A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

With increasing computational resources, environmental models are run at finer grid spacing to resolve the land surface characteristics. The land use/land cover (LULC) data sets input into land surface models are used to assign various default parameters from a look-up tables. The objective of this study is to assess the potential uncertainty in the LULC data and to present a reclassification method for improving the accuracy of LULC data sets. The study focuses on the Southern Great Plains and specifically the Walnut River Watershed in southeastern Kansas, USA. The uncertainty analysis is conducted using two data sets: The National Land Cover Dataset 1992 (NLCD 92) and the Gap Analysis Program (GAP) data set, and a reclassification logic tree. A comparison of these data sets showed that they do not agree for approximately 27% of the watershed. Moreover, an accuracy assessment of these two data sets indicated that neither had an overall accuracy as high as 80%. Using the relationships between land-surface characteristics and LULC, a reclassification of the watershed was conducted using a logical model. This model iteratively reclassified the uncertain pixels according to their surface characteristics. The model utilized normalized difference vegetation index (NDVI) measurements during April and July 2003, elevation, and slope. The reclassification yielded a revised LULC dataset that was substantially improved. The overall accuracy of the revised data set was nearly 93%. The study results suggest: (i) as models adopt finer grid spacings, the uncertainty in the LULC data will become significant; (ii) assimilating NDVI into the land-surface models can reduce the uncertainty due to LULC assignment; (iii) the standard LULC data sets must be used with caution when the focus is on local scale; and (iv) reclassification is a valuable means of improving the accuracy of LULC data sets prior to applying them to local issues or phenomena.     

CT纹理特征分析在孤立性肺结节诊断中的研究进展

肺癌是我国发病率位居首位的恶性肿瘤,胸部CT扫描能提高肺癌的检出率和正确诊断率。早期或没有症状的肺癌大多表现为孤立性肺结节,因此肺部结节的鉴别诊断非常重要。传统CT诊断基于肺结节影像外部及密度特征,对于部分结节,尤其是磨玻璃密度结节的鉴别较困难。CT纹理特征分析基于内部灰度特点变化等多种特征的量化,为孤立性肺结节的良恶性鉴别、预后判断及基因突变预测提供有用参考,从而弥补了传统CT量化评估不足的缺点。本文总结CT纹理特征分析的基本原理、方法和工作流程及其在孤立性肺结节诊断中的应用。     

Gps Ambiguity Resolution as a Classification Problem

In this contribution we explore the analogy between GPS ambiguity resolution on the one hand, and the problem of statistical classification, on the other hand. As classification rules we discuss the Bayes-, the MAP-, the ML- and the Minmax-rule. It is shown to what extent ambiguity resolution may be considered a classification problem. It turns out that both problems show many similarities, although some marked differences exist as well. These similarities and differences are discussed.     

肺结节CT图像纹理特征的初步研究

目的:探讨图像纹理特征分析法在肺结节诊断中的作用。方法:收集104例经临床手术病理证实的肺结节患者,采用水平集模型自动分割、提取肺结节,用灰度共生矩阵法对肺结节的能量、相关、对比度、逆差距和熵等图像纹理特征进行提取,将良、恶性结节的纹理特征进行比较。结果:肺良恶性结节的能量、相关、对比度、逆差距和熵均存在统计学差异(P＜0.05),其中肺良性结节的能量、逆差距、相关性均大于恶性结节,良性结节对比度及熵的均值小于恶性结节。结论:肺良性结节纹理灰度分布均匀、纹理规则、局部灰度反差不大,而肺恶性结节纹理灰度分布不均匀、纹理杂乱、灰度反差明显,因此图像纹理特征对肺结节的诊断有重要的价值。     

流域土地利用/覆被变化的水文效应及洪水响应

干旱与洪涝灾害已成为全球关心的重大问题.土地利用/覆被变化影响雨水的截留、下渗、蒸发等水文要素及其产汇流过程,并进而影响流域出口断面的流量过程,加大流域洪涝灾害发生的频率和强度.深入研究土地利用/覆被变化对洪涝灾害的影响,对于社会经济可持续发展具重要意义.通过分析和总结已进行的有关流域土地利用/覆被变化的水文效应及洪水响应研究工作进展,讨论了其研究内容、方法及现有工作的不足之处.     

