Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines

Crop mapping is one major component of agricultural resource monitoring using remote sensing. Yield or water demand modeling requires that both, the total surface that is cultivated and the accurate distribution of crops, respectively is known. Map quality is crucial and influences the model outputs. Although the use of multi-spectral time series data in crop mapping has been acknowledged, the potentially high dimensionality of the input data remains an issue. In this study Support Vector Machines (SVM) are used for crop classification in irrigated landscapes at the object-level. Input to the classifications is 71 multi-seasonal spectral and geostatistical features computed from RapidEye time series. The random forest (RF) feature importance score was used to select a subset of features that achieved optimal accuracies. The relationship between the hard result accuracy and the soft output from the SVM is investigated by employing two measures of uncertainty, the maximum a posteriori probability and the alpha quadratic entropy. Specifically the effect of feature selection on map uncertainty is investigated by looking at the soft outputs of the SVM, in addition to classical accuracy metrics. Overall the SVMs applied to the reduced feature subspaces that were composed of the most informative multi-seasonal features led to a clear increase in classification accuracy up to 4.3%, and to a significant decline in thematic uncertainty. SVM was shown to be affected by feature space size and could benefit from RF-based feature selection. Uncertainty measures from SVM are an informative source of information on the spatial distribution of error in the crop maps.     

SAR polarimetric change detection for flooded vegetation

Abstract

Due to spatial and temporal variability an effective monitoring system for water resources must consider the use of remote sensing to provide information. Synthetic Aperture Radar (SAR) is useful due to timely data acquisition and sensitivity to surface water and flooded vegetation. The ability to map flooded vegetation is attributed to the double bounce scattering mechanism, often dominant for this target. Dong Ting Lake in China is an ideal site for evaluating SAR data for this application due to annual flooding caused by mountain snow melt causing extensive changes in flooded vegetation. A curvelet-based approach for change detection in SAR imagery works well as it highlights the change and suppresses the speckle noise. This paper addresses the extension of this change detection technique to polarimetric SAR data for monitoring surface water and flooded vegetation. RADARSAT-2 images of Dong Ting Lake demonstrate this curvelet-based change detection technique applied to wetlands although it is applicable to other land covers and for post disaster impact assessment. These tools are important to Digital Earth for map updating and revision.     

Prediction of Anopheles minimus habitat in India—a tool for malaria management

This paper reports research to predict the distribution of An. minimus, a malaria vector in forest fringe areas using GIS to support precision surveys for malaria control. Because An. minimus is a forest‐associated species, generalized thematic maps (1:6?000?000) of forest cover, soil type, altitude, rainfall and temperature were used. Digitization, overlaying, integration and analysis of thematic maps were done using Arc/Info 8.1 NT and Arc/View 3.2 (GIS, ESRI) software. GIS delineated favourable areas for An. minimus where the species is likely to be found, and precision surveys can be conducted. Precision field surveys in selected locations of favourable/non‐favourable areas were carried out. The species could be found in all locations designated as a favourable area and was absent in non‐favourable areas. In two districts, one where the species is reported to have disappeared in the early 1950s and the other where it was not reported in earlier surveys, GIS helped in precision surveys, and An. minimus was found. The technique can quickly cover vast and inaccessible areas and is easy to duplicate in other parts of the world to assist cost‐effective control of malaria. It can also delineate areas favourable for any species of flora and fauna to help precision surveys.     

Assessment of sliver polygons in geographical vector data

As positional error is a major issue in the assessment of spatial data quality, its propagation has been studied widely in map overlaying. However, few studies deal with a manifest consequence of positional error in map overlaying, namely sliver polygons. Sliver polygons are generally treated as awkward by-products that need to be removed quickly. Nevertheless, as they represent spurious areas, their nature and properties carry useful information, for example, for land use/cover assessment. Therefore, next to sliver removal, there is a need for intelligent detection and eventually further analysis of sliver polygons. This article proposes a general, semi-automated method for the assessment of slivers in vector polygon layers. A case study in Flanders (Belgium) illustrates a possible application in area estimation evaluation of land use allocation classes.     

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基于最小二乘支持向量机的城市桥梁震害预测模型研究

针对地震中城市桥梁震害状态具有较强的非线性、复杂性的特点,采用了具有RBF核函数的最小二乘支持向量机(LS-SVM)算法。在大量收集我国地震中城市桥梁震害资料的基础上,将此算法引入桥梁的震害预测中,选取了地震烈度、上部结构、地基失效程度、支座类型、墩台高度、桥梁跨数和场地类别等因素作为模型的特征输入向量,建立了最小二乘支持向量机的桥梁震害预测模型。通过反复地样本训练及模型参数设置,仿真结果表明,该方法具有一定的准确度和可行性。基于最小二乘支持向量机的桥梁震害预测方法是一种可以用于地震中桥梁震害预测的良好方法。     

基于PCA-GASVM的晋陕甘宁地区生态环境评价

我国新型城镇化进程中产生较严重的生态环境问题。选取晋陕甘宁地区为研究对象,从城镇生态压力、状态、响应3个方面构建了生态环境的评价指标体系,并通过2013年中国统计年鉴整理到相关数据,利用PCA-GA-SVM对晋陕甘宁地区生态环境进行评价。结果表明:PCA-GASVM生态环境评价比GASVM评价具有更高的准确率,而且PCA-GASVM比GASVM评价收敛速度更快。甘肃、宁夏的自然保护区占辖区面积比重指标排名比山西、陕西两个省名次靠前,从森林覆盖率分析只有陕西位于第11位,山西、甘肃、宁夏森林覆盖率均位于后20名;从SO₂排放量、烟尘(粉尘)排放量、工业废弃物产生量、废水排放量上分析,在晋陕甘宁地区中,宁夏最优,甘肃次之,陕西、山西排放很严重。晋陕甘宁地区生态环境保护应在"新常态"下要有"新状态",将市场机制与政府的补贴、税收等政策宏观调控相结合,严守生态红线,发展生态经济绿色科技、清洁生产,减少工业废弃物排放,建立环保交易市场,推行污染第三方治理,完善生态补偿制度,走集约、智能、绿色、低碳的新型城镇化道路。     

Geometric Alignments of the Subgrid-Scale Force in the Atmospheric Boundary Layer

In recent years field experiments have been undertaken in the lower atmosphere to perform a priori tests of subgrid-scale (SGS) models for large-eddy simulations (LES). The experimental arrangements and data collected have facilitated studies of variables such as the filtered strain rate, SGS stress and dissipation, and the eddy viscosity coefficient. However, the experimental set-ups did not permit analysis of the divergence of the SGS stress (the SGS force vector), which is the term that enters directly in the LES momentum balance equations. Data from a field experiment (SGS2002) in the west desert of Utah, allows the calculation of the SGS force due to the unique 4 × 4 sonic anemometer array. The vector alignment of the SGS force is investigated under a range of atmospheric stabilities. The eddy viscosity model is likely aligned with the measured SGS force under near-neutral and unstable conditions, while its performance is unsatisfactory under stable conditions.     

Computation of the safety factor for slope stability using discontinuous deformation analysis and the vector sum method

This study introduces the vector sum method into discontinuum-based methods by considering the sliding vector and the stress state of the discrete block system. The sliding direction computation and force projection in the new approach are detailed, and the safety factor is solved by explicit equations. The vector sum method is implemented in the discontinuous deformation analysis (DDA) program and is used to compute the safety factors for two numerical examples. A comparison of the solutions obtained with the theoretical analysis and limit equilibrium analysis demonstrates that the new method is suitable for calculating the safety factor of a slope.