Modelling distribution of marine benthos from hydroacoustics and underwater video

Broad-scale mapping of marine benthos is required for marine resource management and conservation. This study combines textural derivatives based on bathymetry from multibeam hydroacoustics with underwater video observations to model and map sessile biota between 10- and 60-m water depth over 35 km² in Point Addis Marine National Park (MNP), Vic., Australia. Classification tree models and maps were developed for macroalgae (all types, mixed red algae, Ecklonia, and rhodoliths) and sessile invertebrates (all types, sponges, and ascidians). Model accuracy was tested on 25% of the video observation dataset reserved from modelling. Models fit well for most macroalgae categories (correct classification rates of 67–84%), but are not as good for sessile invertebrate classes (correct classification rates of 57–62%). The poor fit of the sessile invertebrate models may be the combined result of grouping organisms with different environmental requirements and the effect of false absences recorded during video interpretation due to poor image quality. Probability maps, binary single-class maps, and multi-class maps supply spatially explicit, detailed information on the distribution of sessile benthic biota within the MNP and provide information at a landscape-scale for ecological investigations and marine management.     

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

In this paper we extend the generalized likelihood uncertainty estimation (GLUE) technique to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. Untransformed binary pattern data already have been used within GLUE to estimate domain‐averaged (zero‐dimensional) likelihoods, yet the pattern information embedded within such sources has not been used to estimate distributed uncertainty. Where pattern information has been used to map distributed uncertainty it has been transformed into a continuous function prior to use, which may introduce additional errors. To solve this problem we use here ‘raw’ binary pattern data to define a zero‐dimensional global performance measure for each simulation in a Monte Carlo ensemble. Thereafter, for each pixel of the distributed model we evaluate the probability that this pixel was inundated. This probability is then weighted by the measure of global model performance, thus taking into account how well a given parameter set performs overall. The result is a distributed uncertainty measure mapped over real space. The advantage of the approach is that it both captures distributed uncertainty and contains information on global likelihood that can be used to condition predictions of further events for which observed data are not available. The technique is applied to the problem of flood inundation prediction at two test sites representing different hydrodynamic conditions. In both cases, the method reveals the spatial structure in simulation uncertainty and simultaneously enables mapping of flood probability predicted by the model. Spatially distributed uncertainty analysis is shown to contain information over and above that available from global performance measures. Overall, the paper highlights the different types of information that may be obtained from mappings of model uncertainty over real and n‐dimensional parameter spaces. Copyright © 2002 John Wiley & Sons, Ltd.     

Probabilistic spatial prediction of categorical data using elliptical copulas

This study uses elliptical copulas and transition probabilities for uncertainty modeling of categorical spatial data. It begins by discussing the expressions of the cumulative distribution function and probability density function of two major elliptical copulas: Gaussian copula and t copula. The basic form of spatial copula discriminant function is then derived based on Bayes’ theorem, which consists of three parts: the prior probability, the conditional marginal densities, and the conditional copula density. Finally, three kinds of parameter estimation methods are discussed, including maximum likelihood estimation, inference functions for margins and canonical maximum likelihood (CML). To avoid making assumptions on the form of marginal distributions, the CML approach is adopted in the real-world case study. Results show that the occurrence probability maps generated by these two elliptical copulas are similar to each other. However, the prediction map interpolated by Gaussian copula has a relatively higher classification accuracy than t copula.     

基于震动图的地震应急专题图编制——以2014年2月12日新疆于田Ms7．3地震余震分布图为例

介绍了ShakeMap烈度图在地震应急专题图制作的背景和意义,对ShakeMap系统进行了介绍,并利用ArcGIS软件将ShakeMap烈度数据进行矢量化,同时展示了地震余震记录数据的获取方法,并以2014年新疆于田7．3级地震余震分布图为例,将其应用在应急专题图件的编制中加以演示。结果表明,应用ShakeMap在专题图制作中能提升图件产出的质量,为辅助决策和震情跟踪给予有力的信息支持。     

Prototypes of Orthoimage Maps as Tools for Geophysical Application

Orthoimage maps have become very popular and frequently produced cartographical outputs in geosciences during recent years. However, the unambiguous terminology, definitions, content and appearance specifications have not been widely researched. This paper deals with the new definition of the orthoimage map, its component delineation, and basic classification. The authors present aspects of topographic and thematic orthoimage maps. The main theoretical achievement of the authors’ research is the determining of the image component and the symbol component of orthoimage map content. The presented orthoimage map concept is applicable in geophysics practise which is demonstrated by three presented topographic and thematic orthoimage maps. They differ according to the relationship between topographic background and thematic content, and between image and symbol component. The image component can be a carrier of thematic geophysical information, or it can be used as topographic background for geophysics-oriented symbol component. All prototypes give examples of how to design, complete and use image-based cartographical products. Those variants might be used as guidelines for future orthoimage map production, especially for the geophysics community.     