地震科学数据的分级分类探讨

在归纳地震科学数据现状的基础上探讨了地震科学数据共享应遵循的基本原则,并按其科学性质和可共享性进行了初步分级分类。结果可作为开展地震科学数据共享工作的参考。     

体数据特征点检测算法综述

三维图像处理技术在当今得到了广泛的应用,体数据是一种重要的三维图像数据类型,基于体数据的三维特征点检测技术作为三维图像配准、分类和识别等方法的第一步,具有特殊的重要意义。本文阐述了基于体数据的特征点检测算法的特点,归纳总结了检测流程,对具有代表性的检测器进行了举例分析,最后给出了检测算法相应的评价方法。     

GIS as a Tool for Seismological Data Processing

—?A computerized application of an integrated seismological GIS model is presented. An object oriented approach of the GIS topology is introduced and the special functions and features of this system are described. A network topology was selected to simulate the network characteristics of seismological data management and analysis. Each seismological entity is considered as a graphical data object, which is associated to other objects by predefined relationships. The graphical user interface introduced by GIS enables to handle seismological software routines and data in a more intuitive way. Examples of interactive processing of seismic waveforms for detecting, locating and characterizing seismic events using GIS visualization capabilities are presented. The benefits of this system during a passive seismic survey in the framework of the CTBT are highlighted.     

Calibration of a Land Subsidence Model Using InSAR Data via the Ensemble Kalman Filter

The application of interferometric synthetic aperture radar (InSAR) has been increasingly used to improve capabilities to model land subsidence in hydrogeologic studies. A number of investigations over the last decade show how spatially detailed time‐lapse images of ground displacements could be utilized to advance our understanding for better predictions. In this work, we use simulated land subsidences as observed measurements, mimicking InSAR data to inversely infer inelastic specific storage in a stochastic framework. The inelastic specific storage is assumed as a random variable and modeled using a geostatistical method such that the detailed variations in space could be represented and also that the uncertainties of both characterization of specific storage and prediction of land subsidence can be assessed. The ensemble Kalman filter (EnKF), a real‐time data assimilation algorithm, is used to inversely calibrate a land subsidence model by matching simulated subsidences with InSAR data. The performance of the EnKF is demonstrated in a synthetic example in which simulated surface deformations using a reference field are assumed as InSAR data for inverse modeling. The results indicate: (1) the EnKF can be used successfully to calibrate a land subsidence model with InSAR data; the estimation of inelastic specific storage is improved, and uncertainty of prediction is reduced, when all the data are accounted for; and (2) if the same ensemble is used to estimate Kalman gain, the analysis errors could cause filter divergence; thus, it is essential to include localization in the EnKF for InSAR data assimilation.     

Testing for Multivariate Outliers in the Presence of Missing Data

—?We consider the problem of multivariate outlier testing for purposes of distinguishing seismic signals of underground nuclear events from training samples based on non-nuclear seismic events when certain data are missing. We consider the case in which the training data follow a multivariate normal distribution. Assume a potential outlier is observed on which k features of interest are measured. Assume further that the available training set of n observations on these k features is available but that some of the observations in the training data have missing features. The approach currently used in practice is to perform the outlier testing using a generalized likelihood ratio test procedure based only on the data vectors in the training data with complete data. When there is a substantial amount of missing data within the training set, use of this strategy may lead to a loss of valuable information. An alternative procedure is to incorporate all n of the data vectors in the training data using the EM algorithm to appropriately handle the missing data in the training set. Resampling methods are used to find appropriate critical regions. We use simulation results and analysis of models fit to Pg/Lg ratios for the WMQ station in China to compare these two strategies for dealing with missing data.     

Results of foF2 Data Testing With the UNDIV Program

The prediction of critical frequency foF2 is of great importance in the area of short-wave telecommunication and ionospheric modelling. The test study of foF2 data should enable the validity of data used in PRIME map generating and testing to be checked. The preliminary results show that some data should not be used because they could affect the latest results of PRIME map generating, testing and improving.     

