Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, Rosalia; Serna, Andrés; Silapan, J.

doi:10.5194/isprs-archives-xli-b8-851-2016

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Fundamentally, the land surface temperature, LST, enables the mapping of urban heat islands (UHIs) [20,21]. However, it is highly influenced in its interactions with the climate, by the land features distribution [25][26][27][28][29]. Figure 4 supports this statement by showing a high positive correlation of LST with both built-up and soil features, detected through spectral indices (Table 1).…”

Section: Background Problem Statement and Study Areamentioning

confidence: 67%

Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

Mfondoum¹,

Gbetkom²,

Hakdaoui³

et al. 2021

AJRS

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Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified "1 = built-up; 0 = Non-Built-up", with an average r 2 =0.78.

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Section: Background Problem Statement and Study Areamentioning

confidence: 67%

Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

Mfondoum¹,

Gbetkom²,

Hakdaoui³

et al. 2021

AJRS

View full text Add to dashboard Cite

show abstract

“…In addition, there is no strict statistical assumptions and requirements of the sampling observation data. For instance, a linear regression model is used in the traditional LUR models, so assumptions of multivariate linear regression need to be tested and satisfied for sample data, such as normal distributions of variables and removed outliers [74,75]. On the contrary, such statistical assumptions are not required in the LUQR models because the quantile regression approach, a robust regression model, is used to fit the relationships between dependent and independent variables [76,77].…”

Section: Methodological Contributionsmentioning

confidence: 99%

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Song

2022

Remote Sensing

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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

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“…The negative impact of urban development is a declining trend in environmental quality due to human activities and urbanization (Zarzoso & Maruotti, 2011). The increasing of vehicles, industries, and built-up land causes air temperature in urban areas to warm significantly (Bernales, 2016). The phenomenon is known as urban heat island (UHI).…”

Section: Introductionmentioning

confidence: 99%

Urban Heat Island Assessment using Remote Sensing Data in West Java, Indonesia: From Literature Review to Experiments and Analyses

Nandi¹,

Dede²

2021

Indonesian J. Sci. Technol

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Urban heat island (UHI) is a significantly increasing temperature that occurs in the urban region due to urbanization and anthropogenic activities. The UHI represents environmental quality decrease and able to change a microclimate in the long term. It phenomenon can be estimated using multi-temporal remote sensing imagery data. This study aims to analyze the spatial dynamics of UHI in the urban region of West Java from 1998 to 2018. We only used remote sensing data from different datasets. Information of land-surface temperature is extracted from Landsat-5 TM and Landsat-8 OLI images using radiative transfer equation which validated using MODIS data in the same period. This study showed that UHI intensity in the urban region of West Java reach 5.11oC in 2018. For 20 years, the land-surface temperature increased to 4.44oC. The UHI distribution is concentrated in the central business district, industrial area, harbor, terminal, airport, and traffic jam zone. The UHI significantly increased in Depok and Cimahi, which are known as satellite cities for the surrounding megapolitan (Jakarta and Bandung Raya). This model has high validity result with a correlation value of 0.74. The UHI management is important to strengthen urban resilience in the environmental field through green open space, green belts, roof gardens, land use and land cover formal direction, and the use of high albedo materials to build construction.

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Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

Cited by 11 publications

References 10 publications

Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Urban Heat Island Assessment using Remote Sensing Data in West Java, Indonesia: From Literature Review to Experiments and Analyses

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