The Digital Belt and Road program in support of regional sustainability

The Belt and Road initiative has a significant focus on infrastructure, trade, and economic development across a vast region, and it also provides significant opportunities for sustainable development. The combined pressure of climate variability, intensified use of resources, and the fragility of ecosystems make it very challenging, however, to achieve future sustainability. To develop the path in a sustainable way, it is important to have a comprehensive understanding of these issues across nations and evaluate them in a scientific and well-informed approach. In this context, the Digital Belt and Road (DBAR) program was initiated as an international venture to share expertise, knowledge, technologies, and data to demonstrate the role of Earth observation science and technology and big Earth data applications to support large-scale development. In this paper, we identify pressing challenges, present the research priorities and foci of the DBAR program, and propose solutions where big Earth data can make significant contributions. This paper calls for further joint actions and collaboration to build a digital silk road in support of sustainable development at national, regional and global levels.     

SensePlace3: a geovisual framework to analyze place–time–attribute information in social media

SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources.     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.     

Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system

Cropland fallows are the next best-bet for intensification and extensification, leading to increased food production and adding to the nutritional basket. The agronomical suitability of these lands can decide the extent of usage of these lands. Myanmar’s agricultural land (over 13.8 Mha) has the potential to expand by another 50% into additional fallow areas. These areas may be used to grow short-duration pulses, which are economically important and nutritionally rich, and constitute the diets of millions of people as well as provide an important source of livestock feed throughout Asia. Intensifying rice fallows will not only improve the productivity of the land but also increase the income of the smallholder farmers. The enhanced cultivation of pulses will help improve nutritional security in Myanmar and also help conserve natural resources and reduce environmental degradation. The objectives of this study was to use remote sensing methods to identify croplands in Myanmar and cropland fallow areas in two important agro-ecological regions, delta and coastal region and the dry zone. The study used moderate-resolution imaging spectroradiometer (MODIS) 250-m, 16-day normalized difference vegetation index (NDVI) maximum value composite (MVC), and land surface water index (LSWI) for one 1 year (1 June 2012–31 May 2013) along with seasonal field-plot level information and spectral matching techniques to derive croplands versus cropland fallows for each of the three seasons: the monsoon period between June and October; winter period between November and February; and summer period between March and May. The study showed that Myanmar had total net cropland area (TNCA) of 13.8 Mha. Cropland fallows during the monsoon season account for a meagre 2.4% of TNCA. However, in the winter season, 56.5% of TNCA (or 7.8 Mha) were classified as cropland fallows and during the summer season, 82.7% of TNCA (11.4 Mha) were cropland fallows. The producer’s accuracy of the cropland fallow class varied between 92 and 98% (errors of omission of 2 to 8%) and user’s accuracy varied between 82 and 92% (errors of commission of 8 to 18%) for winter and summer, respectively. Overall, the study estimated 19.2 Mha cropland fallows from the two major seasons (winter and summer). Out of this, 10.08 Mha has sufficient moisture (either from rainfall or stored soil water content) to grow short-season pulse crops. This potential with an estimated income of US$ 300 per hectare, if exploited sustainably, is estimated to bring an additional net income of about US$ 1.5 billion to Myanmar per year if at least half (5.04 Mha) of the total cropland fallows (10.08 Mha) is covered with short season pulses.     

The past in dust: current trends and future directions in Pleistocene geoarcheology of European loess

Loess is a main archive of Pleistocene landscapes and environments and therefore has an important connection to the preservation and interpretation of Paleolithic sites. In Europe, anthropogenic sites have been found in loess because of past local occupation. At one extreme, sites are well preserved with minimal disturbance often accompanied by embedded proxies to estimate ecological parameters. On the other hand, loess deposits have undergone post-depositional alterations such as weathering, pedogenesis or bioturbation due to changing environmental conditions or other disturbances that obscure anthropogenic sites. We outline the current state of research and connections between Paleolithic archeology and loess research while introducing a series of subsequent regional case studies as part of a special issue. We also make recommendations for future work to incorporate a wider variety of methods to create more robust inferences on hominin and environmental evolution and their connections.     

