Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey
With the latest development and increasing availability of high spatial resolution sensors, earth observation technology offers a viable solution for crop identification and management. There is a strong need to produce accurate, reliable and up to date crop type maps for sustainable agriculture m...
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| Format: | Article |
| Language: | English |
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IJEGEO
2018-08-01
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| Series: | International Journal of Environment and Geoinformatics |
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| Online Access: | http://dergipark.gov.tr/download/article-file/504770 |
| _version_ | 1797916566476554240 |
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| author | M.Tolga Esetlili Filiz Bektas Balcik Fusun Balık Şanl Mustafa Üstüner Kaan Kalkan Çiğdem Göksel Cem Gazioğlu Yusuf Kurucu |
| author_facet | M.Tolga Esetlili Filiz Bektas Balcik Fusun Balık Şanl Mustafa Üstüner Kaan Kalkan Çiğdem Göksel Cem Gazioğlu Yusuf Kurucu |
| author_sort | M.Tolga Esetlili |
| collection | DOAJ |
| description | With the latest development and increasing availability of high spatial resolution sensors, earth observation
technology offers a viable solution for crop identification and management. There is a strong need to produce
accurate, reliable and up to date crop type maps for sustainable agriculture monitoring and management. In this
study, RapidEye, the first high-resolution multi-spectral satellite system that operationally provides a Red-edge
channel, was used to test the potential of the data for crop type mapping. This study was investigated at a
selected region mostly covered with agricultural fields locates in the low lands of Menemen (İzmir) Plain,
TURKEY. The potential of the three classification algorithms such as Maximum Likelihood Classification,
Support Vector Machine and Object Based Image Analysis is tested. Accuracy assessment of land cover maps
has been performed through error matrix and kappa indexes. The results highlighted that all selected classifiers
as highly useful (over 90%) in mapping of crop types in the study region however the object-based approach
slightly outperforming the Support Vector Machine classification by both overall accuracy and Kappa
statistics. The success of selected methods also underlines the potential of RapidEye data for mapping crop
types in Aegean region. |
| first_indexed | 2024-04-10T13:00:29Z |
| format | Article |
| id | doaj.art-652cbc8f5b114dfaa5a7dc38ebbf05fa |
| institution | Directory Open Access Journal |
| issn | 2148-9173 2148-9173 |
| language | English |
| last_indexed | 2024-04-10T13:00:29Z |
| publishDate | 2018-08-01 |
| publisher | IJEGEO |
| record_format | Article |
| series | International Journal of Environment and Geoinformatics |
| spelling | doaj.art-652cbc8f5b114dfaa5a7dc38ebbf05fa2023-02-15T16:13:13ZengIJEGEOInternational Journal of Environment and Geoinformatics2148-91732148-91732018-08-015223124310.30897/ijegeo.442002Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, TurkeyM.Tolga Esetlili0Filiz Bektas Balcik1Fusun Balık Şanl2Mustafa Üstüner3Kaan Kalkan4Çiğdem Göksel5Cem Gazioğlu6Yusuf Kurucu7Ege University, Faculty of Agriculture, Department of Soil Science and Plant Nutrition, 35100 Izmir/ TRIstanbul Technical University, Geomatics Engineering Department, 34469 Maslak, Istanbul, TR Yıldız Technical University, Geomatic Engineering Department, 34220 Esenler, Istanbul, TR Yıldız Technical University, Geomatic Engineering Department, 34220 Esenler, Istanbul, TRÜBİTAK Space Technologies, Ankara, TRIstanbul Technical University, Geomatics Engineering Department, 34469 Maslak, Istanbul, TRIstanbul University, Institute of Marine Sciences and Management, 34134 Vefa Fatih Istanbul TR Ege University, Faculty of Agriculture, Department of Soil Science and Plant Nutrition, 35100 Izmir/ TRWith the latest development and increasing availability of high spatial resolution sensors, earth observation technology offers a viable solution for crop identification and management. There is a strong need to produce accurate, reliable and up to date crop type maps for sustainable agriculture monitoring and management. In this study, RapidEye, the first high-resolution multi-spectral satellite system that operationally provides a Red-edge channel, was used to test the potential of the data for crop type mapping. This study was investigated at a selected region mostly covered with agricultural fields locates in the low lands of Menemen (İzmir) Plain, TURKEY. The potential of the three classification algorithms such as Maximum Likelihood Classification, Support Vector Machine and Object Based Image Analysis is tested. Accuracy assessment of land cover maps has been performed through error matrix and kappa indexes. The results highlighted that all selected classifiers as highly useful (over 90%) in mapping of crop types in the study region however the object-based approach slightly outperforming the Support Vector Machine classification by both overall accuracy and Kappa statistics. The success of selected methods also underlines the potential of RapidEye data for mapping crop types in Aegean region.http://dergipark.gov.tr/download/article-file/504770Crop mappingRapidEyeSupport vector machineObject based classificationMaximum Likelihood Classification |
| spellingShingle | M.Tolga Esetlili Filiz Bektas Balcik Fusun Balık Şanl Mustafa Üstüner Kaan Kalkan Çiğdem Göksel Cem Gazioğlu Yusuf Kurucu Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey International Journal of Environment and Geoinformatics Crop mapping RapidEye Support vector machine Object based classification Maximum Likelihood Classification |
| title | Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey |
| title_full | Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey |
| title_fullStr | Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey |
| title_full_unstemmed | Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey |
| title_short | Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey |
| title_sort | comparison of object and pixel based classifications for mapping crops using rapideye imagery a case study of menemen plain turkey |
| topic | Crop mapping RapidEye Support vector machine Object based classification Maximum Likelihood Classification |
| url | http://dergipark.gov.tr/download/article-file/504770 |
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