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Prof. Michael Gebreslasie
University of KwaZulu-Natal, Westville, Durban 4001

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0 geographic big data analytics
0 Remote sensing & GIS applications
0 enviromental epidemiology
0 Remote Sensing & Gis, Image Processing And Analysis
0 remote sensing and environment

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Remote sensing & GIS applications
Remote Sensing & Gis, Image Processing And Analysis
remote sensing and environment

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Review
Published: 04 June 2021 in Forests
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Forest covers about a third of terrestrial land surface, with tropical and subtropical zones being a major part. Remote sensing applications constitute a significant approach to monitoring forests. Thus, this paper reviews the progress made by remote sensing data applications to tropical and sub-tropical natural forest monitoring over the last two decades (2000–2020). The review focuses on the thematic areas of aboveground biomass and carbon estimations, tree species identification, tree species diversity, and forest cover and change mapping. A systematic search of articles was performed on Web of Science, Science Direct, and Google Scholar by applying a Boolean operator and using keywords related to the thematic areas. We identified 50 peer-reviewed articles that studied tropical and subtropical natural forests using remote sensing data. Asian and South American natural forests are the most highly researched natural forests, while African natural forests are the least studied. Medium spatial resolution imagery was extensively utilized for forest cover and change mapping as well as aboveground biomass and carbon estimation. In the latest studies, high spatial resolution imagery and machine learning algorithms, such as Random Forest and Support Vector Machine, were jointly utilized for tree species identification. In this review, we noted the promising potential of the emerging high spatial resolution satellite imagery for the monitoring of natural forests. We recommend more research to identify approaches to overcome the challenges of remote sensing applications to these thematic areas so that further and sustainable progress can be made to effectively monitor and manage sustainable forest benefits.

ACS Style

Enoch Gyamfi-Ampadu; Michael Gebreslasie. Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review. Forests 2021, 12, 739 .

AMA Style

Enoch Gyamfi-Ampadu, Michael Gebreslasie. Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review. Forests. 2021; 12 (6):739.

Chicago/Turabian Style

Enoch Gyamfi-Ampadu; Michael Gebreslasie. 2021. "Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review." Forests 12, no. 6: 739.

Article
Published: 23 April 2021 in GeoJournal
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Environmental and climatic changes have become issues of global concern partly because of their ability to disrupt activities connected to people’s livelihood. Yet, the emotional distress caused by these changes and the factors responsible for place-based attachment, especially in the Global South, have received scant attention to date. Drawing on the theories of ‘solastalgia’—sadness caused by environmental change and the ensuing emotions it evokes—and place-based attachment, this article analysed the embodied experiences of climatic and environmental changes on rural households in KwaMaye, KwaZulu-Natal, South Africa. Primary data was obtained qualitatively. Findings indicate that environmental and climatic changes, which have manifested in the form of increased soil infertility, soil erosion, mole and termite infestations and increased drought conditions, have undermined farmers’ ability to produce food and engage in livestock production effectively. These circumstances evoked frustrations, increased anxiety, sadness, reduced self-value and self-worth as well as helplessness. Nonetheless, place-based attachment is underpinned by kinship bonds and ancestral heritage. These issues have been discussed within the wider theoretical debates revolving around solastalgia and place-based attachment.

ACS Style

Osadolor O. Ebhuoma; Michael Gebreslasie; Eromose E. Ebhuoma; Llewellyn Leonard. ‘The future looks empty’: embodied experiences of distress triggered by environmental and climatic changes in rural KwaZulu-Natal, South Africa. GeoJournal 2021, 1 -17.

AMA Style

Osadolor O. Ebhuoma, Michael Gebreslasie, Eromose E. Ebhuoma, Llewellyn Leonard. ‘The future looks empty’: embodied experiences of distress triggered by environmental and climatic changes in rural KwaZulu-Natal, South Africa. GeoJournal. 2021; ():1-17.

Chicago/Turabian Style

Osadolor O. Ebhuoma; Michael Gebreslasie; Eromose E. Ebhuoma; Llewellyn Leonard. 2021. "‘The future looks empty’: embodied experiences of distress triggered by environmental and climatic changes in rural KwaZulu-Natal, South Africa." GeoJournal , no. : 1-17.

