Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme
Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Themati...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/14/3120 |
| _version_ | 1811301862265061376 |
|---|---|
| author | Guoyin Cai Huiqun Ren Liuzhong Yang Ning Zhang Mingyi Du Changshan Wu |
| author_facet | Guoyin Cai Huiqun Ren Liuzhong Yang Ning Zhang Mingyi Du Changshan Wu |
| author_sort | Guoyin Cai |
| collection | DOAJ |
| description | Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery. |
| first_indexed | 2024-04-13T07:17:45Z |
| format | Article |
| id | doaj.art-335a500c1dbe48d989dbf77af3de4f3c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:17:45Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-335a500c1dbe48d989dbf77af3de4f3c2022-12-22T02:56:42ZengMDPI AGSensors1424-82202019-07-011914312010.3390/s19143120s19143120Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification SchemeGuoyin Cai0Huiqun Ren1Liuzhong Yang2Ning Zhang3Mingyi Du4Changshan Wu5School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaRemote Sensing Application Center, Ministry of Housing and Urban-Rural Development of the People’s Republic of China, Beijing 100835, ChinaRemote Sensing Application Center, Ministry of Housing and Urban-Rural Development of the People’s Republic of China, Beijing 100835, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaUrban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.https://www.mdpi.com/1424-8220/19/14/3120urban land use/land coverthree-layer classification schemeGF-1 satellite imageryGF-2 satelliteimagery |
| spellingShingle | Guoyin Cai Huiqun Ren Liuzhong Yang Ning Zhang Mingyi Du Changshan Wu Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme Sensors urban land use/land cover three-layer classification scheme GF-1 satellite imagery GF-2 satelliteimagery |
| title | Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme |
| title_full | Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme |
| title_fullStr | Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme |
| title_full_unstemmed | Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme |
| title_short | Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme |
| title_sort | detailed urban land use land cover classification at the metropolitan scale using a three layer classification scheme |
| topic | urban land use/land cover three-layer classification scheme GF-1 satellite imagery GF-2 satelliteimagery |
| url | https://www.mdpi.com/1424-8220/19/14/3120 |
| work_keys_str_mv | AT guoyincai detailedurbanlanduselandcoverclassificationatthemetropolitanscaleusingathreelayerclassificationscheme AT huiqunren detailedurbanlanduselandcoverclassificationatthemetropolitanscaleusingathreelayerclassificationscheme AT liuzhongyang detailedurbanlanduselandcoverclassificationatthemetropolitanscaleusingathreelayerclassificationscheme AT ningzhang detailedurbanlanduselandcoverclassificationatthemetropolitanscaleusingathreelayerclassificationscheme AT mingyidu detailedurbanlanduselandcoverclassificationatthemetropolitanscaleusingathreelayerclassificationscheme AT changshanwu detailedurbanlanduselandcoverclassificationatthemetropolitanscaleusingathreelayerclassificationscheme |