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14 Aug 2020 | SERVIR-HKH

Mapping land cover

The Regional Land Cover Monitoring System (RLCMS) addresses challenges in land management – including difficulties in accessing data, lack of transparency in data collection methodologies, inconsistencies in land cover classification, and limited financial and staff resources – by annually generating high-resolution land cover data for the HKH region.

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High-resolution annual land cover data for the HKH region

Mapping land cover

The Regional Land Cover Monitoring System (RLCMS) addresses challenges in land management – including difficulties in accessing data, lack of transparency in data collection methodologies, inconsistencies in land cover classification, and limited financial and staff resources – by annually generating high-resolution land cover data for the HKH region. The system uses freely available remotesensing data and a cloud-based machine learning architecture to generate land cover maps through a harmonized and consistent regional classification system.

In 2019, we partnered with agencies in Afghanistan, Bangladesh, Myanmar, and Nepal to customize the RLCMS further as per national requirements, and conducted multiple trainings on the system’s development and use. In Nepal, the Forest Research and Training Centre (FRTC) has taken ownership, having allocated its own resources for field validation of the land cover data before final release. The system will be adopted for official reporting on forest cover and provide a basis for other forest-related applications such as national eco-region mapping. In Bangladesh, after a successful pilot in the Chittagong Hill Tracts the Bangladesh Forest Department (BFD) has rolled out the system for the entire country.

Early involvement of FRTC and BFD staff in the co-development of the system has helped build institutional capacities so that they can take the activity forward independently with limited technical backstopping from ICIMOD.

The RLCMS was developed through a joint collaboration among ICIMOD, Asian Disaster Preparedness Center (ADPC), United States Forest Services (USFS), and SilvaCarbon.

The system uses freely available remote-sensing data and a cloud-based machine learning architecture to generate land cover maps through a harmonized and consistent regional classification system.

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