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FRA 2000 - Forest cover mapping & monitoring with NOAA-AVHRR & other coarse spatial resolution sensors







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    Article
    Using Standardized Time Series Land Cover Maps to Monitor the SDG Indicator “Mountain Green Cover Index” and Assess Its Sensitivity to Vegetation Dynamics 2021
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    SDG indicators are instrumental for the monitoring of countries’ progress towards sustainability goals as set out by the UN Agenda 2030. Earth observation data can facilitate such monitoring and reporting processes, thanks to their intrinsic characteristics of spatial extensive coverage, high spatial, spectral, and temporal resolution, and low costs. EO data can hence be used to regularly assess specific SDG indicators over very large areas, and to extract statistics at any given subnational level. The Food and Agriculture Organization of the United Nations (FAO) is the custodian agency for 21 out of the 231 SDG indicators. To fulfill this responsibility, it has invested in EO data from the outset, among others, by developing a new SDG indicator directly monitored with EO data: SDG indicator 15.4.2, the Mountain Green Cover Index (MGCI), for which the FAO produced initial baseline estimates in 2017. The MGCI is a very important indicator, allowing the monitoring of the health of mountain ecosystems. The initial FAO methodology involved visual interpretation of land cover types at sample locations defined by a global regular grid that was superimposed on satellite images. While this solution allowed the FAO to establish a first global MGCI baseline and produce MGCI estimates for the large majority of countries, several reporting countries raised concerns regarding: (i) the objectivity of the method; (ii) the difficulty in validating FAO estimates; (iii) the limited involvement of countries in estimating the MGCI; and (iv) the indicator’s limited capacity to account for forest encroachment due to agricultural expansion as well as the undesired expansion of green vegetation in mountain areas, resulting from the effect of global warming. To address such concerns, in 2020, the FAO introduced a new data collection approach that directly measures the indicator through a quantitative analysis of standardized land cover maps (European Space Agency Climate Change Initiative Land Cover maps—ESA CCI-LC). In so doing, this new approach addresses the first three of the four issues, while it also provides stronger grounds to develop a solution for the fourth issue—a solution that the FAO plans to present to the Interagency and Expert Group on SDG Indicators (IAEG-SDG) at its autumn 2021 session.
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    Brochure, flyer, fact-sheet
    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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    Satellite remote sensing-based forest resources assessment methods for effective management and sustainable development of forests by generation of information on forests and trees outside forest cover
    XV World Forestry Congress, 2-6 May 2022
    2022
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    Satellite based remote sensing methods have proved to be an effective and scientifically proven method for managing and conserving forest data and resources at periodic time intervals. The forest resources monitoring methods provide useful data to forest managers for sustainable forest management at different scale and forest management units. Over the years the scientific management of forest have been a subject globally discussed incorporating the role of environmentalist, conservationist and communities associated with the forest. It has been an unhidden fact that forests have suffered tremendous pressure in developing countries on the pretext of development. It is through effective monitoring and communication of forest information and knowledge that the concerned provincial governments are forced to take remedial measures for protecting the forests. Apart from the government owned forests, termed as Recorded Forest Areas(RFA) in India, Trees outside forests(TOF) are well acknowledged as an important component of forest resources. The ToF, which basically exist as block, linear and scattered plantations on earth are captured using LISS-III sensor of Indian Remote Sensing Satellite. For the national level scale mapping, all patches of area 1hectare and above are considered for estimation. For mapping of ToF patches of size between 0.1-1hectare, high resolution data from LISSIV sensor(5.8metres resolution) is analyzed. It has been now a well-established fact that trees outside RFAs, although in small proportion, contribute significantly to forest conservation and meeting the demand of people towards minor forest produce, firewood etc. The exercise on forest change detection using a hybrid method, is effective in identification of significant forest change. The assessment of forests and ToFs using satellite data and advance image processing tools may be helpful in effective management and long term sustainability of forests in developing countries. Keywords: [Recorded Forest Area, Trees Outside Forest, National Forest Inventory, FSI, Neural Network, Machine Learning] ID: 3622277

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