Evaluating the Scenario of Urban Blue-Green Space in Tezpur Town of Assam using Geo-Technical Approach

doi:10.21203/rs.3.rs-3398688/v1

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Evaluating the Scenario of Urban Blue-Green Space in Tezpur Town of Assam using Geo-Technical Approach

https://doi.org/10.21203/rs.3.rs-3398688/v1

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Urban blue-green space (UBGS) is considered an effective way to mitigate Urban Heat Island (UHI) effects. UBGS not only cools the actual space but also influences the surrounding areas, this phenomenon is termed as urban blue-green space cooling effect. The intensity and density of cooling of the UBGS are one of vital issues of its cooling effects which can play a vital role for urban planners and designers to deal with UHI effects. The present study tries to anatomize the UBGS of urban Tezpur with the help of geo-technology based on the years 1993 and 2023. Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were taken into consideration for the ascertainment of UBGS and UHI. The correlation between LST and NDVI was determined with the aid of Simple Regression analysis. The NDVI values for the years 1993 and 2023 are − 0.32 to 0.70 and − 0.44 and 0.50 respectively. The LST values of the town for the year 1993 are 28.76 to 20.17 and for 2023 the LST value is 29.47 to 20.36. The NDWI value indicates that the water index increases in the water bodies from the year 1993 to 2023

Urban Green Space

Urban Blue Space

Urban Heat Island

Land Surface Temperature

NDVI

NDWI

Landscape patterns and processes are undergoing gradual changes due to urbanization resulting in the decrease of blue-green space (Estoque et al., 2017; Shiflett et al., 2017; Yu et al., 2019) along with the alteration of atmospheric composition in the near-surface layers (Lewis and Maslin, 2015; Oke, et al., 2017). Urban Heat Island (UHI) one of the most typical urban meteorological phenomena is defined as high-temperature urban areas than surrounding rural areas (Manoli et al., 2019; Oke, et al., 2017), UHI is the difference between the highest urban temperature and countryside of a region (Oke, 1973). Stewart (2011), states that the conventional definition of UHI is facing serious challenges and hence, a new schema of Local Climatic Zone (LCZ) to access UHI pattern and intensity was proposed by Stewart and Oke (2012). The urbanization process results in the replacement of natural surface by artificial impervious surface which leads to UHI effects (Lo, et al., 1997). Urban energy consumption, air pollution, water shortage, and extreme hot waves are largely related to UHI effects worldwide (Coseo and Larsen, 2014; Debbage and Shepherd, 2015; Zhang et al., 2013). Du et al., (2019) state that the importance of urban planning and landscape ecology, and mitigation of UHI effects are the major focuses of the recent studies. Undergoing patches of high landscape fragmentation, urbanization leads to climatic change at both local and global levels including the alternation of biogeochemical cycle (Grimm et al., 2008; Yao et al., 2019). Studies have highlighted that UHI effects are strengthened by both urban size and population (Zhang et al., 2015; Ramamurthy and Bou-Zeid, 2017; Wu et al., 2019; Dai et al., 2021).

The Urban landscape research refers to water bodies, farmlands, urban parks, forests, grasslands, and other green spaces as ecological land that have significant cooling effects (Pickett et al., 2011; Gunawardena et al., 2017; Fan et al., 2019), this can be defined as urban blue-green space. Studies suggest that blue and green spaces have Urban Cool Island (UCI) effects (Sun et al., 2012; Anjos and Lopes, 2017; Pelorosso et al., 2017). Gunawardena et al.,(2017) highlight that blue-green space has high thermal capacity and evaporation effects which mitigates UHI effects by cooling down the surrounding microclimate. But to infinitely increase the blue-green area in cities is impracticable due to land use efficiency. The urban researcher states that maximizing the UCI effect with limited space is the most practical alternative (Zhang et al., 2017; Montazeri et al., 2017; Dai et al., 2018).

