scholarly journals Quantifying how urban landscape heterogeneity affects land surface temperature at multiple scales

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Ehsan Rahimi ◽  
Shahindokht Barghjelveh ◽  
Pinliang Dong
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Landscape Metrics ◽  
Land Surface ◽  
Urban Landscape ◽  
Multiple Scales ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Edge Density ◽  
Region Scale

Abstract Background Landscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but some studies have shown their inaccurate results. The objective of the study is to compare landscape metrics and texture-based measures as alternative indices in measuring urban heterogeneity effects on LST at multiple scales. Results The statistical results showed that the correlation between urban landscape heterogeneity and LST increased as the spatial extent (scale) of under-study landscapes increased. Overall, landscape metrics showed that the less fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was seen between edge density (ED) and LST (r = − 0.47) at the sub-region scale. Texture measures showed a stronger relationship (R2 = 34.84% on average) with LST than landscape metrics (R2 = 15.33% on average) at all spatial scales, meaning that these measures had a greater ability to describe landscape heterogeneity than the landscape metrics. Conclusion This study suggests alternative measures for overcoming landscape metrics shortcomings in estimating the effects of landscape heterogeneity on LST variations and gives land managers and urban planners new insights into urban design.

scholarly journals Quantifying how urban landscape heterogeneity affects land surface temperature: A comparison of discrete and continuous approaches

2021 ◽  
Author(s):  
ehsan Rahimi ◽  
Shahindokht Barghjelveh ◽  
Pinliang Dong
Keyword(s):  
Surface Temperature ◽  
Spatial Patterns ◽  
Land Surface Temperature ◽  
Landscape Metrics ◽  
Land Surface ◽  
Urban Landscape ◽  
Landscape Heterogeneity ◽  
Alternative Measures ◽  
Heterogeneity Effects ◽  
Patch Density

Abstract The present study examines the efficiency of discrete and continuous approaches to measuring urban heterogeneity effects on land surface temperature (LST). In the discrete approach, landscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but some studies have shown their inaccurate results. The objective of the study is to compare landscape metrics and alternative approaches to measuring urban heterogeneity effects on LST. We compared landscape metrics results with nine texture-based measures, and two local spatial autocorrelation indices (local Moran’s I and Gi statistics) applied to NDVI and BAI indices as a proxy of the spatial patterns of Tehran vegetation and built-up classes. The statistical results showed that urban landscape heterogeneity had significant impacts on the LST variations, and there was a compatibility between landscape metrics and alternative measures results. Overall results showed that the less-fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was between patch density (PD) and LST (r= -0.71). Higher values of PD have mostly been interpreted to show higher fragmentation, but other landscape metrics and alternative measures declined this conclusion. Our study demonstrated that PD was not a reliable metric and presented no information about the spatial distribution of landscape elements. This study confirms alternative measures for overcoming landscape metrics shortcomings in estimating the effects of landscape heterogeneity on LST variations and gives land managers and urban planners new insights into the urban design.

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

scholarly journals On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

2022 ◽  
Vol 14 (2) ◽  
pp. 279
Author(s):  
Qiong Wu ◽  
Zhaoyi Li ◽  
Changbao Yang ◽  
Hongqing Li ◽  
Liwei Gong ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Landscape Metrics ◽  
Landscape Pattern ◽  
Land Surface ◽  
Landscape Patterns ◽  
Impervious Surfaces ◽  
Multi Scale

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.

scholarly journals A Continental-Scale Classification of Rainfall Seasonality Regimes in Africa Based on Gridded Precipitation and Land Surface Temperature Products

2011 ◽  
Vol 50 (12) ◽  
pp. 2504-2513 ◽  
Author(s):  
Stefanie M. Herrmann ◽  
Karen I. Mohr
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Driving Forces ◽  
Spatial Scales ◽  
Wet Season ◽  
Continental Scale ◽  
Rainfall Seasonality ◽  
The Stability

AbstractA classification of rainfall seasonality regimes in Africa was derived from gridded rainfall and land surface temperature products. By adapting a method that goes back to Walter and Lieth’s approach of presenting climatic diagrams, relationships between estimated rainfall and temperature were used to determine the presence and pattern of humid, arid, and dry months. The temporal sequence of humid, arid, and dry months defined nonseasonal as well as single-, dual-, and multiple-wet-season regimes with one or more rainfall peaks per wet season. The use of gridded products resulted in a detailed, spatially continuous classification for the entire African continent at two different spatial resolutions, which compared well to local-scale studies based on station data. With its focus on rainfall patterns at fine spatial scales, this classification is complementary to coarser and more genetic classifications based on atmospheric driving forces. An analysis of the stability of the resulting seasonality regimes shows areas of relatively high year-to-year stability in the single-wet-season regimes and areas of lower year-to-year stability in the dual- and multiple-wet-season regimes as well as in transition zones.

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).

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