Image courtesy Sensors

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.

Read the full publication at Sensors

 

 

 

 

 

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.

Read the full publication at Remote Sensing

 

 

 

 

HERRERO, SOUTHWORTH, BUNTING, CHILD – Using Repeat Photography to Observe Vegetation Change Over Time in Gorongosa National Park

HANNAH V. HERRERO, JANE SOUTHWORTH, ERIN BUNTING, and BRIAN CHILD

Article first published online: JUN 2017 African Studies Quarterly

ABSTRACT: Protected areas are important conservation tools, as they can be managed to preserve baseline ecosystem health, including that of vegetation dynamics. Understanding long-term ecosystem dynamics within a protected area enables one to understand how this static park landscape responds to outside pressure and changing drivers. In this study, a repeat photography analysis was used to analyze changes in the vegetation pattern and abundance at Gorongosa National Park in Mozambique across seventy-two years of the parks history. Archival photographs dating as far back as 1940 were selected for sites that could be relocated in a subsequent field visit in 2012. Qualitative and quantitative analysis on vegetation abundance by structural group was undertaken using Edwards’ Tabular Key. Results when comparing the photographic pairs show that, in general, tree cover has increased on average from 25 percent to 40 percent over the last seventy-two years. This 15 percent increase may be in response to environmental drivers such as human management, herbivory, fire, and precipitation. Contrary to many recent studies on shrub encroachment in southern Africa, this study finds an increase in tree cover. Such analysis and results are valuable in that they demonstrate long-term ecological change within a managed protected area.

Read the full publication at African Studies Quarterly

 

HERRERO, SOUTHWORTH, BUNTING – Utilizing Multiple Lines of Evidence to Determine Landscape Degradation within Protected Area Landscapes: A Case Study of Chobe National Park, Botswana from 1982 to 2011

Hannah V. Herrero, Jane Southworth, and Erin Bunting

Article first published online: 28 JUL 2016 Remote Sensing

DOI: 10.3390/rs8080623

ABSTRACT: The savannas of Southern Africa are an important dryland ecosystem as they cover up to 54% of the landscape and support a rich variety of biodiversity. This paper evaluates landscape change in savanna vegetation along Chobe Riverfront within Chobe National Park Botswana, from 1982 to 2011 to understand what change may be occurring in land cover. Classifying land cover in savanna environments is challenging because the vegetation spectral signatures are similar across distinct vegetation covers. With vegetation species and even structural groups having similar signatures in multispectral imagery difficulties exist in making discrete classifications in such landscapes. To address this issue, a Random Forest classification algorithm was applied to predict land-cover classes. Additionally, time series vegetation indices were used to support the findings of the discrete land cover classification. Results indicate that a landscape level vegetation shift has occurred across the Chobe Riverfront, with results highlighting a shift in land cover towards more woody vegetation. This represents a degradation of vegetation cover within this savanna landscape environment, largely due to an increasing number of elephants and other herbivores utilizing the Riverfront. The forested area along roads at a further distance from the River has also had a loss of percent cover. The continuous analysis during 1982–2011, utilizing monthly AVHRR (Advanced Very High Resolution Radiometer) NDVI (Normalized Difference Vegetation Index) values, also verifies this change in amount of vegetation is a continuous and ongoing process in this region. This study provides land use planners and managers with a more reliable, efficient and relatively inexpensive tool for analyzing land-cover change across these highly sensitive regions, and highlights the usefulness of a Random Forest classification in conjunction with time series analysis for monitoring savanna landscapes.

Read the full publication at Remote Sensing