Urban blue-green space as a prominent component of the ecosystem, provides large numbers of ecosystem services such as regulating micro-climate, carbon and oxygen fixing, biodiversity conservation, and many others (Tzoulas et al., 2007), it can also mitigate the deterioration of urban environment caused by urbanization (Dang and Li, 2021). Peng et al. (2016) highlight that the UHI effect would be significantly mitigated if the ecological land coverage exceeds more than 70% in urban areas. Studies have found that the cooling effect of blue-green space depends on the shape, size, connectivity, and complexity of the space.

The greenness of the vegetation and blueness of the water bodies is measured by Normalized Difference Vegetation Index (NDVI) (Gunawardena et al., 2017; Kuang et al., 2015; Ren et al., 2016; Santamouris et al., 2018) and Normalized Difference Wetted Index (NDWI) respectively. However, some researchers have proposed that rather than landscape composition, landscape configuration is a prominent factor in the cooling effect of blue-green space (Peng et al., 2016; Sun and Chen, 2017; Yu et al., 2018).

The understanding of the land surface-atmosphere exchange process at both local and global levels can be improved with new perspicacity into LST which would provide a valuable surface state metric for numerous applications (Li et al., 2023). Hollmann et al., (2013) state that the United States (US) Climate Change Science Program, National Aeronautics and Space Administration, as one of the 10 essential climate variables in the land biosphere designated by the Global Climate Observing System, LST has been recognized as an essential Earth surface parameter. LST is an important parameter to measure the urban health status (Chaudhuri and Mishra 2016) as it provides information regarding climate which aids in understanding and anatomizing urban climate (Bendib et al. 2017; Govind and Ramesh 2019). The correlation between LST and urban expansion has been examined by numerous studies (Ali et al. 2017; Nandkeolyar and Kiran 2018; Sahana et al. 2019; Mukherjee and Singh, 2020).

The casual relationship between UHI and urbanization led many researchers to study the mechanism of the urban landscape and its thermal environment (Liu et al., 2018; Silva et al., 2018). Land surface temperature (LST) derived from remotely sensed imagery is generally used to study the spatio-temporal pattern of LST and its relation with urban landscapes with multi-scale modeling (Estoque et al., 2017; Qiao et al., 2019). Recent studies highly emphasize the role of Urban Green Space (UGS) (Gago et al., 2013; Wu and Ren, 2019; Yao et al., 2020) and Urban Blue Space (UBS) in the urban planning process.

Land use and land cover (LULC) change is another prominent parameter used to study urbanization and its impacts (Mukherjee and Singh, 2020). The alteration of LULC is fueled by the conversion of the landscape by anthropogenic activities (Xiao and Weng 2007; Tan et al. 2010). Previous studies highlight that the impact of LULC changes can also be examined with the aid of LST (Feizizadeh et al. 2013; Adams and Smith 2014; Sahana et al. 2016; Tran et al. 2017). The relationship between LST and LULC can be examined by the Normalized Difference Vegetation Index (NDVI) (Nandkeolyar and Kiran 2019; Guha et al. 2018).

As India is going through a massive urbanization process, today one in every three Indians dwells in an urban area (Mukherjee and Singh, 2020), India has about 475 urban agglomerations (UA) or cities (Census, 2011). The rapid pace of urban expansion in India is not only limited to the mega cities but also has impacted other urban agglomerations throughout the country. Studies reveal that urban blue-green studies in India mainly focus on megacities, even in which only green space has the primary focus.

2.1. Description of the Study Area:

The cultural capital of Assam ‘Tezpur’ is situated between 26º 38ʹ North latitude and 92 º 48ʹ East longitudes, covering an area of 40 km². Tezpur is bounded by Rangapara in its north, and Nagaon in the south, while its east and west sides are bounded by Jamugurighat and Dhekiajuli respectively. The climate of the area is sub-tropical monsoon with an average temperature of 21° C in summer and 10° C in winter. The total urban population of Tezpur is 102,505 (Census, 2011).

