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作 者:Lesika Basalumi Lesika Basalumi Charles Joseph Kilawe Ernest William Mauya
机构地区:[1]Department of Ecosystems and Conservation, Sokoine University of Agriculture, Morogoro, Tanzania [2]Department of Forestry and Range Resources, Gaborone, Botswana [3]Forest Engineering and Wood Science, Sokoine University of Agriculture, Morogoro, Tanzania
出 处:《Open Journal of Forestry》2018年第3期429-438,共10页林学期刊(英文)
摘 要:Quantification of the above ground carbon stock (AGC) is important in sustainable forest management and policy advice on climate change mitigation. Traditional ground vegetation survey methods have been used to provide data for estimation of AGC stock but constrained by inadequate time and often too costly. Remote sensing when combined with few ground collected data has the potential of improving forest resource assessment even though this opportunity has not well been utilised. In this study, we mapped AGC through combination of ground survey data collected from 51 permanent sapling plots with Normalized Difference Vegetation Index (NDVI) derived from Landsat 5 Thematic Mapper image. Linkage of the two data sources was made during a training stage of supervised classification. The overall classification accuracy was 98%, suggesting that reliable estimate of AGC for a large area can be made through combination of medium resolution satellite imagery and few samples from the ground.Quantification of the above ground carbon stock (AGC) is important in sustainable forest management and policy advice on climate change mitigation. Traditional ground vegetation survey methods have been used to provide data for estimation of AGC stock but constrained by inadequate time and often too costly. Remote sensing when combined with few ground collected data has the potential of improving forest resource assessment even though this opportunity has not well been utilised. In this study, we mapped AGC through combination of ground survey data collected from 51 permanent sapling plots with Normalized Difference Vegetation Index (NDVI) derived from Landsat 5 Thematic Mapper image. Linkage of the two data sources was made during a training stage of supervised classification. The overall classification accuracy was 98%, suggesting that reliable estimate of AGC for a large area can be made through combination of medium resolution satellite imagery and few samples from the ground.
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