Erin Bunting

Changes in vegetation persistence across global savanna landscapes, 1982–2010

We present a global analysis of the changing face of vegetation persistence in savanna ecosystems by boreal seasons. We utilized nearly 30 years of monthly normalized difference vegetation index data in an innovative time-series approach and developed associated statistical significance tests, making the application of continuous vegetation metrics both more rigorous and more useful to research. We found that 8,000,000–11,000,000 km2 of savanna have experienced significant vegetation decline during each season, while 20,000,000–23,000,000 km2 have experienced an increase in vegetation persistence during each season, relative to the baseline period (1982–1985). In addition, with the exception of the March–April–May season, which is mixed, the pattern of significant vegetation persistence in the Northern Hemisphere is almost exclusively positive, while it is negative in the Southern Hemisphere. This finding highlights the increasing vulnerability of the Southern Hemisphere savanna landscapes; either resulting from changing precipitation regimes (e.g., southern Africa) or agricultural pressures and conversions (e.g., South America).

Dynamics of the relationship between NDVI and SWIR32 vegetation indices in southern Africa: implications for retrieval of fractional cover from MODIS data

ABSTRACT Fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) has been retrieved for Australian tropical savannah based on linear unmixing of the two-dimensional response envelope of the normalized difference vegetation index (NDVI) and short wave infrared ratio (SWIR)32 vegetation indices (VI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The approach assumes that cover fractions are made up of a simple mixture of green leaves, senescent leaves, and bare soil. In this study, we examine retrieval of fractional cover using this approach for a study area in southern Africa with a more complex vegetation structure. Region-specific end-members were defined using Hyperion images from different locations and times of the season. These end-members were applied to a 10-year time series of MODIS-derived NDVI and SWIR32 (from 2002 to 2011) to unmix FPV, FNPV, and FBS. Results of validation with classified high-resolution imagery indicated major bias in estimation of FNPV and FBS, with regression coefficients for predicted versus observed data substantially less than 1.0 and relatively large intercept values. Examination with Hyperion images of the inverse relationship between the MODIS-equivalent SWIR32 index and the Hyperion-derived cellulose absorption index (CAI) to which it nominally approximates revealed: (1) non-compliant positive regression coefficients for certain vegetation types; and (2) shifts in slope and intercept of compliant regression curves related to day of year and geographical location. The results suggest that the NDVI–SWIR32 response cannot be used to approximate the NDVI–CAI response in complex savannah systems like southern Africa that cannot be described as simple mixtures of green leaves, dry herbaceous material high in cellulose, and bare soil. Methods that use a complete set of multispectral channels at higher spatial resolution may be needed for accurate retrieval of fractional cover in Africa.

Utilizing Multiple Lines of Evidence to Determine Landscape Degradation within Protected Area Landscapes: A Case Study of Chobe National Park, Botswana from 1982 to 2011

The savannas of Southern Africa are an important dryland ecosystem as they cover up to 54% of the landscape and support a rich variety of biodiversity. This paper evaluates landscape change in savanna vegetation along Chobe Riverfront within Chobe National Park Botswana, from 1982 to 2011 to understand what change may be occurring in land cover. Classifying land cover in savanna environments is challenging because the vegetation spectral signatures are similar across distinct vegetation covers. With vegetation species and even structural groups having similar signatures in multispectral imagery difficulties exist in making discrete classifications in such landscapes. To address this issue, a Random Forest classification algorithm was applied to predict land-cover classes. Additionally, time series vegetation indices were used to support the findings of the discrete land cover classification. Results indicate that a landscape level vegetation shift has occurred across the Chobe Riverfront, with results highlighting a shift in land cover towards more woody vegetation. This represents a degradation of vegetation cover within this savanna landscape environment, largely due to an increasing number of elephants and other herbivores utilizing the Riverfront. The forested area along roads at a further distance from the River has also had a loss of percent cover. The continuous analysis during 1982–2011, utilizing monthly AVHRR (Advanced Very High Resolution Radiometer) NDVI (Normalized Difference Vegetation Index) values, also verifies this change in amount of vegetation is a continuous and ongoing process in this region. This study provides land use planners and managers with a more reliable, efficient and relatively inexpensive tool for analyzing land-cover change across these highly sensitive regions, and highlights the usefulness of a Random Forest classification in conjunction with time series analysis for monitoring savanna landscapes.

Understanding Long-Term Savanna Vegetation Persistence across Three Drainage Basins in Southern Africa

Across savanna landscapes of southern Africa, people are strongly tied to the environment, meaning alterations to the landscape would impact livelihoods and socioecological development. Given the human–environment connection, it is essential to further our understanding of the drivers of savanna vegetation dynamics, and under increasing climate variability, to better understand the vegetation–climate relationship. Monthly time series of Advanced Very High-Resolution Radiometer (AVHRR)and Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation indices, available from as early as the 1980s, holds promise for the large-scale quantification of complex vegetation–climate dynamics and regional analyses of landscape change as related to global environmental changes. In this work, we employ time series based analyses to examine landscape-level vegetation greening patterns over time and across a significant precipitation gradient. In this study, we show that climate induced reductions in Normalized Difference Vegetation Index (NDVI; i.e., degradation or biomass decline) have had large spatial and temporal impacts across the Kwando, Okavango, and Zambezi catchments of southern Africa. We conclude that over time there have been alterations in the available soil moisture resulting from increases in temperature in every season. Such changes in the ecosystem dynamics of all three basins has led to system-wide changes in landscape greening patterns.

Protected Areas, Climate Change, and Ecosystem Sustainability

Ecosystems around the world are changing at unprecedented rates. Through the use of remote sensing especially time series remote sensing, we can understand the impact of climate change, increased climate variability human pressures impacts on ecosystems at the landscape scale. This article focuses on remote sensing case studies of protected areas (PAs) encompassing diverse ecosystems around the world using a wide variety of methods at varying spatiotemporal scales. While the case studies are diverse they illustrate a key point that PAs need constant monitoring to understand the underlying ecological dynamics impact of human pressure on changing vegetation patterns. In order to establish such a long-term continuous monitoring of diverse regions around the world, time series analyses of remote sensing datasets time series approaches are required. Under climate change, the sustainability of diverse protected area ecosystems is in question. With techniques as described in this article, remote sensing technologies can be used to monitor and understand landscape-level processes and landscape change to identify potential vulnerabilities and tipping points.

Spatiotemporal changes in the size and shape of heat waves over North America

Heat waves are occurring more frequently across the globe and are likely to increase in intensity and duration under climate change. Much work has already been completed on attributing causes of observed heat waves and on modeling their future occurrence, but such efforts are often lacking in exploration of spatial relationships. Based on principles of landscape ecology, we utilized fragmentation metrics to examine the spatiotemporal changes in heat wave shape and occurrence across North America. This methodological approach enables us to examine area, shape, perimeter, and other key metrics. The application of these shape metrics to high-resolution historical (1950–2013) climate data reveals that the total number and spatial extent of heat waves are increasing over the continent, but at an individual heat wave patch level, they are becoming significantly smaller in extent and more complex in shape, indicating that heat waves have become a more widespread and fragmented phenomena.