7 - China

Assessing the Quantitative Relationships Between Impervious Surface Area and Surface Heat Island Effect During Urban Expansion

Abstract

This study investigates the quantitative relationships between impervious surface area (ISA) and surface urban heat island (SUHI) effects during urban expansion using multi-source remote sensing data and geospatial analysis methods. The research focuses on Kunming, China, and examines the temporal and spatial changes in ISA's influence on SUHIs.

Key findings:

1. Urban expansion significantly aggravates SUHIs. There's a general consistency between ISA and land surface temperature (LST) in spatial distribution, but local spatial differentiation is significant. The highest LST areas are not always in the downtown area with the highest ISA but can be scattered in cultivated land and exposed surface areas under development.
2. ISA generally explains the spatial distribution of LST well, showing an obvious positive correlation. A quadratic polynomial function provides the best fit between ISA and LST.
3. Ecological elements like green space and water bodies play a crucial role in alleviating SUHIs. The urban center with the highest ISA coverage may not have a significant SUHI due to the allocation of green space and water bodies.

This research offers a scientific basis for urban planning and ecological environment construction.

Keywords

Urban expansion, Surface urban heat island, Tempo-spatial variation, Geospatial analysis, Plateau lakeside city

Introduction

Urban Heat Island (UHI) Effect

• Definition: Urban areas experience higher temperatures than their suburbs.
• Significance: Represents the impact of urbanization on local/regional microclimates, affecting the urban ecological environment and livability.

UHIs are categorized into:

• Atmospheric UHIs: Measured by air temperature, divided into boundary layer UHIs and canopy layer UHIs.
• Surface UHIs: Defined by land surface temperature (LST) measurements, detected by thermal infrared remote sensing, characterizing surface energy flow.
  • Surface UHIs have greater temporal and spatial differentiation and are more sensitive to surface characteristics and human activities.

Land Surface Temperature (LST)

• Importance: A parameter characterizing surface radiation and energy exchange.
• Application: Widely used to study the spatial patterns of surface UHIs and their relationships with urban surface characteristics.

Urbanization in China and UHI

• Rapid Urbanization: China's urbanization has led to increasingly serious UHIs.
• Negative Impacts of UHI: Aggravates energy consumption and environmental pollution, reduces quality of life and health, causes changes in the urban ecological environment and atmospheric pattern.
• Challenge: Eliminating the UHI effect in the context of urbanization.

Factors Influencing UHI

• Complex mechanisms: Affected by various factors:
  • Landscape pattern of the urban underlying surface
  • Artificial heat sources
  • Urban atmospheric environment
  • Population movement
  • Urban layout and shape
  • Material properties of the urban surface
  • Urban weather and location
• Urban Expansion: Changes in the underlying surface landscape pattern caused by urban expansion are a significant driving factor of the UHI effect.

Impervious Surface Area (ISA)

• Definition: A measure of the urban underlying surface that is closely related to the urban LST.
• Importance: Fundamental to studying surface UHI indicators.
• Impact: Directly affects the vertical radiation balance by modifying surface albedo, specific emissivity, and surface roughness, intensifying the surface sensible heat flux and UHI intensity.

Traditional vs. Remote Sensing Methods

• Traditional Method: Analyzing temperature difference data from statistical yearbooks and surface weather stations.
  • Disadvantages: Single sample, low spatial accuracy, long cycle, high cost, inability to accurately reflect the heterogeneity of urban land cover and thermal environment.
• Remote Sensing Technology: Offers a wide detection range, rapid data acquisition with short time resolution, and a large amount of information.
  • Application: Used to study the relationship between landscape patterns and LST in the context of urban expansion.

Research Gap

• Limited Research on ISA and Ecological Factors: Most studies focus on the mechanism by which land cover/land use affects the thermal regulation of ecological services, with a lack of research on the quantitative relationship between key land cover components (ISA) and ecological factors (LST).
• Geographical Focus: Some domestic scholars have studied the quantitative relationship between ISA and LST, but mainly in megacities, with little research on emerging first-tier cities like Kunming.
• Methodological Limitations: Existing studies may use only a single analysis method, making the specific relationship unclear.

