Formation of Carbon-Enhanced Metal-Poor RR Lyrae Stars

Carbon-enhanced metal-poor (CEMP) stars are considered to be related to the first generation of stars, and responsible for the chemical evolution of the early Galaxy. More than half of them are in binaries, and could be explained by the binary evolution, but the formation channel of them is still not fully understood. Among the hundreds of CEMP stars, there are nine CEMP RR Lyrae stars identified, and at least seven of which are very likely not binaries. The usual binary star evolution channel is difficult to produce such a single star, particularly that of carbon enrichment. One way in which such a single star might be produced is the merger of a helium white dwarf with a Hertzsprung gap (HG) star. We use a stellar evolution program to calculate the models of the merger remnants, and find that the models can reproduce the observed distribution of these CEMP single RR Lyrae stars in terms of surface temperature, gravity, and carbon abundance. Hence, it is extremely possible that the helium white dwarf and HG star merger model is one of the formation channels of the metal-poor carbon-rich RR Lyrae stars.     

Targeting rare earth element bearing mine tailings on Bangka Island,Indonesia, with Sentinel-2 MSI

A laboratory study on rare earth element bearing mine tailings, collected from Bangka Island, Indonesia, reported a new spectral absorption feature at 674 nm associated with Erbium. The present study aims to evaluate the capability of the European Space Agency’s Sentinel-2 MSI sensors to detect this absorption feature from space. An arithmetic band operation is performed on selected visible and near-infrared spectral bands of a Sentinel-2 image. The results show that Sentinel-2 MSI is capable of detecting the 674 nm Erbium-related absorption feature within the particular environmental setting of the study area.     

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.     

Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots,UAV-LiDAR data and Sentinel-2 imagery

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha⁻¹. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R² = 0.62 > 0.52, RMSE = 50.36 Mg ha⁻¹<56.63 Mg ha⁻¹). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R² = 0.62, RMSE = 51.03 Mg ha⁻¹). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.     

Winter cover crops in Dutch maize fields: Variability in quality and its drivers assessed from multi-temporal Sentinel-2 imagery

Planting a cover crop between the main cropping seasons is an agricultural management measure with multiple potential benefits for sustainable food production. In the maize production system of the Netherlands, an effective establishment of a winter cover crop is important for reducing nitrogen leaching to groundwater. Cover crop establishment after maize cultivation is obliged by law for sandy soils and consequently implemented on nearly all maize fields, but the winter-time vegetative ground cover varies significantly between fields. The objectives of this study are to assess the variability in winter vegetative cover and evaluate to what extent this variability can be explained by the timing of cover crop establishment and weather conditions in two growing seasons (2017–2018). We used Sentinel-2 satellite imagery to construct NDVI time series for fields known to be cultivated with maize within the province of Overijssel. We fitted piecewise logistic functions to the time series in order to estimate cover crop sowing date and retrieve the fitted NDVI value for 1 December (NDVI_Dec). We used NDVI_Dec to represent the quality of cover crop establishment at the start of the winter season. The Sentinel-2 estimated sowing dates compared reasonably with ground reference data for eight fields (RMSE = 6.6 days). The two analysed years differed considerably, with 2018 being much drier and warmer during summer. This drought resulted in an earlier estimated cover crop sowing date (on average 19 days) and an NDVI_Dec value that was 0.2 higher than in 2017. Combining both years and all fields, we found that Sentinel-2 retrieved sowing dates could explain 55% of the NDVI_Dec variability. This corresponded to a positive relationship (R² = 0.50) between NDVI_Dec and the cumulative growing degree days (GDD) between sowing date and 1 December until reaching 400 GDD. Based on cumulative GDD derived from two weather stations within Overijssel, we found that on average for the past three decades a sowing date of 19 September (± 7 days) allowed to attain these 400 GDD; this provides support for the current legislation that states that from 2019 onwards a cover crop should be sown before 1 October. To meet this deadline, while simultaneously ascertaining a harvest-ready main crop, in practice implies that undersowing of the cover crop during spring will gain importance. Our results show that Sentinel-2 NDVI time series can assess the effectiveness and timing of cover crop growth for small agricultural fields, and as such has potential to inform regulatory frameworks as well as farmers with actionable information that may help to reduce nitrogen leaching.     

