With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective m...With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.展开更多
This paper proposes a hierarchical word domain assignment algorithm to automatically build domain dictionaries from Machine-Readable Dictionary(MRD).The process for word domain assignment can be divided into three ste...This paper proposes a hierarchical word domain assignment algorithm to automatically build domain dictionaries from Machine-Readable Dictionary(MRD).The process for word domain assignment can be divided into three steps:1) Hierarchical structure constructing;2) Classifier training;3) Word domain assigning.Compared with the traditional methods,the hierarchical word domain assignment algorithm enhances the accuracy of word domain assignment while reducing human efforts on collecting corpus.Experiments on WordNet 2.0 show that 62.53% of the first domain labels are matched with the WordNet Domains 3.0 by using gloss-based word domain assignment,and the performance can be further improved by utilizing the hierarchical relationships among the domain sets.展开更多
A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lowe...A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lower layer relationalintegrity is presented after we analyze and eliminate the covert channels caused by the database integrity.Two SQL statements are extended to process polyinstantiation in the multilevel secure environment.The system based on the multilevel secure relation hierarchical data model is capable of integratively storing and manipulating complicated objects ( e.g. , multilevel spatial data) and conventional data ( e.g. , integer, real number and character string) in multilevel secure database.展开更多
Background： Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can...Background： Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis （FIA） plots. Methods： Each plot had three forest community type labels： （1） ＂FIA＂ types were assigned by the FIA program using a supervised method; （2） ＂USNVC＂ types were assigned via a key based on the U.S. National Vegetation Classification; （3） ＂empirical＂ types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. Results： The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The Iongleaf pine forest community type stood out as the exception： it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as Ioblolly pine types, indicator species metrics were generally high for the USNVC, suggesting that LJSNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables. Conclusions： The classifications have similarities and differences that reflect their differing approaches and Dbjectives. They are most consistent for forest community types that occur in a relatively narrow range of Invironmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, guggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers.展开更多
基金supported by the Beijing Natural Science Foundation(No.JQ20021)the National Natural Science Foundation of China(Nos.61922013,61421001 and U1833203)the Remote Sensing Monitoring Project of Geographical Elements in Shandong Yellow River Delta National Nature Reserve。
文摘With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.
基金supported by the BIT Technology Innovation Program "cloud computing-oriented intelligent processing theory and method of massive language information"underGrant No.3070012231102the BIT Fundamental Research Projects under Grant No.3070012210917
文摘This paper proposes a hierarchical word domain assignment algorithm to automatically build domain dictionaries from Machine-Readable Dictionary(MRD).The process for word domain assignment can be divided into three steps:1) Hierarchical structure constructing;2) Classifier training;3) Word domain assigning.Compared with the traditional methods,the hierarchical word domain assignment algorithm enhances the accuracy of word domain assignment while reducing human efforts on collecting corpus.Experiments on WordNet 2.0 show that 62.53% of the first domain labels are matched with the WordNet Domains 3.0 by using gloss-based word domain assignment,and the performance can be further improved by utilizing the hierarchical relationships among the domain sets.
文摘A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lower layer relationalintegrity is presented after we analyze and eliminate the covert channels caused by the database integrity.Two SQL statements are extended to process polyinstantiation in the multilevel secure environment.The system based on the multilevel secure relation hierarchical data model is capable of integratively storing and manipulating complicated objects ( e.g. , multilevel spatial data) and conventional data ( e.g. , integer, real number and character string) in multilevel secure database.
基金Funding for this work came from the USDA Forest Service Resources Planning Act Assessment,via an agreement with North Carolina State University
文摘Background： Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis （FIA） plots. Methods： Each plot had three forest community type labels： （1） ＂FIA＂ types were assigned by the FIA program using a supervised method; （2） ＂USNVC＂ types were assigned via a key based on the U.S. National Vegetation Classification; （3） ＂empirical＂ types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. Results： The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The Iongleaf pine forest community type stood out as the exception： it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as Ioblolly pine types, indicator species metrics were generally high for the USNVC, suggesting that LJSNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables. Conclusions： The classifications have similarities and differences that reflect their differing approaches and Dbjectives. They are most consistent for forest community types that occur in a relatively narrow range of Invironmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, guggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers.
