Effects of rapid urbanisation on the urban thermal environment between 1990 and 2011 in Dhaka Megacity, Bangladesh

This study investigates the influence of land-use/land-cover (LULC) change on land surface temperature (LST) in Dhaka Megacity, Bangladesh during a period of rapid urbanisation. LST was derived from Landsat 5 TM scenes captured in 1990, 2000 and 2011 and compared to contemporaneous LULC maps. We compared index-based and linear spectral mixture analysis (LSMA) techniques for modelling LST. LSMA derived biophysical parameters corresponded more strongly to LST than those produced using index-based parameters. Results indicated that vegetation and water surfaces had relatively stable LST but it increased by around 2 °C when these surfaces were converted to built-up areas with extensive impervious surfaces. Knowledge of the expected change in LST when one land-cover is converted to another can inform land planners of the potential impact of future changes and urges the development of better management strategies

Assess the effect of different degrees of urbanization on land surface temperature using remote sensing images

AbstractUrbanization is a human-dominated process and has greatly impacted biodiversity, ecosystem processes, and regional climate. In this study we assess the effect of different degrees of urbanization on land surface temperature using remote sensing images. Landsat TM images were used for land surface temperature retrieval using the algorithm proposed by Artis and Carnahan. ALOS multispectral images were used for landcover classification using classification trees in three study areas, namely Xicheng district(A), Haidian district(B), Shijingshan district(C), of different degrees of urbanization in Beijing. Landcover-specific surface temperatures were estimated through an inversion alorithm. At the different degrees of urbanization, reducing the within-pixel coverage ratio of vegetations will result in an land surface temperature rise. Quantitative assessment of the relationship between different degrees of urbanization and land surface temperature was simulated by an urbanization index which integrates the coverage ratio of built-up landcover type and the cell-average NDVI. Urbanization indices of the Xicheng district, Haidian district, Shijingshan district were calculated to be 0.91, 0.72, and, 0.55 respectively. Such results are consistent with the trend of evaluation using quantitative estimation land surface temperature

Multi-Timescale Dynamics of Land Surface Temperature

Spatial and temporal patterns of land surface temperature (LST) have been used in studies of surface energy balance, landscape thermal patterns and water management. An effective way to investigate the landscape thermal dynamics is to utilize the Landsat legacy and consistent records of the thermal state of earth’s surface since 1982. However, only a small proportion of studies emphasize the importance of historical Landsat TIR data for investigating the relationship between the urbanization process and surface thermal properties. This occurred due to the lack of standardized LST product from Landsat and the unevenly distributed remote sensing datasets caused by poor atmospheric effects and/or clouds. Despite the characterization of annual temperature cycles using remote sensing data in previous studies, yet the statistical evidence to confirm the existence of the annual temperature cycle is still lacking. The objectives of the research are to provide statistical evidence for the existence of the annual temperature cycle and to develop decomposition technique to explore the impact of urbanization on surface thermal property changes. The study area is located in Los Angeles County, the corresponding remotely sensed TIR data from Landsat TM over a decadal year (2000-2010) was selected, and eventually a series of 82 cloud-free images were acquired for the computation of LST. The hypothesis technique, Lomb-Scargle periodogram analysis was proposed to confirm whether decadal years’s LSTs showed the annual temperature cycle. Furthermore, the simulated LSTs comprised of seasonality, trend, and noise components are generated to test the robustness of the decomposition scheme. The periodogram analysis revealed that the annual temperature cycle was confirmed statistically with p-value less than 0.01 and the identified periodic time at 362 days. The sensitivity analysis based on the simulated LSTs suggested that the decomposition technique was very robustness and able to retrieve the seasonality and trend components with errors up to 0.6 K. The application of the decomposition technique into the real 82 remote sensing data decomposed the original LSTs into seasonality, trend, and noise components. Estimated seasonality component by land cover showed an agreement with previous studies in Weng & Fu (2014). The derived trend component revealed that the impact of urbanization on land surface temperature ranged from 0.2 K to 0.8 K based on the comparison between urban and non-urban land covers. Further applications of the proposed Lomb-Scargle technique and the developed decomposition technique can also be directed to data from other satellite sensors

Caracterização temporal do município de Campinas usando NDVI, NDBI e temperatura da superfície.

Este estudo tem por objetivo investigar, com o apoio de técnicas de sensoriamento remoto e com base na organização das UTBs (Unidades Territoriais Básicas) do Município de Campinas, as relações entre a temperatura da superfície (TS), o NDVI (índice da diferença normalizada da vegetação) e o NDBI (índice normalizado de diferença de construção) utilizando imagens Landsat 5 TM. As imagens do satélite Landsat 5 foram obtidas em agosto de 1996, 2003 e 2011. Considerando os três anos analisados, o NDVI médio do município aumentou significativamente de 0,18 em 1996 para 0,32 em 2003. Verificou-se redução significativa no valor médio do NDBI, que representou alteração acentuada no uso e na cobertura do solo entre 1996 e 2003 ou 2011. Os resultados identificaram uma relação negativa entre o NDVI e a temperatura da superfície e uma relação positiva entre o NDBI e a temperatura da superfície. Concomitantemente, demonstraram um padrão de ocupação do solo no sentido sul e noroeste do Município de Campinas

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales

DETECTION AND ANALYSIS OF SURFACE URBAN COOL ISLAND USING THERMAL INFRARED IMAGERY OF SALATIGA CITY, INDONESIA

The detection and monitoring of the dynamics of urban micro-climatesneeds to be performedeffectively, efficiently, consistently and sustainably inan effort to improve urban resilience to suchphenomena. Thermal remote sensing posesses surface thermal energy detection capabilities which can be converted into surface temperatures and utilised to analyse the urban micro-climate phenomenon overlarge areas, short periods of time, and at low cost. This paper studies the surface urban cool island (SUCI) effect, the reverse phenomenon of the surface urban heat island (SUHI) effect, in an effort to provide cities with resistance to the urban microclimate phenomenon.The study also aims to detect urban micro-climate phenomena, and to calculate the intensity and spatial distribution of SUCI. The methods used include quantitative-descriptive analysis of remote sensing data, including LST extraction, spectral transformation, multispectral classification for land cover mapping, and statistical analysis. The results show that the urban micro-climate phenomenon in the form of SUHI in the middle of the city of Salatiga is due to the high level of building density in the area experiencing the effect, which mostly has a normal surface temperature based on the calculation of the threshold, while the relative SUCI occurs at the edge of the city. SUCI intensity in Salatiga ranges between -6.71°C and0°C and is associated with vegetation

Spatio-temporal variation of the land surface parameters in Temperature, in King Williams Town, Eastern Cape Province, South Africa

The world is currently experiencing unprecedented urban growth. The influx of people into urban areas from rural areas is motivated by both economic and social factors such as increased employment opportunities. The latter is a result of, in part, industrialization, and the perceived higher standard of living that is often associated with access to better infrastructure. Surface Heat Island (SHI) is a phenomenon whereby urban areas experience higher surface temperatures than the surrounding rural areas. The presence of the SHI in urban areas has a negative impact not only on city dwellers, but also on the environment and the economy. The development of SHI is often associated with patterns of land use and land cover in urban areas. Numerous methods exist that can be used to study SHI’s. Literature suggests that Land Surface Temperature (LST), derived from satellite imagery, is a proven method that produces reliable results. The aim of this study was to evaluate the SHI in King Williams Town by studying the relationship between land surface temperatures, land cover and land cover indices. The derived indices are the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built up Index (NDBI). These indices were selected because they are representative of the land cover scheme used in the research study. This study was conducted in the King Williams town area between the years 1995 and 2018 the land surface temperature was derived from Landsat ETM + high thermal band data. The findings from this study provide an idea on the correlation between satellite derived land surface temperature and the land modification which occurred during the urbanization of King Williams Town during a 23 year period between1995 and 2018. The built up land category was the most influential in the development of high land surface temperature levels , vegetation had an opposite effect as a series of data sets illustrated that vegetated areas had a iv cooling effect on the surface. Water bodies in the study area had an insignificant effect on the Surface temperature levels while the grass lands weren’t as cooling as the vegetation but provided a cooling environment in the study area .The spatial distribution of areas of high surface temperature (hot spots) was discovered to be concentrated in the urban areas of the study area which is in the northwest region of the study area and correlates to the land cover and land cover indices associated with built up and artificial surfaces. The cooler areas or patches of land with lower values of land surface temperature were distributed on the outskirts of the study area away from the CBD and residential areas. This was the case because of the high concentration of vegetation and thicker grass lands in those regions

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Urbanization strongly correlates with land use land cover (LULC) dynamics, which further links to changes in land surface temperature (LST) & urban heat island (UHI) intensity. Each LULC type influences UHI differently with changing climate, therefore knowing this impact & connection is critical. To understand such relations, long temporal studies using remote sensing data play promising role by analysing the trend with continuity over vast area. Therefore, this study is aimed at machine learning centred spatio-temporal analysis of LST and land use indices to identify their intra-urban interaction during 1991–2021 (summer) in Imola city (specifically representing small urban environment) using Landsat-5/8 imageries. It was found that LST in 2021 increased by 38.36% from 1991, whereas average Normalised Difference Built-up Index (NDBI) increased by 43.75%, associating with increased thermal stress area evaluated using ecological evaluation index. Major LULC transformations included green area into agricultural arable-land and built-up. Finally, the modelled output shows that built-up & vegetation index have strongly impacted LST. This study, help to understand the relative impact of land-use dynamics on LST at intra-urban level specifically with respect to the small urban settings. Further assisting in designing and regenerating urban contexts with stable configuration, considering sustainability and liveable climate, for benefit of health of public and fragile population in particular

Urban Green Space Analysis and its Effect on the Surface Urban Heat Island Phenomenon in Denpasar City, Bali

The Urbanization process in Indonesia’s big cities causes adverse environmental impacts such as climate change and land cover change. Urban climate change causes the warming of urban areas compared to rural areas; it is called Urban Heat Island phenomenon. Loss of vegetation due to urban development is one of several causes that contribute to urban heat islands. This study examines the availability of green spaces and their effects on the surface urban heat island in Denpasar city. This study used the spatial approach for Urban Green space mapping with digitizing methods. Landsat 8's thermal band is used for land surface temperature mapping and to conduct a spatial pattern analysis of the SUHI phenomena. The Global Moran’s Index and Local Indicator of Spatial Association (LISA) were used to determine the correlation between urban green space and SUHI. The study result shows that Denpasar City's urban green space area covers 28.22 km2. That's equal to 22.1% of the Denpasar City Administrative area. Denpasar Selatan district has the largest urban green space cover, with 14.19 km2 covered, or 50.27% of all the green space in Denpasar City. The majority of Denpasar is affected by UHI occurrences, except the northern region of North Denpasar and the southern region of South Denpasar. The maximum UHI level reaches 4-5°C, located on the east side of South Denpasar, especially in the Sanur coastal area. According to the spatial pattern study, the association between urban green space and SUHI only exists on the north side of Denpasar. The correlation between low-SUHI intensity clusters and high cover of green space is shown in the same area. However, the association between High-UHI intensity and low green space cover has not significantly happened. It indicated that other factors besides green space could affect the land surface temperature