Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data
Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/22/2719 |
| _version_ | 1819289071193686016 |
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| author | Yan Shi Zhixin Qi Xiaoping Liu Ning Niu Hui Zhang |
| author_facet | Yan Shi Zhixin Qi Xiaoping Liu Ning Niu Hui Zhang |
| author_sort | Yan Shi |
| collection | DOAJ |
| description | Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings. |
| first_indexed | 2024-12-24T03:01:01Z |
| format | Article |
| id | doaj.art-8783bd320c9248528b4940a980f3e2b7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:01:01Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8783bd320c9248528b4940a980f3e2b72022-12-21T17:18:11ZengMDPI AGRemote Sensing2072-42922019-11-011122271910.3390/rs11222719rs11222719Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media DataYan Shi0Zhixin Qi1Xiaoping Liu2Ning Niu3Hui Zhang4Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaGuangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaGuangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Resources and Environment, Academician Laboratory for Urban and Rural Spatial Data Mining of Henan Province, Henan University of Economics and Law, Zhengzhou 450000, ChinaGuangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaLand use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.https://www.mdpi.com/2072-4292/11/22/2719land use and land coverzy-3landsat 8sentinel-1a sarwechat |
| spellingShingle | Yan Shi Zhixin Qi Xiaoping Liu Ning Niu Hui Zhang Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data Remote Sensing land use and land cover zy-3 landsat 8 sentinel-1a sar |
| title | Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data |
| title_full | Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data |
| title_fullStr | Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data |
| title_full_unstemmed | Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data |
| title_short | Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data |
| title_sort | urban land use and land cover classification using multisource remote sensing images and social media data |
| topic | land use and land cover zy-3 landsat 8 sentinel-1a sar |
| url | https://www.mdpi.com/2072-4292/11/22/2719 |
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