Big Earth Data Research Articles

Ambient air pollutants exposure during gestation and incidence risk of hypertensive disorders of pregnancy or preeclampsia in China

The relationships between the exposure to ambient air pollutants during gestation and the incidence of hypertensive disorders in pregnancy (HDPs) or preeclampsia are contradictory. This prospective cohort study enrolled the participants between January 2020 and December 2021 from the Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology. The exposure to ambient air pollutants and daily temperatures were obtained from the ChinaHighAirPollutants dataset and the Big Earth Data Platform for Three Poles, respectively. Logistic regression models were used as single- and two-pollutant models. Restricted cubic splines were applied to each ambient air pollutant exposure to further evaluate the exposure-response relationships. Quantile G-computation approaches were employed to evaluate the cumulative impact of mixed ambient air pollutants on the incidence risk HDPs and preeclampsia. Among 19,325 participants (median age: 30.2 years), 1669 (8.64%) were diagnosed with HDPs and 180 (0.94%) with preeclampsia. While mostly null risk estimates were observed, exposure to PM1, PM2.5, PM10, and NO2 correlated with a decreased incidence risk for HDPs and preeclampsia during most gestational periods. Additionally, our multi-pollutant model presented that an increase by one quartile in the cumulative effect of ambient air pollutants was associated with a significantly decreased incidence risk for HDPs in the trimester before gestation and in the third trimester during gestation, as well as for preeclampsia in the third trimester during gestation. These findings warrant further investigation into the mechanisms underlying these associations.

Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland)

ABSTRACT Accurate, consistent, and high-resolution Land Use & Cover (LUC) information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers, pressures, state, and impacts on land systems. Nevertheless, the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change. Hereafter, we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data, combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region. Findings suggest that time-first approach is a valuable alternative to space-first approaches, accounting for the intra-annual variability of classes, hence improving classification performances. Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest, providing more accurate predictions with lower uncertainty. The produced land cover map has a high accuracy, an improved spatial resolution, while at the same time preserving the statistical significance (i.e. class proportion) of the official national dataset. This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability. However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.

Big Earth Data and its role in sustainability

Big Earth Data and its role in sustainability

CBAS: An International Platform of Digital Technologies Facilitating Sustainable Development Goals

The Chinese Academy of Sciences (CAS) launched the Big Earth Data Science Engineering Program (CASEarth) in 2018, which laid the foundation for the International Research Center of Big Data for Sustainable Development Goals (CBAS). Building on CASEarth’s achievements, CBAS integrates advanced digital technologies to advance the UN Sustainable Development Goals (SDGs) through five key missions: (1) developing SDG data infrastructure and information products via its SDG Big Data Platform, utilizing advanced computing, cloud services, and AI; (2) developing and launching a series of SDG satellites, including SDGSAT-1, which provides crucial Earth observation data through its Open Science Program; (3) providing new knowledge for SDG monitoring and evaluation through annual reports (Big Earth Data in Support of the Sustainable Development Goals, published since 2019) and guiding the development of big data-driven technical solutions and theoretical systems; (4) establishing a think tank for science, technology, and innovation, promoting SDGs through initiatives like the annual International Forum on Big Data for Sustainable Development Goals (FBAS) and the CBAS Fellowship Program; and (5) providing capacity development for SDGs in developing countries through international collaborations, such as the Digital Belt and Road Program (DBAR), which offers professional education and training in big data. Future CBAS efforts will focus on expanding data access, enhancing AI capabilities for SDG indicator monitoring, and strengthening international partnerships to address data gaps and ensure equitable access to technology and expertise for achieving global sustainability.

Transformative crop insurance solution with big earth data: Implementation for potato in India

