scholarly journals The Gradient Effect on the Relationship between the Underlying Factor and Land Surface Temperature in Large Urbanized Region

Land ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
Yixu Wang ◽  
Mingxue Xu ◽  
Jun Li ◽  
Nan Jiang ◽  
Dongchuan Wang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landscape Planning ◽  
Potential Gradient ◽  
Surface Characteristics ◽  
Ordinary Least Squares ◽  
Urban Agglomeration ◽  
Spatial Gradient ◽  
Gradient Effect

Although research relating to the urban heat island (UHI) phenomenon has been significantly increasing in recent years, there is still a lack of a continuous and clear recognition of the potential gradient effect on the UHI—landscape relationship within large urbanized regions. In this study, we chose the Beijing-Tianjin-Hebei (BTH) region, which is a large scaled urban agglomeration in China, as the case study area. We examined the causal relationship between the LST variation and underlying surface characteristics using multi-temporal land cover and summer average land surface temperature (LST) data as the analyzed variables. This study then further discussed the modeling performance when quantifying their relationship from a spatial gradient perspective (the grid size ranged from 6 to 24 km), by comparing the ordinary least squares (OLS) and geographically weighted regression (GWR) methods. The results indicate that: (1) both the OLS and GWR analysis confirmed that the composition of built-up land contributes as an essential factor that is responsible for the UHI phenomenon in a large urban agglomeration region; (2) for the OLS, the modeled relationship between the LST and its drive factor showed a significant spatial gradient effect, changing with different spatial analysis grids; and, (3) in contrast, using the GWR model revealed a considerably robust and better performance for accommodating the spatial non-stationarity with a lower scale dependence than that of the OLS model. This study highlights the significant spatial heterogeneity that is related to the UHI effect in large-extent urban agglomeration areas, and it suggests that the potential gradient effect and uncertainty induced by different spatial scale and methodology usage should be considered when modeling the UHI effect with urbanization. This would supplement current UHI study and be beneficial for deepening the cognition and enlightenment of landscape planning for UHI regulation.

scholarly journals SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 401-405
Author(s):  
M. K. Firozjaei ◽  
M. Makki ◽  
J. Lentschke ◽  
M. Kiavarz ◽  
S. K. Alavipanah
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Incident Angle ◽  
Surface Characteristics ◽  
Slope Aspect ◽  
Biophysical Parameters ◽  
The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

scholarly journals SPATIAL DISAGGREGATION OF LANDSAT-DERIVED LAND SURFACE TEMPERATURE OVER A HETEROGENEOUS URBAN LANDSCAPE USING PLANETSCOPE IMAGE DERIVATIVES

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 115-122
Author(s):  
J. A. Cruz ◽  
J. A. Santos ◽  
A. Blanco
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Urban Landscape ◽  
Bare Soil ◽  
Ordinary Least Squares ◽  
Urban Landscapes ◽  
Ols Regression ◽  
Explanatory Variables

Abstract. Satellite-derived land surface temperature (LST) is frequently utilized to characterize the intensity of urban heat island (UHI) effect in highly urbanized and rapidly urbanizing cities. However, current spaceborne thermal sensors cannot capture temperature variations within heterogeneous urban landscapes at finer scales due to its coarse spatial resolution. This study aims to apply Regression-Kriging (RK) method to downscale a 30-meter Landsat-derived LST to 3 meters using different PlanetScope image derivatives. To avoid multicollinearity, exploratory regression was performed to reduce the initial set of 16 indices to 7 explanatory variables, namely, Enhanced Vegetation Index (EVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Pigment Chlorophyll Ratio Index (NPCRI), Visible Green-based Built-up Index (VgNIR-BI), Mean, Entropy, and Homogeneity. Ordinary Least Squares (OLS) regression was applied to fit the models and the residuals of the best performing models were interpolated using Ordinary Kriging technique and added back to the downscaled LST. The model with the highest accuracy was obtained using the combination of MSAVI, EVI, and Mean, with an R2 of 0.75 and RMSE of 1.12 °C, 0.58 °C, 0.80 °C, and 1.45 °C in estimating the LST of built-up, bare soil, vegetation, and water classes, respectively. The results indicate that the inclusion of textural features in the regression could improve model accuracy without increasing the variance of coefficient estimates. Moreover, RK method (RMSE = 1.10–1.16 °C) was proven to be a reliable downscaling technique because it redistributes the spatial variability of LST that were not preserved in the OLS regression (RMSE = 1.60–1.75 °C).

