Land Surface Temperature Profiling and Its Relationships With Land Indices: A Case Study on Lucknow City

Sarif, Md. Omar; Gupta, R.

doi:10.5194/isprs-annals-iv-5-w2-89-2019

Cited by 15 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Land surface emissivity (LSE) is an important parameter to calculate LST [62,63]. NDVI threshold (NDVI THR ) method was used to calculate LSE because it can differentiate pixels of vegetation, water, and soil significantly [64].…”

Section: Land Surface Emissivitymentioning

confidence: 99%

Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018)

Sarif

Rimal

Stork

2020

IJGI

View full text Add to dashboard Cite

More than half of the world’s populations now live in rapidly expanding urban and its surrounding areas. The consequences for Land Use/Land Cover (LULC) dynamics and Surface Urban Heat Island (SUHI) phenomena are poorly understood for many new cities. We explore this issue and their inter-relationship in the Kathmandu Valley, an area of roughly 694 km2, at decadal intervals using April (summer) Landsat images of 1988, 1998, 2008, and 2018. LULC assessment was made using the Support Vector Machine algorithm. In the Kathmandu Valley, most land is either natural vegetation or agricultural land but in the study period there was a rapid expansion of impervious surfaces in urban areas. Impervious surfaces (IL) grew by 113.44 km2 (16.34% of total area), natural vegetation (VL) by 6.07 km2 (0.87% of total area), resulting in the loss of 118.29 km2 area from agricultural land (17.03% of total area) during 1988–2018. At the same time, the average land surface temperature (LST) increased by nearly 5–7 °C in the city and nearly 3–5 °C at the city boundary. For different LULC classes, the highest mean LST increase during 1988–2018 was 7.11 °C for IL with the lowest being 3.18 °C for VL although there were some fluctuations during this time period. While open land only occupies a small proportion of the landscape, it usually had higher mean LST than all other LULC classes. There was a negative relationship both between LST and Normal Difference Vegetation Index (NDVI) and LST and Normal Difference Moisture Index (NDMI), respectively, and a positive relationship between LST and Normal Difference Built-up Index (NDBI). The result of an urban–rural gradient analysis showed there was sharp decrease of mean LST from the city center outwards to about 15 kms because the NDVI also sharply increased, especially in 2008 and 2018, which clearly shows a surface urban heat island effect. Further from the city center, around 20–25 kms, mean LST increased due to increased agriculture activity. The population of Kathmandu Valley was 2.88 million in 2016 and if the growth trend continues then it is predicted to reach 3.85 million by 2035. Consequently, to avoid the critical effects of increasing SUHI in Kathmandu it is essential to improve urban planning including the implementation of green city technologies.

show abstract

Section: Land Surface Emissivitymentioning

confidence: 99%

Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018)

Sarif

Rimal

Stork

2020

IJGI

View full text Add to dashboard Cite

show abstract

“…A healthy environment condition is characterized by the positive values of NDVI. NDVI plays an important role in urban climate analysis (Guha et al, 2020;M. O. Sarif & Gupta, 2019).…”

Section: 3 Lst Estimationmentioning

confidence: 99%

“…Based on the overall assessment of Table 6, the outputs expose that the maximum increase of LST is related to bare land, built-up, and urban areas, whereas the minimum increase of LST is related to the water body and vegetation areas. This is related to the high evapotranspiration phenomenon from vegetation areas which precisely play important role in the reduction of land surface temperature (Kamali Maskooni et al, 2021;M. O. Sarif & Gupta, 2019).…”

Section: Relationship Between Land Use/land Cover and Lstmentioning

confidence: 99%

Evaluating the Impact of Land Use/Land Cover Changes on the Surface Urban Heat Island intensity: The case study of Kabul city, Afghanistan

