Search

As the rapid increasing of the number of the satellites, the number of facilities of the satellite ground stations is becoming insufficient and the conflicts of serving satellite data missions exist. To make full use of the facilities of the China Remote Sensing Satellite Ground Station (RSGS), scheduling optimization of these facilities was studied in this paper, which was not well-solved by the original method in the operation system of RSGS. The problem is formulated as a continuous time-based mixed integer linear programming (MILP) model. A decomposition optimization algorithm is proposed to solve the large-scale problem, which is difficult to solve using traditional methods. In the first step, the particle swarm optimization (PSO) algorithm is used to optimize the multi-ground station relay tasks, and the multi-ground station optimization problem is transformed into several single ground station optimization problems. Next, in two additional steps, optimization of the facilities at a single ground station is performed to further reduce the size of the model. The proposed method has been applied to operations systems of RSGS, and the approach presented in this article has demonstrated stable performance and sufficient efficiency online, resulting in the automation of facility scheduling. [ABSTRACT FROM AUTHOR]

To assess the status and change trend of forest in China, an indicator framework was developed using SDG sub-indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDG indicators. The main modification include the use of moderate and high spatial resolution satellite data, as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass (AGB). This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover, while time series Landsat data is used to estimate forest AGB across the whole territory of China. The study calculate two SDG sub-indicators: SDG15.1.1 for forest area and SDG15.2.1 for sustainable forest management. The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021, accounting for about 23.51% of the land area. The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha, and the overall trend of forest AGB change in China was positive, albeit with some spatial differences. [ABSTRACT FROM AUTHOR]

The United Nations adopted 17 Sustainable Development Goals (SDGs) to address societal, economic and environmental sustainability issues. The efficiency of SDGs monitoring could be improved by essential variables (EVs), which can help to better deal with massive data, interdisciplinary knowledge and workloads. However, in practice, effectively combining EVs with SDGs monitoring remains challenging. In this paper, we proposed a refining method of essential SDGs variables (ESDGVs) to land degradation. Firstly, we selected northwest China as our experimental region and extracted a group of variables related to land degradation from SDG indicators based on the DPSIR framework. Next, we identify the essential ones using a combined qualitative and quantitative methods with the criteria of feasibility, spatialization, and relevance which considered the issues of data acquisition, monitoring scale, and closeness to the land degradation. Finally, we analysed the monitoring role of ESDGVs. Results show that, compared to conventional observations, ESDGVs facilitate the monitoring and evaluation of regional SDGs with reduced efforts. And both climate and human activities have a facilitating or inhibiting effect on land degradation processes. In the future, we hope to have more mature data sets and consider adding more SDG indicators for ESDGVs' refinement. [ABSTRACT FROM AUTHOR]

Efforts to evaluate the susceptibility of debris flows in large areas, especially in mountainous regions, are often hampered by the alpine and canyon terrain. This paper proposes a convolution neural network (CNN) model named dense residual shuffle net (DRSNet). It is successfully applied to Nujiang Prefecture in Yunnan Province of China, a typical alpine area with frequent debris flows. DRSNet uses digital elevation model, remote sensing, lithology, soil type and precipitation data as input. First, dense connection and residual structure were used to extract the shallow features of various data. Next, channel shuffle, fuse block and fully connection were applied to strengthen the correlation between different shallow features and give inner danger scores. Finally, precipitation as the activation factor was introduced giving the valleys susceptibility. To verify the feasibility of DRSNet, comparative tests were conducted on 7 CNN models and 3 other machine learning (ML) methods. Experimental results show that DRSNet can achieve 78.6% accuracy in debris flow valley classification, which is at least 7.4% higher than common CNN models and 15.2% higher than other ML methods. This article provides new ideas for debris flow susceptibility evaluation. [ABSTRACT FROM AUTHOR]

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn't vary in adjacent space and earth surface property doesn't vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the 'Beijing', 'Dalanzadgad', 'NCU_Taiwan' and 'Kanpur' stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR 'Deep Blue' aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively. [ABSTRACT FROM AUTHOR]

