Forward Support—Cloud Based Mosaic Imagery for Emergency Operations

The Institute for Creative Technologies (ICT) has pursued the creation of One World Terrain (OWT), which aims to provide a set of 3D global terrain capabilities and services that can replicate the coverage and complexities of the operational environment. Research was conducted in support of One World Terrain through development of best practices for the delivery of a raster mosaic via cloud hosting service, created using OptimizeRasters Geoprocoessing Toolbox and the Mosaic Dataset Configuration Script. Though ultimately successful in developing the raster mosaic and hosting it online;JPEG compression lossiness was a key issue with the larger Rose Bowl dataset. Additionally, hosting the imagery via ArcGIS Online was found to increase the compressed file size;making it comparable to the original file size of the data. Future testing should consider usage of an enterprise server to avoid this issue. MRF_LERC compression was identified as the ideal file configuration;and ArcGIS Online was identified as a poor enterprise hosting medium. We have also identified a variety of ways to improve the MDCS script in order to automate the whole process more efficiently.

识别液态和固态水的四波段水指数及其在咸海流域的应用

虽然已有许多水指数被运用于咸海和咸海流域的水域面积分析,然而已有研究尚存在很大的不确定性.由于所使用水指数存在水体误识别的问题,导致不同研究得出的结论差异很大.此外,各种研究采用的咸海流域面积也差异很大,流域面积从约1百万km^(2)到2百万km^(2)不等.因此,我们首先界定咸海流域、锡尔河和阿姆河流域以及它们的上、中、下游的边界,然后提出可识别液态水和固态水的四波段指数(ILSW),以解决传统水指数的误别偏差问题.ILSW基于绿色、红色、近红外和热红外波段反射值,是归一化差异水指数(NDWI)和地表温度(LST)的合成.验证结果显示,ILSW水指数在咸海流域的准确性远远超过其他指数.研究结果表明,南咸海的水域面积平均每年缩小963km^(2),而北咸海几乎没有变化;由于气候变暖,咸海流域上游常年冰雪面积有大幅度退缩趋势,阿姆河上游和锡尔河上游常年冰雪的退缩速率分别为年均6233和384km^(2)/a.气候变化和人类活动引起了严重的水亏问题.自20世纪90年代以来,水亏呈增长趋势,平均每年增长3.778亿m^(3).2011~2020年期间,整个咸海流域平均总水亏量为76.967亿m^(3),而锡尔河流域上游(咸海流域的一个组成部分)水盈余量为25.461亿m^(3).研究结果对制定咸海流域可持续发展战略具有重要意义.

A four-band index for both liquid and solid water and its applications in the Aral Sea Basin

Various investigations have been conducted to analyze the water-coverage area of the Aral Sea and the Aral Sea Basin(ASB). However, the investigations incorporated considerable uncertainty and the used water indices had misclassification problem, which made different research groups present different results. Thus we first ascertain the boundaries of the ASB, the Syr and Amu river basins as well as their upper, middle and lower reaches. Then a four-band index for both liquid and solid water(ILSW) is proposed to address the misclassification problems of the classic water indices. ILSW is calculated by using the reflectance values of the green, red, near infrared, and thermal infrared bands, which combines the normalized difference water index(NDWI) and land surface temperature(LST) together. Validation results show that the ILSW water index has the highest accuracy by far in the Aral Sea Basin. Our results indicate that annual average decline of the water-coverage area was 963 km^(2) in the southern Aral Sea, whereas the northern Aral Sea has experienced little change. In the meanwhile, permanent ice and snow in upper reach of ASB has retreated considerably. Annual retreating rates of the permanent ice and snow were respectively 6233and 3841 km^(2) in upper reaches of Amu river basin(UARB) and Syr river basin(USRB). One of major reasons is that climate has become warmer in ASB. The climate change has caused serious water deficit problem. The water deficit had an increasing trend since the 1990s and its increasing rates was 3.778 billion m^(3) yearly on average. The total water deficit was 76.967 billion m^(3) on average in the whole area of ASB in the 2010s. However, up reaches of Syr river basin(USRB), a component area of ASB, had water surplus of 25.461 billion m^(3). These conclusions are useful for setting out a sustainable development strategy in ASB.

