Authors: Hannah Herrero*, University of Florida, Jane Southworth, University of Florida
Topics: Remote Sensing, Land Use and Land Cover Change, Africa
Keywords: Remote sensing, savannas, national parks, NDVI, MODIS
Session Type: Paper
Start / End Time: 12:40 PM / 2:20 PM
Room: Grand Chenier, Sheraton, 5th Floor
Southern African savannas are an important dryland ecosystem, as they account for up to 54% of the landscape and support a rich variety of biodiversity. These are also 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 hardly distinguishable spectral signatures. Therefore, we take 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 biomass) and blackbody surface temperature data into a rule-based classification in November 2015 (wet season). The resultant rule-based classification was used to extract mean MODIS NDVI values by season over time from 2000-2016. 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 approach for monitoring the health of savanna landscapes, and will be useful for park managers and conservationists globally.