Mapping detention basins and deriving their spatial attributes from airborne LiDAR data for hydrological applications

This paper presents a new technique for mapping detention basins and measuring their spatial attributes using high‐resolution airborne LiDAR (Light Detection and Ranging) data. An efficient least‐cost search algorithm is employed to identify surface depressions from a bare‐earth LiDAR digital elevation model (DEM). Surface depressions are automatically delineated into hydrological objects using the connected component identification and indexing algorithm. Various spatial attributes are derived for these hydrologic objects, including location, perimeter, surface area, depth, storage volume and shape properties. Based on spatial attributes, a rule‐based classifier is established to separate detention basins from other types of surface depressions. We have successfully applied our technique to an urban watershed in the Houston Metropolitan area, Texas. Detention basins at regional and residential subdivision levels are mapped out for the watershed, and measurements on the spatial attributes are derived for each detention basin. The quantitative information derived from LiDAR data provides a scientific basis for formulating an appropriate management plan for detention basins and for assessing their effects on flood control and storm water quality treatment. Copyright © 2007 John Wiley & Sons, Ltd.     

区域开发环境评价中人工智能扩展GIS在累积过程分析中的应用

采用人工智能方法，扩展GIS(地理信息系统)的功能，建立能对累积影响途径全部推理过程做出必要解释的累积效应专家系统。充分发挥其推理及解释的功能，克服GIS在累积效应过程分析和推理中的不足，解决了在区域开发环境影响评价中。用GIS对区域影响环境评价的累积效应影响的时空分析，虽有一定的优势，但不能对累积效应的过程进行分析，即不能确定和分析累积效应的因果关系，不能区分累积方式的难题。研究结果表明，该系统能为区域开发环境影响评价提供决策支撑。     

C/S模式视频监控系统的设计与实现

随着网络技术的发展，计算机网络已越来越成为当今流行的计算环境。计算机的使用方法和体系结构也从早期的单一体系结构，发展成为现在比较流行的C／S体系结构。另一方面，随着社会经济的发展，智能交通系统也已经得到了广泛的应用。介绍了一个高速公路的视频监控系统，详细描述了自动报警功能的设计和实现。     

Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification

We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0% was obtained when all features were used. The highest classification accuracy (79.1%) was obtained when the amount of features was reduced from the initial 328 to the 100 most important using Boruta feature selection algorithm and when ancillary soil and land-use GIS-datasets were used. Although Boruta could rank the importance of features, it could not separate unimportant features from the important ones. Classification accuracy was bit lower (78.7%) when the classification was performed separately on two areas: the areas above and below 1 m vertical distance from the nearest stream. The data split, however, improved the classification accuracy of mire habitat types and streamside habitats, probably because their proportion in the below 1 m data was higher than in the other datasets. It was found that several types of data are needed to get the highest classification accuracy whereas omitting some feature groups reduced the classification accuracy. A major habitat type in the study area was mesic forests in different successional stages. It was found that the inner heterogeneity of different mesic forest age groups was large and other habitat types were often inside this heterogeneity.     

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing ‘optimal segmentation’. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.     

高分辨率遥感影像分类的多示例集成学习

精选示例特征嵌入多示例学习(MILES)算法在对噪声较强的训练样本进行学习时表现出良好的性能,但其判断规则可能带来遥感影像分类结果的不确定性。针对这一问题,提出用Bagging和AdaBoost集成MILES的多示例集成学习算法,使用粗包细分、多样性密度和最大似然分类相结合抑制分类不确定性的方法,实现了高分辨率遥感影像分类中多示例学习与集成学习的组合。采用Quick Bird、IKONOS等高分辨率遥感影像进行试验,结果表明多示例集成学习能有效控制遥感影像分类结果的不确定性,具有良好的应用前景。     

Data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades

This study examined the applicability of data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades. A framework was designed to combine these two techniques. In the framework, 20-m hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer was first merged with 1-m Digital Orthophoto Quarter Quads using a proposed pixel/feature-level fusion strategy. The fused data set was then classified with an ensemble approach based on two contemporary machine learning algorithms: Random Forest and Support Vector Machine. The framework was applied to classify nine vegetation types in a portion of the coastal Everglades. An object-based vegetation map was produced with an overall accuracy of 90% and Kappa value of 0.86. Per-class classification accuracy varied from 61% for identifying buttonwood forest to 100% for identifying red mangrove scrub. The result shows that the framework is promising for automated vegetation mapping in the Everglades.     