scholarly journals Impact of Urban Surface Characteristics and Socio-Economic Variables on the Spatial Variation of Land Surface Temperature in Lagos City, Nigeria

2018 ◽  
Vol 11 (1) ◽  
pp. 25 ◽  
Author(s):  
DMSLB Dissanayake ◽  
Takehiro Morimoto ◽  
Yuji Murayama ◽  
Manjula Ranagalage ◽  
Hepi H. Handayani
Keyword(s):  
Surface Temperature ◽  
Spatial Variation ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Characteristics ◽  
Urban Surface ◽  
Heat Island ◽  
Economic Activities ◽  
Urban Heat

The urban heat island (UHI) and its consequences have become a key research focus of various disciplines because of its negative externalities on urban ecology and the total livability of cities. Identifying spatial variation of the land surface temperature (LST) provides a clear picture to understand the UHI phenomenon, and it will help to introduce appropriate mitigation technique to address the advanced impact of UHI. Hence, the aim of the research is to examine the spatial variation of LST concerning the UHI phenomenon in rapidly urbanizing Lagos City. Four variables were examined to identify the impact of urban surface characteristics and socio-economic activities on LST. The gradient analysis was employed to assess the distribution outline of LST from the city center point to rural areas over the vegetation and built-up areas. Partial least square (PLS) regression analysis was used to assess the correlation and statistically significance of the variables. Landsat data captured in 2002 and 2013 were used as primary data sources and other gridded data, such as PD and FFCOE, were employed. The results of the analyses show that the distribution pattern of the LST in 2002 and 2013 has changed over the study period as results of changing urban surface characteristics (USC) and the influence of socio-economic activities. LST has a strong positive relationship with NDBI and a strong negative relationship with NDVI. The rapid development of Lagos City has been directly affected by conversion more green areas to build up areas over the time, and it has resulted in formulating more surface urban heat island (SUHI). Further, the increasing population and their socio-economic activities including industrialization and infrastructure development have also caused a significant impact on LST changes. We recommend that the results of this research be used as a proxy tool to introduce appropriate landscape and town planning in a sustainable viewpoint to make healthier and livable urban environments in Lagos City, Nigeria

scholarly journals A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

2019 ◽  
Vol 11 (18) ◽  
pp. 2094 ◽  
Author(s):  
Firozjaei ◽  
Alavipanah ◽  
Liu ◽  
Sedighi ◽  
Mijani ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Correlation Coefficient ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Single Channel ◽  
Ordinary Least Squares ◽  
Spatiotemporal Variations ◽  
Biophysical Parameters ◽  
The Impact

Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

scholarly journals A long-term monthly assessment of land surface temperature and normalized difference vegetation index using Landsat data

2021 ◽  
Vol 13 ◽  
Author(s):  
Subhanil Guha
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Negative Correlation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Correlation Method ◽  
Surface Characteristics ◽  
Monthly Variation ◽  
Landsat Images

Abstract The present study assesses the monthly variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) in Raipur City of India using one hundred and eighteen Landsat images from 1988 to 2019. The results show that a monthly variation is observed in the mean LST. The highest mean LST is found in April (38.79oC), followed by May (36.64oC), June (34.56oC), and March (32.11oC).The lowest mean LST is observed in January (23.01oC), followed by December (23.76oC), and November (25.83oC). A moderate range of mean LST is noticed in September (27.18oC), October (27.22oC), and February (27.88oC). Pearson's linear correlation method is used to correlate LST with NDVI. The LST-NDVI correlation is strong negative in October (-0.62), September (-0.55), and April (-0.51). The moderate negative correlation is developed in March (-0.40), May (-0.44), June (-0.47), and November (-0.39). A weak negative correlation is observed in December (-0.21), January (-0.24), and February (-0.29). The change in weather elements and variation in land surface characteristics contribute to the monthly fluctuation of mean LST and LST-NDVI correlation. The study will be an effective one for the town and country planners for their future estimation of land conversion.

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