8
ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
PÁG. 8 - 23
ISSN 0717 - 3997 / 0718 - 3607
This work is part of the “San Francisco de Campeche Climatological Observatory “ research project (Stage 01)” project 036/
UAC/2023 of the Autonomous University of Campeche.
Doctor en Ingeniería
Profesor – Investigador de la Facultad de Ingeniería.
Universidad Autónoma de Campeche, San Francisco de Campeche, México.
https://orcid.org/0000-0003-2081-9913
roacanul@uacam.mx
Magíster en Proyectos de Arquitectura y Urbanismo
Estudiante de la Facultad de Ciencias Químico Biológicas
Instituto de Ecología, Pesquerías y Oceanografía del Golfo de México, San Francisco de Campeche, México.
https://orcid.org/0009-0001-6598-216X
al041220@uacam.mx
Doctor en Ingeniería
Profesor-Investigador de la Facultad de Ingeniería
Universidad Autónoma de Campeche, San Francisco de Campeche, México.
https://orcid.org/0000-0001-7681-8210
oscajmay@uacam.mx
Doctor en Ingeniería
Profesor de la Facultad de Ingeniería
Universidad Autónoma de Campeche, San Francisco de Campeche, México.
https://orcid.org/0000-0002-8331-1888
majimene@uacam.mx
https://doi.org/10.22320/07183607.2024.27.49.01
1
2
3
4
5
Recibido: 25-09-2023
Aceptado: 01-05-2024
ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA
DE SAN FRANCISCO DE CAMPECHE, MÉXICO
SPATIAL-TEMPORAL ANALYSIS
OF HEAT ISLANDS APPLIED TO
THE COASTAL CITY OF SAN
FRANCISCO DE CAMPECHE,
MEXICO
1
ROMÁN CANULTURRIZA 2
KARIANNA AKÉTURRIZA 3
OSCAR MAYTZUC 4
MARIO JIMÉNEZTORRES 5
ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
PÁG. 8 - 23
ISSN 0717 - 3997 / 0718 - 3607
9
The urbanization of the city of San Francisco de Campeche inuences the formation of urban heat islands due to construction
materials, buildings and structures, human activities, lack of vegetation, and transportation infrastructure. Heat islands have
negative consequences such as increased energy consumption and heat stress for the population, contributing to climate change
due to increased greenhouse gas emissions caused by additional energy demand. Cities such as Sydney, Beijing, Nanjing,
Moscow, and Hong Kong are implementing urban planning strategies that promote urban vegetation, the use of reective building
materials, the improvement of public transport, and the promotion of energy efciency in buildings. Landsat satellite images were
used to analyze population growth and urban sprawl to identify heat islands, and a vegetation index analysis was also made.
Regarding the analyses, it was recognized that the temperature increased by approximately 6°C between 1990 and 2022. There
has also been a decrease in vegetation due to the urban sprawl and housing growth, quadrupling the Normalized Difference
Vegetation Index (NDVI) in the 0-0.25 class for the same period. Finally, mitigation measures are proposed to counteract the
effects caused by heat islands in the city.
Keywords: islands, heat, city, coastline
La urbanización de la ciudad de San Francisco de Campeche inuye en la formación de isla de calor urbano debido a materiales
de construcción, edicios y estructuras, actividades humanas, falta de vegetación, e infraestructura de transporte. Las islas
de calor tienen consecuencias negativas como aumento en el consumo de energía y un mayor estrés térmico en la población.
Además, contribuyen al cambio climático debido al aumento de emisiones de gases de efecto invernadero, causadas por la
demanda adicional de energía. Ciudades como Sídney, Beijing, Nanjing, Moscú y Hong Kong están implementando estrategias
de planicación urbana que promueven la vegetación urbana, el uso de materiales de construcción reectantes, la mejora del
transporte público y la promoción de la eciencia energética en edicios. Con el n de identicar islas de calor se utilizaron
imágenes satelitales Landsat. Se analizó el crecimiento de la población y la mancha urbana realizando un análisis de índice
de vegetación. En relación con los análisis realizados, se identicó que la temperatura ha aumentado aproximadamente 6°C
entre los años 1990 y 2022; así como ha disminuido la vegetación ante el crecimiento de la mancha urbana y las viviendas,
cuadruplicando el Índice de Vegetación de Diferencia Normalizada (NDVI) en la clase 0-0.25. Finalmente, se proponen medidas
de mitigación para contrarrestar los efectos que causan las islas de calor en la ciudad.
