Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform
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-...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/10/5/454 |
| _version_ | 1797536531505741824 |
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| author | Lingfei Shi Feng Ling |
| author_facet | Lingfei Shi Feng Ling |
| author_sort | Lingfei Shi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:02:02Z |
| format | Article |
| id | doaj.art-988ba38e7b9f48baa4380a73f464ade4 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T12:02:02Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-988ba38e7b9f48baa4380a73f464ade42023-11-21T16:53:44ZengMDPI AGLand2073-445X2021-04-0110545410.3390/land10050454Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine PlatformLingfei Shi0Feng Ling1College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, ChinaInnovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaAs 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.https://www.mdpi.com/2073-445X/10/5/454local climate zonemulti-source datasetsGoogle Earth Engine |
| spellingShingle | Lingfei Shi Feng Ling Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform Land local climate zone multi-source datasets Google Earth Engine |
| title | Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform |
| title_full | Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform |
| title_fullStr | Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform |
| title_full_unstemmed | Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform |
| title_short | Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform |
| title_sort | local climate zone mapping using multi source free available datasets on google earth engine platform |
| topic | local climate zone multi-source datasets Google Earth Engine |
| url | https://www.mdpi.com/2073-445X/10/5/454 |
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