核电站控制棒驱动机构Canopy焊缝焊接温度场和应力-应变场模拟

目的 研究机加工和拉拔2种成形方式下得到的填充环对Canopy焊缝的影响,获取焊接焊缝成形、焊接残余应力和变形的相关数据,以指导Canopy焊缝焊接工艺。方法 采用数值模拟的方法,建立Canopy焊缝焊接数值分析模型,模拟焊接温度场、焊接残余应力和焊接残余变形。结果 拉拔成形环焊接熔池高度为9 mm,机加工成形环焊接熔池高度为8.3 mm;机加工成形环焊接最大残余应力为255.6 MPa,而拉拔成形环焊接最大残余应力为277.8 MPa,均出现在管座紧贴焊缝的位置;机加工成形环焊接残余变形为0.19 mm,拉拔成形环焊接残余变形为0.186 mm,最大残余变形均出现在焊接起始位置附近,在焊缝与管座交接的位置。结论 熔池形貌直接影响了热影响区域的大小,拉拔Y型环焊接熔池高度更大,焊接的热影响区域更大;拉拔Y型环焊接残余应力略大于机加工Y型环焊接残余应力;机加工成形环和拉拔成形环焊接残余变形相近。     

基于ENVISAT ASAR数据的鄱阳湖湿地生物量反演研究

湿生植被是鄱阳湖湿地生态系统的重要组成部分,生物量的大小是衡量湿地生态系统初级生产力的主要指标之一.本文利用ENVISAT ASAR交替极化(HH,VV)数据对鄱阳湖湿地地区的湿生植被进行生物量反演研究,并在密歇根微波冠层散射(MIMICS)模型模拟分析的基础上利用人工神经网络(ANN)方法来反演生物量.据此计算出鄱阳湖4月份湿生植被的总生物量干重约为1.065×109kg,并给出了生物量分布图.反演结果表明,ENVISAT ASAR数据可以很好地用于湿地植被生物量反演;神经网络生物量反演方法可以有效地表达生物量与后向散射系数之间复杂的非线性关系,从而大大提高反演精度;反演结果的误差主要来自于实地采样、图像配准、反演计算过程中带来的误差.     

云环境下的高效K-Medoids并行算法

传统聚类算法K-Medoids对初始点的选择具有随机性,容易产生局部最优解;替换聚类中心时采用的全局顺序替换策略降低了算法的执行效率;同时难以适应海量数据的运算。针对上述问题,提出了一种云环境下的改进K-Medoids算法,该改进算法结合密度法和最大最小原则得到优化的聚类中心,并在Canopy区域内对中心点进行替换,再采用优化的准则函数,最后利用顺序组合MapReduce编程模型的思想实现了算法的并行化扩展。实验结果表明,该改进算法与传统算法相比对初始中心的依赖降低,提高了聚类的准确性,减少了聚类的迭代次数,降低了聚类的时间。     

A LiDAR-derived canopy density model for tree stem and crown mapping in Australian forests

The retrieval of tree and forest structural attributes from Light Detection and Ranging (LiDAR) data has focused largely on utilising canopy height models, but these have proved only partially useful for mapping and attributing stems in complex, multi-layered forests. As a complementary approach, this paper presents a new index, termed the Height-Scaled Crown Openness Index (HSCOI), which provides a quantitative measure of the relative penetration of LiDAR pulses into the canopy. The HSCOI was developed from small footprint discrete return LiDAR data acquired over mixed species woodlands and open forests near Injune, Queensland, Australia, and allowed individual trees to be located (including those in the sub-canopy) and attributed with height using relationships (r² = 0.81, RMSE = 1.85 m, n = 115; 4 outliers removed) established with field data. A threshold contour of the HSCOI surface that encompassed ∼ 90% of LiDAR vegetation returns also facilitated mapping of forest areas, delineation of tree crowns and clusters, and estimation of canopy cover. At a stand level, tree density compared well with field measurements (r² = 0.82, RMSE = 133 stems ha^− 1, n = 30), with the most consistent results observed for stem densities ≤ 700 stems ha^− 1. By combining information extracted from both the HSCOI and the canopy height model, predominant stem height (r² = 0.91, RMSE = 0.77 m, n = 30), crown cover (r² = 0.78, RMSE = 9.25%, n = 30), and Foliage & Branch Projective Cover (FBPC; r² = 0.89, RMSE = 5.49%, n = 30) were estimated to levels sufficient for inventory of woodland and open forest structural types. When the approach was applied to forests in north east Victoria, stem density and crown cover were reliably estimated for forests with a structure similar to those observed in Queensland, but less so for forests of greater height and canopy closure.     

