中国大陆TMPA降水产品气候态的评估

利用逐日全国降水分析产品,对2012年12月下旬发布的最新版本的TRMM等多源卫星降水估算产品（TMPA）进行评估, 评估时间从2008年9月到2012年8月。从评估结果来看,研究产品3B42V7产品与CPAP产品具有高的相关性,两者的相关系数达到了0.94,相对误差在-0.11％左右,标准偏差约为0.53 mm·d-1。实时产品3B42RT产品与CPAP产品的相关性相对较差,相关系数为0.75,相对误差也上升到了39.3％,标准偏差为1.25 mm·d-1。3B42RT产品在我国西部高估了降水,全年的相对误差为116.60％（春夏秋冬四季分别为104.52%、 105.73%、117.64%和326.60%）;3B42V7产品的相对误差仅为0.82 %（春夏秋冬四季分别为2.25%、1.24%、-5.27%和-24.64%）。     

黄土高原地形复杂区土地利用信息提取方法研究

引入影响土地利用类型分布的坡度数据和对植被覆盖反应敏感的归一化植被指数，以延河流域为研究区域，参考已有的土地利用数据，对地形复杂地区的土地利用信息提取方法进行了研究。结果表明，以监督分类和非监督分类为基础，辅以坡度信息并利用分区和多步骤信息提取方法进行土地利用信息提取，能在一定程度上提高分类精度，研究结果对建立地形复杂地区实用性强的遥感影像分类技术体系具有一定的参考价值。     

基于多尺度、多图幅地理空间基础的输电信息系统的研究与实践

正确、及时、全面地获取输电网络的各种资源信息,并加以提炼、分析,实现输电管理部门间的协同工作和信息共享,为管理者和决策者提供辅助决策依据,从而保障输电网络安全、高效地运行,提高企业的综合经济效益,降低电网运行维护中的损耗[1]。本文提出了采用C/S+B/S两种开发模式相结合的混合体系结构作为系统的总体解决方案。以ESR I公司的ArcG IS系列产品作为基础的GIS开发系统,以.NET的核心语言C#作为系统的最终开发语言。同时,功能强大的数据库软件Oracle和空间数据库引擎ArcSDE为系统统一协调、并发处理以及共享分布各种信息提供了可靠的保障。该系统具有界面友好、方便实用、安全稳定、兼容性强、伸缩性强和可扩展性强等优点。该系统已推广和应用到部分地市,具有极其广阔的推广应用前景。     

基于多目标决策的露采矿山空间整合研究——以江苏省徐州市为例

空间整合是优化露采矿山布局的重要手段,科学制定空间整合方案至关重要。以徐州为例,在分析了影响露采矿山空间整合主要因素的基础上,拟定了空间整合的若干备选方案;以多目标决策理论为支撑,运用专家咨询法、层次分析法、灰色关联度分析等方法,定性结合定量对各备选方案进行综合评价,筛选出最优方案。研究对徐州市矿产资源开发整合具有指导作用,在矿业布局优化研究方面也存在较大理论价值和借鉴意义。     

Geological mapping and mining prospecting in the Aouli inlier (Eastern Meseta,Morocco) based on remote sensing and geographic information systems (GIS)

Landsat 8 Oli, ASTER, and Sentinel 2A satellite images processing was used to map geological formations, lineaments and hydrothermal alteration minerals in the Aouli inlier, as a case study to illustrate the application of digital images processing and Geographic Information System (GIS) in geological mapping and mining prospecting. Principal Component Analysis (PCA) applied to the Landsat images allowed good lithological discrimination and contributed to the updating of available geological maps. The Automatic lineament extraction from Sentinel images revealed the main tectonic structures affecting Aouli inlier. The ratio bands (b5+b7)/b6 and the false color composite (b4/b6, b2/b1, b3/b2) allowed the hydrothermal alteration minerals mapping from Aster images. Combined with available geological data and field observations, the satellite derived data were integrated and analyzed in a GIS software to establish mining prospecting guides. The results showed that the anomaly zones are intimately linked to NNE –SSW and NW –SE oriented faults and to highly fractured areas developing argillic and Fe rich alterations. Verified via field survey, this approach was successfully applied to the Aouli inlier to rapidly target potential areas to be explored in the tactical phase. This provides a model for future prospecting efforts for similar mineral deposits in other areas.©2022 China Geology Editorial Office.     

A Geocomputational Heuristic for Coverage Maximization in Service Facility Siting

Siting service facilities in order to maximize regional coverage is important when budget resources are limited. Various approaches exist for addressing this particular planning problem for discrete or continuous representations of potential facility sites and demand to be served. In cases where both candidate facility sites and service demand are continuous, approaches for maximizing regional coverage have only examined the siting of a single facility. In this article, a geocomputational approach is proposed for addressing multiple facility siting when demand is continuously distributed and facilities may be located anywhere in the region. Emergency warning siren location is used to highlight the developed approach.     