Journal article
Published: 09 March 2021 in Remote Sensing
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Forests contribute significantly to terrestrial biodiversity conservation. Monitoring of tree species diversity is vital due to climate change factors. Remote sensing imagery is a means of data collection for predicting diversity of tree species. Since various sensors have different spectral and spatial resolutions, it is worth comparing them to ascertain which could influence the accuracy of prediction of tree species diversity. Hence, this study evaluated the influence of the spectral and spatial resolutions of PlanetScope, RapidEye, Sentinel 2 and Landsat 8 images in diversity prediction based on the Shannon diversity index (H′), Simpson diversity Index (D1) and Species richness (S). The Random Forest regression was applied for the prediction using the spectral bands of the sensors as variables. The Sentinel 2 was the best image, producing the highest coefficient of determination (R2) under both the Shannon Index (R2 = 0.926) and the Species richness (R2 = 0.923). Both the Sentinel and RapidEye produced comparable higher accuracy for the Simpson Index (R2 = 0.917 and R2 = 0.915, respectively). The PlanetScope was the second-accurate for the Species richness (R2 = 0.90), whiles the Landsat 8 was the least accurate for the three diversity indices. The outcomes of this study suggest that both the spectral and spatial resolutions influence prediction accuracies of satellite imagery.

ACS Style

Enoch Gyamfi-Ampadu; Michael Gebreslasie; Alma Mendoza-Ponce. Evaluating Multi-Sensors Spectral and Spatial Resolutions for Tree Species Diversity Prediction. Remote Sensing 2021, 13, 1033 .

AMA Style

Enoch Gyamfi-Ampadu, Michael Gebreslasie, Alma Mendoza-Ponce. Evaluating Multi-Sensors Spectral and Spatial Resolutions for Tree Species Diversity Prediction. Remote Sensing. 2021; 13 (5):1033.

Chicago/Turabian Style

Enoch Gyamfi-Ampadu; Michael Gebreslasie; Alma Mendoza-Ponce. 2021. "Evaluating Multi-Sensors Spectral and Spatial Resolutions for Tree Species Diversity Prediction." Remote Sensing 13, no. 5: 1033.

Original article
Published: 25 February 2021 in Journal of Sustainable Forestry
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Forest cover change analyses have an essential role in forest management. Thus, this study adopted Landsat satellite imagery to assess the decadal spatiotemporal forest cover changes that occurred between 1989 and 2019 and predicted the 2029 land cover distribution of the Nkandla forest reserve, facing encroachment threats. The support vector machine algorithm and Land Change Modeling were utilized to classify and detect changes that occurred between 1989–1999, 1999–2009, 2009–2019. The Markov Chain Model and Multi-Layer Perceptron were adopted for the future land cover prediction. Consistent changes through inter-transitioning between the land cover types (closed canopy forest, open canopy forest, grassland, and bare sites) were detected. The closed canopy forest increased from 883.46 ha to 1059.23 ha, whereas the open canopy forest declined from 1091.89 ha to 910.60 ha between 1989 and 2019. Generally, the observed changes were caused by ecological processes and human disturbances. The future cover prediction indicated that the closed canopy forest will decline between 2019 and 2029, whereas the open canopy forest, grassland, and bare sites will increase. The information provided through this study will support the management of the Nkandla forest to ensure its continual supply of ecosystem services of national and global importance.

ACS Style

Enoch Gyamfi-Ampadu; Michael Gebreslasie; Alma Mendoza-Ponce. Multi-Decadal Spatial and Temporal Forest Cover Change Analysis of Nkandla Natural Reserve, South Africa. Journal of Sustainable Forestry 2021, 1 -24.

AMA Style

Enoch Gyamfi-Ampadu, Michael Gebreslasie, Alma Mendoza-Ponce. Multi-Decadal Spatial and Temporal Forest Cover Change Analysis of Nkandla Natural Reserve, South Africa. Journal of Sustainable Forestry. 2021; ():1-24.

Chicago/Turabian Style

Enoch Gyamfi-Ampadu; Michael Gebreslasie; Alma Mendoza-Ponce. 2021. "Multi-Decadal Spatial and Temporal Forest Cover Change Analysis of Nkandla Natural Reserve, South Africa." Journal of Sustainable Forestry , no. : 1-24.

Journal article
Published: 29 November 2020 in Forests
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South Africa is reported to experience timber shortages as a result of growing timber demands and pulp production, coupled with the government’s reluctance to grant new forestry permits. Rampant timber theft in the country makes these circumstances worse. The emergence of cloud-based platforms, such as Google Earth Engine (GEE), has greatly improved the accessibility and usability of high spatial and temporal Sentinel-1 and -2 data, especially in data-poor countries that lack high-performance computing systems for forest monitoring. Here, we demonstrate the potential of these resources for forest harvest detection. The results showed that Sentinel-1 data are efficient in detecting clear-cut events; both VH and VV backscatter signals decline sharply in accordance with clear-cutting and increase again when forest biomass increases. When correlated with highly responsive NDII, the VH and VV signals reached the best accuracies of 0.79 and 0.83, whereas the SWIR1 achieved –0.91. A Random Forest (RF) algorithm based on Sentinel-2 data also achieved over 90% accuracies for classifying harvested and forested areas. Overall, our study presents a cost-effective method for mapping clear-cut events in an economically important forestry area of South Africa while using GEE resources.

