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Options and recommendations for a global remote sensing survey of forests

Global Forest Resources Assessment 2010









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    Global forest land-use change 1990-2005: Initial results from a global remote sensing survey 2005
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    The use of satellite imagery can substantially improve knowledge on changes in forest area as a result of deforestation, afforestation and natural forest expansion. This summary outlines some of the initial findings on forest land use and land-use change of a global remote sensing survey carried out to complement the country reporting process as part of the Global Forest Resources Assessments led by the Food and Agriculture Organization of the United Nations (FAO).
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    Book (stand-alone)
    Global forest land-use change from 1990 to 2010: an update to a global remote sensing survey of forests 2017
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    Monitoring the Earth’s global forest resources is important. This note presents the latest results for the extent of forest-land and changes in forest land use for the time period 1990 to 2010. The work is the result of a partnership between FAO, its member countries and the European Commission Joint Research Centre (JRC).
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    Book (stand-alone)
    USE OF NOAA REMOTE SENSING DATA FOR ASSESSMENT OF THE FOREST AREA OF LIBERIA 1993
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    In the context of developing a practicable and cost effective method for obtaining a country’s forest area by remote sensing, the computer processing of NOAA AVHRR HRPT data covering Liberia was investigated. The only cloud-free scene then recorded turned out to be severely and unevenly affected by atmospheric haze. To mitigate the effects of this, the country was divided into six areas (strata) of more uniform haze conditions. Pixel DN values were obtained for forest and adjoining formations on transects within each stratum, for the first four AVHRR channels and three transforms: NDVI (2-1/2 + 1), IND3 (3-2/3 + 2), IND4(4-2/4 + 2). After analyzing the transects and comparing them with the available reference data (a mixture of large scale colour composite Landsat TM and MSS images for 1989 and 1986 respectively), channels 2, 3, 4 and IND3 were retained for processing. This was done by applying three methods to each stratum - thresholding, maximum likelihood classification using cluste ring signatures (hybrid), m.l.c. using training area signatures - and directed at separating the 5 main classes distinguishable on the Landsat images: Closed forest, Disturbed forest, Shifting cultivation and regrowth, Cultivation and Other.

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