scholarly journals Earth Observation for Urban Climate Monitoring: Surface Cover and Land Surface Temperature

2018 ◽  
Author(s):  
Zina Mitraka ◽  
Nektarios Chrysoulakis
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Climate ◽  
Earth Observation ◽  
Surface Cover ◽  
Climate Monitoring

scholarly journals Parametric Design of a Microwave Radiometer for Land Surface Temperature Retrieval from Moon-Based Earth Observation Platform

2020 ◽  
Vol 12 (24) ◽  
pp. 4110
Author(s):  
Linan Yuan ◽  
Jingjuan Liao
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Earth Observation ◽  
Design Parameters ◽  
Observation Data ◽  
The Moon ◽  
Antenna Aperture

Increasing attention is being paid to the monitoring of global change, and remote sensing is an important means for acquiring global observation data. Due to the limitations of the orbital altitude, technological level, observation platform stability and design life of artificial satellites, spaceborne Earth observation platforms cannot quickly obtain global data. The Moon-based Earth observation (MEO) platform has unique advantages, including a wide observation range, short revisit period, large viewing angle and spatial resolution; thus, it provides a new observation method for quickly obtaining global Earth observation data. At present, the MEO platform has not yet entered the actual development stage, and the relevant parameters of the microwave sensors have not been determined. In this work, to explore whether a microwave radiometer is suitable for the MEO platform, the land surface temperature (LST) distribution at different times is estimated and the design parameters of the Moon-based microwave radiometer (MBMR) are analyzed based on the LST retrieval. Results show that the antenna aperture size of a Moon-based microwave radiometer is suitable for 120 m, and the bands include 18.7, 23.8, 36.5 and 89.0 GHz, each with horizontal and vertical polarization. Moreover, the optimal value of other parameters, such as the half-power beam width, spatial resolution, integration time of the radiometer system, temperature sensitivity, scan angle and antenna pattern simulations are also determined.

scholarly journals Thirty Years of Land Use/Land Cover Changes and Their Impact on Urban Climate: A Study of Kano Metropolis, Nigeria

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1106
Author(s):  
Auwalu Faisal Koko ◽  
Yue Wu ◽  
Ghali Abdullahi Abubakar ◽  
Akram Ahmed Noman Alabsi ◽  
Roknisadeh Hamed ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Urban Climate ◽  
Land Use Land Cover ◽  
Spectral Indices ◽  
The Impact

Rapid urban expansion and the alteration of global land use/land cover (LULC) patterns have contributed substantially to the modification of urban climate, due to variations in Land Surface Temperature (LST). In this study, the LULC change dynamics of Kano metropolis, Nigeria, were analysed over the last three decades, i.e., 1990–2020, using multispectral satellite data to understand the impact of urbanization on LST in the study area. The Maximum Likelihood classification method and the Mono-window algorithm were utilised in classifying land uses and retrieving LST data. Spectral indices comprising the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were also computed. A linear regression analysis was employed in order to examine the correlation between land surface temperature and the various spectral indices. The results indicate significant LULC changes and urban expansion of 152.55 sq. km from 1991 to 2020. During the study period, the city’s barren land and water bodies declined by approximately 172.58 sq. km and 26.55 sq. km, respectively, while vegetation increased slightly by 46.58 sq. km. Further analysis showed a negative correlation between NDVI and LST with a Pearson determination coefficient (R2) of 0.6145, 0.5644, 0.5402, and 0.5184 in 1991, 2000, 2010, and 2020 respectively. NDBI correlated positively with LST, having an R2 of 0.4132 in 1991, 0.3965 in 2000, 0.3907 in 2010, and 0.3300 in 2020. The findings of this study provide critical climatic data useful to policy- and decision-makers in optimizing land use and mitigating the impact of urban heat through sustainable urban development.

scholarly journals Land Covers and Climate Impacts on Land Surface Temperature in Putrajaya, Malaysia

2018 ◽  
Vol 3 (10) ◽  
pp. 78-88
Author(s):  
Siti Aekbal Salleh ◽  
Zulkiflee Abd.Latif ◽  
Wan Mohd. Naim Wan Mohd ◽  
Andy Chan
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Publishing House ◽  
Urban Climate ◽  
Climate Impacts ◽  
Climate Data ◽  
Normalised Difference Vegetation Index

This study investigates the influence of surface heterogeneity to the land surface temperature (LST). The land cover changes evaluation and historical climate data comparison were used in this study. Land cover, Normalised Difference Vegetation Index (NDVI), Normalised Difference Built-up Index (NDBI) and LST maps are produced to quantify the impacts of urbanization towards the surface thermal behaviour. The urbanization was set on years 1999 to 2006. While urbanization continued in 2009, the surface temperature was lower than that of 2006. The sea level was notably high during 2006, suggesting the lost of ice extent and evident to the climate change effects. Therefore, the fluctuation of temperature in 1999 to 2009 manifestly influenced by green space and climatic response and not solely caused by urbanization. Keywords: Land surface temperature, Land cover, Urban, Climate. eISSN 2514-751X © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/aje-bs.v3i10.315     

scholarly journals THE EFFECTS OF BUILT-UP AND GREEN AREAS ON THE LAND SURFACE TEMPERATURE OF THE KUALA LUMPUR CITY

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 107-112 ◽  
Author(s):  
N. A. Isa ◽  
W. M. N. Wan Mohd ◽  
S. A. Salleh
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Urban Climate ◽  
Geographical Information ◽  
Landsat 8 ◽  
Kuala Lumpur ◽  
Green Areas

