Urban climate and local climate zone classification in the city of Kochi in India

2020 ◽  
Author(s):  
George Thomas ◽  
Soosen Samuel M. ◽  
Zachariah E. J.
Keyword(s):  
Urban Climate ◽  
Climate Zone ◽  
Local Climate ◽  
The City ◽  
Local Climate Zone

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.

Block-based local climate zone approach to urban climate maps using the UDC model

2020 ◽  
Vol 186 ◽  
pp. 107334
Author(s):  
Lei Jin ◽  
Xinpei Pan ◽  
Lin Liu ◽  
Liru Liu ◽  
Jing Liu ◽  
...  
Keyword(s):  
Urban Climate ◽  
Climate Zone ◽  
Local Climate ◽  
Block Based ◽  
Climate Maps ◽  
Local Climate Zone

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 Intra and inter ‘local climate zone’ variability of air temperature as observed by crowdsourced citizen weather stations in Berlin, Germany

2017 ◽  
Vol 26 (5) ◽  
pp. 525-547 ◽  
Author(s):  
Daniel Fenner ◽  
Fred Meier ◽  
Benjamin Bechtel ◽  
Marco Otto ◽  
Dieter Scherer
Keyword(s):  
Air Temperature ◽  
Climate Zone ◽  
Local Climate ◽  
Weather Stations ◽  
Local Climate Zone

GIS-based mapping of Local Climate Zone in the high-density city of Hong Kong

Urban Climate ◽  
2018 ◽  
Vol 24 ◽  
pp. 419-448 ◽  
Author(s):  
Yingsheng Zheng ◽  
Chao Ren ◽  
Yong Xu ◽  
Ran Wang ◽  
Justin Ho ◽  
...  
Keyword(s):  
Hong Kong ◽  
High Density ◽  
Climate Zone ◽  
Local Climate ◽  
Local Climate Zone
Sign in / Sign up

Export Citation Format

Share Document