ACS Style

Sifiso Xulu; Nkanyiso Mbatha; Kabir Peerbhay; Michael Gebreslasie. Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine. Forests 2020, 11, 1283 .

AMA Style

Sifiso Xulu, Nkanyiso Mbatha, Kabir Peerbhay, Michael Gebreslasie. Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine. Forests. 2020; 11 (12):1283.

Chicago/Turabian Style

Sifiso Xulu; Nkanyiso Mbatha; Kabir Peerbhay; Michael Gebreslasie. 2020. "Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine." Forests 11, no. 12: 1283.

Article
Published: 06 November 2020 in Climatic Change
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There is an urgent need to map the geographic location of climate change risks and vulnerability, especially for cities in sub-Saharan Africa, which are experiencing the greatest urban development challenges and vulnerability to climate change impacts. The aim of this study is to investigate current and projected future heat risk, expressed as a heat stress exposure index using high-resolution climate change projections, and a social vulnerability index, to identify areas of potential future heat stress risk in the Durban (eThekwini) metropolitan area, South Africa. Additionally, this is the first study to use high-resolution downscaled climate change projections under Representative Concentration (RCP) 8.5, to construct the heat exposure index using apparent temperature and increases in minimum temperature and a social vulnerability index, using demographic and socio-economic census and land use data to, derived from principal component analysis (PCA) to spatially characterize heat stress within a South African city. Results show that while heat stress is not a current concern, it is projected to increase and become a future concern, mainly as a function of social vulnerability due to household demographic and infrastructural characteristics, and will be experienced in both the rural and inner-city areas of the metro. This study contributes a heat risk framework to identify locations for specific research and adaptation activities on heat stress risk and for urban planning in sub-Saharan African cities, which are characterized by both rural and urban contexts, to address climate change adaptation targeting and priority setting.

ACS Style

Meryl Jagarnath; Tirusha Thambiran; Michael Gebreslasie. Heat stress risk and vulnerability under climate change in Durban metropolitan, South Africa—identifying urban planning priorities for adaptation. Climatic Change 2020, 163, 807 -829.

AMA Style

Meryl Jagarnath, Tirusha Thambiran, Michael Gebreslasie. Heat stress risk and vulnerability under climate change in Durban metropolitan, South Africa—identifying urban planning priorities for adaptation. Climatic Change. 2020; 163 (2):807-829.

Chicago/Turabian Style

Meryl Jagarnath; Tirusha Thambiran; Michael Gebreslasie. 2020. "Heat stress risk and vulnerability under climate change in Durban metropolitan, South Africa—identifying urban planning priorities for adaptation." Climatic Change 163, no. 2: 807-829.

Journal article
Published: 10 March 2020 in Remote Sensing Applications: Society and Environment
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Natural forest ecosystems are vital environmental resources that provide multiple benefits to society, making it imperative for them to be monitored and mapped for practical management purposes. Satellite remote sensing technology is a new source of data and information for forest management and conservation. This study, therefore, applied Support Vector Machine (SVM) and Random Forest (RF) algorithms to Landsat 8 image for mapping a natural forest in South Africa. The objectives were to classify the forest into specific thematic cover classes that indicate its condition, compare the classification performance of the two algorithms based on their default parameters, and determine the most important variables that contributed to the mapping accuracy. The closed canopy forest was determined as the dominant thematic class, followed in descending order by the open canopy forest, grassland, and bare sites. Both algorithms obtained high classification accuracies of above 95%, though the SVM was slightly superior to the RF. The McNemer test indicated that the difference in performance between the two algorithms was statistically insignificant. The most important variables that contributed to the accuracy were the red, blue, green, Near Infrared and Short-Wave Infrared bands, which is attributed to their sensitivity to vegetation. The information provided through the study can be utilized for the planning, management and prioritization initiatives aimed at the protection and conservation of the forest reserve and similar forest ecosystems. The mapping approach could be used for other natural forest ecosystems to ascertain the spatial coverage of the specific thematic cover for conservation purposes. The SVM is recommended for forest ecosystem mapping as it optimally utilized the capabilities of the spectral bands that reflect their actual importance in the mapping of each cover class. The bands identified as important variables can be incorporated as part of input variables when using Landsat 8 satellite imagery for natural forest mapping.

ACS Style

Enoch Gyamfi-Ampadu; Michael Gebreslasie; Alma Mendoza-Ponce. Mapping natural forest cover using satellite imagery of Nkandla forest reserve, KwaZulu-Natal, South Africa. Remote Sensing Applications: Society and Environment 2020, 18, 100302 .

AMA Style

Enoch Gyamfi-Ampadu, Michael Gebreslasie, Alma Mendoza-Ponce. Mapping natural forest cover using satellite imagery of Nkandla forest reserve, KwaZulu-Natal, South Africa. Remote Sensing Applications: Society and Environment. 2020; 18 ():100302.

