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

Abstract

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°C and 3.31°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. ARTICLE HISTORY

Spatiotemporal Multi-Satellite Biophysical Data Analysis of the Effect of Urbanization on Land Surface and Air Temperature in Baguio City, Philippines

“…Landsat 7 April 2008 image showing scan errors (left) and 2008 yearly composite image (right) taken in Baguio CityNegative correlation was observed between built-up and vegetation, similar to previous studies(Guha et al, 2018, Malik et al, 2019. The highest vegetation area (4,883 ha) and lowest built-up extent (1,094 ha) computed using Landsat-8 data were recorded in 2013; while the lowest vegetation area (3,544 ha) and highest built-up extent were recorded in year 2019.…”

“…The surface reflectance and roughness of different land use types are different, thus leading to differences in land surface temperature (LST). Several studies confirmed that the presence of built-up area can accelerate the effect of UHI, whereas water and green spaces can reduce the UHI intensity (Guha et al, 2018;Song et al, 2014). Aside from LST, changes in land use and cover can also raise the temperature of the local air with several degrees higher than the air temperatures of the surrounding areas (Naserikia et al, 2019).…”

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

“…In the third step, the authors apply non-metric regression to evaluate future urban climate trends in relation to potential land use/land cover changes. Jiang, Fu, and Weng [16] analyze how some biophysical parameters, such as LST, fractional vegetation cover, normalized difference water index, impervious fractions evaporative fraction, and soil moisture, change in relation to land use/land cover alteration in the case study of Indianapolis, USA, during the 2001 investigate the effects of land use/land cover changes on the distribution of LST by using Landsat Thematic Mapper and ETM+ data concerning 1994, 2004 investigate the relationships between land use/land cover change and LST by implementing the split-window algorithm and spectral radiance model in relation to the Sundarbans Biosphere Reserve in India.Various studies concerning the influence of land use/land cover changes on LST variation have been carried out in relation to Italian case studies [19][20][21][22]. Zullo et al [19] study the relationship between LST variations and the increase in urbanized areas from 2001 to 2011 in the Po Valley through different urban growth spatial patterns.…”

“…Various studies concerning the influence of land use/land cover changes on LST variation have been carried out in relation to Italian case studies [19][20][21][22]. Zullo et al [19] study the relationship between LST variations and the increase in urbanized areas from 2001 to 2011 in the Po Valley through different urban growth spatial patterns.…”

Kết Hợp Ảnh Vệ Tinh Landsat 8 Và Sentinel 2 Trong Nâng Cao Độ Phân Giải Nhiệt Độ Bề Mặt

“…Nhiều nghiên cứu trên thế giới và Việt Nam đã sử dụng dữ liệu ảnh viễn thám hồng ngoại nhiệt độ phân giải trung bình như Landsat, Aster trong đánh giá diễn biến nhiệt độ bề mặt ở các đô thị lớn, từ đó chứng minh sự tồn tại của các "đảo nhiệt" đô thị -urban heat islands. Có thể kể đêń cać nghiên cứu của Alipour et al (2004) [1], Balling and Brazel (1988) [2], Cueto et al (2007) [3], Hyung Moo Kim et al (2005) [4], Kumar (2012) [5], Maltick et al (2008) [6], Trịnh Lê Hùng (2014) [7], Yuan et al (2007) [8],…Nhiều nghiên cứu như của Anadababu et al (2018) [9], Bakar et al (2016) [10], Boori et al (2014) [11], Guha et al (2018) [12], Pal and Ziaul (2017) [13], Bùi Quang Thành (2015) [14], Nguyễn Đức Thuận và Phạm Văn Vân (2016) [15], Trần Thị Vân và cộng sự (2009) [16]…đã chứng minh mối quan hệ chặt chẽ giữa nhiệt độ và lớp phủ, trong đó các khu vực có mật độ xây dựng cao và lớp phủ thực vật thưa có nhiệt độ cao hơn rất nhiều so với các khu vực được che phủ bởi lớp phủ thực vật dày.…”