Palabras clave: islas, calor, ciudad, costa
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ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
PÁG. 8 - 23
ISSN 0717 - 3997 / 0718 - 3607
I. INTRODUCTION
Urbanization is one of the human processes with the
most significant environmental and climate impact.
55% of the world’s population lives in cities, which is
expected to increase to 68% by 2050 (Ma et al., 2023).
Harmful agents for health are emitted, which affect local
meteorology. At the same time, urban growth, economic
development, and changes in land use are also a threat
to humans and the ecosystem (Xu et al., 2021), as cities
contribute to global warming, mainly due to the effect
of Urban Heat Islands or UHI.
For example, in the coastal regions of the world, the
effect of the UHI is extreme, changing the regional
meteorology with extreme heat waves and floods,
and the phenomenon is expected to intensify (Qiu et
al., 2023). In these regions, the complexity increases
as a result of the sea breeze that leads the UHI several
kilometers inland until its dissipation (Yun et al., 2020).
It is necessary to understand the phenomenology to
allow the formulation of policies supporting decision-
making and scenario planning that consider: a) Analysis
of the time scale; b) Inclusion of landscape and urban
form, proportion of green and blue areas, improvement
of the albedo, modal distribution of transport; c)
Passive technologies in the building envelope; d) Active
technologies considering artificial climate control; and
e) Public health and citizen participation (Degirmenci et
al., 2021). Therefore, focusing on urban decentralization,
expansion control, green coverage rate, and building
density will improve the thermal environment and air
pollution (Luo & He, 2021).
Currently, there is a lack of knowledge about the
spatio-temporal variation of the intensity of daytime
and nighttime surface UHI. Similarly, resources still
need to be improved to cope with the rapid impacts
of urbanization. In recent years, satellite images have
been used as an alternative to detect UHIs due to
their availability, free access, and extensive registration
history. San Francisco de Campeche is an essential
region because it belongs to the World Heritage list and
is located in a coastal area with rapid urbanization, so
conducting a study focused on the UHI, using satellite
images from the period 1990 – 2020, will quantify
the historical changes in surface and atmospheric
temperature, as well as changes in vegetation cover, to
identify and characterize the UHI. It is also hypothesized
that the results of this study will reveal the areas with
the most significant changes in temperature and
vegetation cover, thus providing a basis for proposing
actions to mitigate the effects of UHI in San Francisco de
Campeche.
II. THEORETICAL FRAMEWORK
Urban Heat Islands (UHI)
UHI are thermal anomalies resulting from the
temperature difference between a surrounding
urban and rural area, where the additional heat
emitted increases the atmospheric temperature (Ortiz
Porangaba et al., 2021). These increase summer cooling
loads and consequent energy consumption, which
leads to higher greenhouse gas emissions (Khare et
al., 2021). This thermal process affects the population
by increasing the local temperature and by releasing
pollutants into the atmosphere and air pollution.
Therefore, it is vital to understand how the components
of cities relate to UHIs to establish improvement
measures in the urban thermal environment and to
reduce air pollution (Kim & Brown, 2021; Liang et al.,
2021). With the rapid spread of urbanization worldwide,
the urban heat island effect substantially and adversely
impacts cities, including energy, environment,
and health conditions. Unfortunately, constructive
geometry and human activities severely intensify the
phenomenon of UHIs (Xu et al., 2021).
It has also been observed that UHIs and air pollution are
responsible for significant health impacts. According
to a World Health Organization (WHO) report, indoor
air pollution caused approximately 3.8 million deaths
in 2016, and about 4.2 million deaths were attributed
to air pollution in the same year. In addition, it is
estimated that 91% of the population lives where the air
quality index exceeds the limits of the WHO guidelines.
Therefore, regarding the figures provided by the WHO,
regulating urbanization could have two-way benefits
(Singh et al., 2020). Urbanization coincides with notable
environmental changes, including vegetation, soil, and
climate (Vasenev et al., 2021). Therefore, understanding
how the components of cities affect UHIs has become
a great challenge for societies that seek to improve the
quality of life through the implementation of urban
planning criteria (Hidalgo García & Arco Díaz, 2021).
The selection of urban planning indicators such as
building density, built area, and green coverage rate,
among others, during the preparation phase for
urban planning, can regulate the intensity of urban
development and the configuration of the urban
thermal environment after the application of the
planning proposal (Luo & He, 2021). This understanding
of the relationship between urban planning indicators
and the formation of the term environment allows
addressing, in greater detail, the thermal aspect in
the planning stage, which helps optimize the urban
ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
PÁG. 8 - 23
ISSN 0717 - 3997 / 0718 - 3607
11
Figure 1. Location of the city of SFC, Mexico. Source: Preparation by the authors.
planning proposal to mitigate the effects of UHIs (Luo &
He, 2021).
