基于ICESat-2 ATLAS数据的DEM高程精度评价

为探究ASTER GDEMV3、SRTM1 DEM和AW3D30 DEM 3种开源DEM数据的高程精度,本文以高精度ICESat-2 ATLAS测高数据为参考数据,利用GIS统计分析、误差相关分析及数理统计对DEM的高程精度进行对比评价。结果表明：①AW3D30的质量最稳定;SRTM1 DEM在平原精度最高;在高原山地精度由高到低依次为AW3D30 DEM、ASTER GDEMV3、SRTM1 DEM。②DEM数据高程精度受地表覆盖影响较大,且与地形因素密切相关,在相同地表覆盖的两个研究区中DEM数据高程精度表现情况不一致,SRTM在平原地表覆盖下精度表现最好,平均误差为3.15 m,AW3D30 DEM在山地地表覆盖下精度表现最好,平均误差为7.61 m。③坡度对DEM数据的高程精度影响较大,在两个研究区3种DEM数据的高程误差均随坡度的增加而增加;坡向对DEM数据的高程精度影响较小,未发现明显的规律。     

Selection of Less Biased Threshold Angles for SAM Classification Using the Real Value–Area Fractal Technique

The accuracy of classification of the Spectral Angle Mapping (SAM) is warranted by choosing the appropriate threshold angles, which are normally defined by the user. Trial‐and‐error and statistical methods are commonly applied to determine threshold angles. In this paper, we discuss a real value–area (RV–A) technique based on the established concentration–area (C–A) fractal model to determine less biased threshold angles for SAM classification of multispectral images. Short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used over and around the Sar Cheshmeh porphyry Cu deposit and Seridune porphyry Cu prospect. Reference spectra from the known hydrothermal alteration zones in each study area were chosen for producing respective rule images. Segmentation of each rule image resulted in a RV–A curve. Hydrothermal alteration mapping based on threshold values of each RV–A curve showed that the first break in each curve is practical for selection of optimum threshold angles. The hydrothermal alteration maps of the study areas were evaluated by field and laboratory studies including X–ray diffraction analysis, spectral analysis, and thin section study of rock samples. The accuracy of the SAM classification was evaluated by using an error matrix. Overall accuracies of 80.62% and 75.45% were acquired in the Sar Cheshmeh and Seridune areas, respectively. We also used different threshold angles obtained by some statistical techniques to evaluate the efficiency of the proposed RV–A technique. Threshold angles provided by statistical techniques could not enhance the hydrothermal alteration zones around the known deposits, as good as threshold angles obtained by the RV–A technique. Since no arbitrary parameter is defined by the user in the application of the RV‐A technique, its application prevents introduction of human bias to the selection of optimum threshold angle for SAM classification.     

遥感在大兴安岭森林覆盖区地质矿产调查中的应用——以黑龙江洛古河1:5万区域地质矿产调查工作为例

大兴安岭地区森林覆盖严重, 气候严寒, 交通极为不便, 野外有效工作时间短, 给区域地质矿产调查工作增加了难度, 急需遥感手段提高成果质量和效率. 在黑龙江大兴安岭洛古河等4幅1:5万区域地质矿产调查工作中, 利用SPOT7、Landsat7/8、ASTER等多种遥感数据开展地质矿产解译, 进行遥感影像分区, 建立地层、构造和侵入岩解译标志, 提取羟基和铁染蚀变异常, 结合水系沉积物测量成果划分了成矿有利区, 有效降低了地质矿产调查强度, 提高了调查效率, 增强了调查质量. 表明遥感技术在大兴安岭高植被覆盖区地质矿产调查过程中能够取得较好效果.     

巴基斯坦山达克矿区ASTER遥感数据蚀变信息提取及找矿预测

巴基斯坦查盖火山岩浆岩带属于特提斯成矿域的重要组成部分之一,是巴基斯坦境内最重要的斑岩型铜矿带,但目前其相关的遥感研究还较少,制约了对该成矿带的找矿潜力分析。本文以山达克矿床及其周边为研究区,对先进星载热辐射与反射辐射计(Advanced Spaceborne Thermal Emission and Reflection Radiometer, ASTER)数据进行RBD (Relative absorption band depth)比值假彩色合成、主成分分析、光谱角制图等处理,获得蚀变遥感异常信息分布特征,通过对蚀变特征和主要控矿要素进行遥感研究,建立了山达克矿床遥感找矿模型并开展成矿预测,为该成矿带矿产勘查提供借鉴。根据建立的遥感找矿模型,圈定了找矿预测靶区10处。对矿区东矿体和矿区北部的2个靶区进行野外验证,证实了提取结果与实际地质事实吻合较好,对矿区岩石样品进行光谱实测,表明样品实测光谱曲线与标准矿物光谱曲线的吸收特征位置高度相似,证实了研究区绢云母化、青磐岩化等蚀变较强且分布较广。结果表明本次研究提取的矿化蚀变结果可信度较高,可为后续找矿勘查工作提供参考。     

