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标题：Towards An Understanding of Uncertainty In Greenhouse Forest Assessments
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作者：S.D. Jones ; G. Richards ; K. Lowell 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B7
页码：747-752
出版社：Copernicus Publications
摘要：Australia's National Carbon Accounting System provides information on land-based sources and sinks of greenhouse gases to fulfil international reporting obligations under the Kyoto Protocol, as well as providing annual estimates to Australia's National Greenhouse Gas Inventory. Manifold is an understanding of change in forest area: afforestation, reforestation and deforestation events. Using a thirty-year archive of Landsat imagery (1972-2002), a set of 12 continent-wide land cover maps, and associated change layers for the 11 intervals was created. A continuous probability network was then used to estimate the probability of a pixel belonging to Forest or Non-Forest classes for each of these 12 dates. These Forest/Non-forest classifications, from successive dates, were then compared on a pixel-by-pixel basis to identify areas of No Change (Forest), No Change (Non-forest), Deforestation, and Regrowth. To gain an understanding of the uncertainty in these change maps, and so that improvements could be made in the mapping technique, a fuzzy evaluation methodology was developed and implemented. A network of ~300 aerial photographs was co-registered to the database and more than ~12,000 points were compared using photo interpretation to validate the matching pixels on each respective change map. The classes used for the photographic interpretation were Definitely Forest, Probably Forest, Unsure, Probably Non-forest, and Definitely Non-forest. Australia-wide the error rates were very low. The 'definite' errors for forest were ~2% and 'definite' errors for non-forest ~4%. Hotspots of uncertainty in forest change errors did emerge however in some forested areas (up to 5.7%). To improve the temporal classification process, a performance analysis was undertaken that cross- referenced reported change in forest area with reported errors in classification. This process will be repeated with each continent- wide land cover map update to provide progressive improvement in the change maps
关键词：Remote Sensing; Vegetation; Land Cover; Analysis; Monitoring; Global-Environmental-Databases