Even though urban areas face multiple environmental
challenges interacting with climate change, including the
UHI effect, vegetation can be a nature-based solution for
UHI mitigation (Tan et al., 2021). The interaction of UHI in
a coastal tropical city may be different from that of cities
in temperate climate zone, affecting it severely. However,
there is a lack of UHI studies focused on coastal tropical
cities (Chew et al., 2021).
Internationally, some studies have been carried out
worldwide in coastal cities such as those of Greece
(Giannaros & Melas, 2012), Oman (Charabi and Bakhit,
2011), in the Caspian Sea (Firozjaei et al., 2023), Istanbul
(Dihkan et al., 2015), China (X. Xu et al., 2023) and in the
Mediterranean Sea (Kassomenos et al., 2022). However,
studies are still emerging in Mexico and the Gulf of
Mexico.
III. CASE STUDY
Case study: Campeche, Mexico
The study was conducted in San Francisco de Campeche
(SFC) (19°50’41’N and 90°32’23’W), the State Capital of
Campeche, Mexico, which is located on the Yucatan
Peninsula, on the shores of the Gulf of Mexico (Figure 1).
San Francisco de Campeche is a fortied historical city and one
of the few walled cities in America. Its historic center and old
neighborhoods have buildings dating from the sixteenth to
the nineteenth centuries, including military, civil, and religious
architecture. Given its historical and commercial context,
the homogeneity of its architecture was declared an Area of
Historical Monuments in 1986, and in 1999, it was included in
the list of World Heritage of Humanity of the United Nations
Educational, Scientic, and Cultural Organization (UNESCO).
It has an area of 3,410.64 km
2
with an average altitude of 5
meters above sea level (Figure 1). It is mainly characterized
by a warm-humid climate with summer rains, which are
distributed in three seasons: “Rains” (June-September), “Norths”
(October-January), and “Dry” (February-May). The city’s average
annual temperature is 27°C, with maximum summer averages
of 29°C and a historical maximum temperature of 43°C (INEGI,
2022).
Demographically, it has 294,077 inhabitants, 32% of the State’s
inhabitants, with a population increase of 25% in the last ten
years (INEGI, 2020). This has led to unplanned urbanization,
which originated from transforming land into housing areas,
thus reducing the green areas within the city. These areas
are identied as having UHI potential, generating an urban
increase in the use of air conditioning, energy demand, and air
pollution.
This type of urban growth pattern in San Francisco de
Campeche is primarily associated with high energy
consumption, which is why this city is considered a case study
whose analysis will help generate a methodology that allows
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ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
PÁG. 8 - 23
ISSN 0717 - 3997 / 0718 - 3607
(1)
(3)
(4)
(5)
(2)
detecting and proposing exportable improvements to
other cities with similar characteristics, such as addressing
public health problems, improving energy eciency,
protecting the environment, and adapting to climate
change.
IV. METHODOLOGY
To identify and characterize UHIs from a temporal
perspective and to contrast them with population growth,
it is proposed to break down the analysis into four phases:
(1) Analyze the population growth of the city of SFC; (2)
Quantify the historical temperature changes (surface and
atmospheric); (3) Quantify the changes in vegetation
cover; and (4) Identify the areas with the most signicant
changes in temperature, vegetation cover and the
relationship between them. Therefore, the methodology
analyzes four temporal elements: the Land Surface
Temperature or LST, Normalized Dierence Vegetation
Index or NDVI, historical population growth, and analysis
of the local temperature history, the latter to reinforce the
analysis of temperature changes.
The historical climate analysis used data from the ERA5
model generated by the European Center for Medium-
term Weather Prediction and local weather stations. The
population analysis is based on the region’s demographic
records.
Landsat satellite images, represented in spectral
bands, were used to calculate the LST and NDVI.
Due to the scarce information available and the
precarious monitoring and observation systems of local
environmental changes, these images are essential for
analyzing and addressing environmental problems in
Latin American cities. Landsat-5TM, Landsat-7TM, Landsat-
8OLI, and Landsat-9OLI images were examined. They
were obtained from the databases of the United States
Geological Survey (USGS, no date). The study analyzed
images between 1990 and 2020 in 5-year intervals
associated with April to characterize the dry season,
which is the hottest in the region.
