The effect of reforestation on carbon sequestration has been extensively studied but there is less understanding of the changes that stand age and vegetation types have on changes in biomass carbon and soil organic ca...The effect of reforestation on carbon sequestration has been extensively studied but there is less understanding of the changes that stand age and vegetation types have on changes in biomass carbon and soil organic carbon(SOC)after reforestation.In this study,150 reforested plots were sampled across six provinces and one municipality in the Yangtze River Basin(YRB)during 2017 and 2018 to estimate carbon storage in biomass and soil.The results illustrate that site-averaged SOC was greater than site-averaged biomass carbon.There was more carbon sequestered in the biomass than in the soil.Biomass carbon accumulated rapidly in the initial 20 years after planting.In contrast,SOC sequestration increased rapidly after 20 years.In addition,evergreen species had higher carbon density in both biomass and soil than deciduous species and economic species(fruit trees).Carbon sequestration in evergreen and deciduous species is greater than in economic species.Our findings provide new evidence on the divergent responses of biomass and soil to carbon sequestration after reforestation with respect to stand ages and vegetation types.This study provides relevant information for ecosystem management as well as for carbon sequestration and global climate change policies.展开更多
Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques which is the discove...Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques which is the discovery of interesting rela-tionships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applications such as pattern recognition, data analysis, and image processing and so forth. However most of the well-known clustering algorithms have some drawbacks which will be presented later when ap-plied in large spatial databases. To overcome these limitations, in this paper we propose a robust spatial clustering algorithm named NSCABDT (Novel Spatial Clustering Algorithm Based on Delaunay Triangulation). Delaunay dia-gram is used for determining neighborhoods based on the neighborhood notion, spatial association rules and colloca-tions being defined. NSCABDT demonstrates several important advantages over the previous works. Firstly, it even discovers arbitrary shape of cluster distribution. Secondly, in order to execute NSCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that NSCABDT does not require so much CPU processing time. Finally it handles efficiently outliers.展开更多
基金The work was supported by the Research Grants Council of the Hong Kong Special Administrative Region,China[grant number 12305116].
文摘The effect of reforestation on carbon sequestration has been extensively studied but there is less understanding of the changes that stand age and vegetation types have on changes in biomass carbon and soil organic carbon(SOC)after reforestation.In this study,150 reforested plots were sampled across six provinces and one municipality in the Yangtze River Basin(YRB)during 2017 and 2018 to estimate carbon storage in biomass and soil.The results illustrate that site-averaged SOC was greater than site-averaged biomass carbon.There was more carbon sequestered in the biomass than in the soil.Biomass carbon accumulated rapidly in the initial 20 years after planting.In contrast,SOC sequestration increased rapidly after 20 years.In addition,evergreen species had higher carbon density in both biomass and soil than deciduous species and economic species(fruit trees).Carbon sequestration in evergreen and deciduous species is greater than in economic species.Our findings provide new evidence on the divergent responses of biomass and soil to carbon sequestration after reforestation with respect to stand ages and vegetation types.This study provides relevant information for ecosystem management as well as for carbon sequestration and global climate change policies.
文摘Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques which is the discovery of interesting rela-tionships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applications such as pattern recognition, data analysis, and image processing and so forth. However most of the well-known clustering algorithms have some drawbacks which will be presented later when ap-plied in large spatial databases. To overcome these limitations, in this paper we propose a robust spatial clustering algorithm named NSCABDT (Novel Spatial Clustering Algorithm Based on Delaunay Triangulation). Delaunay dia-gram is used for determining neighborhoods based on the neighborhood notion, spatial association rules and colloca-tions being defined. NSCABDT demonstrates several important advantages over the previous works. Firstly, it even discovers arbitrary shape of cluster distribution. Secondly, in order to execute NSCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that NSCABDT does not require so much CPU processing time. Finally it handles efficiently outliers.
