The Moon 35 years after Apollo: What's left to learn?

With the cancellation of the Apollo program after Apollo 17 returned from the Moon in 1972, the focus of NASA switched to other areas of the Solar System. Study of the Moon did continue through analysis of the returned samples and remotely sensed data sets (both orbital and surface), as well as through Earth-based telescopic studies. In the 1990s, new orbital data were obtained from several missions (fly-by and orbital), the first being Galileo that allowed the lunar farside to be mapped, followed by global mapping by the Clementine and Lunar Prospector missions.Interest in the Moon started to increase at the beginning of the 21st century as other nations focused their space exploration programs on the Moon. The speech by President Bush in January 2004 put the Moon back into the critical exploration path for NASA, paving the way for humans to return to the lunar surface by 2020. This return will be critical for developing technologies and protocols for the eventual human exploration of other parts of the solar system. At the time of writing (June 2008), the SELENE/Kaguya mission (Japan and Chang’e-1 (China) are orbiting the Moon, with Chandrayaan-1 (India) and Lunar Reconnaissance Orbiter (USA) being scheduled to launch later in 2008.The past (and present) exploration of the Moon begs the question “what's left to be done?” With the renewed focus on the Moon, now that it is on the pathway for the exploration of Mars (and beyond) a similar question has been raised - what should the astronauts do on the Moon? The publication of the New Views of the Moon book [Jolliff et al., 2006. New Views of the Moon, Reviews in Mineralogy, vol. 60. American Mineralogical Society, 721pp] highlighted a number of important scientific questions that remain unanswered as well as posing many more on the basis of the currently available data. These questions resonated in three Lunar Exploration Analysis Group (LEAG) reports pertinent to this discussion, which were also published (on line) during 2006 (http://www.lpi.usra.edu/leag), and in the National Research Council of the National Academies [2007. The Scientific Context for Exploration of the Moon. National Academies Press, Washington, DC, 112pp] report entitled “The Scientific Context for Exploration of the Moon”. This paper synthesizes these recent studies, along with those from the 1980s and 1990s, to emphasize the lunar science questions that remain unanswered. In addition, it summarizes the missions already flown to the Moon along with those that are planned in order to give the reader an idea of exactly what lunar science has been and will be conducted in the hope that it will inspire proposals for missions to address the outstanding science questions.     

Model-based remote sensing algorithms for particulate organic carbon (POC) in the Northeastern Gulf of Mexico

Hydrographic data, including particulate organic carbon (POC) from the Northeastern Gulf of Mexico (NEGOM) study, were combined with remotely-sensed SeaWiFS data to estimate POC concentration using principal component analysis (PCA). The spectral radiance was extracted at each NEGOM station, digitized, and averaged. The mean value and spurious trends were removed from each spectrum. De-trended data included six wavelengths at 58 stations. The correlation between the weighting factors of the first six eigenvectors and POC concentration were applied using multiple linear regression. PCA algorithms based on the first three, four, and five modes accounted for 90, 95, and 98% of total variance and yielded significant correlations with POC with R ² = 0.89, 0.92, and 0.93. These full waveband approaches provided robust estimates of POC in various water types. Three different analyses (root mean square error, mean ratio and standard deviation) showed similar error estimates, and suggest that spectral variations in the modes defined by just the first four characteristic vectors are closely correlated with POC concentration, resulting in only negligible loss of spectral information from additional modes. The use of POC algorithms greatly increases the spatial and temporal resolution for interpreting POC cycling and can be extrapolated throughout and perhaps beyond the area of shipboard sampling.     

关中平原全新世土壤与环境研究

根据蓝田田家村、长安四府村、西安吴家坟和岐山雍川村剖面样品的粒度分析、CaCO3含量测定和微结构的鉴定资料得知,关中平原全新世中期形成的土壤S0具有明显的淀积粘化特征和现代沉积间断条件下发育的土壤剖面特点,该层土壤的构成物质至少有一部分是由先前形成的黄土经成壤作用改造而成的。关中平原S0的粘化类型不仅可以指示当地温湿的气候环境和很少有沙尘暴活动,而且它与西北荒漠区全新世千年尺度上的环境变化存在遥相关关系。全新世晚期发育的黄土L0是在相对冷干条件下形成的狭义土壤,它具有明显的残积粘化和残积—淀积粘化特征,它发育在沙尘暴连续堆积的条件下,形成了厚度大发育弱的粘化层和厚度大而均匀分布的CaCO3淀积层。全新世黄土剖面特征显示,在风尘连续堆积条件下发育的土壤可以不具有明显的剖面分层,厚度可以大于2 m。关中平原全新世L0发育时的气候与西北荒漠区的气候也存在遥相关关系。     

