Comparison on Land-Use/Land-Cover Indices in Explaining Land Surface Temperature Variations in the City of Beijing, China

Khan, Muhammad Sadiq; Ullah, Sami; Chen, Liding

doi:10.3390/land10101018

Cited by 26 publications

(26 citation statements)

References 54 publications

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“…The Pearson's correlation analysis show that the NDVI and LST have a strong negative and significant correlation among most of the provinces in the region (Table 2). These results support the study results of (Guha et al, 2022;Khamchiangta and Dhakal, 2020;Khan et al, 2021).…”

Section: Discussionsupporting

confidence: 92%

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Mohanasundaram

Baghel

Thakur

et al. 2022

Preprint

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Critical applications of satellite data products include monitoring vegetation dynamics using vegetation indices such as Normalized Difference Vegetation Index (NDVI) and thermal characteristics such as Land Surface Temperature (LST) variations across different spatial and temporal scales. One of the major problems with passive remote sensing satellite data products is cloud and shadow cover that void the information from a specific image. The cloud cover problems further amplified during monsoon seasons as the formation of clouds are more frequent during these seasons. The present study proposes temporal aggregation of images and developing Harmonic Analysis of Time Series (HANTS) and Pixel-wise Multiple Linear Regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series of L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI data sets. The calculated root mean square (RMSE) values were ranging from 0.004 to 0.05 and 1.05°C to 3.11°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) calculated at province level show that most of the western provinces showed a healthy vegetation condition than other provinces in the northeast of Thailand.

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Section: Discussionsupporting

confidence: 92%

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Mohanasundaram

Baghel

Thakur

et al. 2022

Preprint

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“…To find the reasons for the differences in the growth and yields in the different geomorphic parts of the landscape, the supervised classification method in the RF algorithm was used to classify and to predict the nutrient elemental indices for the soil and mangos. Compared to general statistical methods, the RF algorithm can accurately conduct index classification, evaluate the importance of each index, generate a tree structure, and determine the importance of each feature as it is an integrated learning method [25,26].…”

Section: Discussionmentioning

confidence: 99%

Characterization of Soil-Plant Leaf Nutrient Elements and Key Factors Affecting Mangoes in Karst Areas of Southwest China

Song

Huang

Yang

et al. 2022

Land

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Baise city is one of the largest producers of mangoes, with this agricultural industry located in the karst region of Southwest China. However, calcium-rich and alkaline soils, severe soil fragmentation, and poor water and fertilizer retention capacity contribute to low mango yields and are key issues that limit the development of the mango industry in karst areas. Our study objectives were to identify the soil factors that limit mango growth and yield in the karst region of Southwest China, and to determine how these growth- and production-limiting conditions vary between landscape positions. This study analyzed the differences in soil nutrient and element contents in mango leaves, and used a Random Forest algorithm to calculate the eigenvalues of the mango leaf and soil elemental indices in the different geomorphic parts (slopes, transition zone, passes, high-yielding depressions, and low-yielding depressions) of the karst peak-cluster depressions. The key factors affecting the mango leaves and soil were screened based on the diagnostic results and the eigenvalues. The results showed that for the elemental contents of Fe, Mg, Ca, and Mn in the mango leaves in the different geomorphic parts of the karst, the peak-cluster depressions were generally deficient and varied significantly. The contents of available B (AB), soil organic matter (SOM), pH, total nitrogen, available Fe, available Mn, alkaline hydrolysis nitrogen, exchangeable Ca (Caex), exchangeable Mg, and other indices in the soils differed significantly, and AB, available Zn, and available K (AK) showed low or very low content levels. In addition, the key soil factors limiting mango yield in the karst areas were AB, fulvic acid, SOM, Fe, Mn, Caex, soil water, and AK; and the key mango leaf factors were Ca, Mn, Fe, Zn, and Mg. Consequently, the characteristics of soil water content, pH, and soil organic matter may be the main drivers affecting the differences in the mango yield and the elemental characteristics. These findings suggest that the addition of organic fertilizer could improve the quality and yield of mangoes in karst areas.

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“…LST increase, one of these interactions, is one of the vital parameters affecting urban environments. The increase in LST, which is one of the biggest sources of concern on a global scale, also causes cities to be more negatively affected by the climate change process ; Khan et al 2021; Yilmaz and Ozturk, 2023).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Urban Land Cover Indices Affecting Urban Heat Island Formation Using Advanced Geo-Spatial Techniques

YILMAZ,

OZTURK

2024

Preprint

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Evaluation of land surface temperature during the climate change process is important in urban planning. The urban thermal environment is closely related to land surface characteristics. The relationship between land surface properties and land surface temperature (LST) is among the current research topics. As a result of advances in geospatial and remote sensing fields, remote sensing-based spectral indices have been developed to investigate land use/land cover (LULC) effects on the urban thermal environment. The study aims to investigate the impact of remote sensing-based LULC indices on LST in Ankara metropolitan city. LST values, which are an important representation of the urban heat island, were calculated from Landsat 8 OLI/TIRS data for 2013, 2018, and 2023. Urban Thermal Field Variance Index (UTFVI) was used to define the urban heat island effect from a thermal perspective. Additionally, to define the urban heat island effect in terms of land cover characteristics, temporal-spatial changes of the LULC indices which are Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-Up Index (NDBI), Normalized Difference Impervious Index (NDISI) has been evaluated. Linear regression analysis was performed to determine the effect of LULC indices on LST. As a result of the analysis, it was determined that NDVI and NDWI had a negative correlation with LST, while NDBI and NDISI had a positive correlation. The highest correlation values belong to 2023. NDVI (R²=0.4944) and NDWI (R²=0.2666) affect the 2023 LST negatively, while NDBI (R²=0.3664) and NDISI (R²=0.6010) affect it positively. While the results show the importance of green vegetation and water surfaces in reducing LST, they show that NDISI, which is a representation of impervious surfaces, has the most impact on increasing LST. The results of the study also reveal the impact and importance of spatial patterns of LULC indices on LST.

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Comparison on Land-Use/Land-Cover Indices in Explaining Land Surface Temperature Variations in the City of Beijing, China

Cited by 26 publications

References 54 publications

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Characterization of Soil-Plant Leaf Nutrient Elements and Key Factors Affecting Mangoes in Karst Areas of Southwest China

Evaluation of Urban Land Cover Indices Affecting Urban Heat Island Formation Using Advanced Geo-Spatial Techniques

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