Simulation of Land Surface Temperature Patterns Over Future Urban Areas—A Machine Learning Approach

Maithani, Sandeep; Nautiyal, Garima; Sharma, Archana; Sharma, Surendra Kumar

doi:10.1007/s12524-022-01590-z

Cited by 6 publications

(3 citation statements)

References 68 publications

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“…In the realm of data analytics, ML is one of the most successful techniques for making predictions using models and algorithms (Angra and Ahuja, 2017;Dhall et al, 2020). Although there is a paucity of research employing ML algorithm to retrieve LST, the technique has been used in other aspects of LST studies, such as spatial downscaling, simulation, addressing meteorological conditions, and similar tasks (Li et al, 2019;Buo et al, 2021;Maithani et al, 2022;Xu et al, 2021). Wang et al, (2022) employed land cover categories and Landsat bands to estimate the LST across the Tibetan plateau using random forest (RF).…”

Section: Introductionmentioning

confidence: 99%

“…The LST by the RF trained model was the most accurate with the lowest root mean square error (RMSE) (1.89 Kelvin), according to Wang et al, who also obtained and examined the LST from the single channel technique, the linear regression model (2.77 Kelvin), and the moderate resolution imaging spectroradiometer product (MOD11A1) (3.62 Kelvin). In order to acquire the LST over Dehradun using an artificial neural network, Maithani et al, (2022) employed built-up densities with a mean absolute error of 1.5° C and 0.9° C, while Rana and Suryanarayana (2022) employed four ML techniques, K nearest neighbour, neural network, regression tree, support vector machine incorporating three indices resulting with an RMSE of 0.54° C, 0.59° C, 0.89° C, and 0.61° C respectively. Mohammad et al, (2022) predicted the LST over a city in Ahmedabad with an RMSE of 0.03° C using XGB regressor.…”

Section: Introductionmentioning

confidence: 99%

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A Generic Machine Learning-Based Framework for Predictive Modeling of Land Surface Temperature

Rengma

Yadav²

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the realm of data analytics, machine learning (ML) is one of the most successful techniques for making predictions. The ability of ML algorithms has also been studied in various aspects of land surface temperature (LST) besides its retrieval. The few investigations on LST retrieval using ML algorithms suggested that it may potentially obtain the LST values incorporating relevant variables of land surface parameters; however, the variables and ML models used differ, and so do their accuracies. The accuracy of the model is affected by the variable's contribution, its quality and quantity, and the fulfilment of each technique's assumptions. Hence this study provides a wide range of LST indicators to be employed for LST retrieval using a widely used ML algorithm, random forest. The ML algorithm framework for LST prediction is illustrated with significant spectral indices and terrain parameters across the highly industrialised district of Jharkhand, India. With the exception of one (aspect) variable, the analysis shows that all 20 variables that were included as independent factors were significant and equally contributed to the model. The model built with all the variables including the aspect of the terrain obtained an RMSE of 1.13 degree Celsius and R2 of 0.48. However, after the removal of aspect, the model obtained an R2 of 0.89 and RMSE of 0.74 °C. The performance of the model on consecutive removal of lesser significant variables are evaluated and the study made clear how crucial it is to consider several environmental or land-use factors that could be pertinent to LST.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Generic Machine Learning-Based Framework for Predictive Modeling of Land Surface Temperature

Rengma

Yadav²

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…There are different approaches and algorithms for LST calculation using Landsat imagery. Du et al [ 21 ] developed a practical split-window algorithm to estimate LST from thermal infrared sensor (TIRS) aboard Landsat 8, obtaining LST with an accuracy better than 1.0 K; Maithani et al [ 22 ] retrieved LST from Landsat thermal datasets using a single-channel algorithm for the Dehradun planning area situated in Uttarakhand (India); Yu et al [ 23 ] compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm, and the single-channel method. Their findings indicated that the LST obtained from the radiative transfer equation-based method, using Landsat band 10, has the highest accuracy with RMSE lower than 1.0 K, while the split-window algorithm has moderate accuracy and the single-channel method has the lowest accuracy.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN

Khachoo

Cutugno

Robustelli

et al. 2023

Sensors

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Continuous and unplanned urbanization, combined with negative alterations in land use land cover (LULC), leads to a deterioration of the urban thermal environment and results in various adverse ecological effects. The changes in LULC and thermal characteristics have significant implications for the economy, climate patterns, and environmental sustainability. This study focuses on the Province of Naples in Italy, examining LULC changes and the Urban Thermal Field Variance Index (UTFVI) from 1990 to 2022, predicting their distributions for 2030. The main objectives of this research are the investigation of the future seasonal thermal characteristics of the study area by characterizing land surface temperature (LST) through the UTFVI and analyzing LULC dynamics along with their correlation. To achieve this, Landsat 4-5 Thematic Mapper (TM) and Landsat 9 Operational Land Imager (OLI) imagery were utilized. LULC classification was performed using a supervised satellite image classification system, and the predictions were carried out using the cellular automata-artificial neural network (CA-ANN) algorithm. LST was calculated using the radiative transfer equation (RTE), and the same CA-ANN algorithm was employed to predict UTFVI for 2030. To investigate the multi-temporal correlation between LULC and UTFVI, a cross-tabulation technique was employed. The study’s findings indicate that between 2022 and 2030, there will be a 9.4% increase in built-up and bare-land areas at the expense of the vegetation class. The strongest UTFVI zone during summer is predicted to remain stable from 2022 to 2030, while winter UTFVI shows substantial fluctuations with a 4.62% decrease in the none UTFVI zone and a corresponding increase in the strongest UTFVI zone for the same period. The results of this study reveal a concerning trend of outward expansion in the built-up area of the Province of Naples, with central northern regions experiencing the highest growth rate, predominantly at the expense of vegetation cover. These predictions emphasize the urgent need for proactive measures to preserve and protect the diminishing vegetation cover, maintaining ecological balance, combating the urban heat island effect, and safeguarding biodiversity in the province.

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Exploring effect of scale dependency in LST downscaling – using convolution neural network-extreme learning machine (CNN-ELM)

Patil,

Maithani,

Sharma

2024

Earth Sci Inform

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Simulation of Land Surface Temperature Patterns Over Future Urban Areas—A Machine Learning Approach

Cited by 6 publications

References 68 publications

A Generic Machine Learning-Based Framework for Predictive Modeling of Land Surface Temperature

A Generic Machine Learning-Based Framework for Predictive Modeling of Land Surface Temperature

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN

Exploring effect of scale dependency in LST downscaling – using convolution neural network-extreme learning machine (CNN-ELM)

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