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An Application of the "traffic lights" Idea to Crop Control in Integrated Administration Control System
Corresponding Author(s) : Beata Hejmanowska
Geomatics and Environmental Engineering,
Vol. 15 No. 4 (2021): Geomatics and Environmental Engineering
Abstract
The aim of the paper is to discuss the idea of marking agricultural parcels in the control of direct payments to agriculture. The method of using remote sensing to monitor crops and mark them according to the idea of "traffic lights" is introduced. Classification into a given "traffic lights" color gives clear information about the status of the parcel. The image classification was done on Sentinel-1 and Sentinel-2 datasets by calculating the NDVI and SIGMA time series in the season from autumn 2016 to autumn 2017. Two approaches are presented: semi-automated and automated classifications. Semi-automated classification based on NDVI_index and SIGMA_index. Automated classification was performed on NDVI by Spectral Angle Mapper method and on SIGMA by Artificial Neural Network (Multilayer Perceptron, MLP method). The following overall accuracy was obtained for NDVI_SAM: 70.35%, while for SIGMA_CNN it was: 62.01%. User accuracy (UA) values were adopted for traffic lights analysis, in machine learning: positive predictive value (PPV). The UA/PPV for rapeseed were in NDVI_index method: 88.1% (6,986 plots), NDVI_SAM: 85.0% (199 plots), SIGMA_index: 61.3% (4,165 plots) and in SIGMA_CNN: 88.9% (2,035 plots). In order to present the idea of "œtraffic lights", a website was prepared using data from the NDVI_index method, which is a trade-off between the number of plots and UA/PPV accuracy.
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- European Commission: Integrated Administration and Control System (IACS). https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy/financing-cap/financial-assurance/managing-payments_en [access: 3.08.2021].
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- Kussul N., Lavreniuk M., Skakun S., Shelestov A.: Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, 2017, pp. 778–782. https://doi.org/10.1109/LGRS.2017.2681128.
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- Zespół Geoinformacji, Fotogrametrii i Teledetekcji Środowiska: Application of the “traffic lights” idea in crops control in Integrated Administration Control System. https://twiki.fotogrametria.agh.edu.pl/c5www/index.php/katedra/gll2021 [access: 3.08.2021].
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- Foody G.M.: Impacts of ignorance on the accuracy of image classification and thematic mapping. Remote Sensing of Environment, vol. 259, 2021, 112367. https://doi.org/10.1016/j.rse.2021.112367.
References
European Commission: Integrated Administration and Control System (IACS). https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy/financing-cap/financial-assurance/managing-payments_en [access: 3.08.2021].
Gov.pl: IT SYSTEMS Integrated Administration and Control System (IACS). https://www.gov.pl/web/arimr-en/it-systems [access: 3.08.2021].
Asseco: ARiMR – Administration and Control System (IACS). https://pl.asseco.com/en/case-study/arimr-administration-and-control-system-iacs-32/ [access: 3.08.2021].
Devos W., Lemoine G., Milenov P., Fasbender D.: Technical guidance on the decision to go for substitution of OTSC by monitoring. Publications Office of the European Union, 2018. http://dx.doi.org/10.2760/693101.
Devos W., Lemoine G., Milenov P., Fasbender D., Loudjani P., Wirnhardt C., Sima A., Griffiths P.: Second discussion document on the introduction of monitoring to substitute OTSC: rules for processing application in 2018–2019. Publications Office of the European Union, 2018. http://dx.doi.org/10.2760/344612.
Devos W., Fasbender D., Lemoine G., Loudjani P., Milenov P., Wirnhardt C.: Discussion document on the introduction of monitoring to substitute OTSC – Supporting non-paper DS/CDP/2017/03 revising R2017/809. Publications Office of the European Union, 2017. http://dx.doi.org/10.2760/258531.
Musiał J., Bojanowski J.: Assessing potential of the Sentinel-2 imagery for monitoring of agricultural fields in Poland. 25th MARS Conference, Praga, 2019. http://www.igik.edu.pl/upload/Poster_EOStat_final(1).pdf [access: 3.08.2021].
