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Open Access Publications from the University of California

Multi-scale vegetation-aeolian transport interaction in drylands: remote sensing and modeling

  • Author(s): Zhang, Junzhe
  • Advisor(s): Okin, Gregory S
  • et al.
Abstract

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.

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