The Study's Focus on Kunming

• Strategic Importance: Kunming is an international commercial center radiating to South and Southeast Asia.
• UHI Sensitivity: High sensitivity to the increasing effect of UHI on tourism development, ecological quality, human settlement, and the urban image.

Research Objectives

1. Analyze the spatial pattern and quantitative change characteristics of ISA and LST.
2. Quantitatively study the relationship between ISA and LST.
3. Explore the impact of urban expansion on the temporal and spatial evolution of the thermal environment.

Methodology

• Data: Landsat TM, ETM+, OLI/TIRS data.
• Approaches: Profile, difference, and regression analysis.

Research Aims

To provide a decision-making basis for Kunming City to effectively alleviate the UHI effect, improve the somatosensory index and the comfort of the living environment, as well as for future urban planning and construction.

Materials & Methods

Study Area: Kunming

• Location: 102°10′-103°40′E, 24°23′-26°22′N, Yunnan Province, China.
• Significance: Provincial capital, core metropolis of the central Yunnan city group, and one of the most important central cities in western China.
• Urban Area: Main urban area consists of five districts (Wuhua, Guandu, Xishan, Panlong, and Chenggong) with 52 townships, covering an area of 2,622 $km^2$.
• Population: In 2018, the registered population of the main urban area was approximately 2.485 million, accounting for 44.37% of Kunming’s registered population.
• Economy: GDP reached 367.12 billion yuan, accounting for approximately 75.59% of Kunming’s GDP.
• Economic Development: The main urban area is the most economically developed and densely populated area in the city, experiencing the fastest urbanization process.
• Topography: Located on the Yunnan-Guizhou Plateau, high in the north, low in the south, uplifted in the middle, and low on the east and west sides. The overall distribution is ladder-shaped from north to south. The urban area is located in Kunming Basin, with an average altitude of 1,891 m.
• Climate: Low-latitude subtropical-plateau mountain monsoon climate, with an average annual temperature of 15 °C, annual precipitation of 1,035 mm, and annual temperature difference of 12–13 °C.
• UHI Specificity: The UHI effect is different from that in other cities due to the complex terrain and the particularity of the climate.
• Urban Expansion: Since the beginning of the 21st century, urban construction has developed rapidly, the ISA has increased significantly, and the urban atmospheric thermal environment has exhibited an increasing temperature trend.
• Research Imperative: It is imperative to explore the interrelationships of the urban expansion of Kunming’s main urban area with its impacts on the formation and evolution of the surface UHI effect.

Data Descriptions and Preprocessing

• Data Selection Period: A narrow data selection period highlights the characteristics of the summer thermal environment, but the Yunnan-Guizhou Plateau receives considerable amounts of rain in summer, resulting in poor data quality.
• Study Findings: Chen’s study (Chen & Zhang, 2017) analyzed the UHI effect in Kunming City and found that the highest UHI intensity appeared in April.
• Landsat Satellite Data: Landsat satellite data (including ETM+, TM and Operational Land Imager (OLI) Thermal Infrared Scanner (TIRS) data) are downloaded from the United States Geological Survey (USGS) website.
• Data Collection Times: Different data collection times employed ranging from April to May (near summer), and clear (cloudless) days of satellite transit are selected to ensure that the imaging quality is good.
• Additional Data: To perform image preprocessing, vector data and DEM data for the main urban area are also acquired.
• Preprocessing Operations: Radiometric calibration, atmospheric correction, and clipping of the research area are performed on the selected data using ENVI software.

LST Retrieval

• Method: Atmospheric correction method (radiative transfer equation method).

• Principle: Subtract the estimated atmospheric influence on the surface thermal radiation from the total thermal radiation observed by the satellite sensor to obtain the surface thermal radiation intensity and then convert this thermal radiation intensity into the LST.

