Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city
Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-08-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2020.1814032 |
| _version_ | 1797679055843098624 |
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| author | Feilin Lai Xiaojun Yang |
| author_facet | Feilin Lai Xiaojun Yang |
| author_sort | Feilin Lai |
| collection | DOAJ |
| description | Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability. |
| first_indexed | 2024-03-11T23:08:51Z |
| format | Article |
| id | doaj.art-e3354fb56b8c40adb07873c16d34a3b5 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:51Z |
| publishDate | 2020-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-e3354fb56b8c40adb07873c16d34a3b52023-09-21T12:34:16ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262020-08-0157683084410.1080/15481603.2020.18140321814032Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American cityFeilin Lai0Xiaojun Yang1Florida State UniversityFlorida State UniversityInformation on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability.http://dx.doi.org/10.1080/15481603.2020.1814032informal settlementsclass ambiguitythermal infrared informationnon-spectral datasupervised classificationrio de janeiro |
| spellingShingle | Feilin Lai Xiaojun Yang Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city GIScience & Remote Sensing informal settlements class ambiguity thermal infrared information non-spectral data supervised classification rio de janeiro |
| title | Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city |
| title_full | Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city |
| title_fullStr | Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city |
| title_full_unstemmed | Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city |
| title_short | Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city |
| title_sort | integrating spectral and non spectral data to improve urban settlement mapping in a large latin american city |
| topic | informal settlements class ambiguity thermal infrared information non-spectral data supervised classification rio de janeiro |
| url | http://dx.doi.org/10.1080/15481603.2020.1814032 |
| work_keys_str_mv | AT feilinlai integratingspectralandnonspectraldatatoimproveurbansettlementmappinginalargelatinamericancity AT xiaojunyang integratingspectralandnonspectraldatatoimproveurbansettlementmappinginalargelatinamericancity |