Next Article in Journal
A Virtual In-Cylinder Pressure Sensor Based on EKF and Frequency-Amplitude-Modulation Fourier-Series Method
Next Article in Special Issue
DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations
Previous Article in Journal
Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security
Previous Article in Special Issue
Empowering Citizens through Perceptual Sensing of Urban Environmental and Health Data Following a Participative Citizen Science Approach
Open AccessArticle

Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

1
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2
Beijing advanced innovation center for future urban design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
3
Remote Sensing Application Center, Ministry of Housing and Urban-Rural Development of the People’s Republic of China, Beijing 100835, China
4
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 101408, China
5
Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3120; https://doi.org/10.3390/s19143120
Received: 26 May 2019 / Revised: 6 July 2019 / Accepted: 13 July 2019 / Published: 15 July 2019
(This article belongs to the Special Issue Urban Remote Sensing and Sustainable Development)
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. View Full-Text
Keywords: urban land use/land cover; three-layer classification scheme; GF-1 satellite imagery; GF-2 satelliteimagery urban land use/land cover; three-layer classification scheme; GF-1 satellite imagery; GF-2 satelliteimagery
Show Figures

Figure 1

MDPI and ACS Style

Cai, G.; Ren, H.; Yang, L.; Zhang, N.; Du, M.; Wu, C. Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme. Sensors 2019, 19, 3120.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop