scholarly journals Spatiotemporal Characterization of Land Cover Impacts on Urban Warming: A Spatial Autocorrelation Approach

2020 ◽  
Vol 12 (10) ◽  
pp. 1631 ◽  
Author(s):  
Chao Fan ◽  
Zhe Wang
Keyword(s):  
Land Cover ◽  
Spatial Autocorrelation ◽  
Land Surface ◽  
Urban Areas ◽  
Spatial Configuration ◽  
Urban Warming ◽  
Green Vegetation ◽  
Urban Rural ◽  
Land Surface Temperatures ◽  
The Cost

There has been an increasing concern of rising temperatures as cities continue to expand and intensify. Urban warming is having significant impacts on the environment that are far beyond city limits. Understanding the development pattern of the urban heat island (UHI) effect is crucial for making action plans to mitigate urban warming. In this study, we combine multitemporal satellite imagery, spatial autocorrelation indices, and statistical analysis into a spatiotemporal study of the surface UHI effect in the Boise-Meridian metropolitan area. A continuous landscape modeling perspective was taken to quantitatively depict the abundance and spatial configuration of green vegetation and built-up areas at a landscape scale. We aim to (1) evaluate the variations in the land surface temperatures (LST) along the urban–rural gradients of Boise for multiple years, (2) identify the relationships of the LST variations with the land cover variables quantified using the spatial autocorrelation indices, and (3) analyze the changing climate in Boise in conjunction with its urbanization pattern over the last two decades. Results show that the region experienced a significant increase in the LST along with a great expansion of urban areas at the cost of agriculture. The warming effect of built-up areas was greater than the cooling effect of green vegetation, suggesting an urgent need for increasing greenspace in the city. Statistical analyses show that clustered vegetation and dispersed built-up features are beneficial for reducing the LST. Our study presents a spatiotemporal framework for analyzing the surface UHI effect from multiple angles. Scientific findings from this study can help make informed policies against urban warming via optimal planning of urban land cover.

Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh

2020 ◽  
Vol 18 ◽  
pp. 100314 ◽  
Author(s):  
Abdulla - Al Kafy ◽  
Md. Shahinoor Rahman ◽  
Abdullah-Al- Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Muhaiminul Islam
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Surface Temperatures ◽  
Land Surface Temperatures

scholarly journals Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

2021 ◽  
Vol 10 (12) ◽  
pp. 809
Author(s):  
Jing Sun ◽  
Suwit Ongsomwang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Decomposition Analysis ◽  
Lulc Change ◽  
Main Components ◽  
The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

scholarly journals Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

2020 ◽  
Vol 12 (24) ◽  
pp. 4067
Author(s):  
Thanhtung Dang ◽  
Peng Yue ◽  
Felix Bachofer ◽  
Michael Wang ◽  
Mingda Zhang
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Dry Season ◽  
Landsat Images ◽  
Surface Temperature Change ◽  
Green Vegetation ◽  
Dry Seasons

Global warming-induced climate change evolved to be one of the most important research topics in Earth System Sciences, where remote sensing-based methods have shown great potential for detecting spatial temperature changes. This study utilized a time series of Landsat images to investigate the Land Surface Temperature (LST) of dry seasons between 1989 and 2019 in the Bac Binh district, Binh Thuan province, Vietnam. Our study aims to monitor LST change, and its relationship to land-cover change during the last 30 years. The results for the study area show that the share of Green Vegetation coverage has decreased rapidly for the dry season in recent years. The area covered by vegetation shrank between 1989 and 2019 by 29.44%. Our findings show that the LST increase and decrease trend is clearly related to the change of the main land-cover classes, namely Bare Land and Green Vegetation. For the same period, we find an average increase of absolute mean LST of 0.03 °C per year for over thirty years across all land-cover classes. For the dry season in 2005, the LST was extraordinarily high and the area with a LST exceeding 40 °C covered 64.10% of the total area. We expect that methodological approach and the findings can be applied to study change in LST, land-cover, and can contribute to climate change monitoring and forecasting of impacts in comparable regions.

The impact of built-up surfaces on land surface temperatures in Italian urban areas

2016 ◽  
Vol 551-552 ◽  
pp. 317-326 ◽  
Author(s):  
Marco Morabito ◽  
Alfonso Crisci ◽  
Alessandro Messeri ◽  
Simone Orlandini ◽  
Antonio Raschi ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Surface Temperatures ◽  
Land Surface Temperatures ◽  
The Impact

scholarly journals ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

2013 ◽  
Vol 6 (2) ◽  
pp. 563-582 ◽  
Author(s):  
S. Faroux ◽  
A. T. Kaptué Tchuenté ◽  
J.-L. Roujean ◽  
V. Masson ◽  
E. Martin ◽  
...  
Keyword(s):  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Climate Models ◽  
Area Index ◽  
Surface Parameters ◽  
Climatic Environment

Abstract. The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

scholarly journals MODELING THE DEVELOPMENTS IN URBANISATION AND RELATIONSHIP WITH VEGETATION COVER IN ALANYA

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 291-297
Author(s):  
B. İşler ◽  
Z. Aslan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Landsat 8 ◽  
World Population ◽  
Land Use Land Cover ◽  
Temperature And Precipitation ◽  
Atmospheric Variables

Abstract. The increase in the world population and the migration of people from rural to urban areas causes an increase in artificial surfaces and causes many negative effects on the ecosystem, regional climate variations and global diversity. Nowadays, as the effects of climate change are felt more and more, it has gained importance in researches on this subject. Therefore, the estimation of the change in the vegetation density for the coming years and the determination of the land use / land cover (LULC) change in cities are very essential for urban planning. In this study, the effects of regional urbanization on vegetation are examined by using satellite data and atmospheric variables. In the vegetation analysis, multi-time index values obtained from TERRA-MODIS satellite, EVI (Enhanced Vegetation Index) and LST (Land Surface Temperature) were taken into account between the years of 2005 and 2018 in Alanya, Turkey. Temperature and precipitation were selected as the atmospheric variables and expected variations in EVI value until 2030 were estimated. In the study employed a wavelet-transformed artificial neural network (WANN) model to generate long-term (12-year) EVI forecasts using LST, temperature and precipitation. The relationship between land use / land cover and urbanization is investigated with NDBI (Normalized Difference Built-up Index) data obtained from the Landsat 8 OLI / TIRS satellite sensor. The simulation results show that The EVI value, which was 0.30 in 2018, will decrease to 0.25 in 2030.

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