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Book (stand-alone)JORDAN - Land Cover Atlas 2019
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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. -
BookletLaunch of the land characterization system (LCHS) software
Report of the proceedings, 23 June 2023
2023Also available in:
No results found.The development and adoption of land cover data standards are critical for supporting global efforts to address the challenges related to climate change, sustainable development, and disaster risk reduction. By providing consistent and interoperable land cover data, these standards can support decision-making processes at all levels, from local to global, and help to ensure that progress towards the UN Sustainability Goals is monitored and reported in a transparent and accountable manner.To support the process, FAO, and the National Research Council of Italy - Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (CNR-STIIMA), developed a new software “land characterization system software (LCHS)” version 1.0 based on international standard i.e. ISO 19144-2 land cover meta language. The LCHS software is designed to implement a system that can evolve with the ISO standard itself. This will guide and facilitate the land cover user community to prepare their land cover classes based on international standards. -
Poster, bannerLand cover mapping in Lao PDR 2019
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No results found.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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