scholarly journals SPATIAL AND TEMPORAL ANALYSIS OF MONTHLY WATER CONSUMPTION AND LAND SURFACE TEMPERATURE (LST) DERIVED USING LANDSAT 8 AND MODIS DATA

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 193-198
Author(s):  
R. Enriquez ◽  
M. Rodriguez ◽  
A. C. Blanco ◽  
I. Estacio ◽  
L. R. Depositario
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Water Consumption ◽  
Land Surface ◽  
Urban Climate ◽  
Temporal Analysis ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
The Relationship

Abstract. Land Surface Temperature (LST) is one of the important factors in monitoring urban climate. Observing the variations of LST can provide a better understanding of the Urban Heat Islands (UHI) phenomenon. The aim of this research is to assess the relationship between the spatial and temporal distribution of LST and water consumption in Zamboanga City for years 2016 and 2017. Data from the city’s water district were used to compute for the per capita water consumption (PCWC) of 49 barangays. Landsat 8 LST data with 30m spatial resolution were computed using inverse Plank function and other parameters such as vegetation proportion and surface emissivity to assess LST spatially while MODIS Terra data with 1km spatial resolution were used to assess LST temporally. Result showed that Landsat LST and PCWC have moderate correlations with p < 0.01: 0.59 and 0.55 for March and April 2016, respectively; 0.49 and 0.56 for March and April 2017, respectively. These indicated that warmer barangays consumed more water. The temporal correlation of the MODIS LST and the computed PCWC equated a −0.71, p < 0.01, correlation. This negative correlation indicated that when LST increases, PCWC decreases, which do not directly indicate that the city consumed less water but rather that the supply was less during warmer months. It was evident as water rationing was experienced during the first quarter of 2016 and intensified on April where the highest LST was recorded. Finally, LST was found of good use in assessing the relationship of temperature and water consumption.

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 Normalized Method for Land Surface Temperature Monitoring on Coastal Reclaimed Areas

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4836 ◽  
Author(s):  
Bahaa Mohamadi ◽  
Shuisen Chen ◽  
Timo Balz ◽  
Khansa Gulshad ◽  
Stephen C. McClure
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Change ◽  
Southern China ◽  
Open Water ◽  
Temporal Analysis ◽  
Urban Heat Islands ◽  
Data Availability ◽  
Robust Detection

The temporal analysis of land surface temperature (LST) has generally been studied using data from the same season, as temperature varies greatly over time. However, the cloud cover in thermal remotely sensed images and the coarse resolution of passive sensor system significantly limits data availability of same season for comparative temporal analysis in many parts of the world. To address this problem, we propose a new method for temporal monitoring of surface temperature based on LST normalization (LSTn); deploying the average open water temperature to normalize LST when monitoring temporal change in the surface temperature of newly coastal reclaimed areas. This method was applied in the Lingding Bay area, Guangdong Province, Southern China. Original LST and LSTn values were calculated for years 1987, 1997, 2007, and 2017. In contrast to the original LST, results show that LSTn can reduce seasonal variability when monitoring temporal change in surface temperatures. Additionally, LSTn revealed pronounced differences between the temperature of impervious surfaces and other land cover types. This method offers more robust detection of surface urban heat islands than original LST in newly developed coastal areas.

Downscaling sub-daily Land Surface Temperature time series for monitoring heat in urban environments

2020 ◽  
Author(s):  
Nikos Alexandris ◽  
Matteo Piccardo ◽  
Vasileios Syrris ◽  
Alessandro Cescatti ◽  
Gregory Duveiller
Keyword(s):  
Time Series ◽  
Heat Stress ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Environments ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fine Spatial Resolution