A common consequence of rapid and uncontrollable urbanization is Urban Heat Island (UHI). It occurs due to the negligence on climate behaviour which degrades the quality of urban climate condition. Recently, addressing urban climate in urban planning through mapping has received worldwide attention. Therefore, the need to identify the significant factors is a must. This study aims to analyse the relationships between Land Surface Temperature (LST) and two urban parameters namely built-up and green areas. Geographical Information System (GIS) and remote sensing techniques were used to prepare the necessary data layers required for this study. The built-up and the green areas were extracted from Landsat 8 satellite images either using the Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI) or Modified Normalize Difference Water Index (MNDWI) algorithms, while the mono-window algorithm was used to retrieve the Land Surface Temperature (LST). Correlation analysis and Multi-Linear Regression (MLR) model were applied to quantitatively analyse the effects of the urban parameters. From the study, it was found that the two urban parameters have significant effects on the LST of Kuala Lumpur City. The built-up areas have greater influence on the LST as compared to the green areas. The built-up areas tend to increase the LST while green areas especially the densely vegetated areas help to reduce the LST within an urban areas. Future studies should focus on improving existing urban climatic model by including other urban parameters.

The Contribution of Anthropogenic Energy Use to Urban Heat Island: Combining Energy Consumption Data with Satellite Observation of Land Surface Temperature

2020 ◽  
Author(s):  
Zhou Yu ◽  
Qi Li ◽  
Ting Sun ◽  
Leiqiu Hu
Keyword(s):  
Energy Consumption ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Energy Use ◽  
Urban Climate ◽  
Building Energy ◽  
Consumption Data ◽  
Building Energy Use ◽  
Urban Heat

<p>Energy consumption, such as building energy use and traffic, is one of the key sources of anthropogenic heat flux in cities (Q<sub>F</sub>), which influences the urban climate. Different methods have been proposed to quantify Q<sub>F</sub>, such as using the inventory data and satellite observations of the land surface temperature. In this study, we develop an analysis framework based on urban surface energy balance and inverse calculation of the expected change of thermodynamic state as a result of different sources of energy consumption. This framework enables us to link the energy consumption data with remotely sensed land surface temperature (LST). Thus, the contribution of different sources of anthropogenic energy consumption to the urban land surface temperature can be readily quantified. We apply this method to ECOSTRESS LST, traffic volume and building energy consumption for cities in the US. We show that the exhaust heat from traffic and building energy use contributes differently to the surface urban heat island effect: the contributions differ in cities with different background climates, urban morphologies and green area fractions. Overall, the combined model-observation framework demonstrates potential in quantifying the impact of two major anthropogenic heating sources on urban climate, in particular with increasingly available high-quality urban energy-use data and fine-resolution satellite observations.  </p>

scholarly journals Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

2020 ◽  
Vol 12 (9) ◽  
pp. 1471 ◽  
Author(s):  
Sofia L. Ermida ◽  
Patrícia Soares ◽  
Vasco Mantas ◽  
Frank-M. Göttsche ◽  
Isabel F. Trigo
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Urban Climate ◽  
Google Earth ◽  
Small Scale ◽  
Temperature Estimation ◽  
Google Earth Engine

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.

scholarly journals Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5336
Author(s):  
Sorin Cheval ◽  
Alexandru Dumitrescu ◽  
Vlad-Alexandru Amihaesei
Keyword(s):  
Surface Temperature ◽  
Urban Area ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Climate ◽  
Temperature Data ◽  
Landsat 8 ◽  
Data Set ◽  
Surface Temperature Data

The Landsat 8 satellites have retrieved land surface temperature (LST) resampled at a 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the city of interest. This paper endorses a procedure for building a long-term gap-free LST data set in an urban area using the high-resolution Landsat 8 imagery. The study is applied on 94 images available through 2013–2018 over Bucharest (Romania). The raw images containing between 1.1% and 58.4% missing LST data were filled in using the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm implemented in the sinkr R packages. The resulting high-spatial-resolution gap-filled land surface temperature data set was used to explore the LST climatology over Bucharest (Romania) an urban area, at a monthly, seasonal, and annual scale. The performance of the gap-filling method was checked using a cross-validation procedure, and the results pledge for the development of an LST-based urban climatology.

scholarly journals Technical note: A view from space on global flux towers by MODIS and Landsat: The FluxnetEO dataset

2021 ◽  
Author(s):  
Sophia Walther ◽  
Simon Besnard ◽  
Jacob A. Nelson ◽  
Tarek S. El-Madany ◽  
Mirco Migliavacca ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Eddy Covariance ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Earth Observation ◽  
Research Network ◽  
Observation System ◽  
Data Sets ◽  
Observation Data ◽  
Earth Observation Data

Abstract. The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at several hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, and Terrestrial Ecosystem Research Network (TERN) / OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the training and validation of ecosystem models. However, insufficient quality, frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions drawn from them. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40° off-nadir. We offer to the community pre-processed Earth observation data in a radius of 2 km around 338 flux sites based on the MCD43A4/A2, MxD11A1 MODIS products and Landsat collection~1 Tier1 and Tier2 products. The data sets we provide can widely facilitate the integration of activities in the fields of eddy-covariance, remote sensing and modelling.

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