Showing papers in "International Journal of Digital Earth in 2017"

Big Data and cloud computing: innovation opportunities and challenges

Abstract: Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

Big Earth Data: a new challenge and opportunity for Digital Earth’s development

Abstract: Digital Earth has seen great progress during the last 19 years. When it entered into the era of big data, Digital Earth developed into a new stage, namely one characterized by ‘Big Earth Data’, confronting new challenges and opportunities. In this paper we give an overview of the development of Digital Earth by summarizing research achievements and marking the milestones of Digital Earth’s development. Then, the opportunities and challenges that Big Earth Data faces are discussed. As a data-intensive scientific research approach, Big Earth Data provides a new vision and methodology to Earth sciences, and the paper identifies the advantages of Big Earth Data to scientific research, especially in knowledge discovery and global change research. We believe that Big Earth Data will advance and promote the development of Digital Earth.

How to assess the success of the open data ecosystem

Abstract: Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government, solving societal problems, and increasing economic value. To describe and monitor the state of open data in countries and organisations, several open data assessment frameworks were developed. Despite high scores in these assessment frameworks, the actual (re)use of open government data (OGD) fails to live up to its expectations. Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem. We have developed a framework, which assesses open data supply, open data governance, and open data user characteristics holistically. This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention. Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data, such as healthcare data. Therefore, open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.

Remote sensing heritage in a petabyte-scale: satellite data and heritage Earth Engine© applications

Abstract: This paper aims to demonstrate results and considerations regarding the use of remote sensing big data for archaeological and Cultural Heritage management large scale applications. For this purpose, the Earth Engine© developed by Google© was exploited. Earth Engine© provides a robust and expandable cloud platform where several freely distributed remote sensing big data, such as Landsat, can be accessed, analysed and visualized. Two different applications are presented here as follows: the first one is based on the evaluation of multi-temporal Landsat series datasets for the detection of buried Neolithic tells (‘magoules’) in the area of Thessaly, in Greece using linear orthogonal equations. The second case exploits European scale multi-temporal DMSP-OLS Night-time Lights Time Series to visualize the impact of urban sprawl in the vicinity of UNESCO World Heritage sites and monuments. Both applications highlight the considerable opportunities that big data can offer to the fields of archaeology and ...

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Abstract: Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html

Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping

Abstract: This study investigates misregistration issues between Landsat-8/ Operational Land Imager and Sentinel-2A/ Multi-Spectral Instrument at 30 m resolution, and between multi-temporal Sentinel-2A images at 10 m resolution using a phase-correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30 m resolution between Landsat-8 and Sentinel-2A images, and 1.2 pixels and 2.8 pixels at 10 m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07 ± ...

Free and open source software for geospatial applications (FOSS4G) to support Future Earth

Abstract: The development, integration, and distribution of the information and spatial data infrastructure (i.e. Digital Earth; DE) necessary to support the vision and goals of Future Earth (FE) will occur in a distributed fashion, in very diverse technological, institutional, socio-cultural, and economic contexts around the world. This complex context and ambitious goals require bringing to bear not only the best minds, but also the best science and technologies available. Free and Open Source Software for Geospatial Applications (FOSS4G) offers mature, capable and reliable software to contribute to the creation of this infrastructure. In this paper we point to a selected set of some of the most mature and reliable FOSS4G solutions that can be used to develop the functionality required as part of DE and FE. We provide examples of large-scale, sophisticated, mission-critical applications of each software to illustrate their power and capabilities in systems where they perform roles or functionality similar...

Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon

Abstract: Previous research has explored the potential to integrate lidar and optical data in aboveground biomass (AGB) estimation, but how different data sources, vegetation types, and modeling algorithms i...

Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics

Abstract: This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO_2 total column (XCO_2) using spatio-temporal geostatistics, which makes full use of the joint spatial and temporal dependencies between observations. The mapping approach considers the latitude-zonal seasonal cycles and spatio-temporal correlation structure of XCO2, and obtains global land maps of XCO_2, with a spatial grid resolution of 1° latitude by 1° longitude and temporal resolution of 3 days. We evaluate the accuracy and uncertainty of the mapping dataset in the following three ways: (1) in cross-validation, the mapping approach results in a high correlation coefficient of 0.94 between the predictions and observations, (2) in comparison with ground truth provided by the Total Carbon Column Observing Network (TCCON), the predicted XCO_2 time series and those from TCCON sites are in good agreement, with an overall bias of 0.01 ppm and a standard deviation of the difference of 1.22 ppm and (3) in comparison with model simulations, the spatio-temporal variability of XCO_2 between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent. The generated mapping XCO_2 data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.

