新疆和田地区耕地面源污染生态风险评价          下载免费PDF全文

田丰收  刘新平  原伟鹏 《干旱区地理》2019,42(2):295-304

随着化肥、农膜等在农业生产中的过量投入，耕地面源污染的程度随之加重。文章选取塔里木河流域上游和田地区为研究区域，依据P-S-R框架理论，构建和田地区耕地面源污染生态风险评价指标体系，加入土壤理化数据，使用生态风险评价模型对和田地区1980 年及2016 年耕地面源污染状况进行生态风险评价，运用耕地生态风险模型、生态风险转移矩阵、Arcgis分析和田地区耕地面源污染时空分异状况。研究结论如下：和田地区1980 年耕地生态风险等级均为II级或III级，呈“中间高，两侧低”分布；2016 年耕地生态风险等级上升至IV级或V级，呈“倒W型”分布，各县耕地面源污染程度较1980 年均有较大幅度的上升，其中墨玉县和于田县在2016 年耕地生态风险等级达到最高的V级，而民丰县因自身生态环境的强脆弱性，同样需要提高关注。根据面源污染“从源头治理”的原则，应切实推进和田地区耕地生态环境保护与治理，提高政府重视程度，增强技术指导，开展试点工作，改善和田地区耕地面源污染现状。  相似文献   

新疆绿洲城镇组群内部经济联系及空间差异测度研究          下载免费PDF全文

李雪梅 《干旱区地理》2019,42(1):180-186

绿洲城镇组群是新疆特殊区域形成的规模相对较小的单一中心空间自组织模式。运用城市中心性指数、城市经济联系模型和Theil系数对新疆八大绿洲城镇组群内部城镇中心性、经济联系及空间差异测度。结果显示：绿洲城镇组群内部的中心城市的中心性职能较强，周边城镇的中心性职能相对较弱，形成了单中心的空间自组织模式；绿洲城镇组群内部经济联系量和经济联系隶属度大小的排序一致，离中心城市的距离越近、经济发展水平越高，经济联系隶属度越高；近10 a年来绿洲城镇组群的整体空间差异一直在扩大，且呈现出继续扩大趋势。在此基础上，提出了建立区域合作协调机制、明确城镇组群发展方向、增强中心城市的辐射带动作用、实现产业合理分工以及构建制度保障体系促进绿洲城镇组群的协同发展。  相似文献   

URANS investigation of the interaction between the free surface and a shallowly submerged underwater vehicle at steady drift     

《Applied Ocean Research》2019

An axisymmetric underwater vehicle (UV) at a steady drift angle experiences the complex three-dimensional crossflow separation. This separation arises from the unfavorable circumferential pressure gradient developed from the windward side toward the leeward side. As is well known, the separated flow in the leeward side gives rise to the formation of a pair of vortices, which affects considerably the forces and moments acting on the UV. In this regard, the main purpose of the present study is to evaluate the role of the leeward vortical flow structure in the hydrodynamic behavior of a shallowly submerged UV at a moderate drift angle traveling beneath the free surface. Accordingly, the static drift tests are performed on the SUBOFF UV model using URANS equations coupled with a Reynolds stress turbulence model. The simulations are carried out in the commercial code STARCCM+ at a constant advance velocity based on Froude number equal to F_n = 0.512 over submergence depths and drift angles ranging from h = 1.1D to h = ∞ and from β = 0 to β = 18.11°, respectively. The validation of the numerical model is partially conducted by using the existing experimental data of the forces and moment acting on the totally submerged bare hull model. Significant interaction between the low-pressure region created by the leeward vortical flow structure and the free surface is observed. As a result of this interaction, the leeward vortical flow structure appears to be largely responsible for the behavior of the forces and moments exerted on a shallowly submerged UV at steady drift.  相似文献   

强震前地磁总场F₀₂测值的空间相关特征研究     

李鸿宇  袁桂平 《地震》2018,38(1):157-166

收集全国102个地磁台站2008年至2015年数字化地磁资料, 运用地磁空间相关法计算每日凌晨02时各台站地磁总场F之间的空间相关系数。 在使用相同的异常判别标准的情况下, 研究时段的17个中强地震中发现在2009年9月19日陕西宁强5.1级地震, 2011年11月1日四川青川5.4级地震, 2013年1月23日辽宁灯塔5.1级地震, 2013年7月22日甘肃岷县6.6级地震和2013年10月31日吉林前郭5.5级地震前均具有较为明显的空间相关低值异常现象。 通过总结5个震例的异常特征, 笔者发现其异常形态极其相似, 且平均的异常持续时间为20天, 而地震就发生在异常开始后3个月内; 同时, 地震发生在异常集中区中心附近, 且这个异常区域大小在500 km左右。 这一研究结果对于进一步分析地震前地磁空间相关异常特征积累了丰富的资料。  相似文献   

