Diffraction of multidirectional random waves by multiple rectangular submarine pits     

Hong Sik Lee  A. Neil Williams  Bo Hyung Lee  Jeill Oh 《Ocean Engineering》2003,30(1):1287-106

A numerical model is presented to predict the interaction of multidirectional random surface waves with one or more rectangular submarine pits. The water depth is assumed uniform and the method involves the superposition of diffraction solutions based on linearized shallow water wave theory obtained by a two-dimensional boundary integral approach. The incident wave conditions are specified using a discrete form of the Mitsuyasu directional spectrum. The present numerical model has been validated through comparisons with previous theoretical results for regular waves. Good agreement was obtained in all cases. Based on these comparisons it is concluded that the present numerical model is an accurate and efficient tool to predict the wave field around multiple submarine pits and navigation channels in many practical situations.  相似文献   

两种海蜇毒素的分子标记研究     

苏秀榕  杨春  黄晓春  李太武 《海洋与湖沼》2006,37(3):206-210

采用聚丙烯酰胺凝胶电泳技术研究了8种同工酶在海蜇的刺胞和中胶层的表达特异性,利用RAPD技术对海蜇刺胞组织的DNA标记进行研究。结果表明,作为生物体防御清除自由基的SOD,在刺胞和中胶层均有表达。而与酯类化合物代谢相关的EST、维持细胞正常的能量代谢的ATPase,能在海蜇和口冠海蜇的刺胞中表达,而不能在中胶层中表达,所以,EST和ATPase可作为刺胞毒素的分子标记。碳水化合物代谢中重要的酶类MDH和ADH、清除细胞内H2O2的POD、催化磷酸单酯水解的重要酶类(与磷脂的转移、消化、吸收等活动密切相关的)ACP、在体外碱性环境下能水解有机磷脂键而产生一个有机基团和无机磷酸根的ALP,这5种酶仅在毒性较强的口冠海蜇刺胞中表达,MDH和ACP活性很高,在毒性相对较弱的海蜇刺胞中不表达。所以,这5种酶可作为海蜇毒性强弱的标记。以两种海蜇刺胞DNA为模板,S11、S32、S38、S95等4个随机引物的RAPD谱图差异明显,亦可作为毒素强弱的间接分子标记。  相似文献   

非线性随机海浪模型的一种新形式   总被引：5，自引：1，他引：5  

刘新安  黄培基 《海洋与湖沼》1997,28(3):310-314

以反映随机海浪非线性的波面高度分布高阶矩为参量，提出一种新形式的非线性随机海浪模型，在三阶近似下具体导出其波面高度的表达工和推导出二阶谱。本文模式为Ｌｏｎｇｕｅｔ－Ｈｇｇｉｎｓ模式的另一种新的数学表示。  相似文献   

A note on wave energy dissipation over steep beaches     

T.T. Janssen  J.A. Battjes 《Coastal Engineering》2007

This paper revisits the derivation of the parametric surf zone model proposed by Baldock et al. [Baldock, T. E., Holmes, P., Bunker, S. & Van Weert, P. 1998 Cross-shore hydrodynamics within an unsaturated surf zone. Coast. Eng. 34, 173–196.]. We show that a consistent use of the proposed Rayleigh distribution for surf zone wave heights results in modification of the expressions for the bulk dissipation rate and enhanced dissipation levels on steep beaches and over-saturated surf zone conditions. As a consequence, the modification proposed herein renders the model robust even on steep beaches where it could otherwise develop a shoreline singularity.  相似文献   

Boundary element modeling of multidirectional random waves in a harbor with partially reflecting boundaries     

