Big Remotely Sensed Data: tools, applications and experiences

Big Remotely Sensed Data: tools, applications and experiences

Remote Sensing of Environment xxx (xxxx) xxx–xxx Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsev...

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Remote Sensing of Environment xxx (xxxx) xxx–xxx

Contents lists available at ScienceDirect

Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse

Big Remotely Sensed Data: tools, applications and experiences F. Casua, M. Manuntaa, P.S. Agramb, R.E. Crippenb a b

IREA-CNR, Italy JPL-Caltech, United States

A R T I C L E I N F O Keywords: Big Data

The increased availability of large remote sensing datasets is generating heightened interest within the geoscience community, and more generally within human society. Indeed, remote sensing datasets that have commonly been analyzed as single scenes, or neighboring scenes, or temporally sequential scenes can now be analyzed en masse. This is due to the accumulation of large data volumes through time by increasing numbers of satellites, data access efficiencies due to technical advances and policy changes, and advances in hardware and software processing capabilities. For example, over five million Landsat images have been acquired and archived via several Landsat missions over 40 + years. Free access to these data combined with modern computer technologies (e.g. Cloud Computing) allows researchers to ask new scientific questions that can be answered by formulating algorithms applicable to all or much of the entire archive. Similarly, the Sentinel constellation of the European Copernicus programme, with its free and open access data policy, is generating a large volume of satellite imagery (SAR, optical, and altimetry) for operational and monitoring activities that will support diverse scientific and commercial communities, thus enabling the use of remote sensing to significantly impact society more directly than ever before. The large volume of data and its analysis can be succinctly captured in a unique concept that we borrow from IT jargon: Big Data. To complete the transition to the Big Data era of remote sensing, new tools, applications, solutions and algorithms have to be invented and developed to fully benefit from the vast amount of information which would otherwise remain under-exploited. Accordingly, the Special Issue “Big Remotely Sensed Data: tools, applications and experiences” is focused on the technological aspects of handling and processing the data as well as on the possibility that the Big Data paradigm offers to the remote sensing environmental sciences (e.g., build new cross domain applications, discover new insights from different data uses). This also drives the choice of specific words in the Special Issue headline: tools, applications and experiences.

Tools are fundamental aspects for processing and handling the large volumes of data. They are the basis for the Big Data paradigm itself (Big Data cannot be analyzed by humans without an automated tool). Therefore, we include a variety of papers showing innovative and powerful tools to address the complexity of processing large amounts of remotely sensed data. De Luca et al. (2017) propose a new algorithm for efficiently processing thousands of interferometric SAR (InSAR) data by exploiting the Amazon Web Services Cloud Computing facilities, while Gorelick et al. (2017) describe the capabilities of the Google Earth Engine (GEE) platform to lower the access barrier to high performance computing infrastructures for remote sensing scientists. Bhangale et al. (2017) and Peternier et al. (2017) developed new efficient Landsat and SAR processing tools, respectively, based on GPU computation capacities. Pérez-Suay et al. (2017) developed an innovative classification technique for large-scale applications. Likewise, building web-based ondemand processing tools, for instance for crop monitoring (Azzari and Lobell, 2017; Liu et al., 2017) and river analysis (Isikdogan et al., 2017), is also another innovation that meets the computational challenge of Big Data processing. However, tools are useless without a target application. So we included papers that focus on remote sensing applications and use Big Data to find new ways for addressing known (and unknown) specific application issues or propose the use of remote sensing for inventing new applications which were not possible before the Big Data era. A large variety of themes have been addressed: water use, extent and monitoring is discussed by Senay et al. (2017) and Khandelwal et al. (2017), which exploit historical Landsat archives and MODIS data, respectively; crop yields, precision farming, and/or mapping at large scale are treated in Azzari et al. (2017) with Landsat and Google Earth Engine, and McCarty et al. (2017), using Landsat and very high resolution satellite imagery; snow and land cover dynamics using long time archives are investigated by Dariane et al. (2017) and Huang et al. (2017), respectively; ground deformation detection through InSAR data is addressed by Cigna and Sowter (2017), who generate an Earth

E-mail address: [email protected] (F. Casu). http://dx.doi.org/10.1016/j.rse.2017.09.013 Received 30 July 2017; Accepted 14 September 2017 0034-4257/ © 2017 Elsevier Inc. All rights reserved.

