scholarly journals Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

2020 ◽  
Vol 12 (3) ◽  
pp. 440 ◽  
Author(s):  
Ruci Wang ◽  
Hao Hou ◽  
Yuji Murayama ◽  
Ahmed Derdouri
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Social Life ◽  
Series Data ◽  
Rapid Urbanization ◽  
Cooling Effects ◽  
The Relationship ◽  
The City

Rapid urbanization is one of the most concerning issues in the 21st century because of its significant impacts on various fields, including agriculture, forestry, ecology, and climate. The urban heat island (UHI) phenomenon, highly related to the rapid urbanization, has attracted considerable attention from both academic scholars and governmental policymakers because of its direct influence on citizens’ daily life. Land surface temperature (LST) is a widely used indicator to assess the intensity of UHI significantly affected by the local land use/cover (LULC). In this study, we used the Landsat time-series data to derive the LULC composition and LST distribution maps of Nanjing in 2000, 2014, and 2018. A correlation analysis was carried out to check the relationship between LST and the density of each class of LULC. We found out that cropland and forest in Nanjing are helping to cool the city with different degrees of cooling effects depending on the location and LULC composition. Then, a Cellar Automata (CA)-Markov model was applied to predict the LULC conditions of Nanjing in 2030 and 2050. Based on the simulated LULC maps and the relationship between LST and LULC, we delineated high- and moderate-LST related risk areas in the city of Nanjing. Our findings are valuable for the local government to reorganize the future development zones in a way to control the urban climate environment and to keep a healthy social life within the city.

scholarly journals Influence of Land Use and Land Cover Change on Land surface temperature

2021 ◽  
Vol 283 ◽  
pp. 01038
Author(s):  
Jing Sun ◽  
Jing He
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Rapid Urbanization ◽  
Lulc Changes ◽  
The Mean ◽  
The Relationship

The rapid urbanization process has recently led to significant land use and land cover (LULC) changes, thereby affecting the climate and the environment. The purpose of this study is to analyze the LULC changes in Hefei City, Anhui Province, and their relationship with land surface temperature (LST). To achieve this goal, multitemporal Landsat data were used to monitor the LULC and LST between 2005 and 2015. The study also used correlation analysis to analyze the relationship between LST, LULC, and other spectral indices (NDVI, NDBI, and NDWI). The results show that the built-up land has expanded significantly, transforming from 488.26 km2 in 2005 to 575.64 km2 in 2015. It further shows that the mean LST in Hefei city has increased from 284.0 K in 2005 to 285.86 K in 2015. The results also indicate that there is a positive correlation between LST and NDVI and NDBI, while there is a negative correlation between LST and NDWI. This means that urban expansion and reduced water bodies will lead to an increase in LST.

scholarly journals Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

2021 ◽  
Vol 13 (2) ◽  
pp. 323
Author(s):  
Liang Chen ◽  
Xuelei Wang ◽  
Xiaobin Cai ◽  
Chao Yang ◽  
Xiaorong Lu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Maximum Temperature Difference ◽  
Rapid Urbanization ◽  
The Impact ◽  
The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

scholarly journals Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9115 ◽  
Author(s):  
Muhammad Amir Siddique ◽  
Liu Dongyun ◽  
Pengli Li ◽  
Umair Rasool ◽  
Tauheed Ullah Khan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Rapid Urbanization ◽  
K Value ◽  
Vegetation Water ◽  
Landsat Satellite

