Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes

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−shr...

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Main Authors: Hannah Victoria Herrero, Jane Southworth, Erin Bunting, Romer Ryan Kohlhaas, Brian Child
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/19/16/3456
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spelling doaj-292a7d41f6024954971e1c66a9f46efc2020-11-25T02:30:14ZengMDPI AGSensors1424-82202019-08-011916345610.3390/s19163456s19163456Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna LandscapesHannah Victoria Herrero0Jane Southworth1Erin Bunting2Romer Ryan Kohlhaas3Brian Child4Department of Geography, University of Tennessee, 1000 Philip Fulmer Way, Room 315, Knoxville, TN 37996-0925, USADepartment of Geography, University of Florida, 3141 Turlington Hall, Gainesville, FL 32611, USADepartment of Geography, Michigan State University, 673 Auditorium Rd., Room 215, East Lansing, MI 48824, USAOak Hall School, 8009 SW 14 Ave., Gainesville, FL 32607, USADepartment of Geography, University of Florida, 3141 Turlington Hall, Gainesville, FL 32611, USASouthern 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.https://www.mdpi.com/1424-8220/19/16/3456remote sensingsavanna scienceNDVItemperatureMODIStime seriesLandsatZambiaprotected areasclassificationsSouth Luangwa National Park
collection DOAJ
language English
format Article
sources DOAJ
author Hannah Victoria Herrero
Jane Southworth
Erin Bunting
Romer Ryan Kohlhaas
Brian Child
spellingShingle Hannah Victoria Herrero
Jane Southworth
Erin Bunting
Romer Ryan Kohlhaas
Brian Child
Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes
Sensors
remote sensing
savanna science
NDVI
temperature
MODIS
time series
Landsat
Zambia
protected areas
classifications
South Luangwa National Park
author_facet Hannah Victoria Herrero
Jane Southworth
Erin Bunting
Romer Ryan Kohlhaas
Brian Child
author_sort Hannah Victoria Herrero
title Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes
title_short Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes
title_full Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes
title_fullStr Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes
title_full_unstemmed Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes
title_sort integrating surface-based temperature and vegetation abundance estimates into land cover classifications for conservation efforts in savanna landscapes
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2019-08-01
description 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.
topic remote sensing
savanna science
NDVI
temperature
MODIS
time series
Landsat
Zambia
protected areas
classifications
South Luangwa National Park
url https://www.mdpi.com/1424-8220/19/16/3456
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