Jahangirnagar University Environmental Bulletin
ISSN: 2304-3326Contents Available at: https://www.juniv.edu/department/env
Land Use Land Cover Dynamics and its Signature on Land Surface Temperature in Savar, Bangladesh
Taslima Akter Sathe1, Syed Hafizur Rahman2*
1Graduate Student, Department of Environmental Sciences, Jahangirnagar University, Dhaka-1342, Bangladesh.
2Professor, Department of Environmental Sciences, Jahangirnagar University, Dhaka-1342, Bangladesh.
Abstract
Savar Upazila is rapidly expanding as a secondary city in Bangladesh. Rapid urbanization during past decades has resulted in a significant decrease in vegetation, which has had a negative impact on the climate and hampered environmental resilience. This study aims to determine the effect of land use and land cover (LULC) changes on Land Surface Temperature (LST) variation in the Savar area from 2011 to 2021. For 2011 and 2021, multi-temporal Landsat 5 ETM+ and Landsat 8 TM/OLI pre-processed satellite images were utilized to categorize LULC classes, and surface temperature bands were used to calculate LST. The investigation was conducted using remote sensing (RS) and geographic information system (GIS) techniques. The Maximum Likelihood Algorithm (MLA) was used to extract LULC maps under supervised conditions, with an overall accuracy of 90.4% on average. In 10 years, the measured LST has exhibited a rising trend in lowest (2.42°C), maximum (6.39°C), and mean (4°C) temperature.
With growing Build-up (21.5%) and decreasing vegetation (by 10%), the area of low temperatures (2011) has transformed into an area of high temperature (2021). For both years, the highest temperature is observed in the Build-up area, with the greatest positive change (+23.17%) in 10 years. The study found that the increase in impermeable surfaces and the decline in vegetation were the most important factors in the LST increase. The outcomes of this research will assist urban planners and policymakers in developing a long-term urban land development strategy.
Keywords: LST, Climate Change, Maximum Likelihood Algorithm, LULC, Secondary City.
Introduction
The demand for accommodations, food, agricultural production and shelter is increasing as the world’s population grows. As a result of anthropogenic activity, the land cover characteristic is changing to accommodate the rising need of population, replacing vegetated areas with impermeable surfaces, and therefore inadvertently causing climate change (Igun and Williams 2018; Nzoiwu et al., 2017). Many scholars have recently focused their efforts on better understanding the driving elements in changing local and regional climates for anthropogenic activities such as land use changes (Imran et al., 2021). The conversion of natural vegetated surfaces into impermeable built-up surfaces, in particular, is to blame for regional climate change (Argueso et al., 2014).
Savar Upazila, located around 24 kilometers northwest of the capital Dhaka, is a neighboring
connected city, and since 1949, it has been fast rising as a secondary city in Bangladesh (Rahman & Rahman, 2021). In addition to the tannery and readymade garment industries, Savar is home to the country’s Export Processing Zone (EPZ). Several studies on land use change and land value in the Savar municipality have been conducted as a result of the transformation of Savar Upazila from rural to suburban and urban (Rahman & Rahman, 2021). However, the effect of LULC change on LST over time in the Savar area is yet to be investigated. For this reason, we performed the study to determine the impact of LULC changes on LST over a decade. Although different studies have been carried out to investigate the LULC and LST relationship, the designed approach for testing the research hypothesis is unique to the secondary city.
GIS and Remote Sensing are often used technologies to analyze LULC changes and accompanying LST (Bhuiyan et al., 2020a, b; Jahan et al., 2021). This study is unique in that it will utilize multitemporal Landsat 5 and Landsat 8 satellite images for 2011 and 2021 to define LULC classes and investigate the transition from vegetation to built-up areas and the fluctuation in LST in the studied area. This research will contribute in the preservation and development of the natural resources of the secondary city. The link investigated in this study will be useful in a variety of domains, including government governance, urban planning, climate change, and professional environmental temporal monitoring.
