Developing Practical Skills for Regional Land Cover Mapping Using a Cloud Computing Platform: An Example of Capacity Building for Service Delivery
Abstract
Capacity building is best achieved through the co-development of a purpose build service. Land cover maps are essential for informing policy development and planning efforts. However, organizations routinely report a suite of challenges to using land cover maps, including infrequent updates, classification systems that do not accommodate multiple users, lack of accuracy assessments, inaccessibility or data confined to political boundaries. We present the co-creation of a regional land cover monitoring system aimed at alleviating these challenges. The system was designed with a service delivery and participatory capacity building approach with stakeholders of the SERVIR land cover thematic group through a series of workshops with representatives from thirteen countries in the Lower Mekong and Hindu Kush Himalaya. They co-developed a modular, customizable land cover monitoring system architecture using publically available data and a free cloud based computing platform, Google Earth Engine. Stakeholders specified their national land cover typology and agreed on a shared typology. Then contributed existing reference data and collected additional information to fill gaps to create a series of thematic biophysical map layers, such as percent tree cover. These thematic layers were then selected and combined into multi-class land cover maps that meet user needs using a customizable assembly logic. With stakeholder mapping and needs identified, the resulting service design was adapted to users with capacity building workshops customized to specific requirements aimed towards meeting agreed upon outputs and outcomes. This included specific workshops for Myanmar for developing consistent and accurate land cover maps for UNFCCC and FAO reporting requirements. Overall, the accessibility and transparency of the system results in dramatically increased information and technology exchange within and between member organizations. Using biophysical input data layers is beneficial as map inputs can be easily updated to make use of better reference data, improvements in classification methods and remote sensing technologies. The system is built using freely available data and is customizable to national needs it can easily be adopted by other regional land management efforts around the world.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMPA54A..02T
- Keywords:
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- 0480 Remote sensing;
- BIOGEOSCIENCESDE: 4337 Remote sensing and disasters;
- NATURAL HAZARDSDE: 6319 Institutions;
- POLICY SCIENCESDE: 6630 Workforce;
- PUBLIC ISSUES