海平面升降变化对贵州紫云礁体生长的控制

以贵州紫晚二叠世碳酸盐台地边缘礁为例,运用高频层序地层学原理和方法,精细地划分了紫云礁层序的各级单元,并对其内部沉积构成进行了详细研究,探讨海平面和变化对礁体生长的控制,研究结果表明,晚二叠世紫云礁 合体为发育在台地边缘坡折带的一个三级层序,由１３个准层序组成,并可识别出低位、海侵、高位３个体系域,低位期的下切谷 边缘坡折带碳酸盐台地的暴露、海侵期和高位期礁的３种生长方式的增生及每种生长方式的特定     

华北板块北缘东段拼贴带地质—地球物理特征

华北板块北缘东段（吉林省境内）地质构造的研究历来为学者所关注,研究成果颇多。笔者通过最新的地质－地球物理资料的分析研究,认为：该带重磁场的总体特征,即近乐西向展布的地球物理场态,被北东向或与之相联系的其它方向的重磁场带或梯度带分隔、切割、和改造;区域构造框架－－古东西构造,为中生代北东向等构造改造、包容及其它各复合关系,具有较高的一致性。通过对该带的５个构造重磁场区段之一的富尔河区段的剖析,确证生     

Incentives for field hydrology and data sharing: collaboration and compensation: reply to “A need for incentivizing field hydrology,especially in an era of open data”*

ABSTRACT

We thank Allen and Berghuijs for continuing the discussion on field hydrology and data sharing and discuss two incentives to promote data collection and sharing in hydrological sciences: a collaborative attitude and additional funding to make data publicly available.     

黄淮海平原冬小麦生长期旱情分析

黄淮海平原降水较少且年际、季际变化大,因此水分供应状况成为该区作物,特别是以冬春季为主要生育期的冬小麦的主限制因素。鉴于作物实际蒸散量与潜在蒸散量关系依赖于作物生长状况和土壤水分的事实,提出反映作物缺水状况的干旱指标———作物水分胁迫指数（ＣＷＳＩ）。计算黄淮海平原冬小麦生长期间的ＣＷＳＩ,并分析其在自然降水条件及适量灌溉条件下的时空分异规律     

综合RS与GIS方法提取青藏高原冲川变化信息：以布喀塔格峰为例

冰川变化的常规观测方法在高原的大部分地区无法实施。遥感与地理信息系统技术的发展为研究冲川变化提供了有效的手段,本文以位于昆仑山脉中段的布喀塔格山峰冰川为例,利用１９７３年至１９９４年的ＲＢＶ,ＭＳＳ和ＴＭ遥感资料为信息源,综合目视判读与统计分析方法提取冰川界限,形成冰川边界图,并规一化坐标系统,以ＧＩＳ为工具分析该冲川群的变化情况,计算冲川进退变化速率。     

分层随机抽样下全球30 m火烧迹地产品验证

火烧迹地是全球及区域碳循环和气候变化等研究所需的重要参数之一,卫星遥感技术为快速获取大区域火烧迹地空间分布信息提供了有效手段。中国科学院基于Landsat系列卫星数据研发了首个30 m空间分辨率全球火烧迹地产品GABAM (Global Annual Burned Area Map)。遥感数据产品的精度验证对产品使用具有重要意义,迄今尚未有研究机构对GABAM产品精度进行独立评价和分析。为系统评价GABAM产品精度,利用2010年全球30 m空间分辨率火烧迹地产品(GABAM2010)开展精度验证研究工作,在全球和几个陆地生物群落中估算了产品精度,并探索了全球遥感专题产品精度验证的技术框架。基于分层随机抽样选择80个非重叠的泰森多边形区域TSA (Thiessen Scene Areas),采用误差矩阵和6个精度指标对GABAM2010产品做全面精度评价和分析,以满足火烧迹地产品用户的使用要求。结果表明:在全球范围内,GABAM2010产品的错分率和漏分率分别为24.32%和31.60%,总体精度为97.85%;由于数据质量(如条带、云)等的影响,火烧迹地的范围会被低估,对于较容易发生火灾的生物群落,如热带亚热带草原区域,GABAM2010产品精度较高;在生物群落内部,高密度火烧迹地区域的精度高于低密度火烧迹地区域。     

An IEEE value loop of human-technology collaboration in geospatial information science

ABSTRACT

Geosensing and social sensing as two digitalization mainstreams in big data era are increasingly converging toward an integrated system for the creation of semantically enriched digital Earth. Along with the rapid developments of AI technologies, this convergence has inevitably brought about a number of transformations. On the one hand, value-adding chains from raw data to products and services are becoming value-adding loops composed of four successive stages – Informing, Enabling, Engaging and Empowering (IEEE). Each stage is a dynamic loop for itself. On the other hand, the “human versus technology” relationship is upgraded toward a game-changing “human and technology” collaboration. The information loop is essentially shaped by the omnipresent reciprocity between humans and technologies as equal partners, co-learners and co-creators of new values.

The paper gives an analytical review on the mutually changing roles and responsibilities of humans and technologies in the individual stages of the IEEE loop, with the aim to promote a holistic understanding of the state of the art of geospatial information science. Meanwhile, the author elicits a number of challenges facing the interwoven human-technology collaboration. The transformation to a growth mind-set may take time to realize and consolidate. Research works on large-scale semantic data integration are just in the beginning. User experiences of geovisual analytic approaches are far from being systematically studied. Finally, the ethical concerns for the handling of semantically enriched digital Earth cover not only the sensitive issues related to privacy violation, copyright infringement, abuse, etc. but also the questions of how to make technologies as controllable and understandable as possible for humans and how to keep the technological ethos within its constructive sphere of societal influence.     

