Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully...Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.展开更多
This article takes a forestry company in Heyuan City of Guangdong Province for example,to research the FMUs(Forest Management Units)-oriented subcompartment data sharing technology,and realize the software platform ba...This article takes a forestry company in Heyuan City of Guangdong Province for example,to research the FMUs(Forest Management Units)-oriented subcompartment data sharing technology,and realize the software platform based on Visual Basic development environment.The software platform can perform the normalization processing,cadastral number coding,data extraction,data updating and other pragmatic operations on subcompartment data,so as to achieve the subcompartment data sharing between forest management unit and Guangdong forest resources center.展开更多
森林景观是以森林生态系统为主体而构成的景观，其分类是确定景观构成要素及空间分布格局（陆元昌等，2005）。但景观异质性依时间和空间尺度变化而存在差异，在大尺度下空间变异中的“噪声”成分在另一种较小尺度下表现为“结构性”成...森林景观是以森林生态系统为主体而构成的景观，其分类是确定景观构成要素及空间分布格局（陆元昌等，2005）。但景观异质性依时间和空间尺度变化而存在差异，在大尺度下空间变异中的“噪声”成分在另一种较小尺度下表现为“结构性”成分，一个景观单元在小尺度上呈现异质性（邬建国，2000），而在大尺度上可能具有均质性（赵玉涛等，2002）。不同尺度景观要素具有不同程度均质性（Goberna et al．，2007）。森林景观分类是一个基于土地利用类型、植被和环境条件等因素而将其划分为多级分类体系的科学过程（朱耀军等，2011）。因此，森林景观分类具有尺度特性，不同尺度下森林景观分类的构成要素及构成要素等级不同。展开更多
基金supported by the National Technology Extension Fund of Forestry,Forest Vegetation Carbon Storage Monitoring Technology Based on Watershed Algorithm ([2019]06)Fundamental Research Funds for the Central Universities (No.PTYX202107).
文摘Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.
基金part of the project "Researches of Southern China's Forestry Strategy" and "Improvement of the Forest Resources Monitoring System of China" Funded by the State Forestry Administration of China"Researches of Sustainable Management and Demonstration of Urban Forests" Funded by the Bureau of Forestry and Gardening of Guangzhou Municipality
文摘This article takes a forestry company in Heyuan City of Guangdong Province for example,to research the FMUs(Forest Management Units)-oriented subcompartment data sharing technology,and realize the software platform based on Visual Basic development environment.The software platform can perform the normalization processing,cadastral number coding,data extraction,data updating and other pragmatic operations on subcompartment data,so as to achieve the subcompartment data sharing between forest management unit and Guangdong forest resources center.
文摘森林景观是以森林生态系统为主体而构成的景观，其分类是确定景观构成要素及空间分布格局（陆元昌等，2005）。但景观异质性依时间和空间尺度变化而存在差异，在大尺度下空间变异中的“噪声”成分在另一种较小尺度下表现为“结构性”成分，一个景观单元在小尺度上呈现异质性（邬建国，2000），而在大尺度上可能具有均质性（赵玉涛等，2002）。不同尺度景观要素具有不同程度均质性（Goberna et al．，2007）。森林景观分类是一个基于土地利用类型、植被和环境条件等因素而将其划分为多级分类体系的科学过程（朱耀军等，2011）。因此，森林景观分类具有尺度特性，不同尺度下森林景观分类的构成要素及构成要素等级不同。
