Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

被引:273
|
作者
Guha, Subhanil [1 ]
Govil, Himanshu [1 ]
Dey, Anindita [2 ]
Gill, Neetu [3 ]
机构
[1] Natl Inst Technol Raipur, Dept Appl Geol, Raipur, Madhya Pradesh, India
[2] Nazrul Balika Vidyalaya, Dept Geog, Guma, W Bengal, India
[3] Chhattisgarh Council Sci & Technol, Raipur, Madhya Pradesh, India
关键词
Land surface temperature (LST); normalized difference vegetation index (NDVI); normalized difference builtup index (NDBI); land use/land cover (LU-LC); urban thermal field variance index (UTFVI); urban heat island (UHI); URBAN HEAT-ISLAND; EMISSIVITY RETRIEVAL; VEGETATION COVER; USE/LAND COVER; TM DATA; IMPACT; URBANIZATION; COOL; VARIABILITY; SHANGHAI;
D O I
10.1080/22797254.2018.1474494
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU-LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15 degrees C and 3.31 degrees C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone.
引用
收藏
页码:667 / 678
页数:12
相关论文
共 50 条
  • [1] Availability of Land Surface Temperature Using Landsat 8 OLI/TIRS Science Products
    Park, SeongWook
    Kim, MinSik
    [J]. KOREAN JOURNAL OF REMOTE SENSING, 2021, 37 (03) : 463 - 473
  • [2] Downscaling Land Surface Temperature via Assimilation of LandSat 8/9 OLI and TIRS Data and Hypersharpening
    Alparone, Luciano
    Garzelli, Andrea
    [J]. Remote Sensing, 2024, 16 (24)
  • [5] USING A SPLIT-WINDOW ALGORITHM FOR THE RETRIEVAL OF THE LAND SURFACE TEMPERATURE VIA LANDSAT-8 OLI/TIRS
    Auntarin, Chavarit
    Chunpang, Poramate
    Chokkuea, Wutthisat
    Laosuwan, Teerawong
    [J]. GEOGRAPHIA TECHNICA, 2021, 16 : 30 - 42
  • [6] Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS Data
    Govil, Himanshu
    Guha, Subhanil
    Diwan, Prabhat
    Gill, Neetu
    Dey, Anindita
    [J]. DATA MANAGEMENT, ANALYTICS AND INNOVATION, ICDMAI 2019, VOL 1, 2020, 1042 : 171 - 184
  • [7] RETRIEVING LAND SURFACE TEMPERATURE FROM LANDSAT 8 TIRS DATA USING RTTOV AND ASTER GED
    Meng, Xiangchen
    Li, Hua
    Du, Yongming
    Liu, Qinhuo
    Zhu, Jinshan
    Sun, Lin
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 4302 - 4305
  • [8] Surface Solar Radiation Modeling from Landsat 8 OLI/TIRS Satellite Data
    Benharrats, Farah
    Mahi, Habib
    [J]. 2020 5TH INTERNATIONAL CONFERENCE ON RENEWABLE ENERGIES FOR DEVELOPING COUNTRIES (REDEC), 2020,
  • [9] Land surface temperature retrieval from Landsat 8 OLI/TIRS images based on back-propagation neural network
    Zhang, Bo
    Zhang, Meng
    Hong, Danfeng
    [J]. INDOOR AND BUILT ENVIRONMENT, 2021, 30 (01) : 22 - 38
  • [10] High-Resolution Sea Surface Temperature Retrieval from Landsat 8 OLI/TIRS Data at Coastal Regions
    Jang, Jae-Cheol
    Park, Kyung-Ae
    [J]. REMOTE SENSING, 2019, 11 (22)