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   

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   

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   

Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems of the Western Ghats,India     

《Limnologica》2020

Partitioning beta diversity into its two components of spatial turnover and nestedness is a more robust method for checking spatial variability in biological communities than calculating the total beta diversity alone. The relative contribution of spatial turnover and nestedness has been used to test the effects of climatic, environmental, spatial and temporal variables on community composition. In this study, we tested the effects of environmental factors and microhabitat features on total beta diversity and its spatial turnover and nestedness components using a comprehensive dataset of aquatic Heteroptera collected from four types of permanent freshwater habitats (i.e. streams, ponds, rock tanks and reservoirs) in the Western Ghats of India. We observed that communities in all four types of habitats were predominantly shaped by dissimilarity caused due to spatial turnover (>85 %). Each type of habitat showed the presence of one or more species uniquely associated with it, which might contribute to the turnover between communities. The abiotic environment (climatic factors, topological factors, soil characteristics and microhabitat features) as well as assemblage structure differed significantly between habitat types. Communities in each type of habitat were affected by different environmental factors, such as precipitation and temperature patterns for streams, altitude and rocky substrate for rock tanks, and soil characteristics and the presence of aquatic macrophytes for ponds and reservoirs. Assemblages observed in the four types of permanent habitats are thus compositionally distinct due to species replacements between local communities, which in turn are strongly influenced by environmental variables. Similar to previous studies, our results show that spatial turnover largely measures the same phenomenon as total beta diversity on a regional scale.  相似文献   

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?     

《地学前缘(英文版)》2020,11(3):871-883

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.  相似文献   

Two-stage permutation tests for determining homogeneity within a spatial cluster     

Qiliang Liu  Wenkai Liu  Min Deng  Yaolin Liu 《International journal of geographical information science》2019,33(9):1718-1738

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.  相似文献   

A comparative analysis of gridding systems for point-based land cover/use analysis     

Wasim Shoman  Ugur Alganci  Hande Demirel 《国际地球制图》2019,34(8):867-886

For spatial analyses, raster land cover/use maps are converted into points, where each point holds attribute of its corresponding land cover/use. However, these are not identical in terms of areas or shapes; thus assigning a point to each isolated shape is not an adequate solution and for that gridding is suggested. Square, hexagon and triangle are among the basic land use gridding systems where each of them has its own advantages in such process. This research aims to compare the systems in providing accurate representations of the original land cover/use maps, assess the data loss while increasing resolution and suggest suitable gridding system. The research finds the errors in area and feature numbers as criteria for selected classes. Modules that find out errors in each scale considering each criterion and class alone are proposed. The modules suggest both the best system for each criterion alone and for combined criteria.  相似文献   

基于LEAP模型的城市交通低碳发展路径研究——以广州市为例     

黄莹  郭洪旭  廖翠萍  赵黛青 《气候变化研究进展》2019,15(6):670-683

以广州市为例,应用长期能源替代规划系统（LEAP）模型,通过设置政策情景、低碳情景和绿色低碳情景,模拟不同发展情景下广州交通领域未来的能源消费需求和CO₂排放趋势,分析城市低碳发展的方向和路径。结果显示,随着城镇化进程的加快和生产生活运输需求的增加,广州交通领域碳排放总量将持续增长,但增长速度有所放缓。政策情景下,广州交通领域的CO₂排放将于2035年左右达到峰值,严重滞后于广州市提出的碳排放总量达峰目标;低碳和绿色低碳情景下,通过加大低碳政策措施的力度,达峰时间有望分别提前到2025年和2023年。要实现城市交通的低碳发展,促进交通碳排放提前达峰,需要大力发展铁路和水路运输,全面落实公交优先发展战略,有效控制小汽车数量和出行频率,不断提高交通工具的清洁化和能效水平,逐步形成各种运输方式协调发展的综合交通运输体系,推动城市交通低碳发展。  相似文献