2.2. Database and Data Sources:

The relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) plays a vital role in the determination of Urban Green Space and Urban Heat Island. The measurement of Urban Heat Island (UHI) is one of the prominent ways of determining the trend of declining temperature towards the outskirts the determination of Urban Green Space and urban health of Tezpur town of Assam, Landsat data of from different years were used. Landsat 5 TM for 1993 and Landsat 8 OLI for 2023 were taken into consideration for obtaining the changing trend. The details of the data used for the study are shown in Table 1.

Table 1

Remote Sensing Data Used in the Study
Sensors	Date	Bands	Resolution	Purpose
Landsat 5 TM	30/04/1993	B3 and B4 B2 and B4 B6	30 m 30 m 120 m	NDVI NDWI LST
Landsat 8 OLI	11/05/2023	B4 and B5 B3 and B5 B10	30 m 30 m 100 m	NDVI NDWI LST

2.3. Data Processing and Methodology:

For the anatomization of NDVI, NDWI, and LST, satellite data were employed in ArcGIS 10.3. Spectral bands 4 and 5 of Landsat 8 were used for NDVI and NDWI while band 10 was used for LST extraction. The relationship between Urban Blue-Green Space and LST was established with the aid of Simple Linear Regression. The LST maps were later converted into Urban Heat Island (UHI). The methodological flowchart (Fig. 2) highlights the details of the methods of the study.

2.3.1. Landuse Landcover (LULC) Change:

The Landuse and landcover (LULC) change has been a great important in recent studies for monitoring physic-cultural changes and managing natural resources (Rawat, et al., 2013; Kumari, et al., 2014; Twisa and Buchroithner, 2019). Landuse and landcover are two composite terms that are frequently used conversely to describe features on the land surface (Vargo, et al., 2013; Liping, et al., 2018; Gondwe, et al., 2021). An increasing number of anthropogenic activities lead to alterations of the land surface on a global scale (Lambin, et al., 2001; Saikia and Saikia, 2020).

2.3.2. Normalized Difference Vegetation Index (NDVI):

The amount of vegetation cover along with the greenness of an area can be measured with the help of the Normalized difference vegetation index (NDVI). A high NDVI value highlights a high degree of greenness as well as healthy green (Curran, 1980). NDVI is used based on the vegetation responses to absorption and reflection of near-infrared and red lights; it is one of the prominent methods that are largely used in detecting land cover and land use change on a spatio-temporal scale. NDVI values range from − 1 to 1, where 0 indicates the presence of urban areas, the negative value existence of water bodies, and the positive value presence of green cover (Sahana et al. 2016; Babalola and Akinsanola 2016). NDVI is calculated using the formula 1.

NDVI = \(\frac{(\text{N}\text{I}\text{R} – \text{R}\text{e}\text{d})}{(\text{N}\text{I}\text{R} + \text{R}\text{e}\text{d})}\) (1)

2.3.3. Land Surface Temperature (LST):

For the detection of Urban Heat Island (UHI) and surface energy and water balance, Land Surface Temperature acts as a vital parameter (Zhang et al. 2008; Kustas and Anderson 2009; Karnieli et al. 2010). The spectral radiance method (Sahana et al. 2016; Tran et al. 2017; Mukherjee and Singh 2020) was used for the recovery of surface temperature from Landsat data.

To calculate LST for the year 1993, Landsat 5-TM (Spectral Band 6) was taken into consideration. Formula 2 was used to retrieve spectral radiance from the Landsat 5 image.