Please cite this article as: Casu, F., Remote Sensing of Environment (2017), http://dx.doi.org/10.1016/j.rse.2017.09.013

Remote Sensing of Environment xxx (xxxx) xxx–xxx

F. Casu et al.

snow cover variability via cloud-free MODIS snow cover product in Central Alborz Region. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.05.042. (Available online 2 June 2017, ISSN 0034-4257). De Luca, C., Zinno, Ivana, Manunta, Michele, Lanari, Riccardo, Casu, Francesco, 2017. Large areas surface deformation analysis through a cloud computing P-SBAS approach for massive processing of DInSAR time series. Remote Sens. Environ. http:// dx.doi.org/10.1016/j.rse.2017.05.022. (Available online 3 June 2017, ISSN 00344257). Giuliani, G., Dao, Hy, De Bono, Andrea, Chatenoux, Bruno, Allenbach, Karin, De Laborie, Pierric, Rodila, Denisa, Alexandris, Nikos, Peduzzi, Pascal, 2017. Live Monitoring of Earth Surface (LiMES): a framework for monitoring environmental changes from Earth Observations. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.05. 040. (Available online 31 May 2017, ISSN 0034-4257). Gorelick, N., Hancher, Matt, Dixon, Mike, Ilyushchenko, Simon, Thau, David, Moore, Rebecca, 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.06.031. (Available online 6 July 2017, ISSN 0034-4257). Hu, X., Oommen, Thomas, Lu, Zhong, Wang, Teng, Kim, Jin-Woo, 2017. Consolidation settlement of Salt Lake County tailings impoundment revealed by time-series InSAR observations from multiple radar satellites. Remote Sens. Environ. http://dx.doi.org/ 10.1016/j.rse.2017.05.023. (Available online 1 June 2017, ISSN 0034-4257). Huang, H., Chen, Yanlei, Clinton, Nicholas, Wang, Jie, Wang, Xiaoyi, Liu, Caixia, Gong, Peng, Yang, Jun, Bai, Yuqi, Zheng, Yaomin, Zhu, Zhiliang, 2017. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.02.021. (Available online 6 March 2017, ISSN 0034-4257). Isikdogan, F., Bovik, Alan, Passalacqua, Paola, 2017. RivaMap: an automated river analysis and mapping engine. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse. 2017.03.044. (Available online 13 April 2017, ISSN 0034-4257). Kalia, A.C., Frei, M., Lege, T., 2017. A Copernicus downstream-service for the nationwide monitoring of surface displacements in Germany. Remote Sens. Environ. http://dx. doi.org/10.1016/j.rse.2017.05.015. (Available online 16 June 2017, ISSN 00344257). Khandelwal, A., Karpatne, Anuj, Marlier, Miriam E., Kim, Jongyoun, Lettenmaier, Dennis P., Kumar, Vipin, 2017. An approach for global monitoring of surface water extent variations in reservoirs using MODIS data. Remote Sens. Environ. http://dx.doi.org/ 10.1016/j.rse.2017.05.039. (Available online 21 June 2017, ISSN 0034-4257). Lewis, A., Oliver, Simon, Lymburner, Leo, Evans, Ben, Wyborn, Lesley, Mueller, Norman, Raevksi, Gregory, Hooke, Jeremy, Woodcock, Rob, Sixsmith, Joshua, Wu, Wenjun, Tan, Peter, Li, Fuqin, Killough, Brian, Minchin, Stuart, Roberts, Dale, Ayers, Damien, Bala, Biswajit, Dwyer, John, Dekker, Arnold, Dhu, Trevor, Hicks, Andrew, Ip, Alex, Purss, Matt, Richards, Clare, Sagar, Stephen, Trenham, Claire, Wang, Peter, Wang, Lan-Wei, 2017. The Australian Geoscience Data Cube — Foundations and lessons learned. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.03.015. (Available online 12 April 2017, ISSN 0034-4257). Liu, Q., Klucik, Rudy, Chen, Chao, Grant, Glenn, Gallaher, David, Lv, Qin, Shang, Li, 2017. Unsupervised detection of contextual anomaly in remotely sensed data. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.01.034. (Available online 8 February 2017, ISSN 0034-4257). McCarty, J.L., Neigh, C.S.R., Carroll, M.L., Wooten, M.R., 2017. Extracting smallholder cropped area in Tigray, Ethiopia with wall-to-wall sub-meter WorldView and moderate resolution Landsat 8 imagery. Remote Sens. Environ. http://dx.doi.org/10. 1016/j.rse.2017.06.040. (Available online 8 July 2017, ISSN 0034-4257). Pérez-Suay, A., Amorós-López, Julia, Gómez-Chova, Luis, Laparra, Valero, Muñoz-Marí, Jordi, Camps-Valls, Gustau, 2017. Randomized kernels for large scale Earth observation applications. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017. 02.009. (Available online 8 March 2017, ISSN 0034-4257). Peternier, A., Boncori, John Peter Merryman, Pasquali, Paolo, 2017. Near-real-time focusing of ENVISAT ASAR Stripmap and Sentinel-1 TOPS imagery exploiting OpenCL GPGPU technology. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.04. 006. (Available online 14 April 2017, ISSN 0034-4257). Senay, G.B., Schauer, Matthew, Friedrichs, MacKenzie, Velpuri, Naga Manohar, Singh, Ramesh K., 2017. Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States. Remote Sens. Environ. http://dx.doi. org/10.1016/j.rse.2017.05.005. (Available online 18 May 2017, ISSN 0034-4257).