Materials and Methods Study Area
Savar Upazila, Bangladesh’s secondary city, is located at 23.8583° N latitude and 90.2667° E longitude in the Dhaka district of Bangladesh (Figure 1). The study area is approximately 285 square kilometers or 28,593 hectares (ha). The municipality of Savar Upazila is made up of 12 unions. It is bordered on the north by Kaliakair and Gazipur Sadar Upazilas, on the south by Keraniganj Upazila, on the east by Dhamrai and Singair Upazilas, and on the west by Mirpur, Mohammadpur, Pallabi, and Uttara Thanas of Dhaka City. This area is bordered by a number of rivers. In terms of agricultural and other socio-economic activities, the Dhalashwari River has a considerable impact on the studied region. The two primary economic sectors in Savar are agriculture and manufacturing.
The research area’s spatial data were acquired in GeoTIFF format files from USGS EarthExplorer satellite imagery of 2021 and 2011 (EarthExplorer.usgs.gov, 2022). In 2011, two satellite images covering the whole study area were acquired. The specifics about the imagery that were used are also supplied. (Table 1)
Both Landsat 8 and Landsat 5 atmospherically corrected (Level 2) Surface Reflectance (SR) and Surface Temperature (ST) bands images were obtained. Daytime imageries were taken since land surface temperature is connected to solar activity. For the investigation, imageries with less than 10% land cloud coverage were gathered in order to produce a sharper image and a more accurate outcome ((Konteoes & Stakenborg, 1990).
Data Acquisition
Table 1 Information of satellite imageries collected from metadata.
Year Date of acquisition
Satellite Sensor Type
Cloud Coverage
Path Row Collection Category 2021 18/04/21 Landsat 8 OLI/TIRS 3.44 % 137 43 T1
2011 7/04/11 Landsat 5 ETM+ 2% 137 43 T1
2011 7/04/11 Landsat 5 ETM+ 0% 137 44 T1
Figure 1: Location of Study Area Map of Savar Upazilla, Dhaka , Bangladesh (Rahman and Rahman 2021).
©2021 by M.L. Rahman and S.H.
Rahman
Image Processing and Classification
All raster images were loaded to QGIS, as all processes were carried out in this software. The image (path 137, row 43) of Landsat 8 OLI/TIRS was clipped with study area shape file. For Landsat 5 ETM+ two images (path 137, row 43; path 137, row 44) were mosaicked and then clipped with study area shape file. Surface reflectance bands with different composite raster combinations were used to produce False Colour Composite (FCC). For image classification four LULC classes were considered (Table 2).
Figure 2: Research Process Diagram
Table 2 LULC class descriptions.
LULC Classes Description
Build-up Residential, commercial and industrial services, transportation network.
Vegetation Trees, grassland, cropland, and fallow land.
Water River, wetlands, lakes, ponds, and reservoirs
Bare Soil Vacant land, open space, sand, bare soils, and landfill sites.
More than 25 training inputs were taken for each class except Bare soil for supervised image classification. As Bare soil covered a small area, 15 training inputs were possible to take for this class. For improved image categorization, indices such as the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Water Index (NDWI) have been developed. Based on field observations, Google Earth, and various combinations of the band, the other land use land cover (LULC) in the pictures may be distinguished. During image categorization, the maximum likelihood approach was used. MLC (maximum likelihood classification) is a reliable approach with little risk of misinterpretation (Sisodia et al., 2014).
After categorizing, the accuracy-test was used to perform post-image processing. Google satellite map was utilized for ground-truthing, with five reference polygons for each class.
The accuracy evaluation was based on the creation of an error matrix and the calculation of overall accuracy, producer accuracy, user accuracy, and the Kappa statistic.
Retrieval of Land Surface Temperature
To derive brightness temperature from Landsat 8 OLI/TIRS and Landsat 5 ETM+ data, thermal band 10 and band 6 were used as inputs, respectively (Earth Resources Observation and Science, 2020). First, the DN values of Landsat 8’s ST band 10 and Landsat 5’s ST band 6 were converted to satellite temperature (BT) in degrees Celsius.
Conversion of DN value into Brightness temperature (℃)
BT = (MT . Qcal + AT)- 273.15 (1)
Where BT is Brightness Temperature in degree Celsius, Qcal is the DN of the surface temperature band, M.T. is surface temperature multiplicative scaling factor (unitless) , AT is surface temperature additive scaling factor (unitless).
BT = (0.00341802. Qcal +149.0)- 273.15 (2) Equation (2) was used both for Landsat 8 and Landsat 5 ST bands.
