Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China
Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in L...
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my.um.eprints.194092018-09-26T02:01:57Z http://eprints.um.edu.my/19409/ Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China Yang, X. Chen, L. Li, Y. Xi, W. Chen, L. G Geography. Anthropology. Recreation TH Building construction Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0 % in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9 % (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9 % and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features. Kluwer (now part of Springer) 2015 Article PeerReviewed Yang, X. and Chen, L. and Li, Y. and Xi, W. and Chen, L. (2015) Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China. Environmental Monitoring and Assessment, 187 (7). ISSN 0167-6369 http://dx.doi.org/10.1007/s10661-015-4667-3 doi:10.1007/s10661-015-4667-3 |
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G Geography. Anthropology. Recreation TH Building construction Yang, X. Chen, L. Li, Y. Xi, W. Chen, L. Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China |
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Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0 % in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9 % (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9 % and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features. |
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Yang, X. Chen, L. Li, Y. Xi, W. Chen, L. |
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Yang, X. Chen, L. Li, Y. Xi, W. Chen, L. |
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Yang, X. |
title |
Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China |
title_short |
Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China |
title_full |
Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China |
title_fullStr |
Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China |
title_full_unstemmed |
Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China |
title_sort |
rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from lianyungang city, china |
publisher |
Kluwer (now part of Springer) |
publishDate |
2015 |
url |
http://eprints.um.edu.my/19409/ http://dx.doi.org/10.1007/s10661-015-4667-3 |
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