• Formula: $Lλ = [ελB(T_s) + (1 - ε)L↓]τ + L↑$ (1)

  • Where:
    • $Lλ$ is the thermal infrared radiance detected by the satellite.
    • $ε$ is the surface specific emissivity.
    • $T_S$ is the true surface temperature (K).
    • $B(T_s)$ is the blackbody radiance.
    • $τ$ is the transmittance of the atmosphere in the thermal infrared band.
    • $L↓$ is the atmospheric downward radiance.
    • $L↑$ is the atmospheric upward radiance (W/ $m^2$/sr/$μm$).

• Blackbody Radiance : $B(T_S)$ is calculated as an intermediate step:

  • $B(T_S) = [Lλ - L↑] / τε$\ (2)

• Ts is obtained by Planck’s formula:
  $TS = K2 / ln(K1 / B(T_s) + 1)$\ (3)

  • Where K1 and K2 are constants specific to the Landsat sensor used.

• Parameters: Atmospheric profile parameters and surface emissivity.

• Atmospheric Profile Parameters: Obtained from NASA’s website, entering imaging time and central longitude and latitude.

• Surface Emissivity: Divided into the emissivity of water bodies, natural surfaces, and urban areas, following Qin’s study (Qin et al., 2004).

• LST Normalization: LST at each time is normalized to the range of [0–1] to better compare and analyze the change characteristics of the surface UHI in space at different times.

  • Formula: $Ni = (Ti - tmin) / (tmax - tmin)$(4)
    • Where
      • $N_i$ is the normalized LST of the i-th pixel.
      • $T_i$ represents the LST of the i-th pixel.
      • $t<em>{min}$ and $t</em>{max}$ represent the minimum and maximum LST values, respectively.

• Temperature Zones: Divided into five levels (low-temperature (LT) zone, medium-low temperature (MLT) zone, medium-temperature (MT) zone, medium-high temperature (MHT) zone, and high-temperature (HT) zone) according to the density segmentation method proposed by Xu’s study (Xu et al., 2014).

• UHI Areas: MHT and HT zones are defined as the UHI areas.

ISA Retrieval

• Impervious Surfaces: Mainly buildings and artificial ground features such as roads, parking lots, public squares, and roofs.
• Building Area Index (BAI): Used to assess the extent of urban expansion.
  • Formula: $BAI = (Blue - NIR) / (Blue + NIR)$(5)
    • where Blue and NIR are the reflectivity values in the blue and near-infrared bands of the image, respectively.
• Modified Normalized Difference Water Index (MNDWI): Used to remove water bodies to improve extraction accuracy for impervious surfaces.
  • Formula: $MNDWI = (Green - MIR1) / (Green + MIR1)$(6)
    • where Green and MIR1 are the reflectivity value in the green and Mid-infrared 1 bands of the image.
• Water Extraction Thresholds: Based on continuous testing, the threshold values of water extraction in different periods are as follows: 2002, MNDWI ≥ −0.05; 2008, MNDWI ≥ 0.0; 2014, MNDWI ≥ 0.1; 2020, MNDWI ≥ 0.1.
• Accuracy Assessment: Based on visual interpretation, 210 training samples are randomly selected to verify the accuracy of the extracted ISA. The overall accuracy was above 86% (Except for the value of 83.64% in 2008) and the Kappa coefficient was above 0.78 (Except for the value of 0.73 in 2008).
• ISA Normalization: Extracted impervious ISA is normalized for quantitative analysis.
  • Formula: $NDISA = (BAI - BAImin) / (BAImax - BAImin)$(7)
    • where BAImax and BAImin generally take the maximum and minimum values within a certain confidence range.
• NDISA Levels: Divided into 5 levels according to an equal-interval density division: low-ISA (LISA:0-0.2), medium-low ISA (MLISA:0.2-0.4), medium-ISA (MISA:0.4-0.6), medium-high ISA (MHISA:0.4-0.8) and high-ISA (HISA:0.8-1).

