According to the factors that confirm the shape of surface, it is classified into two categories: arc surface and curve surface The method to confirm the category of surfaces and the plotting methods are discussed in...According to the factors that confirm the shape of surface, it is classified into two categories: arc surface and curve surface The method to confirm the category of surfaces and the plotting methods are discussed in this paper, which provide guidance for parts plotting.展开更多
Data from vegetation succession study is usually a matrix of plot × species × time.It is difficult to analyse this kind of data at present.A simple technique,clustercentering ordination is described in this ...Data from vegetation succession study is usually a matrix of plot × species × time.It is difficult to analyse this kind of data at present.A simple technique,clustercentering ordination is described in this paper,which can be used to analyse such a matrix directly.The data of 5 permanent plots in 7 years of inlands and grasslands in Kootwijki,the Netherlands,is analysed using this method as an application example.The results obviously illustrate the trend,direction and speedof community succession and can be easily interbrated.This suggests that clustercentering ordination is an effective and time-saving technique in study of vegetation succession.展开更多
The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied...The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.展开更多
文摘According to the factors that confirm the shape of surface, it is classified into two categories: arc surface and curve surface The method to confirm the category of surfaces and the plotting methods are discussed in this paper, which provide guidance for parts plotting.
文摘Data from vegetation succession study is usually a matrix of plot × species × time.It is difficult to analyse this kind of data at present.A simple technique,clustercentering ordination is described in this paper,which can be used to analyse such a matrix directly.The data of 5 permanent plots in 7 years of inlands and grasslands in Kootwijki,the Netherlands,is analysed using this method as an application example.The results obviously illustrate the trend,direction and speedof community succession and can be easily interbrated.This suggests that clustercentering ordination is an effective and time-saving technique in study of vegetation succession.
文摘The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.