Assessment of a Random Forest Classifier in Urban Local Climate Zone Classification Using Sentinel-2 and PALSAR-2

2021 ◽  
Author(s):  
Chaomin Chen ◽  
Hasi Bagan ◽  
Xuan Xie ◽  
Luwen Tan ◽  
Yoshiki Yamagata
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Climate Zone ◽  
Local Climate ◽  
Local Climate Zone ◽  
Sentinel 2

scholarly journals Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

2018 ◽  
Vol 10 (10) ◽  
pp. 1572 ◽  
Author(s):  
Chunping Qiu ◽  
Michael Schmitt ◽  
Lichao Mou ◽  
Pedram Ghamisi ◽  
Xiao Zhu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Landsat 8 ◽  
Climate Zone ◽  
Local Climate ◽  
Feature Importance ◽  
Local Climate Zone ◽  
Sentinel 2

Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.

scholarly journals EFFECT OF THE TRAINING SET CONFIGURATION ON SENTINEL-2-BASED URBAN LOCAL CLIMATE ZONE CLASSIFICATION

2018 ◽  
Vol XLII-2 ◽  
pp. 931-936 ◽  
Author(s):  
C. P. Qiu ◽  
M. Schmitt ◽  
P. Ghamisi ◽  
X. X. Zhu
Keyword(s):  
Training Sample ◽  
Majority Voting ◽  
Training Set ◽  
Sample Number ◽  
Climate Zone ◽  
Local Climate ◽  
Physical Environmental Factors ◽  
The Impact ◽  
Local Climate Zone ◽  
Sentinel 2

As any supervised classification procedure, also Local Climate Zone (LCZ) mapping requires reliable reference data. These are usually created manually and inevitably include label noise, caused by the complexity of the LCZ class scheme as well as variations in cultural and physical environmental factors. This study aims at evaluating the impact of the training set configuration, i.e. training sample number and imbalance, on the performance of Canonical Correlation Forests (CCFs) for a classification of the 11 urban LCZ classes. Experiments are carried out based on globally available Sentinel-2 imagery. Besides multi-spectral observations, different index measures extracted from the images as well as the Global Urban Footprint (GUF) and Open Street Map (OSM) layers are fed into the CCFs classifier. The results show that different LCZs favor different configurations in terms of training sample number and balance. Based on the findings, majority voting of different predictions from different configurations is proposed and performed. This way, a significant accuracy improvement can be achieved.

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

2021 ◽  
Vol 13 (10) ◽  
pp. 1902
Author(s):  
Chaomin Chen ◽  
Hasi Bagan ◽  
Xuan Xie ◽  
Yune La ◽  
Yoshiki Yamagata
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Grid Cell ◽  
Classification Model ◽  
Textural Features ◽  
Climate Zone ◽  
Local Climate ◽  
Dual Polarized ◽  
Local Climate Zone ◽  
Sentinel 2

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.

scholarly journals Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 454
Author(s):  
Lingfei Shi ◽  
Feng Ling
Keyword(s):  
Large Scale ◽  
Urban Climate ◽  
Google Earth ◽  
Positive Contribution ◽  
Climate Zone ◽  
Local Climate ◽  
Google Earth Engine ◽  
Occurrence Matrix ◽  
Local Climate Zone ◽  
Sentinel 2

As one of the widely concerned urban climate issues, urban heat island (UHI) has been studied using the local climate zone (LCZ) classification scheme in recent years. More and more effort has been focused on improving LCZ mapping accuracy. It has become a prevalent trend to take advantage of multi-source images in LCZ mapping. To this end, this paper tried to utilize multi-source freely available datasets: Sentinel-2 multispectral instrument (MSI), Sentinel-1 synthetic aperture radar (SAR), Luojia1-01 nighttime light (NTL), and Open Street Map (OSM) datasets to produce the 10 m LCZ classification result using Google Earth Engine (GEE) platform. Additionally, the derived datasets of Sentinel-2 MSI data were also exploited in LCZ classification, such as spectral indexes (SI) and gray-level co-occurrence matrix (GLCM) datasets. The different dataset combinations were designed to evaluate the particular dataset’s contribution to LCZ classification. It was found that: (1) The synergistic use of Sentinel-2 MSI and Sentinel-1 SAR data can improve the accuracy of LCZ classification; (2) The multi-seasonal information of Sentinel data also has a good contribution to LCZ classification; (3) OSM, GLCM, SI, and NTL datasets have some positive contribution to LCZ classification when individually adding them to the seasonal Sentinel-1 and Sentinel-2 datasets; (4) It is not an absolute right way to improve LCZ classification accuracy by combining as many datasets as possible. With the help of the GEE, this study provides the potential to generate more accurate LCZ mapping on a large scale, which is significant for urban development.

scholarly journals Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks

2020 ◽  
Vol 12 (21) ◽  
pp. 3552
Author(s):  
Cheolhee Yoo ◽  
Yeonsu Lee ◽  
Dongjin Cho ◽  
Jungho Im ◽  
Daehyeon Han
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Urban Form ◽  
Suggested Approach ◽  
Climate Zone ◽  
Local Climate ◽  
Form And Function ◽  
Building Components ◽  
Local Climate Zone ◽  
Sentinel 2

Recent studies have enhanced the mapping performance of the local climate zone (LCZ), a standard framework for evaluating urban form and function for urban heat island research, through remote sensing (RS) images and deep learning classifiers such as convolutional neural networks (CNNs). The accuracy in the urban-type LCZ (LCZ1-10), however, remains relatively low because RS data cannot provide vertical or horizontal building components in detail. Geographic information system (GIS)-based building datasets can be used as primary sources in LCZ classification, but there is a limit to using them as input data for CNN due to their incompleteness. This study proposes novel methods to classify LCZ using Sentinel 2 images and incomplete building data based on a CNN classifier. We designed three schemes (S1, S2, and a scheme fusion; SF) for mapping 50 m LCZs in two megacities: Berlin and Seoul. S1 used only RS images, and S2 used RS and building components such as area and height (or the number of stories). SF combined two schemes (S1 and S2) based on three conditions, mainly focusing on the confidence level of the CNN classifier. When compared to S1, the overall accuracies for all LCZ classes (OA) and the urban-type LCZ (OAurb) of SF increased by about 4% and 7–9%, respectively, for the two study areas. This study shows that SF can compensate for the imperfections in the building data, which causes misclassifications in S2. The suggested approach can be excellent guidance to produce a high accuracy LCZ map for cities where building databases can be obtained, even if they are incomplete.

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