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Soil mapping in Lao PDR









​FAO. 2019. Soil mapping in Lao PDR. Ventiane. 1 p. CC-BY-SA-NC 3.0 IGO


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    Land cover mapping in Lao PDR 2019
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    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.
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    State of the art agricultural land cover maps for the Lao People's Democratic Republic​
    Part of the Land Resources Information Management System (LRIMS)
    2021
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    The leaflet presents the activities of the second component of the project “Strengthening Agro-climatic Monitoring and Information Systems (SAMIS) to improve adaptation to climate change and food security in Lao PDR”. In collaboration with The Department of Agricultural Land Management (DALaM) under the Ministry of Agriculture (MAF) has, with financial support of FAO Laos and technical support from International FAO experts, produced the first national agricultural land cover map in the country. It has been generated using a random forest machine learning approach to identify different land uses from satellite imagery and is in both, technical standard and accuracy, state of the art. The map includes major production systems of Lao PDR, including shifting cultivation. In its first released version, the following land cover classes are depicted: paddy rice, annual crops, steep slope agriculture (shifting agriculture), maize, cassava, sugarcane, tea plantations, coffee plantations, orchards and other plantations, sparse natural vegetation, dense natural vegetation, bare areas, built-up areas, and water surfaces. The pixel resolution of the map is 10m, while for temporal resolution images across the whole year of analysis are used. It is calibrated with 2,740 field observation data and has currently an estimated error of 10%, the acceptable norm based on FAO expertise.
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    Brochure, flyer, fact-sheet
    Soil mapping for the Lao People's Democratic Republic
    Part of the Land Resources Information Management System (LRIMS)
    2021
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    The leaflet presents the activities of the second component of the project “Strengthening Agro-climatic Monitoring and Information Systems (SAMIS) to improve adaptation to climate change and food security in Lao PDR”. The Department of Agricultural Land Management (DALaM) under the Ministry of Agriculture (MAF) has, with financial support of FAO Laos and financial support by GEF, produced the national soil map in the country. Soil mapping is the process of delineating natural bodies of soils, classifying and grouping the delineated soils into map units, and capturing soil property information for interpreting and depicting soil spatial distribution on a map and containing a database of soil properties (soil pH, textures, organic matter, depths of horizons, etc.). It is typically the end result of a soil survey inventory. The map will be used by DALaM for calculating suitability and crop productivity today and in the future. The map will feed into the Land Resource Information Management System (LRIMS), a support tool for socio-economic and agricultural policy planning that provides crop modelling for the past and the future.

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