Body motion in liquid under ice plate with snow cover

The motion of a submarine in liquid under an ice plate covered with flooded snow is considered. The ice is modelled as an elastic plate and the snow cover is modelled as a viscous layer on the top of the plate. The submarine is modelled as a slender solid of revolution with scale 1:300. The experimental and theoretical study of the influence of the viscous snow layer on deflections of the floating ice plate is conducted. The viscous layer reduces the amplitudes of flexural-gravity waves. The greatest influence of the viscous layer on the plate deflections is achieved for velocities of the submarine, where the waves of maximum amplitude are generated. Theoretical results are in good qualitative and quantitative agreement with the model experiments.     

新疆地区土地覆被遥感数据的一致性研究

鉴于新疆地区对中国乃至中亚有着特殊的战略意义,本文针对不同数据源及分类系统在土地覆被数据的空间分布上缺乏互通性问题,结合2010年目视解译土地利用现状遥感监测数据、GlobeLand30和GlobCover2009共3种土地覆被数据,采用类型相似分析、类型混淆分析、混淆矩阵分析、空间一致性分析4种方法开展精度评价及一致性分析,以期对土地覆被数据在中国西北干旱区的适用性及适用范围提供有效建议。结果表明,3种土地覆被数据对新疆地区土地覆被类型构成基本一致,且对裸地类型的辨识度最高;新疆地区中高度一致区域占新疆总面积的95%;3种数据两两对比时,总体精度在64.11%~72.57%之间,其中目视解译数据/GlobeLand 30组合表现出最高水平,且仍有提高空间,反映出目前相同卫星传感器是提升精度评价结果的重要因素之一,且不同分类系统、分类方法、空间分辨率及卫星过境时间等因素对精度评价结果也会产生巨大影响。为解决此类问题,利用多源土地覆被遥感数据的融合技术提高数据精度,或是利用深度学习对遥感影像资料进行精确地解译和判读,将是今后全球土地覆被制图及应用领域的主要发展趋势。     

我国南方西南和中东部区域两次持续性低温雨雪过程与关键环流系统的关系研究

利用NCEP再分析资料和国家气象信息中心提供的753站逐日气温和降水资料,对比分析了我国南方西南和中东部区域两次持续10 d以上的低温雨雪过程,结果表明:(1)两次过程中欧亚大陆中高纬东亚大槽均加深,但环流形势有差异。西南过程呈现"北高南低"形势,关键脊区在贝加尔湖,而中东部区域过程"北高南低"和"西高东低"形势共存,关键脊区从乌拉尔山延伸至贝加尔湖。两次过程异常的环流与北大西洋向东传播的波列有关。(2)西南过程关键脊区提前过程3 d发展并东移至贝加尔湖,形成稳定形势;而中东部区域过程关键脊区提前过程一周发展,在开始日达最强。两次过程均伴随蒙古高压东移南压使地面降温,500 hPa关键脊区超前蒙古高压2 d变化。西南过程降温主要受到冷平流和绝热冷却影响,而中东部区域过程主要受到冷平流的影响。(3)西南过程水汽来自孟加拉湾,只受南支槽支配。中东部区域过程水汽来自孟加拉湾、南海和西太平洋,由南支槽和西太平副热带高压的共同影响。两次过程水汽正收支主要来自南边界。     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.     

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.     

MODIS数据的水浇地提取

水浇地是土地覆盖分类系统中的一个重要类型, 依据水分亏缺指数(WDI)与土壤水分的相关性, 计算了监测时间内WDI的变化情况, 用降雨资料去除了由于降雨引起的WDI的变化, 进而提取了研究区的水浇地分布。结果表明：(1)提取出的水浇地在数量上与统计资料相比, 除山西省偏差相对较大外, 其余各省偏差均小于7%, 提取出的结果与统计资料具有可比性。(2)提取出的水浇地在空间上多成片出现在河流、湖泊、水库附近、灌区和绿洲上, 与已知的水浇地集中区一致。(3)利用TM影像对提取结果进行了初步判断, 其中山西省的正确率较低, 只有64%, 其余各省正确率均在70%以上, 新疆的正确率最高为92%。     

北极河流径流量变化及影响因子分析

利用最新的北极径流资料(R-arcticNET V3.0和Arctic RIMS),对进入北冰洋的4条主要河流的季节及年代际变化进行诊断分析。结果表明:入海径流在4-6月的大幅增加主要是由气温达到融点后积雪融化造成的,降水的作用次之。总的来说,欧亚区域在过去的70年里入海径流量是增加的,而北美区域在近30年里入海径流量是减小的,但注入北冰洋的径流总量是增大的。值得注意的是入海径流的年代际变化在不同季节增减趋势显著不同,夏季和秋季径流量减小,而其他两个季节径流量增大。进一步分析了影响入海径流变化的一些气候因子,结果表明:北大西洋涛动(北太平洋指数)与欧亚(北美)区域的冬、春季径流量存在正相关。春季气温与春季径流量呈正相关,而与夏季径流量呈负相关。降水与径流量基本上为同步正相关。春季积雪覆盖面积与春季径流量为负相关,而与夏季径流量为正相关,夏季积雪覆盖面积与夏季的径流量为正相关。     

Contribution of a pathway through the Arctic Ocean to the re cent reduction in the ice cover

The sea ice cover in the Arctic Ocean has been reducing and hit the low record in the summer of 2007. The anomaly was extremely large in the Pacific sector. The sea level height in the Bering Sea vs. the Greenland Sea has been analyzed and compared with the current meter data through the Bering Strait. A recent peak existed as a consequence of atmospheric circulation and is considered to contribute to inflow of the Pacific Water into the Arctic Basin. The timing of the Pacific Water inflow matched with the sea ice reduction in the Pacific sector and suggests a significant increase in heat flux. This component should be included in the model prediction for answering the question when the Arctic sea ice becomes a seasonal ice cover.     

ERA5再分析云量在大气环境评估中的适用性研究

以河北省7个国家级气象站为例,利用1979—2013年ERA5云量和地面实测云量数据,依国标推荐法计算混合层高度,通过公式推导出通风量和大气自净能力指数,比较两套数据的计算结果,对ERA5再分析云量在大气环境评估中的适用性进行了研究;采用ERA5云量计算2013年3月至2021年2月的大气自净能力指数,对其与PM2.5浓度之间的关系进行分析。结果表明：①两种云量资料计算的月平均混合层高度、通风量和大气自净能力指数的相对误差近似符合正态分布,主要集中在0～5%;各月混合层高度：相对误差＞通风量＞大气自净能力指数,相关性则相反。秋冬季的相对误差（相关系数）大都低（高）于春夏季,邢台、石家庄和保定的误差明显小于其他站。②利用两种云量数据计算的大气自净能力指数的多年平均相对误差最小,通风量的次之,历年值的相关性和偏差也是中南部站点的更为理想。③2013年3月至2021年2月,各站月平均大气自净能力指数和PM2.5浓度呈反向相关,中南部站点的相关性更为显著。     

基于3S的荣成湾沿岸土地覆被变化及驱动力研究

运用3S技术对最近40年来荣成湾沿岸土地覆被动态变化及驱动力进行研究.结果表明,该区海岸湿地土地覆被总面积和陆域土地覆被总面积变化不大,但其各自类型结构和分布均发生了显著调整,而且二者变化在时间上不同步.从面积上讲,前者以1981年为界,渴湖和沙地先减少后波动增加,滩涂和沼泽地先增加后波动减少;养殖池从无到有并迅速增加...