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.     

Detection and mapping of bracken fern weeds using multispectral remotely sensed data: a review of progress and challenges

Bracken fern is one of the major invasive plants distributed all over the world currently threatening socio-economic and ecological systems due to its ability to swiftly colonize landscapes. The study aimed at reviewing the progress and challenges in detecting and mapping of bracken fern weeds using different remote sensing techniques. Evidence from literature have revealed that traditional methods such as field surveys and modelling have been insufficient in detecting and mapping the spatial distribution of bracken fern at a regional scale. The applications of medium spatial resolution sensors have been constrained by their limited spatial, spectral and radiometric capabilities in detecting and mapping bracken fern. On the other hand, the availability of most of these data-sets free of charge, large swath width and their high temporal resolution have significantly improved remote sensing of bracken fern. The use of commercial satellite data with high resolution have also proven useful in providing fine spectral and spatial resolution capabilities that are primarily essential to offer precise and reliable data on the spatial distribution of invasive species. However, the application of these data-sets is largely restricted to smaller areas, due to high costs and huge data volumes. Studies on bracken fern classification have extensively adopted traditional classification methods such as supervised maximum likelihood classifier. In studies where traditional methods performed poorly, the combination of soft classifiers such as super resolution analysis and traditional methods of classification have shown an improvement in bracken fern classification. Finally, since high spatial resolution sensors are expensive to acquire and have small swath width, the current study recommends that future research can also consider investigating the utility of the freely available recently launched sensors with a global footprint that has the potential to provide invaluable information for repeated measurement of invasive species over time and space.     

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.     

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 novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.     

Design principles for origin-destination flow maps

Origin-destination flow maps are often difficult to read due to overlapping flows. Cartographers have developed design principles in manual cartography for origin-destination flow maps to reduce overlaps and increase readability. These design principles are identified and documented using a quantitative content analysis of 97 geographic origin-destination flow maps without branching or merging flows. The effectiveness of selected design principles is verified in a user study with 215 participants. Findings show that (a) curved flows are more effective than straight flows, (b) arrows indicate direction more effectively than tapered line widths, and (c) flows between nodes are more effective than flows between areas. These findings, combined with results from user studies in graph drawing, conclude that effective and efficient origin-destination flow maps should be designed according to the following design principles: overlaps between flows are minimized; symmetric flows are preferred to asymmetric flows; longer flows are curved more than shorter or peripheral flows; acute angles between crossing flows are avoided; sharp bends in flow lines are avoided; flows do not pass under unconnected nodes; flows are radially distributed around nodes; flow direction is indicated with arrowheads; and flow width is scaled with represented quantity.     

蚀变矿物填图技术在斑岩型铜矿找矿勘查中的应用—以滇西北香格里拉松诺矿床为例

松诺铜矿区位于香格里拉格咱斑岩铜矿带红山-普朗铜多金属成矿亚带中段,矿区剥蚀程度较低,具有寻找斑岩型铜矿的潜力。本次蚀变矿物填图工作采用近红外光谱矿物分析技术,识别出绿泥石、绿帘石、高岭石、云母类、蒙脱石类及伊利石等6类主要蚀变矿物,蚀变类型为青磐岩化、绢英岩化;结合物探、化探异常分布,圈定3处找矿靶区,经对KHT2、KHT3钻孔验证,深部均圈定多层铜矿化体。结果表明,近红外光谱矿物分析技术在斑岩型铜多金属矿床勘查评价中可以较好地划分热液矿化蚀变带,进一步明确斑岩型矿化-蚀变中心,为探矿工程部署提供依据。     

金沙江白格滑坡残留体稳定性分析与防治对策

金沙江白格滑坡在经历了2018年10月和11月的2次滑动堵江后,在其后缘仍存在K1、K2和K3等3处规模较大的残留崩滑体,并有再次失稳堵江的可能。目前关于残留体稳定性的研究还存在较大争议,多从影响因素及成因机制等方面进行定性分析,其结果依赖于评价者的经验,差异较大。在野外调查的基础上,针对白格滑坡所在区工程地质条件及其残留体变形特征,选取坡高、坡度、坡面形态、临空面、地下水出露、裂缝发育和变形等7个因素作为评价因子,基于模糊综合评判模型,运用层次分析法对白格滑坡残留体进行分区稳定性评价,并提出相应的防治对策。评价结果表明:K1与K2残留体稳定性较差;K3残留体基本稳定,与实际调查状况较吻合。研究结果可为同类型滑坡的评价与防治提供参考。     

ASTER mapping of gypsum deposits of Thumrait region of southern Oman

This study demonstrates the use of ASTER data for the mapping of gypsum deposits and associated geological formations that occurred in the Thumrait region of southern Oman. The measurement of spectra over samples of gypsum in the 1,300–2,500 nm wavelength using a PIMA spectrometer showed the presence of distinct absorptions at 1400–1600, 1750, 1940, 2,100, and 2,400 nm characteristics to O? H stretching, H₂O combinations, and S? O bending overtones and stretching, respectively. Studying the unique spectral absorption characters of gypsum samples, we developed a false color composite (FCC) and an image by decorrelation stretch using the spectral bands 7, 3, and 2 of ASTER. The results FCC showed the regions of gypsum occurrences, and the decorrelated image discriminated the gypsum occurrences from other geological formations of the area. The study of surface mineralogy of the region using the VNIR‐SWIR bands by the spectral angle mapper method showed the presence of sulfate, carbonate, and clay minerals of the geological formations in the study area. We compared the results of ASTER with the results obtained using spectral bands 12, 8, and 4 of Sentinel‐2A processed by the same methods. The study showed that the spectral bands of ASTER can be used for mapping the gypsum and associated geological formations.     

黑龙江省浅覆盖区地物化特征与找矿标志——以黑河市340高地金矿化区为例

黑龙江省黑河市340高地金矿点大地构造上位于兴安地块东北缘,多宝山Cu-Au-Mo-W-Fe成矿亚带内,其地貌为浅覆盖区,找矿较为困难。本文采用浅钻技术取样,对研究区进行基岩填图,圈定出以硅化为中心、青磐岩化为外围的蚀变矿化带。对基岩样品进行岩石地球化学分析,通过主成分分析,确定有6个主成分特征值大于1,其中第二主成分与金元素关系密切,是研究区有效的找矿标志。高精度磁测结果显示区内构造以NNW向为主,且其线性低值异常或与蚀变引起的退磁有关。对区内的地物化异常进行工程查证,发现金矿点1处,圈定预测靶区1处。