The dynamic population distributions by activity type (e.g. working, shopping or in-home) are vital for resource allocation, urban planning and epidemic containment. Although studies have incorporated individual-level human mobility data to map population distribution by activity type, access to such data is hindered due to privacy issues and they rely on auxiliary data to provide priori activity knowledge. This paper presents a method for generating the population dynamics by activity type. We first introduce more readily available sequential snapshot data to construct the population mixture model, then decompose the population mixture, and finally estimate the dynamic population size for each activity. We test the method in the central districts of Guangzhou city, China, based on real-time Tencent user density data. Correlation analysis and accuracy assessment prove that our method can accurately estimate hourly distributions for populations engaging in working, stay-at-home, and socializing activities. The temporal distribution of the working population reproduces the regular work scenarios and socializing population displays complex spatial patterns. We also find that there is an underlying relationship between a region's function and its dynamic population structure. The presented method has great potential for application and could provide new insight for studying urban dynamic functions. [ABSTRACT FROM AUTHOR]

In map multiscale visualization, typification is the process of replacing original objects, such as buildings, using a smaller number of objects while maintaining initial geometrical and distribution characteristics. During the past few decades, many vector-based methods for building typification have been developed, whereas raster-based methods have received less attention. In this paper, a new method for the typification of buildings with different distribution patterns called superpixel building typification (SUBT) is developed based on raster data. Using this method, buildings with different distribution patterns, such as linear, grid and irregular patterns, are first grouped by image connected component detection and superpixel analysis. Then, the new positions for building typification are determined by superpixel resegmentation. Finally, a new representation of the buildings is determined through analysis of the orientation and shape of the buildings in each superpixel. To test the proposed SUBT method, buildings from both cities and countrysides in China are applied to perform typification. The experimental results show that the proposed SUBT method can realize typification for buildings with linear, grid and irregular distributions while effectively maintaining the original distribution characteristics of the buildings. [ABSTRACT FROM AUTHOR]

The Russian Far East is a region between China and the Russian Arctic with a diverse climatological, geophysical, oceanic, and economical characteristic. The southern region is located in the Far East monsoon sector, while the northern parts are affected by the Arctic Ocean and cold air masses penetrating far to the south. Growing economic activities and traffic connected to the China Belt and Road Initiative together with climate change are placing an increased pressure upon the Russian Far East environment. There is an urgent need to improve the capacity to measure the atmospheric and environmental pollution and analyze their sources and to quantify the relative roles of local and transported pollution emissions in the region. In the paper, we characterize the current environmental and socio-economical landscape of the Russian Far East and summarize the future climate scenarios and identify the key regional research questions. We discuss the research infrastructure concept, which is needed to answer the identified research questions. The integrated observations, filling in the critical observational gap at the Northern Eurasian context, are required to provide state-of-the-art observations and enable follow-up procedures that support local, regional, and global decision making in the environmental context. [ABSTRACT FROM AUTHOR]

