Special Issue "Google Earth Engine and Cloud Computing Platforms: Methods and Applications in Big Geo Data Science"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Guest Editor
Prof. Mattia Crespi Website E-Mail
Geodesy and Geomatics Division—DICEA, Sapienza University of Rome, Italy
Interests: remote sensing big data analysis; optical and SAR satellite remote sensing, photogrammetry, and stereo-SAR; 3D terrain and objects modeling; GNSS positioning and monitoring; GNSS seismology
Guest Editor
Dr. Andrea Nascetti Website E-Mail
Geoinformatics Division, Department of Urban Planning and Environment, KTH Royal Institute of Technology, Stockholm, Sweden
Interests: remote sensing big data, photogrammetry, SAR, machine learning, 3D modelling, Google Earth Engine
Guest Editor
Dr. Roberta Ravanelli Website E-Mail
Geodesy and Geomatics Division—DICEA, Sapienza University of Rome, Italy
Interests: geo big data and remote sensing big data analysis, image and point cloud processing; low-cost close-range 3D modelling

Special Issue Information

Dear Colleagues,

According to the well-known sentence “80% of data is geographic”, much of the data in the world can be geo-referenced. Geospatial data describe objects and things with locations given in a spatial reference frame, which now generally means the global spatial reference frame (often called WGS84), which is connected to the Global Navigation Satellite Systems. Geospatial data can be collected and analyzed using a variety of geomatic sensors and methodologies (GNSS and terrestrial surveying, photogrammetry and remote sensing, laser scanning, mobile mapping, geo-located sensors, geo-tagged web contents, and volunteered geographic information—VGI). Among them, those related to remote sensing play a pivotal role, since petabyte-scale archives of remote sensing data have become freely available from the EU Copernicus Program and multiple U.S. Government agencies (NASA, USGS, and NOAA).

This is the why the efficient geospatial big data handling, particularly remote sensing data, is of key importance. In this respect, it is necessary to change the way these data are visualized, processed, and analyzed, in order to make them truly available to the wide community of non-remote sensing experts, who indeed need remote sensing big data to investigate, monitor, and model a large and continuously growing variety of Earth systems, social, and economic processes.

Currently, cloud infrastructures can provide the required flexibility to manage (for both storage and computation) such huge amounts of data and to efficiently process them, thus making possible analyses that were previously thought unfeasible, due to data volume and computational restrictions. In this respect, Google Earth Engine (GEE) is a cloud-based platform that makes it easy to access both multi-temporal remote sensing big data and high-performance computing resources for processing these datasets. Also, GEE users can upload their own non-public data in reserved areas and process them together the public ones, performing synergic data fusion and integration. GEE is also designed to help researchers easily disseminate their results to other researchers, policy makers, and even the general public, to support a variety of management decisions or simply to share scientific results.

Research papers focusing on both methodology and applications by using GEE across different geographic scales are welcome, as well as contributions related to other public-domain platforms with goals similar to GEE (i.e., ESA DIAS and ESA TEPs—Thematic Exploitation Platforms).

Potential topics for this Special Issue include but are not limited to the following:

  • Remote Sensing Big Data analysis and integration with other geospatial data (i.e., GNSS, social media data);
  • Multi-Sensor and multi-resolution data analysis;
  • Machine and deep learning for remote sensing;
  • Land-use and land-cover change monitoring and modeling;
  • Urban and population dynamics characterization;
  • Water resources monitoring and modeling;
  • Forests and vegetation dynamics monitoring and modeling;
  • Ecosystem response to the climate change.

Prof. Mattia Crespi
Dr. Andrea Nascetti
Dr. Roberta Ravanelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Geospatial and remote sensing big data
  • Big data analysis and dissemination
  • Big data comparison, integration, and fusion
  • Cloud computing platforms
  • Google Earth Engine
  • Earth system, social, and economic processes
  • Monitoring and modeling
  • Methodology
  • Applications.

