AI Article Synopsis

  • - The eco-type map of Barotseland, Zambia, identifies land units based on vegetation and flood/drought exposure, created from input by 77 local participants in three villages.
  • - Researchers used Landsat satellite images from 2014 and various indices (Water Index, NDVI, NBR) to assess water levels and vegetation types, ensuring accuracy by excluding burned and built areas.
  • - The resulting map, with 81% accuracy and a 30m pixel resolution, serves as a valuable tool for agricultural and conservation efforts in the Barotse Floodplain.

Article Abstract

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 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141788PMC
http://dx.doi.org/10.1016/j.dib.2018.07.009DOI Listing

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