Estimating uncertainty associated with water stages from a single SAR image

It is the goal of remote sensing to infer information about objects or a natural process from a remote location. This invokes that uncertainty in measurement should be viewed as central to remote sensing. In this study, the uncertainty associated with water stages derived from a single SAR image for the Alzette (G.D. of Luxembourg) 2003 flood is assessed using a stepped GLUE procedure. Main uncertain input factors to the SAR processing chain for estimating water stages include geolocation accuracy, spatial filter window size, image thresholding value, DEM vertical precision and the number of river cross sections at which water stages are estimated. Initial results show that even with plausible parameter values uncertainty in water stages over the entire river reach is 2.8 m on average. Adding spatially distributed field water stages to the GLUE analysis following a one-at-a-time approach helps to considerably reduce SAR water stage uncertainty (0.6 m on average) thereby identifying appropriate value ranges for each uncertain SAR water stage processing factor. For the GLUE analysis a Nash-like efficiency criterion adapted to spatial data is proposed whereby acceptable SAR model simulations are required to outperform a simpler regression model based on the field-surveyed average river bed gradient. Weighted CDFs for all factors based on the proposed efficiency criterion allow the generation of reliable uncertainty quantile ranges and 2D maps that show the uncertainty associated with SAR-derived water stages. The stepped GLUE procedure demonstrated that not all field data collected are necessary to achieve maximum constraining. A possible efficient way to decide on relevant locations at which to sample in the field is proposed. It is also suggested that the resulting uncertainty ranges and flood extent or depth maps may be used to evaluate 1D or 2D flood inundation models in terms of water stages, depths or extents. For this, the extended GLUE approach, which copes with the presence of uncertainty in the observed data, may be adopted.     

Bayesian updating of flood inundation likelihoods conditioned on flood extent data

Previously we have detailed an application of the generalized likelihood uncertainty estimation (GLUE) procedure to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. This method was applied to two sites where a single consistent synoptic image of inundation extent was available to test the simulation performance of the method. In this paper, we extend this to examine the predictive performance of the method for a reach of the River Severn, west‐central England. Uniquely for this reach, consistent inundation images of two major floods have been acquired from spaceborne synthetic aperture radars, as well as a high‐resolution digital elevation model derived using laser altimetry. These data thus allow rigorous split sample testing of the previous GLUE application. To achieve this, Monte Carlo analyses of parameter uncertainty within the GLUE framework are conducted for a typical hydraulic model applied to each flood event. The best 10% of parameter sets identified in each analysis are then used to map uncertainty in flood extent predictions using the method previously proposed for both an independent validation data set and a design flood. Finally, methods for combining the likelihood information derived from each Monte Carlo ensemble are examined to determine whether this has the potential to reduce uncertainty in spatially distributed measures of flood risk for a design flood. The results show that for this reach and these events, the method previously established is able to produce sharply defined flood risk maps that compare well with observed inundation extent. More generally, we show that even single, poor‐quality inundation extent images are useful in constraining hydraulic model calibrations and that values of effective friction parameters are broadly stationary between the two events simulated, most probably reflecting their similar hydraulics. Copyright © 2004 John Wiley & Sons, Ltd.     

Influence of river channel morphology and bank characteristics on water surface boundary delineation using high‐resolution passive remote sensing and template matching

Accurate mapping of water surface boundaries in rivers is an important step for monitoring water stages, estimating discharge, flood extent, and geomorphic response to changing hydrologic conditions, and assessing riverine habitat. Nonetheless, it is a challenging task in spatially and spectrally heterogeneous river environments, commonly characterized by high spatiotemporal variations in morphology, bed material, and bank cover. In this study, we investigate the influence of channel morphology and bank characteristics on the delineation of water surface boundaries in rivers using high spatial resolution passive remote sensing and a template‐matching (object‐based) algorithm, and compare its efficacy with that of Support Vector Machine (SVM) (pixel‐based) algorithm. We perform a detailed quantitative evaluation of boundary‐delineation accuracy using spatially explicit error maps in tandem with the spatial maps of geomorphic and bank classes. Results show that template matching is more successful than SVM in delineating water surface boundaries in river sections with spatially challenging geomorphic landforms (e.g. sediment bar structures, partially submerged sediment deposits) and shallow water conditions. However, overall delineation accuracy by SVM is higher than that of template matching (without iterative hierarchical learning). Vegetation and water indices, especially when combined with texture information, improve the accuracy of template matching, for example, in river sections with overhanging trees and shadows – the two most problematic conditions in water surface boundary delineation. By identifying the influence of channel morphology and bank characteristics on water surface boundary mapping, this study helps determine river sections with higher uncertainty in delineation. In turn, the most suitable methods and data sets can be selectively utilized to improve geomorphic/hydraulic characterization. The methodology developed here can also be applied to similar studies on other geomorphic landforms including floodplains, wetlands, lakes, and coastlines. Copyright © 2014 John Wiley & Sons, Ltd.     

