Deriving Landscape-Scale Vegetation Cover and Aboveground Biomass in a Semi-Arid Ecosystem Using Imaging Spectroscopy

Poley, Andrew

doi:10.18122/b25q72

Cited by 3 publications

(4 citation statements)

References 65 publications

(119 reference statements)

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“…In the mid‐elevations, aspect, topography, and hydrology influence the vegetation distribution (Burke et al ., 1989). North through east‐facing colluvial hollows and riparian corridors contain the densest shrub coverage across the mid‐elevation landscape (Poley, 2017).…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal changes in aeolian redistribution of sediments and nutrients following fire

Roehner

Pierce

Yager

2020

Earth Surf Processes Landf

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Wildfires can profoundly alter rates, magnitudes, and ecological influences of aeolian redistribution of sediments and nutrients. This study examines the influence of fire in a semi‐arid ecosystem using 2 years of continuous passive dust trap data in the northern Great Basin, USA. We analyse the mass flux, organic material content, grain size distribution, and geochemistry of the collected samples to trace the fingerprint of the 2015 Soda Fire through space and time. In areas not affected by fire, dust is characterized by silt‐sized median grains, a geochemical signature consistent with a playa source area, and spatially consistent but seasonally variable dust flux rates. Following fire, dust flux increases significantly within and near the burned area. At burned and topographically sheltered sites, dust deposition in the eighth month following fire was 190% higher than dust deposition 2 years post‐revegetation. Topographically exposed sites recorded only modest increases in dust deposition following fire. Analysis of organic matter indicates all dust samples (both burned and unburned) contained an average of 45% organic matter compared to a watershed average of 1.6% organic matter in soils. Geochemical and seasonal dust deposition data from 12 dust traps at a range of elevations indicate that with the removal of stabilizing vegetation after wildfire, differences in topographic position and wind direction lead to preferential redistribution of material across a burned landscape over hillslope scales (0–10 km). We posit post‐fire aeolian redistribution of locally derived material to topographically controlled positions is an important control on the spatial variability of soil depth and characteristics in drylands with complex topography. © 2020 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Spatial and temporal changes in aeolian redistribution of sediments and nutrients following fire

Roehner

Pierce

Yager

2020

Earth Surf Processes Landf

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“…Previous studies have indicated that aeolian inputs to soils contain vital carbon resources for semi-arid landscapes (McTainish and Strong,Reynolds et al, 2001). Will (2017) observed average watershed wide SOC of 1.79% across the RCEW. Soil samples collected from the A-horizon of soils in Murphy…”

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confidence: 82%

“…In the mid-elevations, aspect, topography, and hydrology influence vegetation distribution (Burke et al, 1989). North through east facing colluvial hollows and riparian corridors contain the densest shrub coverage across the mid-elevation landscape (Poley, 2017).…”

Section: Site Descriptionmentioning

confidence: 99%

Post-Fire Variation in Aeolian Deposition in the Northern Great Basin

Pierce¹,

Roehner²,

Glenn³

et al.

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Aeolian processes play a significant role in the redistribution of sediment and nutrients in sparsely vegetated sagebrush-steppe ecosystems. When fire is introduced to the landscape, decreased surface roughness and associated threshold friction velocities allow for the increased mobility of surface sediments and burnt organic material, mobilizing previously stable material. Once material is entrained, interactions between a dynamic atmosphere and complex topography control the spatial distribution of aeolian deposition over a landscape. Given the significant impact of fire on aeolian processes in semi-arid deserts, we posit that postfire aeolian redistribution of material is an important control on the spatial variability of soil depth and characteristics in semi-arid deserts with complex topography. Our study uses over two years of continuous passive dust trap data collected following the Soda Fire of August 2015 in the northern Great Basin. We analyze the mass flux, organic material content, grain size distribution, and geochemistry of the collected samples to trace the fingerprint of the Soda Fire through space and time. As such, the results of this study will inform research on postfire sediment and carbon redistribution, the spatial variability of soil characteristics, and landform evolution in western rangelands. Our results indicate that seasonal variation in aeolian mass flux is pronounced, with the fall months generating the highest rates of dust flux. Immediately following the Soda Fire of August 2015, the mass flux of both sediment and organic material increased by two to threefold within and proximal to the burned area. Increases v in flux lasted on the landscape until the revegetation of the burned area in the spring of 2016, leaving roughly 8 months of disturbed soil surface conditions. Samples impacted by fire contained 88% fine silt and clay-sized material while undisturbed samples averaged 94%, indicating a temporary increase in the particle size distribution within the burned area. A geochemical comparison of regional and local dust and its sources also indicates a pulse of local sediment mobility following the fire through an increase in the relative concentrations of Titanium (found in local soil) and a decrease in the relative concentrations of Barium and Strontium (found in regional soluble salts). We interpret the cessation in local mobility after revegetation to adequate surface roughness provided from spring "green up" (grasses and forbes) to return vertical fluxes of organic matter and sediment to within pre-disturbance fluctuations. Recent studies in the northern Great Basin have found aspect-controlled differences in soil depth and volumetric water content in mid-elevation sagebrush-steppe ecosystems. North-facing aspects tend to have deeper soils, with higher organic content and greater volumetric water contents throughout the water year than south-facing slopes. Our results indicate that local material is suspended and deposited over small scales (0-10 km) to spatially controlled loc...