Perceptual complexity of soil-landscape maps: a user evaluation of color organization in legend designs using eye tracking

Abstract: We compared the ability of two legend designs on a soil-landscape map to efficiently and effectively support map reading tasks with the goal of better understanding how the design choices affect user performance. Developing such knowledge is essential to design effective interfaces for digital earth systems. One of the two legends contained an alphabetical ordering of categories, while the other used a perceptual grouping based on the Munsell color space. We tested the two legends for 4 tasks with 20 experts (in geography-related domains). We analyzed traditional usability metrics and participants’ eye movements to identify the possible reasons behind their success and failure in the experimental tasks. Surprisingly, an overwhelming majority of the participants failed to arrive at the correct responses for two of the four tasks, irrespective of the legend design. Furthermore, participants’ prior knowledge of soils and map interpretation abilities led to interesting performance differences between ...

A hierarchical spatiotemporal adaptive fusion model using one image pair

Abstract: Image fusion techniques that blend multi-sensor characteristics to generate synthetic data with fine resolutions have generated great interest within the remote sensing community. Over the past dec...

Development of an operational land water mask for MODIS Collection 6, and influence on downstream data products

Abstract: Data from the Moderate Resolution Imaging Spectro-radiometer (MODIS) on-board the Earth Observing System Terra and Aqua satellites are processed using a land water mask to determine when an algorithm no longer needs to be run or when an algorithm needs to follow a different pathway. Entering the fourth reprocessing (Collection 6 (C6)) the MODIS team replaced the 1 km water mask with a 500 m water mask for improved representation of the continental surfaces. The new water mask represents more small water bodies for an overall increase in water surface from 1% to 2% of the continental surface. While this is still a small fraction of the overall global surface area the increase is more dramatic in certain areas such as the Arctic and Boreal regions where there are dramatic increases in water surface area in the new mask. MODIS products generated by the on-going C6 reprocessing using the new land water mask show significant impact in areas with high concentrations of change in the land water mask. Her...

Simulation of permafrost changes on the Qinghai–Tibet Plateau, China, over the past three decades

Abstract: Permafrost is one of the largest elements of the terrestrial cryosphere and is extremely sensitive to climate change. Based on mean annual ground temperature (MAGT) data from 189 boreholes on the Qinghai–Tibet Plateau (QTP), terrain factors, and climate data from China Meteorological Forcing Dataset, we propose a new mean annual ground air temperature (MAGAT) statistical model between meteorological parameters with subsurface temperatures to simulate permafrost distribution and variation of MAGT on the QTP over the past three decades (1981–2010). Validation of the model with MAGT data from 13 boreholes and permafrost maps of the QTP indicated that the MAGAT model is applicable to simulate the distribution and evolution of permafrost on the QTP. Simulation results show that the spatiotemporal MAGT of permafrost significantly increased by 0.37°C, or 0.25°C/10 yr, and the total area of permafrost decreased by 2.48 × 105 km2 on the QTP over the past three decades. Regionally, the changes of permafrost...

A model-service deployment strategy for collaboratively sharing geo-analysis models in an open web environment

Abstract: Geo-analysis models can be shared and reused via model-services to support more effective responses to risks and help to build a sustainable world. The deployment of model-services typically requires significant effort, primarily because of the complexity and disciplinary specifics of geo-analysis models. Various modelling participants engage in the collaborative modelling process: geo-analysis model resources are provided by model providers, computational resources are provided by computational resource providers, and the published model-services are accessed by model users. This paper primarily focuses on model-service deployment, with the basic goal of providing a collaboration-oriented method for modelling participants to conveniently work together and make full use of modelling and computational resources across an open web environment. For model resource providers, a model-deployment description method is studied to help build model-deployment packages; for computational resource providers, ...