洞穴石笋指标量化研究进展

洞穴石笋因分布广、可精确测年、δ18O信号可进行区域对比等优势逐渐成为重要的陆相地质载体.然而,由于量化研究不足,δ18O气候环境意义解译尚处于经验化阶段,特别是亚洲季风区石笋δ18O指标解释存在诸多争议.本文试通过总结已有石笋δ18O、δ13C、微量元素、年层厚度等指标量化研究成果,石笋生长机理模拟实验及相关技术方法,分析其存在问题.结合其它地质材料量化方法,从现代器测资料校准、多指标开发、集成树轮材料等方面提出加强现代监测,促进机理研究,定量认识现代洞穴石笋年～季节尺度沉积旋回与洞穴环境关系,建立量化经验模型,以发挥石笋年代学优势,推动区域或全球对比.     

GLOBAL sensitivity analysis,GIS and multi‐criteria evaluation for a sustainable planning of a hazardous waste disposal site in Spain

A novel application of Sensitivity Analysis is presented. Useful applications of Global SA (GSA) already exist in the field of numerical modelling. In this paper, we explore the joint use of GSA, Geographical Information Systems (GIS) and Multi‐Criteria Evaluation. In this preliminary case study, 11 factors have been used to find the best place to locate a hazardous waste landfill. Two variance‐based methods (Sobol' and E‐FAST) are used to compute sensitivity indices in order to identify the factors that determine the variance of the model output. The results show that only three factors jointly account for 97% of the output variance. This information is employed to make a simplification of the original model, retaining only these three influential factors. In addition, if the SA is carried out in a pilot area where the spatial properties are similar to those of the whole region, we can infer the results to the whole area. This procedure achieves the goal of the study with an optimized allocation of resources for GIS data acquisition.     

GIS,multi‐criteria and multi‐factor spatial analysis for the probability assessment of the existence of illegal landfills

This work deals with the identification of potentially contaminated areas using remote sensing, geographic information systems (GIS) and multi‐criteria spatial analysis. The identification of unknown illegal landfills is a crucial environmental problem in all developed and developing countries, where a large number of illegal waste deposits exist as a result of fast, and relatively unregulated, industrial growth over the past century. The criteria used to perform the spatial analysis are here selected by considering the characteristics which are ‘desirable’ for an illegal waste disposal site, chiefly related to the existence of roads for easy access and to a low population density which facilitates unnoticed dumping of illegal waste materials. A large dataset describing known legal and illegal landfills and the context of their location (population, road network, etc.) was used to perform a spatial statistical analysis to select factors and criteria allowing for the identification of the known waste deposits. The final result is a map describing the likelihood of an illegal waste deposit to be located at any arbitrary location. Such a probability map is then used together with remote sensing techniques to narrow down the set of possibly contaminated sites (Silvestri and Omri, 2008 Silvestri, S. and Omri, M. 2008. A method for the remote sensing identification of uncontrolled landfills: formulation and validation.. International Journal of Remote Sensing, 29(4): 975–989. [Taylor & Francis Online] , [Google Scholar]), which are candidates for further analyses and field investigations. The importance of the integration of GIS and remote sensing is highlighted and represents a key instrument for environmental management and for the spatially‐distributed characterization of possible uncontrolled landfill sites.     

Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area

Spectral feature fitting (SFF) is a commonly used strategy for hyperspectral imagery analysis to discriminate ground targets. Compared to other image analysis techniques, SFF does not secure higher accuracy in extracting image information in all circumstances. Multi range spectral feature fitting (MRSFF) from ENVI software allows user to focus on those interesting spectral features to yield better performance. Thus spectral wavelength ranges and their corresponding weights must be determined. The purpose of this article is to demonstrate the performance of MRSFF in oilseed rape planting area extraction. A practical method for defining the weighted values, the variance coefficient weight method, was proposed to set up criterion. Oilseed rape field canopy spectra from the whole growth stage were collected prior to investigating its phenological varieties; oilseed rape endmember spectra were extracted from the Hyperion image as identifying samples to be used in analyzing the oilseed rape field. Wavelength range divisions were determined by the difference between field-measured spectra and image spectra, and image spectral variance coefficient weights for each wavelength range were calculated corresponding to field-measured spectra from the closest date. By using MRSFF, wavelength ranges were classified to characterize the target's spectral features without compromising spectral profile's entirety. The analysis was substantially successful in extracting oilseed rape planting areas (RMSE ≤ 0.06), and the RMSE histogram indicated a superior result compared to a conventional SFF. Accuracy assessment was based on the mapping result compared with spectral angle mapping (SAM) and the normalized difference vegetation index (NDVI). The MRSFF yielded a robust, convincible result and, therefore, may further the use of hyperspectral imagery in precision agriculture.