Estimation of soil losses within plots as affected by different agricultural land management

Abstract

Proper agricultural land management strategies improve soil structural properties, thereby reducing soil loss by water erosion. This study was conducted to estimate soil losses from plots of different agricultural land management using the Water Erosion Prediction Project (WEPP) (95.7) model. The study took place in a semiarid region in Kenya. The mean annual rainfall was 694 mm. The WEPP (95.7) model was initially used to estimate total sediment loading from the catchment into a reservoir. The estimate was about 2871 t corresponding to an average sedimentation rate of 1063 t km^?2 year^?1, which was about 76% of the measured total sediment inflow into the reservoir. Soil losses were estimated within 10 plots on the catchment of different sizes and slopes with the following treatments: conventional tillage (hand hoeing) with maize and soybean intercropping (HOCOBE); conservation tillage (disc plough) with maize and soybean intercropping (COBEAN); conservation tillage with only maize cultivation (CNTCORN); and conservation tillage with only soybean cultivation (CNTBEAN). The soil loss reduction of COBEAN, CNTCORN and CNTBEAN relative to HOCOBE ranged between 27–47%, 16–29% and 12–25%, respectively, depending on the size and slope of the plot. In general, conservation tillage reduced soil loss relative to conventional tillage. However, with conservation tillage, the single cropping system resulted in greater soil loss than the intercropping system. In the case of single cropping with conservation tillage, the soil loss reduction for maize ranged between 4 and 9%, relative to soybean. Overall, the study showed that there would be a significant reduction of soil losses from plots if conservation tillage with an intercropping system (maize and soybean) were to be adopted on agricultural lands in semiarid regions.     

Ultra-Compact H II Regions

This article reviews how observations of dense molecular gas can help us understand the dynamics and evolution of ultra-compact H II (UCHII) regions. Particular attention is paid to the cometary objects, which it is argued are likely to be the dominant class. Thermal molecular lines in emission and absorption are considered as well as the mainly dynamical information from masers. Most of the UCHII regions studied at high resolution have hot cores ahead them which makes interpretation of molecular observations much more complex. Significant support for the champagne flow model for cometary regions can been seen, adding to the evidence from studies of the ionised gas. How the prevalence of cometary objects may fit into a triggered, sequential picture of massive star formation is outlined.     

Using a maximum entropy model to optimize the stochastic component of urban cellular automata models

ABSTRACT

The stochastic perturbation of urban cellular automata (CA) model is difficult to fine-tune and does not take the constraint of known factors into account when using a stochastic variable, and the simulation results can be quite different when using the Monte Carlo method, reducing the accuracy of the simulated results. Therefore, in this paper, we optimize the stochastic component of an urban CA model by the use of a maximum entropy model to differentially control the intensity of the stochastic perturbation in the spatial domain. We use the kappa coefficient, figure of merit, and landscape metrics to evaluate the accuracy of the simulated results. Through the experimental results obtained for Wuhan, China, the effectiveness of the optimization is proved. The results show that, after the optimization, the kappa coefficient and figure of merit of the simulated results are significantly improved when using the stochastic variable, slightly improved when using Monte Carlo methods. The landscape metrics for the simulated results and actual data are much closer when using the stochastic variable, and slightly closer when using the Monte Carlo method, but the difference between the simulated results is narrowed, reflecting the fact that the results are more reliable.     

Mining spatiotemporal association patterns from complex geographic phenomena

ABSTRACT

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.     

Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data

ABSTRACT

We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization.     

寒区隧道保温设防长度工程实践与研究进展综述北大核心CSCD

保温法是目前寒区隧道建设中应用最为广泛的一种冻害防治方法。通过敷设保温材料可以减缓隧道结构、围岩体与洞内空气的热量交换过程,进而减小或避免衬砌与围岩体内的季节冻融,实现冻害防治的目的。在工程设计中,隧道保温段的敷设长度和厚度是两个关键参数,其中敷设厚度相对容易确定,但是敷设长度的确定目前缺乏统一的标准和简便可靠的方法,给隧道保温设计带来了一定的难度和不确定性。基于此,对包括现有铁路和公路规范要求、经验公式、工程类比法、理论解析法、数值模拟法等寒区隧道保温段敷设长度确定方面的工程实践、研究进展和挑战进行了系统的总结,并在此基础上提出保温设防设计用气象数据的选取方法、保温设防长度确定的依据、隧道进出口的差异性,以及季节冻土与多年冻土区隧道的差异等未来工程实践和科学研究仍需解决和研究的重点,以期能够为寒区隧道保温防冻工程设计难题的解决提供参考。     

