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Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia
This eco-type map presents land units with distinct vegetation and exposure to floods (or droughts) in three villages in the Barotseland, Zambia. The knowledge and eco-types descriptions were collected from participatory mapping and focus group discussions with 77 participants from Mapungu, Lealui,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141788/ https://www.ncbi.nlm.nih.gov/pubmed/30246096 http://dx.doi.org/10.1016/j.dib.2018.07.009 |
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author | Del Rio, Trinidad Groot, Jeroen C.J. DeClerck, Fabrice Estrada-Carmona, Natalia |
author_facet | Del Rio, Trinidad Groot, Jeroen C.J. DeClerck, Fabrice Estrada-Carmona, Natalia |
author_sort | Del Rio, Trinidad |
collection | PubMed |
description | This eco-type map presents land units with distinct vegetation and exposure to floods (or droughts) in three villages in the Barotseland, Zambia. The knowledge and eco-types descriptions were collected from participatory mapping and focus group discussions with 77 participants from Mapungu, Lealui, and Nalitoya. We used two Landsat 8 Enhanced Thematic Mapper (TM) images taken in March 24th and July 14th, 2014 (path 175, row 71) to calculate water level and vegetation type which are the two main criteria used by Lozi People for differentiating eco-types. We calculated water levels by using the Water Index (WI) and vegetation type by using the Normalized Difference Vegetation Index (NDVI). We also calculated the Normalized Burn Ratio (NBR) index. We excluded burned areas in 2014 and built areas to reduce classification error. Control points include field data from 99 farmers’ fields, 91 plots of 100 m(2) and 65 waypoints randomly selected in a 6 km radius around each village. We also used Google Earth Pro to create control points in areas flooded year-round (e.g., deep waters and large canals), patches of forest and built areas. The eco-type map has a classification accuracy of 81% and a pixel resolution of 30 m. The eco-type map provides a useful resource for agriculture and conservation planning at the landscape level in the Barotse Floodplain. |
format | Online Article Text |
id | pubmed-6141788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-61417882018-09-21 Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia Del Rio, Trinidad Groot, Jeroen C.J. DeClerck, Fabrice Estrada-Carmona, Natalia Data Brief Environmental Science This eco-type map presents land units with distinct vegetation and exposure to floods (or droughts) in three villages in the Barotseland, Zambia. The knowledge and eco-types descriptions were collected from participatory mapping and focus group discussions with 77 participants from Mapungu, Lealui, and Nalitoya. We used two Landsat 8 Enhanced Thematic Mapper (TM) images taken in March 24th and July 14th, 2014 (path 175, row 71) to calculate water level and vegetation type which are the two main criteria used by Lozi People for differentiating eco-types. We calculated water levels by using the Water Index (WI) and vegetation type by using the Normalized Difference Vegetation Index (NDVI). We also calculated the Normalized Burn Ratio (NBR) index. We excluded burned areas in 2014 and built areas to reduce classification error. Control points include field data from 99 farmers’ fields, 91 plots of 100 m(2) and 65 waypoints randomly selected in a 6 km radius around each village. We also used Google Earth Pro to create control points in areas flooded year-round (e.g., deep waters and large canals), patches of forest and built areas. The eco-type map has a classification accuracy of 81% and a pixel resolution of 30 m. The eco-type map provides a useful resource for agriculture and conservation planning at the landscape level in the Barotse Floodplain. Elsevier 2018-07-09 /pmc/articles/PMC6141788/ /pubmed/30246096 http://dx.doi.org/10.1016/j.dib.2018.07.009 Text en © 2018 Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Environmental Science Del Rio, Trinidad Groot, Jeroen C.J. DeClerck, Fabrice Estrada-Carmona, Natalia Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia |
title | Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia |
title_full | Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia |
title_fullStr | Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia |
title_full_unstemmed | Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia |
title_short | Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia |
title_sort | integrating local knowledge and remote sensing for eco-type classification map in the barotse floodplain, zambia |
topic | Environmental Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141788/ https://www.ncbi.nlm.nih.gov/pubmed/30246096 http://dx.doi.org/10.1016/j.dib.2018.07.009 |
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