Evaluation of the effect of geographical parameters on the formation of the land surface temperature by applying OLS and GWR, A case study Shiraz City, Iran

Kashki, Abdolreza; Karami, Mahmoud; Zandi, Rahman; Roki, Zohreh

doi:10.1016/j.uclim.2021.100832

Cited by 58 publications

(15 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The regression analysis results presented in this study suggest that the GWR model can produce good results and provide detailed information at the local scale. According to the adjusted R 2 values, the GWR model can explain 84% of the variation in the land surface temperature in the study area [69,70]. From north to south, there was a trend from a negative correlation to a positive NDVI correlation, with the values of the adjusted NDVI R 2 ranging from −3.159 to 4.195.…”

Section: Discussionmentioning

confidence: 96%

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Baqa

Fang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity—Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000–2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000–2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth.

show abstract

Section: Discussionmentioning

confidence: 96%

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Baqa

Fang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The OLS method assumes that the sample regression model is closest to the observation, and provides a global model of the variable to predict and shows the least deviation from observation (de Souza & Junqueira, 2005; Kashki et al, 2021). The formula for the OLS model is as follows:

Z_{O L S} (M A A T_{0}) = δ_{0} + \sum_{m = 1}^{x} [δ_{m} (M A A T_{0}) \times Y_{m} (M A A T_{0})] + ε_{0}

Where :

Z_{OLS} ({MAAT}_{0})

is the predicted MAAT at the geographical location of

{italicMAAT}_{0}

.…”

Section: Methodsmentioning

confidence: 99%

Land use and cover change in Northeast China and its impacts on the Xing'an permafrost in 1980s–2010s

Wang

Jin

et al. 2022

Land Degrad Dev

View full text Add to dashboard Cite

Boreal forest and wetland have important influences on the development and protection of the ecosystem‐dominated Xing'an permafrost. However, the responses of different ecosystems to climate change and the impacts on the underlying permafrost are still unclear. Here, based on the multi‐period land use/land cover (LULC) data and long‐time series of air temperature, combined with the ordinary least squares (OLS) and ordinary kriging (OK) methods, the effects of land use and cover change (LUCC) on the distribution of mean annual air temperature (MAAT) and permafrost in Northeast China were analyzed. From 1980s to 2010s, MAAT showed an upward trend (0.025°C per yr) and extents of permafrost showed a decreasing trend (−3668 km2yr−1) in Northeast China. Permafrost degradation mainly occurred in forested land and grassland, with areal reductions of 4.0106 × 104 and 3.8754 × 104 km2, respectively. The transformation of LULC aggravates the degradation of permafrost. The conversions of forested land and grassland to cultivated land and forested land to grassland resulted in the shrinkage of permafrost extent by 6233 km2 from 1980s to 2010s . Our results confirm the significant impacts of LUCC on the Xing'an permafrost resulting in its degradation. Additionally, they can provide a scientific basis for ecological environment protection and restoration and sustainable development of boreal forest and wetland ecosystems in permafrost regions of Northeast China.

show abstract

“…The OLS model is the basic model of spatial modeling and the benchmark of analysis. In spatial modeling with OLS, it is assumed that the coefficients or parameters of the statistical model are constant relative to the position, and the influence of the change of spatial position is not considered, which is considered the weakness of this method in spatial modeling [58]. The formula is as follows:…”

Section: Ordinary Least Squares (Ols)mentioning

confidence: 99%

Coupling Coordination Relationship and Driving Mechanism between Urbanization and Ecosystem Service Value in Large Regions: A Case Study of Urban Agglomeration in Yellow River Basin, China

Zhang

Liu

Feng

et al. 2021

IJERPH

View full text Add to dashboard Cite

Mastering the coupling and coordination relationship and driving mechanism of urbanization and ecosystem service value (ESV) is of great significance to ecological protection and regional sustainable development. In this paper, the coupling coordination model, geographic detector and GWR model are used to analyze the spatio-temporal coupling interaction between urbanization and ESV and the spatial differentiation characteristics of influencing factors from 1995 to 2018. The results of the study are as follows: (1) During the study period, cities in the Yellow River Basin experienced accelerated urban expansion, and the ESV of forests, water and wetlands increased, which offset the reduction in ESV due to the expansion of construction land and farmland and grassland. (2) The degree of coupling and coordination between the two gradually improved, but the overall situation showed a low-level coupling and coordination process. Mild coupling coordination gradually increased, reaching an increase of 38.10%; severe imbalance types tended to disappear, decreasing by 52.38%, and coupling subtypes developed from lagging urbanization to ESV backward types. The high-value areas of the coupling coordination degree are distributed in the high-value areas of ESV in the north of the upper reaches, and the low-value areas are distributed in the cities of Henan and Shandong with high urbanization levels in the downstream and most resource-based cities in the middle reaches. (3) In addition, the spatial intensity of the effect of each dominant factor on the degree of coupling coordination is different. Economic growth, technological development, environmental regulations and the proportion of forest land have positive and belt-shaped alienation characteristics for the coupling and coordination of the two, and infrastructure and temperature show negative driving characteristics. Therefore, the coupling and coordination relationship between ESV and urbanization should be clarified to help future urban planning. On the basis of determining the regional environmental carrying capacity and the adjustment direction of the rational planning of land resources, the impact of urban barriers formed by administrative boundaries and natural geographical conditions on the development of urban agglomerations should be broken to achieve the overall high-quality and coordinated development of the basin.

show abstract

Evaluation of the effect of geographical parameters on the formation of the land surface temperature by applying OLS and GWR, A case study Shiraz City, Iran

Cited by 58 publications

References 40 publications

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Land use and cover change in Northeast China and its impacts on the Xing'an permafrost in 1980s–2010s

Coupling Coordination Relationship and Driving Mechanism between Urbanization and Ecosystem Service Value in Large Regions: A Case Study of Urban Agglomeration in Yellow River Basin, China

Contact Info

Product

Resources

About