Recent advances in earthquake monitoring I: Ongoing revolution of seismic instrumentation

Seismic networks have significantly improved in the last decade in terms of coverage density, data quality, and instrumental diversity. Moreover, revolutionary advances in ultra-dense seismic instruments, such as nodes and fiber-optic sensing technologies, have recently provided unprecedented high-resolution data for regional and local earthquake monitoring. Nodal arrays have characteristics such as easy installation and flexible apertures, but are limited in power efficiency and data storage and thus most suitable as temporary networks. Fiber-optic sensing techniques, inclu-ding distributed acoustic sensing, can be operated in real time with an in-house power supply and connected data storage, thereby exhibiting the potential of becoming next-generation permanent networks. Fiber-optic sensing techniques offer a powerful way of filling the observation gap particularly in submarine environments. Despite these technological advancements, various challenges remain. First, the data characteristics of fiber-optic sensing are still unclear. Second, it is challenging to construct software infrastructures to store, transfer, visualize, and process large amount of seismic data. Finally, innovative detection methods are required to exploit the potential of numerous channels. With improved knowledge about data characteristics, enhanced software infrastructures, and suitable data processing techniques, these innovations in seismic instrumentation could profoundly impact observational seismology.     

作物病虫害高光谱遥感进展与展望

作物病虫害作为影响农作物品质、产量及威胁粮食安全的主要因素,仅依靠人工田间调查对其进行监测已不能满足当下农业生产精准高效的需求。高光谱遥感作为能够获取地表物体连续波谱信息的遥感技术,已经成为当下作物病虫害监测识别的重要手段。本文对作物病虫害高光谱遥感监测识别的研究进展进行综述,通过对该领域发表文献的统计以及对主要机构、团队、数据源的分析,明确了病虫害高光谱遥感监测的研究热点和趋势;在此基础上,分析高光谱技术及其作物病虫害的监测识别机理,从病虫害胁迫探测、分类识别、危害严重度定量分析及早期检测四个方面综述相关技术及研究现状;通过探讨当下高光谱遥感病虫害监测识别面临的挑战,提出作物病虫害标准图谱库的建立、星载高光谱传感器的完善以及星空地一体化监测平台的搭建是当前作物病虫害高光谱遥感监测识别技 术落到实处的关键,也是未来发展的重点方向。     

全局局部细节感知条件随机场的高分辨率遥感影像建筑物提取

条件随机场模型由于其较强的上下文信息建模能力,被广泛应用于建筑物提取任务中。然而,面对高分辨率遥感影像丰富的地物信息,基于条件随机场的提取方法存在建筑物边界模糊的问题。本文提出了一种全局局部细节感知条件随机场框架,该框架提出全局局部一体化D-LinkNet,在有效利用多尺度建筑物信息的同时保留局部结构信息,解决了传统条件随机场一元势能丢失边界信息的问题。同时,该框架融合分割先验以缓解建筑物类内光谱差异较大的影响,利用更大尺度的上下文信息来精确提取建筑物,并引入局部类别标记代价从而保持细节信息以获取清晰的建筑物边界。实验结果表明,该框架在WHU卫星和航空数据集上的精度评价指标均优于其他对比方法,其IoU分别达89.82%和91.72%,对于复杂场景下的建筑物信息能够获得较好的提取效果。     