Separating the individual effects of climate variability and human activities on streamflow is more important than just knowing their combined effects. In this paper, using a scenario-based hydrological simulation approach, the streamflow changes caused by climate variability and two different types of human activities (i.e. land-use change and large reservoirs operations) as well as the contribution rates of these three factors over 272 sub-basins in the Yangtze river basin were quantified and compared among 5 different periods (i.e. 1988–1992 (P1), 1993–1997 (P2), 1998–2002 (P3), 2003–2007 (P4) and 2008–2012 (P5)). Results demonstrate that, at the annual scale, climate variability played a leading role in the change in outflow of most sub-basins. With regard to the seasonal variations in discharge at Datong station, climate factors played a predominant role during P1-P2 and P2-P3. Since the Three Gorges Reservoir began operating in 2003, the discharge was enhanced by reservoirs in January-May and reduced by reservoirs in July-December. Reservoir and climate factors codetermined seasonal streamflow change during P3-P4 and P4-P5. Land-use change made the smallest contribution to seasonal discharge fluctuations. This study can support decision-making in regional water resources planning and management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Geoportals are a consolidated web-based solution to provide open spatial data sharing and online geo-information management. Their roles and possible advancements according to the Digital Earth vision and implementation require investigations. This paper presents a review of the literature concerning geoportals and serves the following primary purposes. First, various geoportal approaches for discovering and accessing Earth observation data and geo-information, mainly with scientific purposes, are summarized according to their characteristics and functionalities. Second, current major challenges in geoportals are identified in terms of functionalities, technologies, and especially big data support, from geoportal cases of China. Finally, based on lessons learned from the international and Chinese geoportals, solutions and recommendations for the challenges in geoportals are proposed in terms of their architectures, services, and technologies. The results show that geoportals usually provide access to distributed data systems, offering maps, data discovery, and data downloads. Some of them are also capable of offering online analysis and processing service, enhanced semantic search engines, and dynamic visualization tools. The strength of geoportals could lead to a full-fledged online Digital Earth system that could provide better data sharing and dissemination solutions to the challenges posed by big data. [ABSTRACT FROM AUTHOR]

Urbanization is one of the most important social and economic phenomena in the world today. This paper reviews the formation of megacities and summarizes the main problems, challenges and opportunities faced by the sustainable development of such large megacities. Issues discussed include the problems of land subsidence in megacities, environmental issues, traffic problems and energy supply aspects. The sustainable development of megacities in other parts of the world provided the references and experiences for the countermeasures of megacity planning and development in China. The vision of Digital Earth and Digital Cities can play a major role in the efficient management and sustainable growth of such megacities all around the world. [ABSTRACT FROM AUTHOR]

COSMO-SkyMed is a constellation of four X-band high-resolution radar satellites with a minimum revisit period of 12 hours. These satellites can obtain ascending and descending synthetic aperture radar (SAR) images with very similar periods for use in the three-dimensional (3D) inversion of glacier velocities. In this paper, based on ascending and descending COSMO-SkyMed data acquired at nearly the same time, the surface velocity of the Yiga Glacier, located in the Jiali County, Tibet, China, is estimated in four directions using an offset tracking technique during the periods of 16 January to 3 February 2017 and 1 February to 19 February 2017. Through the geometrical relationships between the measurements and the SAR images, the least square method is used to retrieve the 3D components of the glacier surface velocity in the eastward, northward and upward directions. The results show that applying the offset tracking technique to COSMO-SkyMed images can be used to derive the true 3D velocity of a glacier's surface. During the two periods, the Yiga Glacier had a stable velocity, and the maximum surface velocity, 2.4 m/d, was observed in the middle portion of the glacier, which corresponds to the location of the steepest slope. [ABSTRACT FROM AUTHOR]

Surface supply relationships between glaciers and lakes are needed to analyze and understand hydrological processes at regional and global scales. However, these supply relationships still cannot be extracted efficiently by existing methods. This paper proposes an automatic and efficient approach to extracting surface supply relationships between glaciers and lakes based on meltwater flow paths. The approach includes two stages: (1) identifying direct connections between objects (i.e. glaciers and lakes) based on flow direction derived from digital terrain analysis on a gridded digital elevation model (DEM) and (2) deriving all (or user-specified) kinds of surface supply relationships based on graph search. All computation-intensive steps in this approach have been parallelized; and all steps in the proposed approach have been integrated as an automatic program. Results for the Tibetan Plateau show that given outline data for glaciers and lakes and the Shuttle Radar Topography Mission DEM, the proposed approach can automatically derive diverse surface supply relationships under user-specified restrictions on the attributes of the supply route. The parallelization in the approach effectively improves the computing efficiency. The proposed approach could also be applied to developing a detailed fundamental dataset of supply relationships between glaciers and lakes for other region or period. [ABSTRACT FROM AUTHOR]