Chicago/Turabian Style

Enoch Gyamfi-Ampadu; Michael Gebreslasie; Alma Mendoza-Ponce. 2020. "Mapping natural forest cover using satellite imagery of Nkandla forest reserve, KwaZulu-Natal, South Africa." Remote Sensing Applications: Society and Environment 18, no. : 100302.

Journal article
Published: 26 June 2019 in Forests
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Drought limits the production of plantation forests, notably in the drought-prone Zululand region of South Africa. During the last 40 years, the country has faced a series of severe droughts, however that of 2015 stands out as the most extreme and prolonged. The 2015 drought impaired forest productivity and led to widespread tree mortality in this region, but the identification of tree response to drought stress remains uncertain because of its spatial variability. To address this problem, a method that can capture drought patterns and identify trees with similar reactions to drought stress is desired. This could improve the accuracy of detecting trees suffering from drought stress which is key for forest management planning. In this study, we aimed to evaluate the utility of unsupervised mapping approaches in compartments of Eucalyptus trees with similar drought characteristics based on the Normalized Difference Water Index (NDWI) and to demonstrate the value of cloud-based Google Earth Engine (GEE) resources for rapid landscape drought monitoring. Our results showed that calculating distances between pixels using three different matrices (Random Forest (RF) proximity, Euclidean and Manhattan) can accurately detect similarities within a dataset. The RF proximity matrix produced the best measures, which were clustered using Wards hierarchical clustering to detect drought with the highest overall accuracy of 87.7%, followed by Manhattan (85.9%) and Euclidean similarity measures (79.9%), with user and producer results between 84.2% to 91.2%, 42.8% to 98.2% and 37.2% to 94.7%, respectively. These results confirm the value of the RF proximity matrix and underscore the capability of automatic unsupervised mapping approaches for monitoring drought stress in tree plantations, as well as the value of using GEE for providing cost effective datasets to resource stricken countries.

ACS Style

Sifiso Xulu; Kabir Peerbhay; Michael Gebreslasie; Riyad Ismail. Unsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa. Forests 2019, 10, 531 .

AMA Style

Sifiso Xulu, Kabir Peerbhay, Michael Gebreslasie, Riyad Ismail. Unsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa. Forests. 2019; 10 (7):531.

Chicago/Turabian Style

Sifiso Xulu; Kabir Peerbhay; Michael Gebreslasie; Riyad Ismail. 2019. "Unsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa." Forests 10, no. 7: 531.

Articles
Published: 02 January 2019 in Journal of Land Use Science
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Urbanization is one of the most significant and irreversible forms of land change, but we lack empirical evidence of these changes in the Global South. This is the first study to quantify past and explore future land change for the Durban metropolitan, using Land Change Modeler (LCM) software. Results show between 1994–2016, the total changed area was 118,403 ha (47% of landscape) and eleven transition categories were responsible for these changes. Three scenarios were explored: Scenario 1: business as usual (BAU), Scenario 2: green space protection (GSP), and Scenario 3: integrated rapid public transport network (IRPTN), up to the year 2076. BAU and IRPTN show similar projected spatial change concentrated in the west and north. GSP shares temporal change trends with BAU, but projects spatial change concentrated in the north and south. We discuss the utility of this modelling approach to understand land change processes useful for climate change planning.

ACS Style

Meryl Jagarnath; Tirusha Thambiran; Michael Gebreslasie. Modelling urban land change processes and patterns for climate change planning in the Durban metropolitan area, South Africa. Journal of Land Use Science 2019, 14, 81 -109.

AMA Style

Meryl Jagarnath, Tirusha Thambiran, Michael Gebreslasie. Modelling urban land change processes and patterns for climate change planning in the Durban metropolitan area, South Africa. Journal of Land Use Science. 2019; 14 (1):81-109.

Chicago/Turabian Style

Meryl Jagarnath; Tirusha Thambiran; Michael Gebreslasie. 2019. "Modelling urban land change processes and patterns for climate change planning in the Durban metropolitan area, South Africa." Journal of Land Use Science 14, no. 1: 81-109.

