Assessment and monitoring of land cover dynamics are essential for the sustainable management of natural resources, environmental protection, and food security. During several consultations organized by ICIMOD under its SERVIR Hindu Kush Himalaya (SERVIR-HKH)Initiative, stakeholders identified the need for a land cover monitoring system that produces frequent, high-quality land cover maps using a consistent regional classification scheme compatible with national needs. To address these needs, SERVIR-HKH undertook the development of the Regional Land Cover Monitoring System (RLCMS) as one of its services.
The service aims to develop an operational system for annual land cover mapping and change analysis using a standard methodology and consistent datasets, and is a collaborative effort between SERVIR-HKH at ICIMOD and SERVIR-Mekong at the Asian Disaster Preparedness Center, with additional support from the United States Forest Services (USFS) and SilvaCarbon. The RLCMS uses the architecture of a state-of-the-art cloud-based remote-sensing technology, such as Google Earth Engine (GEE), and a standard set of input data sources to generate high-quality land cover maps on a regular basis at the regional and national levels. The National Land Cover Monitoring Systems (NLCMS) for Afghanistan, Myanmar, and Nepal will generate consistent land cover data at the national level, which can also be harmonized at the regional level in the RLCMS.
In Bangladesh, the Bangladesh Forest Department (BFD) has been analyzing and using land cover data for the national forest inventory, estimation of forest reference emission levels, and other forest management purposes.
The training on developing an NLCMS using Google Earth Engine for forest and land resources management is aimed at building capacity of BFD staff in the use of new and emerging technologies.
The specific objectives of the training workshop are:
- To understand the concept of national land cover monitoring system through recent trends, techniques, and earth observation data products
- To gain hands-on experience using the GEE platform for the implementation a national land cover monitoring system
- To understand procedures in creating land cover primitives and primitive classifiers
- To design, test, and refine procedures for assembling data primitives and other information sources into explicit land cover classes
- To specify appropriate accuracy assessment procedures and products that will provide critical context to users of the data