Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams

Remote sensing has rarely been used as a tool to map and monitor submerged aquatic vegetation (SAV) in rivers, due to a combination of insufficient spatial resolution of available image data and strong attenuation of light in water through absorption and scattering. The latter process reduces the possibility to use spectral reflectance information to accurately classify submerged species. However, increasing availability of very high resolution (VHR) image data may enable the use of shape and texture features to help discriminate between species by taking an object based image analysis (OBIA) approach, and overcome some of the present limitations.This study aimed to investigate the possibility of using optical remote sensing for the detection and mapping of SAV. It firstly looked at the possibilities to discriminate submerged macrophyte species based on spectral information only. Reflectance spectra of three macrophyte species were measured in situ across a range of submergence depths. The results showed that water depth will be a limiting factor for the classification of species from remote sensing images. Only Spiked Water Milfoil (Myriophyllum spicatum) was indicated as spectrally distinct through ANOVA analysis, but subsequent Jeffries–Matusita distance analysis did not confirm this. In particular Water Crowfoot (Ranunculus fluitans) and Pondweed (Potamogeton pectinatus) could not be discriminated at 95% significance level. Spectral separability of these two species was also not possible without the effect of an overlying water column.Secondly, the possibility to improve species discrimination, using spatial and textural information was investigated for the same SAV species. VHR image data was acquired with a Near Infrared (NIR) sensitive DSLR camera from four different heights including a telescopic pole and a Helikite UAS. The results show that shape and texture information can improve the detection of the spectrally similar Pondweed and Water Crowfoot from VHR image data. The best performing feature ‘length/width ratio of sub-objects’ was obtained through expert knowledge. All of the shape and texture based features performed better at species differentiation than the spectrally based features.In conclusion this study has shown that there is considerable potential for the combination of VHR data and OBIA to map SAV in shallow stream environments, which can benefit species monitoring and management.     

Balancing the source terms in a SPH model for solving the shallow water equations

A shallow flow generally features complex hydrodynamics induced by complicated domain topography and geometry. A numerical scheme with well-balanced flux and source term gradients is therefore essential before a shallow flow model can be applied to simulate real-world problems. The issue of source term balancing has been exhaustively investigated in grid-based numerical approaches, e.g. discontinuous Galerkin finite element methods and finite volume Godunov-type methods. In recent years, a relatively new computational method, smooth particle hydrodynamics (SPH), has started to gain popularity in solving the shallow water equations (SWEs). However, the well-balanced problem has not been fully investigated and resolved in the context of SPH. This work aims to discuss the well-balanced problem caused by a standard SPH discretization to the SWEs with slope source terms and derive a corrected SPH algorithm that is able to preserve the solution of lake at rest. In order to enhance the shock capturing capability of the resulting SPH model, the Monotone Upwind-centered Scheme for Conservation Laws (MUSCL) is also explored and applied to enable Riemann solver based artificial viscosity. The new SPH model is validated against several idealized benchmark tests and a real-world dam-break case and promising results are obtained.     

Simulation of dam-break waves on movable beds using a multi-stage centered scheme

This paper presents the application of the multi-stage first-order centered scheme GMUSTA to solve a two-phase flow model with four equations for simulating dam-break floods without and with sediment transport.Computation of generalized Riemann invariants can be particularly complex and costly in simulating dam-break floods with sediment transport.GMUSTA numerical scheme,which does not require complete knowledge of the eigenstructure of the hyperbolic mathematical model,offers a suitable and attractive option.The quality of the dam-break flood simulations with GMUSTA scheme is verified by comparing the results against laboratory tests and some experimental data available in the literature,on fixed and mobile bed conditions,with different bed materials and flush or stepped bottoms.The numerical results reproduce quite well the experimental evidence,proving that the model is capable of predicting the temporal evolution of the free-surface and the bed.The GMUSTA scheme,which is not only simple to implement but also both accurate and computationally efficient,is proposed as an appropriate tool for integrating non-equilibrium sediment-transport models.     

