Integrating airborne remote sensing and field campaigns for ecology and Earth system science

Chadwick, Kristi; Brodrick, Philip G.; Grant, Kathleen; Goulden, Tristan; Henderson, Amanda; Falco, Nicola; Wainwright, Haruko M.; Williams, Kenneth H.; Bill, Markus; Breckheimer, Ian; Brodie, Eoin L.; Steltzer, Heidi; Williams, Charles F.; Blonder, Benjamin; Chen, Jiancong; Dafflon, Baptiste; Damerow, Joan; Hancher, M.; Khurram, Aizah; Lamb, Jack; Lawrence, C. R.; McCormick, Maeve; Musinsky, John; Pierce, Samuel; Polussa, Alexander; Porro, Maceo Hastings; Scott, Andea; Singh, Hans W.; Sørensen, P.; Varadharajan, Charuleka; Bizuayehu, Whitney,; Maher, Kate

doi:10.1111/2041-210x.13463

Cited by 38 publications

(50 citation statements)

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“…The thickness of a soil layer regulates the hydrologic response, including surface and base flow runoff, water partitioning, evapotranspiration, plant-available water, and water and nutrient residence time (Fan et al, 2019). It also determines hillslope stability (or landslide potential), channel initiation, drainage density, and other geomorphic processes (Dietrich et al, 1995). More-over, soils hold the largest reservoir of organic carbon in the terrestrial ecosystem and function as a reservoir of other elements' accumulation, sequestration, and biogeochemical reactions (Grant and Dietrich, 2017;Tokunaga et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The soil layer, here defined as the mobile regolith layer, extends from the land surface to the top of the saprolite layer or bedrock (if there is no saprolite layer). Process-based geomorphologic models describe the soil thickness with mass conservation based on the balance between (1) soil transport (i.e., erosion and deposition) on the land surface and (2) soil production resulting from the bedrock-to-soil or saproliteto-soil weathering at the bottom of the soil layer (Catani et al, 2010;Dietrich et al, 1995;Heimsath et al, 2001Heimsath et al, , 1997Nicótina et al, 2011;Roering et al, 1999Roering et al, , 2001Tesfa et al, 2009). These two processes are controlled by vegetation cover, topographic gradient, biogenic processes, and climate forcing.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have used curvature -defined as the secondorder derivative of elevation -as a proxy for soil thickness (Patton et al, 2018;Tesfa et al, 2009) because hillslope curvature has an inverse (either linear or nonlinear) relationship with soil production rate based on mass conservation laws (Dietrich et al, 1995;Heimsath et al, 1997;Jackson et al, 2017). However, some studies show that curvature is a secondary or less significant variable than other topographic or land cover features in predicting soil thickness (Roering et al, 2010;Shangguan et al, 2017;Taylor et al, 2013;Tesfa et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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A hybrid data–model approach to map soil thickness in mountain hillslopes

et al. 2021

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Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid data–model approach to map soil thickness in mountain hillslopes

et al. 2021

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“…We assume that the geological map and topographic metrics can capture the variability of soil properties, since Gillin et al (2015) documented strong correlations between topographic metrics and soil properties. In addition, we used four airborne datasets (Texts S1): an airborne electromagnetic (AEM) survey acquired in fall 2017 (Minsley and Ball, 2018;Uhlemann et al, submitted;Zamudio et al, 2020), LiDAR and hyperspectral data collected by the National Ecological Observation Network (NEON) team in June 2018 (Chadwick et al, 2020), and NASA Airborne Snow Observatory (ASO) data collected in April 2018 (doi.org/10.5067/M4TUH28NHL4Z).…”

Section: Site and Datamentioning

confidence: 99%

Watershed zonation approach for tractably quantifying above-and-belowground watershed heterogeneity and functions

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Uhlemann

Franklin

et al. 2021

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Abstract. In this study, we develop a watershed zonation approach for characterizing watershed organization and function in a tractable manner by integrating multiple spatial data layers. Recognizing the coupled ecohydrogeological-biogeochemical interactions that occur across bedrock through canopy compartments of a watershed, we hypothesize that (1) suites of above/belowground properties co-varying with each other, (2) hillslopes are representative units for capturing watershed-scale heterogeneity, (3) remote sensing data layers and clustering methods can be used to identify watershed hillslope zones having unique distributions of bedrock-through-canopy properties relative to neighboring parcels, and (4) property suites associated with the identified zones can be used to understand zone-based functions, such as response to early snowmelt or drought, and associated solute exports to the river. We demonstrate this concept using unsupervised clustering methods that synthesizes airborne remote sensing data (LiDAR, hyperspectral, and electromagnetic surveys) along with satellite and streamflow data collected in the East River Watershed, Crested Butte, Colorado, USA. Results show that, (1) hillslope-average elevation and slope are significantly correlated with near-surface bedrock electrical resistivity (top 20 m), (2) elevation and aspect are independent controls on plant and snow signatures, (3) the correlation between hillslope-averaged above- and below- ground properties are significantly higher than pixel-by-pixel correlation and (4) K-means, hierarchical clustering, and Gaussian mixture clustering methods generate similar zonation patterns across the watershed. Using independently collected data, it is shown that the identified zones provide information about zone-based watershed functions, including foresummer drought sensitivity and river nitrogen exports. The approach is expected to be extensible to other sites and generally useful for guiding the selection of hillslope experiment locations and informing model parameterization.

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“…(Fig. 3) serve as a powerful starting point for mapping and measuring aspects of ecosystem biogeochemistry and biodiversity (Ustin et al 2004, Chadwick et al 2020 and providing valuable information for conservation practitioners, policymakers, and land managers (Asner et al 2017). NEON AOP data are the only free source of repeat airborne imaging spectroscopy data, offering far greater spectral resolution than other publicly available datasets (Fig.…”

Section: Aop Data Opportunitiesmentioning

confidence: 99%

Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research

et al. 2021

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Integrating airborne remote sensing and field campaigns for ecology and Earth system science

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 38 publications

References 57 publications

A hybrid data–model approach to map soil thickness in mountain hillslopes

A hybrid data–model approach to map soil thickness in mountain hillslopes

Watershed zonation approach for tractably quantifying above-and-belowground watershed heterogeneity and functions

Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research

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