ContextCrop insurance has become an indispensable risk management tool in the agricultural sector because globally crops are being exposed to multiple hazards. The lack of reliable crop yield data has impacted the sustenance of area-yield crop insurance schemes. Index-based insurance, which links pay-outs to crop performance proxies rather than measured losses, is being explored to improve the effectiveness of crop insurance contracts. ObjectiveThis paper presents an innovative crop insurance scheme that has replaced the existing ‘area-yield’ approach using bias-prone crop yield estimates with the ‘area-crop performance approach’ using objectively measured satellite indices. MethodSatellite-based crop mapping, satellite and weather-based crop health indicators, field data collection and analysis, composite index generation, and insurance loss assessment are major tasks in the project. Data of Sentinel-1 and 2 satellites, weather datasets and mobile app-based field data from transplantation to harvesting of the crop constituted a huge repository of the database in this project. Metrics derived from established satellite indices, such as NDVI, LSWI and Backscatter, along with weather indices, were synthesized into a composite index of crop performance called Crop Health Factor (CHF). The input data matrix of the CHF model included eight input indicators. After data normalization, weights for these indicators were generated using the entropy technique, a proven method of information measurement that produces balanced relationships and unbiased weights. The CHF was first generated with data from the past years (2016–2019), and the resulting weights were then applied to the normalized data of the current year (2020). ResultsThe current crop insurance scheme, using CHF data instead of yield data, was implemented in the state of West Bengal, India, covering about 500,000 ha of potato across and one thousand insurance units in the 2020 crop season. The CHF and yield data from past years showed similar patterns in the majority of cases. The indemnity level was set at 70 % of the normal CHF, which was the average CHF of past years. Loss assessment and compensation payouts for the current year were determined by the extent of CHF reduction beyond the indemnity level. SignificanceThis new index-insurance scheme has many advantages over the conventional yield-based scheme in terms of transparency, objectivity and ease of implementation. There is scope for improving the composite index with additional features. Such technology-driven index-insurance schemes for field crops are expected to bring a paradigm shift in the crop insurance sector, giving rise to new business models and benefitting all the stakeholders.

The origin and research progress of Big Earth Data

The origin and research progress of Big Earth Data

An Overview of GIS-RS Applications for Archaeological and Cultural Heritage under the DBAR-Heritage Mission

In recent decades, the application of GIS and RS in archaeological and cultural heritage (ACH) has witnessed a notable surge both in terms of quantity and scope. During the initial implementation period (2016–2021) of the Digital Belt and Road Heritage (DBAR-Heritage) working group, several instances of GIS-RS-based applications in support of cultural heritage conservation have merged. In this paper, in order to discuss the great potential of GIS and RS on the Silk Road, an overview of GIS- and RS-based applications in ACH is first presented. In a substantial portion of the published scientific literature, the identification and comprehension of archaeological sites, the monitoring and risk assessment of cultural heritage, and the management and visualization of cultural heritage data are highlighted. Following this, five illustrative case studies from the DBAR-Heritage working group are presented to exemplify how the integration of GIS and RS serves as key approaches in recognizing and appreciating cultural heritage. These selected case studies showcase the utilization of multi-source data for the identification of linear sites; detailed, refined monitoring and assessment of the Angkor Wat heritage; and the reconstruction of the Silk Road routes. These instances serve as the cornerstone for highlighting current trends in GIS and RS applications in ACH along the Silk Road. These methodologies efficiently integrate multi-source geospatial data and employ multidisciplinary approaches, ultimately furnishing sophisticated and intelligent tools for the exploration and management of archaeological and cultural heritage in the era of Big Earth Data. Subsequently, a comprehensive discussion on the merits and challenges of GIS and RS applications in ACH is presented, followed by an exploration of the current application trends. Finally, the prospects for the widespread application of GIS and RS in ACH along the Silk Road are outlined in accordance with the operational plan of DBAR-Heritage during its second implementation phase.

Progress on achieving environmental SDGs assessed from Big Earth Data in China

Progress on achieving environmental SDGs assessed from Big Earth Data in China

Earth Observation in the EMMENA Region: Scoping Review of Current Applications and Knowledge Gaps

Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services.

Adopting GPU computing to support DL-based Earth science applications

ABSTRACT With the advancement of Artificial Intelligence (AI) technologies and accumulation of big Earth data, Deep Learning (DL) has become an important method to discover patterns and understand Earth science processes in the past several years. While successful in many Earth science areas, AI/DL applications are often challenging for computing devices. In recent years, Graphics Processing Unit (GPU) devices have been leveraged to speed up AI/DL applications, yet computational performance still poses a major barrier for DL-based Earth science applications. To address these computational challenges, we selected five existing sample Earth science AI applications, revised the DL-based models/algorithms, and tested the performance of multiple GPU computing platforms to support the applications. Application software packages, performance comparisons across different platforms, along with other results, are summarized. This article can help understand how various AI/ML Earth science applications can be supported by GPU computing and help researchers in the Earth science domain better adopt GPU computing (such as supermicro, GPU clusters, and cloud computing-based) for their AI/ML applications, and to optimize their science applications to better leverage the computing device.

Mapping potential wetlands by a new framework method using random forest algorithm and big Earth data: A case study in China's Yangtze River Basin

Mapping potential wetlands by a new framework method using random forest algorithm and big Earth data: A case study in China's Yangtze River Basin

Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020

Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda).

