[目的]分析1990—2015年乌苏里江流域内中国和俄罗斯两国区域土地覆被变化特征和差异,为该流域内人地关系研究和土地资源管理提供科学参考。[方法]以Landsat TM/OLI遥感影像为数据源,采用基于面向对象的遥感分类方法,提取乌苏里江流域1...[目的]分析1990—2015年乌苏里江流域内中国和俄罗斯两国区域土地覆被变化特征和差异,为该流域内人地关系研究和土地资源管理提供科学参考。[方法]以Landsat TM/OLI遥感影像为数据源,采用基于面向对象的遥感分类方法,提取乌苏里江流域1990年和2015年的土地覆被信息,分析乌苏里江流域土地覆被变化的主要特征。[结果]1990—2015年的26 a间,乌苏里江流域内农田、湿地面积变化较为明显。农田呈扩张趋势,面积增加6089.69 km 2,增长率18.4%。其中,5416.7 km 2农田由湿地转化而来,农田扩张主要发生在中国境内区域。湿地、林地面积分别减少了5683.51和844.09 km 2,减少率分别为56.4%和3.51%。俄罗斯境内土地覆被变化作用强度较弱,各土地覆被类型间变化率均不超过2%。[结论]气候、地形、农业宏观政策和农业生产方式等是推动乌苏里江流域土地覆被变化的影响因素;其中,农业生产方式的转变和规模的扩大是该流域土地覆被变化的最主要驱动力。展开更多
The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemic...The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemical anomaly detection. In this paper, the isolation forest model is used to detect geochemical anomalies and it does not require geochemical data to satisfy a particular distribution. By constructing a tree to traverse the average path length of all data, anomaly scores are used to characterize the anomaly and background fields, and the optimal threshold is selected to identify geochemical anomalies. Taking 1∶200 000 geochemical exploration data of Fusong area in Jilin Province, NE China as an example, Fe2O3 and Pb were selected as the indicator elements to identify geochemical anomalies, and the results were compared with traditional statistical methods. The results show that the isolation forest model can effectively identify univariate geochemical anomalies, and the identified anomalies results have significant spatial correlation with known mine locations. Moreover, it can identify both high value anomalies and weak anomalies.展开更多
文摘[目的]分析1990—2015年乌苏里江流域内中国和俄罗斯两国区域土地覆被变化特征和差异,为该流域内人地关系研究和土地资源管理提供科学参考。[方法]以Landsat TM/OLI遥感影像为数据源,采用基于面向对象的遥感分类方法,提取乌苏里江流域1990年和2015年的土地覆被信息,分析乌苏里江流域土地覆被变化的主要特征。[结果]1990—2015年的26 a间,乌苏里江流域内农田、湿地面积变化较为明显。农田呈扩张趋势,面积增加6089.69 km 2,增长率18.4%。其中,5416.7 km 2农田由湿地转化而来,农田扩张主要发生在中国境内区域。湿地、林地面积分别减少了5683.51和844.09 km 2,减少率分别为56.4%和3.51%。俄罗斯境内土地覆被变化作用强度较弱,各土地覆被类型间变化率均不超过2%。[结论]气候、地形、农业宏观政策和农业生产方式等是推动乌苏里江流域土地覆被变化的影响因素;其中,农业生产方式的转变和规模的扩大是该流域土地覆被变化的最主要驱动力。
基金Supported by National Key Basic Research Development Planning Project(No.2015CB453005)
文摘The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemical anomaly detection. In this paper, the isolation forest model is used to detect geochemical anomalies and it does not require geochemical data to satisfy a particular distribution. By constructing a tree to traverse the average path length of all data, anomaly scores are used to characterize the anomaly and background fields, and the optimal threshold is selected to identify geochemical anomalies. Taking 1∶200 000 geochemical exploration data of Fusong area in Jilin Province, NE China as an example, Fe2O3 and Pb were selected as the indicator elements to identify geochemical anomalies, and the results were compared with traditional statistical methods. The results show that the isolation forest model can effectively identify univariate geochemical anomalies, and the identified anomalies results have significant spatial correlation with known mine locations. Moreover, it can identify both high value anomalies and weak anomalies.