Atmospheric pollution assessment near potential source of natural aerosols in the South Gobi Desert region,China

ABSTRACT

Rapid economic growth, a high degree of urbanization and the proximity of a large number of desert and semidesert landscapes can have a significant impact on the atmosphere of adjacent territories, leading to high levels of atmospheric pollution. Therefore, identifying possible sources of atmospheric pollution is one of the main tasks. In this study, we carried out an analysis of spatial and temporal characteristics of five main atmospheric pollutants (PM_2.5, PM₁₀, SO₂, NO₂, and CO) near potential source of natural aerosols, affecting seven cities (Wuhai, Alashan, Wuzhong, Zhongwei, Wuwei, Jinchang, Zhangye), located in immediate proximity to the South Gobi deserts. The results, obtained for the period from 1 January 2016 to 31 December 2018, demonstrate total concentrations of PM_2.5 and PM₁₀ are 38.2 ± 19.5 and 101 ± 80.7 μg/m³ exceeding the same established by the Chinese National Ambient Air Quality Standard (CNAAQS), being 35 and 70 μg/m³, respectively. Based on the data from Moderate Resolution Imaging Spectroradiometer (MODIS) for the whole period, Clean Сontinental (71.49%) and Mixed (22.29%) types of aerosols prevail in the region. In the spring and winter seasons maximum concentrations of pollutants and high values of Aerosol Optical Depth (AOD) in the region atmosphere are observed. PM_2.5 and PM₁₀ ratio shows the presence of coarse aerosols in the total content with value 0.43. The highest concentrations of pollutants were in the period of dust storms activity, when PM_2.5 and PM₁₀ content exceeded 200 and 1000 µg/m³, and AOD value exceeded 1. UV Aerosol Index (UVAI), Aerosol Absorbing Optical Depth (AAOD), and Single Scattering Albedo (SSA), obtained from Ozone Monitoring Instrument (OMI), demonstrate the high content of dust aerosols in the period of sandstorms. Analysis of backward trajectories shows that dust air masses moved from North to Northwest China, affecting large deserts such as Taklamakan, Gurbantunggut, Badain Jaran, Tengger, and Ulan Buh deserts.     

Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions

ABSTRACT

Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technologies that can somehow support them in quantifying and mapping risks. Remotely sensed data, with special focus on free ones, can certainly provide the most of information they need, making possible to better calibrate insurance fees in space and time. In this work, a prototype of service based on free remotely sensed data is proposed with the aim of supporting insurance companies’ strategies. The service is thought to calibrate annual insurance rates, longing for their reduction at such level that new customers could be attracted. The study moves from the entire Piemonte region (NW Italy), to specifically focus onto the Cuneo province (Southern Piemonte), which is mainly devoted to agriculture. MODIS MOD13Q1-v6 and Sentinel-2 L2A image time series were jointly used. NDVI maps from MODIS data were useful to describe the midterm phenological trends of main crops at regional level in the period 2000–2018; differently, Sentinel-2 data permitted to map local crop differences at field level in 2016 and 2017 years. With reference to MODIS data, the average phenological behavior of main crop classes in the area, obtained from the CORINE Land Cover map Level 3, was considered using a time series decomposition approach. Trend analyses showed that the most of the crop classes alternated three phases (about 7 years) suggesting that, presently, this is probably the time horizon to be considered to tune mid-term algorithms for risk estimates in the agricultural context. Crop classes trends were consequently split into three phases and each of them modeled by a first-order polynomial function used to update correspondent insurance risk rate. Sentinel-2 data were used to map phenological anomalies at field level for the 2016 and 2017 growing seasons; shifts from class average behavior were considered to locally and temporarily tune insurance premium around its average trend as described at the previous step. Synthesizing, one can say that this approach, integrating MODIS and Sentnel-2 data, makes possible to locally and temporarily calibrate premiums of indexed insurance policies by describing the average trends of crop performance (NDVI) at regional level by MODIS data and refining it at field and specific crop level by Sentinel-2 data.     

Analyzing ice dynamics using Sentinel-1 data at the Solheimajoküll Glacier,Iceland

ABSTRACT

The climate in southern Iceland has warmed over the last 70 years, resulting in accelerated glacier dynamics at the Solheimajoküll glacier. In this study, we compare glacier terminus locations from 1973 to 2018, to changes in climate across the study area, and we derive ice-surface velocities (2015–2018) from satellite remote-sensing imagery (Sentinel-1) using the offset-tracking method. There have been two regional temperature trends in the study period: cooling (1973–1979) and warming (1980–2018). Our results indicate a time lag of about 20 years between the onset of glacier retreat (?53 m/year since 2000) and the inception of the warming period. Seasonally, the velocity time series suggest acceleration during the summer melt season since 2016, whereas glacier velocities during accumulation months were constant. The highest velocities were observed at high elevations where the ice-surface slope is the steepest. We tested several scenarios to assess the hydrological time response to glacier accelerations, with the highest correlations being found between one and 30 days after the velocity estimates. Monthly correlation analyses indicated inter-annual and intra-annual variability in the glacier dynamics. Additionally, we investigate the linkage between glacier velocities and meltwater outflow parameters as they provide useful information about internal processes in the glacier. Velocity estimates positively correlate with water level and negatively correlate with water conductivity between April and August. There is also a disruption in the correlation trend between water conductivity and ice velocity in June, potentially due to a seasonal release of geothermal water.