青藏高原河流网络高分CubeSat遥感监测

河流网络是地表水循环的重要组成部分,如何实现河流网络动态监测已成为河流遥感研究的热点。近年来,以PlanetScope为代表的CubeSat小卫星已具备了米级空间分辨率、1 d重访周期的优势,这为河流网络高时空分辨率动态监测提供了可能。本文以青藏高原长江源区的通天河流域（227 km²）为研究区,选取2017-05—2017-10 5期3 m空间分辨率CubeSat遥感影像,增强河流横纵剖面特征自动化提取了河流网络,研究了通天河流域河流网络动态变化,对比分析了3 m CubeSat与30 m Landsat 8、10 m Sentinel-2所提取的河流网络,以及5种现有水体数据集（GRWL,GSW,FROM-GLC 2017,OpenStreetMap,HydroSHEDS）。研究结果表明：（1）研究区内河流网络5月水系密度较低（0.38 km^-1）,7—8月河流网络进入丰水期,水系密度显著增加至0.61 km^-1,9月河流网络进入平水期,水系密度趋于平稳（0.53 km^-1）,随后迅速退化并于10月开始冻结,水系密度迅速降低至0.37 km^-1;（2）采用高空间分辨率CubeSat所提取的河流网络能够识别更多细小河流（河宽3—30 m）,CubeSat所提取的河流总长分别为Landsat 8、Sentinel-2所提取河流总长的1.6倍和1.3倍;（3）CubeSat所提取的河流网络水系密度高于现有水体数据集（2.9—12.4倍）,弥补了现有水体数据集无法反映细小河流的不足。     

IEU-Net高分辨率遥感影像房屋建筑物提取

房屋建筑物作为人类活动的主要场所,快速准确地将其从高分辨率遥感影像中提取出来,对促进遥感信息在防灾减灾、城镇管理等方面的应用具有重要意义。本文基于深度学习,提出了高分辨率遥感影像房屋建筑物像素级精确提取方法。首先,针对样本图像边缘像素特征不足现象,以U-Net模型为基础提出IEU-Net模型,设计了全新的忽略边缘交叉熵函数IELoss并将其作为损失函数,另外添加Dropout和BN层在避免过拟合的同时提高模型训练速度和鲁棒性。其次,为解决模型特征丰富度有限的问题,引入形态学建筑物指数MBI,与遥感影像RGB波段一同参与到模型的分类过程。最后,在模型预测时与IELoss相对应采用忽略边缘预测策略从而获得最佳建筑物提取结果。实验对比分析表明：本文方法能有效克服样本边缘像素特征不足问题并抑制道路、建筑物阴影对结果的影响,提升高分辨率遥感影像中房屋建筑物的提取精度。     

深度学习遥感图像标注系统研究

随着深度学习的发展,遥感影像处理技术也从传统机器学习算法向深度学习转变,然而,用于遥感图像的训练数据集却十分稀少,且数据标注困难。本文将GIS技术与图像标注技术相结合,基于Flask Web框架设计一个可用于海量遥感数据的标注系统。该系统可用于海量遥感数据的数据框标注、数据类别标注,以及目标关键点标注,同时能将标注数据导出为深度学习训练最常用的COCO数据集和VOC2007两种格式。     

无/缺水下地形数据的高原堰塞湖水量遥感估算

水量遥感动态监测对于高原堰塞湖风险评估、预报预警和处置决策等具有重要意义。针对高原无资料或缺资料区,充分利用空天遥感技术,文章提出了一种无/缺水下地形数据的高原堰塞湖水量遥感定量估算方法。该方法首先通过遥感水域面积提取,获取堰塞湖淹没空间范围;进而采用不规则复杂多边形中线定位算法,确定堰塞湖中心线位置;然后基于河道中心特定点高程信息,结合局部河道比降估算,生成堰塞湖水下地形河道中线约束因子;再根据河道边坡高程信息和水下地形约束因子自适应拟合出局部堰塞河道的水下未知地形;最后通过三维曲面离散积分实现堰塞湖水量遥感动态定量估算。实验以东帕米尔高原的萨雷兹堰塞湖为研究区,展开遥感水量调查与局部验证研究,结果表明:萨雷兹堰塞湖当前水域面积约为89.09 km²,水量约为162.49亿m³;这一结果与专家预估的水资源量155—165亿m³基本吻合。经局部模拟实验精度对比验证,模拟结果与实际数据动态误差总体控制在10%以内,相关系数达到0.95(P<0.01,双尾),进一步证明了算法的鲁棒性和估算结果的可信度。为无/缺水下地形数据的高原堰塞湖水量遥感估算提供了一种有效的方法,实现了水下地形未知的高原堰塞湖水量遥感快速反演与定量测算。