Published Papers (3 papers)

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Research

Open AccessArticle
Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine
Remote Sens. 2019, 11(15), 1824; https://doi.org/10.3390/rs11151824 - 04 Aug 2019
Abstract
The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China’s river basins, the surface water in the [...] Read more.
The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China’s river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km2). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages. Full article
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Open AccessArticle
Improvement of Remote Sensing-Based Assessment of Defoliation of Pinus spp. Caused by Thaumetopoea pityocampa Denis and Schiffermüller and Related Environmental Drivers in Southeastern Spain
Remote Sens. 2019, 11(14), 1736; https://doi.org/10.3390/rs11141736 - 23 Jul 2019
Abstract
This study used Landsat temporal series to describe defoliation levels due to the Pine Processionary Moth (PPM) in Pinus forests of southeastern Andalusia (Spain), utilizing Google Earth Engine. A combination of remotely sensed data and field survey data was used to detect the [...] Read more.
This study used Landsat temporal series to describe defoliation levels due to the Pine Processionary Moth (PPM) in Pinus forests of southeastern Andalusia (Spain), utilizing Google Earth Engine. A combination of remotely sensed data and field survey data was used to detect the defoliation levels of different Pinus spp. and the main environmental drivers of the defoliation due to the PPM. Four vegetation indexes were also calculated for remote sensing defoliation assessment, both inside the stand and in a 60-m buffer area. In the area of study, all Pinus species are affected by defoliation due to the PPM, with a cyclic behavior that has been increasing in frequency in recent years. Defoliation levels were practically equal for all species, with a high increase in defoliation levels 2 and 3 since 2014. The Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) exhibited similar overall (p < 0.001) accuracy in the assessment of defoliation due to the PPM. The synchronization of NDII-defoliation data had a similar pattern for all together and individual Pinus species, showing the ability of this index to adjust the model parameters based on the characteristics of specific defoliation levels. Using Landsat-based NDII-defoliation maps and interpolated environmental data, we have shown that the PPM defoliation in southeastern Spain is driven by the minimum temperature in February and the precipitation in June, March, September, and October. Therefore, the NDII-defoliation assessment seems to be a general index that can be applied to forests in other areas. The trends of NDII-defoliation related to environmental variables showed the importance of summer drought stress in the expansion of the PPM on Mediterranean Pinus species. Our results confirm the potential of Landsat time-series data in the assessment of PPM defoliation and the spatiotemporal patterns of the PPM; hence, these data are a powerful tool that can be used to develop a fully operational system for the monitoring of insect damage. Full article
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Open AccessArticle
Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China’s Eastern Coastal Zone circa 2015
Remote Sens. 2019, 11(8), 924; https://doi.org/10.3390/rs11080924 - 16 Apr 2019
Cited by 1
Abstract
Accurate and up-to-date tidal flat mapping is of much importance to learning how coastal ecosystems work in a time of anthropogenic disturbances and rising sea levels, which will provide scientific instruction for sustainable management and ecological assessments. For large-scale and high spatial-resolution mapping [...] Read more.
Accurate and up-to-date tidal flat mapping is of much importance to learning how coastal ecosystems work in a time of anthropogenic disturbances and rising sea levels, which will provide scientific instruction for sustainable management and ecological assessments. For large-scale and high spatial-resolution mapping of tidal flats, it is difficult to obtain accurate tidal flat maps without multi-temporal observation data. In this study, we aim to investigate the potential and advantages of the freely accessible Landsat 8 Operational Land Imager (OLI) imagery archive and Google Earth Engine (GEE) for accurate tidal flats mapping. A novel approach was proposed, including multi-temporal feature extraction, machine learning classification using GEE and morphological post-processing. The 50 km buffer of the coastline from Hangzhou Bay to Yalu River in China’s eastern coastal zone was taken as the study area. From the perspective of natural attributes and unexploited status of tidal flats, we delineated a broader extent comprising intertidal flats, supratidal barren flats and vegetated flats, since intertidal flats are major component of the tidal flats. The overall accuracy of the resultant map was about 94.4% from a confusion matrix for accuracy assessment. The results showed that the use of time-series images can greatly eliminate the effects of tidal level, and improve the mapping accuracy. This study also proved the potential and advantage of combining the GEE platform with time-series Landsat images, due to its powerful cloud computing platform, especially for large scale and longtime tidal flats mapping. Full article
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