Historically, Lop Nur was a large and famous salt lake that acted as an important geographic position along the ancient ‘Silk Road’, and was associated with the surrounding old civilizations, such as Loulan and Haitou. However, it dried up before 1972. It shows a clear ‘Ear’ feature on synthetic aperture radar (SAR) images. The objective of this paper is to interpret Lop Nur's environmental evolution during its drying-up process based on an analysis of its sodium sedimentary feature. The genetic algorithm-partial least squares approach is introduced as a modeling method to retrieve the subsurface sodium content from polarimetric parameters obtained by Cloude decomposition. As a result, theR2and root-mean-square error can reach 0.7 and 9.1 g/kg. It is suggested that the subsurface salt content was the primary reason for the bright-grey strips textures on SAR images. Furthermore, our results show that the sodium content along the same strip changed, with its distribution exhibiting consistency with the lake body's movement driven by the strong wind. In future, high-precision topographical data will be considered, and should be helpful in the analysis of lake body movement. The method of this paper can also be applied in other similar dried salt lakes. [ABSTRACT FROM PUBLISHER]

The geographical condition is a very important component of a country's national condition, and geographical conditions monitoring (GCM) has been of great concern to the Chinese government. GCM has a close relation with ‘Digital China’ and is a concrete embodiment of Digital China. This paper discusses the content and classification of GCM. In accordance with application areas, GCM can be divided into fundamental monitoring, thematic monitoring, and disaster monitoring. The application areas perspective includes the content of the three other perspectives, like the monitoring elements, the monitoring scope, and the monitoring cycle and fully reflects the essence of the GCM. Fundamental monitoring mainly focuses on monitoring all of the geographical elements, which provides a basis for follow-up thematic monitoring; thematic monitoring is a special type of designated subject monitoring that concerns the public or the government; disaster monitoring focuses on the dynamic monitoring of the pre-disaster and disaster periods for natural disasters. The monitoring results will provide timely information for governments so that they can meet management or decision-making requirements. A GCM case study in the area of the Qinghai — Tibet plateau was made, and some concrete conclusions were drawn. Finally, this paper presents some thoughts on conducting GCM. [ABSTRACT FROM PUBLISHER]

Global change has a significant impact on the lives of humankind. Earth observation can help to better understand our earth and cope with global change. With the availability of more reliable environmental data sets, digital earth is becoming a popular way to monitor the Earth and provide information to researchers and decision makers on environment protection, disaster mitigation, and social benefits. Therefore, accessing data with lowering costs is essential for digital earth. Nevertheless, there are big challenges in ensuring the feasibility of access to Chinese remote sensing data. This paper outlines some of the main challenges in realizing data sharing, provides an analysis of the core reasons leading to these challenges, and proposes recommendations to overcome the challenges. Amongst the main challenges are differences in data policy to gain access to satellite data, diverse data formats, and delivery mechanisms. The major challenge for the decision makers is to define a more open policy and for the scientist the challenge is to implement these polices for the benefit of all. This paper proposes that governments should adopt policies encouraging more open distribution and access to their data, in order to generate an improved digital earth with increased benefits to human society. [ABSTRACT FROM PUBLISHER]

Digitizing the land surface temperature (Ts) and surface soil moisture (mv) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrievingTsandmvusing the dual-polarizedTbof Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situTsandmvin southern China showed the average root mean square errors (RMSE) ofTsandmvretrievals reach 2.42 K (R2= 0.61,n= 351) and 0.025 g cm−3(R2= 0.68,n= 663), respectively. The results were also validated using global in situTs(n= 2362) andmv(n= 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R2= 0.86) and 0.039 g cm−3(R2= 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging SpectroradiometerTs(R2= 0.57; RMSE = 2.91 k) and ECV_SMmv(R2= 0.51; RMSE = 0.045 g cm−3), respectively. Overall, this paper indicates an effective way to jointly modelingTsandmvusing passive microwave remote sensing. [ABSTRACT FROM AUTHOR]

