap size and vegetation height of 0.97 and 0.89 in the summer, respectively. The RMSE of bare soil gap size and vegetation height are 0.2 m and 6.72 cm in the summer, respectively. Based on these results, we found that drone‐based remote sensing proved to be an efficient and highly accurate method that serves as a complement to field measurements for rangeland indicator estimation. We discussed the possible applications of drone‐based products on arid and semiarid rangelands: the spatially explicit input of an ecological model, to detect and characterize non‐stationarity, and to detect landscape anisotropy.
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This content will become publicly available on March 15, 2026
Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics
Abstract Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2(NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks withR2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks.
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- Award ID(s):
- 2425290
- PAR ID:
- 10654210
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- John Wiley & Sons, Inc.
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 17
- Issue:
- 3
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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