Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.     

Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots,UAV-LiDAR data and Sentinel-2 imagery

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha⁻¹. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R² = 0.62 > 0.52, RMSE = 50.36 Mg ha⁻¹<56.63 Mg ha⁻¹). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R² = 0.62, RMSE = 51.03 Mg ha⁻¹). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.     

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.     

Numerical simulation of nearshore circulation on field topography under random wave environment

In order to investigate surf zone hydrodynamics through two-dimensional numerical simulations of nearshore circulation under random wave environment, a nearshore circulation model, SHORECIRC, and a random wave model, SWAN, were combined and utilized. Using this combined model, a numerical simulation of the October 2, 1997 SandyDuck field experiment was performed. For this simulation, field topography and an input offshore spectrum were constructed using observed data sets synchronized with the experiment. The wave-breaking model in SWAN was modified by using breaker parameters varied according to bottom slope. The simulation results were compared with the experimental data, which revealed a well-developed longshore current, as well as with results using other combinations which were SHORECIRC and its original monochromatic wave-driver, and SHORECIRC and the default of SWAN. The results from the novel combined model agreed well with the experimental data. The results of the present simulation also indicate that alongshore field topography influences shear fluctuation of longshore currents.     

Numerical simulation of random wave slamming on structures in the splash zone

The numerical investigation of random wave slamming on superstructures of marine structures in the splash zone is presented in this paper. The impact pressures on the underside of the structure are computed based on the improved volume of fluid method (VOF). The governing equations are Reynolds time-averaged equations and the two equation k– model. The third order upwind difference scheme is applied to the convection term to reduce the effect of numerical viscosity. The numerical wave flume with random wave-maker suitable for VOF is established. Appropriate moving contact-line boundary conditions are introduced to the model wave in contact with and separated from the underside of structure. Parametric studies have been carried out for different incident waves, structure dimensions and structure clearance. The numerical results are verified by the experimental results.     

Optimal design of two-dimensional wings in ground effect using multi-objective genetic algorithm

The shape optimization of the 2-dimensional wing in ground effect (WIG) has been performed by the integration of CFD (computational fluid dynamics) and MOGA (multi-objective genetic algorithm). Because of the trade-off between the aerodynamic forces and the height stability, it is difficult to satisfy the design requirements of efficiency and stability at the same time. In this study, the lift coefficient, the lift-drag ratio and the static height stability are chosen as the objective functions to obtain the optimal wing profiles of a WIG craft. An NACA0015 airfoil is used for the baseline model; the aerodynamic characteristics of the base model are compared with that of the optimal solutions. The profile of the airfoil is constructed by four Bezier curves with fourteen control points resulting in the eighteen coordinates, which are adopted as the design variables. The optimal solutions of the multi-objective optimization are not unique but a set of the non-dominated optima: the Pareto frontiers or a Pareto set. As the results of the multi-objective optimization, the forty Pareto optima, which include high-lift, high-efficiency, and more stable airfoils on the edge of the 3-dimensional objective space, are obtained at thirty evolutions of the generation.     

基于加权约束的单体建筑物点云表面重建算法

建筑物点云表面重建在高精度城市测绘、虚拟现实等领域有十分广泛的应用前景。由于建筑物的几何形态多变,重建算法普遍存在计算速率慢、拟合精度低和模型结构不完整的问题。为此,本文以单体建筑物为研究对象,提出基于加权约束的单体建筑物点云表面重建算法,在表面初始化过程中充分考虑数据对结构拟合的贡献。在此基础上,构建基于正则集的单体建筑物表面重建算法,实现建筑物拟合过程中的加权拟合误差、近邻结构平滑的同步优化。针对多类建筑物三维点云的实验结果表明,相比传统的建筑物重建策略,本文的加权约束方法可根据不同类型的点云数据设计自适应权重,并选择模型拟合中最优的权重函数,在高噪声、低精度点云数据下能得到更高精度的单体建筑物表面模型。     

Mapping the farming-pastoral ecotones in China

Conventional farming-pastoral ecotones methods of delineating were not quantitative and could not fully show their spatial distribution. The present paper attempts to develop quantitative methods for mapping farming-pastoral ecotones in China. Nine indicators, related to temperature, precipitation and altitude aspects, were selected to quantify ecological susceptibility of vegetation （crops and forage）. Methods of analytic hierarchy process （AHP） and expert score ranking combined with fuzzy set theory were applied to assign the weight for each indicator and to define the membership functions. The geographic information system （GIS） was used to manage the spatial database and conduct the spatial analysis. According to the spatial calculation of evaluation model integrated with GIS, the ecological susceptibility of vegetation （crops and forage） was mapped. Three different zones, pastoral area, farming-pastoral ecotones and farming area, were classified by spatial cluster analysis and the maximum likelihood classification for the numeric map of vegetation ecological susceptibility by GIS. This map was validated by the economic statistical result based on the ratio of the output value from animal husbandry in total output value of agriculture by the National Bureau of Statistics in China, indicating that the mapping of the farming-pastoral ecotones may be accepted.     

人工育珠对三角帆蚌(Hyriopsis cumingii)遗传基因影响的初步研究

采用酚-氯仿和试剂盒两种提取法对三角帆蚌怀珠群与非怀珠群各6个个体进行基因组DNA的提取,然后在优化RAPD(随机扩增多态性DNA)检测条件基础上,从80个随机引物中筛选出12个扩增较好且多态性强的引物进行RAPD扩增,产物通过水平板琼脂糖凝胶电泳和垂直板聚丙烯酰胺凝胶电泳两种方法进行检验并对DNA的多态性进行分析。结果显示在怀珠群和非怀珠群检测到的位点数、多态位点比例、平均Shannon多态性信息指数、平均Nei's基因多样性指数、群体内个体间平均遗传相似率和平均遗传距离分别为100、33%、0.1927、0.1324、0.904、0.096,95、47.37%、0.2711、0.1879、0.861、0.139,群体间的平均遗传距离为0.821,非怀珠群的变异性大于怀珠群;本研究还获得了两群体各自的特异扩增谱带及两群体间表达差异较大谱带,这些位点很可能是由人工育珠所引起。根据MEGA4.0软件的UPGMA和NJ程序构建的分子系统树可直观地将两群体分开。     

Adaptive Predictive Inverse Control of Offshore Jacket Platform Based on Rough Neural Network

The offshore jacket platform is a complex and time-varying nonlinear system,which can be excited of harmful vibration by external loads.It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model.In this paper,an adaptive inverse control method is proposed on the basis of novel rough neural networks (RNN) to control the harmful vibration of the offshore jacket platform,and the offshore jacket platform model is established by dynamic stiffness matrix (DSM) method.Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory,RNN is utilized to identify the predictive inverse model of the offshore jacket platform system.Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads,and to deal with the delay problem caused by signal transmission in the control process.The numerical results show that the constructed novel RNN has advantages such as clear structure,fast training speed and strong error-tolerance ability,and the proposed method based on RNN can effectively control the harmfid vibration of the offshore jacket platform.