Exploring LiDAR-RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR

Scanning Light Detecting and Ranging (LiDAR), Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) were analyzed to determine (1) which of the three sensor systems most accurately predicted forest biomass, and (2) if LiDAR and SAR/InSAR data sets, jointly considered, produced more accurate, precise results relative to those same data sets considered separately. LiDAR ranging measurements, VHF-SAR cross-sectional returns, and X- and P-band cross-sectional returns and interferometric ranges were regressed with ground-estimated (from dbh) forest biomass in ponderosa pine forests in the southwestern United States. All models were cross-validated. Results indicated that the average canopy height measured by the scanning LiDAR produced the best predictive equation. The simple linear LiDAR equation explained 83% of the biomass variability (n = 52 plots) with a cross-validated root mean square error of 26.0 t/ha. Additional LiDAR metrics were not significant to the model. The GeoSAR P-band (λ = 86 cm) cross-sectional return and the GeoSAR/InSAR canopy height (X-P) captured 30% of the forest biomass variation with an average predictive error of 52.5 t/ha. A second RaDAR-FOPEN collected VHF (λ ∼ 7.8 m) and cross-polarized P-band (λ = 88 cm) cross-sectional returns, none of which proved useful for forest biomass estimation (cross-validated R² = 0.09, RMSE = 63.7 t/ha). Joint consideration of LiDAR and RaDAR measurements produced a statistically significant, albeit small improvement in biomass estimation precision. The cross-validated R² increased from 83% to 84% and the prediction error decreased from 26.0 t/ha to 24.9 t/ha when the GeoSAR X-P interferometric height is considered along with the average LiDAR canopy height. Inclusion of a third LiDAR metric, the 60th decile height, further increased the R² to 85% and decreased the RMSE to 24.1 t/ha. On this 11 km² ponderosa pine study area, LiDAR data proved most useful for predicting forest biomass. RaDAR ranging measurements did not improve the LiDAR estimates.     

Reflectance seasonality and its relation to the canopy leaf area index in an eastern Siberian larch forest: Multi-satellite data and radiative transfer analyses

Reliable monitoring of seasonality in the forest canopy leaf area index (LAI) in Siberian forests is required to advance the understanding of climate-forest interactions under global environmental change and to develop a forest phenology model within ecosystem modeling. Here, we compare multi-satellite (AVHRR, MODIS, and SPOT/VEGETATION) reflectance, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and LAI with aircraft-based spectral reflectance data and field-measured forest data acquired from April to June in 2000 in a larch forest near Yakutsk, Russia. Field data in a 30 × 30-m study site and aircraft data observed around the field site were used. Larch is a dominant forest type in eastern Siberia, but comparison studies that consider multi-satellite data, aircraft-based reflectance, and field-based measurement data are rarely conducted. Three-dimensional canopy radiative transfer calculations, which are based on Antyufeev and Marshak's [Antyufeev, V.S., & Marshak, A.L. (1990). Monte Carlo method and transport equation in plant canopies, Remote Sensing of Environment, 31, 183-191] Monte Carlo photon transport method combined with North's [North, P.R. (1996). Three-dimensional forest light interaction model using a Monte Carlo method, IEEE Transactions on Geoscience and Remote Sensing, 34(4), 946-956] geometric-optical hybrid forest canopy scene, helped elucidate the relationship between canopy reflectance and forest structural parameters, including several forest floor conditions. Aircraft-based spectral measurements and the spectral response functions of all satellite sensors confirmed that biases in reflectance seasonality caused by differences in spectral response functions among sensors were small. However, some reflectance biases occur among the near infrared (NIR) reflectance data from satellite products; these biases were potentially caused by absolute calibration errors or cloud/cloud shadow contamination. In addition, reflectance seasonality in AVHRR-based NIR data was very small compared to other datasets, which was partially due to the spring-to-summer increase in the amount of atmospheric water vapor. Radiative transfer simulations suggest that bi-directional reflectance effects were small for the study site and observation period; however, changes in tree density and forest floor conditions affect the absolute value of NIR reflectance, even if the canopy leaf area condition does not change. Reliable monitoring of canopy LAI is achieved by minimizing these effects through the use of NIR reflectance difference, i.e., the difference in reflectance on the observation day from the reflectance on a snow-free/pre-foliation day. This may yield useful and robust parameters for multi-satellite monitoring of the larch canopy LAI with less error from intersensor biases and forest structure/floor differences. Further validation with field data and combined use of other index (e.g. normalized difference water index, NDWI) data will enable an extension of these findings to all Siberian deciduous forests.     

