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BUNTING, CHILD, HERRERO, SOUTHWORTHIntegrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes

Hannah Victoria Herrero, Jane Southworth, Erin Bunting, Romer Ryan Kohlhaas, and Brian Child

Article first published online: 07 AUG 2019 Sensors

DOI: 10.3390/s19163456

ABSTRACT: Southern African savannas are an important dryland ecosystem, as they account for up to 54% of the landscape, support a rich variety of biodiversity, and are areas of key landscape change. This paper aims to address the challenges of studying this highly gradient landscape with a grass–shrub–tree continuum. This study takes place in South Luangwa National Park (SLNP) in eastern Zambia. Discretely classifying land cover in savannas is notoriously difficult because vegetation species and structural groups may be very similar, giving off nearly indistinguishable spectral signatures. A support vector machine classification was tested and it produced an accuracy of only 34.48%. Therefore, we took a novel continuous approach in evaluating this change by coupling in situ data with Landsat-level normalized difference vegetation index data (NDVI, as a proxy for vegetation abundance) and blackbody surface temperature (BBST) data into a rule-based classification for November 2015 (wet season) that was 79.31% accurate. The resultant rule-based classification was used to extract mean Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI values by season over time from 2000 to 2016. This showed a distinct separation between each of the classes consistently over time, with woodland having the highest NDVI, followed by shrubland and then grassland, but an overall decrease in NDVI over time in all three classes. These changes may be due to a combination of precipitation, herbivory, fire, and humans. This study highlights the usefulness of a continuous time-series-based approach, which specifically integrates surface temperature and vegetation abundance-based NDVI data into a study of land cover and vegetation health for savanna landscapes, which will be useful for park managers and conservationists globally.

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ecological-modelingBUNTING, SOUTHWORTH – Utilization of the SAVANNA model to analyze future patterns of vegetation cover in Kruger National Park under changing climate

Erin L. Bunting, Timothy Fullman, Gregory Kiker, Jane Southworth

Article first published online: 17 OCT 2016 Ecological Modelling

DOI: 10.1016/j.ecolmodel.2016.09.012


Within southern Africa’s savanna ecosystems, woody and herbaceous species have differing growth characteristics that allow a tenuous co-existance. The high dependence of humans on the landscape, through agricultural production, tourism, and natural resource extraction makes understanding savanna vegetation dynamics essential. Studies analyzing resilience of savannas suggest potential state changes in vegetation structure from continuous grasslands with sporadic woody cover to less biologically productive landscapes. One of the biggest questions in this landscape is the impact of climate change. The spatially explicit SAVANNA model is used to analyze the impact of climate change on vegetation cover across Kruger National Park’s (KNP) main land system classifications (Satara, Skukuza, Letaba, and Phalaborwa). Manipulating climate inputs and management regimes allowed us to analyze the resilience of savanna vegetation under multiple Intergovernmental Panel on Climate Change (IPCC) scenarios. Trends in future climate indicate an increase in temperatures greater than 1.0 °Celsius and a slight decrease in precipitation by 2080. Model results indicate a long-term decrease in multiple size classes of vegetation across all the four land systems. However, the model runs show differing response to climate change between the woody and herbaceous cover types. Spatial trends across the park follow closely with the north-south climate gradient. The most spatially distinct land system was Skukuza, which exhibited some of the highest initial net primary production (NPP) values and also the greatest decreases in NPP into the future. While this region is projected to lose large proportions of its herbaceous and shrub vegetation it is projected to increase in tree green leaf, mostly related to increasing fine leaf vegetation (Acacia sp.). The northern land systems were already dominated by mopane, but under all model scenarios mopane will increase in Letaba and Phalaborwa. This mopane increase will offset the loss of herbaceous and shrub vegetation, resulting in little to no decrease in NPP across time for these land systems. This work illustrates that landscape resilience is not only impacted by the severity of changing climate but the degree to which we manage such systems.

Read the full publication at Ecological Modelling