Assessing the spatio-temporal variability of vegetation productivity in Africa: quantifying the relative roles of climate variability and human activities

Abstract: Quantitative attribution at the individual pixel level of the relative contributions of climate variability and human activities to vegetation productivity dynamics across Africa is generally lacking. This is because of the difficulty in establishing a baseline or potential vegetation against which the relative impacts of these factors can be assessed. This study addresses these gaps. First, annual potential net primary productivity (NPPP) for 2000–2014 was estimated for Africa using a model constructed from samples of NPP and environmental covariates from protected areas. Second, trends in NPPP, actual NPP (NPPA), and human-appropriated NPP (NPPH = NPPP − NPPA) were estimated and used in quantifying the relative contributions of climate and human activities to NPP dynamics. Over 2000–2014, NPP improvement was largely concentrated in equatorial and northern Africa, while subequatorial Africa exhibited the most NPP decline. Parts of Mali, Burkina Faso, and the central Africa region are associated w...

Spatially explicit urban green indicators for characterizing vegetation cover and public green space proximity: a case study on Brussels, Belgium

Abstract: Cities often have a substantial green infrastructure, which provides local ecosystem services that improve the quality of life of urban residents. These services should be explicitly addressed in u...

Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

Abstract: Forests of the Sierra Nevada (SN) mountain range are valuable natural heritages for the region and the country, and tree height is an important forest structure parameter for understanding the SN forest ecosystem. There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution. In this study, we presented a method to map wall-to-wall forest tree height (defined as Lorey’s height) across the SN at 70-m resolution by fusing multi-source datasets, including over 1600 in situ tree height measurements and over 1600 km2 airborne light detection and ranging (LiDAR) data. Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements, and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System (GLAS) footprints. Finally, the random forest algorithm was used to model the SN tree height from these GLAS tree heights, ...

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Abstract: The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–...

Phenology and classification of abandoned agricultural land based on ALOS-1 and 2 PALSAR multi-temporal measurements

Abstract: Agricultural crop abandonment negatively impacts local economy and environment since land, as a resource for agriculture, is not optimally utilized. To take necessary actions to rehabilitate abando...

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

Abstract: Pushed by the Internet of Things (IoT) paradigm modern sensor networks monitor a wide range of phenomena, in areas such as environmental monitoring, health care, industrial processes, and smart cities. These networks provide a continuous pulse of the almost infinite activities that are happening in the physical space and are thus, key enablers for a Digital Earth Nervous System. Nevertheless, the rapid processing of these sensor data streams still continues to challenge traditional data-handling solutions and new approaches are being requested. We propose a generic answer to this challenge, which has the potential to support any form of distributed real-time analysis. This neutral methodology follows a brokering approach to work with different kinds of data sources and uses web-based standards to achieve interoperability. As a proof of concept, we implemented the methodology to detect anomalies in real-time and applied it to the area of environmental monitoring. The developed system is capable of ...

Integrating global land cover datasets for deriving user-specific maps

Abstract: Global scale land cover (LC) mapping has interested many researchers over the last two decades as it is an input data source for various applications. Current global land cover (GLC) maps often do not meet the accuracy and thematic requirements of specific users. This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets. We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends. We used a regression kriging method to integrate Globcover-2009, LC-CCI-2010, MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets. Overall correspondence of the integrated GLC map with reference LC was 80% based on 10-fold cross-validation using 24,681 sample sites. This is globally 10% and regionally 6–13% higher than the input map correspondences. Based on LC class presence probability maps, expected LC proportion maps at coarser resolution were created and used for...

Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm

Abstract: This paper presents an approach to process raw unmanned aircraft vehicle (UAV) image-derived point clouds for automatically detecting, segmenting and regularizing buildings of complex urban landscapes. For regularizing, we mean the extraction of the building footprints with precise position and details. In the first step, vegetation points were extracted using a support vector machine (SVM) classifier based on vegetation indexes calculated from color information, then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings. In the second step, we first determined the building boundary points with a modified convex hull algorithm. Then, we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm. Then, two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction con...

Sustainable geotourism using digital technologies along a rural georoute in Monsagro (Salamanca, Spain)

Abstract: Rural geotourism looks at the natural resources of the territory as a means of promoting a specialized, educational, sustainable tourism. This is an increasingly sought-after activity. This paper presents a rural georoute across a high mountain village, highlighting the remarkable value of the geological heritage of biological trace fossils and physical sedimentary structures exposed on street pavements and facades of houses. A series of tourist georesources were created and implemented: educational and interpretive panels, videos, QR codes, geoapps and games, all of which promote and disseminate the exceptional geological content and the history of the earth through the use of new technologies (smartphones, ipod, etc.). All this is intended as a means to make geotourism a natural tourism, favoring experiences, whilst explaining the natural environment and its temporal and spatial dimensions, offering opportunities for socio-economic development and job creation in rural areas with problems of dep...

