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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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DE CARVALHOUrban vegetation loss and ecosystem services: The influence on climate regulation and noise and air pollution

Roberta Mendonça De Carvalho and Claudio Fabian Szlafsztein

Article first published online: 5 NOV 2018 Environmental Pollution

DOI: 10.1016/j.envpol.2018.10.114

ABSTRACT: Ecosystem services are present everywhere, green vegetation coverage (or green areas) is one of the primary sources of ecosystem services considering urban areas sustainability and peoples urban life quality. Urban vegetation cover loss decreases the capacity of nature to provision ecosystem services; the loss of urban vegetation is also observed within the Amazon. This study aims at identifying urban vegetation loss and relate it to the provision of ecosystem services of reduction of air quality, reduction of air pollution, and climate regulation. Urban vegetation coverage loss was calculated using NDVI on LANDSAT 5 imagery over a 23-year period from 1986 to 2009. NDVI thresholds were arbitrarily selected, and complemented by in locus observation, to establish guidelines for quantitative (area) and qualitative (density) evolution of green cover, divided in six different categories, named as water, bare soil, poor vegetation, moderate vegetation, dense vegetation and very dense vegetation. Data on air pollution, noise pollution and temperature were outsourced from previous works. Measurement show a significant loss of very dense, dense and moderate vegetation coverage and an increase in poor vegetation and bare soil areas, in accordance to increase in air and noise pollution, and local temperature, and provides positive refashions between the loss of urban green coverage and decrease in ecosystem services.

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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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YANG – Interpolation of Groundwater Depth based on Data Assimilation

MA Huan, YUE Depeng, YANG Di, YU Qiang, ZHANG Qibin, HUANG Yuan

Article first published online: 27 APR 2017 Transactions of the Chinese Society of Agricultural Machinery (in English)

DOI: 10.6041/j.issn.1000-1298.2017.04.027

ABSTRACT: Groundwater monitoring is limited by practical conditions, and only limited monitoring results can be obtained when it is observed. As a kind of geostatistical interpolation method, cooperative Kriging (co-Kriging) method can effectively represent the transformation of discrete point-like information to planar continuous information. Dengkou County, a typical county in the arid region of Northwest China, was selected as the study area. The sampled data from 40 groundwater sampling sites in 2015 was selected as the main variable. And this data optimized by EnKF was used as the basic data of co-Kriging interpolation. The evapotranspiration results and NDVI data were selected as the covariates. Co-Kriging interpolation was carried out by using the sampled data from 40 groundwater sampling sites in August, 2015, as the main variable, which were optimized by EnKF, and the evapotranspiration results and NDVI data were used as the covariates. Meanwhile, the results of co Kriging interpolation without using EnKF model and Kriging interpolation optimized by EnKF model were used to verify the accuracy. The results showed that the spatial distribution trend of groundwater depth was basically the same at large scale, the value in the southern desert region was higher, and the spatial distribution showed obvious geography regularity. The most significant improvement was achieved with EnKF model. Based on this improvement, the mean error, root mean square error and mean standard error were all better than those without assimilation, with the mean error of 0.2705m. Compared with the ordinary Kriging interpolation method, co-Kriging model took the synergistic effect of evapotranspiration and NDVI into consideration, and the precision was obviously improved. The mean error was decreased by 0.4097m, the root mean square error was decreased by 0.0784m and the mean standard error was decreased by 1.0167m. This study can provide a scientific basis for spatial visualization simulation and reasonable management of water resources in arid areas.

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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.

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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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MOLLALOZoonotic cutaneous leishmaniasis in northeastern Iran: a GIS-based spatio-temporal multi-criteria decision-making approach


Article first published online: 02 MAR 2016 Epidemiology & Infection

DOI: 10.1017/S0950268816000224

ABSTRACT: Zoonotic cutaneous leishmaniasis (ZCL) constitutes a serious public health problem in many parts of the world including Iran. This study was carried out to assess the risk of the disease in an endemic province by developing spatial environmentally based models in yearly intervals. To fill the gap of underestimated true burden of ZCL and short study period, analytical hierarchy process (AHP) and fuzzy AHP decision-making methods were used to determine the ZCL risk zones in a Geographic Information System platform. Generated risk maps showed that high-risk areas were predominantly located at the northern and northeastern parts in each of the three study years. Comparison of the generated risk maps with geocoded ZCL cases at the village level demonstrated that in both methods more than 90%, 70% and 80% of the cases occurred in high and very high risk areas for the years 2010, 2011, and 2012, respectively. Moreover, comparison of the risk categories with spatially averaged normalized difference vegetation index (NDVI) images and a digital elevation model of the study region indicated persistent strong negative relationships between these environmental variables and ZCL risk degrees. These findings identified more susceptible areas of ZCL and will help the monitoring of this zoonosis to be more targeted.

Read the full publication at Epidemiology & Infection.