Land cover mapping in Lao PDR
No hay miniatura disponible
Fecha
Autores
Título de la revista
ISSN de la revista
Título del volumen
Editor
FAO ;
Resumen
Descripción
Cropland cover mapping using machine learning in Lao PDR
The GIS unit of the Department of Agricultural Land Management (DALaM) of the Ministry of Agriculture and Forest of Lao PDR is develop a new national level cropland cover map. Working through the project “Strengthening Agro-climatic Monitoring and Information Systems (SAMIS) to improve adaptation to climate change and food security in Lao PDR” funded by GEF and implemented by FAO, the activity is inserted in a broader exercise focusing on developing a national level decision making schemes for long term land planning.
Filtered composition and mosaicking is run using the SEPAL, a cloud computing-based platform for autonomous land monitoring using remotely-sensed data. It allows users to access powerful cloud-computing resources to query, access and process satellite data quickly and efficiently for creating advanced analyses. The cropland cover map is developed using the ESA Sentinel 2A sensor and classified using the Land Cover Classification System (LCCS), the ISO standard (ISO 19144-1) classification system developed by FAO and UNEP.