Historical growth of the population of the urban
conurbation area
Data were collected from population growth and
its relationship with the urbanized area. These were
collected from local records, such as the Municipal Urban
Development Program of Campeche 2020-2040 (SEDATU,
2020), the Campeche Urban Development Director
Program 2008-2033 (PDU, in Spanish), and the Municipal
Program of Territorial Ecological Management (PMOET, in
Spanish).
Earth’s surface temperature
To obtain this data, images of band 6 were used for Landsat-
5TM and Landsat-7TM, and band 10 for Landsat-8OLI and
Landsat-9OLI. The calculation consists of 4 steps (X. Li et al.,
2016) :
1. Spectral radiance , for TM images is
obtained with Eq.1, where is the digital value of the pixel
in a range of 0-255, and , the maximum and minimum
values of the pixels in the thermal band, and and , the
scaled maximum and minimum spectral radiances. For
the OLI images, this was calculated from Eq.2 (considering
radiation at the top of the atmosphere or TOA radiance),
where was the correction for band 10, and ML and AL
represented multiplicative and additive factors for the
reheating of the radiance to a certain band.
2. Luminous intensity temperature or Bright Temperature (BT)
Eq.3, where K1 and K2 are thermal conversion constants
associated with the type of satellite image (TM or OLI).
3. Land Surface Emissivity (LSE) Eq.4, indicates the average
emissivity of an element on the land surface from the NDVI,
where and are the maximum and minimum of the NDVI.
4. Estimation of the LST, given by Eq.5, where is the
wavelength of the emitted radiance (µm), h=; s is
Boltzman’s constant, and c is the speed of light.
Surface temperature time series
For this analysis, the temperature record from 1940 to 2023
was used, obtained from two sources:
• From 1940-2022, from the ERA5 model (https://cds.
climate.copernicus.eu), for air temperature records at 2 m
above the Earth’s surface to identify increases in the city
over time. This value is calculated in one-hour intervals
by interpolating between the lowest level of the model
and the land surface.
ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
PÁG. 8 - 23
ISSN 0717 - 3997 / 0718 - 3607
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(6)
Figure 2. a) Housing behavior and urban sprawl in Campeche
(above), b) Housing behavior compared to the population of
Campeche (below). Source: Preparation by the authors.
For the Landsat-5TM and Landsat-7TM images, spectral bands
4 and 3 were used for the NIR and R values, while for Landsat-
8OLI and Landsat-9OLI, bands 5 and 6 apply. The NDVI values
range between ±1.0, while green vegetation is between the
values of 0.2-0.8 (Wang et al., 2020).
V. RESULTS
Analysis of historical population growth
Figure 2 presents the historical demographic growth, built
housing, and impact on urban fragmentation in the city
from 1950 to 2019 (last census). Figure 2 contrasts the city’s
population growth concerning built-up housing. Over the
70 years, the state’s population has grown by 87%, with
the highest increases occurring in 1970 and 2019. A third
of the increase has happened in the last ten years. On the
other hand, the number of housing units has grown even
faster, increasing by 91% since 1980. In particular, real estate
expansion has grown by 38% since 2000, which is associated
with demographic growth. These results are linked to the
increase in urban sprawl (Figure 2), a product of urban
expansion to the south and east of the city. In the eighties
and nineties, housing growth was concentrated in the
southern and southeastern areas. Changes in land use are
directly associated with the increase in land temperature.
Land surface temperature (LST)
Figure 3 compiles the LST maps from 1990 to 2020 in
ve-year intervals, cataloging the surface temperature in 5
color ranges: Blue (< 20°C), light blue (20-25°C), green (25-
30°C), yellow (30-35°C), orange (35-40°C), and red (>40°C).
During the nineties, the city did not exceed 25°C at a land
level at the hottest time of the year, with the oldest and
most central neighborhoods having higher temperatures,
• 2022-2023, from a multifunctional wireless weather
station located within the city at the coordinates 19.85°N
– 90.50°W. The temperature data series was collected from
October 2022 to April 2023, recording every 10 minutes.
Normalized difference vegetation index
1. This indicator checks the condition of vegetation from
near-infrared (NIR) and red (R) bands from the Landsat
images. Its estimation was made through the following
formula Eq. 6 (H. Li et al., 2018):
a product of the reduced urban sprawl. In later decades
(2000-2020), the LST exceeded 30°C due to urban
expansion to the city’s surrounding areas. This is consistent
with the emergence of housing neighborhoods in the east
and west, which caused an expansion of 37% on forest
land.