Elastoplastic multiphase model for simulating the response of piled raft foundations subject to combined loadings

A multiphase model and corresponding computational time‐saving finite element code is proposed in this paper for predicting the settlements experienced by a piled raft foundation when subject to the combined action of vertical and lateral loadings. This model, which is formulated in the framework of an elastoplastic behaviour for the soil and the reinforcing piles as well, explicitly accounts for the shear and flexural behaviour of the latter. Starting from a simple analytical example where all the concepts attached to this model are clearly illustrated, the main stages leading to its finite element implementation are then presented. The numerical tool thus elaborated, is applied to the simulation of a pile‐reinforced strip foundation submitted to a horizontally applied seismic load in addition to a permanent vertical load. One of the key results of such a simulation in terms of design recommendation, lies in the conclusion that, while the shear and flexural contributions of the reinforcement play quite a negligible role in the case of a vertical load (as compared with their axial resistance), they remain absolutely essential for withstanding the seismic lateral loading. Copyright © 2006 John Wiley & Sons, Ltd.     

Changes in land cover and shallow landslide activity: A case study in the Spanish Pyrenees

The Pyrenees, like many other mountain areas in Europe, have experienced depopulation and land abandonment during the 20th century. This has encouraged vegetation recovery in formerly occupied areas, including reforestation to promote woodland. The objective of this study is to analyse the effects of these changes on shallow landsliding, a process responsible for erosion and land degradation in many mountain areas. A sequence of aerial images reveals a slight decrease in the landslide occurrence rate in the last half of the 20th century and a parallel increase in the landslide extinction rate, i.e. the rate at which evidence of landslide activity is removed by vegetation colonisation. A logistic regression routine was used to assess the influence of land use and vegetation recovery in the occurrence of shallow landslides. The result shows that the former arable fields on the valley slopes still facilitate landsliding, even after land abandonment and revegetation by shrubs or trees. A shift in the topographic location of landslides was also detected, pointing to an increased importance of water redistribution in the slopes after prolonged rainfall periods.     

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.     

丁字湾近岸海底沉积物地层单元划分及沉积特征

通过对172 km的高分辨率浅地层剖面与6个钻孔的综合对比,对丁字湾近岸海底沉积物进行细分,自上而下划分为D0,D1,D2,D3共4个反射界面,SU1,SU2,SU3共3个沉积单元,该文总结了每个沉积单元的埋深、分布及特征,按照各个层序形成时的相对海平面位置及其升降变化态势,划分了海侵、低位、高位3个体系域,分别对应滨海相、陆相、滨海相沉积相。查明了丁字湾近岸浅地层结构及沉积特征,为该区的发展规划、海洋工程建设提供地质依据。     

肥城市城市规划区浅层地温能开发利用适宜性评价

在充分收集基础地质、水文地质、地热地质等资料基础上,结合钻探施工、现场热响应试验测试、井温测量、实验室测试等手段,对奥陶纪碳酸盐岩类裂隙岩溶水水位埋深、水温展布进行了测量,确定水位埋深在65～75m,埋深最大者位于白云山公园南、沙窝社区西南一带,埋深达约100m。平面地温最高温分布于研究区西部,西大封—朱庄—金槐—冉庄村一带,温度达到18℃以上。对肥城市城市规划区浅层地温能开发利用进行了适宜性评价,地下水地源热泵适宜中等区位于大面积地区,适宜差区位于泰肥铁路以北、白云山公园、龙山公园、母猪山一带。地埋管地源热泵适宜中等区位于大面积地区,适宜差区位于白云山公园、龙山公园一带的生态保护区。肥城市城市规划区浅层地温能资源丰富、开采条件便利,潜力巨大,具有良好的发展浅层地温能的基础条件。     

Analytical analysis of hydraulic effect of vegetation on shallow slope stability with different root architectures

A parametric study is performed to investigate hydraulic effect of vegetation on shallow slope stability with different root architectures in an infinite slope. Calculated results show during the first one hour of rainfall (181 mm/day), the exponential root architecture has higher ability to maintain shallow slope stability than the parabolic one. Under light rainfall (i.e., 20 mm/day) for 24 h, hydraulic effect of vegetation is more important inside root zone than outside root zone, while it is the opposite for rainfall intensities of 181 and 394 mm/day over the same duration. Parabolic rooted slope is more sensitive to rainfall pattern than bare one.     

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DE–LSSVM_SLP. The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model. In this research, a GIS database with 129 historical landslide records in the Quy Hop area (Central Vietnam) has been collected to establish the hybrid model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the performance of the newly constructed model. Experimental results show that the proposed model has high performances with approximately 82% of AUCs on both training and validating datasets. The model’s results were compared with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 Decision Trees. The result comparison demonstrates that the DE–LSSVM_SLP deems best suited for the dataset at hand; therefore, the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.