A New Retracking Technique for Brown Peaky Altimetric Waveforms

A new Brown-Peaky (BP) retracker has been developed for peaky waveforms that usually appear within ～10 km to the coastline. The main feature of the BP is that it fits peaky waveforms using the Brown model without introducing a peak function. The retracking strategy first detects the peak location and width of a waveform using an adaptive peak detection method, and then estimates retracking parameters using a weighted least squares (WLS) estimator. The WLS assigns a downsized weight to corrupted waveform gates, but an equal weight to other normal waveform gates. The BP retracker has been applied to 4-year Jason-1 waveform (2002–2006) in two Australian coastal zones. The results retracked by BP, MLE4 and ALES retrackers have been validated against tide-gauge observations located at Burnie, Lorne and Broome. The comparison results show that three retrackers have similar performance over open oceans with the correlation coefficient (～0.7) and RMSE (～13 cm) between altimetric and tide-gauge sea levels for distance >7 km offshore. The main improvement of BP retracker occurs for distance ≤7 km to the coastline, where validation results indicate that data retracked by BP are more accurate (15–21 cm) than those by ALES (16–24 cm) and MLE4 (19–37 cm).     

基于连续帧间差分的抛弃式探头三维下沉运动处理方法研究

抛弃式探头由无人机装载,能够在较远目标区域和危险海域开展海洋水文环境剖面参数的测量。通过安装不同传感器,可以实现对温度、盐度的剖面测量,其深度的测量采用数学方法计算得到。针对双摄像机水箱实验获得的5个不同攻角实验结果,分析了常用的运动目标的检测方法,最终选择基于连续帧间差分法,确定探头的三维坐标位置,进而得到探头下沉运动的三维运动轨迹和速度曲线等信息。探头从水面释放后攻角在下沉过程中不断调整,改变运动姿态,同时伴随自身的旋转,抵消水平方向阻力作用,初始攻角产生的深度测量误差主要体现在加速过程,探头达到匀速运动后测量误差不变,在不考虑横流的情况下,探头最后以匀速垂直下落运动。     

宁夏生态移民村空间剥夺测度及影响因素

生态移民村是一种具有扶贫脱贫与生态保护双重意义的特殊乡村类型,是乡村振兴不可或缺的重要对象。在空间重构过程中形成的空间剥夺现象,是当前生态移民村全面振兴面临的一个新问题。为探究生态移民村空间剥夺规律,以宁夏70个生态移民村2017年相关数据为基础,构建了以收入就业、教育培训、社会生活、居住环境及公共服务可达性为主要内容的空间剥夺指标体系并进行水平测度,进而运用地理加权回归和地理探测器对其影响因子进行探测。结果显示：① 宁夏生态移民村空间剥夺整体水平较低,但总指数均值相对于非生态移民村显著高出0.023;② 宁夏生态移民村空间剥夺时空分异特征明显,总指数均值在时间上呈现“先较低—后上升—再下降”趋势,在空间上呈现“北部低—中部高—南部次高”分布;③ 民族构成、地形地貌、移民时段、依托资源和经济区带是影响生态移民村空间剥夺的重要因子,前3项对空间剥夺总指数的解释力分别达到了22.4%、10.6%和14.0%。空间剥夺水平测度为生态移民生产生活评价和政策调整完善提供新的客观依据,相关部门可据此优化调节生态移民村空间资源配置,增加其获取资源的能力和机会,推动生态移民村全面振兴。     

喜马拉雅山脉中段土地覆被的垂直分异特征

喜马拉雅山脉中段的珠穆朗玛峰等地,海拔高差巨大、生境复杂多变、土地覆被类型多样且植被垂直带谱完整,是全球范围内研究土地覆被垂直变化的理想场所。本文基于30 m空间分辨率的土地覆被数据（2010年）和DEM数据,在ArcGIS和Matlab平台的支持下,提出并运用脊线法、样带法和扇区法3种山地南北坡划分方法,研究了喜马拉雅山土地覆被垂直分布与结构差异。结果表明：① 山地土地覆被分布具有明确的垂直地带性结构特征,喜马拉雅中部土地覆被垂直带谱为南六北四式,土地覆被垂直带谱中具有人类活动的特点。② 南北坡之间的土地覆被垂直带谱差异明显,南坡土地覆被类型完整多样,北坡相对简单;对同类型土地覆被而言,南坡较北坡分布高程低、幅度宽。③ 依据各类型分布面积比随海拔变化情况,土地覆被类型在南北坡上的垂直分布可分为4种模式：冰川雪被、稀疏植被和草地为单峰分布型,裸地为南单峰北双峰分布型。④ 3种划分方法中,南坡的土地覆被垂直带结构具有相似性,而北坡的土地覆被垂直带结构存在差异,扇区法较好地反映了土地覆被自然分布格局。     