In this study, Plank’s function was used after conversion to satellite temperature for the calculation of Land Surface Temperature.
Calculation of Land Surface Temperature through Plank’s Equation
𝐿𝑆𝑇 =
𝐵𝑇1+𝜆.𝐵𝑇𝜌 ∗ln(𝜀) (3)
Where 𝜆 = central band wavelength of emitted radiance in meter, BT = satellite brightness temperature in Celcius, 𝜌 = 1.438× 10−2mK, 𝜀 = emissivity (calculated by using the Eq. (3)).
ε = 0. 004 ∗ P𝑉+ 0. 986 (4) Where P𝑉 = proportion of vegetation (calculated by using Eq. (5))
P𝑉 = [ NDVI−NDVImin
NDVImax−NDVImin]2 (5) NDVI was calculated by the following Equation.
𝑁𝐷𝑉𝐼 =𝑁𝐼𝑅−𝑅
𝑁𝐼𝑅+𝑅 (6)
Where NIR is the Near Infrared band and R is the Red band of the satellite images.
This whole process was followed for both Landsat 5 and Landsat 8 ST bands.
Results and Discussion Analysis of LULC changes
In 2011, Vegetation (42.81 %) and Build-up (39 %) occupied the vast territory (Figure 3).
Water and Bare soil made up 6.7 % and 11.04 % of the total, respectively (Table 3). In 2021, the proportion of vegetation fell to 32.15 %, the amount of bare soil has decreased to 4%, and the amount of Water has decreased to 3.27% (Table 4). The percentage of Built-up area increased to 60.49 percent, which is a major change in land use. In ten years, there is a nearly 10% loss of vegetation and a 21.5 % rise in Built-up area. It appears that the region of Vegetation, Water, and Bare Soil has been turned into build-up. In 2021, the main land cover class has replaced by the Built-up area as major land use class. These changes can be seen in a visual representation of LULC change in QGIS from 2011 to 2021. (Figure 3)
Figure 3 Landuse Landcover Changes from 2011 to 2021.
For defining the confusion areas, ground verification was done. An overall accuracy of 98.1268% for 2021 and 82.71% for 2021, on average 90.4% (Table 3 & 4) was found.
Table 3: Land-use Landcover Classification Accuracy (2011) Land Cover
Classes
Area (km2) Percentage (%)
UA (%) PA (%) Overall Accuracy (%)
98.1268 Vegetation 122.3991 42.80939 100.0 96.0131
Water 19.2798 6.743158 90.4564 100.0
Build-up 111.6387 39.04591 97.0954 99.7486 Bare Soil 32.5989 11.40155 99.162 100.0 Kappa hat coefficient = 0.9709
Table 4: Landuse Landcover Classification Accuracy (2021) Land Cover
Classes
Area (km2) Percentage (%)
UA (%) PA (%) Overall
Accuracy (%)
82.7173
Vegetation 91.935 32.15449 100.0 65.0411
Water 9.3438 3.268017 100.0 100.0
Build-up 172.9494 60.48948 71.43 100.0
Bare Soil 11.6883 4.088012 100.0 100.0
Kappa hat coefficient = 0.7004
The Kappa Kat Co-efficient for 2011 and 2021 maps were 0.9709 and 0.7004, on average 0.836 (Table 3 & 4)
Change in LST from 2011 to 2021
To visualize the LST from low to high, the maps are symbolized light blue to dark red, correspondingly (Figure 4). From 2011 to 2021, the overall LST of the Savar area increased (Figure 4). In 2011, the minimum LST was 26.63°C, rising to 29.05°C in 2021. (Figure 5(a)).
As well as, the maximum LST was 44.52°C (2011), rising to 50.91°C (2021) (Figure 5(a)). In ten years, the minimum and maximum temperatures have risen by 2.42°C and 6.39°C, respectively. The change in mean LST of the study area can be used to better understand the pattern of variation. The mean temperature of LST is likewise on the rise (Figure 5(a)).
Within Savar, the mean LST has risen by over 4°C in the last ten years, which concerns earth and its inhabitants.
Area Based LST Distribution
The magnitude of the red area in the LST map of 2011 (Figure 4) is less than the magnitude in the map of 2021 (Figure 4), indicating that the area with high LST has increased in 2021.