Comparing expert judgement and numerical criteria for hydrograph evaluation

Abstract

This paper investigates the relationship between expert judgement and numerical criteria when evaluating hydrological model performance by comparing simulated and observed hydrographs. Using a web-based survey, we collected the visual evaluations of 150 experts on a set of high- and low-flow hydrographs. We then compared these answers with results from 60 numerical criteria. Agreement between experts was found to be more frequent in absolute terms (when rating models) than in relative terms (when comparing models), and better for high flows than for low flows. When comparing the set of 150 expert judgements with numerical criteria, we found that most expert judgements were loosely correlated with a numerical criterion, and that the criterion that best reflects expert judgement varies from expert to expert. Overall, we identified two groups of 10 criteria yielding an equivalent match with the expertise of the 150 participants in low and high flows, respectively. A single criterion common to both groups (the Hydrograph Matching Algorithm with mean absolute error) may represent a good indicator for the overall evaluation of models based on hydrographs. We conclude that none of the numerical criteria examined here can fully replace expert judgement when rating hydrographs, and that both relative and absolute evaluations should be based on the judgement of multiple experts.

Editor D. Koutsoyiannis     

Developing environmental standards for abstractions from UK rivers to implement the EU Water Framework Directive / Développement de standards environnementaux sur les prélèvements d'eau en rivière au Royaume Uni pour la mise en œuvre de la directive cadre sur l'eau de l'Union Européenne

Abstract

Under the European Union Water Framework Directive, Member States must put in place a river basin planning framework to determine what measures are necessary to maintain and improve the ecological status for all surface water bodies. The governmental organisations legally responsible for implementing the Directive in the UK have recognised that an appropriate river flow regime is fundamental to maintain a healthy river and, as a result, they need to regulate abstractions and effluent discharges and ensure sufficient water is released from impoundments. This paper reports on the process of producing environmental standards that define the maximum abstraction allowable from UK rivers, to leave sufficient flow to maintain a healthy river ecosystem. As there are currently insufficient data available to determine the relationships between river flow and ecological status empirically, expert knowledge was captured through a series of workshops at which leading UK freshwater scientists defined maximum levels of river flow regime alteration that would achieve ecological objectives for different river water body types. For the least ecologically sensitive rivers, maximum abstractions in the range 15–35% of the natural flow were proposed, depending on the flow magnitude and time of year. For the most sensitive rivers, the maximum abstraction proposed was in the range 7.5–25%. The knowledge was used by the responsible UK authorities to develop environmental standards. The authorities subsequently used the environmental standards to determine regulatory standards that could be implemented within practical constraints and current licensing policies.     

Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

ABSTRACT

This paper proposes a new classification method for spatial data by adjusting prior class probabilities according to local spatial patterns. First, the proposed method uses a classical statistical classifier to model training data. Second, the prior class probabilities are estimated according to the local spatial pattern and the classifier for each unseen object is adapted using the estimated prior probability. Finally, each unseen object is classified using its adapted classifier. Because the new method can be coupled with both generative and discriminant statistical classifiers, it performs generally more accurately than other methods for a variety of different spatial datasets. Experimental results show that this method has a lower prediction error than statistical classifiers that take no spatial information into account. Moreover, in the experiments, the new method also outperforms spatial auto-logistic regression and Markov random field-based methods when an appropriate estimate of local prior class distribution is used.