甘肃省地震应急专题图的设计与实现

地震应急期间,标准清晰的专题图因其快速、直观、信息量大等优点,能及时有效地提供灾区基本情况和灾情信息,在地震灾害应急指挥及救援管理中发挥着重要作用。地震应急图件的制作流程涉及基础数据的处理、总体设计、符号设计、制图综合、打印出图等多个环节,甘肃省地震应急专题图存在应急图件制作不规范、产品产出时效性不高的问题。通过不同比例尺数据库的建立、地震应急专题图和产品目录的设计、专题图模板的制作、本地化快速出图软件的部署,使震后应急期间各类专题图件产出快速、标准化、产品化,使甘肃省地震应急快速制图的能力和水平得到提升。     

Optimization of groundwater-monitoring networks for identification of the distribution of a contaminant plume

A new methodology is proposed to optimize monitoring networks for identification of the extent of contaminant plumes. The optimal locations for monitoring wells are determined as the points where maximal decreases are expected in the quantified uncertainty about contaminant existence after well installation. In this study, hydraulic conductivity is considered to be the factor that causes uncertainty. The successive random addition (SRA) method is used to generate random fields of hydraulic conductivity. The expected value of information criterion for the existence of a contaminant plume is evaluated based on how much the uncertainty of plume distribution reduces with increases in the size of the monitoring network. The minimum array of monitoring wells that yields the maximum information is selected as the optimal monitoring network. In order to quantify the uncertainty of the plume distribution, the probability map of contaminant existence is made for all generated contaminant plume realizations on the domain field. The uncertainty is defined as the sum of the areas where the probability of contaminant existence or nonexistence is uncertain. Results of numerical experiments for determination of optimal monitoring networks in heterogeneous conductivity fields are presented.     

Evaluative Zoning of Seawater Areas Based on Environmental and Fishery Characteristics

The principles and methods of comprehensive evaluative mapping of seawater areas are considered using the Barents Sea as an example. The areas are classified using a digital map database represented by a series of electronic thematic maps. The digital thematic maps are based on a raster version of a geographic information system (GIS).     

地震应急专题地图快速化产出研究

文章简要介绍了基于ArcGIS的地震应急专题地图的制作,探讨了地震应急专题地图的特点、制作方法和产出流程。提出,通过制作专题地图模板,可实现地震应急专题地图的快速产出,为地震应急决策和灾后救助提供及时有效的信息支撑。     

Soil property variation mapping through data mining of soil category maps

Spatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd.     

基于MQTT协议的地震专题图自动生成和推送系统的设计与实现

为增强地震专题图发布的时效性,提升地震专题图服务能力,设计了一套基于MQTT协议的地震专题图自动生成和推送系统.该系统通过移动互联网与地震速报系统之间保持长连接,利用MQTT协议实时接收地震速报信息,根据地震烈度衰减模型生成地震影响场,然后运用ArcPy站点包进行相关空间分析和自动出图,最后利用企业微信的开发接口,实现...     

Bayesian maximum entropy and data fusion for processing qualitative data: theory and application for crowdsourced cropland occurrences in Ethiopia

Categorical data play an important role in a wide variety of spatial applications, while modeling and predicting this type of statistical variable has proved to be complex in many cases. Among other possible approaches, the Bayesian maximum entropy methodology has been developed and advocated for this goal and has been successfully applied in various spatial prediction problems. This approach aims at building a multivariate probability table from bivariate probability functions used as constraints that need to be fulfilled, in order to compute a posterior conditional distribution that accounts for hard or soft information sources. In this paper, our goal is to generalize further the theoretical results in order to account for a much wider type of information source, such as probability inequalities. We first show how the maximum entropy principle can be implemented efficiently using a linear iterative approximation based on a minimum norm criterion, where the minimum norm solution is obtained at each step from simple matrix operations that converges to the requested maximum entropy solution. Based on this result, we show then how the maximum entropy problem can be related to the more general minimum divergence problem, which might involve equality and inequality constraints and which can be solved based on iterated minimum norm solutions. This allows us to account for a much larger panel of information types, where more qualitative information, such as probability inequalities can be used. When combined with a Bayesian data fusion approach, this approach deals with the case of potentially conflicting information that is available. Although the theoretical results presented in this paper can be applied to any study (spatial or non-spatial) involving categorical data in general, the results are illustrated in a spatial context where the goal is to predict at best the occurrence of cultivated land in Ethiopia based on crowdsourced information. The results emphasize the benefit of the methodology, which integrates conflicting information and provides a spatially exhaustive map of these occurrence classes over the whole country.     