This study mainly focuses on revealing an ancient water landscape at the Longcheng site in the northern Chaohu Lake Basin using very high-resolution (VHR) GeoEye-1 imagery. First, prior to classification, the GeoEye-1 image was processed following atmospheric and geometric correction. The supervised classification was carried out in order to show the land-cover situation in the Longcheng area. The overall classification accuracy was 89.98%, with a kappa coefficient of 0.87. The moat system around the city walls was discovered by using rule-based object-oriented segmentation of the postclassified image, and the other walls of ancient Longcheng were manually identified from the pansharpened VHR GeoEye-1 image. Finally, a map of the ancient water landscape containing the ancient city, wall and moat at the Longcheng site was produced. This paper demonstrates that VHR remote sensing has the ability to uncover an ancient water landscape and provide new insights for archaeological and paleoenvironmental studies. [ABSTRACT FROM AUTHOR]

Yangbajain contains the largest geothermal energy power station in China. Geothermal explorations in Yangbajain first started in 1976, and two plants were subsequently built in 1981 and 1986. A large amount of geothermal fluids have been extracted since then, leading to considerable surface subsidence around the geothermal fields. In this paper, InSAR time series analysis is applied to map the subsidence of the Yangbajain geothermal fields during the period from December 2011 to November 2012 using 16 senses of TerraSAR-X stripmap SAR images. In the case of the TerraSAR-X data, most orbital fringes were removed using precise orbits during the interferometric processing. However, residual orbital ramps remain in some interferograms due to the uncertainties in the TerraSAR-X orbits. To remove the residual orbital ramps, we estimated a best-fit ‘twisted plane’ for each epoch interferogram using quadratic polynomial models based on a network approach. This method removes most of the long-wavelength signals, including orbit ramps and atmospheric effects. The vertically stratified component (Topography Correlated Atmospheric Delay, TCAD) was also removed using a network approach. If the influence of seasonal frozen ground (SFG) is not taken into consideration, our results show that the subsidence rate around power plant I (the south plant) is approximately 20 mm/yr with a peak of 30 mm/yr. The subsidence rate around power plant II (the north plant) is approximately 10 mm/yr, when accounting for the influence of SFG on the power plant and its surrounding ground surface. Our results show that ground motion is caused by seasonal frozen ground and is strongly related to the temperature change. [ABSTRACT FROM PUBLISHER]

The Chinese Government and citizens face enormous challenges of disaster management as widespread devastation, economic damages, and loss of human lives caused by increasing natural disasters. Disaster management requires a complicated iterative process that includes disaster monitoring, early detection, forecasting, loss assessment, and efficient analysis of disaster reduction. Each task typically involves the use of technologists and multiple geospatial information resources, including sensors, data sources, models, geo-tools, software packages, and computing resources. However, most existing disaster management systems operate in a typical passive data-centric mode, where resources cannot be fully utilized. This impediment is partially being addressed by the increasingly complex application requirements and the growing availability of diverse resources. In this paper, we summarize and analyze the practical problems experienced by the National Disaster Reduction Application System of China. To address the issues of data-centric, centralized, isolated solutions, we propose a novel Focusing Service Mechanism, which is capable of scheduling and allocating for optimum utilization of multiple resources, to dynamically generate collaborative and on-demand disaster information services. We also demonstrate the design and implementation of the Integrated Disaster Information Service System (IDISS). Through the service strategies ofVirtualizing,Wrapping, andIntegrating, disaster-related resources are constructed into services in the IDISS. These services are dynamically aggregated into focusing service chains, for diverse disaster management tasks. Actual applications illustrate that the proposed service system can significantly improve the capability of disaster management in China. [ABSTRACT FROM PUBLISHER]

China is one of the most disaster-prone countries in the world. Currently, the disaster prevention and relief mechanism in China is mainly based on single disaster types and is implemented by different ministries and divisions in single administrative regions. Subsequently, the available resources, including data, services, materials, and human resources, cannot be shared and used effectively. Based on the idea of an observation system of systems and a business system of systems, this paper presents an integrated framework for a Chinese National Disaster Reduction System of Systems (CNDRSS) to address this issue. The CNDRSS framework aims to achieve data sharing and collaboration among different disaster-related ministries/institutions by providing one-stop services for all phases of disaster management and linking together existing and planned disaster-related business systems and observation systems. The key technologies use federated databases and a web service to integrate multiple disaster management systems among different ministries/institutions and a sensor web to integrate airborne, space-borne, andin-situobservations through the web service. These event-driven focused-services connecting the various observations, processing, and mapping processes can meet the requirements for complex disaster-chain systems. [ABSTRACT FROM AUTHOR]