In the areas of the historical center and the east, an
increase in temperature caused by deforestation was
observed, exceeding 35°C. In addition, the tendency to
generate zones that reach or exceed 40°C is interesting.
This indicates that, in 30 years, a coastal city with a small
population, such as the case study, has increased the land-
level temperature by approximately 10°C.
To visualize the behavior of the temperature at ground level,
information was extracted from 24 points identied with
more signicant change throughout the city for each of the
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ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
PÁG. 8 - 23
ISSN 0717 - 3997 / 0718 - 3607
Figure 3. Land temperature maps for the case study: a)1990; b)1995; c)2000; d)2015; e)2020. Source: Preparation by the authors.
Figure 4. Information extraction points and Series for points 1 and 12. Source: Preparation by the authors.
(a)
(b)
ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
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15
Figure 5. NDVI maps for the case study: a)1990; b) 1995; c)2000; d)2020; e)2022. Source: Preparation by the authors.
analyzed images (Figure 4). The graph shows the changes in
temperature for points 1 and 12, with an average increase of 6°C
from 1990 to 2020.
Vegetation analysis
The NDVI values were grouped into ve vegetation classes:
Very scarce (<0), scarce (0-0.25), reduced (0.25-0.50), acceptable
(0.50-0.75), and abundant (>0.75), as can be seen in Figure
5. Figure 4 shows that, in 1990, the urban area was reduced
and concentrated in the city center, with sparse vegetation
dominating. However, a signicant portion of what at that time
represented the city’s periphery (currently the southern and
eastern areas) retained acceptable levels of vegetation. Five years
later, a reduction in vegetation is visible in the east of the city,
coinciding with the demographic increase and the number of
buildings. In 2020, the reduction of vegetation extended to the
south and southeast of the city, where more than 90% of the
urban core is in the category of scarce vegetation, contributing
to the rise in temperature.
Table 1 compiles the evolution of the NDVI in the last 20 years.
The least frequent category that has been reduced the most
is “very scarce vegetation,” which has gone from 16.38ha to
0.38ha. On the other hand, the “scarce” category has been the
most representative and the only one that has grown, while the
extensions in the “abundant” vegetation category are almost
imperceptible. During these two decades, there has been a
tendency to reduce urban vegetation, putting the population
at risk from the tropical climate heat waves without green areas
or urban vegetation to cushion them.
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Classes
Area in Hectares
1990 1995 2000 2015 2020 2022
<0 116.45 16.52 579.16 0.32 0.14 0.38
0-0.25 181.75 386.60 54.27 410.3 515.75 433.59
0.25-0.5 371.60 230.85 1.76 224.4 119.29 200.94
0.5-0.75 65.36 1.22 0 0.17 0.01 0.27
>0.75 0 0 0 0 0 0
Table 1. Details of the normalized difference vegetation index from 1990 to 2022. Source: Preparation by the authors.
Figure 6. Relationship between NDVI and LST for the case study in the years: a) 1990; b) 1995; c) 2000; d) 2015; e) 2020; f) 2022. Source:
Preparation by the authors.
ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
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17
Figure 7. Temperature behavior in SFC in the different decades of study. Source: Preparation by the authors
Figure 8. Hourly temperature data series in the period January 1940 - December 2022. b) Series of annual maximum temperature values between
1940 and 2022. Source: Preparation by the authors.
1940 1950 1960 1970 1980 1990 2000 2010 2020
18.0
19.5
21.0
22.5
24.0
25.5
27.0
28.5
30.0
Temperatura (°C)
Año
Temperatura
0 20000 40000 60000 80000
15
20
25
30
35
40
Temperatura (°C)
Temperatura en la década de 1950 Temperatura en la década de 1970
Temperatura en la década de 2010
Temperatura en la década de 1990
0 20000 40000 60000 80000
15
20
25
30
35
40
0 20000 40000 60000 80000
15
20
25
30
35
40
Temperatura (°C)
Datos
0 20000 40000 60000 80000
10
15
20
25
30
35
40
Datos
When vegetation aects the distribution of the LST, a
reasonable approach to determine the spatiotemporal
changes is to identify the relationships between the
LST and the NDVI. Figure 6 illustrates the negative
relationship between the NDVI and LST values. Between
1990 and 1995, the LST values did not exceed 35°C,
while the NDVI was distributed between 0.8 and -0.1 on
average. This results in negative and very steep regression
slopes, suggesting vegetation softened the thermal
eect. The correlations tend to be more horizontal from
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ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
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Figure 9. Data record of the climatological season; the date format is month/day/year. Source: Preparation by the authors.
