Vegetation-aeolian transport interaction strongly affects ecosystem function, landform, and dust emission in drylands. Vegetation strongly modulates the pattern of wind-driven transport in drylands; however, the interaction between vegetation and aeolian transport is complex and vary across the different spatial scales. Moreover, this interaction also results in the strengthening of soil erosions and the loss of nutrients by blowing and flushing away soil particles, which have been recognized as the primary components of desertification. Recent studies indicate that climate change has been taking place and is predicted to become more common in arid and semiarid regions, amplifying aeolian processes and changing vegetation pattern. Therefore, measurement, monitoring, and assessment of vegetation-aeolian transport interaction became important. Previous studies have only focused on the interaction at a single spatial scale. The overall goal of my dissertation is to provide a comprehensive investigation of the vegetation-aeolian transport interaction from a multi-scale perspective.

In this study, a drone-based remote sensing method was created to characterize biophysical indicators in a grass-shrub ecosystem at a landscape scale. This drone-based remote sensing method was proved to be an efficient, high accuracy, and low-cost method that serves as an alternative to field measurements to provide tempo-spatial continuous observation of vegetation pattern and landform change. A machine learn-based data assimilation method was developed to predict the biophysical indicators in the arid and semiarid rangelands of Western U.S. This machine learning-based data assimilation method was applied on the arid and semiarid rangelands of Western U.S. to build the first-ever distribution maps of several biophysical indicators. Based on the prediction of these biophysical indicators, a semi-physical model was designed to estimate the vertical dust flux in the Western U.S. and the results were validated by satellite remote sensing product. Last, an ecological model was developed to simulate the impact of aeolian transport on vegetation pattern and landform at a landscape scale. This model successfully imitated the impact of aeolian transport on vegetation community. The study of this dissertation improved the understanding of vegetation-aeolian transport interaction.

Drylands cover more than 40% of the earth land surface and are home for more than 2 billion people. Despite the harsh environment and the extreme aridity conditions, drylands are among the most diverse ecosystems that contribute to more than 30 % of the global terrestrial net primary production. Assessing vegetation dynamics through remote sensing for dryland surfaces remain a challenge due to the appearance of soil and non-photosynthetic material that usually cause non-linear scattering of light. This aspect of remote sensing is investigated in this dissertation using mechanistic and unmixing remote sensing approaches to assess net primary productivity of the Chihuahuan Desert. The unmixing approach was demanded by the difficulties associated with assessing vegetation productivity with mechanistic remote sensing. We found that including soil and non-photosynthetic surface covers contribute to the predictions of NPP with Multiple Endmember Spectral Mixture Analysis, Random Forest and stepwise regression.

Erosion is considered one of the main complex drivers that contribute to vegetation cover change. The feedbacks between wind erosion and other environmental, and biological driver have not been fully studied. Wind erosion remains one of the most understudied drivers of shrub encroachment despite the evidence of the association of aeolian transport with grass cover decline and shrub encroachment. In this research, we introduce wind erosion processes as major disturbances to vegetation covers in drylands affecting the health and mortality of vegetation species. We investigated the damaging effects of wind transport on major vegetation community types in the Chihuahuan Desert using a linear wind tunnel. We quantified the damaging effects of sandblasting using plant mapping methods to emphasize leaf loss, color change, stem loss and plant height change. Our sandblasting experiment shows some similarities between the grasses and the shrubs in the response to sandblasting. However, the grasses were more sensitive to sandblasting than the shrubs due to their growing point and growth form. Our finding might explain shrub encroachment phenomena in drylands

Aeolian and fluvial processes are fundamental drivers of arid land dynamics because of their effects on soil surfaces and microtopographies. In this research we investigate the coupling effects of wind and water transport in erosion and deposition in ephemeral streams in Moab, UT covering dry and wet periods. We used structure from motion and drone technologies to survey stream over 16 months period to investigate the soil surface elevation change due to wind and water activities. We performed differencing analysis and quantified soil erosion and deposition volume for 5 survey periods. The streams show a significant net soil erosion and deposition over the survey periods which indicate direct interaction between aeolian and fluvial processes in development and changes of channel morphologies.