2000—2020年兴都库什东部冰川区物质平衡变化及其影响因素

利用SRTM DEM和ASTER立体像对数据获取的DEM分析了2000—2020年兴都库什东部的冰川物质平衡,并结合CRU TS 4.04气象数据探讨了气温、降水、地形和冰湖对南、北冰川区物质平衡空间差异的影响。结果表明：2000—2020年兴都库什东部冰川区物质平衡为（-0.02±0.04） m w.e.·a^-1,冰川整体呈现微弱的负物质平衡状态。从坡向来看,南坡以正物质平衡冰川居多,北坡以负物质平衡冰川居多。从南、北两个子区域来看,北部冰川区物质平衡为（0.07±0.04） m w.e.·a^-1,南部冰川区物质平衡为（-0.32±0.04） m w.e.·a^-1。北部冰川面积规模大,所处海拔区间高,南部则相反。北部冰川区处于较高的海拔区间且冬季气温较低,导致夏季升温所产生的冰川消融的影响被削弱,冰川物质平衡的分布与降水分布在空间上具有一致性。南部冰川区出现的强烈物质亏损主要是由于夏季气温的急剧升高和冰川处于较低的海拔区间。南、北区域冰前湖和冰面湖面积不断扩大的空间差异性,也在一定程度上加剧了该地区冰川物质平衡的空间差异。     

多源数据在甘肃山羊坝地区找矿勘查中的应用

针对单一遥感数据已难以满足地质找矿工作需求的问题,本次研究综合使用雷达数据、光学数据及其他非遥感数据共同服务于地质找矿。以甘肃山羊坝地区为研究区,选择ASTER多光谱遥感数据,采用植被抑制法+特征向量主成分分析法,提取研究区的蚀变信息;选择PALSAR雷达数据,采用聚焦、多视、滤波、辐射定标、地理编码和增强处理等一系列处理方法制作雷达强度图,提取研究区构造信息。最后利用GIS平台进行遥感、地质及化探等信息的集成与综合分析,最终圈定了具有找矿前景的矿产资源靶区,野外查证发现一处金矿点。此次研究获得了良好的找矿效果,表明同时使用雷达数据、光学数据及其他非遥感数据的综合找矿方法,对本地区金矿找矿勘查具有重要的指导作用。     

Impacts of DEM resolution,source, and resampling technique on SWAT-simulated streamflow

The sensitivity of streamflow simulated with the Soil and Water Assessment Tool (SWAT) model to Digital Elevation Model (DEM) resolution, DEM source and DEM resampling technique is still poorly understood. The objective of this study is to compare SWAT model streamflow estimates in the Johor River Basin (JRB), Malaysia for DEMs differing in resolution (from 20 to 1500 m), sources (Shuttle Radar Topography Mission: SRTM v4.1, Advanced Space-borne Thermal Emission and Reflection Radiometer: ASTER GDEM2, EarthEnv-DEM90 and Global Multi-resolution Terrain Elevation Data 2010: GMTED2010) and resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority). The key findings were as follows: (1) SRTM v4.1 (Root Mean Square Error (RMSE) = 11.16 m) and EarthEnv-DEM90 (RMSE = 12.4 m) had better vertical accuracy over the JRB compared to the ASTER GDEM2 (RMSE = 16.95 m); (2) Accurate annual streamflow simulations were obtained by using nearly all of the DEM resolutions, as pointed out by a relative error (RE) lower than 7% from 20 to 50 m and from 100 to 800 m DEMs; (3) Prediction errors were the lowest for ASTER GDEM2 (RE = 3.9%), followed by SRTM v4.1 (RE = 5.4%), EarthEnv-DEM90 (RE = 6.3%), and GMTED2010 (RE = 7.3%); (4) the majority and nearest neighbour resampling techniques performed the best (RE of 6.0%), followed by bilinear interpolation (RE of 7.2%) and cubic convolution (7.5%). The study indicates that DEM resolution is the most sensitive SWAT model DEM parameter compared to DEM source and DEM resampling technique for streamflow simulation within SWAT.     

基于光学立体和InSAR的Grove山地区DEM建立和分析

本文分别利用光学立体和In SAR技术生成了东南极Grove山地区15 m分辨率的ASTER DEM和20 m分辨率的In SAR DEM。在利用ASTER立体像对生成DEM的过程中引入ICESat测高数据作为高程控制以减少错误匹配,提高DEM垂直精度;而在利用ERS tandem数据生成DEM后,选取ICESat测高数据对In SAR DEM进行倾斜面纠正,以消除基线不精确估计等带来的影响。通过与未作控制的ICESat测高数据进行比较,评价了两种DEM的精度并对误差进行了分析。同时,比较了两种DEM的差异,并分析了造成这些差异的原因,探讨了两种技术生成南极冰盖DEM的优势和不足。最后结合两DEM的优势,融合生成了Grove山地区高精度的DEM。     

An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field,India

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9 × 9 pixels for ASTER and 27 × 27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.