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Mogens Humlekrog Greve

Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Standard

Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control. / Chellasamy, Menaka; Ferre, Ty; Greve, Mogens Humlekrog.

I: International Journal of Applied Earth Observation and Geoinformation, Bind 49, 49, 2016, s. 10.23.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Chellasamy, M, Ferre, T & Greve, MH 2016, 'Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control', International Journal of Applied Earth Observation and Geoinformation, bind 49, 49, s. 10.23. https://doi.org/10.1016/j.jag.2016.01.008

APA

Chellasamy, M., Ferre, T., & Greve, M. H. (2016). Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control. International Journal of Applied Earth Observation and Geoinformation, 49, 10.23. [49]. https://doi.org/10.1016/j.jag.2016.01.008

CBE

Chellasamy M, Ferre T, Greve MH. 2016. Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control. International Journal of Applied Earth Observation and Geoinformation. 49:10.23. https://doi.org/10.1016/j.jag.2016.01.008

MLA

Chellasamy, Menaka, Ty Ferre, og Mogens Humlekrog Greve. "Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control". International Journal of Applied Earth Observation and Geoinformation. 2016, 49. 10.23. https://doi.org/10.1016/j.jag.2016.01.008

Vancouver

Chellasamy M, Ferre T, Greve MH. Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control. International Journal of Applied Earth Observation and Geoinformation. 2016;49:10.23. 49. https://doi.org/10.1016/j.jag.2016.01.008

Author

Chellasamy, Menaka ; Ferre, Ty ; Greve, Mogens Humlekrog. / Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control. I: International Journal of Applied Earth Observation and Geoinformation. 2016 ; Bind 49. s. 10.23.

Bibtex

@article{eb81c229256a45f28144f4fd34999b64,
title = "Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control",
abstract = "Beginning in 2015, Danish farmers are obliged to meet specific crop diversification rules based on total land area and number of crops cultivated to be eligible for new greening subsidies. Hence, there is a need for the Danish government to extend their subsidy control system to verify farmers’ declarations to war-rant greening payments under the new crop diversification rules. Remote Sensing (RS) technology has been used since 1992 to control farmers’ subsidies in Denmark. However, a proper RS-based approach is yet to be finalised to validate new crop diversity requirements designed for assessing compliance under the recent subsidy scheme (2014–2020); This study uses an ensemble classification approach(proposed by the authors in previous studies) for validating the crop diversity requirements of the new rules. The approach uses a neural network ensemble classification system with bi-temporal (spring and early summer) WorldView-2 imagery (WV2) and includes the following steps: (1) automatic computation of pixel-based prediction probabilities using multiple neural networks; (2) quantification of the classification uncertainty using Endorsement Theory (ET); (3) discrimination of crop pixels and validation of the crop diversification rules at farm level; and (4) identification of farmers who are violating the requirements for greening subsidies. The prediction probabilities are computed by a neural net-work ensemble supplied with training samples selected automatically using farmers declared parcels(field vectors containing crop information and the field boundary of each crop). Crop discrimination is performed by considering a set of conclusions derived from individual neural networks based on ET. Verification of the diversification rules is performed by incorporating pixel-based classification uncertainty or confidence intervals with the class labels at the farmer level. The proposed approach was tested with WV2 imagery acquired in 2011 for a study area in Vennebjerg, Denmark, containing 132 farmers, 1258 fields, and 18 crops. The classification results obtained show an overall accuracy of 90.2{\%}. The RS-based results suggest that 36 farmers did not follow the crop diversification rules that would qualify for the greening subsidies. When compared to the farmers’ reported crop mixes, irrespective of the rule, the RS results indicate that false crop declarations were made by 8 farmers, covering 15 fields. If the farmers’reports had been submitted for the new greening subsidies, 3 farmers would have made a false claim;while remaining 5 farmers obey the rules of required crop proportion even though they have submitted the false crop code due to their small holding size. The RS results would have supported 96 farmers for greening subsidy claims, with no instances of suggesting a greening subsidy for a holding that the farmer did not report as meeting the required conditions. These results suggest that the proposed RS based method shows great promise for validating the new greening subsidies in Denmark.",
author = "Menaka Chellasamy and Ty Ferre and Greve, {Mogens Humlekrog}",
year = "2016",
doi = "10.1016/j.jag.2016.01.008",
language = "English",
volume = "49",
pages = "10.23",
journal = "International Journal of Applied Earth Observation and Geoinformation",
issn = "0303-2434",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Evaluating an ensemble classification approach for crop diversityverification in Danish greening subsidy control