Sahak

Hotaki

Ahmadi

et al. 2022

Preprint

View full text Add to dashboard Cite

The purpose of this paper is to detect the areas which are affected by urban heat island (UHI) based on the relationship between land surface temperature and land use /land cover (LU/LC) changes during 1998, 2011, and 2020 for the major city of Kabul, the capital of Afghanistan, using multispectral and multi-temporal Landsat data (TM and OLI/TIRS). To achieve the objectives, the emissivity corrected land surface temperature method was examined to calculate Land Surface Temperature (LST), and the Land-use/Land-cover map was prepared using the support vector machine (SVM) supervised algorithm. The LU/LC was categorized into five major classes (vegetation, water-body, built-up, bare-land, and soil). According to the LST map estimated by processing the thermal band of the satellite image, areas influenced by Urban Heat Island (UHI) were detected to evaluate their anomalies to the existing LU/LC types. The findings of LST illustrate, that the mean recorded LST in 1998 was 39.42˚C, whereas the mean recorded LST in 2020 was 41.25˚C, which demonstrates a 1.83 ˚C increase for the whole study period. Specifically in 1998 only five districts (1,16,9,15, and 19) were affected by surface UHIs, while in 2011, the surface UHIs influenced areas increased to eight districts (15, 16, 1, 21, 17, 12, 13, and 8), and in 2020, the surface UHIs affected regions are improved to ten districts (1,19, 15,16, 9,17, 22, 11, 13, and 2) over the Kabul City. These variations and improvements are mostly due to the status of LU/LC in the study area, and it demonstrates the strong link between land cover and land surface temperature. Furthermore, Normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) were also extracted. The relationship of NDVI and NDBI with LST was evaluated. Based on the results, a strong negative relationship between LST and NDVI was observed, while a positive relationship between LST and NDBI was recorded. The findings of this work show that an increase in non-evaporation areas and a decline in the greenery surfaces increased the LST. Consequently, the outcomes of this study are significant for decision-makers and urban planners to manage the drawbacks of urbanization temperature in arid and semi-arid areas.

show abstract

“…Some studies rigorously found similar facts in Taipei city of Taiwan [15], Phoenix city of the United States of America (USA) [16], Singapore [10], Dhaka city of Bangladesh [17], Kathmandu valley of Nepal [18], Nanjing city of China [19], Beijing city of China [20], Tokyo city of Japan [21], Tehran city of Iran [13], 70 selected cities of Europe [22], Hong Kong [23], Baltimore-DC metropolitan area of the USA [24], and Cairo city of Egypt [25]. At the same time, researchers have discussed how their changing aspects affect and transform the environment of the city landscape in various Indian cities, such as Kolkata [26], Delhi and Mumbai [27], Chandigarh [28], Hyderabad [29], Noida [30], Lucknow [31], and Raipur [32]. Mal et al (2020) conducted a study on the relationship of LST with LULC and elevation in the Ganga River basin, which includes major cities such as Kolkata, Patna, Allahabad (now Prayagraj), Varanasi, Lucknow, Kanpur, New Delhi, and Kathmandu during 2001-2019 using 1 km of MODIS Terra datasets [33].…”

Section: Introductionmentioning

confidence: 99%

Assessing Local Climate Change by Spatiotemporal Seasonal LST and Six Land Indices, and Their Interrelationships with SUHI and Hot–Spot Dynamics: A Case Study of Prayagraj City, India (1987–2018)

2022

View full text Add to dashboard Cite

LST has been fluctuating more quickly, resulting in the degradation of the climate and human life on a local–global scale. The main aim of this study is to examine SUHI formation and hotspot identification over Prayagraj city of India using seasonal Landsat imageries of 1987–2018. The interrelationship between six land indices (NDBI, EBBI, NDMI, NDVI, NDWI, and SAVI) and LST (using a mono-window algorithm) was investigated by analyzing correlation coefficients and directional profiling. NDVI dynamics showed that the forested area observed lower LST by 2.25–4.8 °C than the rest of the city landscape. NDBI dynamics showed that the built-up area kept higher LST by 1.8–3.9 °C than the rest of the city landscape (except sand/bare soils). SUHI was intensified in the city center to rural/suburban sites by 0.398–4.016 °C in summer and 0.45–2.24 °C in winter. Getis–Ord Gi* statistics indicated a remarkable loss of areal coverage of very cold, cold, and cool classes in summer and winter. MODIS night-time LST data showed strong SUHI formation at night in summer and winter. This study is expected to assist in unfolding the composition of the landscape for mitigating thermal anomalies and restoring environmental viability.

show abstract

Land Surface Temperature Profiling and Its Relationships With Land Indices: A Case Study on Lucknow City

Cited by 15 publications

References 19 publications

Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018)

Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018)

Evaluating the Impact of Land Use/Land Cover Changes on the Surface Urban Heat Island intensity: The case study of Kabul city, Afghanistan

Assessing Local Climate Change by Spatiotemporal Seasonal LST and Six Land Indices, and Their Interrelationships with SUHI and Hot–Spot Dynamics: A Case Study of Prayagraj City, India (1987–2018)

Contact Info

Product

Resources

About