







































71 

GeoPlanning 
Journal of Geomatics and Planning                                                                                                              Vol. 11, No. 1, 2024     
 

Original Research 

Tracking the Temporal Changes in Land 

Surface Temperature, Vegetation, and Built-up 

Patterns in Rizal Province, Philippines using 

Landsat Imagery 

Pauline Angela Sobremonte-Maglipon1,2* Anne Olfato-Parojinog1,2 King 

Joshua Almadrones-Reyes3,4 James Eduard Limbo-Dizon3,4 and Nikki 

Heherson A. Dagamac1,2,3,4 

1. Department of Biological Sciences, College of Science, University of Santo Tomas, España, 

Manila, 1008, Philippines  

2. The Graduate School, University of Santo Tomas, España, Manila, 1008, Philippines  

3. Research Center for the Natural and Applied Sciences University of Santo Tomas, España, 

Manila, 1008, Philippines  

4. Advanced Educational Program, Thai Nguyen University of Agriculture and Forestry Quyết 

Thắng, Thái Nguyên, Vietnam 

DOI: 10.14710/geoplanning.11.1.71-84 

Abstract 

The Rizal Province was subjected to a series of natural and human-induced disturbances throughout the years. Currently, 

the area is undergoing urbanization which in turn results in shifts in the extent of impervious surfaces that can intensify 

heat-related health concerns, increase energy consumption for cooling, and alter local weather patterns. This study uses 

remote sensing images from to quantify the various environmental considerations that remain undocumented and unmapped 

for areas caused by changes in land use and land cover from Landsat Collection 1- Level 1 (Landsat 4-5 ™ C1- Level 1 & 

Landsat 8 OLI/ TIRS C1 Level 1) and calculated three parameters namely, (i) Land surface temperature (LST), (ii) 

Normalized Difference Vegetation Index (NDVI), and (iii) the Normalized Difference Built-up Index (NDBI). The results 

showed the following: (i) an increase in the vegetation cover from 1993-2020 showed a decrease in LST from 29.34°C to 

24.03°C, (ii) the relationship between LST and NDBI is directly proportional, whereas an inversely proportional relationship 

can be observed between LST and NDVI, and (iii) there is a fluctuating LST due to the changes in the land cover of the 

study site for almost three decades. This implicates the extensive shift in the ambient temperature of Rizal which further 

emphasizes the effects of the modification in certain land use land cover classifications, especially in vegetation cover and 

urban development. This highlights how human-induced and natural factors significantly contribute to the release of heat 

and ambient temperature, thus, accentuating the need for sustainable urban planning. 

Copyright © 2024 GJGP-Undip 

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license 

1. Introduction  

For the past several decades, urbanization in various megacities especially in developing countries such as 

the Philippines has been steadily expanding in response to the increase in human population. As a result of this, 

urban development continuously ensues to meet the living demands of the rapidly growing population (Doygun 

& Alphan, 2006; Ramachandra et al., 2015; Buchori et al., 2022). Unfortunately, the built up of urban areas has 

led to the increase in land surface temperature (LST) due to the accumulation of solar heat retention from 

infrastructural materials such as cement, asphalt, concrete, and steel, all of which are characterized to have high 

heat capacities (Stempihar et al., 2012; Uddin et al., 2022; Morris et al., 2017). In addition, other forms of human-

induced heat emissions including fuel and biomass burning are contributing factors to the increase of LST 

e-ISSN: 2355-6544 
 
Received: 31 October 2023;  
Accepted: 13 February 2024;  
Published: 08 March 2024. 
 
Keywords:  
LST, NDVI, NDBI, Remote 
Sensing 
 
*Corresponding author(s)  
email: pmaglipon@gmail.com 
 

 

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72 

(Ekwurzel et al., 2017). This concept is otherwise known as the Urban Heat Island (UHI) effect. The observed 

warming in urban areas corresponds to the land-use changes, usually that from vegetation to settlements (Fang 

et al., 2011). The consequences of the UHI effect extend beyond mere temperature increases; it can significantly 

alter urban ecological systems and exert various ecological and environmental impacts on urban climates, 

hydrological situations, soil properties, atmospheric environments, biological habitats, material cycles, energy 

metabolism, and residents' health (Yang et al., 2016). These changes can render urban areas more vulnerable to 

economic and socio-political perturbations (Chase et al., 1999; Vitousek et al., 1997; Kasperson et al., 1995). 