Lλ = \(\left(\frac{\text{L} \text{M}\text{a}\text{x}{\lambda } – \text{L} \text{M}\text{i}\text{n}{\lambda }}{\text{Q} \text{C}\text{a}\text{l} \text{M}\text{a}\text{x}-\text{Q} \text{C}\text{a}\text{l} \text{M}\text{i}\text{n}}\right)\) × (Q Cal – Q Cal Min) + L Minλ (2)

Here Lλ represents spectral radiance; L Maxλ and L Minλ are the Radiance Maximum and Radiance Minimum values of Band 6. Q Cal is the Band 6 of Landsat 5 (Thermal Infrared). Q Cal Max and Q Cal Min are the Quantized and Calibrated Maximum, Minimum values of Band 6. The retrieved spectral radiance was converted to Land Surface Temperature (LST) by using the formula 3.

LST = \(\frac{{\text{K}}_{2}}{\text{L}\text{n}(\frac{{\text{K}}_{1}}{\text{L}{\lambda }}+ 1)}\) − 273.15 (3)

Here LST represents Land Surface Temperature; K₁ and K₂ are thermal conversion constants. According to Xu and Chen (2004), the absolute zero (approx. −273.15) is added to the above formula to convert the temperature units to the Celsius scale.

For the calculation of LST, Landsat 8 OLI (Spectral band 10) of the year 2023 was taken into consideration. Formula 4 was used to retrieve spectral radiance from Landsat 8 images.

Lλ = ML × Q_cal + AL - O_i (4)

Here Lλ is spectral radiance, ML represents band-specific multiplicative rescaling factor, Q_cal is thermal band, AL represents band-specific additive rescaling factor and O_i is the correlation value for band 10 i.e. 0.29. The retrieved spectral radiance was converted to surface temperature using the following formula 5 after the extraction process.

T_B = \(\frac{{\text{K}}_{2}}{\text{L}\text{n}(\frac{{\text{K}}_{1}}{\text{L}{\lambda }}+ 1)}\) − 273.15 (5)

Here T_B represents surface temperature; K₁ and K₂ are thermal conversion constants. Xu and Chen (2004) state that to convert the temperature units to Celsius, absolute zero (approx. −273.15) is added to the above formula. Vegetation Fraction (F_V) and Land Surface Emissivity were used to retrieve surface temperature from band 10 of the Landsat 8 data, values from the above two equations. Vegetation Fraction was computed using the formula 6 (Carlson and Ripley 1997).

F_V = \(\left(\frac{\text{N}\text{D}\text{V}\text{I} - {\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{i}\text{n}}}{{\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{a}\text{x}} - {\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{i}\text{n}}}\right)\)² (6)

Here NDVI_min is the minimum NDVI value highlighting the presence of bare soil and NDVI_max is the maximum NDVI value highlighting the presence of healthy vegetation (Guha et al., 2018). Emissivity (ε) was calculated with the aid of formula 7.

ε = 0.004 × F_V + 0.986 (7)

The Land Surface Temperature (LST) was measured using the formula 8 (Weng et al. 2004; Guha et al. 2018).

LST = \(\frac{{T}_{B}}{1+ \left(\frac{{\lambda \sigma T}_{B}}{hc}\right) \text{L}\text{n} {\epsilon }}\) (8)

Here h represents Plank’s constant (6.626 × 10^− 34 Js), λ is effective wavelength (10.8 mm for band 10 in Landsat 8 data), σ is Boltzmann constant (1.38 × 10^− 23 J/K) and c is the velocity of light in vacuum (2.998 × 10^− 8 m/s).

2.3.4. Regression Analysis:

For summarizing the LST, NDWI, and NDVI values, statistical approaches were used after the determination of LST, NDWI, and NDVI. Simple Regression Analysis was employed to ascertain the correlation between LST and NDWI, LST and NDVI. The values were derived from each pixel with the aid of ArcGIS 10.3 and a correlation of the same was gleaned using Microsoft Excel.