Geographic information science in the era of geospatial big data: A cyberspace perspective

The advent of information and communication technology and the Internet of Things have led our society toward a digital era.The proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to virtual.This transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different perspectives.For instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

生态环境曲面建模基本定理及其应用

在高精度曲面建模方法和地球表层系统建模基本定理研究结果基础上,演绎提出了生态环境曲面建模基本定理.以京津冀地区为案例,对基于生态环境曲面建模基本定理的空间升尺度、空间降尺度、空间插值、数据融合和模型-数据同化等算法进行了实证研究,与传统算法精度进行了比较分析.结果表明,由于基于生态环境曲面建模基本定理的各种算法综合了外蕴量信息和内蕴量信息,同时运用了理论上完善的信息综合方法,使海拔高度曲面的升尺度均方根误差至少降低了9m,年平均气温未来情景的降尺度精度至少提高16%,年平均气温过去变化趋势的数据融合精度至少提高70%,年平均降雨量过去变化趋势的空间插值精度至少提高0.2%,碳储量的模型-数据同化精度提高了40%.文章最后讨论了生态曲面建模基本定理亟待解决的五大理论问题和四大应用基础问题.

A new approach to modeling the fade-out threshold of coronavirus disease

On 21 December of 2019,four lower respiratory tract samples were collected from patients with pneumonia of unknown cause in Wuhan,China[1].This disease was later diagnosed as a coronavirus disease.The World Health Organization(WHO)named the coronavirus disease as COVID-19 on 11 February and declared a pandemic on 11 March 2020.The COVID-19 is a previously unknown disease with no tools available for pharmaceutical management at the early stage of its outbreak.

Space- and ground-based CO_2 measurements： A review

The climate warming is mainly due to the increase in concentrations of anthropogenic greenhouse gases, of which CO_2 is the most important one responsible for radiative forcing of the climate. In order to reduce the great estimation uncertainty of atmospheric CO_2 concentrations, several CO_2-related satellites have been successfully launched and many future greenhouse gas monitoring missions are planned. In this paper, we review the development of CO_2 retrieval algorithms, spatial interpolation methods and ground observations. The main findings include: 1) current CO_2 retrieval algorithms only partially account for atmospheric scattering effects; 2) the accurate estimation of the vertical profile of greenhouse gas concentrations is a long-term challenge for remote sensing techniques; 3) ground-based observations are too sparse to accurately infer CO_2 concentrations on regional scales; and 4) accuracy is the primary challenge of satellite estimation of CO_2 concentrations. These findings, taken as a whole, point to the need to develop a high accuracy method for simulation of carbon sources and sinks on the basis of the fundamental theorem of Earth's surface modelling, which is able to efficiently fuse space- and ground-based measurements on the one hand and work with atmospheric transport models on the other hand.

Dynamics of the COVID-19 basic reproduction numbers in different countries

The reproduction number,R,is the average number of secondary infectious cases produced by one infectious case during a disease outbreak[1].When a population is totally susceptible,R becomes the basic reproduction number,R_(0).It is a key parameter regulating the transmission dynamics of a pandemic[2].R_(0)provides an indication of whether the introduction of disease will result in a localized burnout or signal the beginning of a pandemic that could move through all geographic scales[3].

Quantum machine learning of eco-environmental surfaces

The computer advances of the past century can be traced to the increase in their numbers on chips that has accompanied the miniaturization of transistors.However,computers are nearing the fundamental limits of such miniaturization[1].Many practical problems require huge amounts of computational resources that exceed the capabilities of today's computers.A 54-qubit quantum computer on the other hand can solve in minutes a problem that would take a classical machine 10,000 years[2].

A fundamental theorem for eco-environmental surface modelling and its applications

We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.