A-FPN算法及其在遥感图像船舶检测中的应用

光学遥感图像船舶检测主要面临两个挑战:光学遥感图像背景复杂,船舶检测易受海浪、云雾及陆地建筑等多方面干扰;遥感图像分辨率低,船舶目标小,对于其分类与定位带来很大困难;针对上述问题,在FPN的基础上,提出一种融入显著性特征的卷积神经网络模型A-FPN (Attention-Based Feature Pyramid Networks)。首先,利用卷积提取图像特征金字塔;然后,利用顶层金字塔逐级构建显著特征层,抑制背景信息,通过金字塔顶层的细粒度特征提高浅层特征的表达能力,构建自上而下的多级显著特征映射结构;最后利用Softmax分类器进行多层级船舶检测。A-FPN模型利用显著性机制引导不同感受下的特征进行融合,提高了模型的分辨能力,对遥感图像处理领域具有重要应用价值。实验阶段,利用公开的遥感目标检测数据集NWPU VHR-10中的船舶样本进行测试,准确率为92.8%,表明A-FPN模型适用于遥感图像船舶检测。     

Variations in glacier volume and snow cover and their impact on lake storage in the Paiku Co Basin,in the Central Himalayas

Glaciers and snow cover are important constituents of the surface of the Tibetan Plateau. The responses of these phenomena to global environmental changes are sensitive, rapid and intensive due to the high altitudes and arid cold climate of the Tibetan Plateau. Based on multisource remote sensing data, including Landsat images, MOD10A2 snow product, ICESat, Cryosat-2 altimetry data and long-term ground climate observations, we analysed the dynamic changes of glaciers, snow melting and lake in the Paiku Co basin using extraction methods for glaciers and lake, the degree-day model and the ice and lake volume method. The interaction among the climate, ice-snow and the hydrological elements in Paiku Co is revealed. From 2000 to 2018, the basin tended to be drier, and rainfall decreased at a rate of −3.07 mm/a. The seasonal temperature difference in the basin increased, the maximum temperature increased at a rate of 0.02°C/a and the minimum temperature decreased at a rate of −0.06°C/a, which accelerated the melting from glaciers and snow at rates of 0.55 × 10⁷ m³/a and 0.29 × 10⁷ m³/a, respectively. The rate of contribution to the lake from rainfall, snow and glacier melted water was 55.6, 27.7 and 16.7%, respectively. In the past 18 years, the warmer and drier climate has caused the lake to shrink. The water level of the lake continued to decline at a rate of −0.02 m/a, and the lake water volume decreased by 4.85 × 10⁸ m³ at a rate of −0.27 × 10⁸ m³/a from 2000 to 2018. This evaluation is important for understanding how the snow and ice melting in the central Himalayas affect the regional water cycle.     

毛乌素沙区1961-2016年降水特征

基于毛乌素沙区10个气象站1961-2016年观测资料，应用Mann-Kendall方法和t检验法对各气象站年降水量进行了突变检验，借助小波分析讨论了各气象站年降水量的周期特征，根据降水量等值线划分结果对整个研究区分区分析了年、季、月和日尺度上的降水变化特征，并在两个时段上分析了季节性降水的差异。结果表明：毛乌素沙区年降水量空间特征差异明显，东部亚区呈上升趋势，中西部亚区呈下降趋势，但变化趋势不显著且无突变发生；降水年内分配不均，干湿季分明，降水集中在5-9月，夏秋季降水占全年降水比重大，季、月和日尺度降水量存在梯度递减变化；年降水量的年际变化过程存在多重时间尺度的自相似结构；近26年的冬春季降水增加显著，但降水波动幅度小于前30年。     

南极中山站全天信息采集系统

南极高原拥有独特的天文观测优势,为了对南极中山站夜天文观测条件进行实测研究,中国科学院云南天文台专门研制了一套具有耐低温、自动除雪除霜等适应南极气候特征的全自动全天信息采集系统,该系统可以提供实时的全天云量、天光背景和全天图像,并将信息推送到网页实时显示。介绍了系统的研制及为适应南极气候进行的耐低温实验,统计分析了中山站2016~2017年的全天信息数据,结果显示,中山站2016和2017年的可观测时间为772.21 h和437.38 h,可观测夜数为93 d和51 d,天光背景最大真实值为22.05 Mag/arcsec 2,年平均气温为-10.6℃,最高气温19.1℃,最低气温为-44℃,2016年平均相对湿度为55.2%。     

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.     

2000—2015 年伊犁河谷植被覆盖时空变化特征

利用 2000—2015 年的 EOS／MODIS 数据，采用趋势分析、Hurst 指数、变异系数法对伊犁河 谷植被时空变化及未来趋势进行分析，结果显示：空间分布上，伊犁河谷植被覆盖度呈北部、南部、 东部偏高，西部、中部偏低的分布特征；时间变化上，2000—2015 年，伊犁河谷植被覆盖度波动减 小，减速为 6.25%·(10 a)^-1；区域分布上，伊犁河谷植被表现为低波动变化，波动程度中等以及下占 73.16%，波动程度高的区域占 26.84%。未来预测表明，伊犁河谷植被覆盖呈退化趋势，其中，持续 退化的面积占 57.55%，持续改善的面积占 13.51%。