2000 due to an increase in temperature values, which
reach 40°C, and a decrease in noticeable vegetation,
which acquires an average maximum NDVI between 0.4
and a minimum of up to -0.4. From 2015 to 2020, the NDVI
values are grouped in intervals of 0 and 0.4, with some
minimum values reaching -0.2 or 0.5. The temperature
remains close to 40°C; in 2020, it even approaches 50°C.
For 2022, the trend of the NDVI values is maintained,
and a reduction in the (surface) temperature values is
observed, reaching values close to 35°C. This reduction of
the NDVI is indicative of deforestation due to the urban
area’s expansion and the temperature increases, which
indicate the presence of UHIs in the city.
Surface temperature time series
The temperature data were acquired at a location in
the coastal zone 15 km away from the city, derived
from the resolution of the meshing of the ERAS model,
corresponding to 0.25°C, at intervals of 28km. Figure
7 shows the temperature behavior from the annual
perspective and the analysis in four specic decades
(1950, 1970, 1990, and 2010). There has been a gradual
increase in temperature since 1970, an increase in
maximum temperatures in recent decades, and a
decrease in minimum temperatures due to thermal
warming in the region.
Figure 8 above shows the temperature series with an
average value of 26.21 °C, a minimum of 14.01 °C, and a
maximum of 36.95 °C, reached in 2020. According to the
graph below, the maximum recorded value was 33.66°C
in 1940, while in 2020, it was 35.83°C. Likewise, the series’
trend line indicates that the temperature has increased by
1.30°C and reached highs of 34.66°C.
Figure 9 presents the data for October 2022 to May
31
st
, 2023, recording a minimum temperature of 16°C, a
maximum of 40°C, and an average of 27.3°C. An upward
trend is observed that begins in April and extends until
the middle of May when the values reach 40 °C
VI. DISCUSSION
The results for the case study exhibit an interrelation between
the lack of urban design and planning, which, together with
the UHIs, results in high thermal retention of solar radiation
impacting buildings, pavements, materials, and surfaces, a
situation very similar to that reported by Tian et al. (2021). This
also coincides with what was reported by Han et al. (2022), who
nd that coastal cities experience the most changes because
emerging cities experience growth without policies or planning,
causing rapid expansions in proportion, density, and regularity.
This is contrary to cities with planned development, where more
natural areas such as green surfaces and urban parks are built
to improve the environment and reduce thermal stress. This
supports the hypothesis that the analysis of population growth
contributes to identifying the UHIs.
In the case study, the eect of UHIs is increased due to the
relative humidity typical of coastal cities, which can vary from
60% to 100% throughout the day. For the case of Singapore,
Chew et al. (2021) mention that relative humidity, being related
to temperature increases during the day and night, indicates a
daily variation of up to 3°C they also use data measured in the
eld with stations.
The use of images to identify the UHIs is widespread. For
example, in the city of Thessaloniki, Greece (Giannaros & Melas,
2012), images were used to identify the UHIs. They also used
temperature data measured at stations. However, Giannaros and
Melas (2012) incorporated wind speed and thermal comfort into
their analysis, nding variations of up to 4 °C.
On the other hand, the Landsat imaging application in the study
conducted in Istanbul by Dihkan et al. (2015) stands out since
they analyze the 1984 – 2011 period to nd the LST, identifying
the land uses/cover (LULC) and its temporal and spatial changes,
nding a relationship between LST and LULC, which originate
the HUS with temperatures close to 50°C. In the city of Muscat,
Oman, the authors Charabi and Bakhit (2011) use meteorological
observations to infer the spatio–temporal changes analyzed
during one year. These studies support the hypothesis that
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REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
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ISSN 0717 - 3997 / 0718 - 3607
19
satellite images can successfully quantify historical surface
temperature and vegetation coverage changes.
The phenomenon of UHIs has adverse eects on the social-
urban ecosystem, such as an increase in the electricity
consumption in buildings, a reduction of thermal comfort,
and a decrease in air quality, aecting the health of residents
and leading to higher mortality. A recent study in the
metropolitan city of Bangkok (Thailand) on physical factors
driving the urban heat island found that the average annual
temperature of a city with more than one million inhabitants
is between 1K and 3K higher than that of the surrounding
rural areas (Khamchiangta & Dhakal, 2019).
When the intensication of UHIs occurs, there is an increase
in the mortality of children and older adults, as well as in
respiratory and cardiovascular diseases and even cancer
(Hidalgo García & Arco Díaz, 2021; Hidalgo-García & Arco-
Díaz, 2023; Yao et al., 2022). Changes in land use patterns,
combined with population growth and the heat generated
by human activity, drastically alter the climate as Ullah et al.