Assessing effectiveness of nature-based solution with big earth data: 60 years mangrove plantation program in Bangladesh coast

BackgroundIn the climate change context, nature-based solution (NBS) is considered one of the effective tools to increase the resilience of socio-ecological system. The concept coincides with the government’s attempts of afforestation and reforestation programs that have been going on for 60 years in Bangladesh. This study, therefore, envisaged understanding how NBS (mangrove afforestation and reforestation) works to promote climate change resilience through the synthetization of remote sensing-based big earth data, statistical tools, and models. The study took the entire coast of Bangladesh except for Sundarbans Reserve Forest and rolled back to 1962 to work on 60 years’ time series data. Declassified CORONA satellite imagery along with Landsat satellite imagery was used, which is the first-ever attempt in the remote sensing-based ecosystem work in Bangladesh.ResultsThe study's main innovation is to spatially establish the effectiveness of the NBS. The study critically assessed and estimated stable lands and their socio-economic benefits as part of the effectiveness of the NBS. As part of the NBS-derived benefits in the context of climate change, it estimated the sequestrated carbon in mangrove forests. A significant positive relationship was observed between the increase of mangroves and stable lands. Near about 448,011 ha of agricultural land was stabilized due to the NBS intervention whose economic value is 18,837 million USD. In addition, 29,755.71 kt of carbon have been sequestrated due to NBS program.ConclusionsThe concept of NBS is still in the development stage and very little or no work has been done so far in measuring and labeling the effectiveness of the NBS. Therefore, our study can innovatively contribute to the scientific community to show the effectiveness of the NBS in three domains (social, economic and ecological) in the changing climatic scenario.

A comprehensive and up-to-date web-based interactive 3D emergency response and visualization system using Cesium Digital Earth: taking landslide disaster as an example

ABSTRACT As with the fast advances in the technologies of big Earth data and information communication, Web-based 3D GIS system has come a long way from a few years ago. These advances reflect in many aspects of 3D GIS such as higher real-time performance, enhanced interactivity, more realistic 3D visualization effect and improved user interface. This paper aims to present a comprehensive and up-to-date 3D Web GIS for Emergency Response using the current vue.js web application framework and the well-known Cesium API, taking landslide disaster as an example. Building upon recent advances in WebGL technology, we developed a suite of enhanced 3D spatial analysis functions, including interactive route planning, instant text/image/video messaging being incorporated into both 3D WebGL page and mobile GIS applications, and progressive 3D construction and AR visualization using LiDAR and camera over local emergency network or internet. Moreover, professional functions such as landslide susceptibility mapping, landslide monitoring, spatial temporal contingency plan management, landslide information management, personnel and equipment management, and communication are all implemented and integrated in the 3D GIS system. Most of the functions of the system are implemented using open-source projects, which is beneficial to the development of the 3D GIS research community.

Urban-rural disparities of carbon storage dynamics in China's human settlements driven by population and economic growth

China has experienced a rapid expansion of human settlement in both urban and rural areas over the past three decades. Regarding the impacts on carbon storage, previous studies that only focus on certain ecosystems cannot reflect urban-rural disparities, resulting in the carbon storage changes in human settlement remaining unknown. In this study, we aimed to explore China's urban-rural disparities in human settlement expansion and direct impacts on carbon storage by using the big Earth data technology. The results showed that from 1990 to 2018, the total amount of China's human settlement expansion reached 175,703.80 km2, and the inner-city, peri-urban, and rural components accounted for 21.00 %, 20.18 %, and 58.82 %, respectively. Along with the general tendency of impervious surface area (ISA) growth, there was more soil organic carbon (SOC) (1254.33 TgC) being sealed beneath ISA (0–100 cm depth), compared to a huge reduction in vegetation biomass carbon (VBC) (91.44 TgC) during the study period. The results further indicated that the change density of either VBC or SOC presented a slightly rising trend along the urban-rural gradient, due to the increasingly common encroachment on vegetation and soil types with higher carbon content. We also found that socioeconomic drivers had a greater influence in urban areas than rural areas, and the related correlation exhibited a descending trajectory in both urban and rural areas. There is thus an urgent need to preserve lands with abundant carbon storage and contain the waste of land resources in rural areas. All stakeholders should pay more attention to concerted and targeted regulation policies for well-planned and eco-friendly human settlement expansion such as enhancing rural land use efficiency and promoting large-scale afforestation and continuous urban greening, which will be critical not only for guiding sustainable urbanization all over China but also for mitigating climate change for the entire world.

