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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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BUNTING, SOUTHWORTH, HERRERO, RYAN, WAYLENUnderstanding Long-Term Savanna Vegetation Persistence across Three Drainage Basins in Southern Africa

Erin L. Bunting , Jane Southworth, Hannah Herrero, Sadie J. Ryan, and Peter Waylen

Article first published online: 25 JUN 2018 Remote Sens. 2018, 10(7), 1013

DOI: 10.3390/rs10071013

ABSTRACT: 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.

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International Journal of Remote Sensing 37SOUTHWORTH, BUNTING – Dynamics of the relationship between NDVI and SWIR32 vegetation indices in southern Africa: implications for retrieval of fractional cover from MODIS data

Michael J. Hill, Qiang Zhou, Qingsong Sun, Crystal B. Schaaf, Jane Southworth, Niti B. Mishra, Cerian Gibbes, Erin Bunting, Thomas B. Christiansen. & Kelley A. Crews

Article first published online: 02 Mar 2016 International Journal of Remote Sensing

DOI: 10.1080/01431161.2016.1154225

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.

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