Furthermore, by 2021, light blue patches have almost vanished (Figure 4). Figure 5(b) indicates the area of the research region covered by various LST distributions. LST under 30℃ was 49.04 km2 over time; it decreases and falls to 0.07 km2 in the year 2021. The area under the LST class > 40 °C, on the other hand, had grown with time. In 2011, the area of LST classes 45-49°C and above 50°C was nil, but by 2021, it had grown to 0.66 km2 and 0.05 km2, respectively. It is observed that the area of LST between 30-34(℃) was higher in the year 2011. In 2021, most areas shifted to LST between 35-39(℃). This phenomenon also proves that LST is increasing in the study area over time.
Figure 4 Changes in LST from 2011 to 2021
Figure 5(a) Variation in LST from 2011 to 2021.
Figure 5(b) Area Based LST Distribution of 2011 and 2021.
Vegetation cover loss and increase in LST with urban expansion
It is shown that Vegetation loss has occurred (10%) and Build-up area has increased (21.5%) in ten years (Table 3 & 4). Decreased area of Vegetation, Water and Bare soil is converted to Build-up area. The main LST class covering the maximum area is LST 30-34 (℃) and 35-39 (℃) in year 2011 and 2021, respectively (Figure 5(b)). The area of LST class between 30-34 (℃) has decreased by 40.26% and the area of LST class 35-39 (℃) has increased by 49.74%, with 10% Vegetation loss and 21.5% Build-up area expansion in 2021 (Figure 5(b)).
LST distribution over different LULC classes
In 2011, the highest temperatures were in the Build-up areas (46.37°C) and Bare soil areas (43.65°C), while the lowest temperatures were in the Water (26.63°C) and Vegetation (26.74°C) on the LULC (Figure 2) and LST (Figure 3) maps. In 2021, the highest temperatures were in the Build-up (50.91°C) and Bare soil (46.68°C) zones, while the lowest temperatures were in the Water (29.27°C) and Vegetative area (29.05°C). It is obvious that urban expansion can raise radiant surface temperature by replacing natural vegetation with built-up surfaces such as concrete, stone, metal, and asphalt.
In addition, the mean LST of Vegetation, Water, Build-up, and Bare Soil has risen by 3.13°C, 2.63°C, 3.76°C, and 5.11°C, respectively (Figure 6(a) & 6(b)). Thus, Bare soil and Built-up areas have experienced a large shift in mean LST over the last ten years, whilst Water and Vegetation have experienced a small change. As Bare soil and Build-up seem quite similar in image classification, the change in mean temperature also had shown high variation over time.
Discussion
According to the study, the dominant land cover in 2011 was vegetation, which was replaced by Build-up or urban areas in 2021. Between 2011 and 2021, the area of vegetation has declined by over 10% (30.46 km2) as a result of constructing infrastructures such as brickfields, artificial afforestation, and clearing crops to make way for development activities.
Due to an altered built-up area, farmland, riverine areas filling up with sand, river embankment, developed settlement, and infrastructure, the area’s second LULC class, Water, declined by 3.27 percent (9.94 km2). The built-up area of the study area has risen by more
Figure 6(a) LST distribution over different LULC classes (2011)
Figure 6(b) LST distribution over different LULC classes (2021)
than 1.5 times (61.31 km2) due to the expansion of the Savar Upazila’s industrial infrastructure over the last decade. In this study, it was discovered that urbanization is rapidly and unpredictably expanding, as well as unsustainable. The amount of bare soil has also decreased as a result of urbanization. LST has an increasing trend from 2011 to 2021 along with each LULC class. But the change is greater in the Build-up area and Bare soil. Bare soil shows a higher mean LST increase than Build-up area, this is possible for some misleading training sample as these two classes seem quite similar in composite raster and close value of NDVI. Also, these two classes have quite a similar temperature range.
The study discovered a link between LST and LULC alteration. The increase in LST is accelerated by LULC change over time. In some regions, especially where vegetation has been lost and urban expansion has occurred, LST (50.9°C) has increased dramatically.
