Likai Zhu

Understanding spatial and temporal dynamics of land surface phenology (LSP) and its driving forces are critical for providing information relevant to short- and long-term decision making, particularly as it relates to climate response planning. With the third generation Global Inventory Monitoring and Modeling System (GIMMS3g) Normalized Difference Vegetation Index (NDVI) data and environmental data from multiple sources, we investigated the spatio-temporal changes in the start of the growing season (SOS) in southern African savannas from 1982 through 2010 and determined its linkage to environmental factors using spatial panel data models. Overall, the SOS occurs earlier in the north compared to the south. This relates in part to the differences in ecosystems, with northern areas representing high rainfall and dense tree cover (mainly tree savannas), whereas the south has lower rainfall and sparse tree cover (mainly bush and grass savannas). From 1982 to 2010, an advanced trend was observed predominantly in the tree savanna areas of the north, whereas a delayed trend was chiefly found in the floodplain of the north and bush/grass savannas of the south. Different environmental drivers were detected within tree- and grass-dominated savannas, with a critical division being represented by the 800 mm isohyet. Our results supported the importance of water as a driver in this water-limited system, specifically preseason soil moisture, in determining the SOS in these water-limited, grass-dominated savannas. In addition, the research pointed to other, often overlooked, effects of preseason maximum and minimum temperatures on the SOS across the entire region. Higher preseason maximum temperatures led to an advance of the SOS, whereas the opposite effects of preseason minimum temperature were observed. With the rapid increase in global change research, this work will prove helpful for managing savanna landscapes and key to predicting how projected climate changes will affect regional vegetation phenology and productivity.

Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)- vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001–2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982–2010) data found that for MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas.

This research examines the integration and potential uses of linkages between climate dynamics, savanna vegetation and landscape level processes within a highly vulnerable region, both in terms of climate variability and social systems. We explore the combined applications of two time-series methodologies: (1) climate signals detected in tree ring growth, from published literature, chronologies from the International Tree-Ring Data Bank, and minimal preliminary field data; and (2) new primary production (NPP) data of vegetation cover over time derived from remotely sensed analyses. Both time-series are related to the regional patterns of precipitation, the principle driver of plant growth in the area. The approach is temporally and spatially multiscalar and examines the relationships between vegetation cover, type and amount, and precipitation shifts. We review literature linking dendrochronology, climate, and remotely sensed imagery, and, in addition, provide unique preliminary analyses from a dry study site located on the outer limit of the Okavango Delta. The work demonstrates integration across the different data sources, to provide a more holistic view of landscape level processes occurring in the last 30-50 years. These results corroborate the water-limited nature of the region and the dominance of precipitation in controlling vegetation growth. We present this integrative analysis of vegetation and climate change, as a prospective approach to facilitate the development of long-term climate/vegetation change records across multiple scales.

To obtain a better understanding of the variability in net primary production (NPP) in savannas is important for the study of the global carbon cycle and the management of this particular ecosystem. Using satellite and precipitation data sets, we investigated the variations in NPP in southern African savannas from 1982 to 2010, and disentangled the relationships between NPP and precipitation by land cover classes and mean annual precipitation (MAP) gradients. Specifically, we evaluate the utility of the third generation Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) dataset, in comparison with Moderate-resolution Imaging Spectroradiometer (MODIS) derived NPP products, and find strong relationships between the overlapping data periods (2000–2010), such that we can apply our model to derive NPP estimates to the full 29-year NDVI time-series. Generally, the northern portion of the study area is characterized by high NPP and low variability, whereas the southern portion is characteristic of low NPP and high variability. During the period 1982 through 2010, NPP has reduced at a rate of −2.13 g∙C∙m⁻²∙yr⁻¹ (p < 0.1), corresponding to a decrease of 6.7% over 29 years, and about half of bush and grassland savanna has experienced a decrease in NPP. There is a significant positive relationship between mean annual NPP and MAP in bush and grassland savannas, but no significant relationship is observed in tree savannas. The relationship between mean annual NPP and MAP varies with increases in MAP, characterized as a linear relationship that breaks down when MAP exceeding around 850–900 mm.