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Geospatial information for sustainable food systems

Land Cover Mapping to support natural resources assessment and conflict mitigation strategies in South Sudan

The assessment of the distribution and the temporal dynamics of natural resources as well as human activities is fundamental baseline information needed for sustainable management of the land, particularly in zones with severe environmental challenges (e.g. scarcity of natural resources, frequent natural hazards). Land cover data are therefore essential to address the increasing concerns in regards to food and nutrition security, planning a shift from subsistence to sustainable commercial agriculture and increasing the resilience of livelihoods to threats and crises in a changing climate.

However, compelling reliable land cover information is challenging in a country such as South Sudan, with a total surface of approximately 620 thousands kmq. It is a complex landscape, with many areas still insecure
and inaccessible during large part of the year.

In 2011, FAO published the Land cover atlas of the Republic of South Sudan” based on the integration of Landsat and SPOT images. The dataset, developed within the “Sudan Institutional Capacity Programme: Food Security Information for Action” (SIFSIA) funded by European Union, in collaboration with the Government of South Sudan, received a very positive consensus but it was scarcely applied to effective national land use planning.

In November 2017, FAO decided to develop an updated land cover dataset, with the objective to produce baseline georeferenced information to serve a number of applications within a portfolio of projects:

  • Strengthening the Livelihoods Resilience of Pastoral and Agro-Pastoral Communities in South Sudan’s cross border areas with Sudan, Ethiopia, Kenya and Uganda.
  • Strengthening the resilience of household to food insecurity in South Sudan.
  • Sustainable Agriculture for Economic Resilience.

The rationale for an updated land cover dataset are manifold:

  1. The land cover of South Sudan has rapidly changed in the last years, requiring an update of the previous dataset: settlements to host refugees and Internally Displaced Persons (IDPs), deforestation and degradation of woodlands for biofuel production land conversion to agriculture. The inclusion of a 10 Km buffer around the country boundaries allows capture of those transboundary patterns (e.g. the livestock movements) not mapped in previous assessments.
  2. Newly available Earth Observation missions such as the European Space Agency (ESA) Copernicus programme, allow us to apply multi-temporal and multi-spectral analysis both with optical and radar images at an unprecedented level of temporal and spatial resolution. This, combined with online cloud-computing platforms will allow improved thematic and spatial accuracy combined with time and cost-effective results.
  3. The availability of a new International Standard on Land Cover Classification will allow us to publish the dataset with a land cover legend that better meets the needs of rigorous land cover “formalization” required for map-making, with the user-friendliness and flexibility required by end-users. This will overcome some of the limitations of the previous land cover, where the high number of mixed classes, made the use of the dataset, challenging.

Approach to Land Cover Mapping

The FAO approach to land cover mapping, combine innovative technologies, multi-temporal high-resolution imagery, standards and tools developed specifically to assess land characteristics.

1. Standards

With the objective to describe the land in a non-ambiguous way, the approach ensures interoperability and comparability of national, regional and global land cover classification systems, FAO has contributed significantly to the development of the Land Cover Meta Language (LCML) which became a joint FAO/ISO standard (ISO 19144-2:2012) and is based on the original FAO Land Cover Classification System (LCCS).

LCML is used to standardize the process of classifying land rather than providing a fixed classification system. It creates a set of standard diagnostic
attributes (biotic and abiotic basic objects, their properties
and characteristics) spatially and temporally arranged.

2. Imagery

Sentinel-2 is the primary source of remote sensed imagery. It is a polar-orbiting, multispectral high-resolution imaging mission for land monitoring, developed by the European Space Agency (ESA), within the Copernicus programme. The two satellites are equipped with a Multi-Spectral Instruments with a spatial resolution of 10 meters, in the visible and infrared bands, a revisit time of 5 days that coupled with a wide swath, allow to have free cloud coverage of the whole area of study. Time series images for the whole 2017 (January to November) have been accessed and pre-processed at level 2C (geometric, radiometric and atmospheric corrected) in order to have a comparable time series to be analyzed. Sentinel-1 images are being used to provide additional information to support land cover interpretation, in particular to assess temporal dynamics of water resources.

3. Analysis

Object-based Image Analysis (OBIA) will be applied to delineate and classify land cover features. Multi-temporal optical and radar images will be used to characterize the land cover across the whole year, including patterns of vegetation growth, fire occurrence, water dynamics. OBIA provides several advantages respects to traditional raster-based analysis, in terms of improved accuracy and ability to identify complex and heterogeneous functionalland cover classes. Sample training data for each class will be entered into a machine-learning algorithm (i.e. random forest classifier) to provide an initial interpretation of the “object-features”. Local expert-knowledge, supported by time series of vegetation index profiles, multi-temporal radar images and ancillary information will further detail and validate the result of interpretation.

4. Capacity building

A first workshop to enhance capacity on land cover mapping, geospatial management and field data collection was organized in Juba in February 2018 for local geospatial experts and Natural Resource Officers. Capacity building is a critical component on land cover mapping that ensures validation, endorsement and sustainability of final products.

Applications

The new land cover dataset will allow to map the current occurrence of natural resources, human settlements and human activities in South Sudan and within neighboring countries. It will represent the most innovative and updated dataset developed for South Sudan, that integrate high-resolution multi-temporal imagery, Object-based image analysis and machine-learning algorithms and LCML, to support the Natural Resource Management strategy and land use planning. The national land cover dataset will allow upscaling and extrapolating observation on test pilot areas (i.e. field data observation or sub-meter Remote Sensing analysis) at national level for many potential applications:

  • Identification and suitability of livestock migratory routes
  • Participatory and community mapping for development
    of conflict mitigation strategies
  • Estimation of charcoal production and impact
    on natural wood biomass
  • Assessment of Socio-Economic Systems
  • Assessment of landscape complexity and hotspots
    mapping (e.g. cropland\grassland interspersion)
  • Supporting crop monitoring.