Información geoespacial para lograr sistemas alimentarios sostenibles

Strengthening technical capacities for land cover monitoring in Angola

Land cover is critical for various Sustainable Development Goals, for planning and monitoring sustainable land and water management, including for food security and resilient agriculture. With the recent technological progress and access to information, land cover information is becoming increasingly valuable for many applications. However, harmonization and consistency between land cover remains challenging.

In the context of Angola, accurate and updated land cover information is required to support various local and national ongoing programmes such as against land degradation. The project Sustainable Land Management in target landscapes of Central Angola (GCP/ANG/055/GFF) aims at strengthening technical capacities and revert land degradation trends in agriculture. Under this project, a one-month training programme from March 2nd to April 11th 2022 on land cover monitoring was organized. Nine technical experts from Angola were trained on (1) Land cover legend preparation, (2) Land Cover Classification System, (3) Remote Sensing, CollectEarth Online and SEPAL, (4) Collection of training samples, (5) Quality  check  of  collected  training  data  and  land  cover preparation and (6) updated land cover for identified Area of Interest.

During this training, Fatima Mushtaq highlighted the importance of sharing knowledge and technical expertise to build critical mass for GIS/RS and land cover monitoring.

This training contributes to strengthen land cover monitoring capacities and use of accurate land cover information for national purposes. The geospatial technologies used during the training include cloud computing platforms, remote sensing, Geographical Information System (GIS), machine learning techniques and their integrated approaches have been proving to provide the most efficient and nature-based solutions in the context of land and water monitoring and management including agricultural production. The training used FAO open-source platform for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), FAO Land Cover Legend Registry LCLR web portal, Land Cover Classification System version 3 (LCCS3) software based on Land Cover Meta Language (LCML) and Collect Earth Online (CEO) to prepare land cover legend and datasets at local, national, and regional level.