基于GIS的石家庄市雾霾分析     

苏亚聪  史娟  徐爽 《北京测绘》2020,(2):233-237

为了得出雾霾气象成因机制、影响因素和时空分布特征,以石家庄市区与郊县为研究区域,将2013年9月至2016年12月石家庄市各市区、郊县的PM2.5历史监测数据中的有效数据进行了数据分析处理得到雾霾浓度数据,还有温度、降水、风速、地形和人口密度等数据,运用GIS分析的方法,模拟绘制石家庄雾霾的时空分布图、雾霾与各影响因子的专题对比图,得到雾霾形成机制的因子、雾霾的时空分布规律、雾霾季节变化特征、雾霾与地形间的关系等;运用数据分析软件OriginPro8.SR3分析雾霾浓度与风速数据、降雨数据、温度数据间的相关性。  相似文献   

Event-based classification for global study of river flood generating processes     

Lina Stein  Francesca Pianosi  Ross Woods 《水文研究》2020,34(7):1514-1529

Better understanding of which processes generate floods in a catchment can improve flood frequency analysis and potentially climate change impacts assessment. However, current flood classification methods are either not transferable across locations or do not provide event-based information. We therefore developed a location-independent, event-based flood classification methodology that is applicable in different climates and returns a classification of all flood events, including extreme ones. We use precipitation time series and very simply modelled soil moisture and snowmelt as inputs for a decision tree. A total of 113,635 events in 4155 catchments worldwide were classified into one of five hydro-climatological flood generating processes: short rain, long rain, excess rainfall, snowmelt and a combination of rain and snow. The new classification was tested for its robustness and evaluated with available information; these two tests are often lacking in current flood classification approaches. According to the evaluation, the classification is mostly successful and indicates excess rainfall as the most common dominant process. However, the dominant process is not very informative in most catchments, as there is a high at-site variability in flood generating processes. This is particularly relevant for the estimation of extreme floods which diverge from their usual flood generation pattern, especially in the United Kingdom, Northern France, Southeastern United States, and India.  相似文献   

基于深度学习理论的无人机影像分类     

阳成 《北京测绘》2020,(4):481-484

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。  相似文献   

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.  相似文献   

基于遥感水边线的潮滩面积提取方法研究          下载免费PDF全文

张媛媛  高志强  刘向阳  许宁 《海洋开发与管理》2018,35(3):56-61

潮滩作为动态变化的后备土地资源,对其研究具有重要意义。文章以江苏省如东县为研究区,使用环境资源卫星(HJ1A/1B)影像为数据源,应用面向对象技术创建分类规则,实现批量半自动提取水边线,随后利用DSAS软件处理水边线集获得潮位点集,基于最外边界法实现高(低)潮位点的提取,进而获得潮滩范围,估算的如东县潮滩面积为55 182hm~2。最后利用误差矩阵进行精度验证(Kappa系数为0.945)。结果表明:该方法对于潮滩面积提取具有适用性,无论在空间域还是时间域都具有一定的推广性。  相似文献   

Google Earth Engine平台支持下的赣南柑橘果园遥感提取研究   总被引：1，自引：0，他引：1  

徐晗泽宇  刘冲  王军邦  齐述华 《地球信息科学学报》2018,20(3):396-404

赣南地区是中国柑橘主产区,柑橘种植产业经数十年发展已具较大规模。本文利用Google Earth Engine平台,使用2140景Landsat影像进行像元级融合,重构目标年份季节最小云量影像集,构建多维分类特征集,利用随机森林分类算法,实现了1990、1995、2000、2005、2010和2016年赣南柑橘果园的分布制图。结果表明：利用Google Earth Engine平台可实现大量遥感影像数据的高效处理;最小云量影像合成方法能够有效解决多云多雨地区高质量光学影像获取困难的问题;以最小云量影像合成构建的数据集,使用随机森林分类算法能够有效提取赣南柑橘果园,分类平均总体精度和Kappa系数分别为93.15%和0.90,分类效果良好;赣南柑橘果园面积由1990年9.77 km²扩大为2016年2200.34 km²,2005年以后呈大规模扩张趋势,果园分布由零星分布,逐步形成连片化的聚集分布特点,柑橘果园用地的主要来源为林地、灌丛和耕地。  相似文献   

