The main components of the RemoteAgri system, which is able to ingest remote sensing data from external repositories, handle and process them on the server-side upon a user request for a given AOI
| K. Karantzalos | A. Karmas | A. Tzotsos |
| RSLab, NTUA, Athens, Greece | EOfarm P.C., Athens, Greece |
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| karank@central.ntua.gr | karmas@EOfarm.com | tzotsos@EOfarm.com |
Abstract: In this paper, novel geospatial services are presented which are able to process on the server-side numerous remote sensing data based on big data frameworks like Hadoop and Rasdaman. The developed system itself features several software modules that orchestrate the different image processing algorithms responsible for the production of consistent value-added maps like canopy greenness and leaf area index. Through distributed multitemporal analysis, the entire crop growth cycle can be continuously monitored through the analysis of time-series observations. These observations cover multiple crop growth cycles, offering invaluable information by linking weather statistical data with the start, the end and the duration of each growth cycle enabling critical decisions by direct comparison with the current crop growth state. |
This work is based on:
Karantzalos K., Karmas A., Tzotsos, A., 2015. RemoteAgri: Processing Online Big Earth Observation Data for Precision Agriculture, 10th European Conference on Precision Agriculture, pp.421-428.
Karmas and Karantzalos, 2016. Scalable Geospatial Services for the Production of Time Series and Value-added Maps in Agriculture and Water Quality Monitoring, Proceedings of the 2016 Conference on Big Data from Space (ESA BiDS'16) [slides]
Karmas A., Tzotsos A., Karantzalos K., 2016. Big Geospatial Data for Environmental and Agricultural Applications, Yu and Guo (eds.), Big Data Concepts, Theories and Applications, Springer International Publishing, DOI 10.1007/978-3-319-27763-9.
The main components of the RemoteAgri system, which is able to ingest remote sensing data from external repositories, handle and process them on the server-side upon a user request for a given AOI |
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The derived NDVI time series from all available satellite multispectral data over an agricultural area near Chalastra in Northern Greece. Season’s start and length can be observed (middle row) as well as the corresponding canopy greenness levels after the application of the developed geospatial services. With light blue color the mean NDVI of the AOI and with the dark blue the ones after the online, moving average curve fitting |
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Multitemporal weather data indicating the mean/max air temperatures and precipitation levels from a weather station near the agricultural area of the previous figure |
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Seasonality maps indicating the day of year (DOY) that NDVI reached its peak value are shown for 2014 and 2015 in an agricultural area near Thessaloniki in Northern Greece |
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VIDEO 1 |
VIDEO 2 |