基于ASTER与Sentinel-2A融合数据的云南普朗铜矿化蚀变信息提取

云南普朗铜矿区是我国提高战略性金属矿产铜矿资源的主要潜力地段,该矿区位于多云多雨山地高原区,采用传统地面调查方法开展找矿勘查工作受限较大,而遥感技术可在其中发挥重要作用。鉴于此,本研究选取ASTER和Sentinel-2A多光谱遥感数据,在光谱协同统一两种数据源的地表反射率基础上,采用高通滤波(High-Pass Filtering,HPF)的融合方法兼顾两种数据在短波红外波段和可见光近红外波段的优势,获取了具有高空间分辨率和高光谱保真度的ASTER-Sentinel-2A融合数据集;进而利用"比值+主成分分析法"与"主成分分析+能谱-面积法",开展了普朗铜矿区矿化遥感蚀变信息识别,并通过深入分析及野外验证,提出了对普朗铜矿蚀变信息分布特征及找矿潜力地带的新认识:(1)在原蚀变填图工作中判定为青磐岩化带的东部区域,重新进行了蚀变带的划分;(2)东部区域中新划分的钾化硅化带和绢英岩化带,具有较大找矿潜力。本研究可为普朗铜矿找矿勘查提供参考。     

CSAMT与AMT数据“拼接”处理——以湖南仁里铌钽矿床7号剖面为例

音频大地电磁测深(AMT)与可控源音频大地电磁测深(CSAMT)均为深部勘查常用方法。AMT方法采集频带较宽,但存在“死频带”,在“死频带”范围无法完全得到地质体响应。CSAMT信噪比高,但通常受发射功率的限制,收发距离一般放置不够远,容易过早进入“近区”。本文展示了将两种方法数据拼接组合的一种处理方式:首先在前人理论研究的基础上,利用CSAMT和AMT均匀半空间模型进行了正演计算,结果表明,CSAMT远区与AMT数据拼接具有可行性;选择湖南仁里铌钽矿床7号剖面的野外实测数据进行实验,将同测点位置的CSAMT远区数据和AMT同测点同模式数据相“拼接”,合成新AMT数据;最后进行反演,反演结果与钻探结果对应较好,表明该方法具有一定的效果。     

2011~2020年勘查地球化学数据处理研究进展

我国建立了包含海量数据的高质量的勘查地球化学数据库,为矿产勘查、环境评价和地质调查等提供了重要的数据支撑。如何高效处理勘查地球化学数据,并从中发掘和识别深层次信息一直是勘查地球化学学科研究的热点和前沿领域。本文在系统调研国内外学者过去十年发表的论著基础上,对勘查地球化学数据处理方法进行分析与对比,从勘查地球化学数据库建设、地球化学异常识别及其不确定性评价等方面概述了我国近十年来在该领域取得的主要研究进展,包括:(1)分形与多重分形模型由于考虑了地球化学空间模式的复杂性和尺度不变性,在全球范围内得到极大的发展和推广,我国学者引领了基于分形与多重分形的勘查地球化学数据处理;(2)机器学习和大数据思维开始在该领域启蒙,并迅速得到关注,正在成为研究热点和前沿领域,我国学者率先开展基于机器学习算法的勘查地球化学大数据挖掘研究;(3)我国学者需要进一步加强勘查地球化学数据缺失值处理以及成分数据闭合效应研究。今后该领域应进一步加强对弱缓地球化学异常识别、异常不确定性评价以及异常识别与其形成机理相结合等方面的研究。     

地球科学大数据的管理与共享：以英国地质调查局为例

大数据驱动的研究范式正在引起地学领域的革命,而海量大数据的有效管理和共享是数据高效利用的前提。英国地质调查局作为最早成立的国家地质调查局,拥有海量的地学数据资源,通过近年来对数字化工作的全面推进,在数据的开放共享方面走在了世界各国的最前沿。文章对英国地质调查局的数据资源管理和数据共享方式进行了分析调研,详细介绍了他们的一站式管理平台——开放地学的主体组成,以及他们与同行合作建设的数据库。开放地学全面汇总了地调局内的数据资源,并通过一系列数据共享将所有数据集有机链接,通过数据和模型的巧妙结合,在满足用户数据需求的同时,对数据的应用进行了全方位的拓展,是地球系统科学研究框架下地球科学数字化工作的良好典范。     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.