Hong Sik Lee  A. Neil Williams 《Ocean Engineering》2002,29(1):39-58

A numerical model is presented for the prediction of the wave field due to the diffraction of directional random waves in a harbor of arbitrary shape with partially reflecting boundaries. The water depth is assumed uniform and the method is based upon the superposition of diffraction solutions for monochromatic waves obtained by a two-dimensional boundary integral equation approach. The incident wave conditions are specified using a discrete form of the Mitsuyasu directional spectrum. The present numerical model has been validated through comparisons with previous experimental data and theoretical results for both regular and random wave diffraction by offshore breakwaters and in harbors. Good agreement was obtained in all cases. Based on these comparisons it is concluded that the present numerical model is an accurate and efficient tool to predict the wave field inside a harbor or around a breakwater in many practical applications.  相似文献   

High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (H_canopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 H_canopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 H_canopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 H_canopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted H_canopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in H_canopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools.  相似文献   

Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR.  相似文献   

Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data     

《International Journal of Applied Earth Observation and Geoinformation》2015

Land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) and above-ground biomass are some of the key factors limiting agricultural production and ecosystem functioning. Leaf N and biomass can be used as indicators of rangeland quality and quantity. Conventional methods for assessing these vegetation parameters at landscape scale level are time consuming and tedious. Remote sensing provides a bird-eye view of the landscape, which creates an opportunity to assess these vegetation parameters over wider rangeland areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The estimation of above-ground biomass has been hindered by the signal saturation problems using conventional vegetation indices. The objective of this study is to monitor leaf N and above-ground biomass as an indicator of rangeland quality and quantity using WorldView-2 satellite images and random forest technique in the north-eastern part of South Africa. Series of field work to collect samples for leaf N and biomass were undertaken in March 2013, April or May 2012 (end of wet season) and July 2012 (dry season). Several conventional and red edge based vegetation indices were computed. Overall results indicate that random forest and vegetation indices explained over 89% of leaf N concentrations for grass and trees, and less than 89% for all the years of assessment. The red edge based vegetation indices were among the important variables for predicting leaf N. For the biomass, random forest model explained over 84% of biomass variation in all years, and visible bands including red edge based vegetation indices were found to be important. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability, and is important for rangeland assessment and monitoring.  相似文献   

基于随机抽样一致性算法的稳健点云平面拟合方法   总被引：1，自引：0，他引：1  

李孟迪  蒋胜平  王红平 《测绘科学》2015,40(1):102-106

针对点云数据平面拟合过程中存在粗差及异常值等问题,文章提出一种基于随机抽样一致性算法(RANSAC)的稳健平面拟合方法。该方法以RANSAC算法为基础并结合特征值法,通过设置一定的准则,剔除点云数据中存在的粗差及异常值,达到获得理想平面拟合参数的目的。运用此算法对仿真数据及实测数据进行平面拟合,并与传统算法进行比较,结果表明该方法可以很好地适应于点云数据中存在粗差及异常值的情况,获得较好的平面参数估计值,是一种稳健的平面拟合算法。  相似文献   

中尺度暴雨集合预报系统研发中的初值扰动试验   总被引：8，自引：1，他引：7       下载免费PDF全文

李俊  杜钧  王明欢  崔春光 《高原气象》2009,28(6):1365-1375

针对2006年5月24～25日一次暴雨过程, 通过一系列初值扰动试验探讨实际业务中建立集合预报系统的方法。运用45 km的WRF模式构建一个11个成员的集合预报系统来比较分析不同的扰动方案、 扰动的空间结构和扰动振幅对集合预报的影响, 结果表明： （1）初值扰动的空间结构对暴雨集合预报的离散度影响很关键, 而扰动振幅的影响却居次要地位。具有动力学结构的孵化扰动明显优于随机扰动。（2）集合预报比单一控制预报提供了更有价值的预报信息。例如在该个例控制预报中漏报的湖北监利强降水中心, 在集合预报中有20%的概率, 并且实况被包含在集合预报的预报范围之中。集合平均预报也明显优于控制预报\.例如矫正了在控制预报中明显虚报的鄂东北的大暴雨中心, 且集合平均预报的暴雨中心落在实际观测暴雨中心的附近。（3）集合离散度较好地反映了实际降水过程的可预报性。例如应用孵化扰动, 其离散度的空间结构同降水预报误差的空间分布大致对应。  相似文献