2.3.5. Normalized Difference Water Index (NDWI):

Normalized Difference Water Index (NDWI) (Mc Feeters, 1996), a two-band ratio approach is widely used for the delineation of water bodies. NDWI makes use of the normalized ratio of near-infrared (NIR) and green bands for delineating the water feature. It highlights the water attribute and represses non-water particulars and hence, distinguishes water from non-water by selecting a suitable threshold value. Xu (2006) states that NDWI is the combination of the difference of spectral responses between the NIR and green features, reckoning the string absorption of NIR by water bodies and its high reflectance by soil and vegetation. Thus, NDWI is defined as the normalized ratio between the high reflective bands (ρh) and low reflective band (ρl) (Xie et al., 2016)

NDWI = \(\frac{(\text{G}\text{r}\text{e}\text{e}\text{n} – \text{N}\text{I}\text{R})}{(\text{G}\text{r}\text{e}\text{e}\text{n} + \text{N}\text{I}\text{R})}\) (9)

3.1. Landuse and Landcover Change:

In the present study, the LULC classes were assigned four classes viz. water bodies, vegetation, settlement, and other lands (Figs, 3-A and 3-B). The total area and percentage of different LULC classes of the years 1993 and 2023 are showcased in Table 2. As depicted in Table 2, Tezpur town witnessed a tremendous increase in its settlement area and a systematic decrease in its vegetative area and other lands within the studied period.

Table 2

LULC Classes of Tezpur Town in Temporal Scale
LULC Classes	Area in sq km		Area in %
LULC Classes	1993	2023	1993	2023
Settlement	11.123	20.625	34.40	63.78
Vegetation	13.545	7.776	41.89	24.05
Water Body	0.659	1.146	2.04	3.54
Others	7.011	2.791	21.68	8.63
Total Area	32.338	32.338	100.00	100.00

A visible change of 29.38% (9.5502 sq. km.) was observed in the settlement area of Tezpur. Similarly, the water bodies also highlighted a positive change of about 1.50% (0.487 sq. km.) from 1993 to 2023. On the other hand, vegetative areas and other land showcased a negative change. The vegetative and other areas of the town decreased by 17.84% (5.769 sq. km.) and 13.05% (4.22 sq. km.) within the period respectively. As highlighted by Figs. 3-A and B, the settlement area initially (1993) at the middle part of the southern Tezpur town sprawled to the entire town by 2023 resulting in urban sprawl and the decrease of vegetative lands and other areas. The water bodies of the region also represented a positive change mainly in the eastern part of the town.

3.2. NDVI Distribution:

Liu et al. (2015) state that factors like slope, topography, availability of radiation, etc. influence the spatial variation of NDVI. Normalized difference vegetation index (NDVI) of Tezpur town for the years 1993 and 2023 are showcased in Fig. 4-A and B respectively for the four LULC classes.

As seen in Figs. 4-A and B, the NDVI values varied between − 0.32 and 0.70 in the year 1993 which reduced to -0.44 and 0.50 in the year 2023. The highest NDVI exhibited during the studied periods was in the year 1993.

3.3. LST Distribution:

Figure 6A and B showcase the Land Surface Temperature (LST) of Tezpur town. The LST values of the town for the year 1993 are 28.76 to 20.17 and for 2023 the LST value is 29.47 to 20.36. Average values of LST for the years 1993 and 2023 were recorded at 23.99 and 26.16 respectively.

3.4. Correlation Between NDVI and LST:

Figures 6-A and B represent the correlation between NDVI and LST of Tezpur town. The trend line of both the figures highlights the highly negative correlation between the NDVI and LST. A coefficient of determination (r²) of 0.187 for 1993 and 0.163 for 2023 is showcased by the Linear Regression. The correlation values were − 0.43 and − 0.40 for the years 1993 and 2023 respectively.