社区关系、效益感知与亲旅游行为研究：一个有调节的中介作用模型          下载免费PDF全文

李东  崔春雨  马长发  关靖云  陈玥彤  王玉清 《干旱区地理》2020,43(4):1098-1107

目的地居民作为旅游活动的重要参与主体,对旅游业的行为态度不仅影响着游客的感知和体验,也对目的地旅游业的可持续发展起着重要作用。在旅游支持态度与亲环境行为等相关研究的基础上,提出亲旅游行为的概念,表达居民促进旅游业在当地更好发展的行为意向。以社会表征理论研究框架为基础,构建“社区关系—效益感知—行为态度”模型,实证考察了社区关系对亲旅游行为的影响,探讨了旅游效益感知的中介作用和旅游事件依恋的调节作用。结果表明：社区关系对目的地居民经济效益、社会效益、环境效益感知均具有显著的正向影响,积极的旅游影响感知又对亲旅游行为产生显著的正向影响作用。居民对经济、社会、环境效益的感知在社区关系与亲旅游行为之间存在多重链式中介作用,传统的中介模型低估了旅游效益感知的影响作用。旅游事件依恋正向调节了社区关系与旅游社会效益感知、环境效益感知的关系。研究结论为揭示旅游介入情境下社区关系、效益感知与亲旅游行为之间的影响机制提供了一定的理论依据,对于目的地社区旅游开发与管理具有一定的实践参考价值。  相似文献   

土地利用视角下城市交通事故驱动机理的研究进展     

王成  谢波 《地理科学进展》2020,39(9):1597-1606

城镇化与机动交通的快速发展,引发了城市土地利用与交通系统的重塑,导致城市交通安全问题日益严峻。为了优化土地利用布局并改善交通安全,需要从土地利用视角开展交通事故的驱动机理研究。国内外该方面研究形成了以交通流量和交通速度为主要中介因素联系土地利用与交通事故的经典理论框架,却忽略了源于土地利用并深刻影响交通安全的交通需求因素,导致缺乏“土地利用—交通需求—交通事故”完整路径链的研究。论文通过综述该领域文献,在归纳总结城市交通事故影响因素的基础上,揭示土地利用视角下交通事故的驱动机理并探讨未来研究方向。研究指出,土地利用的多维属性特征对交通事故具有重要影响,土地利用与交通系统的动态匹配关系及其对出行行为的影响是揭示交通事故驱动机理的关键突破口,对于构建交通安全导向的城市土地利用模式具有重要的理论与实践意义。  相似文献   

地理大数据挖掘——目标、内涵与研究问题     

裴韬  宋辞  郭思慧  舒华  刘亚溪  杜云艳  马廷  周成虎 《地理学报(英文版)》2020,30(2):251-266

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Quick Phase Identification for Dense Seismic Array with Aid from Long Term Phase Records of Co-located Sparse Permanent Stations     

CHEN Yini  LI Jun  WANG Zhenjie  ZHAO Mengqi  YU Tiehong  LI Danning  YU Junyi  WANG Weitao 《中国地震研究》2020,34(3):328-342

The phase identification and travel time picking are critical for seismic tomography, yet it will be challenging when the numbers of stations and earthquakes are huge. We here present a method to quickly obtain P and S travel times of pre-determined earthquakes from mobile dense array with the aid from long term phase records from co-located permanent stations. The records for 1 768 M ≥ 2.0 events from 2011 to 2013 recorded by 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and rectilinearity. The reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage, which are further refined with AR-AIC. Totally, 718 573 P picks and 512 035 S picks are obtained from mobile stations, which are 28 and 22 times of those from permanent stations, respectively. By comparing the automatic picks with manual picks from 88 permanent stations, for M ≥ 3.0 events, 81.5% of the P-pick errors are smaller than 0.5 second and 70.5% of S-pick errors are smaller than 1 second. For events with a lower magnitude, 76.5% P-pick errors fall into 0.5 second and 69.5% S-pick errors are smaller than 1 second. Moreover, the Pn and Sn phases are easily discriminated from directly P/S, indicating the necessity of combining traditional auto picking and integrating machine learning method.  相似文献   

Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km² in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R² = 0.69 and RMSE_% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.  相似文献   

Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery     

《International Journal of Applied Earth Observation and Geoinformation》2020

Coffee berry necrosis is a fungal disease that, at a high level, significantly affects coffee productivity. With the advent of surface mapping satellites, it was possible to obtain information about the spectral signature of the crop on a time scale pertinent to the monitoring and detection of plant phenological changes. The objective of this paper was to define the best machine learning algorithm that is able to classify the incidence CBN as a function of Landsat 8 OLI images in different atmospheric correction methods. Landsat 8 OLI images were acquired at the dates closest to sampling anthracnose field data at three times corresponding to grain filling period and were submitted to atmospheric corrections by DOS, ATCOR, and 6SV methods. The images classified by the algorithms of machine learning, Random Forest, Multilayer Perceptron and Naive Bayes were tested 30 times in random sampling. Given the overall accuracy of each test, the algorithms were evaluated using the Friedman and Nemenyi tests to identify the statistical difference in the treatments. The obtained results indicated that the overall accuracy and the balanced accuracy index were on an average around 0.55 and 0.45, respectively, for the Naive Bayes and Multilayer Perceptron algorithms in the ATCOR atmospheric correction. According to the Friedman and Nemenyi tests, both algorithms were defined as the best classifiers. These results demonstrate that Landsat 8 OLI images were able to identify an incidence of the coffee berry necrosis by means of machine learning techniques, a fact that cannot be observed by the Pearson correlation.  相似文献