Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area. Issue 7 (1st April 2019)

• Record Type: Journal Article
• Authors: Deng, Ziwei; Zhu, Xiang; He, Qingyun; Tang, Lisha
• Is Part Of: Advances in space research. Volume 63:Issue 7(2019)
• Page Start: 2144
• Page End: 2154
• Publication Date: 2019-04-01
• Subjects: Land use/land cover -- Landsat8 -- Multisource fusion -- Time series -- MODIS; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2018.12.005
• Languages: English
• ISSNs: 0273-1177

Abstract

Abstract: It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.