Accurate fire severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in the Kalahari Desert of Southern Botswana. The frequent wildfires in this arid savanna region predominantly cause topkill, where vegetation experiences above-ground combustion, but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush fire severity and post-burn coppicing/regeneration events of woody vegetation. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 sq. km plot within the Modisa Wildlife Reserve, extensively burnt by the high-intensity Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.5% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Logistic regression analysis revealed a >50% probability of woody vegetation regrowth in high severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in the Kalahari.

Light absorbing impurities (LAIs), like dust and black carbon (BC), initiate powerful albedo feedbacks when deposited on snow cover, yet due to a scarcity of observations radiative forcing by LAIs is often neglected, or poorly constrained, in climate and hydrological models. This has important consequences for regions like the Colorado River Basin, where dust deposition to mountain snow cover frequently occurs in the upper basin in the springtime, a relatively new phenomenon since western expansion of the US. Previous work showed that dust on snow (DOS) enhances snowmelt by 3-7 weeks, shifts timing and intensity of runoff, and reduces total water yield.

Here, advanced methods are presented to measure, model, and monitor DOS in the hydrologically sensitive Colorado River Basin. A multi-year multi-site spatial variability analysis indicates the heaviest dust loading comes from point sources in the southern Colorado Plateau, but also shows that lower levels of dust loading from diffuse sources still advances melt by 3-4 weeks. A high-resolution snow property dataset, including vertically resolved measurements of snow optical grain size and dust/BC concentrations, confirms that impurity layers remain in the layer in which they are deposited and converge at the surface as snow melts: influencing snow properties, rapidly reducing snow albedo, and increasing snowmelt rates. The optical properties of deposited impurities, which are mainly dust, are determined using an inversion technique from measurements of hemispherical reflectance and particle size distributions. Using updated optical properties in the snow+aerosols radiative transfer model SNICAR improves snow albedo modeling over a more general dust characterization, reducing errors by 50% across the full range of snow reflectance. Radiative forcing by LAIs in the CRB, estimated directly from measurements and updated optical properties, is most strongly controlled by dust concentrations in the uppermost surface layer, as dust comprises 99%+ of the impurity mixture, and therefore, dominates absorption. Coupling the physically based snow model SNOWPACK, modified to track dust layers, to SNICAR, simulates the impacts of DOS radiative forcing on snow properties. This improved understanding, and representation, of DOS processes has important implications for assessing regional impacts of LAIs, for representing LAIs in climate and hydrologic models, for remote sensing of these processes.

Savannas are an important environment that make up over one fifth of the planet’s terrestrial area, but our current understanding of the ecohydrological mechanisms that produce this woody-grass coexistence remain limited. It has been assumed that in savannas there is no direct competition occurring belowground between woody vegetation and grasses. Although the assertion that two layers of roots are present has been questioned as a consistent representation of belowground woody-grass interactions in savannas. Therefore, it is necessary to repartition woody and grass roots into three-layers to provide a more consistent representation of the belowground competition observed in savannas. Then developing numerical models provides a means through which these hypotheses can be compared, and analyzed. The results show that reallocating roots into three-layers provides a far more consistent representation of belowground competition occurring between woody vegetation and grasses in savannas environments, as well as a better understanding of the ecohydrological mechanisms influencing the woody-grass distribution.

Dryland environments are experiencing shifting ecogeomorphic patterns due to climatic changes and anthropogenic activities, resulting in a shift from grasslands to shrub-dominated landscapes. This dissertation investigates the effects from increasingly variable monsoonal precipitation and ecogeomorphic connectivity on perennial grass growth, litter distribution, and soil organic matter in drylands, with a focus on grass-shrub ecotones. Field experiments were conducted in the Chihuahuan Desert at the Jornada Basin Long-Term Ecological Research (LTER) site using a precipitation manipulation system and connectivity modifiers (ConMods) to assess their effects on plant productivity, recruitment, and soil nutrient distribution. Results show that reducing connectivity, combined with increased monsoonal precipitation, can enhance perennial grass productivity and recruitment, and affect the distribution of soil organic matter and non-photosynthetic vegetation. These findings contribute to our understanding of how aeolian processes and shifting precipitation regimes will shape vegetation patterns and soil properties in dryland environments under future climate scenarios. This research provides insights into potential mitigation strategies for combating shrub encroachment and promoting the sustainability of dryland ecosystems.