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Remote Sens. 2017, 9(10), 1048; https://doi.org/10.3390/rs9101048

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

1
Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
2
GeoAnalysis, Budapest, 1134 Hungary
3
Department of Civil and Environmental Engineering, University of Rhode Island , Kingston, RI 02881, USA
4
NASA Goddard Space Flight Center, Terrestrial Information Systems Laboratory, Greenbelt, MD 20771, USA
*
Author to whom correspondence should be addressed.
Received: 24 July 2017 / Revised: 8 September 2017 / Accepted: 3 October 2017 / Published: 14 October 2017
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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. View Full-Text
Keywords: automated processing; workflow; real time; very high spatial resolution; surface reflectance; satellite image; smallholder farming; agriculture automated processing; workflow; real time; very high spatial resolution; surface reflectance; satellite image; smallholder farming; agriculture
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Stratoulias, D.; Tolpekin, V.; de By, R.A.; Zurita-Milla, R.; Retsios, V.; Bijker, W.; Hasan, M.A.; Vermote, E. A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture. Remote Sens. 2017, 9, 1048.

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