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“…Known spectral endmembers ('pure' pixels, either from known locations in the images, or a reference signal) can then inform unmixing of the pixel into its likely constituents. This process has been used with remarkable success, even in shrub-steppe environments [Poley, 2017] It may not be possible to pick a singular date that can minimize confusion between all classes since phenologies of semi-arid vegetation can be disparate [Gallagher, 2018].…”

Section: Phenology and Time-seriesmentioning

confidence: 99%

Remote Sensing Time-Series Analysis, Machine Learning, and K-Means Clustering Improves Dryland Vegetation and Biological Soil Crust Classification

Enterkine¹

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Dryland and semi-arid vegetation communities, although appearing to the casual observer as relatively simplistic and homogeneous, are in fact the opposite. Upon further inspection, semi-arid vegetation is highly complex and heterogeneous at almost any scale. The same holds true for biological soil crust. Growing concern about global changes in climate, nutrient cycles, and land use have required increasing scrutiny of our understanding of these communities and all of their constituents, as we seek to improve forecasting models and inform land management decisions. This thesis aims to provide insight to the paradigm of how we create and interpret vegetation classifications in a semi-arid ecosystem. In the first chapter, I examine the potential of new remote sensing imaging platforms in combination with machine learning algorithms and cloud computing as they apply to time-series analyses for vegetation classification. The results of this indicate that sinusoidal approximations ("Harmonic Models") of vegetation indices are able to predict vegetation cover with nearly the same accuracy as monthly composites, and that a combination of both perform no better than either. Additionally, I examine how assigning classes to training data (e.g. species-level, plant functional type) influence the classification accuracy, interpretability, and potential uses. Stricter class membership requirements at increasingly aggregated scales (e.g. PFT) lead to greater accuracies. Finding the implication of this conclusion unsatisfactoryat the extreme, everything was either cheatgrass or bare, the shrub class succumbing almost entirely to vii errors of omission-I investigated approaching other methods to assign classes to field data that captured more of the realities of semi-arid vegetation within our study area. To this extent, k-means clustering was used to determine what community classes were present in the field data. The outcome of this approach is a class where each potential constituent cover has a known distribution. Overall accuracies were found to be lower for this approach. However, the classification outcomes quantify overlapping distributions of cover types (e.g. 'sagebrush' or 'shrub') between classes. These accuracies are assessed using 'fuzzy' confusion matrices. This enables more information to be preserved through the remote sensing classification process, and reserves more interpretation for the map user than a typical 'hard' classification. Importantly the distributions of cover types are likely most representative of field conditions and thus more useful to land managers making holistic decisions about restoration or fuel management. For the second chapter, I delved deeper into the potentials of new remote sensing and computing platforms to predict biological soil crust cover. The growing field of research on biological soil crust points to potentially significant implications for nutrient and water cycling, in addition to positive effects on native vascular vegetation. However, spatial data are lacking due to remot...

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Deriving Landscape-Scale Vegetation Cover and Aboveground Biomass in a Semi-Arid Ecosystem Using Imaging Spectroscopy

Cited by 3 publications

References 65 publications

Spatial and temporal changes in aeolian redistribution of sediments and nutrients following fire

Spatial and temporal changes in aeolian redistribution of sediments and nutrients following fire

Post-Fire Variation in Aeolian Deposition in the Northern Great Basin

Remote Sensing Time-Series Analysis, Machine Learning, and K-Means Clustering Improves Dryland Vegetation and Biological Soil Crust Classification

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