Csillik O., Belgiu M., Asner G.P., Kelly M.: Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2. Remote Sensing, vol. 11, no. 10, 2019, 1257 https://doi.org/10.3390/rs11101257.
Hütt C., Waldhoff G., Bareth G.: Fusion of Sentinel-1 with official topographic and cadastral geodata for crop-type enriched LULC mapping using FOSS and open data. ISPRS International Journal of Geo-Information, vol. 9, no. 2, 2020, 120. https://doi.org/10.3390/ijgi9020120.
Van Tricht K., Gobin A., Gilliams S., Piccard I.: Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping. A case study for Belgium. Remote Sensing, vol. 10, no. 10, 2018, 1642. https://doi.org/10.3390/rs10101642.
Brinkhoff J., Vardanega J., Robson A.: Land cover classification of nine perennial crops using Sentinel-1 and -2 data. Remote Sensing, vol. 12, no. 1, 2020, 96. https://doi.org/10.3390/rs12010096.
Maponya M., van Niekerk A., Mashimbye Z.: Pre-harvest classification of crop types using a Sentinel-2 time-series and machine learning. Computers and Electronics in Agriculture, vol. 169, 2020, 105164. https://doi.org/10.1016/j.compag.2019.105164.
Hejmanowska B., Mikrut S., Głowienka E., Michałowska K., Kramarczyk P., Pirowski T.: Expertise on the use of Sentinel 1 and 2 images to monitor the agricultural activity of ARIMR beneficiaries. 2018. http://home.agh.edu.pl/~galia/img/Raport_ARIMR_AGH_2018_EN_haslo.pdf.
Hejmanowska B., Mikrut S., Głowienka E., Kramarczyk P., Pirowski T.: The use of hyperspectral data to monitor the agricultural activity of the ARMA benefi- ciaries and support its business processes. 2019. http://home.agh.edu.pl/~galia/img/Raport_ARIMR_AGH_2019_EN_haslo.pdf.
Hejmanowska B., Kramarczyk P., Głowienka E., Mikrut S.: Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images. Remote Sensing, vol. 13, no. 16, 2021, 3176. https://doi.org/10.3390/rs13163176.
Rouse J., Haas R., Schell J., Deering D., Harlan J.: Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. NASA/ GSFC Type III Final Report, Greenbelt, MD, 1974.
Laur H.: ERS-1 SAR Calibration: Derivation of Backscattering Coefficient σo in ERS-1 SAR PRI Products. ESA/ESRIN, Issue 1, Rev. 0, October 1992.
Filipponi F.: Sentinel-1 GRD Preprocessing Workflow. Proceedings, vol. 18, no. 1, 2019. 11. https://doi.org/10.3390/ECRS-3-06201.
Kussul N., Lavreniuk M., Skakun S., Shelestov A.: Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, 2017, pp. 778–782. https://doi.org/10.1109/LGRS.2017.2681128.
Fawcett T.: An introduction to ROC analysis. Pattern Recognition Letters, vol. 27, iss. 8, 2006, pp. 861–874. https://doi.org/10.1016/j.patrec.2005.10.010.
Congalton R.G.: A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, vol. 37, iss. 1, 1991, pp. 35–46. https://doi.org/10.1016/0034-4257(91)90048-B.
Zespół Geoinformacji, Fotogrametrii i Teledetekcji Środowiska: Application of the “traffic lights” idea in crops control in Integrated Administration Control System. https://twiki.fotogrametria.agh.edu.pl/c5www/index.php/katedra/gll2021 [access: 3.08.2021].
Morales-Barquero L., Lyons M.B., Phinn S.R., Roelfsema C.M.: Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources. Remote Sensing, vol. 11, no. 19, 2019, 2305. https://doi.org/10.3390/rs11192305.
Foody G.M.: Impacts of ignorance on the accuracy of image classification and thematic mapping. Remote Sensing of Environment, vol. 259, 2021, 112367. https://doi.org/10.1016/j.rse.2021.112367.