Studying the dynamic changes in the coastline of the northeastern Caspian Sea is significant since the level of the Caspian is unstable,and the coastline change can cause enormous damage to the ecology,economy,and pop...Studying the dynamic changes in the coastline of the northeastern Caspian Sea is significant since the level of the Caspian is unstable,and the coastline change can cause enormous damage to the ecology,economy,and population of the coastal part of Kazakhstan.In this work,we use remote sensing and Geographic Information System(GIS)technologies to study the changes in the coastline of the northeastern Caspian Sea and predict the extent of flooding with increasing water levels.The proposed methodology for creating dynamic maps can be used to monitor the coastline and forecast the extent of flooding in the area.As a result of this work,the main factors affecting changes in the coastline were identified.After analyzing the water level data from 1988 to 2019,it was revealed that the rise in water level was observed from 1980 to 1995.The maximum sea level rise was recorded at-26.04 m.After that,the sea level began to fall,and between 1996 and 2009,there were no significant changes;the water level fluctuated with an average of-27.18 m.Then,a map of the water level dynamics in the Caspian Sea from 1988 to 2019 was compiled.According to the dynamics map,water level rise and significant coastal retreat were revealed,especially in the northern part of the Caspian Sea and the northern and southern parts of Sora Kaydak.The method for predicting the estimated flooding area was described.As a result,based on a single map,the flooding area of the northeast coast was predicted.A comparative analysis of Landsat and SRTM data is presented.展开更多
研究表明大地震之前由于地表温度的变化会引起长波辐射OLR(Outgoing Longwave Radiation)数据异常,但目前缺乏有效的技术来提取异常。我们提出了一种基于随机传感器和鞅理论的异常数据挖掘算法ADRM (Abnormality Detection based on Ran...研究表明大地震之前由于地表温度的变化会引起长波辐射OLR(Outgoing Longwave Radiation)数据异常,但目前缺乏有效的技术来提取异常。我们提出了一种基于随机传感器和鞅理论的异常数据挖掘算法ADRM (Abnormality Detection based on Randomized Transducer and Power Martingales),经过实验对比能有效挖掘异常。本数据集记录了尼泊尔地区2009-2018年10年间的NOAA卫星的OLR数据和经过异常数据挖掘后的相应数据序列。数据集在地域上,以尼泊尔地震震中为中心的周边地域划分为同样经纬度2.5°×2.5°为单位的25个网格;时间上,定义每个年度是从上一年的9月28日到下一年的9月28日,共计366天,2009-2018年10年的数据。数据集存储为1个.xls文件,数据量为3.92 MB。基于该数据集的研究成果分别发表在《地球信息科学学报》(2018年20卷8期)和《IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing》(2018年11卷8期)。展开更多
文摘Studying the dynamic changes in the coastline of the northeastern Caspian Sea is significant since the level of the Caspian is unstable,and the coastline change can cause enormous damage to the ecology,economy,and population of the coastal part of Kazakhstan.In this work,we use remote sensing and Geographic Information System(GIS)technologies to study the changes in the coastline of the northeastern Caspian Sea and predict the extent of flooding with increasing water levels.The proposed methodology for creating dynamic maps can be used to monitor the coastline and forecast the extent of flooding in the area.As a result of this work,the main factors affecting changes in the coastline were identified.After analyzing the water level data from 1988 to 2019,it was revealed that the rise in water level was observed from 1980 to 1995.The maximum sea level rise was recorded at-26.04 m.After that,the sea level began to fall,and between 1996 and 2009,there were no significant changes;the water level fluctuated with an average of-27.18 m.Then,a map of the water level dynamics in the Caspian Sea from 1988 to 2019 was compiled.According to the dynamics map,water level rise and significant coastal retreat were revealed,especially in the northern part of the Caspian Sea and the northern and southern parts of Sora Kaydak.The method for predicting the estimated flooding area was described.As a result,based on a single map,the flooding area of the northeast coast was predicted.A comparative analysis of Landsat and SRTM data is presented.
文摘研究表明大地震之前由于地表温度的变化会引起长波辐射OLR(Outgoing Longwave Radiation)数据异常,但目前缺乏有效的技术来提取异常。我们提出了一种基于随机传感器和鞅理论的异常数据挖掘算法ADRM (Abnormality Detection based on Randomized Transducer and Power Martingales),经过实验对比能有效挖掘异常。本数据集记录了尼泊尔地区2009-2018年10年间的NOAA卫星的OLR数据和经过异常数据挖掘后的相应数据序列。数据集在地域上,以尼泊尔地震震中为中心的周边地域划分为同样经纬度2.5°×2.5°为单位的25个网格;时间上,定义每个年度是从上一年的9月28日到下一年的9月28日,共计366天,2009-2018年10年的数据。数据集存储为1个.xls文件,数据量为3.92 MB。基于该数据集的研究成果分别发表在《地球信息科学学报》(2018年20卷8期)和《IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing》(2018年11卷8期)。
