Patterns and Processes of Vegetation Dynamics

A broad theme of my research is centered on understanding patterns of seasonal vegetation dynamics and long-term trends. This includes a diverse range of topics such as detecting changes in global trends in vegetation greenness from satellite data and assessing simulated trends in vegetation productivity and water use efficiency in Africa.

Lab member Rubaya Pervin is currently developing and testing a number of novel remote sensing data fusion methods for detecting savanna shrub, grass, and bare soil cover. We will use these high-resolution datasets to validate coarser resolution data, and to examine patterns and drivers of the shrub-grass coexistence in the SW US. We are interested in the following questions: Do shrub cover patterns in these ecosystems follow a characteristic power law distribution, and if not, why not? Are these patterns mostly regulated by “bottom-up” vegetation-moisture interactions, or “top-down” disturbance controls? 

Papers under this theme are (see publications for links to urls and pdfs):

  • Traore et al. (2014) 1982-2010 Trends of Light use Efficiency and Inherent Water use Efficiency in African vegetation: Sensitivity to Climate and Atmospheric CO2 Concentrations, Remote Sensing, 6, 8923-8944. [Key highlight: long-term trends in vegetation dynamics and carbon uptake were not well-captured by the ORCHIDEE model in semi-arid regions (e.g. the Sahel).]
  • Wang et al. (2015) Has the advancing onset of spring vegetation green-up slowed down or changed abruptly over the past three decades? Global Ecology and Biogeography, 24, 621-631.
  • MacBean et al. (2015), Using satellite data to improve the leaf phenology of a global terrestrial biosphere model, Biogeosciences, 12, 7185-7208.
  • Smith et al. (2018), Chlorophyll fluorescence better captures seasonal and interannual gross primary productivity dynamics across dryland ecosystems of southwestern North America, Geophys. Res. Lett., 45, 748–75. [Key highlight: SIF data capture dryland GPP dynamics better than NDVI due to a decoupling between vegetation photosynthetic activity and greenness during drought periods]

My collaboration with Abdoul Traore and my work on phenology data assimilation , which showed that constraining vegetation dynamics using satellite-derived NDVI data was not able to improve the model–data discrepancy in the seasonal cycle and trend in vegetation productivity in dry tropical and semi-arid regions, led to my recent research on understanding coupled carbon-water-vegetation dynamics in semi-arid regions.