Related items
Showing items related by metadata.
-
Poster, bannerSoil mapping in Lao PDR 2019
Also available in:
No results found.Soil 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 soil 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. The soil map based on the World Soil Classification Systems is ongoing. The samples were finalized in the provinces of Phongsaly, Bokeo, Luangnamtha, Xayabouly, Oudomxay, Luangprabang, Houaphanh, Champasack, Salavan, Xekong, and Attapue. All soil profiles records name of the profile, soil depth, date, land use type, complete soil description, GPS coordinates, photos of the soil profiles, and description of the land use situation. The laboratory analysis is ongoing. So far, half of the soil samples were analyzed and the map will be ready the end of 2019. -
Book (stand-alone)JORDAN - Land Cover Atlas 2019
Also available in:
No results found.The Jordanian Land Cover Database and Atlas were developed under the Regional Food Security Analysis Network (RFSAN) project. The main objective of the project is to increase and improve provision of goods and services from agriculture, forestry and fisheries in a sustainable manner as well as to increase the understanding of the bio-physical conditions of land in Jordan. The Land Cover Atlas of the Hashemite Kingdom of Jordan provides information on the land cover distribution by sub-national administrative boundaries (governorates and districts) provided by the Royal Geographic Centre (RJGC). The Land Cover Database is compliant with the ISO\FAO standard (ISO 19144-2:2012) based on the land cover classification system (LCCS): Land Cover Meta Language (LCML). LCML was implemented to support the standardization and integration of a national land cover classification system across the world. It provides a set of standard diagnostic attributes that are independent of the scale of interpretation. Its use advocates for a more transparent and comparable way of reporting land cover information. The LCML land cover legend was designed with the software LCCSv3. The main data source includes multispectral Sentinel-2 imagery at 10 m of spatial resolution acquired from April to November 2016 and ancillary georeferenced data (land cover and land use map, vegetation cover, soil map) obtained from different institutions. Sentinel-2 imagery were pre-processed and mosaicked to provide a temporal sequence of free-cloud, calibrated images. Then, an Object-Based Image Analysis workflow was applied to segment the images into homogeneous polygons, that were interpreted according to their spectral, texture and shape characteristics supported by vegetation indices and ancillary datasets. Post-processing finally removed incoherent classifications, clipping and dissolving polygons to official boundaries. The final database comprises 1 million polygons classified according to the LCCS Legend distinguished into 34 classes (23 aggregated classes). The statistical analysis of land cover aggregated class distribution is organized into two sections: • National Land Cover Data Base (LCDB). • LCDB by governorates. This work represents a substantial contribution to understanding land cover and land processes in the Hashemite Kingdom of Jordan and provides valuable baseline data to further monitor land changes in the future. -
Brochure, flyer, fact-sheetState of the art agricultural land cover maps for the Lao People's Democratic Republic
Part of the Land Resources Information Management System (LRIMS)
2021Also available in:
No results found.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.
Users also downloaded
Showing related downloaded files
No results found.