Journal article
Published: 28 November 2018 in Southern Forests: a Journal of Forest Science
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Commercial forestry plantations are an important and valuable segment of the South African economy and forest managers are required to maximise and sustain forest productivity. However, various factors such as the outbreak of damaging agents are constantly hampering forest health and thus decrease productivity. It is therefore important to detect the presence and spread of these agents within plantation forests, a task efficiently achieved using remote sensing technology. A wide assortment of sensors with varying resolutions are available and have been extensively used for this purpose. This paper reviews the current status of remote sensing of forest health in South Africa by providing insight on the latest developments on the use of the technology in forest plantations. A systematic search was executed on Google Scholar, ScienceDirect® and EBSCOhost® databases that identified 627 articles of which 29 made reference to remote sensing of forest health in South Africa. Four key results were found: (1) the latest technology is capable of detecting and monitoring forest health with great accuracy, especially with the adoption of machine learning methods; (2) studies employing remote sensing to characterise forest health have burgeoned since 2006 with even more applying hyperspectral data; (3) most studies were spatially concentrated in the KwaZulu-Natal Midlands region around Pietermaritzburg with only a few over the Western Cape; and (4) the remote detection of pest outbreaks and pathogens have received much attention followed by alien invasive plants and a few studies directed to fragmentation. Present and future partnerships may open up opportunities for exploit- ing remote sensing further; this should address growing expectations from government and industry for more detailed and accurate information concerning the health and condition of South Africa's plantation forests.

ACS Style

Sifiso Xulu; Michael T Gebreslasie; Kabir Y Peerbhay. Remote sensing of forest health and vitality: a South African perspective. Southern Forests: a Journal of Forest Science 2018, 81, 91 -102.

AMA Style

Sifiso Xulu, Michael T Gebreslasie, Kabir Y Peerbhay. Remote sensing of forest health and vitality: a South African perspective. Southern Forests: a Journal of Forest Science. 2018; 81 (2):91-102.

Chicago/Turabian Style

Sifiso Xulu; Michael T Gebreslasie; Kabir Y Peerbhay. 2018. "Remote sensing of forest health and vitality: a South African perspective." Southern Forests: a Journal of Forest Science 81, no. 2: 91-102.

Journal article
Published: 30 August 2018 in Forests
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South Africa has a long history of recurrent droughts that have adversely affected its economic performance. The recent 2015 drought has been declared the most serious in 26 years and impaired key agricultural sectors including the forestry sector. Research on the forests’ responses to drought is therefore essential for management planning and monitoring. The effects of the latest drought on the forests in South Africa have not been studied and are uncertain. The study reported here addresses this gap by using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) and precipitation data retrieved and processed using the JavaScript code editor in the Google Earth Engine (GEE) and the corresponding normalized difference infrared index (NDII), Palmer drought severity index (PDSI), and El Niño time series data for KwaMbonambi, northern Zululand, between 2002 and 2016. The NDVI and NDII time series were decomposed using the Breaks for Additive Seasonal and Trend (BFAST) method to establish the trend and seasonal variation. Multiple linear regression and Mann–Kendall tests were applied to determine the association of the NDVI and NDII with the climate variables. Plantation trees displayed high NDVI values (0.74–0.78) from 2002 to 2013; then, they decreased sharply to 0.64 in 2015. The Mann–Kendall trend test confirmed a negative significant (p = 0.000353) trend between 2014 and 2015. This pattern was associated with a precipitation deficit and low NDII values during a strong El Niño phase. The PDSI (−2.6) values indicated severe drought conditions. The greening decreased in 2015, with some forest remnants showing resistance, implying that the tree species had varying sensitivity to drought. We found that the plantation trees suffered drought stress during 2015, although it seems that the trees began to recover, as the NDVI signals rose in 2016. Overall, these results demonstrated the effective use of the NDVI- and NDII-derived MODIS data coupled with climatic variables to provide insights into the influence of drought on plantation trees in the study area.

ACS Style

Sifiso Xulu; Kabir Peerbhay; Michael Gebreslasie; Riyad Ismail. Drought Influence on Forest Plantations in Zululand, South Africa, Using MODIS Time Series and Climate Data. Forests 2018, 9, 528 .

AMA Style

Sifiso Xulu, Kabir Peerbhay, Michael Gebreslasie, Riyad Ismail. Drought Influence on Forest Plantations in Zululand, South Africa, Using MODIS Time Series and Climate Data. Forests. 2018; 9 (9):528.

Chicago/Turabian Style

Sifiso Xulu; Kabir Peerbhay; Michael Gebreslasie; Riyad Ismail. 2018. "Drought Influence on Forest Plantations in Zululand, South Africa, Using MODIS Time Series and Climate Data." Forests 9, no. 9: 528.

Journal article
Published: 01 July 2018 in Atmospheric Environment
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ACS Style

S.K. Sangeetha; V. Sivakumar; Michael Gebreslasie. Long-range transport of SO2 over South Africa: A case study of the Calbuco volcanic eruption in April 2015. Atmospheric Environment 2018, 185, 78 -90.

AMA Style

S.K. Sangeetha, V. Sivakumar, Michael Gebreslasie. Long-range transport of SO2 over South Africa: A case study of the Calbuco volcanic eruption in April 2015. Atmospheric Environment. 2018; 185 ():78-90.

