Multi-temporal RapidEye Tasselled Cap data for land cover classification
Land cover mapping can be seen as a key element to understand the spatial distribution of habitats and thus to sustainable management of natural resources. Multi-temporal remote sensing data are a valuable data source for land cover mapping. However, the increased amount of data requires effective m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-01-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/22797254.2019.1701560 |
| _version_ | 1819296021832794112 |
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| author | Christoph Raab B. Tonn M. Meißner N. Balkenhol J. Isselstein |
| author_facet | Christoph Raab B. Tonn M. Meißner N. Balkenhol J. Isselstein |
| author_sort | Christoph Raab |
| collection | DOAJ |
| description | Land cover mapping can be seen as a key element to understand the spatial distribution of habitats and thus to sustainable management of natural resources. Multi-temporal remote sensing data are a valuable data source for land cover mapping. However, the increased amount of data requires effective machine learning algorithms and data compression approaches. In this study, the Random Forest and C 5.0 classification algorithms were applied to (1) a multi-temporal Tasselled-Cap-transformed, (2) top of atmosphere and (3) surface reflectance RapidEye time-series. The overall accuracies ranged from 91.44% to 91.80%, with only minor differences between algorithms and datasets. The McNemar test showed, however, significant differences between the Tasselled-Cap-transformed and untransformed mapping results in most cases. The temporal profiles for the Tasselled-Cap-transformed RapidEye data indicated a good separability between considered classes. The phenological profiles of vegetated surfaces followed a typical green-up curve for the Greenness Tasselled-Cap-index. A permutation-based variable importance measure indicated that late autumn should be considered as most important phenological phase contributing to the classification model performance. The results suggested that the RapidEye Tasselled Cap Transformation, which was designed for agricultural applications, can be an effective data compression tool, suitable to map heterogeneous landscapes with no measurable negative impact on classification accuracy. |
| first_indexed | 2024-12-24T04:51:29Z |
| format | Article |
| id | doaj.art-55fe9cf8c0eb4c1883366c7c0d48b169 |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-12-24T04:51:29Z |
| publishDate | 2019-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-55fe9cf8c0eb4c1883366c7c0d48b1692022-12-21T17:14:32ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542019-01-0152165366610.1080/22797254.2019.17015601701560Multi-temporal RapidEye Tasselled Cap data for land cover classificationChristoph Raab0B. Tonn1M. Meißner2N. Balkenhol3J. Isselstein4University of GoettingenUniversity of GoettingenInstitut für Wildbiologie Göttingen und Dresden e.VUniversity of GoettingenUniversity of GoettingenLand cover mapping can be seen as a key element to understand the spatial distribution of habitats and thus to sustainable management of natural resources. Multi-temporal remote sensing data are a valuable data source for land cover mapping. However, the increased amount of data requires effective machine learning algorithms and data compression approaches. In this study, the Random Forest and C 5.0 classification algorithms were applied to (1) a multi-temporal Tasselled-Cap-transformed, (2) top of atmosphere and (3) surface reflectance RapidEye time-series. The overall accuracies ranged from 91.44% to 91.80%, with only minor differences between algorithms and datasets. The McNemar test showed, however, significant differences between the Tasselled-Cap-transformed and untransformed mapping results in most cases. The temporal profiles for the Tasselled-Cap-transformed RapidEye data indicated a good separability between considered classes. The phenological profiles of vegetated surfaces followed a typical green-up curve for the Greenness Tasselled-Cap-index. A permutation-based variable importance measure indicated that late autumn should be considered as most important phenological phase contributing to the classification model performance. The results suggested that the RapidEye Tasselled Cap Transformation, which was designed for agricultural applications, can be an effective data compression tool, suitable to map heterogeneous landscapes with no measurable negative impact on classification accuracy.http://dx.doi.org/10.1080/22797254.2019.1701560land coverrandom forestc 5.0rapideyephenological correctiontasselled cap transformationclassification |
| spellingShingle | Christoph Raab B. Tonn M. Meißner N. Balkenhol J. Isselstein Multi-temporal RapidEye Tasselled Cap data for land cover classification European Journal of Remote Sensing land cover random forest c 5.0 rapideye phenological correction tasselled cap transformation classification |
| title | Multi-temporal RapidEye Tasselled Cap data for land cover classification |
| title_full | Multi-temporal RapidEye Tasselled Cap data for land cover classification |
| title_fullStr | Multi-temporal RapidEye Tasselled Cap data for land cover classification |
| title_full_unstemmed | Multi-temporal RapidEye Tasselled Cap data for land cover classification |
| title_short | Multi-temporal RapidEye Tasselled Cap data for land cover classification |
| title_sort | multi temporal rapideye tasselled cap data for land cover classification |
| topic | land cover random forest c 5.0 rapideye phenological correction tasselled cap transformation classification |
| url | http://dx.doi.org/10.1080/22797254.2019.1701560 |
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