Land use and topography as predictors of nutrient levels in a tropical catchment

The influence of landscape on nutrient concentration and yield was analyzed in a tropical catchment, the Guare River in northern Venezuela. Spatial and temporal variation in nitrate, SRP and total P were determined in 15 sites located along the river mainstem and tributaries. Higher nitrate concentrations and yields were reported from upper sites and both decreased in the downstream direction along the river mainstem. These trends appear to be related to more pronounced slopes and larger proportions of agricultural and forest lands in subcatchments located in the upper part of the basin, and dense algal mats in the lower reaches. Nitrate values were higher during periods of high discharge, suggesting that nitrate is primarily transported by shallow subsurface flow. SRP represented between 60 and 80% of total P. Phosphorus concentrations were homogeneous along the river mainstem and showed little seasonal variation, while yields were higher in the upper basin. Multiple regression identified slope, runoff and agriculture as primary predictors of nitrate and phosphorus across the watershed, which suggests that both natural and anthropogenic landscape characteristics have a strong influence on nutrient levels in the Guare catchment.     

Testing the Quality of Sea-Level Data Using the GECCO Adjoint Assimilation Approach

Besides providing an estimate of the changing ocean state, an important result of the dynamically consistent estimating the circulation and climate of the ocean (ECCO) state estimate approach is the provision of a posterior model–data residuals which contain important information about elements in the assimilated observations that are inconsistent with the model dynamics or with the information present in other ocean data sets that are being used as constraints in the assimilation procedure. Based on decreased GECCO2 model–data residuals, upon using the altimeter data through the ESA climate change initiative (cci) sea-level (SL) project, we show here that the recently reprocessed ESA SL_cci altimeter data set (SL1) has been improved relative to the earlier AVISO altimetry data set and is now more consistent with the GECCO2 estimate and with the information about the changing ocean state embedded in other ocean data sets. The improvement can be shown to exist separately for both TOPEX/POSEIDON and ERS data sets. The study reveals that especially in regions characterized by small sea surface height (SSH) variability and small signal-to-noise ratio in the SSH data, improvements can be on the order of 30% of previously existing model–data residuals. However, in some regions we can find degradations, particulary in those where GECCO2 has little skill in representing the altimeter data and where evaluation of the products with GECCO2 is thus not advisable. Upon the assimilation of the new SL1 data set, the GECCO2 synthesis was further improved. However, adding the sea surface temperature (SST) from the SST_cci project as additional constrain, no further impact can be identified.     

Aerial Orthophoto and Airborne Laser Scanning as Monitoring Tools for Land Cover Dynamics: A Case Study from the Milicz Forest District (Poland)

The paper presents the results from the study concerning the application of airborne laser scanning (ALS) data and derived raster products like the digital surface model (DSM) and the digital terrain model (DTM) for the assessment of the degree of change of the land use based on the forest succession example. Simultaneously, an automated method of ALS data processing was developed based on the normalized (nDSM) and cadastral GIS information. Besides delivering precise information on forest succession, ALS technology is an excellent tool for time-changes spatial analyses. Usage of the ALS data can support the image interpretation process decreasing the subjectivity of the operator. In parallel, a manual vectorization and object classification (object-based image analysis—OBIA) were performed; both based on aerial orthophoto and ALS data. By using integrated ALS point clouds and digital aerial images, one can obtain fast OBIA processing and the determination of areas where the land cover has changed. The Milicz District (central west part of Poland) was chosen as the test site where ALS was to be performed in 2007, together with the digital aerial photos (Vexcel camera; pixel 0.15 m; CIR). The aerial photos were then processed to a CIR orthophoto. The area of study consisted of 68 private parcels (some of them were abandoned; 68.57 ha; scanned cadastral maps from the local survey office; land use information) in the direct neighbourhood of the State Forest, on which a forest succession could often be observed. The operator vectorized forest (trees and shrubs) succession areas on the 2D CIR orthophoto. They were then compared with the results from the OBIA and GIS analysis, based on the normalized digital surface model. The results showed that areas with high vegetation cover were three times larger than the official land cover database (cadastral maps).     