Geospatial Measurement Analysis

1. Profile Analysis:

• Application: Compare, measure, and analyze the difference between a set of samples from one dimension, then qualitatively and quantitatively summarize the results.
• Method: Profiles of the NDLST and NDISA values are plotted for the four phases of the image along the main expansion direction of the city (from Cuihu to Chenggong), and the value of NDISA and NDLST corresponding to the pixels passing through the profile line are extracted for quantitative analysis.

1. Difference Analysis:

• Application: Solve the difference in a relevant or corresponding quantity; in remote sensing, it involves subtracting the gray value of the corresponding pixel in the multi-temporal image.
• Method: Analyze the differences in the NDLST and NDISA among the four phases of the images to characterize the spatial heterogeneity of urban expansion and the migration and evolution of the surface UHI in the main urban area of Kunming.

1. Regression Analysis:

• Application: Determine the correlation between dependent variables and some independent variables, establish a regression equation with good correlation, and extrapolate it to predict the change of dependent variables in the future.
• Method: Reclassify NDISA at an interval of 0.01, calculate the average of the NDLST in each interval based on its partition statistics function using ArcGIS software, and perform regression analysis on the average value of NDLST and NDISA in the interval using SPSS software to calculate the correlation between them.

Results

Analysis of ISA Expansion Characteristics

• Spatial Distribution: Impervious surfaces of the four periods are consistent in overall spatial distribution, concentrated in areas dominated by houses, factories, roads, and public squares (built-up areas), while areas with lower NDISA are mostly distributed on natural surfaces such as vegetation and farmland.
• NDISA Trends: Areas with higher ISA values have gradually expanded, reflecting Kunming’s rapid development since the turn of the 21st century, with the largest expansion and the most obvious changes occurring during 2008–2014.
• Urban Expansion Direction: Kunming’s urban expansion presents the characteristics of “extending from the north to the south”, with urban development transitioning from the model of “ring Cuihu Lake” to “ring Dianchi Lake”.
• ISA Classification Statistics: The ISA in the main urban area of Kunming has shown an overall increasing trend from 2002 to 2020.
  • High-ISA (HISA): area increased from 194.13 $km^2$ in 2002 to 344.42 $km^2$ in 2020, an increase of 150.29 $km^2$, and the proportion of impervious surfaces has increased by 6.09%.
  • Medium-High ISA (MHISA): the proportion fluctuates, with proportions first decreasing, then increasing, then decreasing again. Over these 18 years the proportion overall increased by 0.34%.
  • Medium-ISA (MISA): The area has decreased by 319.99 $km^2$, and the proportion has dropped by 13.19%.
  • Medium-Low ISA (MLISA): The proportion first increased then decreased. Throughout 2002–2020, the proportion showed a decreasing trend, falling by −2.54%.
  • Low-ISA (LISA): increased by 9.3% in 18 years
• Overall Trends: HISA, MISA, and LISA changed the most; despite increased urbanization, the urban green environment continued to improve.

Analysis of the Surface UHIs Temporal and Spatial Evolution Characteristics

• NDLST Value Range: [0–1], corresponding to temperature variations from low to high.
• Spatial Distribution: Overall spatial distributions of the LST and impervious surfaces are consistent. Higher NDLST values are mainly found in urban built-up areas, while areas covered by water bodies and vegetation are predominantly cold.
• Temporal Variation: Spatial LST distributions in the images at different times exhibit different characteristics. The area with the most obvious temperature increase was the transition zone from the Third Ring Road to Chenggong District.
• Heat Island Evolution: During 2014–2020, the high-temperature area spread across the entire study area with a contiguous and concentrated distribution. Significant heat island in the main city center in 2020 compared to 2014.
• LST Classification Statistics: Reveal the following:
  • LT area decreased year by year during 2002–2020 with a total decrease of 191.65 $km^2$. (7.18%)
  • The MLT area first increased and then decreased, with the overall area decreasing by 78.26 $km^2$.
  • The MT area reached its maximum in 2014, showing a trend of first increasing and then decreasing over the past 18 years (Decrease of 65.58 $km^2$).
  • The MHT area fluctuated, first decreasing and then increasing (overall increase of 72.76 $km^2$).
  • The HT area first decreased and then increased, showing an overall growth trend with a final increase of 262.38 $km^2$ over the past 18 years.
• Overall Thermal Environment: From 2002 to 2020, the MHT and HT areas exhibited an increasing trend, while the LT, MLT, and MT areas gradually decreased, indicating that the thermal environment in the main urban area of Kunming has continuously intensified.
• Urbanization Stage: The period of 2008–2020 was a stage of rapid urbanization (rate of increase of 15.72%). Although the intensity of the heat island has increased, the MT area has always been dominant, which makes it difficult for the main urban area of Kunming to display an extreme climate, and thus, the urban atmospheric environment tends to be mild.

