Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples

Liang Cheng,Kun Tan,Li Feng,Junshi Xia,Pei Liu,Sicong Liu,Peijun Du

doi:10.3390/rs6109458

Abstract

Remote sensing imagery has been widely used in urban growth and environment analysis with many effective and advanced strategies being developed. However, most of these approaches are separated from each other. There is an urgent need to combine different modules into some practical processing chains. Firstly, we present a comprehensive analysis of key processing chains in applying remote sensing images to urban environment analysis from such aspects as Land Use/Land Cover (LULC), urban landscape ecology, Urban Heat Islands (UHIs), vegetation and water monitoring, change detection, urban ecological security assessment and urban environmental mapping. Secondly, an integrated system, namely Urban Environment Analysis System (UEAS), is implemented based on the aforementioned processing chains to analyze urban environment using multi-temporal and multi-source remotely sensed data. Several case studies are demonstrated to confirm the effectiveness of the integrated system and the combined processing chains. The contributions of this paper lie in introducing ensemble learning to urban environment remote sensing, combining remote sensing derived information with thematic models for urban environment assessment, and developing an integrated system for urban environment analysis.

Highlights

Effective and sustainable urban management increasingly requires advanced techniques to obtain various and up-to-date information on the pattern, state, characteristics, and development of an urban environment [1]
We present a comprehensive analysis of key techniques for remote sensing analysis of urban environment, such as Land Use/Land Cover (LULC), urban landscape ecology, Urban Heat Islands (UHIs), vegetation and water extraction, change detection and urban ecological security assessment
Landscape pattern is the spatial arrangement of landscape elements in different sizes and shapes

Summary

Introduction

Effective and sustainable urban management increasingly requires advanced techniques to obtain various and up-to-date information on the pattern, state, characteristics, and development of an urban environment [1]. Sensed imagery is an effective data source for urban environment analysis that is inherently suited to provide information on urban land cover characteristics and their changes over time at various spatial and temporal scales [2,3,4,5,6]. Many algorithms and models have been developed to analyze urban environment [5,6,7,8,9,10,11,12,13]. Since different remotely sensed images have various spatial, temporal, spectral, and radiometric characteristics, it is challenging to produce accurately thematic information and quantitative indicators using only one model

Methods

Results

Conclusion