Image courtesy Applied Sciences.

BUNTING, CHILD, HERRERO, KHATAMI, MUIR, SOUTHWORTHAn Evaluation of Vegetation Health in and around Southern African National Parks during the 21st Century (2000–2016)

Hannah Herrero, Jane Southworth, Carly Muir, Reza Khatami, Erin Bunting, and Brian Child

Article first published online: 30 MAR 2020 Applied Sciences

DOI: 10.3390/app10072366

ABSTRACT: Roughly 65% of the African continent is classified as savanna. Such regions are of critical importance given their high levels of biological productivity, role in the carbon cycle, structural differences, and support of large human populations. Across southern Africa there are 79 national parks within savanna landscapes. Understanding trends and factors of vegetation health in these parks is critical for proper management and sustainability. This research strives to understand factors and trends in vegetation health from 2000 to 2016 in and around the 79 national parks across southern Africa. A backward stepwise regression was used to understand the factors (e.g., precipitation, population density, and presence of transfrontier conservation areas) affecting the normalized difference vegetation index (NDVI) during the 21st century. There was a statistically significant positive (p < 0.05) relationship between mean annual precipitation and NDVI, and a significant negative relationship between population density and NDVI. To monitor vegetation trends in and around the parks, directional persistence, a seasonal NDVI time series-based trend analysis, was used. Directional persistence is the net accumulation of directional change in NDVI over time in a given period relative to a fixed benchmarked period. Parks and buffer zones across size classes were compared to examine differences in vegetation health. There was an overwhelmingly positive trend throughout. Additionally, national parks, overall, had higher amounts of positive persistence and lower amounts of negative persistence than the surrounding buffer zones. Having higher positive persistence inside of parks indicates that they are functioning favorably relative to the buffer zones in terms of vegetation resilience. This is an important finding for park managers and conservation overall in Southern Africa.

Read the full publication at Applied Sciences

 

 

 

Image courtesy Remote Sensing

KHATAMI, MUIR, SOUTHWORTHOperational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Reza Khatami, Jane Southworth, Carly Muir, Trevor Caughlin, Alemayehu N. Ayana, Daniel G. Brown, Chuan Liao, and Arun Agrawal

Article first published online: 16 MAR 2020 Remote Sensing

DOI: 10.3390/rs12060954

ABSTRACT: Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.

Read the full publication at Remote Sensing.

Image courtesy Remote Sensing.

CHILD, HERRERO, KHATAMI, SOUTHWORTH, WAYLEN, YANGA Healthy Park Needs Healthy Vegetation – The Story of Gorongosa National Park in the 21st Century

Hannah Herrero, Peter Waylen, Jane Southworth, Reza Khatami, Di Yang, Brian Child

Article first published online: 03 FEB 2020 Remote Sensing

DOI: 10.3390/rs12030476

ABSTRACT: Understanding trends or changes in biomass and biodiversity around conservation areas in Africa is important and has economic and societal impacts on the surrounding communities. Gorongosa National Park, Mozambique was established under unique conditions due to its complex history. In this study, we used a time-series of Normalized Difference Vegetation Index (NDVI) to explore seasonal trends in biomass between 2000 and 2016. In addition, vegetation directional persistence was created. This product is derived from the seasonal NDVI time series-based analysis and represents the accumulation of directional change in NDVI relative to a fixed benchmark (2000–2004). Trends in precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was explored from 2000–2016. Different vegetation covers are also considered across various landscapes, including a comparison between the Lower Gorongosa (savanna), Mount Gorongosa (rainforest), and surrounding buffer zones. Important findings include a decline in precipitation over the time of study, which most likely drives the observed decrease in NDVI. In terms of vegetation persistence, Lower Gorongosa had stronger positive trends than the buffer zone, and Mount Gorongosa had higher negative persistence overall. Directional persistence also varied by vegetation type. These are valuable findings for park managers and conservationists across the world.