(2019) have evidenced. This situation is analogous to that
observed in SFC, where land use changes, population and
urban growth have changed the city’s temperature.
The studies examined on UHIs agree that urbanization
causes changes in the physical characteristics of the natural
landscape and urban land use, resulting in the disappearance
of large areas of vegetation and modifying the local climate
(Zhao et al., 2011). In the San Francisco de Campeche
case study, the NDVI allowed seeing the areas with the
most changes, nding a relationship between the LST and
vegetation coverage, similar to that of Hidalgo García & Arco
Díaz (2021) and Hu et al. (2020), who associated NDVI with
LST, nding a negative correlation; i.e., there is a reduction
in NDVI values as LST values increase. This conrms the
hypothesis that changes in vegetation cover will facilitate the
identication of UHI areas.
In SFC, it is seen that the reduction in NDVI values is due to
the construction of housing units. This can be compared
with what is documented by Ciacci et al. (2022) that, in cities,
the changes generated by the construction sector represent
27% of global greenhouse gas emissions. Therefore, diverse
mitigation strategies have been proposed and applied to
reduce the risk of UHIs. The ones that stand out are urban
green spaces, green roofs, vertical greening or green walls,
water bodies, cold materials, and changes in urban geometry
(Ciacci et al., 2022). Planning and design that modify the
characteristics of the surrounding environment could reduce
the UHIs. Replacing trees and vegetation with less permeable
material surfaces minimizes the natural eects of shading,
water evaporation from the soil, and leaf evapotranspiration,
so the reverse process would maximize them.
Studies have been conducted to maximize the
natural eects of shading strategies, focusing on UHI
mitigation measures and their impact on building
energy consumption and outdoor thermal comfort
(Tian et al., 2021). The implementation of sustainable
urban infrastructure, sustainable rain management, and
reduction of anthropogenic heat have been proposed,
as well as the implementation of mitigation measures
in construction, such as protection from solar radiation,
minimization of heat inltration, maintenance of thermal
comfort, and the planning of urban areas together with
urban development measures such as reforestation,
green infrastructure, and reduction of anthropogenic heat
(Leal Filho et al., 2017). In addition, it can inuence public
policies, certications, and regulations, which allow, as
well as the methodology applied in Europe on optimal
protability (European Parliament, 2010), outlining the
most cost-eective measures to rebuild buildings, focusing
on economic aspects or interventions to achieve an NZEB
(Nearly zero–energy buildings) standard, or energy and
environmental certications in the urban environment. An
example of them is the Italian regulations, which regulate
the development of the urban environment to comply with
the Kyoto Protocols, highlighting the role of trees (Ciacci
et al., 2022). However, this lacks strategies, methodologies,
regulations, and public policies that reduce the eects of
UHIs.
In the last three decades, San Francisco de Campeche
has experienced an increase in its temperature, which
has resulted in a 40% increase in electricity consumption,
as reported by SENER (2023), coinciding with what was
pointed out by Tian et al. (2021) who indicate that, in
countries with warm climates, every 1°C increase leads to
a 1.66% increase in electricity consumption. Implementing
any of the strategies above in the case study could
substantially impact the thermal-urban environment if used
primarily in the project’s design stage.
Although no strategy for UHI reduction is applied in this
research, some implemented in other regions that, due
to SFC’s characteristics, could be practical and replicable
are proposed. An example of this is that urban greening
can purify the air, regulate the temperature, and improve
the urban ecosystem. On the other hand, urban green
space lowers the air temperature, mitigates pollution, and
reduces the energy used for cooling. The use of green roofs
can inuence the urban environment since they represent
between 20% and 25% of a city’s surface (Besir & Cuce,
2018). They can also reduce indoor temperatures on the
top floor by up to 3.4°C (Tam et al., 2016).
In Hong Kong, a study on urban heat island mitigation
strategies showed that with 60% green cover, the air
20
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ISSN 0717 - 3997 / 0718 - 3607
temperature could be reduced between 0.65°C-1.45°C and
the annual energy savings were estimated at 3.4x10
7
kWh
and 7.6x10
7
kWh, respectively (Peng & Jim, 2015). In the
same way, green walls are smaller in size, have high
aesthetic value, and can mitigate UHIs by reducing the
wall’s temperature to save energy, with thermal insulation
provided by vegetation, cooling evapotranspiration, and
screening against the wind. Pan and Chu (2016) showed
that a green wall can save 16% of a building’s energy
consumption.