Correlation between the ISA and LST Based on Profile Analysis

• NDISA trends: In 2002, the areas with higher NDISA were concentrated mainly in the range from the city center (Cuihu) to 7.5 km along the profile, expanding to 12 km in 2008 and to 25.6–27.81 km in 2014. By 2020, the areas with both higher and lower NDISA values would expand with an alternating distribution.
• Temperature Profile: The NDLST of the area closest to the city center is not excessively high. The highest temperatures in 2002, 2008 and 2014 occurred at 28.6–30.1 km, 25.6-27.1 km and 13.6–15.1 km from the city center, while in 2020 they appeared at 4.5–6.0 km. While the temperature profile doesn't show the typical heat island phenomenon, the overall temperature change trend is consistent with the urban expansion trend.
• Common Features: The profiles of the four phases revealed three common features:
  • NDISA isn't very high (approximately 0.2–0.6) in the area of 27.8–31.8 km, while NDLST is relatively high. These areas are mainly suburban arable land, with extensive bare ground.
  • Both NDLST and NDISA are relatively low in the 33.8–35.7 km area. These areas are mainly in the suburbs, with extensive green space.
  • Within 3 km of the city center, NDISA is very high (close to 1), while NDLST is not the highest. These areas also contain a large number of water bodies and green spaces.

Correlation between ISA and LST based on Difference Analysis

• NDISA Difference Analysis
  • Range of ISA difference: [-1, 1]. Positive value indicates urban expansion, negative value indicates urban contraction. The closer the value is to the endpoint, the more obvious the change.
  • 2002-2008: Urban construction mainly around the Second Ring Road. Chenggong University Town also began construction. Average difference = 0.0004 (slow construction).
  • 2008-2014: Expansion most evident near Changshui Airport and Third Ring Road to Guandu Lakeside, followed by university towns. Forest fire in Xishan District. Average difference = 0.0317 (rapid expansion).
  • 2014-2020: Most obvious expansion in Northeast Airport New Area. Average difference = -0.0372. Reflects increased ecological protection, urban greening, and land resource integration.
  • 2002-2020: Rapid urbanization. Chenggong University Town and Airport New Area show the most obvious growth.
• NDLST Difference Analysis
  • Consistent with NDISA in terms of spatial distribution and temporal evolution. LST changes with urban construction development, but spatial variations are significantly different.
  • 2002-2008: Temperature increase mainly along the Second Ring Road, Airport New Area, and Chenggong University Town. Large water bodies such as Dianchi Lake and Yangzong Lake exhibited obvious cooling trends.
  • 2008-2014: Opposite trend. Temperatures increased significantly in large water bodies (Dianchi Lake, Yangzong Lake) and Xishan Mountains (forest fires). Guandu District lakeside area also warmed significantly. Cooling in the main areas of urban expansion. Lower average LST in 2014 (0.38) than in 2008 (0.49).
  • 2014-2020: Significant temperature increase throughout the region. Cooling in Chenggong University Town and Xishan District (vegetation restoration).
  • 2002-2020: Significant warming trend with the increase of ISA. The most obvious areas of temperature increase are mainly distributed on the newly built land from the downtown of the main urban area to Chenggong University Town, showing characteristics of “C” shape distribution along Dianchi Lake from north to south.
• Thermal Environment Condition Classification:
  • Zhang's study (Zhang et al., 2007) has thermal environment classification which this study references and builds upon.
  • Values can be divided by changes in LST values into: Significantly improved (−1, −0.3], moderately improved (−0.3, −0.15], slightly improved (−0.15, −0.05], basically unchanged (−0.05, 0.05], slightly deteriorated (0.05, 0.15], moderately deteriorated (0.15, 0.3] and significantly deteriorated (0.3, 1], for a total of 7 grades. Each level corresponds to a Roman numeral followed by a digit.
• NDISA Average Value Discussion
  • Improved Thermal Conditions: Negative NDISA values, correlating areas with reduction of urban construction, showing its impact on conditions.
  • Deterioration of Environment: Positive NDISA values, due to the increase of urban construction, indicating a high rate of solar radiation absorption.
  • Trends: Expansion of UHI is considerably affected through urban expansion. Green space and NDISA values correlate because urban building demands also require more green spaces around construction in the late phase of planning.
  • Consistency: Study parallels that of Xie's where thermal deterioration is found on both the old and newly constructed land due to increased ISA.
  • Instability: The STD demonstrates a degree of instability on the LST because urbanization focuses on both construction density and quality of life.
• Regression Analysis: Discusses mitigation measures by improving underlying surface types.

