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  • Mapping vital ecosystem services by integrating local knowledge and remote sensing data

    References

    Estrada-Carmona, N., Attwood, S., Cole, S.M., Remans, R., DeClerck,F. 2016 (submitted). “Ecosystem services framework to guide crop diversi- fication for nutrition and ecosystem health in Barotse Floodplain.” Ecology and Society. TEEB (2015) TEEB for Agriculture & Food: an interim report, United Nations Environment Programme, Geneva, Switzerland.

    Authors

    Presenting: Trinidad del Rio t.delrio@utwente.nl1

    Natalia Estrada Carmona2, Jeroen Groot 3,Louise Willemen1, Fabrice DeClerck2

    Primary affiliations 1Faculty of Geo-information Science and Earth Observations (ITC) University of Twente, the Netherlands. 2Bioversity International, France 3Farming System Ecology FSE, Wageningen University, the Netherlands

    1. Background and Research Objective

    • Wetlands support high levels of biodiversity and provide vital ecosystem services to society. • Wetlands are facing increased pressures from land degradation and agricultural expansion. • The Barotse region, located in the western province in Zambia, experiences a four to five month “hungry” season with limited food access. • Growing interest on: How land use configuration leads to providing the goods and services upon which human well-being is dependent? (TEEB 2015).

    This study aims to integrate of local and scientific knowledge by developing a land type

    map which is associated with vital ecosystem services for a dynamic landscape

    Figure 3. The understanding of the local classification and management of the land type units from the Barotse region associated to their provision of goods and services is the cornerstone of this study.

    Figure 2. Schematic map of the Barotse floodplain with approximate land types location and description linked to the ecosystem services provided (Estrada-Carmona et al., 2016, submitted).

    Figure 1. The location of the study area in Zambia (37.5 thousand km2). The white square shows the location of the study area.

    Figure 6. Detailed representation of a 5 x 3 km subset of the study area showing examples of land type units associated through the understanding of the local classification to ecosystem services provision.

    Figure 5. Land type classification map. The red square indicates the location of the detailed representation (Figure 6).

    Case study in the Barotse Floodplain, Zambia

    Local knowledge: land types

    classifica�on

    Field measurements:

    farm plots and natural areas

    Water Index NIR + SWIR 2

    NDVI (vegeta�on) NDVI = (NIR – RED)/ (NIR + RED)

    187 of water and vegeta�on

    combina�ons

    11 land types related to ES supply

    Ground data for classifica�on and

    valida�on

    Subareas according to Macro and micro-

    topography

    Grouping criteria according to land types water and vegeta�on characteris�cs

    corroborate cut-off values for categoriza�on of RS images

    Figure 4. Diagram showing the followed steps for the creation of the land type map integrating local and scientific knowledge. The input images used were Landsat 8 Enhanced Thematic Mapper (TM) images from March 24th and July 14th, 2014 for the analysis (path 175, row 71).

    Fertile soil suitable for growing several vegetables but has flooding risk (lost of harvest)

    Small fertile hills in the plain allow agriculture during the dry period: usually rice in the bottom part and maize, tomato or cucurbits family sp. on the top. Flooding control

    Fertile areas in the plain are suitable for rice and some vegetables cultivation during the dry (short) period. Also for animal grazing. They get flooded during the wet season

    Roads enhance connectivity for trading of goods

    Permanent ponds provide fish, water for drinking and for agriculture

    Dry and sandy areas are suitable for growing drought resistant crops and some fruit trees. Also provides firewood

    Forest in the upland provide with construction materials, firewood, wild food, pollination, animal grazing area (specially in the wet season), suit- able for casaba cultivation and wildlife habitat

    3. Results

    • The integration of local and scientific knowledge with remote sensing resulted in a map that displays the distribution of land units which are relevant to their users (the Barotse inhabitants) (Figure 5). • Understanding the local use of different land units in agricultural landscapes is essen- tial to support effective decision making and is vital to untangle the complex interac- tions between people, nature and agricultural landscapes. • Ground truthing measured the land type map’s overall accuracy and a Kappa value of 82.1 and 78% respectively. • Soil fertility and the seasonal risk of flooding or droughts are the main land type factors that determine the what, where and when of crop selection and management. Therefore, this land type map represents a useful guide that supports decisions for sustainable development in rural areas (Figure 6).

    Mapping vital ecosystem services in dynamic floodplains considering the local knowledge is a promising tool that supports effective decision and

    planning for food and nutritional security

    2. Methodology

    • The study area corresponded to the Barotse floodplain and surroundings, located in the western province in Zambia (Figure 1). • Using the local land type characterization based in local knowledge (Figure 2), this land type map was created using information from land types locations and manage- ment of farm plots and natural areas collected through interviews to smallholder farmers distributed in the region, participatory activities and fieldwork done in 2014 (Figure 3).

    • Combining categorized water and vegetation categorized rasters, we used the previously collected field measurements and the land types characteristics of water content and vegetation cover to identify and map 11 classes corresponding to main previously identified land types. • Additional ground truthing data (91 plots of 100 m2 and 70 waypoints) was used for the accuracy assessment (Figure 4).