Chicago/Turabian Style

S.K. Sangeetha; V. Sivakumar; Michael Gebreslasie. 2018. "Long-range transport of SO2 over South Africa: A case study of the Calbuco volcanic eruption in April 2015." Atmospheric Environment 185, no. : 78-90.

Journal article
Published: 26 June 2018 in South African Medical Journal
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Background. South Africa (SA) in general, and KwaZulu-Natal (KZN) Province in particular, have stepped up efforts to eliminate malaria. To strengthen malaria control in KZN, a relevant malaria forecasting model is important.Objectives. To develop a forecasting model to predict malaria cases in KZN using the Seasonal Autoregressive Integrated Moving Average (SARIMA) time series approach.Methods. The study was carried out retrospectively using a clinically confirmed monthly malaria case dataset that was split into two. The first dataset (January 2005 - December 2013) was used to construct a SARIMA model by adopting the Box-Jenkins approach, while the second dataset (January - December 2014) was used to validate the forecast generated from the best-fit model.Results. Three plausible models were identified, and the SARIMA (0,1,1)(0,1,1)12 model was selected as the best-fit model. This model was used to forecast malaria cases during 2014, and it was observed to fit closely with malaria cases reported in 2014.Conclusions. The SARIMA (0,1,1)(0,1,1)12 model could serve as a useful tool for modelling and forecasting monthly malaria cases in KZN. It could therefore play a key role in shaping malaria control and elimination efforts in the province.

ACS Style

O Ebhuoma; Michael Gebreslasie; L Magubane. A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa. South African Medical Journal 2018, 108, 573 -578.

AMA Style

O Ebhuoma, Michael Gebreslasie, L Magubane. A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa. South African Medical Journal. 2018; 108 (7):573-578.

Chicago/Turabian Style

O Ebhuoma; Michael Gebreslasie; L Magubane. 2018. "A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa." South African Medical Journal 108, no. 7: 573-578.

Journal article
Published: 10 May 2018 in International Journal of Environmental Research and Public Health
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Climate change has resulted in rising temperature trends which have been associated with changes in temperature extremes globally. Attendees of Conference of the Parties (COP) 21 agreed to strive to limit the rise in global average temperatures to below 2 °C compared to industrial conditions, the target being 1.5 °C. However, current research suggests that the African region will be subjected to more intense heat extremes over a shorter time period, with projections predicting increases of 4–6 °C for the period 2071–2100, in annual average maximum temperatures for southern Africa. Increased temperatures may exacerbate existing chronic ill health conditions such as cardiovascular disease, respiratory disease, cerebrovascular disease, and diabetes-related conditions. Exposure to extreme temperatures has also been associated with mortality. This study aimed to consider the relationship between temperatures in indoor and outdoor environments in a rural residential setting in a current climate and warmer predicted future climate. Temperature and humidity measurements were collected hourly in 406 homes in summer and spring and at two-hour intervals in 98 homes in winter. Ambient temperature, humidity and windspeed were obtained from the nearest weather station. Regression models were used to identify predictors of indoor apparent temperature (AT) and to estimate future indoor AT using projected ambient temperatures. Ambient temperatures will increase by a mean of 4.6 °C for the period 2088–2099. Warming in winter was projected to be greater than warming in summer and spring. The number of days during which indoor AT will be categorized as potentially harmful will increase in the future. Understanding current and future heat-related health effects is key in developing an effective surveillance system. The observations of this study can be used to inform the development and implementation of policies and practices around heat and health especially in rural areas of South Africa.

ACS Style

Thandi Kapwata; Michael T. Gebreslasie; Angela Mathee; Caradee Yael Wright. Current and Potential Future Seasonal Trends of Indoor Dwelling Temperature and Likely Health Risks in Rural Southern Africa. International Journal of Environmental Research and Public Health 2018, 15, 952 .

AMA Style

Thandi Kapwata, Michael T. Gebreslasie, Angela Mathee, Caradee Yael Wright. Current and Potential Future Seasonal Trends of Indoor Dwelling Temperature and Likely Health Risks in Rural Southern Africa. International Journal of Environmental Research and Public Health. 2018; 15 (5):952.

Chicago/Turabian Style

Thandi Kapwata; Michael T. Gebreslasie; Angela Mathee; Caradee Yael Wright. 2018. "Current and Potential Future Seasonal Trends of Indoor Dwelling Temperature and Likely Health Risks in Rural Southern Africa." International Journal of Environmental Research and Public Health 15, no. 5: 952.