Figure 10 shows a before-and-after of a proposal to reforest
an urban sector and the proposal to place a green wall in
a house. The sector is on 59
th
Street, located in the historic
center. This street connects the Puerta de Mar with the
Puerta de Tierra of the walled city, becoming the city’s most
popular and crowded meeting point, the so-called “heart
of the Historic Center of Campeche.” The Figure’s house
represents the modern constructions in San Francisco de
Campeche.
Finally, urban planning can improve the urban climate by
meeting the needs of residents (Zhao et al., 2011). Together
with urban design, this has a realistic environmental
meaning and can mitigate the effect of UHIs in some urban
regions by optimizing urban morphology (Q. Hu et al., 2016).
The size, geometric shape, and vegetation cover are the
urban morphology factors that impact the thermal stress
of the city (Liang et al., 2021)Given this, SFC requires
urban planning that allows it to develop and decrease
UHIs. The results of this study provide a watershed that
will enable deepening the analysis of UHIs, from their
origin to reduction strategies. This will be useful for
urban planners (engineers, architects, among others),
public health officials, and government actors.
VII. CONCLUSIONS
The UHI analysis has been consolidated as an
indispensable component in understanding the current
urban climate. This research reveals the importance of
complementing traditional climate and weather studies,
such as the agricultural calendar and rainfall periods,
among others, with historical temperature analyses and
satellite images. These provide a crucial perspective on
addressing the challenges of unplanned urban growth.
Integrating data on UHIs with urban planning and
design observations is presented as a comprehensive
approach to mitigating the adverse effects of
disorganized urban development.
Figure 10. Intervention proposals. a) Before, b) Reforestation, c) Before, d) Green wall placement. Source: Preparation by the authors.
(a) (b)
(c) (d)
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21
The UHIs identified in the city of SFC focus on areas
with a high density of urban infrastructure, where the
presence of buildings is predominant and vegetation
is scarce. This urban concentration has been associated
with a significant increase in surface temperature, with
a rise of 6°C recorded from 1990 to 2022. At the same
time, a marked decrease in vegetation cover has been
observed, quadrupling the values of the Normalized
Difference Vegetation Index in the 0 – 0.25 class in the
same period. These findings reflect an increasing trend
in temperature, especially evidenced by the series of
annual maximum values of the period.
The UHI in SFC is due to environmental degradation that
currently disturbs the population’s comfort, mainly in
April. This increase in temperature causes an increase in
electricity consumption to maintain thermal comfort, in
addition to generating adverse effects on public health.
Additional variables that may influence the formation
and intensity of UHIs are suggested. Relative humidity
and wind speed are important factors that can modulate
the effects of UHIs and should be considered in future
studies. In addition, a detailed thermal comfort analysis
provides a more complete understanding of how
climatic conditions affect the subjective perception of
temperature and human well-being. Identifying the
periods of greatest discomfort and comparing these
parameters during the day and night generates a more
accurate assessment of the risks associated with UHIs
and guides adaptation and mitigation strategies.
Ultimately, to effectively address the challenges caused
by UHIs and to improve the quality of urban habitat, it
is necessary to implement various political strategies,
leading to the modification of public policies based
on urban planning results, which considers short-term
actions such as revegetation with local vegetation
and the creation of green spaces, in addition to the
incorporation of innovative interventions such as the
development of green infrastructures and walls. By
promoting urban vegetation and improving vegetation
cover, air temperatures can be reduced, UHI’s effects
can be mitigated, and profitable and sustainable
environments for urban dwellers can be generated.
These interventions reduce energy consumption,
improve urban biodiversity, and create recreational and
functional public spaces.
In this way, UHI analysis emerges as a crucial research
area to address the different challenges of disorganized
urban growth. By integrating data on UHI with urban
planning and design considerations, progress is
demonstrated toward sustainable, resilient, and livable
cities capable of mitigating the adverse impacts of urban
development without control for present and future
generations.
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ANÁLISIS ESPACIOTEMPORAL DE ISLAS DE CALOR APLICADO EN LA CIUDAD COSTERA DE SAN FRANCISCO DE CAMPECHE, MÉXICO
ROMÁN CANUL-TURRIZA, KARIANNA AKÉ-TURRIZA, OSCAR MAY-TZUC, MARIO JIMÉNEZ-TORRES
REVISTA URBANO Nº 49 / MAYO 2024 - OCTUBRE 2024
PÁG. 8 - 23
ISSN 0717 - 3997 / 0718 - 3607
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