Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.     

Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®

This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS^® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS^® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP.     

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.     

SensePlace3: a geovisual framework to analyze place–time–attribute information in social media

SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources.     

TRANSIT: a GIS toolbox for estimating the duration of ancient sail-powered navigation

Cost-surface analysis in Geographic Information System (GIS) environment has been less frequently used in the study of ancient sail navigation than in other studies of the human past. Navigation cost-surface analysis entails the use of GIS tools that are versatile but not very easy to grasp and to put to work. This article describes an ArcGIS toolbox built to facilitate cost-surface analysis of ancient sail navigation. It estimates the navigation time from a start location, considering parameters relevant for the generation of an accumulated anisotropic cost-surface, automating the complex workflow required to meaningfully pre- and post-process the data. Acknowledging the limitations inherent to the tool, and to the modeling of a complex matter such as sail navigation, the toolbox is first described and then used in a worked example. Historically recorded voyages in the Mediterranean during classical antiquity are compared to estimated durations generated by the toolbox. In spite of structural and expected limitations, the results indicate that the proposed toolbox may produce reasonable estimates. These should be thought of as values gravitating around, not matching, likely past durations. The estimated values may prove useful as an indication of the order of magnitude of past voyages’ duration, and as frame of reference in measuring ancient maritime space through time.     

A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.     

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.     

测井地质学前世、今生与未来——写在《测井地质学·第二版》出版之时

测井地质学以地质学和测井学的方法理论为指导,综合运用各种测井信息,来解决基础地质和石油地质的地质问题。经过数十年发展,测井地质学在油气勘探开发各个环节得到广泛应用。非常规油气的兴起使得测井地质学正面临多重挑战和全新探索,亟需建立针对非常规油气的测井地质学综合方法理论体系。本文以《测井地质学·第二版》出版为契机,系统归纳了测井地质学的起源及发展历程、主要研究内容和研究方法流程。然后总结了测井资料与地震、地质信息的匹配性,并分析了不同探测特性测井方法纵向分辨率区间特征。在此基础上评述了测井地质学在井旁构造解析、沉积学特征研究、层序地层划分、地应力方向判别及大小计算、井壁裂缝识别与评价、烃源岩评价以及非常规油气“七性关系”综合评价当中的应用。但由于测井资料的负载能力有限性、测井与地质信息属性不对应性以及测井资料本身的多解性,使得测井地质学在测井—地质转换、非常规油气测井评价及其与人工智能融合方面还存在一定的问题。因此加强基础岩石物理研究,挖掘测井曲线中包含的地质信息,并与人工智能相结合,将拓展测井地质学研究的精度和广度,从而使其未来可更好地应用至非常规油气测井评价等实践工作中。     

山西长治盆地中更新世气候转型期环境变化特征

中更新世气候转型(MPT)是第四纪环境变化的一个重要时期,该时期气候模式、气候周期等均发生了明显变化,然而不同区域对中更新世气候转型的响应特征存在明显差异.本研究选取了长治盆地XZK4钻孔中更新世转型前后(1.8～0.1 Ma)共207个样品,通过古地磁定年,运用粒度端元分析和孢粉分析,阐述了长治盆地中更新世转型期的环境变化与湖泊演化特征.结果显示,1.8 Ma以来,孢粉组合中乔木花粉含量多高于60％,但个别时段草本植物花粉占优,显示区域植被多数阶段为森林,但存在明显冷期,并且1.1 Ma和0.55 Ma是研究区环境转变的重要转折点.中更新世转型期前(～1.1 Ma):沉积物粒度总体较细,黏土含量多高于25％,EM1组分(湖相沉积)多高于60％,EM3(河流相组分)在个别时段出现,总体显示该阶段沉积物组成以湖相沉积为主,个别阶段受到河流影响大;中更新世转型时期(1.1～0.55 Ma):沉积物粒度较上一阶段更细,黏土含量(平均为30％)、EM1占比和孢粉浓度均达到研究段最高,沉积物颜色以棕灰色为主,EM3组分占比降至最低,其中1.1～0.95 Ma喜冷的云杉属花粉出现,显示该时期研究区气候总体偏冷湿,湖泊面积较1.1 Ma之前扩大;中更新世转型后(～0.55 Ma):沉积物粒度明显变粗,达到钻孔最高值,沉积物颜色变黄,EM2(风成组分)和EM3占主导,草本植物含量增加,尤其是喜干的蒿属花粉增加明显,表明气候较之前变干,沉积物类型以风力沉积为主,湖泊消亡.综合对比显示,构造运动是影响长治古湖消长的主要驱动力,气候变干加剧了湖泊消亡.     

Deformation patterns of upper Quaternary strata and their relation to active tectonics,Po Basin,Italy

Despite increased application of subsurface datasets below the limits of seismic resolution, reconstructing near‐surface deformation of shallow key stratigraphic markers beneath modern alluvial and coastal plains through sediment core analysis has received little attention. Highly resolved stratigraphy of Upper Pleistocene to Holocene (Marine Isotope Stage 5e to Marine Isotope Stage 1) alluvial, deltaic and coastal depositional systems across the southern Po Plain, down to 150 m depth, provides an unambiguous documentation on the deformation of previously flat‐lying strata that goes back in time beyond the limits of morphological, historical and palaeoseismic records. Five prominent key horizons, accurately selected on the basis of their sedimentological characteristics and typified for their fossil content, were used as highly effective stratigraphic markers (M1 to M5) that can be tracked for tens of kilometres across the basin. A facies‐controlled approach tied to a robust chronology (102 radiocarbon dates) reveals considerable deformation of laterally extensive nearshore (M1), continental (M2 and M3) and lagoon (M4 and M5) marker beds originally deposited in a horizontal position (M1, M4 and M5). The areas where antiformal geometries are best observed are remarkably coincident with the axes of buried ramp anticlines, across which new seismic images reveal substantially warped stratal geometries of Lower Pleistocene strata. The striking spatial coincidence of fold crests with the epicentres of historic and instrumental seismicity suggests that deformation of marker beds M1 to M5 might reflect, in part at least, syntectonically generated relief and, thus, active tectonism. Precise identification and lateral tracing of chronologically constrained stratigraphic markers in the ¹⁴C time window through combined sedimentological and palaeoecological data may delineate late Quaternary subsurface stratigraphic architecture at an unprecedented level of detail, outlining cryptic stratal geometries at the sub‐seismic scale. This approach is highly reproducible in tectonically active Quaternary depositional systems and can help to assess patterns of active deformation in the subsurface of modern alluvial and coastal plains worldwide.