Direct retrieval of the shape of leaf spectral albedo from multiangular hyperspectral Earth observation data

Physically-based retrieval of vegetation canopy properties from remote sensing data presumes a knowledge of the spectral albedo of the basic scattering unit, i.e. leaf. In this paper, we present a novel method to directly retrieve the spectral dependence of leaf single-scattering albedo of a closed broadleaf forest canopy from multiangular hyperspectral satellite imagery. The new algorithm is based on separating the reflected signal into a linear (first-order) and non-linear (diffuse) reflectance component. A limitation of the proposed algorithm is that the leaf single-scattering albedo ω(λ) is retrieved with an accuracy of a structural parameter (called a₀) which, in turn, depends on canopy bidirectional gap probability, ratio of leaf reflectance to transmittance, and distribution of leaf normals. The structural parameter (a₀) was found to depend on tree-level structural parameters, such as tree height and volume of a single crown, but not the amount of leaf area.     

Characterizing forest canopy structure with lidar composite metrics and machine learning

A lack of reliable observations for canopy science research is being partly overcome by the gradual use of lidar remote sensing. This study aims to improve lidar-based canopy characterization with airborne laser scanners through the combined use of lidar composite metrics and machine learning models. Our so-called composite metrics comprise a relatively large number of lidar predictors that tend to retain as much information as possible when reducing raw lidar point clouds into a format suitable as inputs to predictive models of canopy structural variables. The information-rich property of such composite metrics is further complemented by machine learning, which offers an array of supervised learning models capable of relating canopy characteristics to high-dimensional lidar metrics via complex, potentially nonlinear functional relationships. Using coincident lidar and field data over an Eastern Texas forest in USA, we conducted a case study to demonstrate the ubiquitous power of the lidar composite metrics in predicting multiple forest attributes and also illustrated the use of two kernel machines, namely, support vector machine and Gaussian processes (GP). Results show that the two machine learning models in conjunction with the lidar composite metrics outperformed traditional approaches such as the maximum likelihood classifier and linear regression models. For example, the five-fold cross validation for GP regression models (vs. linear/log-linear models) yielded a root mean squared error of 1.06 (2.36) m for Lorey's height, 0.95 (3.43) m for dominant height, 5.34 (8.51) m²/ha for basal area, 21.4 (40.5) Mg/ha for aboveground biomass, 6.54 (9.88) Mg/ha for belowground biomass, 0.75 (2.76) m for canopy base height, 2.2 (2.76) m for canopy ceiling height, 0.015 (0.02) kg/m³ for canopy bulk density, 0.068 (0.133) kg/m² for available canopy fuel, and 0.33 (0.39) m²/m² for leaf area index. Moreover, uncertainty estimates from the GP regression were more indicative of the true errors in the predicted canopy variables than those from their linear counterparts. With the ever-increasing accessibility of multisource remote sensing data, we envision a concomitant expansion in the use of advanced statistical methods, such as machine learning, to explore the potentially complex relationships between canopy characteristics and remotely-sensed predictors, accompanied by a desideratum for improved error analysis.     

磷营养胁迫对冬小麦冠层光谱的影响

因为磷素重要的营养作用,其胁迫的存在影响冬小麦的正常生长。借助地面遥感仪器获取冬小麦在磷营养胁迫下的多个生育期里的冠层光谱数据并对其影响特征进行了分析。利用因子分析方法提取主因子与含有丰富信息的光谱变量,并结合极显著水平(0.01)的均值比较与检验过程考察了冬小麦冠层光谱,确定了对磷营养胁迫敏感的光谱波段:760nm,810nm和870nm与950nm,并在此基础上结合冬小麦对磷素的吸收利用特征选定了运用冠层光谱敏感波段反射率探测和区分磷营养胁迫的关键生育期:拔节期。结果同时表明,对冬小麦磷营养胁迫而言,近红外区间(760nm~1100nm)光谱反射特征的区分能力要强于可见光区。本文同时指出了研究与发展利用遥感技术进行营养胁迫监测的方法和着重点。