Verification and Assessment of Real-time Forecasts of Two Extreme Heavy Rain Events in Zhengzhou by Operational NWP Models     

钟水新  庄妍  胡胜  陈子通  丁伟钰  冯业荣  邓涛  刘显通  张艳霞  徐道生  戴光丰  蒙伟光 《热带气象学报(英文版)》2021,27(4):406-417

In the present study, the performances of the NWP models on two heavy rainfalls on 20 July and 22 August 2021 over Henan Province were investigated. The impacts of the water vapor transport to the extreme rainfall were further discussed. The results showed that the regional model system in the Guangzhou Meteorological Service generally showed high scores on the extreme rainfall over Henan. The maximum 24h accumulative rainfall by the 24h forecasts by the CMA-GD reached 556 mm over Henan Province. The 24-h and 48-h Threat Score (TS) of heavy rainfall reached 0.56 and 0.64. The comparisons of the Fraction Skill Score (FSS) verifications of the heavy rainfall by CMA-GD and CMA-TRAMS at the radium of 40km reached 0.96 and 0.87. The water vapor transport to the extreme rainfall showed that the vertically integrated water vapor transport (IVT) of the whole layer before the occurrence of the heavy rainfall exhibited a double-eyes distribution in case 7 · 20. The north eye over Henan reached the same magnitude of IVT as the typhoon eye (Cempaka) over south China. The IVT over the lower troposphere (<500 hPa) showed an overwhelming magnitude than the upper level, especially in the planetary boundary layer (<700 hPa). More practical technical needs to be developed to improve its performances on the forecasting of extreme rainfall, as well as more experiments need to be conducted to examine the effects of the specific terrain and physical schemes on the extreme rainfall.  相似文献   

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow     

Michael J. Friedel  Massimo Buscema  Luiz Eduardo Vicente  Fabio Iwashita  Andréa Koga-Vicente 《International Journal of Digital Earth》2018,11(7):670-690

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.  相似文献   

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data     

Milad Janalipour 《International Journal of Digital Earth》2018,11(12):1241-1268

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.  相似文献   

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery     

Erik H. Schmidt  Budhendra L. Bhaduri  Nicholas Nagle  Bruce A. Ralston 《地理信息系统科学与遥感》2018,55(6):860-879

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.  相似文献   

The effects of visual realism,spatial abilities,and competition on performance in map-based route learning in men     

Arzu Çöltekin  Rebecca Francelet  Kai-Florian Richter  John Thoresen  Sara Irina Fabrikant 《制图学和地理信息科学》2018,45(4):339-353

We report on how visual realism might influence map-based route learning performance in a controlled laboratory experiment with 104 male participants in a competitive context. Using animations of a dot moving through routes of interest, we find that participants recall the routes more accurately with abstract road maps than with more realistic satellite maps. We also find that, irrespective of visual realism, participants with higher spatial abilities (high-spatial participants) are more accurate in memorizing map-based routes than participants with lower spatial abilities (low-spatial participants). On the other hand, added visual realism limits high-spatial participants in their route recall speed, while it seems not to influence the recall speed of low-spatial participants. Competition affects participants’ overall confidence positively, but does not affect their route recall performance neither in terms of accuracy nor speed. With this study, we provide further empirical evidence demonstrating that it is important to choose the appropriate map type considering task characteristics and spatial abilities. While satellite maps might be perceived as more fun to use, or visually more attractive than road maps, they also require more cognitive resources for many map-based tasks, which is true even for high-spatial users.  相似文献   

基于深度学习的岩石矿物智能识别     

任伟  张盛  乔计花  黄金明 《地质论评》2021,67(Z1):67z1281-67z1282

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雅鲁藏布江流域极端降水模拟及预估     

高佳佳  杜军 《冰川冻土》2021,43(2):580-588

为了解雅鲁藏布江流域汛期极端降水的变化规律,推算一定重现期的极端降水量分位数,通过百分位法、Hill图法、年交叉率法选取阈值,借助广义帕累托分布函数（GPD）对流域极端降水频率进行了分析。结果表明：99百分位时的阈值为流域内各站点的最佳阈值,且各站点超阈值序列通过了M-K的平稳性检验,无明显突变。拟合效果通过K-S检验,各站点拟合的极端降水理论频数和实测频数基本相符。尺度参数的大值区位于流域下游,表明该地区的极值波动大;形状参数正值区位于流域中上游地区,说明发生破纪录降水事件的概率较大,拟合结果与实际观测一致。从5年一遇和10年一遇的极端降水值来看,雅江流域除拉孜站外,其他地区降水极值均超过30 mm,日喀则地区的降水极值达50 mm;各地区20年一遇和30年一遇的降水极值增长的非常缓慢。通过与实际极端降水值对比分析得出,GPD拟合计算出的重现期水平基本符合实际,即具有一定的合理性。  相似文献   