Read the full publication at Remote Sensing

 

 

 

 

Come find out about the University of Florida in South Africa program  at the Study Abroad Fair on Wednesday, January 29 from 10 AM to 3 PM at the Reitz Union North Lawn.

We will also be hosting an informational session on our University of Florida in South Africa program on Wednesday, February 5th from 5-6 PM in Turlington Hall 3018.

University of Florida in South Africa
Summer A: May 26th – June 20th
Students will be in the field on safari in one of Africa’s prime wildlife areas! Imagine earning 6 credits while also gaining an understanding of the challenges, complexities and beauty of National Parks in South Africa. A truly incredible experience!
The program will primarily take place in Kruger National Park where students will go on safari, conduct reserve walks with Rangers, visit animal rescue facilities, and much more! Housing, lodging and food are all included.
Eligibility Requirements:
-Open to ALL majors
-GPA 2.5 or higher; student must be in good standing
-Interest in Africa & Conservation
-Previously taken GEA 3600: Geography of Africa (recommended but not required)
-Email the program director, Dr. Jane Southworth, to meet & discuss further trip details >> jsouthwo@ufl.edu

APPLICATION DEADLINE: February 15th, 2020

Spaces are limited to only 14, and fill quickly!
Apply online at: www.internationalcenter.ufl.edu

Shape and structure of traditional brickfields visible in high-resolution Google Earth imagery (top) and digitized brickfield locations overlaid on false color composite Landsat imagery across the study period (bottom). Image courtesy International Journal of Geo-Information.

MEHEDY, SOUTHWORTHMapping Time-Space Brickfield Development Dynamics in Peri-Urban Area of Dhaka, Bangladesh

Mohammad Mehedy Hassan, Levente Juhász, and Jane Southworth

Article first published online: 11 OCT 2019 International Journal of Geo-Information

DOI: 10.3390/ijgi8100447

ABSTRACT: Due to the high demand for cheap construction materials, clay-made brick manufacturing has become a thriving industry in Bangladesh, with manufacturing kilns heavily concentrated in the peripheries of larger cities and towns. These manufacturing sites, known as brickfields operate using centuries-old technologies which expel dust, ash, black smoke and other pollutants into the atmosphere. This in turn impacts the air quality of cities and their surroundings and may also have broader impacts on health, the environment, and potentially contribute to global climate change. Using remotely sensed Landsat imagery, this study identifies brickfield locations and areal expansion between 1990 and 2015 in Dhaka, and employs spatial statistics methods including quadrat analysis and Ripley’s K-function to analyze the spatial variation of brickfield locations. Finally, using nearest neighbor distance as density functions, the distance between brickfield locations and six major geographical features (i.e., urban, rural settlement, wetland, river, highway, and local road) were estimated to investigate the threat posed by the presence of such polluting brickfields nearby urban, infrastructures and other natural areas. Results show significant expansion of brickfields both in number and clusters between 1990 and 2015 with brickfields increasing in number from 247 to 917 (total growth rate 271%) across the Dhaka urban center. The results also reveal that brickfield locations are spatially clustered: 78% of brickfields are located on major riverbanks and 40% of the total are located in ecologically sensitive wetlands surrounding Dhaka. Additionally, the average distance from the brick manufacturing plant to the nearest urban area decreased from 1500 m to 500 m over the study period. This research highlights the increasing threats to the environment, human health, and the sustainability of the megacity Dhaka from brickfield expansion in the immediate peripheral areas of its urban center. Findings and methods presented in this study can facilitate data-driven decision making by government officials and city planners to formulate strategies for improved brick production technologies and decreased environmental impacts for this urban region in Bangladesh.

Read the full publication at International Journal of Geo-Information.

Dr. Meshari Alenezi, Dr. Di Yang, Dr. Jane Southworth, Dr. Hannah Herrero, and Dr. Xavier Haro-Carrión at the 2019 University of Florida Summer Commencement.
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