Accuracy of data
Despite the use of a well-established methodology to extract the spatiotemporal distribution of LST, slight mistakes may still exist. Cloud-free images of the study area are required to obtain an accurate LST of the study area. Even if the land cloud cover in satellite images was less than 10%, it was not exactly zero for each image. For this reason , it differed slightly from the data collected in the field (Dar et al., 2019; Kafy et al., 2021). As a result of these issues, the LST distribution in any location may be skewed. For validation, maximum and minimum temperature data from the Worldweatheronline website in the studied region were acquired.
The average LST were considered to verify the outcome. Because weather stations collect data on an hourly basis and the spatial distribution was taken into account in the study. The average LST value during the day is between the minimum and maximum air temperature (Table 5). Furthermore, LST values are not the same parameter as air temperature. During the day, LST might be more extreme than the air temperature.
Table 5: Validating LST result with air temperature
Date Average (LST
in℃)
Min (Air
Temperature in℃)
Max (Air
Temperature in
℃)
Humidity (%)
18/04/2021 35.50 26 40 55
07/04/2011 31.56 24 38 43
[Source: www.worldweatheronline.com]
Conclusion
The present study identified the reason of rising LST in the Savar area over time. Over the last decade, the Build-up area has rapidly increased (from 39.05 % to 60.49 %). Vegetation, Water, and Bare soil, on the other hand, have all reduced dramatically. The quantity of territory with a high-temperature range has grown, as the mean LST has risen drastically (almost 4°C) in tandem with the increase of impermeable surfaces. From 2011 to 2021, the largest positive increase was seen in the Build-up area (+23.17 %) in comparison with
vegetation (+12.5%). The assessed LST classes in respect to the area showed that lower recorded temperature zones in 2011 were replaced by the higher temperature zone in 2021. In 2021, around 8% of the region is in the maximum temperate (>40°C) zone.
Due to its location in a monsoon-dominated region, the climate in Savar is humid. In this rapidly human-settling urban area, higher temperatures paired with excessive humidity could result in a significant increase in public health risk. It is important to monitor and predict the change in LULC and the city’s LST pattern in order to mitigate the impact of Urban Heat Island and the thermal discomfort caused by unchecked urbanization. To avoid catastrophic consequences in the next years, the city must focus urgently on reducing the urban heat phenomenon. Some adaptation measures can be implemented during the early stages of city design, while others can be used later. In Italy, a green and cool roof design resulted in a considerable temperature reduction of 2-5°C (Costanzo et al., 2016). In Hong Kong, city planning with abundant water bodies reduced the metropolitan temperature by 0.7°C (Fung and Jim, 2020) and by 3°C in Japan (Fung and Jim, 2020). (Imam Syafii et al., 2017). In the United Kingdom (Armson et al., 2012), China (Sun and Chen, 2017), Germany (Sodoudi et al., 2018), Ethiopia (Feyisa et al., 2014), Egypt (Aboelata and Sodoudi, 2019), and Copenhagen, greening urban spaces resulted in a 1-6°C temperature drop compared to other adaptation strategies (Yang et al., 2020). For a secondary city like Savar, these adaptation methods could be beneficial.
Acknowledgement
We acknowledged the USGS archives authority for providing the satellite images with free of cost. We are thankful to the Department of Environmental Sciences, Jahangirnagar University for providing the opportunity to conduct the study. Authors are also very to the unanimous reviewers for their valuable comments to scrutinize our manuscript.
References
Aboelata, A., Sodoudi, S., 2019. Evaluating the effect of trees on UHI mitigation and reduction of energy usage in different built up areas in Cairo. Building and Environment, 168, 106490.
https://doi.org/10.1016/j.buildenv.2019.106490
Argüeso, D., Evans, J. P., Fita, L., Bormann, K. J., 2014. Temperature response to future urbanization and climate change. Climate Dynamics, 42(7), 2183–2199. https://doi.org/10.1007/s00382-013- 1789-6
Armson, D., Stringer, P., Ennos, A. R., 2012. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban Forestry & Urban Greening, 11(3), 245–255.
https://doi.org/10.1016/j.ufug.2012.05.002
Bhuiyan, M. A. E., Witharana, C., Liljedahl, A. K., Jones, B. M., Daanen, R., Epstein, H. E., Kent, K., Griffin, C. G., Agnew, A., 2020a. Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery. Journal of Imaging, 6(9), 97.