2011~2020年勘查地球化学数据处理研究进展     

左仁广  王健  熊义辉  王子烨 《矿物岩石地球化学通报》2021,40(1):81-93

我国建立了包含海量数据的高质量的勘查地球化学数据库,为矿产勘查、环境评价和地质调查等提供了重要的数据支撑。如何高效处理勘查地球化学数据,并从中发掘和识别深层次信息一直是勘查地球化学学科研究的热点和前沿领域。本文在系统调研国内外学者过去十年发表的论著基础上,对勘查地球化学数据处理方法进行分析与对比,从勘查地球化学数据库建设、地球化学异常识别及其不确定性评价等方面概述了我国近十年来在该领域取得的主要研究进展,包括:(1)分形与多重分形模型由于考虑了地球化学空间模式的复杂性和尺度不变性,在全球范围内得到极大的发展和推广,我国学者引领了基于分形与多重分形的勘查地球化学数据处理;(2)机器学习和大数据思维开始在该领域启蒙,并迅速得到关注,正在成为研究热点和前沿领域,我国学者率先开展基于机器学习算法的勘查地球化学大数据挖掘研究;(3)我国学者需要进一步加强勘查地球化学数据缺失值处理以及成分数据闭合效应研究。今后该领域应进一步加强对弱缓地球化学异常识别、异常不确定性评价以及异常识别与其形成机理相结合等方面的研究。  相似文献   

基于多源遥感影像的长春市城市建成区提取     

王博帅  蒲东川  李婷婷  牛雪峰 《吉林大学学报(地球科学版)》2021,51(4):1284-1294

夜光遥感影像记录的城市灯光与人类活动密切相关,已广泛应用于城市信息提取。珞珈一号作为新一代夜光遥感数据源,比以往的夜光数据具有更高的空间分辨率和光谱分辨率,可以更清晰地表达城市建成区范围和内部结构。本文利用珞珈一号夜光遥感影像,通过人类居住指数（human settlement index, HSI）、植被覆盖和建筑共同校正的城市夜光指数（vegetation and build adjusted nighttime light urban index, VBANUI）及支持向量机（support vector machine, SVM）监督分类3种方法对长春市城市建成区进行提取,并与利用NPP/VIIRS（suomi national polar-orbiting partnership/visible infrared imaging radiometer suite）夜光遥感影像、采用同样方法得到的结果对比。结果显示：本文提出的VBANUI提高了传统植被覆盖校正的城市夜光指数（vegetation adjusted nighttime light urban index, VANUI）的提取精度,使用珞珈一号夜光遥感影像通过VBANUI提取的城市建成区结果最优,其Kappa系数为0.80,总体分类精度为90.74%;使用珞珈一号和NPP/VIIRS夜光遥感影像通过HSI按最佳阈值提取城市建成区的Kappa系数分别为0.75和0.72,总体分类精度分别为88.27%和86.54%;复合数据的SVM监督分类法中Landsat-NDBI、Landsat-NDBI-VIIRS、Landsat-NDBI-LJ和Landsat-NDBI-LJlog的Kappa系数分别为0.602、0.627、0.643和0.681,总体分类精度分别为81.11%、81.52%、82.25%和84.48%。研究结果表明：3种提取方法下,均为使用珞珈一号夜光遥感影像的结果优于使用NPP/VIIRS夜光遥感影像的结果,证明相比于NPP/VIIRS夜光遥感影像,珞珈一号夜光遥感影像更适用于城市尺度的建成区范围提取。  相似文献   

机器学习与成矿预测:以闽西南铁多金属矿预测为例     

张振杰  成秋明  杨玠  武国朋  葛云钊 《地学前缘》2021,28(3):221-235

作为近年来爆炸式发展的方法模型,机器学习为地质找矿提供了新的思维和研究方法。本文探讨矿产预测研究的理论方法体系,总结机器学习在矿产预测领域的特征信息提取和信息综合集成两个方面的应用现状,并讨论机器学习在矿产资源定量预测领域面临的训练样本稀少且不均衡、模型训练中缺乏不确定性评估、缺少反哺研究、方法选择等困难和挑战。进一步以闽西南马坑式铁矿为实例论述基于机器学习方法的矿产预测基本流程:(1)通过成矿系统研究建立成矿模型,确定矿床控矿要素;(2)通过勘查系统研究建立找矿模型,并为评价预测提供相关的勘查数据;(3)通过预测评价系统研究,建立预测模型,并提取预测要素;(4)利用机器学习模型对预测要素进行信息综合集成,获取成矿有利度图;(5)对预测性能和结果进行不确定性评估;(6)找矿靶区/成矿远景区圈定及资源量估算。最后,总结建立以地学大数据和地球系统理论为指导,以“地球系统-成矿系统-勘查系统-预测评价系统”为研究路线的基于地学大数据的矿产资源定量预测理论和方法体系的研究愿景。  相似文献