A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images

Yibo Huang,Yang Hong,Cheng Tong,Binjie Chen,Pengbo Liu,Jinsong Deng,Hongquan Wang

doi:10.3390/rs11101230

Abstract

Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A data from 1996 to 2016 in Hangzhou City, which is the central metropolis of the Yangtze River Delta in China. In this study, orthorectification was first applied on the SPOT and Sentinel-2A images to guarantee precise geometric correction which outperformed the conventional polynomial transformation method. After pre-processing, PCA and hybrid classifier were used together to enhance and extract change information. Accuracy assessment combining stratified random and user-defined plots sampling strategies was performed with 930 reference points. The results indicate reasonable high accuracies for four periods. It was further revealed that the proposed method yielded higher accuracy than that of the traditional post-classification comparison approach. On the whole, the developed methodology provides the effectiveness of monitoring urban expansion and green space change in this study, despite the existence of obvious confusions that resulted from compound factors.

Highlights

Rapid urbanization and exploitation of natural resources have raised increasing concerns over environmental issues and made the urban area into a fragile region
This study has presented the usefulness of multi-date and multi-sensor SPOT and Sentinel data, Principal Component Analysis (PCA)-based change enhancement and hybrid classification method in successfully monitoring urban sprawl and green space change of Hangzhou City for the past 20 years
The methodology developed in this study was based on an adequate understanding of sensor characteristics, data availability, landscape features, data-related limitation, and change detection techniques employed in practice

Summary

Introduction

Rapid urbanization and exploitation of natural resources have raised increasing concerns over environmental issues and made the urban area into a fragile region. It is especially true in the coastal developed urban area in China [1,2,3,4,5]. The green spaces usually include community parks, woodlands, nature reserves and agricultural lands [10]. Monitoring urban expansion and green space change has been the primary task for urban management and been elevated to the forefront of research and practical applications [12,13]. A large number of urban remote sensing investigations since the launch of the first SPOT satellite in 1986 have implied that the tool is already operational and perhaps worthy of widespread development and application [15,16,17]

Objectives

Methods

Results

Discussion

Conclusion