Journal article
Published: 03 November 2017 in South African Journal of Geomatics
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Up to date forest inventory data has become increasingly essential for sustainable planning and management of a commercial forest plantation. Forest inventory data may be collected in the form of traditional field based approaches or using remote sensing techniques. The aim of this study was to examine the utility of the partial least squares regression (PLSR), random forest (RF) and a PLSR-RF hybrid machine learning approach for the prediction of four forest structural attributes: (basal area, volume, dominant tree height and mean tree height) within a commercial Eucalyptus forest plantation using a combination of spectral and textural information of high spatial resolution (0.15m) remote sensing data. The best model for this study was produced for mature E. dunnii species for dominant tree height using the PLSR-RF hybrid model (R2 = 0.82 and RMSE = 2.07m). The results of this study highlight the robustness and potential of the PLSR-RF hybrid model for the prediction of forest structural attributes using high resolution imagery within a commercial Eucalyptus forest plantation.

ACS Style

Nicole Reddy; Michael Gebreslasie; Riyad Ismail. A hybrid partial least squares and random forest approach to modelling forest structural attributes using multispectral remote sensing data. South African Journal of Geomatics 2017, 6, 377 .

AMA Style

Nicole Reddy, Michael Gebreslasie, Riyad Ismail. A hybrid partial least squares and random forest approach to modelling forest structural attributes using multispectral remote sensing data. South African Journal of Geomatics. 2017; 6 (3):377.

Chicago/Turabian Style

Nicole Reddy; Michael Gebreslasie; Riyad Ismail. 2017. "A hybrid partial least squares and random forest approach to modelling forest structural attributes using multispectral remote sensing data." South African Journal of Geomatics 6, no. 3: 377.

Journal article
Published: 01 November 2017 in Acta Tropica
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Although malaria morbidity and mortality are greatly reduced globally owing to great control efforts, the disease remains the main contributor. In Zambia, all provinces are malaria endemic. However, the transmission intensities vary mainly depending on environmental factors as they interact with the vectors. Generally in Africa, possibly due to the varying perspectives and methods used, there is variation on the relative importance of malaria risk determinants. In Zambia, the role climatic factors play on malaria case rates has not been determined in combination of space and time using robust methods in modelling. This is critical considering the reversal in malaria reduction after the year 2010 and the variation by transmission zones. Using a geoadditive or structured additive semiparametric Poisson regression model, we determined the influence of climatic factors on malaria incidence in four endemic provinces of Zambia. We demonstrate a strong positive association between malaria incidence and precipitation as well as minimum temperature. The risk of malaria was 95% lower in Lusaka (ARR=0.05, 95% CI=0.04-0.06) and 68% lower in the Western Province (ARR=0.31, 95% CI=0.25-0.41) compared to Luapula Province. North-western Province did not vary from Luapula Province. The effects of geographical region are clearly demonstrated by the unique behaviour and effects of minimum and maximum temperatures in the four provinces. Environmental factors such as landscape in urbanised places may also be playing a role.

ACS Style

Nzooma M. Shimaponda-Mataa; Enala Tembo-Mwase; Michael Gebreslasie; Thomas Achia; Samson Mukaratirwa. Reprint of “Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression ”. Acta Tropica 2017, 175, 60 -70.

AMA Style

Nzooma M. Shimaponda-Mataa, Enala Tembo-Mwase, Michael Gebreslasie, Thomas Achia, Samson Mukaratirwa. Reprint of “Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression ”. Acta Tropica. 2017; 175 ():60-70.

Chicago/Turabian Style

Nzooma M. Shimaponda-Mataa; Enala Tembo-Mwase; Michael Gebreslasie; Thomas Achia; Samson Mukaratirwa. 2017. "Reprint of “Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression ”." Acta Tropica 175, no. : 60-70.

Journal article
Published: 01 May 2017 in Journal of Infection and Public Health
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ACS Style

Osadolor Ebhuoma; Michael Gebreslasie; Lethumusa Magubane. Modeling malaria control intervention effect in KwaZulu-Natal, South Africa using intervention time series analysis. Journal of Infection and Public Health 2017, 10, 334 -338.

AMA Style

Osadolor Ebhuoma, Michael Gebreslasie, Lethumusa Magubane. Modeling malaria control intervention effect in KwaZulu-Natal, South Africa using intervention time series analysis. Journal of Infection and Public Health. 2017; 10 (3):334-338.

Chicago/Turabian Style

Osadolor Ebhuoma; Michael Gebreslasie; Lethumusa Magubane. 2017. "Modeling malaria control intervention effect in KwaZulu-Natal, South Africa using intervention time series analysis." Journal of Infection and Public Health 10, no. 3: 334-338.

Journal article
Published: 16 November 2016 in Geospatial Health
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Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF) statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI)], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

ACS Style

Thandi Kapwata; Michael T. Gebreslasie. Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa. Geospatial Health 2016, 11, 1 .

AMA Style

Thandi Kapwata, Michael T. Gebreslasie. Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa. Geospatial Health. 2016; 11 (3):1.

Chicago/Turabian Style

Thandi Kapwata; Michael T. Gebreslasie. 2016. "Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa." Geospatial Health 11, no. 3: 1.

