Optimizing GPS-guidance transit route for cable crane collision avoidance using artificial immune algorithm

GPS Solutions - A cable crane collision accident usually causes serious damage to workers, equipment and materials. Owing to the emergence of global position system (GPS), some new collision...     

《遥感学报》20年:从热点到前沿

为反映《遥感学报》及中国遥感学科近20年的发展趋势,针对《遥感学报》在1997年—2015年期间出版的所有1804篇论文进行了统计分析,给出了国内高校及研究机构的发文量排名和受国家自然科学基金资助的发文情况,并对比了常设栏目和专题栏目的论文发文量及被引用情况。通过比较4个时间段内论文关键词的共现关系,反映出随着新的卫星载荷不断发射,观测技术由单一观测变为多源卫星观测,遥感定量反演模型由简单变得更为复杂,遥感技术应用也由最初的测绘、国土调查等单一应用逐渐变为多学科交叉应用,特别是在近年来国内外社会热点的驱动下,遥感技术已开始在灾害应急、全球变化、大气污染、粮食安全等领域发挥出越来越重要的作用。此外,无论是在当前遥感学科发展还是国家需求的现状下,都迫切需要建立国家级的遥感应用综合信息系统,以提高对环境和资源的宏观调控能力,为中国经济和社会可持续发展战略、布局和趋势预测,为资源管理、环境保护、防灾减灾以及实现资源环境、经济、社会的宏观调控,提供科学的数据和决策支持。     

珠三角基塘系统研究回顾及展望

基塘系统是中国劳动人民独创的低洼地利用方式和生态循环农业模式,珠三角基塘系统在历史上曾起到重要的作用,但目前其面积萎缩、生态功能退化。为了推动珠三角传统农业文化遗产的保护、促进区域人地关系协调发展,论文结合文献计量学方法对珠三角基塘系统研究文献进行梳理和概括,发现在基(田)塘生产力、区域水陆生态农业循环模式规律、社会经济发展与传统农业文化遗产保护的关系等方面已经展开研究;但是珠三角基塘系统演变机理研究比较薄弱,区域特色的农业文化遗产保护途径探索还鲜见。建议未来加强珠三角基塘系统演变机理、系统重构技术、农业文化遗产保护价值与途径的研究,以此深化基塘系统理论及实践研究,为协同区域农业生产格局与农业生态系统耦合关系提供支撑。     

山东沂沭河流域2000BC前后古文化兴衰的环境考古

Neolithic culture series in the Yishu River Basin developed in the order of Beixin culture-Dawenkou culture-Longshan culture-Yueshi culture. During the early and middle stage of Longshan culture （4600-4300 cal. yr BP）, the climate in the Yishu River Basin was warm and wet. Paddy-oriented agriculture planted paddy was very developed. The society was flourishing with great amount of archaeological sites. The cooling starting in 4200 cal. yr BP made the paddies shortfall in output or even no seeds were gathered. This situation intensified the discrepancy between population and resource. The scarcity in natural resource led to substantial decrease in population and subsequent drop in archaeological sites. About 4000 cal. yr BP Longshan culture was displaced by Yueshi culture which was relatively un- derdeveloped, simple and unsophisticated.     

Distribution of arsenic and mercury in subtropical coastal beachrock,Gulf of Mannar,India

An assessment of coastal pollution was made on the basis of trace element concentrations (arsenic — As, mercury — Hg) in the Gulf of Mannar. The beachrock samples were collected along the coastal tracts between Rameswaram and Kanyakumari. The samples were dried and digested to determine the As and Hg using atomic absorption spectrophotometer (AAS-air-acetylene and nitrous oxide method). The As and Hg accumulation status of the beachrock was assessed using geo-accumulation index values (I _geo). The accumulation of As and Hg in the beachrock ranges from 2.75 to 20.72 μg g⁻¹ and from 0.06 to 0.31 μg g⁻¹, respectively. The As and Hg concentrations in the beachrocks are compared with crustal average values and average of other region sediments. The possible source of the contamination is from atmospheric deposition and anthropogenic activities.     

Inversion of Vegetation Components Based on the Spectral Mixture Analysis Using Hyperion Data

Hyper spectral remote sensing is widely used to identify ground objects as a result of the advantages of ground radiation intensity characteristics and spectral position characteristics, in which inversion of vegetation components is the difficult point and hotspot. In this study, Huma county of Heilongjiang Province was selected as the study area, the canopy spectra of four types of typical vegetation were measured in situ firstly, including mongolian oak, cotton grass, lespedeza and white birch. Then, on the basis of analyzing the canopy spectral characteristics and their parameterization, the spectral differences of different vegetations were located, and the parameterization method of characteristics identification was determined. Finally, Hyperion data were used to calculate the canopy albedos based on the bidirectional reflectance model of vegetation canopies, and to map the vegetation components in the study area by use of linear spectral mixture model. The results showed that inversion of vegetation components in high vegetation-covered area was accurate using the canopy albedos and liner spectral mixture model, and was identical with the field sampling, which validated the feasibility of canopy albedos and liner spectral mixture model for the inversion of vegetation components.     

Extraction of Alteration Mineral Information from Moderate Remote Sensing Images Using MPS Method

Although alteration minerals related to metallogenesis is very important in mineral exploration, information of alteration mineral is weakly expressed in remote sensing imagery, which is often subject to interfering noise and sometimes limited in spectral and spatial resolutions. Because of easy access, moderate images are the main sources of alteration mineral information. Therefore, it is very important to develop alteration mineral information extraction methods from remote sensing images. In this paper, a combined method based on Mask, principal component analysis (PCA) and support vector machine method (SVM) was used to extract alteration mineral information from Enhanced thematic mapper plus remote sensing data with limited spectral and spatial resolutions. First, a mask image of the remote sensing imagery was created to remove interference information such as vegetation, shadow and water. Then, PCA was employed to collect sample data relating to iron, argillic, and carbonatization alteration. Finally, SVM was used to deal with alteration anomaly and build a feature extraction model of high accuracy. The Mask-PCA-SVM model is used to extract alteration mineral information from remote sensing images of Hatu area, Xinjiang Uygur Autonomous Regions, China. The results show that the new methods proposed in this paper can coincide well with known deposits occurrences, rate reached 86.51%. While, the consistent rate with known deposits of the ratio model, PCA model and Spectral angle mapper model were only 3.37, 65.08 and 69.05% respectively. This suggests that the proposed model can find the actual distribution of mineral deposits more effectively by reducing interference to a greater degree.     

A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover

Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlapping tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.