https://doi.org/10.3390/jimaging6090097
Bhuiyan, M. A. E., Witharana, C., Liljedahl, A. K., 2020b. Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types. Journal of Imaging, 6(12), 137.
https://doi.org/10.3390/jimaging6120137
Costanzo, V., Evola, G., Marletta, L., 2016. Energy savings in buildings or UHI mitigation? Comparison between green roofs and cool roofs. Energy and Buildings, 114, 247–255.
https://doi.org/10.1016/j.enbuild.2015.04.053
Dar, I., Qadir, J., Shukla, A., 2019. Estimation of LST from multi-sensor thermal remote sensing data and evaluating the influence of sensor characteristics. Annals of GIS, 25(3), 263–281.
https://doi.org/10.1080/19475683.2019.1623318
Feyisa, G. L., Dons, K., Meilby, H., 2014. Efficiency of parks in mitigating urban heat island effect: An example from Addis Ababa. Landscape and Urban Planning, 123, 87–95.
https://doi.org/10.1016/j.landurbplan.2013.12.008
Fung, C. K. W., Jim, C. Y., 2020. Influence of blue infrastructure on lawn thermal microclimate in a subtropical green space. Sustainable Cities and Society, 52, 101858.
https://doi.org/10.1016/j.scs.2019.101858
Igun, E., Williams, M., 2018. Impact of urban land cover change on land surface temperature. Global Journal of Environmental Science and Management, 4(1), 47–58.
https://doi.org/10.22034/gjesm.2018.04.01.005
Imran, H. M., Hossain, A., Islam, A. K. M. S., Rahman, A., Bhuiyan, M. A. E., Paul, S., Alam, A., 2021.
Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh. Earth Systems and Environment, 5(3), 667–693.
https://doi.org/10.1007/s41748-021-00243-4
Imam Syafii, N., Ichinose, M., Kumakura, E., Jusuf, S. K., Chigusa, K., Wong, N. H., 2017. Thermal environment assessment around bodies of water in urban canyons: A scale model study.
Sustainable Cities and Society, 34, 79–89. https://doi.org/10.1016/j.scs.2017.06.012
Jahan, K., Pradhanang, S. M., Bhuiyan, M. A. E., 2021. Surface Runoff Responses to Suburban Growth:
An Integration of Remote Sensing, GIS, and Curve Number. Land, 10(5), 452.
https://doi.org/10.3390/land10050452
Kafy, A.-A., Islam, M., Sikdar, S., Ashrafi, T. J., Al-Faisal, A., Islam, M. A., Rakib, A. A., Khan, M. H.
H., Sarker, M. H. S., Ali, M. Y., 2021. Remote Sensing-Based Approach to Identify the Influence of Land Use/Land Cover Change on the Urban Thermal Environment: A Case Study in Chattogram City, Bangladesh. In Re-envisioning Remote Sensing Applications. CRC Press.
Kontoes, C., Stakenborg, J. 1990. Availability of cloud-free Landsat images for operational projects.
The analysis of cloud-cover figures over the countries of the European Community. International Journal of Remote Sensing, 11(9), 1599-1608.
Nzoiwu, C. P., Agulue, E. I., Mbah, S., Igboanugo, C. P., 2017. Impact of Land Use/Land Cover Change on Surface Temperature Condition of Awka Town, Nigeria. Journal of Geographic Information System, 9(6), 763–776. https://doi.org/10.4236/jgis.2017.96047
Rahman, Md. L., Rahman, S. H., 2021. Detection of Land Use Land Cover Changes Using Remote Sensing and GIS Techniques in a Secondary City in Bangladesh. Grassroots Journal of Natural Resources, 4(3), 132–146. https://doi.org/10.33002/nr2581.6853.040311
Sisodia, P. S., Tiwari, V., Kumar, A., 2014. Analysis of Supervised Maximum Likelihood Classification for remote sensing image. International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), 1–4. https://doi.org/10.1109/ICRAIE.2014.6909319
Yang, G., Yu, Z., Jørgensen, G., Vejre, H., 2020. How can urban blue-green space be planned for climate adaption in high-latitude cities? A seasonal perspective. Sustainable Cities and Society, 53, 101932. https://doi.org/10.1016/j.scs.2019.101932
