Worldview2数据在西昆仑老并—赞坎矿集区铁矿调查中的应用

正赞坎—老并矿集区位于新疆塔什库尔干县,属于西昆仑成矿带塔合曼—西若达坂铁矿成矿区,研究区高寒缺氧,地形切割强烈,人烟稀少,交通极为不便,面积性的野外矿产地质调查工作面临挑战,难以获取系统、全面、翔实的资料(王俊峰等,2013)。遥感技术作为当今矿产调查的主要技术手段之一,越来越得到重视,特别是高分辨率的WorldView-Ⅱ数据能够提供全色0.46 m和多光谱     

湖南长城岭-凤凰山地区遥感蚀变信息提取与成矿预测

长城岭-凤凰山地区位于湘南多金属矿集区,找矿前景较好。通过Landsat-8遥感影像解译长城岭-凤凰山一带的线-环结构,发现长城岭"Ф"型构造特征;根据实测围岩蚀变光谱特征,选用主成分分析法和比值+主成分分析法,基于landsat-8OLI数据提取了长城岭-凤凰山地区铁染、羟基及硅化蚀变异常信息,基于Worldview-Ⅱ数据提取背首-凤凰山地区铁染蚀变异常信息。经野外验证及多元地学信息对比证实蚀变提取结果的准确性,比值+主成分分析法具有更好的提取效果,高分辨率影像提取该区蚀变信息具有更高的精度,并圈定了成矿远景区。     

Quantifying shoreline change in Funafuti Atoll,Tuvalu using a time series of Quickbird,Worldview and Landsat data

Funafuti Atoll, Tuvalu is located in the southwestern Pacific Ocean, which has experienced some of the highest rates of global sea-level rise over the past 60 years. Atoll islands are low-lying accumulations of reef-derived sediment that provide the only habitable land in Tuvalu, and are considered vulnerable to the myriad possible impacts of climate change, especially sea-level rise. This study examines the shoreline change of twenty-eight islands in Funafuti Atoll between 2005 and 2015 using 0.65 m QuickBird, 0.46 m WorldView-2, and 0.31 m WorldView-3 imagery using an image segmentation and decision tree classification. Shoreline change estimates are compared to previous study that used a visual interpretation approach. The feasibility of estimating island area with Landsat-8 Operational Land Imager (OLI) data is explored using CLASlite software. Results indicate a 0.13% (0.35 ha) decrease in net island area over the study time period, with 13 islands decreasing in area and 15 islands increasing in area. Substantial decreases in island area occurred on the islands of Fuagea, Tefala and Vasafua, which coincides with the timing of Cyclone Pam in March, 2015. Comparison between the WorldView-2 shoreline maps and those created from Landstat-8 indicate that the estimates tend to be in higher agreement for islands that have an area > 0.5 ha, a compact shape, and no built structures. Ten islands had > 90% agreement, with percent disagreements ranging from 2.78 to 100%. The methods and results of this study speak to the potential of automated EoV shoreline monitoring through segmentation and classification tree approach, which would reduce down data processing and analysis time. With the growing constellation of high and medium spatial resolution satellite-based sensors and the development of semi or fully automated image processing technology, it is now possible to remotely assess the short and medium-term shoreline dynamics on dynamic atolls. Landsat estimates were reasonably matched to those derived from fine resolution imagery, with some caveats about island size and shape.