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,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018-08-01
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| Series: | Data in Brief |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340918307820 |
| _version_ | 1811299395266674688 |
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| author | Trinidad Del Rio Jeroen C.J. Groot Fabrice DeClerck Natalia Estrada-Carmona |
| author_facet | Trinidad Del Rio Jeroen C.J. Groot Fabrice DeClerck Natalia Estrada-Carmona |
| author_sort | Trinidad Del Rio |
| collection | DOAJ |
| 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 m2 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. Keywords: Thematic map, Landsat-8 satellite data, Barotseland, Vegetation types, Geographical distribution, GIS |
| first_indexed | 2024-04-13T06:35:21Z |
| format | Article |
| id | doaj.art-363b1aa5564d4f498eacdb705835f8a6 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-13T06:35:21Z |
| publishDate | 2018-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-363b1aa5564d4f498eacdb705835f8a62022-12-22T02:57:56ZengElsevierData in Brief2352-34092018-08-011922972304Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, ZambiaTrinidad Del Rio0Jeroen C.J. Groot1Fabrice DeClerck2Natalia Estrada-Carmona3University of Twente, The Netherlands; Wageningen University & Research, The NetherlandsWageningen University & Research, The NetherlandsBioversity International, FranceWageningen University & Research, The Netherlands; Bioversity International, France; Corresponding author at: Bioversity International, France.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 m2 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. Keywords: Thematic map, Landsat-8 satellite data, Barotseland, Vegetation types, Geographical distribution, GIShttp://www.sciencedirect.com/science/article/pii/S2352340918307820 |
| spellingShingle | Trinidad Del Rio Jeroen C.J. Groot Fabrice DeClerck Natalia Estrada-Carmona Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia Data in Brief |
| 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 |
| url | http://www.sciencedirect.com/science/article/pii/S2352340918307820 |
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