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Land cover assessment and consideration of its dynamics can underpin sustainable natural resources management, environmental protection, and food security. During multiple stakeholder consultations carried out by the International Centre for Integrated Mountain Development (ICIMOD) under its SERVIR Hindu Kush Himalaya (SERVIR-HKH) Initiative, stakeholders identified the need for a land cover monitoring system that produces high-quality land cover maps using a consistent regional classification scheme compatible with national needs. To address these needs, SERVIR-HKH undertook the Regional Land Cover Monitoring System (RLCMS) as one of its services.
ICIMOD, Kathmandu, Nepal
18 November 2019 to
22 November 2019
The service aims to develop an operational system for annual land cover mapping and change analysis using a standard methodology and consistent datasets. It 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 cloud- based, state-of-the-art remote sensing science and technology, such as Google Earth Engine, 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 will generate consistent land cover data at the national level which can also be harmonized at the regional level in the RLCMS.In Afghanistan, government agencies use land cover data for agriculture, the national forest inventory, and other forest management purposes. National-level land cover assessments were carried out in 1972, 1993, and 2010 using satellite data from different sensors; each assessment used different land cover classes/legends. No land cover assessments at the national level have been conducted.
In this aspect, the SERVIR-HKH Initiative and partners are now convening a production workshop focused on building team capacity and refining algorithms for deriving key land cover “primitive” elements, devising methods for assembling the data primitives into target land cover classes, and formulating explicit accuracy assessment procedures. The five-day event will train partner teams in the use and implementation of primitive development, data assembly algorithms, accuracy assessment procedures and implications, and documentation of how these elements fit within the overall system.