Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1007/s10393-017-1307-0 |
Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa | |
Malahlela, Oupa E.1,2; Olwoch, Jane M.1,3; Adjorlolo, Clement2 | |
通讯作者 | Malahlela, Oupa E. |
来源期刊 | ECOHEALTH
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ISSN | 1612-9202 |
EISSN | 1612-9210 |
出版年 | 2018 |
卷号 | 15期号:1页码:23-40 |
英文摘要 | Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1: 2 (presence/absence) were generated at buffer distances of 0.5-20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (q < 0.05) and yielded a deviance (D 2) of 36%. The significantly positive relationship (q < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a1 index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall. |
英文关键词 | Vhembe District Municipality Malaria SAVI Landsat 5 |
类型 | Article |
语种 | 英语 |
国家 | South Africa ; Namibia |
收录类别 | SCI-E |
WOS记录号 | WOS:000435527600004 |
WOS关键词 | DIFFERENCE WATER INDEX ; SPECTRAL REFLECTANCE ; DISTRIBUTION MODEL ; VEGETATION INDEX ; PSEUDO-ABSENCES ; TRANSMISSION ; SELECTION ; FOREST ; RISK ; NDWI |
WOS类目 | Biodiversity Conservation ; Ecology ; Environmental Sciences |
WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/208670 |
作者单位 | 1.Univ Pretoria, Dept Geog Geoinformat & Meteorol, Private Bag X20, ZA-0028 Hatfield, South Africa; 2.South African Natl Space Agcy SANSA, Earth Observat Directorate, ZA-0001 Pretoria, South Africa; 3.Southern African Sci Serv Ctr Climate Change & Ad, Windhoek 91100, Namibia |
推荐引用方式 GB/T 7714 | Malahlela, Oupa E.,Olwoch, Jane M.,Adjorlolo, Clement. Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa[J],2018,15(1):23-40. |
APA | Malahlela, Oupa E.,Olwoch, Jane M.,&Adjorlolo, Clement.(2018).Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa.ECOHEALTH,15(1),23-40. |
MLA | Malahlela, Oupa E.,et al."Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa".ECOHEALTH 15.1(2018):23-40. |
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