Next Article in Journal
Design and Mechanical Sensitivity Analysis of a MEMS Tuning Fork Gyroscope with an Anchored Leverage Mechanism
Previous Article in Journal
Nondestructive Analysis of Debonds in a Composite Structure under Variable Temperature Conditions
Version is current.
Open AccessArticle

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

1
Department of Geography, University of Tennessee, 1000 Philip Fulmer Way, Room 315, Knoxville, TN 37996-0925, USA
2
Department of Geography, University of Florida, 3141 Turlington Hall, Gainesville, FL 32611, USA
3
Department of Geography, Michigan State University, 673 Auditorium Rd., Room 215, East Lansing, MI 48824, USA
4
Oak Hall School, 8009 SW 14 Ave., Gainesville, FL 32607, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3456; https://doi.org/10.3390/s19163456
Received: 21 June 2019 / Revised: 31 July 2019 / Accepted: 3 August 2019 / Published: 7 August 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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. View Full-Text
Keywords: remote sensing; savanna science; NDVI; temperature; MODIS; time series; Landsat; Zambia; protected areas; classifications; South Luangwa National Park remote sensing; savanna science; NDVI; temperature; MODIS; time series; Landsat; Zambia; protected areas; classifications; South Luangwa National Park
Show Figures

Figure 1

MDPI and ACS Style

Herrero, H.V.; Southworth, J.; Bunting, E.; Kohlhaas, R.R.; Child, B. Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes. Sensors 2019, 19, 3456.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop