scholarly journals Vegetation index-based biomass model and Land Surface Temperature (LST) from urban green spaces in Bandung City derived from multispectral imageries

2021 ◽  
Vol 747 (1) ◽  
pp. 012060
Author(s):  
A L Suti ◽  
I P Ash Shidiq ◽  
Rokhmatulloh ◽  
A Wibowo
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Green Spaces ◽  
Urban Green ◽  
Urban Green Spaces

scholarly journals Multi-Temporal Land Surface Temperature and Vegetation Greenness in Urban Green Spaces of Puebla, Mexico

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 155
Author(s):  
Filoteo Gomez-Martinez ◽  
Kirsten M. de Beurs ◽  
Jennifer Koch ◽  
Jeffrey Widener
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Population Expansion ◽  
Urban Heat Islands ◽  
Green Spaces ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Heat

The urban heat island (UHI) effect is a global problem that is likely to grow as a result of urban population expansion. Multiple studies conclude that green spaces and waterbodies can reduce urban heat islands. However, previous studies often treat urban green spaces (UGSs) as static or limit the number of green spaces investigated within a city. Cognizant of these shortcomings, Landsat derived vegetation and land surface temperature (LST) metrics for 80 urban green spaces in Puebla, Mexico, over a 34-year (1986–2019) and a 20-year (2000–2019) period were studied. To create a photo library, 73 of these green spaces were visited and the available land cover types were recorded. Green spaces with Indian laurel were found to be much greener and vegetation index values remained relatively stable compared to green spaces with mixed vegetation cover. Similarly, green spaces with large waterbodies were cooler than those without water. These results show that larger green spaces were significantly cooler (p < 0.01) and that size can explain almost 30% of temperature variability. Furthermore, green spaces with higher vegetation index values were significantly cooler (p < 0.01), and the relationship between greenness and temperature strengthened over time.

scholarly journals Analysis of the Cooling Effect of Urban Green Spaces in Mitigating Micro-climate Change Using Geospatial Techniques in Adama City, Central Ethiopia

2021 ◽  
Author(s):  
Biratu Bobo Merga ◽  
Kenate Worku Tabor ◽  
Girma Alemu
Keyword(s):  
Climate Change ◽  
Urban Planning ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Green Space ◽  
Green Spaces ◽  
Urban Green ◽  
Urban Green Spaces

Abstract Nowadays, addressing urban climate in urban planning through mapping has got world-wide attention. Greening urban environment is one of the best mechanisms to combat the effects of micro-climate change. Therefore, this study aims at analyzing the cooling effects of Urban Green Spaces (UGS) in mitigating micro-climate change in Adama City with special emphasis on land surface temperature variation with respective to vegetation cover for the last two decades i.e. from 2000 to 2020. Three different remotely sensed data of Landsat7 ETM+ (2000 and 2010) as well as Landsat8 OLI/TIRS (2020) were used in the study. The consistent land surface temperature data were retrieved from Landsat7 ETM+ and Landsat8 OLI/TIRS using mono window and split window algorithms, respectively. Regression and correlation analysis among Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were also performed in Statistical Package for Social Science (SPSS V23). The study reveals that the proportion of Urban Green Spaces (UGS) to other land use/land cover particularly, dense vegetation cover were reduced from 29.3Km2(21.20%) in 2000 to 18.17Km2 (13.15%) in 2020. The main land dynamic process, which could considerably contribute to the increase in Land Surface Temperature, was the rapid expansion of built-up areas in the study area. The model produced through multiple linear regression analysis clearly indicates that the two urban parameters (built up and green areas) contributed 75.2% of the Land Surface Temperature (LST) variations in Adama City. The cooling efficiency (CE) and the threshold value of efficiency (TVoE) of green space in Adama City were calculated as 5.5 ± 0.5 ha. This finding implies that when Adama City municipality implements urban planning, allocating a green space area of 5.5 ± 0.5 ha reduces surface temperature by about 2.85 0C which is the most efficient to reduce heat effects. The study suggests that strengthening of plan execution capacity, public participation in urban planning and strengthening the development of urban green spaces as an important strategy to mitigate the effects of micro-climate change.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

2021 ◽  
Vol 13 (2) ◽  
pp. 323
Author(s):  
Liang Chen ◽  
Xuelei Wang ◽  
Xiaobin Cai ◽  
Chao Yang ◽  
Xiaorong Lu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Maximum Temperature Difference ◽  
Rapid Urbanization ◽  
The Impact ◽  
The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

scholarly journals Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

2018 ◽  
Vol 7 (7) ◽  
pp. 275 ◽  
Author(s):  
Bipin Acharya ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Laxman Khanal ◽  
Shahid Naeem ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Dengue Fever ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Remotely Sensed ◽  
Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

scholarly journals Dynamics of human-induced lakes and their impact on land surface temperature in Toshka Depression, Western Desert, Egypt

2021 ◽  
Author(s):  
Rasha Abou Samra
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Research Area ◽  
Western Desert ◽  
Severe Drought ◽  
Land Use Land Cover

Abstract Land surface temperature (LST) is a significant environmental variable that is appreciably influenced by land use /land cover changes. The main goal of this research was to quantify the impacts of land use/land cover change (LULC) from the drying of Toshka Lakes on LST by remote sensing and GIS techniques. Landsat series TM and OLI satellite images were used to estimate LST from 2001 to 2019. Automated Water Extraction Index (AWEI) was applied to extract water bodies from the research area. Optimized Soil-Adjusted Vegetation Index (OSAVI) was utilized to predict the reclaimed land in the Toshka region until 2019. The results indicated a decrease in the lakes by about 1517.79 km2 with an average increase in LST by about 25.02 °C between 2001 and 2019. It was observed that the dried areas of the lakes were converted to bare soil and are covered by salt crusts. The results indicated that the land use change was a significant driver for the increased LST. The mean annual LST increased considerably by 0.6 °C/y between 2001 and 2019. A strong negative correlation between LST and Toshka Lakes area (R-square = 0.98) estimated from regression analysis implied that Toshka Lakes drying considerably affected the microclimate of the study area. Severe drought conditions, soil degradation, and many environmental issues were predicted due to the rise of LST in the research area. There is an urgent need to develop favorable strategies for sustainable environmental management in the Toshka region.

scholarly journals Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Tahmid Anam Chowdhury ◽  
◽  
Md. Saiful Islam ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Land Use Land Cover ◽  
Remarkable Change

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

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