Regression Analysis of ISA and LST

• Functions Compared: Analyzes the relation between ISA and LST by using Linear, Exponential, Logarithmic, Polynomial, and Power.
• Polynomial Fitting: Best fitting model equation used quadratic polynomial (higher regression coefficient).
• NDISA relation to NDLST: The graphs in the figure support that areas with higher NDISA have increased temperature. Monotonic correlation exists in a range of 0-0.3, but the correlation is insignificant in ranges above 0.7.
• Highest Value of NDLST: Areas with the highest values of NDLST are in the range of .4-.5, corresponding that this value is the threshold that divides urban surfaces of nature and man-made construction.
• "Inverted-U Shape": Polynomial fit between NDISA ad NDLST resulted in regression curves characterizing this shape (increasing with each other, slowing down, stabilizes eventually).
• Pearson's Correlation: Ranges between 0.8-0.9, indicating a strong relation between the expansion of ISA, urban surface temperature, and expansion of UHI.

Discussion

Analysis of Spatial Relationship between ISA and LST

• ISA and LST Spatial Characteristics:
  • Overall: Consistent in spatial distribution patterns.
  • Local: Highest LST areas not concentrated in Kunming's city center (highest ISA) but distributed in cultivated land in the north and developing areas around Dianchi Lake.
• ISA and LST have consistent spatial distribution patterns because of urban built-up regions and increased heat capacity as well as conductivity. Previously published studies like those of Song, Yuan, and Cui also validate the claim that there's a strong UHI and ISA relationship.
• Areas with Highest LST isn't Kunming City center but distributed along the cultivated land along the northern parts and Dianchi lake because of better ecological implementation.
  • Improvement of city center through greenery creates shade & evapotranspiration alleviate UHI and is validated across cities like findings in Estoque's study.
  • Bodies of water also create help reduce LST increase in areas such as artificial lakes and wetlands because it takes longer for water to rise in temperature and create LST in the area (cold patches).
  • Buildings block solar radiation by being bigger over larger surface area, therefore, having less solar radiation compared to areas like the city's north where the bare soil landscape is. Zhang also validates that shading aids in cooling by around 10 ⁰C for plants and 20-30 ⁰C for roofs, respectively.
  • The mountain dry land along the north without crops creates a strong environment for endothermic behavior, therefore, also affecting the increasing LST rates. Red soil also follows the same trend as it absorbs solar radiation at great rates.