Land use effects on climate in China as simulated by a regional climate model

A regional climate model (RegCM3) nested within ERA40 re-analyzed data is used to investigate the climate effects of land use change over China. Two 15-year simulations (1987―2001), one with current land use and the other with potential vegetation cover without human intervention, are conducted for a domain encompassing China. The climate impacts of land use change are assessed from the difference between the two simulations. Results show that the current land use (modified by anthropogenic ac- tivities) influences local climate as simulated by the model through the reinforcement of the monsoon circulation in both the winter and summer seasons and through changes of the surface energy budget. In winter, land use change leads to reduced precipitation and decreased surface air temperature south of the Yangtze River, and increased precipitation north of the Yangtze River. Land use change signifi- cantly affects summer climate in southern China, yielding increased precipitation over the region, de- creased temperature along the Yangtze River and increased temperature in the South China area (south-end of China). In summer, a reduction of precipitation over northern China and a temperature rise over Northwest China are also simulated. Both daily maximum and minimum temperatures are affected in the simulations. In general, the current land use in China leads to enhanced mean annual precipitation and decreased annual temperature over south China along with decreased precipitation over North China.     

Deep Artificial Neural Networks as a Tool for the Analysis of Seismic Data

The number of seismological studies based on artificial neural networks has been increasing. However, neural networks with one hidden layer have almost reached the limit of their capabilities. In the last few years, there has been a new boom in neuroinformatics associated with the development of third-generation networks, deep neural networks. These networks operate with data at a higher level. Unlabeled data can be used to pretrain the network, i.e., there is no need for an expert to determine in advance the phenomenon to which these data correspond. Final training requires a small amount of labeled data. Deep networks have a higher level of abstraction and produce fewer errors. The same network can be used to solve several tasks at the same time, or it is easy to retrain it from one task to another. The paper discusses the possibility of applying deep networks in seismology. We have described what deep networks are, their advantages, how they are trained, how to adapt them to the features of seismic data, and what prospects are opening up in connection with their use.     

Testing techniques to quantify drumlin height and volume: synthetic DEMs as a diagnostic tool

Glacial bedform height (H) and volume (V) likely preserve important information about the behaviour of former ice sheets. However, large systematic errors exist in the measurement of H and V. Three semi‐automated methods to isolate drumlins from other components of the landscape (e.g. trees, hills) as portrayed by NEXTMap have recently been devised; however, it is unclear which is most accurate. This paper undertakes the first quantitative comparison of such readily implementable methods, illustrating the use of statistically representative ‘synthetic landscapes’ as a diagnostic tool. From this analysis, guidelines for quantifying the 3D attributes of drumlins are proposed. Specifically, to avoid obtaining incorrect estimates caused by substantial systematic biases, interpreters should currently take three steps: declutter the digital elevation model for estimating H but not V; remove height data within the drumlin; then interpolate across the resultant hole to estimate a basal surface using Delaunay triangulation. Results are demonstrated through analysis of drumlins in an area in western central Scotland. The guidance arguably represents the best current advice for subglacial bedforms in general, highlighting the need for more studies into the quality of mapped data using synthetic landscapes. Copyright © 2014 John Wiley & Sons, Ltd.     

Testing benthic foraminiferal distributions as a contemporary quantitative approach to biomonitoring estuarine heavy metal pollution

Biomonitoring of estuarine pollution is the subject of active research, and benthic foraminifera are an attractive group to use for these purposes due to their ubiquitous presence in saline water and wide diversity. Here, we describe a case study of biomonitoring using benthic foraminifera in the French Mediterranean lagoon, Bages-Sigean lagoon. In this case, the major pollutants of interest are heavy metals in the sediment, particularly contaminated by Cu and Cd derived from industrial and agricultural sources. The foraminiferal assemblages of the Bages-Sigean lagoon are typical of normal paralic environments, but unusually almost completely lack agglutinated forms. The density of benthic foraminifera was shown to be more influenced by the sediment characteristics rather than heavy metal pollution. However, the relative abundance of Quinqueloculina bicostata was shown to increase in the most polluted areas and we propose that this taxon may be used as an indicator of heavy metal pollution.