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Stratoulias D, Tolpekin V, de By RA, et al. A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture. Remote Sens (Basel). 2017;9(10):1048doi: 10.3390/rs9101048.
Stratoulias, D., Tolpekin, V., de By, R. A., Zurita-Milla, R., Retsios, V., Bijker, W., Alfi Hasan, M., & Vermote, E. (2017). A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture. Remote sensing, 9(10), 1048. https://doi.org/10.3390/rs9101048
Stratoulias, Dimitris, et al. "A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture." Remote sensing vol. 9,10 (2017): 1048. doi: https://doi.org/10.3390/rs9101048
Stratoulias D, Tolpekin V, de By RA, Zurita-Milla R, Retsios V, Bijker W, Alfi Hasan M, Vermote E. A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture. Remote Sens (Basel). 2017;9(10):1048. doi: 10.3390/rs9101048. Epub 2017 Oct 14. PMID: 32704488; PMCID: PMC7340489.
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Remote Sens (Basel). 2017;9(10):1048. doi: 10.3390/rs9101048. Epub 2017 Oct 14.

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture.

Remote sensing

Dimitris Stratoulias, Valentyn Tolpekin, Rolf A de By, Raul Zurita-Milla, Vasilios Retsios, Wietske Bijker, Mohammad Alfi Hasan, Eric Vermote

Affiliations

  1. Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands; [email protected] (V.T.); [email protected] (R.A.d.B.); [email protected] (R.Z.-M.); [email protected] (V.R.); [email protected] (W.B.).
  2. GeoAnalysis, Budapest, 1134 Hungary.
  3. Department of Civil and Environmental Engineering, University of Rhode Island , Kingston, RI 02881, USA; [email protected].
  4. NASA Goddard Space Flight Center, Terrestrial Information Systems Laboratory, Greenbelt, MD 20771, USA; [email protected].

PMID: 32704488 PMCID: PMC7340489 DOI: 10.3390/rs9101048

Abstract

Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

© 2017 The Author(s).

Keywords: agriculture; automated processing; real time; satellite image; smallholder farming; surface reflectance; very high spatial resolution; workflow

Conflict of interest statement

The authors declare no conflicts of interest. The funding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or

References

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