3.5. NDWI Distribution:

Figure 7A and B showcase the Normalized Difference Water Index (NDWI) of Tezpur town for the years 1993 and 2023 respectively. The distributional pattern of NDWI explains that a small channel is passing amidst the Tezpur town. Similarly, a large tank is also observed in the middle of the southwestern part of the town. The water index increased in both the water bodies from the year 1993 to 2023. On the other hand, the lower middle part of the town also showcases a large-scale water body with an increasing trend from 1993 to 2023, but the pattern is the misinterpretation of the software due to the radiometric error of satellite data.

The uninterruptedly growing impact of global warming is exposing the urban areas to a nodus of intensifying UHI effects. The academic attention to the UHI impacts can provide urban planners and designers with feasible strategies to address the growing UHI issues. This paper tries to anatomize and evaluate the scenario of UBGS of the studied Tezpur town. The Tezpur town experienced an increasing trend of UHI during the studied period like any other town such as Shanghai (Tan et al., 2010); Beijing (Yao et al., 2020); Wuhan (Wu et al., 2019); Seremban, Malaysia (Aburas et al., 2015); Guwahati (Chetia et al., 2020); Jorhat (Neog and Hazarika 2022); Surat and Bharuch (Mukherjee and Singh, 2020),

Several factors such as low evapotranspiration as a result of less green cover, hindrance in airflow because of high rugosity, release of anthropogenic heat in large amounts, and absorption of solar radiation because of low albedo (Santamouris et al., 2007; Nuruzzaman, 2015). Yang et al. (2016) studied the urban heat island effects in which the researchers emphasized the importance of water bodies in balancing urban temperature. The study highlighted the role played by the urban rivers and lakes in reducing the UHI effects in Bozhou, China. Urban and economic development often results in the decline of green space in a region. Seremban, Malaysia witnessed a negative trend in its vegetative cover during the years 1990 and 2010 due to land use activities (Aburas et al., 2015).

The present paper tries to adopt a comprehensive approach with the exercise of the linear regression method for incepting a correlation between LST and NDVI for the determination of the UHI phenomenon in the town. The study highlights a negative correlation between the LST and NDVI in the region. Thus, showcasing the influence of the UHI effect in the area, however, the presence of the river Brahmaputra in its eastern and southern parts to a certain extent influences the local climate of the region and helps in mitigating extreme UHI effects.

Thus, it can be concluded that with the increasing impact of global warming, the impacts of UHI effects also tend to increase exposing the towns and cities to various existing and newfangled issues. However, this effect can be mitigated with the implementation of proper strategies. The study brings a crucial conclusion that the land use and land cover of a region can greatly influence its urban health including the mitigation of UHI to a larger extent like that of the presence of the Brahmaputra river near Tezpur town. The paper paves the way for planners, designers, and policymakers for adaptation approaches and strategies for robust urban planning.

Conflict of Interest:

The authors declare that they have no conflict of interest.

Acknowledgment:

The authors wish heartfelt thanks to the Centre for Studies in Geography, Dibrugarh University; the Department of Geography, DHSK College, Dibrugarh of Assam, India and the Department of Geography, Rajiv Gandhi University of Arunachal Pradesh, India for giving such an opportunity to conduct the research smoothly.

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Editorial decision: Major revisions
17 Dec, 2023
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Evaluating the Scenario of Urban Blue-Green Space in Tezpur Town of Assam using Geo-Technical Approach

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Description of the Study Area:

2.2. Database and Data Sources:

2.3. Data Processing and Methodology:

2.3.1. Landuse Landcover (LULC) Change:

2.3.2. Normalized Difference Vegetation Index (NDVI):

2.3.3. Land Surface Temperature (LST):

2.3.4. Regression Analysis:

2.3.5. Normalized Difference Water Index (NDWI):

3. Results and Analysis

3.1. Landuse and Landcover Change:

3.2. NDVI Distribution:

3.3. LST Distribution:

3.4. Correlation Between NDVI and LST:

3.5. NDWI Distribution:

4. Discussion and Conclusion

Declarations

Conflict of Interest:

Acknowledgment:

References

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Version 1