Virtual earth cloud: a multi-cloud framework for enabling geosciences digital ecosystems

ABSTRACT Humankind is facing unprecedented global environmental and social challenges in terms of food, water and energy security, resilience to natural hazards, etc. To address these challenges, international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan. The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions (providing the best available knowledge) and avoid potentially negative impacts on society and the environment. Such knowledge platforms must build on the recent and next coming digital technologies that have transformed society – including the science and engineering sectors. Big Earth Data (BED) science aims to provide the methodologies and instruments to generate knowledge from numerous, complex, and diverse data sources. BED science requires the development of Geoscience Digital Ecosystems (GEDs), which bank on the combined use of fundamental technology units (i.e. big data, learning-driven artificial intelligence, and network-based computing platform) to enable the development of more detailed knowledge to observe and test planet Earth as a whole. This manuscript contributes to the BED science research domain, by presenting the Virtual Earth Cloud: a multi-cloud framework to support GDE implementation and generate knowledge on environmental and social sustainability.

An optimized hexagonal quadtree encoding and operation scheme for icosahedral hexagonal discrete global grid systems

ABSTRACT Although research on the discrete global grid systems (DGGSs) has become an essential issue in the era of big earth data, there is still a gap between the efficiency of current encoding and operation schemes for hexagonal DGGSs and the needs of practical applications. This paper proposes a novel and efficient encoding and operation scheme of an optimized hexagonal quadtree structure (OHQS) based on aperture 4 hexagonal discrete global grid systems by translation transformation. A vector model is established to describe and calculate the aperture 4 hexagonal grid system. This paper also provides two different grid code addition algorithms based on induction and coordinate transformation. We implement the transformation between OHQS codes and geographic coordinates through the , and coordinate systems. Compared with existing schemes, the scheme in this paper greatly improves the efficiency of the addition operation, neighborhood retrieval and coordinate transformation, and the coding is more concise than other aperture 4 hexagonal DGGSs. The encoding operation based on the coordinate system is faster than the encoding operation based on the induction and addition table. Spatial modeling based OHQS DGGSs are also provided. A case study with rainstorms demonstrated the availability of this scheme.

Modeling carbon storage in urban vegetation: Progress, challenges, and opportunities

Urban vegetation (UV) and its carbon storage capacity are critical for terrestrial carbon cycling and global sustainable development goals (SDGs). With complex spatial distribution, composition and ecological functions, UV is essential for global carbon cycling and climate change. Therefore, improving UV carbon storage capacity modeling is a research hotspot that deserves extensive investigation. However, the uniqueness of UV lead to great challenges in carbon storage modeling, including (1) limitations in data and algorithms due to complex and sensitive urban environments; (2) the severe scarcity of in-city field observation data (e.g., EC towers and field surveys); (3) difficulty in parameter inversion (e.g., canopy height, LAI, etc.); (4) poor transferability when migrating estimation models from natural vegetation to urban scenarios. The progress in carbon storage modeling in urban settings is reviewed, with detailed discussions on carbon storage modeling methods and major challenges. We then propose strategies to overcome existing challenges, including (1) implementing novel and improved remote sensing (RS) techniques (e.g., hyper-spectral, LiDAR, carbon satellites, etc.) to obtain enhanced structural and functional information on UV; (2) improving critical nodes of the earth observation sensor network, especially the distribution of EC towers in urban settings; (3) leveraging “Model-Data Fusion” technology by integrating big earth data with carbon estimation models to reduce the uncertainty in UV carbon storage estimations. This review provides new insights for modeling UV carbon storage and is expected to help the research community to achieve a better understanding of UV towards carbon neutrality.

Classification framework and semantic labeling for Big Earth Data

ABSTRACT Big Earth Data refers to the multidimensional integration and association of scientific data, including geography, resources, environment, ecology, and biology. An effective data classification system and label management strategy are important foundations for long-term management of data resources. The objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth Data Science Engineering Program (CASEarth). This study constructed two sets of classification and coding systems that realize classification by mapping each other; namely, the geosphere-level and Sustainable Development Goals (SDGs) indicator classifications. This technique was based on natural language processing technology and solved problems with subject-word segmentation, weight calculation, and dynamic matching. A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,100. Furthermore, we expect our study to provide the methodology and technical support for user-oriented classification and label management services for Big Earth Data.

Urgent need to strengthen unified management of scientific data of natural resources

At present, the institutional restructuring and business restructuring of the Ministry of natural resources have been basically completed, and business integration has become the focus of the work. Earth data science is combination of earth science and data science, and is a frontier cross field of earth science research and a paradigm shift of data-driven scientific discovery. This paper reviews the progress of big earth data research in 2020 from the aspects of earth data science program, scientific data governance, and earth science data sharing. It focuses on the Deep-Time Digital Earth Science Program, Big Earth Data Science Engineering Project and achievements in global data aggregation, authentication, repository, policies, standards, and identification, as well as practices of the earth science data centers, databases, data models, data integration, and data publishing. The paper points out countermeasures and suggestions for promoting scientific management of natural resources.