Analysis of the Quantitative Relationship between ISA and LST

• Linear Correlation Between ISA and LST: Many scholars analyzed the quantitative relationship between ISA and LST by using linear regression analysis and concluded that there was a linear correlation between the two parameters (Cao et al., 2011; Li et al., 2011; Yuan & Bauer, 2007).
• Quantitative Relation is not linear: The quantitative relationship between ISA and LST is not a simple linear model. They found that the exponential function is the best model (Mao et al., 2015; Tan & Xu, 2013; Wei, Tan & Wang, 2014).
• Quadratic Model the best fitting: Results show that the quadratic polynomial function is the best fitting model for Kunming ISA and LST.
• Compared (Linear, Exponential, Quadratic) to quantitative data: The regression coefficent R to the power of 2 resulted in a relation strength of Q > L > E, therefore supporting the quadric function for a stronger and better model.
• Q Analysis: At the beginning, NDLST rises rapidly, then gradually slow down, and finally tends to be stable. Every 0.1 increase in NDISA is associated with a average between 0.02-0.03 of the NDLST increase across time. However, higher density of NDISA, temperature drops approximately by the range of 0.004-0.014 of NDLST.
• Reason for polynomial fit is that surface vegetation that exists in ISA areas with lower temperature has vegetation loss and the soil heats more because there is less shade. However, this relationship and study differs from other scholars because they were in urban built up areas, compared to the discussed urban and bare soil landscape. Other factors need to be considered such as if just built up areas would affect the overall data gathered.

Future Trends In UHI

• Dynamic Quantitative Analysis: Study used remote sensing to avoid interference of subjective views as well as providing a comprehensive characteristic over the past 20 years.
• Shortcomings:
  • Kunming is mainly under cloudy weather, making data collection and analysis harder to produce quality data.
  • Study used Landsat image every 6 years, creating a gap of analysis in between and may have ignored slight changes. Further research and usage will need to require more precise images and different factors must be consider in order to more precisely relieve the UHI effect in the city.

Analysis of Mitigation Measures for UHI

• Urban Planning: Urbanization construction on the UHI effect includes changing underlying surfaces which make green landscape scarce, thus increasing sensible heat flux.
• Consideration Factors:
  • Thermal Characteristics (materials, buildings, and roads have low SHC)
  • Air Circulation (Intensive ISA doesn't contribute towards air circulation as well as impede emission and storage of heat)
• Solutions:
  • Expanding Vegetation (Albedo affected by the state of what is green creates spatial config on UHI increase and decreasing effect).
  • Spatial Config of the space between ISA and greenery.
  • Cooling bodies of water (Specific heat affect regional temperature by determining surface characteristics. Water can determine how cities can maintain microclimate and increase wetland areas alongside it).
  • Improve bare surface use through the ground cover to retain water and nutrients for plants on them (Organic mulch technology).

Conclusions

1. Urban Expansion:

• From 2002 to 2020, Kunming’s urban area significantly expanded.
• HISA increased by 150.29 $km^2$ .
• Development shifted from around Cuihu Lake to around Dianchi Lake.

1. SUHI Trends:

• The SUHI area increased by 335.14 $km^2$.
• Distribution transitioned from sheet-like to strip-like to concentrated plane.

1. Difference Analysis:

• Improved areas: Negative NDISA image values.
• Deteriorated areas: Positive NDISA image values.
• ISA and LST spatial/temporal changes are consistent.

1. Profile Analysis:

• Highest LST area not in the downtown area (highest ISA).
• Scattered in cultivated land/developing areas in the north.
• Large exposed surface areas are the main temperature driver.

1. Regression Analysis:

• ISA effectively indicates LST spatial distribution.
• Positive correlation: ISA and LST.
• Quadratic polynomial function: Best-fit model.

1. Ecological Elements:

• Density and spatial configuration of green space/water bodies alleviate SUHIs.
• Future planning should optimize the utilization of urban underlying surfaces.

1. Future Research:

• The interaction of effects of the warming/cooling from natural and human forces should be taken into account and accounted for. Combining more advanced data usage with economic development status for both social and economic analysis for better measurements of UHI.