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An assessment of remote sensing-based drought index over different land cover types in southern Africa

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dc.contributor.author Marumbwa, FM
dc.contributor.author Cho, Moses A
dc.contributor.author Chirwa, PW
dc.date.accessioned 2020-10-05T08:49:33Z
dc.date.available 2020-10-05T08:49:33Z
dc.date.issued 2020-07
dc.identifier.citation Marumbwa, F.M., Cho, M.A. and Chirwa, P.W. 2020. An assessment of remote sensing-based drought index over different land cover types in southern Africa. International Journal of Remote Sensing, v41(19), pp 7368-7382. en_US
dc.identifier.issn 0143-1161
dc.identifier.issn 1366-5901
dc.identifier.uri https://www.tandfonline.com/doi/full/10.1080/01431161.2020.1757783
dc.identifier.uri https://doi.org/10.1080/01431161.2020.1757783
dc.identifier.uri http://hdl.handle.net/10204/11588
dc.description Copyright: 2020 Taylor & Francis. This is the preprint version of the work. For access to the published version, kindly visit the publisher's website. en_US
dc.description.abstract An understanding of drought and land cover interaction plays a crucial role in vegetation vulnerability studies and land use planning. However, there is paucity of information on drought, land cover and land use interaction in southern Africa. We analysed the drought impact on land cover using Globcover land cover data and Vegetation Condition Index (VCI) for the 2015 to 2016 season. The 2015 to 2016 season was chosen because it was the worst drought in southern Africa since the 1980s. We developed a novel land cover social pixels’ or ‘village pixels’ which represents rural communities. The Kruskal–Wallis test was used to evaluate whether there is a significant difference in drought impact among the land cover classes. The response of each land cover to drought impact was calculated by correlating Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI). Our results reveal that the evergreen forests and the flooded vegetation were the most severely affected by the 2015–2016 drought. However, the lowest VCI values were recorded within the village pixels land cover, indicating the vulnerability of rural communities to drought impacts. The vegetation response to drought impact ranged from 2 months (crops) to 8 months (flooded vegetation). With regards to drought recurrence (1998 to 2018), the crop and grassland land cover recorded the highest drought frequency whilst the forest had the least drought frequency. en_US
dc.language.iso en en_US
dc.publisher Taylor & Francis en_US
dc.relation.ispartofseries Worklist;23730
dc.subject Drought en_US
dc.subject Land use en_US
dc.subject Land cover en_US
dc.title An assessment of remote sensing-based drought index over different land cover types in southern Africa en_US
dc.type Article en_US
dc.identifier.apacitation Marumbwa, F., Cho, M. A., & Chirwa, P. (2020). An assessment of remote sensing-based drought index over different land cover types in southern Africa. http://hdl.handle.net/10204/11588 en_ZA
dc.identifier.chicagocitation Marumbwa, FM, Moses A Cho, and PW Chirwa "An assessment of remote sensing-based drought index over different land cover types in southern Africa." (2020) http://hdl.handle.net/10204/11588 en_ZA
dc.identifier.vancouvercitation Marumbwa F, Cho MA, Chirwa P. An assessment of remote sensing-based drought index over different land cover types in southern Africa. 2020; http://hdl.handle.net/10204/11588. en_ZA
dc.identifier.ris TY - Article AU - Marumbwa, FM AU - Cho, Moses A AU - Chirwa, PW AB - An understanding of drought and land cover interaction plays a crucial role in vegetation vulnerability studies and land use planning. However, there is paucity of information on drought, land cover and land use interaction in southern Africa. We analysed the drought impact on land cover using Globcover land cover data and Vegetation Condition Index (VCI) for the 2015 to 2016 season. The 2015 to 2016 season was chosen because it was the worst drought in southern Africa since the 1980s. We developed a novel land cover social pixels’ or ‘village pixels’ which represents rural communities. The Kruskal–Wallis test was used to evaluate whether there is a significant difference in drought impact among the land cover classes. The response of each land cover to drought impact was calculated by correlating Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI). Our results reveal that the evergreen forests and the flooded vegetation were the most severely affected by the 2015–2016 drought. However, the lowest VCI values were recorded within the village pixels land cover, indicating the vulnerability of rural communities to drought impacts. The vegetation response to drought impact ranged from 2 months (crops) to 8 months (flooded vegetation). With regards to drought recurrence (1998 to 2018), the crop and grassland land cover recorded the highest drought frequency whilst the forest had the least drought frequency. DA - 2020-07 DB - ResearchSpace DP - CSIR KW - Drought KW - Land use KW - Land cover LK - https://researchspace.csir.co.za PY - 2020 SM - 0143-1161 SM - 1366-5901 T1 - An assessment of remote sensing-based drought index over different land cover types in southern Africa TI - An assessment of remote sensing-based drought index over different land cover types in southern Africa UR - http://hdl.handle.net/10204/11588 ER - en_ZA


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