Browsing by Author "Ding, Junyan"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Exploring the Relationship between Monthly Precipitation and EVI Vegetation Productivity Index of Serengeti Nation Park
    (2012-09-13) Ding, Junyan; Hall-Beyer, Mryka
    Precipitation has significant impact on the vegetation productivity in the Serengeti area of eastern Africa, an arid and semi-arid region. Previous studies indicate that the response of vegetation productivity to precipitation varies as the consequence of difference in total precipitation, soil properties, and vegetation type. In order to explore and explain the non-stationary relation between vegetation productivity and each of the mean and the variation of precipitation, I examine the correlation between vegetation and precipitation of Serengeti National Park using monthly precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) derived Enhanced Vegetation Index (EVI) images from 2001 to 2009. First, I compute the mean EVI and mean precipitation based on monthly images. To separate the mean and variation of precipitation, the mean precipitation is subtracted from each of the original precipitation images; the new series images represent the variation of precipitation. Then the new series of variation of precipitation is subjected to Fourier PCA (principal component analysis) analysis to generate a few (usually five) most representative PCA components. Among the PCA components, PC1 and PC3 are found not correlated with mean precipitation and thus are used together to represent the variation of precipitation of the entire period. To explore the non-stationary relation between EVI and precipitation, geographically weighted regression (GWR) models are used with mean precipitation and PC1 and PC3 as independent variables and mean EVI as dependent variable. Three GWR models are created with 1) mean precipitation alone, 2) PCA components, and 3) both mean precipitation and PCA components together, as independent variables. Finally, global linear least square models are used to detect how the correlations between mean EVI and precipitation are affected by soil WHC (water holding capacity) and N content, and vegetation type (independent variables). It is found that the highest correlation between mean EVI and mean precipitation occurs at north SNP (Serengeti National Park), and decreases towards both the west and east; the highest correlation between mean EVI and the variation of precipitation occurs in the north SNP and the south-east regions, and decreases towards the north and west. The correlation between mean EVI and both mean and variation of precipitation together are maximized at the west side of north SNP and decrease towards the south-east. The amount of precipitation and soil water holding capacity have a significant impact on the correlations between mean EVI and both mean precipitation and the variation of precipitation; soil nitrogen content has significant impact on the correlation between mean EVI and the variation of precipitation; only forest, woody savanna, and savanna are found to have significant impact on the correlation between mean EVI and the variation of precipitation.
  • Thumbnail Image
    ItemOpen Access
    In Search of Physical Explanation of Co-development of Plants and Landscape
    (2018-06-11) Ding, Junyan; Johnson, Edward Arnold; Martin, Yvonne E.; Post, John R.; Yeung, Edward C.
    The physical structure of the landscape is the subject of geology and geomorphology based on physical and chemical principles of material and energy movement under climatic and tectonic forces. The biological inhabitants of this physical landscape, area subject rooted in biological, ecological and evolutionary theories, but the primary focus has been on how the physical environment shaped life on land. It is only recently that we begin to recognize the evolution of the abiotic and biotic worlds are intimately intertwined via complex feedback loops of physical hydrological, and biological pathways, and at a range of spatial and temporal scales. In my thesis, I addressed a subset of these complex interactions: the coevolution of the landform organized as hills-valleys and the plants that occupy the different landscape positions. I used an integrated modeling approach that captures the first-order, well recognized, and mechanistic links to explore feedbacks and to shed new lights on the coupled evolution of the landform and the plants. The landscape is a battleground of tectonics (mountain building) vs. denudation (weathering/erosion that destroy the mountains). The landscape structure, e.g. hillslope shape/size and stream density, depends on the relative dominance of diffusive type erosion (e.g., soil creep and shallow landslides) vs. the advective type (e.g., runoff erosion and channel incision). Climate exerts strong regulation on the dominance erosion processes with increasing rainfall intensity resulting in more advective type erosion landform. The result is a landscape evolution model, with three levels of diffusive vs. advective dominances. Diffusion dominated landscapes tend to have wider/longer hillslopes, more rounded hilltops, and fewer streams (i.e., less dissected). This will have consequences to water movement on land and the resulting soil hydrology. These distinct landforms lead to distinct patterns of soil hydrology. A simple and powerful way to characterize soil hydrology is the Topographic Index (TI) a proxy of equilibrium of water table depth, defined for each point in a watershed as the ratio of drainage area above the point (proxy of rain caught upstream) and local slope at the point (proxy for outflow). Using TI, I translated the three landscape structures above into three hydrologic structures. Higher TI occurs in flat valley bottoms with a large slope above it, low TI on ridge tops, and intermediate TI on hillslopes. These patterns will influence the distribution of plants genetically adapted to different soil moisture conditions. Plants have evolved genetic traits to survive and optimize their performance in given environmental niches. Soil hydrology is a powerful discriminator of plants according to their drought and waterlogging tolerance. To a first order watershed, I represented plants in three tolerance curves: hydric (waterlogging tolerant), xeric (drought tolerant), and mesic (moderate moisture tolerance), each with a preferred distribution along the TI index. This allows me to connect hydrology to plant distribution for each tolerance type. Each plant type occupies a distinct hydrologic position in all three landscapes, but more mesic plants are found in diffusive landscapes. Water and nutrients move from soils to roots, stems, shoots, leaves, and to the atmosphere through a series of resistant flow paths. Plant hydraulic traits reflect evolutionary trade-offs between efficiency and safety. For leaves, denser veins, lower stomatal resistance, wider and larger leaves all decrease leaf hydraulic resistance and increase net primary productivity (NPP), but the same traits also increase risks of embolism and leaf death. I developed a leaf NPP optimization model to explore the joint adjustment of several leaf traits to different levels of soil water stress. It offered a theoretical framework to explain leaf traits seen across hydrologic gradients; e.g., narrow-leaved species tend to occupy xeric habitats on hilltops, and species of large and wide leaves occupy hydric habitats in valleys. In this thesis, by using aboveground traits, I provided a mechanistic connection between abundance and distribution of plant functional groups (PFG) on landscape and the geomorphic processes that created it. It sets the foundation for further investigating how landscape composition of PFGs can regulate topography and hillslope hydrology under the regulation of changing climate thus close the feedback cycle. A missing component here is a mechanistic expressing of soil water-root interaction that creates the dynamic rooting structure across landscape. This should be the focus of future study.