Journal article
Published: 06 November 2016 in Acta Tropica
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Although malaria morbidity and mortality are greatly reduced globally owing to great control efforts, the disease remains the main contributor. In Zambia, all provinces are malaria endemic. However, the transmission intensities vary mainly depending on environmental factors as they interact with the vectors. Generally in Africa, possibly due to the varying perspectives and methods used, there is variation on the relative importance of malaria risk determinants. In Zambia, the role climatic factors play on malaria case rates has not been determined in combination of space and time using robust methods in modelling. This is critical considering the reversal in malaria reduction after the year 2010 and the variation by transmission zones. Using a geoadditive or structured additive semiparametric Poisson regression model, we determined the influence of climatic factors on malaria incidence in four endemic provinces of Zambia. We demonstrate a strong positive association between malaria incidence and precipitation as well as minimum temperature. The risk of malaria was 95% lower in Lusaka (ARR = 0.05, 95% CI = 0.04–0.06) and 68% lower in the Western Province (ARR = 0.31, 95% CI = 0.25–0.41) compared to Luapula Province. North-western Province did not vary from Luapula Province. The effects of geographical region are clearly demonstrated by the unique behaviour and effects of minimum and maximum temperatures in the four provinces. Environmental factors such as landscape in urbanised places may also be playing a role.

ACS Style

Nzooma M. Shimaponda-Mataa; Enala Tembo-Mwase; Michael Gebreslasie; Thomas Achia; Samson Mukaratirwa. Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression. Acta Tropica 2016, 166, 81 -91.

AMA Style

Nzooma M. Shimaponda-Mataa, Enala Tembo-Mwase, Michael Gebreslasie, Thomas Achia, Samson Mukaratirwa. Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression. Acta Tropica. 2016; 166 ():81-91.

Chicago/Turabian Style

Nzooma M. Shimaponda-Mataa; Enala Tembo-Mwase; Michael Gebreslasie; Thomas Achia; Samson Mukaratirwa. 2016. "Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression." Acta Tropica 166, no. : 81-91.

Journal article
Published: 04 November 2016 in Parasites & Vectors
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Schistosomiasis is a snail-borne disease endemic in sub-Saharan Africa transmitted by freshwater snails. The distribution of schistosomiasis coincides with that of the intermediate hosts as determined by climatic and environmental factors. The aim of this paper was to model the spatial and seasonal distribution of suitable habitats for Bulinus globosus and Biomphalaria pfeifferi snail species (intermediate hosts for Schistosoma haematobium and Schistosoma mansoni, respectively) in the Ndumo area of uMkhanyakude district, South Africa. Maximum Entropy (Maxent) modelling technique was used to predict the distribution of suitable habitats for B. globosus and B. pfeifferi using presence-only datasets with ≥ 5 and ≤ 12 sampling points in different seasons. Precipitation, maximum and minimum temperatures, Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), pH, slope and Enhanced Vegetation Index (EVI) were the background variables in the Maxent models. The models were validated using the area under the curve (AUC) and omission rate. The predicted suitable habitats for intermediate snail hosts varied with seasons. The AUC for models in all seasons ranged from 0.71 to 1 and the prediction rates were between 0.8 and 0.9. Although B. globosus was found at more localities in the Ndumo area, there was also evidence of cohabiting with B. pfiefferi at some of the locations. NDWI had significant contribution to the models in all seasons. The Maxent model is robust in snail habitat suitability modelling even with small dataset of presence-only sampling sites. Application of the methods and design used in this study may be useful in developing a control and management programme for schistosomiasis in the Ndumo area.

ACS Style

Tawanda Manyangadze; Moses John Chimbari; Michael Gebreslasie; Pietro Ceccato; Samson Mukaratirwa. Modelling the spatial and seasonal distribution of suitable habitats of schistosomiasis intermediate host snails using Maxent in Ndumo area, KwaZulu-Natal Province, South Africa. Parasites & Vectors 2016, 9, 1 -10.

AMA Style

Tawanda Manyangadze, Moses John Chimbari, Michael Gebreslasie, Pietro Ceccato, Samson Mukaratirwa. Modelling the spatial and seasonal distribution of suitable habitats of schistosomiasis intermediate host snails using Maxent in Ndumo area, KwaZulu-Natal Province, South Africa. Parasites & Vectors. 2016; 9 (1):1-10.

Chicago/Turabian Style

Tawanda Manyangadze; Moses John Chimbari; Michael Gebreslasie; Pietro Ceccato; Samson Mukaratirwa. 2016. "Modelling the spatial and seasonal distribution of suitable habitats of schistosomiasis intermediate host snails using Maxent in Ndumo area, KwaZulu-Natal Province, South Africa." Parasites & Vectors 9, no. 1: 1-10.