基于2 m格网点云数据的高精度DEM制作第三次国土调查坡度图

地形地貌随着时间的推移时刻发生变化,以高精度数字高程模型（EDE）数据制作坡度图,进而计算田坎系数,更精准地进行耕地面积计算及统计。以2 m格网点云数据生成的DEM数据为基础,按照第三次全国国土调查的相关技术要求,根据耕地坡度分级要求进行分级,生成坡度分级栅格数据图。对坡度分级栅格数据图进行矢量化,生成坡度分级矢量化数据。对矢量化数据进行图斑综合、界线平滑、拓扑重建、数据裁切等处理,制作完成调查区域坡度图及相关属性数据制作。并对生产的坡度图成果进行分析,找出不同尺度格网生产的坡度图的技术差异,并对高精度坡度的应用进行了展望。     

新冠病毒疫情期间复工复产卫星遥感监测

本文利用美国新一代极轨卫星NPP搭载的VIIRS载荷数据反演获得的热点数据,以及美国NASA的Aura卫星搭载的OMI载荷的二氧化氮对流层柱浓度资料,对中国地区2020-01—02及2019年同期,以及抗击疫情期间和后期复工复产的情况进行监测。监测结果表明：（1）2020-02较1月份及2019年同期相比,工业企业的耗能水平却出现显著降低,NO₂浓度呈现显著下降趋势。（2）全国工业热源点数和分布范围及密度自2020-02-03正式复工后稳步提升。湖北省除武汉、黄石和鄂州地区仍可监测到少数热点外,其他地区2月份均未再监测到任何热点,也证明了湖北基本没有复工迹象。（3）疫情得到基本控制之后,京津冀地区工业热源点有显著增加,但新增区域一般仍处于低能耗水平。从全国大气NO₂柱浓度监测来看,复工开始前后与2019年同期相比,污染物浓度均呈现明显下降趋势,说明复工强度有限。（4）到3月初工业热源点及其能耗水平出现显著升高,中国大部分地区的热源企业虽已逐步复工生产,说明工业产能较2月有显著提升,但生产规模或产能并未完全恢复。     

基于二三维一体化的智慧警务地理信息平台建设与研究

"智慧警务"是"智慧城市"的重要组成部分,也是社会治安防控体系发展的必然趋势,应提高认识,积极探索和论证建设"智慧警务"的模式和方法。本文阐述了基于二三维一体化的智慧警务地理信息平台建设内容和总体框架,并详细地介绍了警用地理信息系统及智慧警务应用系统建设内容和技术路线。     

卫星测高数据监测青藏高原湖泊2010年—2018年水位变化

青藏高原湖泊水位变化是气候变化和生态环境变化研究的重要指标。随着Cryosat-2观测数据的日益丰富和处理技术的提升,可以有效监测更多湖泊的水位变化信息。本研究构建了基于噪声去除技术、改进的波形重跟踪处理算法（ImpMWaPP）和误差混合动态模型为一体的高精度湖泊水位序列提取方法,利用Cryosat-2 SARIn数据获取到133个青藏高原湖泊2010年—2018年的高精度水位序列,并分析了这些湖泊水位变化的时空变化特征。总体上,青藏高原湖泊的水位继续呈上升趋势,但上升速度较2003年—2009年趋缓,年均变化率0.159 m/a。从地域分布上,北部湖泊的水位上升最为显著,而南部湖泊的水位则趋于稳定。从时间上,2010年—2012年和2016年—2018年,大多数湖泊的水位呈现快速上涨,而其他时间水位相对稳定或略有下降。     

O2-O2云反演算法及其在TROPOMI上的应用

利用遥感技术对大气环境污染进行监测时,云是影响痕量气体反演精度的重要因素,因此在痕量气体反演中需要对云的影响进行校正,通常使用的云参数主要是有效云量和云压。本文基于O₂-O₂ 477 nm吸收波段构建了O₂-O₂云反演算法：首先,根据有效云量和云高与连续反射率和O₂-O₂斜柱浓度之间的对应关系,结合假定的云模型利用VLIDORT辐射传输模型建立关于有效云量和云压的查找表;然后,通过差分吸收光谱技术拟合卫星载荷观测的大气层顶辐射,获得O₂-O₂斜柱浓度并计算连续反射率;最后,结合辅助数据,根据查找表进行插值反演获得有效云量和云压。通过将算法应用到OMI观测数据,将反演结果与OMCLDO2产品进行对比验证,有效云量和云压空间分布一致,相关系数R均超过0.97;并还将该算法应用于下一代大气成分监测仪器TROPOMI,与FRESCO+产品对比,有效云量和云压空间分布基本一致,当地表类型为海洋时,有效云量相关系数R大于0.97,云压相关系数R大于0.94,云压反演结果存在一定的区别;通过将O₂-O₂云反演算法和FRESCO+云压反演结果与CALIOP Cloud Layer产品进行对比,结果表明,在低云情况下,O₂-O₂云反演算法线性回归方程斜率为0.782,截距为198.0 hPa,相关系数R为0.850,算法表现优于FRESCO+。而在高云情况下,FRESCO+反演结果更接近CALIOP云压结果。在OMI和TROPOMI上的应用表明O₂-O₂云反演算法在大气云反演中具有较高的准确性和可行性,可以为大气痕量气体反演的校正提供云参数,并为中国同类型卫星载荷的云反演算法提供算法参考。