Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

Han, Lujia; Dahlgren, Randy A.; Larsen, Royce E.; Devine, Scott M.; Roche, Leslie M.; Geen, Anthony T. O’; Wong, Amy; Covello, S.; Jin, Yufang

doi:10.3390/rs11050595

Cited by 33 publications

(47 citation statements)

References 63 publications

(76 reference statements)

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“…facing, 24% north facing, and 2% east facing (Liu et al, 2019). Soils formed from a mélange of sedimentary bedrock ranging from sandstones to shales.…”

Section: Study Site Descriptionmentioning

confidence: 99%

“…A 3DR Solo quadcopter (UAV), equipped with a MicaSense RedEdge camera (MicaSense Inc., Seattle, WA; five spectral bands: blue (465-485 nm), green (550-570 nm), red (663-673 nm), red-edge (712-722 nm), and near-infrared (820-860 nm) (NIR)), was used for ten flights over the 10-ha catchment during the growing season for the two years (Liu et al, 2019). Six flights were made from November 11, 2016 to April 30, 2017, and four from January 18, 2018 to April 14, 2018.…”

Section: Uav Flights and Reflectance Datamentioning

confidence: 99%

“…Six flights were made from November 11, 2016 to April 30, 2017, and four from January 18, 2018 to April 14, 2018. Further details about flight plans and image processing to produce illumination-corrected red and NIR reflectance values used to calculate normalized difference vegetation index (NDVI) at 30-cm resolution are available in Liu et al (2019). Mean growing season NDVI, red, red-edge, and NIR values, as opposed to individual monthly reflectance data, were used in statistical models to reduce the number of correlated covariates considered.…”

Section: Uav Flights and Reflectance Datamentioning

confidence: 99%

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Terrain attributes and forage productivity predict catchment-scale soil organic carbon stocks

et al. 2020

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Accurate assessments of soil organic carbon (SOC) stocks are needed at multiple scales given their importance to both local soil health and global C cycles. Rangelands cover 54% of California, representing a large stock of SOC, but existing SOC estimates are uncertain. To improve understanding of fine-resolution SOC stocks in complex terrain and provide guidance to rangeland SOC inventories, we grid-sampled 105 locations (21-m grid cells) at two depths (0-10 and 10-30 cm) in a 10-ha annual grassland catchment in California's Central Coast Range. Soils were analyzed for bulk density, coarse fragments, SOC and texture. Monthly aerial imagery was acquired by an unmanned aerial vehicle to compare surface reflectance during two contrasting years (wet vs. dry) to SOC stocks. We found that the 0-30 cm soil thickness held 3.64 ± 0.71 kg SOC m −2 (mean ± SD) with a range of 1.97-5.49 kg SOC m −2. The 0-10 cm soil thickness stored 47% of the 0-30 cm SOC stock with SOC concentrations twice as high in the 0-10 cm layer (1.40 ± 0.38%) as in the 10-30 cm layer (0.71 ± 0.15% SOC). Multiple linear regression (MLR) models explained 50-57% of SOC variability at 0-30 and 10-30 cm, but only 25% of variability at 0-10 cm. Based on cross-validation tests, MLR outperformed spatial interpolation methods and Random Forest models, best explaining SOC stocks with five environmental covariates: wet-year greenness, mean curvature, elevation, insolation, and slope. Lower hillslope positions, concave landforms, and enhanced wet-year greenness were associated with more SOC, and explained 11%, 24%, and 31% of variability in 0-30 cm SOC stocks, respectively. This study demonstrates that the accuracy of regional-scale SOC mapping of California rangelands benefits from considering microclimatic and topographic controls at the catchment-scale, in addition to broader scale mineralogical and macroclimatic controls identified in previous SOC studies.

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“…facing, 24% north facing, and 2% east facing (Liu et al, 2019). Soils formed from a mélange of sedimentary bedrock ranging from sandstones to shales.…”

Section: Study Site Descriptionmentioning

confidence: 99%

Section: Uav Flights and Reflectance Datamentioning

confidence: 99%

Section: Uav Flights and Reflectance Datamentioning

confidence: 99%

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Terrain attributes and forage productivity predict catchment-scale soil organic carbon stocks

et al. 2020

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“…Terrain characteristics were derived using the ArcGIS Desktop 10.5 software from a 1.86-cm per pixel digital surface model of the study area, which was created using photogrammetric imagery captured by a drone on March 9, 2017 (Table S1, Liu et al, 2019). Seven ground control locations showed a mean RMSE of 6 cm (all dimensions).…”

Section: Catchment Terrain Characteristicsmentioning

confidence: 99%

“…The study site was selected as an example of complex topography in California annual rangelands, including summit, shoulder, backslope, footslope, and concave-linear-convex surface curvatures with slopes ranging from 0 to 20° (Figure 1). Of the total catchment area, 45% was south facing, 29% was west facing, 24% was north facing, and 2% was east facing (Liu et al, 2019). Soils formed from a mélange of sedimentary bedrock and colluvium ranging from sandstone to shale.…”

Section: Study Site Descriptionmentioning

confidence: 99%

Microclimate–forage growth linkages across two strongly contrasting precipitation years in a Mediterranean catchment

O’Geen

Larsen²,

Dahlke

et al. 2019

Ecohydrology

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Given the complex topography of California rangelands, contrasting microclimates affect forage growth at catchment scales. However, documentation of microclimate-forage growth associations is limited, especially in Mediterranean regions experiencing pronounced climate change impacts. To better understand microclimate-forage growth linkages, we monitored forage productivity and rootzone soil temperature and moisture (0-15 and 15-30 cm) in 16 topographic positions in a 10-ha annual grassland catchment in California's Central Coast Range. Data were collected through two strongly contrasting growing seasons, a wet year (2016-17) with 287-mm precipitation and a dry year (2017-18) with 123-mm precipitation.Plant-available soil water storage (0-30 cm) was more than half full for most of the wet year; mean peak standing forage was 2790 kg ha −1 (range: 1597-4570 kg ha −1 ).The dry year had restricted plant-available water and mean peak standing forage was reduced to 970 kg ha −1 (range: 462-1496 kg ha −1 ). In the wet year, forage growth appeared energy limited (light and temperature): warmer sites produced more forage across a 3-4°C soil temperature gradient but late season growth was associated with moister sites spanning this energy gradient. In the dry year, the warmest topographic positions produced limited forage across a 10°C soil temperature gradient until late season rainfall in March. Linear models accounting for interactions between soil moisture and temperature explained about half of rapid, springtime forage growth variance. These findings reveal dynamic but clear microclimate-forage growth linkages in complex terrain, and thus, have implications for rangeland drought monitoring and dryland ecosystems modeling under climate change.

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Rangeland management

Sandhage‐Hofmann

2023

Encyclopedia of Soils in the Environment

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Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

Cited by 33 publications

References 63 publications

Terrain attributes and forage productivity predict catchment-scale soil organic carbon stocks

Terrain attributes and forage productivity predict catchment-scale soil organic carbon stocks

Microclimate–forage growth linkages across two strongly contrasting precipitation years in a Mediterranean catchment

Rangeland management

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