Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery
Land Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/10/2501 |
| _version_ | 1827740234244161536 |
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| author | Suzanna Cuypers Andrea Nascetti Maarten Vergauwen |
| author_facet | Suzanna Cuypers Andrea Nascetti Maarten Vergauwen |
| author_sort | Suzanna Cuypers |
| collection | DOAJ |
| description | Land Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic Object-Based Image Analysis (GEOBIA). In this study, we utilize very high-resolution (VHR) optical imagery with a resolution of 50 cm to improve object recognition for GEOBIA LULC classification. We focused on the city of Nice, France, and identified ten LULC classes using a Random Forest classifier in Google Earth Engine. We investigate the impact of adding Gray-Level Co-Occurrence Matrix (GLCM) texture information and spectral indices with their temporal components, such as maximum value, standard deviation, phase and amplitude from the multi-spectral and multi-temporal Sentinel-2 imagery. This work focuses on identifying which input features result in the highest increase in accuracy. The results show that adding a single VHR image improves the classification accuracy from 62.62% to 67.05%, especially when the spectral indices and temporal analysis are not included. The impact of the GLCM is similar but smaller than the VHR image. Overall, the inclusion of temporal analysis improves the classification accuracy to 74.30%. The blue band of the VHR image had the largest impact on the classification, followed by the amplitude of the green-red vegetation index and the phase of the normalized multi-band drought index. |
| first_indexed | 2024-03-11T03:22:04Z |
| format | Article |
| id | doaj.art-1bc1966a4d1e43c285af533e017ad7d0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:22:04Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1bc1966a4d1e43c285af533e017ad7d02023-11-18T03:06:01ZengMDPI AGRemote Sensing2072-42922023-05-011510250110.3390/rs15102501Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 ImagerySuzanna Cuypers0Andrea Nascetti1Maarten Vergauwen2Department of Civil Engineering, Geomatics Section, Faculty of Engineering Technology, KU Leuven, 3001 Leuven, BelgiumDepartment of Geography, Faculty of Science, University of Liège, Place du 20 Août 7, 4000 Liège, BelgiumDepartment of Civil Engineering, Geomatics Section, Faculty of Engineering Technology, KU Leuven, 3001 Leuven, BelgiumLand Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic Object-Based Image Analysis (GEOBIA). In this study, we utilize very high-resolution (VHR) optical imagery with a resolution of 50 cm to improve object recognition for GEOBIA LULC classification. We focused on the city of Nice, France, and identified ten LULC classes using a Random Forest classifier in Google Earth Engine. We investigate the impact of adding Gray-Level Co-Occurrence Matrix (GLCM) texture information and spectral indices with their temporal components, such as maximum value, standard deviation, phase and amplitude from the multi-spectral and multi-temporal Sentinel-2 imagery. This work focuses on identifying which input features result in the highest increase in accuracy. The results show that adding a single VHR image improves the classification accuracy from 62.62% to 67.05%, especially when the spectral indices and temporal analysis are not included. The impact of the GLCM is similar but smaller than the VHR image. Overall, the inclusion of temporal analysis improves the classification accuracy to 74.30%. The blue band of the VHR image had the largest impact on the classification, followed by the amplitude of the green-red vegetation index and the phase of the normalized multi-band drought index.https://www.mdpi.com/2072-4292/15/10/2501GEOBIALULCtemporal analysisGoogle Earth EngineGLCMVHR |
| spellingShingle | Suzanna Cuypers Andrea Nascetti Maarten Vergauwen Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery Remote Sensing GEOBIA LULC temporal analysis Google Earth Engine GLCM VHR |
| title | Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery |
| title_full | Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery |
| title_fullStr | Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery |
| title_full_unstemmed | Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery |
| title_short | Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery |
| title_sort | land use and land cover mapping with vhr and multi temporal sentinel 2 imagery |
| topic | GEOBIA LULC temporal analysis Google Earth Engine GLCM VHR |
| url | https://www.mdpi.com/2072-4292/15/10/2501 |
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