Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

Aqiang Yang,Guoqing Sun

doi:10.3390/ijgi6030059

Abstract

Rapid urbanization dramatically changes the local environment. A hybrid classification method is designed and applied to multi-temporal Landsat images and ancillary data to obtain land cover change datasets. A support vector machine (SVM) classifier is used to classify multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) images that were collected in 2000 at the pixel level. These images are also segmented with the mean shift method. The impervious surface is refined based on a combination of the segmented objects and the SVM classification results. The changed areas in 1990 and 2010 are determined by comparing the Thematic Mapper (TM) and ETM+ images via the re-weighted multivariate alteration detection transformation method. The TM images that were masked as changed areas in 1990 and 2000 are input into the SVM classifier. Land cover maps for 1990 and 2010 are produced by combining the unchanged area in 2000 with the new classes of the changed areas in 1990 and 2010. Land cover change has continuously accelerated since 1990. Remarkably, arable land decreased, while the impervious surface area significantly increased.

Highlights

Land cover and land use provide information that improves our understanding of the interactions between humans and the environment [1]
The dramatic land cover changes around urban agglomerations cause a variety of problems, such as regional climate change, the urban heat island effect and increased greenhouse gas emissions [15,16,17,18,19]
Many climate models, such as the Regional Atmospheric Modeling System (RAMS), the Penn State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model (MM5) and the Weather Research and Forecasting Model (WRF), require land cover as input data to analyze the effects of land cover changes on the climate [20,21,22,23,24]

Summary

Introduction

Land cover and land use provide information that improves our understanding of the interactions between humans and the environment [1]. Land cover directly affects the physical characteristics of the land surface, such as soil moisture, albedo, temperature and transpiration, so many scientific studies require information regarding the spatial distribution and dynamic changes of land cover [5,6,7]. The dramatic land cover changes around urban agglomerations cause a variety of problems, such as regional climate change, the urban heat island effect and increased greenhouse gas emissions [15,16,17,18,19]. Using climate models to simulate the effects of land cover changes on the regional climate in the BTT region requires long-term land cover products

Objectives

Methods

Findings

Discussion

Conclusion