Combination of Sentinel-2 and PALSAR-2 for Local Climate Zone Classification: A Case Study of Nanchang, China

Chaomin Chen,Hasi Bagan,Xuan Xie,Yoshiki Yamagata,Yune La

doi:10.3390/rs13101902

Abstract

Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR-2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate.

Highlights

With continuous urbanization and the increasing settlement in global cities, natural landscapes are constantly converted to impervious surfaces in urban areas, altering the natural surface energy and water balances, which often results in altered climatic conditions in urban areas and the formation of the urban heat island (UHI) phenomenon [1,2,3]
The main objectives of this study were (i) to classify different combinations of spectral, backscattering, and textural features in Sentinel-2 and PALSAR-2, (ii) to assess the importance and contribution of the input features from Sentinel-2 multispectral instrument (MSI) imagery and PALSAR-2 data to local climate zone” (LCZ) classification, and (iii) to compare the advantages and disadvantages of the resampling method and the grid-cell-based method in the process of LCZ mapping, and to perform spatial statistical analysis of the best LCZs map and land surface temperatures (LSTs) derived from summer nighttime Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal imagery
The accuracies of the classification were evaluated in terms of user’s accuracy (UA), producer’s accuracy (PA), and overall accuracy (OA), which were derived from the confusion matrix based on test pixels [61]

Summary

Introduction

With continuous urbanization and the increasing settlement in global cities, natural landscapes are constantly converted to impervious surfaces in urban areas, altering the natural surface energy and water balances, which often results in altered climatic conditions in urban areas and the formation of the urban heat island (UHI) phenomenon [1,2,3]. As a key topic in urban climate studies, the concept of a “local climate zone” (LCZ) was introduced in 2012 by Stewart and Oke [4] to quantify the relationship between urban morphology and the UHI phenomenon. LCZs provide a standardized framework to link land cover types and urban morphology with corresponding thermal properties, so LCZs have been the systematic criteria for UHI comparisons [5]. Database and Access Portal Tools (WUDAPT) project was developed as a new global initiative to produce standardized LCZ maps [6,7,8]. Because remote sensing data are Remote Sens. It is necessary to explore the combination of multi-source remote sensing data to generate LCZ mapping

Objectives

Methods

Results

Discussion

Conclusion