surface displacement time series of large areas in the UK, while Hu et al. (2017) use multi-sensor and multi-temporal InSAR data to extract the surface deformation on tailing impoundments. An innovative cross domain application is addressed on Cai et al. (2017), who identify city structures via the joint exploitation of remotely sensed and social network data. Finally, case experiences demonstrate the effectiveness of scientific research in providing added value in addressing real world problems. In this regard, it is worth noting the availability of several projects and initiatives aimed to exploit and make available remotely sensed data for understanding and managing actual problems that can span from the monitoring of environmental changes (Giuliani et al., 2017) to the detection of ground deformation at national scale via InSAR data (Kalia et al., 2017; Costantini et al., 2017), going through the creation of a comprehensive set of tools, storage and processing facilities for the analysis and fruition of entire remote sensing archives over one continent (Lewis et al., 2017). Thanks to the high quality of the selected contributions, we believe that the Special Issue provides the reader with a clear perspective of the current state of remote sensing, which is now fully entering the Big Data era. A special thank you goes to all the reviewers that, with their professional support and constructive comments, have contributed to make this Special Issue possible. Last, but not least, we are indebted to Managing Editor Betty Schiefelbein, our special issue Editor-in-Chief Jing M. Chen, and all the RSE Editors-in-Chief for their assistance, guidance and patience in working with us. References Azzari, G., Lobell, D.B., 2017. Landsat-based classification in the cloud: an opportunity for a paradigm shift in land cover monitoring. Remote Sens. Environ. http://dx.doi.org/ 10.1016/j.rse.2017.05.025. (Available online 1 June 2017, ISSN 0034-4257). Azzari, G., Jain, Meha, Lobell, David B., 2017. Towards fine resolution global maps of crop yields: testing multiple methods and satellites in three countries. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.04.014. (Available online 10 May 2017, ISSN 0034-4257). Bhangale, U., Durbha, Surya S., King, Roger L., Younan, Nicolas H., Vatsavai, Rangaraju, 2017. High performance GPU computing based approaches for oil spill detection from multi-temporal remote sensing data. Remote Sens. Environ. http://dx.doi.org/ 10.1016/j.rse.2017.03.024. (Available online 10 April 2017, ISSN 0034-4257). Cai, J., Huang, Bo, Song, Yimeng, 2017. Using multi-source geospatial big data to identify the structure of polycentric cities. Remote Sens. Environ. http://dx.doi.org/10.1016/ j.rse.2017.06.039. (Available online 6 July 2017, ISSN 0034-4257). Cigna, F., Sowter, Andrew, 2017. The relationship between intermittent coherence and precision of ISBAS InSAR ground motion velocities: ERS-1/2 case studies in the UK. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.05.016. (Available online 30 May 2017, ISSN 0034-4257). Costantini, M., Ferretti, Alessandro, Minati, Federico, Falco, Salvatore, Trillo, Francesco, Colombo, Davide, Novali, Fabrizio, Malvarosa, Fabio, Mammone, Claudio, Vecchioli, Francesco, Rucci, Alessio, Fumagalli, Alfio, Allievi, Jacopo, Ciminelli, Maria Grazia, Costabile, Salvatore, 2017. Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-SkyMed radar data. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.07.017. (Available online 4 August 2017). Dariane, A.B., Khoramian, Amin, Santi, Emanuele, 2017. Investigating spatiotemporal

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