Archeological crop marks identified from Cosmo-SkyMed time series: the case of Han-Wei capital city, Luoyang, China

Abstract: The development of spaceborne Synthetic Aperture Radar (SAR) technology declares that the golden era of SAR remote sensing in archeology is approaching; however, nowadays its methodology framework is still lacking due to the inadequate case studies validated by ground-truths. In this study, we investigated the crop marks using multi-temporal Cosmo-SkyMed data acquired in 2013 by applying a two-step decision-tree classifier in conjunction with a spatial analysis in an area of archeological interest nearby the archeological site of Han-Wei capital city (1900–1500 BP), in Luoyang, China. The time-series backscattering anomalies related to the wheat growth cycle were identified and then further validated in two zones by geophysical investigations (Ground Penetration Radar and electrical measurements) and in a third zone by archeological excavations made after the SAR data acquisition. This study provides a new approach for the relic detection, shallowly buried and covered by the crop vegetation, by te...

Multidimensional and quantitative interlinking approach for Linked Geospatial Data

Abstract: Linked Data is known as one of the best solutions for multisource and heterogeneous web data integration and discovery in this era of Big Data. However, data interlinking, which is the most valuable contribution of Linked Data, remains incomplete and inaccurate. This study proposes a multidimensional and quantitative interlinking approach for Linked Data in the geospatial domain. According to the characteristics and roles of geospatial data in data discovery, eight elementary data characteristics are adopted as data interlinking types. These elementary characteristics are further combined to form compound and overall data interlinking types. Each data interlinking type possesses one specific predicate to indicate the actual relationship of Linked Data and uses data similarity to represent the correlation degree quantitatively. Therefore, geospatial data interlinking can be expressed by a directed edge associated with a relation predicate and a similarity value. The approach transforms existing sim...

Exploring the reasons for the seasons using Google Earth, 3D models, and plots

Abstract: Public understanding of climate and climate change is of broad societal importance. However, misconceptions regarding reasons for the seasons abound amongst students, teachers, and the public, many...

The second law of geography for a spatially enabled economy

Abstract: Location has proven axiomatic as an economic variable throughout human history. Tobler’s first law of geography introduced the importance of location; in that, near things are more related than far things. In an age of digital economies, a new research frontier exists where everything is more related to everything else and has an increased economic value from spatially enabled technology. The accessibility of digital-spatial information has brought economic geographers to a new understanding of markets within a Digital Earth framework. The importance of location to economic value can be expected to grow as the Internet of Things develops in sophistication. New business models enter and disrupt established markets with innovative spatially enabled approaches. A successful penetration of established markets suggests a new business model for financial and functional utility by engaging spatially enabled assets. The second law of geography is introduced as a conceptual framework to comprehend the econ...

Challenges and strategies for the visual exploration of complex environmental data

Abstract: In this opinion paper, we, a group of scientists from environmental-, geo-, ocean- and information science, argue visual data exploration should become a common analytics approach in Earth system science due to its potential for analysis and interpretation of large and complex spatio-temporal data. We discuss the challenges that appear such as synthesis of heterogeneous data from various sources, reducing the amount of information and facilitating multidisciplinary, collaborative research. We argue that to fully exploit the potential of visual data exploration, several bottlenecks and challenges have to be addressed: providing an efficient data management and an integrated modular workflow, developing and applying suitable visual exploration concepts and methods with the help of effective and tailored tools as well as generating and raising the awareness of visual data exploration and education. We are convinced visual data exploration is worth the effort since it significantly facilitates insight into environmental data and derivation of knowledge from it.

Wāhi, a discrete global grid gazetteer built using linked open data

Abstract: Discrete global grid systems have become an important component of Digital Earth systems. However, previously there has not existed an easy way to map between named places (toponyms) and the cells of a discrete global grid system. The lack of such a tool has limited the opportunities to synthesize social place-based data with the more standard Earth and environmental science data currently being analyzed in Digital Earth applications. This paper introduces Wāhi, the first gazetteer to map entities from the GeoNames database to multiple discrete global grid systems. A gazetteer service is presented that exposes the grid system and the associated gazetteer data as Linked Data. A set of use cases for the discrete global grid gazetteer is discussed.

Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009

Abstract: Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.