基于三种多变量不安定指数分析模型的汶川县地质灾害易发性评价

采用多变量不安定指数分析法模型并加以改进,应用于汶川县地质灾害易发性评价。选取坡度、坡向、地层岩性、距断层距离、植被覆盖率及距水系距离六项影响因子,结合四川省自然资源厅发布的汶川县地质灾害隐患点数据,以幂次相乘、线性累加、幂次累加这三种不同的不安定指数分析法模型分别得到了研究区地质灾害易发性分区图,并用接受者操作特性曲线（Receiver Operating Characteristic curve, ROC curve）验证了各种模型的评价性能。结果表明:（1）对本案例而言,幂次相乘模型相较其它两种模型具有最高的精度;（2）汶川县地质灾害“极高”“高”“中”“低”“极低”易发区的面积占比分别为:19.3%、24.6%、19.2%、19.3%、17.6%,且研究区地质灾害易发性较高的区域多分布在断裂带附近。本研究成果可为区域地质灾害防治工作提供理论借鉴和技术参考。     

柴北缘东段及周缘侏罗系发育特征及其对构造活动的响应

为研究柴北缘东部及周缘侏罗系发育特征及其对构造活动的响应,笔者等基于柴北缘东段红山、霍布逊及德令哈凹陷侏罗系野外地质调查,通过地层接触关系追踪、二维地震资料解释以及岩性岩相突变特征分析,同时与中国西北地区其他典型的盆地侏罗系发育特征进行对比分析,研究表明,在中国西北多个板块的相互作用的构造背景之下,柴北缘东段燕山期构造运动Ⅰ幕发生在早—中侏罗世,以古地理与古构造环境较为均一、发育粗碎屑凹陷、每个凹陷内具有幕式沉积充填特征,与中国西北其他盆地对比,此幕构造运动波及面大,但强度较弱,推测与中国西北周边各板块碰撞造山之后的伸展作用相关;燕山期构造运动Ⅱ幕发生在晚侏罗世—早白垩世,此幕构造运动导致古地理与古构造环境向新的环境转变,以中—上侏罗统之间的角度不整合、白垩系及晚侏罗统的残缺不全为特征,推测与拉萨地块与欧亚板块碰撞以及蒙古—鄂霍茨克洋的关闭及Kolyma—Omolon地块的碰撞有关。     

层次分析法和GIS技术在河南省雷电灾害风险区划中的应用

根据自然灾害风险评估理论,利用闪电定位资料、地理信息数据、社会经济数据以及雷电灾情等数据,采用层次分析法（AHP法）,从致灾因子的危险性、承灾体的暴露度和承灾体的脆弱性方面,研究雷电灾害风险评估及区划方法,建立起评价指标与风险评估的定量关系,形成了河南省雷电灾害风险评估的方法.同时,结合GIS技术,形成了致灾因子危险性分布图、承灾体的暴露度分布图和承灾体的脆弱性分布图,最终叠加形成河南省雷电灾害综合风险区划图.区划结果表明：高风险区主要位于豫东和豫西北大部分地区,低风险区主要位于豫北和豫西南部分地区.     

陆域冻土区天然气水合物地球化学勘查中顶空气轻烃应用模式试验

为探索顶空气轻烃技术在陆域冻土区天然气水合物地球化学勘查中的应用,选择祁连山木里已知天然气水合物矿区和哈拉湖未知区作为方法技术的试验区.试验指标包括土壤顶空气、土壤酸解烃、岩芯顶空气轻烃测井、甲烷碳同位素.研究表明:祁连山木里已知天然气水合物矿藏上方,土壤顶空气轻烃组分以甲烷为主,甲烷所占比例非常高,90.6％的样品C1含量都在78％以上,甲烷含量显著高于常规油气盆地;未发现天然气水合物的哈拉湖试验区,无明显的土壤顶空气轻烃异常现象.顶空气轻烃异常模式为:平面上,矿藏上方近地表土壤中存在明显的顶空气甲烷强异常;剖面上,在天然气水合物稳定带上方呈现"前缘晕"异常特征;其空间分布与祁连山水合物矿藏展布空间具有套合关系.甲烷碳同位素和烃类组成判断地表油气化探异常为热解成因,与祁连山冻土区天然气水合物的气体为同一成因来源.陆域冻土区天然气水合物顶空气轻烃地球化学勘查技术试验结果表明:顶空气轻烃地球化学方法既可以判断水合物成因类型,也可以圈出水合物矿藏范围.