Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data
Imagery from recently launched high spatial resolution WorldView-3 offers new opportunities for crop identification and landcover assessment. Multispectral WorldView-3 at 1.6m spatial resolution and LANDSAT8 images covering an extent of 100Km² in humid ecology of Nigeria were used for crop and la...
| Main Authors: | , , |
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| Format: | Conference Proceedings |
| Language: | Inglés |
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2016
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| Online Access: | https://hdl.handle.net/10568/75882 |
| _version_ | 1855533509199790080 |
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| author | Alabi, Tunrayo Haertel, M. Chiejile, S. |
| author_browse | Alabi, Tunrayo Chiejile, S. Haertel, M. |
| author_facet | Alabi, Tunrayo Haertel, M. Chiejile, S. |
| author_sort | Alabi, Tunrayo |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Imagery from recently launched high spatial resolution WorldView-3 offers new opportunities for crop
identification and landcover assessment. Multispectral WorldView-3 at 1.6m spatial resolution and
LANDSAT8 images covering an extent of 100Km² in humid ecology of Nigeria were used for crop and
landcover identification. Three supervised classification techniques (maximum likelihood(MLC), Neural
Net clasifier(NNC) and support vector machine(SVM)) were used to classify WorldView-3 and
LANDSAT8 into four crop classes and seven non-crop classes. For accuracy assessment, kappa coefficient,
producer and user accuracies were used to evaluate the performance of all three supervised classifiers. NNC
performed best with an overall accuracy(OA) of 92.20, kappa coefficient(KC) of 0.83 in landcover
identification using WorldView-3. This was closely followed by SVM with an OA of 91.77%, KC of 0.83.
MLC performed slightly lower at an OA of 91.25% and KC of 0.82. Classification of crops and landcover
with LANDSAT8 was best with MLC classifier with an OA of 92.12% , KC of 0.89. Cassava at younger
than 3 months old could not be identified correctly by all classifiers using WorldView-3 and LANDSAT8
products. In summary WorldView-3 and LANDSAT8 data had satisfactory performance in identifying
different crop and landcover types though at varying degrees of accuracies. |
| format | Conference Proceedings |
| id | CGSpace75882 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| record_format | dspace |
| spelling | CGSpace758822023-02-15T06:37:58Z Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data Alabi, Tunrayo Haertel, M. Chiejile, S. cassava maize neural network Imagery from recently launched high spatial resolution WorldView-3 offers new opportunities for crop identification and landcover assessment. Multispectral WorldView-3 at 1.6m spatial resolution and LANDSAT8 images covering an extent of 100Km² in humid ecology of Nigeria were used for crop and landcover identification. Three supervised classification techniques (maximum likelihood(MLC), Neural Net clasifier(NNC) and support vector machine(SVM)) were used to classify WorldView-3 and LANDSAT8 into four crop classes and seven non-crop classes. For accuracy assessment, kappa coefficient, producer and user accuracies were used to evaluate the performance of all three supervised classifiers. NNC performed best with an overall accuracy(OA) of 92.20, kappa coefficient(KC) of 0.83 in landcover identification using WorldView-3. This was closely followed by SVM with an OA of 91.77%, KC of 0.83. MLC performed slightly lower at an OA of 91.25% and KC of 0.82. Classification of crops and landcover with LANDSAT8 was best with MLC classifier with an OA of 92.12% , KC of 0.89. Cassava at younger than 3 months old could not be identified correctly by all classifiers using WorldView-3 and LANDSAT8 products. In summary WorldView-3 and LANDSAT8 data had satisfactory performance in identifying different crop and landcover types though at varying degrees of accuracies. 2016 2016-06-29T08:16:45Z 2016-06-29T08:16:45Z Conference Proceedings https://hdl.handle.net/10568/75882 en Limited Access Alabi, T., Haertel, M. & Chiejile, S. (2016). Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data. proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2016). (pp. 109-120), Setubal, Portugal |
| spellingShingle | cassava maize neural network Alabi, Tunrayo Haertel, M. Chiejile, S. Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data |
| title | Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data |
| title_full | Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data |
| title_fullStr | Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data |
| title_full_unstemmed | Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data |
| title_short | Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data |
| title_sort | investigating the use of high resolution multi spectral satellite imagery for crop mapping in nigeria crop and landuse classification using worldview 3 high resolution multispectral imagery and landsat8 data |
| topic | cassava maize neural network |
| url | https://hdl.handle.net/10568/75882 |
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