&lt;p&gt;The frequency of extreme heat related events is rising. This places the ever growing number of urban dwellers at higher risk. Quantifying these phenomena is important for the development and monitoring of climate change adaptation and mitigation policies. In this context, earth observations offer increasing opportunities to assess these phenomena with an unprecedented level of accuracy and spatial reach. Satellite thermal imaging systems acquire Land Surface Temperature (LST) which is fundamental to run models that study for example hotspots and heatwaves in urban environments.&lt;/p&gt;&lt;p&gt;Current instruments include TIRS on board Landsat 8 and MODIS on board of Terra satellites. These provide LST products on a monthly basis at 100m and twice per day at 1km respectively. Other sensors on board geostationary satellites, such as MSG and GOES-R, produce sub-hourly thermal images. For example the SEVIRI instrument onboard MSG, captures images every 15 minutes. However, this is done at an even coarser spatial resolution, which is 3 to 5 km in the case of SEVIRI. Nevertheless, none of the existing systems can capture LST synchronously with fine spatial resolution at a high temporal frequency, which is a prerequisite for monitoring heat stress in urban environments.&lt;/p&gt;&lt;p&gt;Combining LST time series of high temporal resolution (i.e. sub-daily MODIS- or SEVIRI-derived data) with products of fine spatial resolution (i.e. Landsat 8 products), and potentially other related variables (i.e. reflectance, spectral indices, land cover information, terrain parameters and local climatic variables), facilitates the downscaling of LST estimations. Nonetheless, considering the complexity of how distinct surfaces within a city heat-up differently during the course of a day, such a downscaling is meaningful for practically synchronous observations (e.g. Landsat-8 and MODIS Terra&amp;#8217;s morning observations).&lt;/p&gt;&lt;p&gt;The recently launched ECOSTRESS mission provides multiple times in a day high spatial resolution thermal imagery at 70m. Albeit, recording the same locations on Earth every few days at varying times. We explore the associations between ECOSTRESS and Landsat-8 thermal data, based on the incoming radiation load and distinct surface properties characterised from other datasets. In our approach, first we upscale ECOSTRESS data to simulate Landsat-8 images at moments that coincide the acquisition times of other sensors products. In a second step, using the simulated Landsat-8 images, we downscale LST products acquired at later times, such as MODIS Aqua (ca. 13:30) or even the hourly MSG data. This composite downscaling procedure enables an enhanced LST estimation that opens the way for better diagnostics of the heat stress in urban landscapes.&lt;/p&gt;&lt;p&gt;In this study we discuss in detail the concepts of our approach and present preliminary results produced with the JEODPP, JRC's high throughput computing platform.&lt;/p&gt;

scholarly journals ASSESSMENT OF RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND NORMALIZED DIFFERENT VEGETATION INDEX USING LANDSAT IMAGES IN SOME REGIONS OF DIYALA GOVERNORATE

2021 ◽  
Vol 52 (4) ◽  
pp. 793-801
Author(s):  
Al-Jbouri & Al-Timimi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Conditions ◽  
Landsat 8 ◽  
Landsat Images ◽  
Remote Sensing Techniques ◽  
The Relationship

Agriculture is the most important and most dependent economic activity and influenced by climatic conditions as the climate elements represented by solar radiation, temperature, wind and relative humidity. Therefore, is necessary that analyze and understand the relationship between climate and agriculture. The aim of this study to assessment the relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) for three regions of Diyala Governorate in Iraq (Al Muqdadya, Baladrooz, and Baquba) by through using of remote sensing techniques and geographic information system (GIS).The Normalized difference vegetation index NDVI and land surface temperature (LST) were used in two of the Landsat-5 ETM + and Landsat-8 OLI satellite imagery during the years 1999 and 2019.  The results showed that increased in NDVI and decreased in LST for 2019, while for 1999 increased in LST and decreased in NDVI for the three regions. Finally, the regression was used to obtain that correlation between LST and NDVI. It was concluded that the correlation coefficient between NDVI and LST is negative, where the strongest correlation was 0.76 for Baquba and weakest correlation was 0.55 for Muqdadyia.

scholarly journals FUSION OF LANDSAT- 8 THERMAL INFRARED AND VISIBLE BANDS WITH MULTI-RESOLUTION ANALYSIS CONTOURLET METHODS

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 77-81 ◽  
Author(s):  
F. Farhanj ◽  
M. Akhoondzadeh
Keyword(s):  
Surface Temperature ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Information ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Land surface temperature image is an important product in many lithosphere and atmosphere applications. This image is retrieved from the thermal infrared bands. These bands have lower spatial resolution than the visible and near infrared data. Therefore, the details of temperature variation can't be clearly identified in land surface temperature images. The aim of this study is to enhance spatial information in thermal infrared bands. Image fusion is one of the efficient methods that are employed to enhance spatial resolution of the thermal bands by fusing these data with high spatial resolution visible bands. Multi-resolution analysis is an effective pixel level image fusion approach. In this paper, we use contourlet, non-subsampled contourlet and sharp frequency localization contourlet transform in fusion due to their advantages, high directionality and anisotropy. The absolute average difference and RMSE values show that with small distortion in the thermal content, the spatial information of the thermal infrared and the land surface temperature images is enhanced.

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.

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.

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