Mekong Development of a Regional Land Cover Monitoring System In the Lower Mekong Region a Joint Effort Between SERVIR-Mekong and Partners - Aekkapol Aekakkararungroj SERVIR-Mekong Asian Disaster Preparedness Center
SERVIR From Space to Village SCO NASA Mesoamerica West Africa East Africa HKH Lower Mekong
SERVIR-Mekong: Demand Driven Activity Regional Needs and Outcomes Request for Technical Assistance Needs Assessment National Opportunities Cambodia, Lao PDR, Burma, Thailand, Vietnam
Regional Land Cover Monitoring System (RLCMS) Objectives To develop a unified regional (satellite-based) land cover monitoring system to serve user defined objectives To produce annual land cover maps of the Lower Mekong Countries: Cambodia, Lao PDR, Myanmar, Thailand, and Vietnam for 2000 to 2015, To provide an analysis tool that can easily be used for different time-series.
Regional Land Cover Monitoring System Approach and Expected outcome A robust remote sensing cloud- based system that Is developed collaboratively Produces consistent products at regular intervals Unified high-quality land cover maps that serves the expressed needs of multiple users in the region Uses transparent, well documented, open source approach Includes quality control / quality assurance methodology that integrates information from multiple sources Map products improved policy, planning and decision making among a broad range of sub-national, national and regional users. A system leveraging collaboration and partnership in land cover monitoring in the Lower Mekong Region.
Production and operational framework of the RLCMS Establish Land Cover Typology 2 1 Develop Land Cover Algorithms Mangrove Grassland/Shrubland Bare Land Forest Water Wetland Agriculture Urban Rice 4 Define Spatial and Temporal Data Requirements 5 4 3 Define End User Objectives 5 Land Cover Assemblage Reference Data Field Imagery Obs. Obs. 6 Land Cover Map Production 7 Accuracy Assessment 6
Establish Land Cover Typology 2 1 Define Spatial and Temporal Data Requirements Define End User Objectives
Establish Land Cover Typology 2 1 Develop Land Cover Algorithms Mangrove Grassland/Shrubland Bare Land Forest Water Wetland Agriculture Urban Rice 4 Define Spatial and Temporal Data Requirements 4 3 Define End User Objectives Reference Data Field Obs. Imagery Obs.
BIOPHYSICAL Definitions Forest General Type IPCC Definitions Possible Biophysical Attributes Difficulty Forest Land (IPCC) This category includes all land with woody vegetation consistent with thresholds used to define forest land in the national GHG inventory, sub-divided into managed and unmanaged, and also by ecosystem type as specified in the IPCC Guidelines 3. It also includes systems with vegetation that currently fall below, but are expected to exceed, the threshold of the forest land category. % Canopy cover Easy Vegetation height class Hard health, vigor Easy Seasonal Greenness Medium
BUILD / IMPORT DATA PRIMITIVES Canopy Cover Canopy Height Forest Water
Field Reference High Resolution Image Reference
Establish Land Cover Typology 2 1 Develop Land Cover Algorithms Mangrove Grassland/Shrubland Bare Land Forest Water Wetland Agriculture Urban Rice 4 Define Spatial and Temporal Data Requirements 5 4 3 Define End User Objectives 5 Land Cover Assemblage Reference Data Field Obs. Imagery Obs.
Canopy Cover Canopy Height Forest Rule Set Land Cover Map Urban Agriculture
Establish Land Cover Typology 2 1 Develop Land Cover Algorithms Mangrove Grassland/Shrubland Bare Land Forest Water Wetland Agriculture Urban Rice 4 Define Spatial and Temporal Data Requirements 5 4 3 Define End User Objectives 5 Land Cover Assemblage Reference Data Field Obs. Imagery Obs. 6 Accuracy Assessment 6
Establish Land Cover Typology 2 1 Develop Land Cover Algorithms Mangrove Grassland/Shrubland Bare Land Forest Water Wetland Agriculture Urban Rice 4 Define Spatial and Temporal Data Requirements 5 4 3 Define End User Objectives 5 Land Cover Assemblage Reference Data Field Obs. Imagery Obs. 6 Land Cover Map Production 7 Accuracy Assessment 6
RLCMS components Google Earth Engine Google AppSpot Collect Earth Google Earth Engine (GEE) performs image analysis based on land cover type algorithm. A user-friendly site/app on Google s appspot exposes the application to users An open-source high resolution image viewing and reference data collection system (MAPCHA/ Collect Earth).
RLCMS Partnership System Development Capacity building Consultation End user operation
RLCMS progress and update
Capacity building and Collaboration Two RCLMS workshops (>100 participants) Three Google Earth Engine ToT trainings in Thailand, Cambodia, Viet Nam ( >90 participants; ) Training materials is developed and translated to national languages
Collaborative effort in RLCMS System development Land cover typology development. Land cover algorithm developed GEE scripts for land cover types (primitives) drafted NDVI Canopy Cover percentage Tree Height Developing Online reference collection system (MAPCHA/Collect Earth)
Next steps Finalizing land cover primitive classes for assembly into final classes Accuracy assessment Advance GEE training RCLMS workshops to introduce initial result Map production dissemination
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