基于Hyperion影像的涩北气田油气信息提取

 对柴达木地区涩北气田地质地理环境下的蚀变矿物进行分析，结合卫星高光谱遥感数据Hyperion的图谱，对已知气田区与背景区光谱特征进行相关分析，确定了932.64~1 346.25 nm与2 002.06~2 385.5 nm为油气信息识别的有利波长范围； 利用光谱角制图（SAM）技术提取了涩北气田油气的空间分布信息和台吉乃尔含气构造等远景区，为高光谱遥感油气勘探提供了有效技术方法与途径。     

遥感技术在南盘江地区油气烃类检测中的应用

应用陆地卫星 TM 资料对南盘江地区进行了烃类微渗漏蚀变信息检测研究。岩石土壤地球化学特征分析表明,该区不同程度地存在着“褪红”、“粘土矿化”、“碳酸盐矿化”等烃类蚀变异常现象。选择 FeO/Fe_2O_3作为褪红的成分因子;(K_2O+Na_2O+Al_2O_3)、(K_2O+Na_2O)/Al_2O_3、(K_2O+Na_2O+Al_2O_3)/S∶O_2和 K_2O/Na_2O 等作为粘土矿化的成分因子;CaO+MgO 和 CaO/MgO 作为碳酸盐矿化的成分因子。利用 TM1/TM3、TM1/TM4识别褪红蚀变信息,TM5/TM7、TM5/TM4用干识别粘土矿化和碳酸盐矿化蚀变信息。建立了地球化学—光谱—遥感图像三位一体的遥感找油气模式,根据油气遥感色调异常对南盘江地区进行了评价和分类。     

流速仪法测流最小水深计算公式推导

在<河流流量测验规范>中,规定了流速仪法测流垂线、流速测点的分布位置和布置测点时的最小水深,[1]也有这方面的刊载,并列举了部分计算公式,但公式考虑不够全面,而且还不符合规范要求.本文依据规范,按流速仪测点布置情况,分析推导出了具体完整的计算公式,并对测点布置方案选择提出了相应的建议.     

Spectral monitoring of moorland plant phenology to identify a temporal window for hyperspectral remote sensing of peatland

Recognising the importance of the timing of image acquisition on the spectral response in remote sensing of vegetated ecosystems is essential. This study used full wavelength, 350–2500 nm, field spectroscopy to establish a spectral library of phenological change for key moorland species, and to investigate suitable temporal windows for monitoring upland peatland systems. Spectral responses over two consecutive growing seasons were recorded at single species plots for key moorland species and species sown to restore eroding peat. This was related to phenological change using narrowband vegetation indices (Red Edge Position, Photochemical Reflectance Index, Plant Senescence Reflection Index and Cellulose Absorption Index); that capture green-up and senescence related changes in absorption features in the visible to near infrared and the shortwave infrared. The selection of indices was confirmed by identifying the regions of maximum variation in the captured reflectance across the full spectrum. The indices show change in the degree of variation between species occurring from April to September, measured for plant functional types. A discriminant function analysis between indices and plant functional types determines how well each index was able to differentiate between the plant functional groups for each month. It identifies April and July as the two months where the species are most separable. What is presented here is not one single recommendation for the optimal temporal window for operational monitoring, but a fuller understanding of how the spectral response changes with the phenological cycle, including recommendations for what indices are important throughout the year.     

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity–hue–saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.     

A simple two-band semi-analytical model for retrieval of specific absorption coefficients in coastal waters

A non-linear iterative method is used to replace the traditional spectral slope technique in initializing the total absorption decomposition model. Based on comparison of absorption coefficient by QAA and two-band semi-analytical model (TSAA) models with field measurements collected from the West Florida Shelf waters and Bohai Sea, it is shown that both models are effective in estimating absorption coefficients from the West Florida Shelf waters, but the TSAA model is superior to the QAA model. Use of the TSAA model in estimating absorption coefficient in the West Florida Shelf and Bohai Sea decreases the uncertainty of estimation by 1.3–74.7% from the QAA model. The TSAA model’s sensitivity to the input parameters was evaluated by varying one parameter and keeping the others fixed at their default values. Our results indicate that the TSAA model has quite a strong noise tolerance to addressing the field data of the total absorption coefficient.     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

Estimate of heavy metals in soil and streams using combined geochemistry and field spectroscopy in Wan-sheng mining area,Chongqing, China

Heavy metals contaminated soils and water will become a major environmental issue in the mining areas. This paper intends to use field hyper-spectra to estimate the heavy metals in the soil and water in Wan-sheng mining area in Chongqing. With analyzing the spectra of soil and water, the spectral features deriving from the spectral of the soils and water can be found to build the models between these features and the contents of Al, Cu and Cr in the soil and water by using the Stepwise Multiple Linear Regression (SMLR). The spectral features of Al are: 480 nm, 500 nm, 565 nm, 610 nm, 680 nm, 750 nm, 1000 nm, 1430 nm, 1755 nm, 1887 nm, 1920 nm, 1950 nm, 2210 nm, 2260 nm; The spectral features of Cu are: 480 nm, 500 nm, 610 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1920 nm, 2150 nm, 2260 nm; And the spectral features of Cr are: 480 nm, 500 nm, 610 nm, 715 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1755 nm, 1920 nm, 1950 nm. With these features, the best models to estimate the heavy metals in the study area were built according to the maximal R². The R² of the models of estimating Al, Cu and Cr in the soil and water are 0.813, 0.638, 0.604 and 0.742, 0.584, 0.513 respectively. And the gradient maps of these three types of heavy metals’ concentrations can be created by using the Inverse distance weighted (IDW).The gradient maps indicate that the heavy metals in the soil have similar patterns, but in the North-west of the streams in the study area, the contents are of great differences. These results show that it is feasible to predict contaminated heavy metals in the soils and streams due to mining activities by using the rapid and cost-effective field spectroscopy.     

FR－Ⅰ和FR－Ⅱ型射电源的关系—谱特征

本文比较了两类射电源ＦＲ－Ⅰ和ＦＲ－Ⅱ型的射电－光学—Ｘ－ｒａｙ的谱特征。ＦＲ－Ⅰ射电源的射电－光学谱比ＦＲ－Ⅱ射电源平得多（△αｒｏ≈０．１９）；但对于光学－Ｘ－ｒａｙ和射电－Ｘ－ｒａｙ谱，ＦＲ－Ⅰ却比ＦＲ－Ⅱ陡得多（△αｏｘ≈０．４０；△αｒｘ≈０．１０）。初步结果表明，ＦＲ－Ⅱ（Ｇ）可能是ＦＲ－Ⅱ（Ｑ）向ＦＲ－Ⅰ过渡的中间状态。