Rapid urbanization is changing the existing patterns of land use land cover (LULC) globally, which is consequently increasing the land surface temperature (LST) in many regions. The present study is focused on estimating current and simulating future LULC and LST trends in the urban environment of Chaoyang District, Beijing. Past patterns of LULC and LST were identified through the maximum likelihood classification (MLC) method and multispectral Landsat satellite images during the 1990–2018 data period. The cellular automata (CA) and stochastic transition matrix of the Markov model were applied to simulate future (2025) LULC and LST changes, respectively, using their past patterns. The CA model was validated for the simulated and estimated LULC for 1990–2018, with an overall Kappa (K) value of 0.83, using validation modules in IDRISI software. Our results indicated that the cumulative changes in built-up to vegetation area were 74.61 km2 (16.08%) and 113.13 km2 (24.38%) from 1990 to 2018. The correlation coefficient of land use and land cover change (LULCC), including vegetation, water bodies and built-up area, had values of r =  − 0.155 (p > 0.005), −0.809 (p = 0.000), and 0.519 (p > 0.005), respectively. The results of future analysis revealed that there will be an estimated 164.92 km2 (−12%) decrease in vegetation area, while an expansion of approximately 283.04 km2 (6% change) will occur in built-up areas from 1990 to 2025. This decrease in vegetation cover and expansion of settlements would likely cause a rise of approximately ∼10.74 °C and ∼12.66 °C in future temperature, which would cause a rise in temperature (2025). The analyses could open an avenue regarding how to manage urban land cover patterns to enhance the resilience of cities to climate warming. This study provides scientific insights for environmental development and sustainability through efficient and effective urban planning and management in Beijing and will also help strengthen other research related to the UHI phenomenon in other parts of the world.

scholarly journals MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS

2016 ◽  
Vol XLI-B8 ◽  
pp. 851-856 ◽  
Author(s):  
A. M. Bernales ◽  
J. A. Antolihao ◽  
C. Samonte ◽  
F. Campomanes ◽  
R. J. Rojas ◽  
...  
Keyword(s):  
Land Use ◽  
Linear Regression ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Use Land Cover ◽  
Spatial Metrics ◽  
Class Level ◽  
Multi Linear Regression ◽  
The Relationship

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

scholarly journals MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS

2016 ◽  
Vol XLI-B8 ◽  
pp. 851-856 ◽  
Author(s):  
A. M. Bernales ◽  
J. A. Antolihao ◽  
C. Samonte ◽  
F. Campomanes ◽  
R. J. Rojas ◽  
...  
Keyword(s):  
Land Use ◽  
Linear Regression ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Use Land Cover ◽  
Spatial Metrics ◽  
Class Level ◽  
Multi Linear Regression ◽  
The Relationship

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

scholarly journals Effect of Surface Temperature on Energy Consumption in a Calibrated Building: A Case Study of Delhi

Climate ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 71
Author(s):  
Priyanka Kumari ◽  
Sukriti Kapur ◽  
Vishal Garg ◽  
Krishan Kumar
Keyword(s):  
Land Use ◽  
Energy Consumption ◽  
Land Cover ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Heat Island ◽  
Rapid Urbanization ◽  
Urban Heat

Rapid urbanization and associated land-use changes in cities cause an increase in the demand for electricity by altering the local climate. The present study aims to examine the variations in total energy and cooling energy demand in a calibrated building energy model, caused by urban heat island formation over Delhi. The study used Sentinel-2A multispectral imagery for land use and land cover (LULC) of mapping of Delhi, and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for land surface temperature (LST) mapping during March 2018. It was observed that regions with dense built-up areas (i.e., with built-up area greater than 90%) had a higher annual land surface temperature (LST), i.e., 293.5 K and urban heat island intensity (UHII) ranging from 0.9 K–5.9 K. In contrast, lower annual values of LST (290K) and UHII (0.0–0.4 K) were observed in regions with high vegetation cover (53%). Statistical analysis reveals that a negative correlation exists between vegetation and nighttime LST, which is further confirmed by linear regression analysis. Energy simulations were performed on a calibrated building model placed at three different sites, identified on the basis of land use and land cover percentage and annual LST. Simulation results showed that the site located in the central part of Delhi displayed higher annual energy consumption (255.21 MWh/y) compared to the site located in the rural periphery (235.69 MWh/y). For all the three sites, the maximum electricity consumption was observed in the summer season, while the minimum was seen in the winter season. The study indicates that UHI formation leads to increased energy consumption in buildings, and thus UHI mitigation measures hold great potential for energy saving in a large city like Delhi.

scholarly journals Characterizing the relationship between land use land cover change and land surface temperature

2017 ◽  
Vol 124 ◽  
pp. 119-132 ◽  
Author(s):  
Duy X. Tran ◽  
Filiberto Pla ◽  
Pedro Latorre-Carmona ◽  
Soe W. Myint ◽  
Mario Caetano ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Use Land Cover ◽  
The Relationship
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