Due to spatial and temporal variability an effective monitoring system for water resources must consider the use of remote sensing to provide information. Synthetic Aperture Radar (SAR) is useful due to timely data acquisition and sensitivity to surface water and flooded vegetation. The ability to map flooded vegetation is attributed to the double bounce scattering mechanism, often dominant for this target. Dong Ting Lake in China is an ideal site for evaluating SAR data for this application due to annual flooding caused by mountain snow melt causing extensive changes in flooded vegetation. A curvelet-based approach for change detection in SAR imagery works well as it highlights the change and suppresses the speckle noise. This paper addresses the extension of this change detection technique to polarimetric SAR data for monitoring surface water and flooded vegetation. RADARSAT-2 images of Dong Ting Lake demonstrate this curvelet-based change detection technique applied to wetlands although it is applicable to other land covers and for post disaster impact assessment. These tools are important to Digital Earth for map updating and revision. [ABSTRACT FROM PUBLISHER]

China will, as a component of 'Digital Earth,' establish a Geomatics Informatization Technology System (GITS) which is characterized by real-time acquisition, automatic processing, networking service, and socialized application with fundamental geographical information. The basic composition of GITS is proposed. GITS covers four layers and six platforms. The four layers are data acquisition, processing, management, and application and services. The six platforms are informatic geodetic datum, high-precision geo-spatial data acquisition, automatic geo-spatial data processing, grid-based geo-spatial information management, comprehensive geo-spatial information sharing and service, and geo-spatial information integration and application. The informatic geodetic datum platform provides a carrier for all the four layers and a base for the other five platforms. The high-precision geo-spatial data acquisition platform belongs to the acquisition layer. The automatic geo-spatial data processing platform belongs to the processing layer. The grid-based geo-spatial information management platform belongs to the management layer and is a bridge connecting geo-spatial data acquisition and spatial information sharing service and integrated applications. The comprehensive geo-spatial information sharing and service platform belongs to the application and service layer. Finally, this paper presents thoughts for constructing GITS. [ABSTRACT FROM AUTHOR]

The understanding that mankind should reasonably exploit and utilize earth resources and effectively protect the planet on which we live, is now widely accepted. However, effective actions can only be conducted if we better understand and visualize the earth. To meet this need, digital earth science and technology have been put forward and developed. This paper introduces the evolution and development process of digital earth, and presents an overview by reviewing and analyzing the 1999 and 2009 Beijing Declaration on Digital Earth, the scientific and commercial digital earth systems, global and regional digital earth research, and some existing platforms of digital earth science. It also presents some thoughts about digital earth's future development. [ABSTRACT FROM AUTHOR]

Digital Earth is an information expression of the real Earth, and is a new way of understanding the Earth in the twenty-first century. This paper introduces a Digital Earth Prototype System (DEPS) developed at the Chinese Academy of Sciences (CAS) and supported by the Knowledge Innovation Program of the Chinese Academy of Sciences. Discussions are made to the theoretical model and technical framework of the Digital Earth, and its related key technologies on spatial information processing, spatial data warehouse technology, virtual reality technology, high-performance and parallel computing. The DEPS consists of seven sub-systems including the spatial data, metadata, model database, Grid geoscience computing, spatial information database, maps service and virtual reality. Meanwhile, we developed a series of application systems such as the environment monitoring for the Olympic Games 2008 in Beijing, natural disasters evaluation, digital city, digital archeology, Asia regional aerosol and climate change. The DEPS/CAS displayed the application ability and potential of the Digital Earth in three levels: the global, national and regional. [ABSTRACT FROM AUTHOR]