基于ASP.NET MVC的海域海岸带整治修复项目信息管理系统的设计和实现          下载免费PDF全文

于新宇  李家  郝杰  闫吉顺 《海洋开发与管理》2018,35(2):71-73+93

文章在国家大力开展海域海岸带整治修复的背景下,利用Visual Studio 2013开发平台和SQL Server 2014数据库软件,采用ASP.NET MVC架构设计海域海岸带整治修复项目信息管理系统,实现整治修复项目信息的快速录入,并可进行查询、展示和互动等操作。相比传统信息管理系统,本系统大大提高了可维护性、可扩展性、灵活性和封装性,可促进我国海域海岸带整治修复项目信息管理实现现代化。  相似文献   

Trends in aerosol optical properties over Eastern Europe based on MODIS-Aqua     

《地学前缘(英文版)》2020,11(6):2169-2181

This study provides characteristics of aerosol columnar properties, measured over ten countries in Eastern Europe from 2002 to 2019. Aerosol optical depth (AOD) and Ångström exponent (AE) were obtained with the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 merged Dark Target and Deep Blue aerosol product. The product is validated using ground-based Aerosol Robotic Network (AERONET) situated at Minsk, Belsk, Moldova and Kyiv. The results showed that 76.15% of retrieved AOD data are within the expected error. It was established that 64.2% of AOD points are between 0 and 0.2 and 79.3% of all AE points are over 1. Mean AOD values in the region vary from 0.130 ​± ​0.04 (Moldova) to 0.193 ​± ​0.03 (Czech Republic) with mean value in the region 0.162 ​± ​0.05. Seasonal mean AOD (AE) values were at the maximum during the summer from 0.231 ​± ​0.05 (1.482 ​± ​0.09 in winter) to minimum 0.087 ​± ​0.04 during the winter (1.363 ​± ​0.17 in summer). Gradual AOD reduction is observed in all countries with annual trend from −0.0050 (Belarus) to −0.0029 (Russia). Finally, the relationship between AOD and AE was studied to classify various aerosol types and showed seasonal non-uniformity of their contribution depending on variation in sources. The entire region is under significant impact of various aerosol types, including clean continental (СС), mixed (MX) and anthropogenic/burning (AB) aerosols types that are at 59.77%, 24.72%, and 12.97% respectively. These results form an important basis for further regional studies of air quality and distribution of sources of pollution.  相似文献   

页岩油的储层特征及其影响因素分析——以沾化凹陷罗家地区为例     

贾屾  姜在兴 《中国地质调查》2020,7(2):13-20

为查明沾化凹陷罗家地区古近系沙河街组三段下亚段页岩油的储层特征及其影响要素,通过岩心、薄片、扫描电镜等多种资料综合分析,开展页岩油储层岩性、储层空间类型、影响因素及页岩油储层评价参数的研究。结果表明: 综合矿物成分和沉积构造2个因素,可将研究区页岩油储层岩性划分为7种类型; 不同岩性储集空间发育有较大差异,纹层状泥质灰岩和纹层状灰岩储集空间最为发育,纹层状灰质泥岩储集空间较发育,块状泥岩储集空间发育一般,块状泥质灰岩、块状灰质泥岩及纹层状粉砂岩储集空间发育较差; 研究区页岩油储层储集空间的发育主要受矿物成分、沉积构造、有机质含量和赋存方式以及成岩作用的影响。以此为基础,选取方解石含量、纹层状构造、总有机碳(Total Organic Carbon,TOC)含量、镜质体反射率(R_o)及孔隙度作为页岩油储层评价参数,将沾化凹陷罗家地区页岩油储层分为优质储层、有利储层和不利储层3类。  相似文献   

Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition.  相似文献