Test of statistical means for the extrapolation of soil depth point information using overlays of spatial environmental data and bootstrapping techniques

Hydrological modelling depends highly on the accuracy and uncertainty of model input parameters such as soil properties. Since most of these data are field surveyed, geostatistical techniques such as kriging, classification and regression trees or more sophisticated soil‐landscape models need to be applied to interpolate point information to the area. Most of the existing interpolation techniques require a random or regular distribution of points within the study area but are not adequate to satisfactorily interpolate soil catena or transect data. The soil landscape model presented in this study is predicting soil information from transect or catena point data using a statistical mean (arithmetic, geometric and harmonic mean) to calculate the soil information based on class means of merged spatial explanatory variables. A data set of 226 soil depth measurements covering a range of 0–6·5 m was used to test the model. The point data were sampled along four transects in the Stubbetorp catchment, SE‐Sweden. We overlaid a geomorphology map (8 classes) with digital elevation model‐derived topographic index maps (2–9 classes) to estimate the range of error the model produces with changing sample size and input maps. The accuracy of the soil depth predictions was estimated with the root mean square error (RMSE) based on a testing and training data set. RMSE ranged generally between 0·73 and 0·83 m ± 0·013 m depending on the amount of classes the merged layers had, but were smallest for a map combination with a low number of classes predicted with the harmonic mean (RMSE = 0·46 m). The results show that the prediction accuracy of this method depends on the number of point values in the sample, the value range of the measured attribute and the initial correlations between point values and explanatory variables, but suggests that the model approach is in general scale invariant. Copyright © 2009 John Wiley & Sons, Ltd.     

Assessment of river habitat in Brandenburg, Germany

A habitat assessment of 1,707 km of rivers in the Federal State of Brandenburg, Germany, was undertaken using the desk-based ‘overview method’. This method includes the analysis of different sources such as topographic and thematic maps, reports, remote sensing data, and interviews with authorities. It compares actual habitat conditions with natural conditions, and assesses the human disturbances using a seven-step scale. Results show, that in Brandenburg more than 50% of surveyed river units are heavily (class 5) to totally disturbed (class 7) and only slightly more than 20% are undisturbed (class 1) to little disturbed (class 2). Main human disturbances are canalization, bank stabilizations, flood control, migration barriers, and agricultural and urban development in the floodplain. All survey data and maps are embedded in a geographical information system (GIS) that not only allows future analysis and use in river restoration management by experts, but also serves as information for the public. The river habitat map of Brandenburg is part of the river habitat map of the entire Germany.     

ArcGis图层组在地震应急专题制图中的应用探索

总结地震应急专题图的特点,以应急专题图模板制作为实例,讨论了地震应急专题图制作过程中遇到的不同震级下地图负载过大或过小的问题,探索利用ArcGis图层组解决该问题的方法,初步确立地震应急专题图的图层组分组比例尺范围。以便于快速、美观制作地震应急专题图,更好的服务于地震应急工作。     

Tsunami Probability in the Caribbean Region

We calculated tsunami runup probability (in excess of 0.5 m) at coastal sites throughout the Caribbean region. We applied a Poissonian probability model because of the variety of uncorrelated tsunami sources in the region. Coastlines were discretized into 20 km by 20 km cells, and the mean tsunami runup rate was determined for each cell. The remarkable ~500-year empirical record compiled by O’Loughlin and Lander (2003) was used to calculate an empirical tsunami probability map, the first of three constructed for this study. However, it is unclear whether the 500-year record is complete, so we conducted a seismic moment-balance exercise using a finite-element model of the Caribbean-North American plate boundaries and the earthquake catalog, and found that moment could be balanced if the seismic coupling coefficient is c = 0.32. Modeled moment release was therefore used to generate synthetic earthquake sequences to calculate 50 tsunami runup scenarios for 500-year periods. We made a second probability map from numerically-calculated runup rates in each cell. Differences between the first two probability maps based on empirical and numerical-modeled rates suggest that each captured different aspects of tsunami generation; the empirical model may be deficient in primary plate-boundary events, whereas numerical model rates lack backarc fault and landslide sources. We thus prepared a third probability map using Bayesian likelihood functions derived from the empirical and numerical rate models and their attendant uncertainty to weight a range of rates at each 20 km by 20 km coastal cell. Our best-estimate map gives a range of 30-year runup probability from 0–30% regionally.