AU - Chellasamy, Menaka

AU - Ferre, Ty

AU - Greve, Mogens Humlekrog

PY - 2016

Y1 - 2016

N2 - Beginning in 2015, Danish farmers are obliged to meet specific crop diversification rules based on total land area and number of crops cultivated to be eligible for new greening subsidies. Hence, there is a need for the Danish government to extend their subsidy control system to verify farmers’ declarations to war-rant greening payments under the new crop diversification rules. Remote Sensing (RS) technology has been used since 1992 to control farmers’ subsidies in Denmark. However, a proper RS-based approach is yet to be finalised to validate new crop diversity requirements designed for assessing compliance under the recent subsidy scheme (2014–2020); This study uses an ensemble classification approach(proposed by the authors in previous studies) for validating the crop diversity requirements of the new rules. The approach uses a neural network ensemble classification system with bi-temporal (spring and early summer) WorldView-2 imagery (WV2) and includes the following steps: (1) automatic computation of pixel-based prediction probabilities using multiple neural networks; (2) quantification of the classification uncertainty using Endorsement Theory (ET); (3) discrimination of crop pixels and validation of the crop diversification rules at farm level; and (4) identification of farmers who are violating the requirements for greening subsidies. The prediction probabilities are computed by a neural net-work ensemble supplied with training samples selected automatically using farmers declared parcels(field vectors containing crop information and the field boundary of each crop). Crop discrimination is performed by considering a set of conclusions derived from individual neural networks based on ET. Verification of the diversification rules is performed by incorporating pixel-based classification uncertainty or confidence intervals with the class labels at the farmer level. The proposed approach was tested with WV2 imagery acquired in 2011 for a study area in Vennebjerg, Denmark, containing 132 farmers, 1258 fields, and 18 crops. The classification results obtained show an overall accuracy of 90.2%. The RS-based results suggest that 36 farmers did not follow the crop diversification rules that would qualify for the greening subsidies. When compared to the farmers’ reported crop mixes, irrespective of the rule, the RS results indicate that false crop declarations were made by 8 farmers, covering 15 fields. If the farmers’reports had been submitted for the new greening subsidies, 3 farmers would have made a false claim;while remaining 5 farmers obey the rules of required crop proportion even though they have submitted the false crop code due to their small holding size. The RS results would have supported 96 farmers for greening subsidy claims, with no instances of suggesting a greening subsidy for a holding that the farmer did not report as meeting the required conditions. These results suggest that the proposed RS based method shows great promise for validating the new greening subsidies in Denmark.

AB - Beginning in 2015, Danish farmers are obliged to meet specific crop diversification rules based on total land area and number of crops cultivated to be eligible for new greening subsidies. Hence, there is a need for the Danish government to extend their subsidy control system to verify farmers’ declarations to war-rant greening payments under the new crop diversification rules. Remote Sensing (RS) technology has been used since 1992 to control farmers’ subsidies in Denmark. However, a proper RS-based approach is yet to be finalised to validate new crop diversity requirements designed for assessing compliance under the recent subsidy scheme (2014–2020); This study uses an ensemble classification approach(proposed by the authors in previous studies) for validating the crop diversity requirements of the new rules. The approach uses a neural network ensemble classification system with bi-temporal (spring and early summer) WorldView-2 imagery (WV2) and includes the following steps: (1) automatic computation of pixel-based prediction probabilities using multiple neural networks; (2) quantification of the classification uncertainty using Endorsement Theory (ET); (3) discrimination of crop pixels and validation of the crop diversification rules at farm level; and (4) identification of farmers who are violating the requirements for greening subsidies. The prediction probabilities are computed by a neural net-work ensemble supplied with training samples selected automatically using farmers declared parcels(field vectors containing crop information and the field boundary of each crop). Crop discrimination is performed by considering a set of conclusions derived from individual neural networks based on ET. Verification of the diversification rules is performed by incorporating pixel-based classification uncertainty or confidence intervals with the class labels at the farmer level. The proposed approach was tested with WV2 imagery acquired in 2011 for a study area in Vennebjerg, Denmark, containing 132 farmers, 1258 fields, and 18 crops. The classification results obtained show an overall accuracy of 90.2%. The RS-based results suggest that 36 farmers did not follow the crop diversification rules that would qualify for the greening subsidies. When compared to the farmers’ reported crop mixes, irrespective of the rule, the RS results indicate that false crop declarations were made by 8 farmers, covering 15 fields. If the farmers’reports had been submitted for the new greening subsidies, 3 farmers would have made a false claim;while remaining 5 farmers obey the rules of required crop proportion even though they have submitted the false crop code due to their small holding size. The RS results would have supported 96 farmers for greening subsidy claims, with no instances of suggesting a greening subsidy for a holding that the farmer did not report as meeting the required conditions. These results suggest that the proposed RS based method shows great promise for validating the new greening subsidies in Denmark.

U2 - 10.1016/j.jag.2016.01.008

DO - 10.1016/j.jag.2016.01.008

M3 - Journal article

VL - 49

SP - 10.23

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 0303-2434

M1 - 49

ER -