The pioneering observation of the UHI phenomenon initially introduced by Luke Howard in 1818 

indicated the global acknowledgment of a substantial urban climatic and environmental predicament (Zhou & 

Chen, 2018). UHI, widely prevalent across urban landscapes, encompasses elevated heat capacity, thermal 

conductivity, and predominantly impervious surfaces, which amplify heat absorption and retention relative to 

rural locales (Gaur et al., 2018; Naikoo et al., 2022). Additionally, the proliferation of urban structures aggravates 

heat absorption by means of recurrent reflections and absorption, much as the presence of establishments and 

buildings reduce urban air circulation, further compounding heat accumulation (He et al., 2007; Zhou & Chen, 

2018). Because of this, megacities confront steadily escalating UHI magnitudes, exacerbating discomfort in the 

urban thermal setting, especially during warmer seasons, thereby establishing prolonged reliance on air 

conditioning by inhabitants, hence, giving rise to an extended levels of anthropogenic heat discharge (Zhou et 

al. 2011; Zhou & Chen, 2018). Similar studies highlight mitigating UHI implications to diminish the adverse 

effects on urban inhabitants’ health and well-being, advocating for a deeper understanding and definite 

interventions in urban development and design to foster a holistic approach towards sustainable and resilient 

urban planning. 

An analysis that can evaluate the UHI effect is the LST analysis, which is considered to be one of the most 

vital domains in assessing the physical systematic series of surface energy (Chen et al., 2013). LST is the land 

surface’s radiative skin temperature measured by means of a remote sensor. Additionally, it is approximated in 

distinction to geostationary satellites’ top-of-atmosphere brightness temperatures taken from their infrared 

spectral channels. Information gathered by means of this analysis dispenses data regarding both temporal as well 

as spatial variations of the land (Chen et al., 2013). This concept is generally used in a wide range of fields 

spanning critical concepts in environmental science such as evapotranspiration, hydrological cycle, urban 

climate, climate change monitoring, vegetation records, and other related studies (Guha et al., 2017; Chen et al., 

2013). The acquisition of LST through multi-temporal remote sensing may be further supported by indices such 

as (1) the Normalized Difference Vegetation Index (NDVI) and (2) the Normalized Difference Built-up Index 

(NDBI). Simply put, NDVI is used as an indicator for vegetation cover, whereas NDBI is for levels of 

urbanization. These are vital indices as they are used for correlation (Bala et al., 2018; Malik et al., 2019). The 

analysis of LST combined with NDVI and NDBI data can provide indications regarding the correlation among 

vegetation and build-up indices as well as surface temperature (Chen et al., 2013). Substantial alterations in terms 

of LULC are brought about by anthropogenic and natural factors impacting heat emission of LST (Malik et al., 

2018). The results that may be collated in this component of the study can illustrate the extensive shift in the 

ambient temperature of Rizal, which can establish evidence of the implications of the changes in land use land 

cover (LULC) classifications in the area.  

Several studies have been carried out in the Philippines that utilize remote sensing to investigate, as well 

as address issues regarding urbanization and its effects. For instance, are the two recent studies that have been 

conducted in Metropolitan Manila. Limbo-Dizon and Dagamac (2023) detect how the coastline of Manila was 

affected by the rapid urban development over the past decades. Almadrones-Reyes and Dagamac (2022) assessed 

LULC and LST changes in Metropolitan Manila using Landsat imagery. Similarly, Tiangco et al. (2008), 

investigated the LST of Metro Manila, nonetheless, Advanced Spaceborne Thermal Emission and Reflection 

Radiometer (ASTER) was used. On the other hand, Tinoy et al. (2019), generated spatiotemporal hot and cold 

spot occurrence maps by utilizing LST images in Davao City. While numerous studies have explored 

urbanization and its impacts in the Philippines, focusing primarily on metropolitan areas like Manila, and Davao, 

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73 

research in semi-urban regions such as the Province of Rizal remains limited (Limbo-Dizon & Dagamac, 2023; 

Almadrones-Reyes & Dagamac, 2022; Tiangco et al., 2008; Tinoy et al., 2019). This particularly serves a 

significant purpose as semi-urban areas are known to be transitional zones between natural and human-modified 

landscapes (Meeus & Gulinck, 2008). With that, this study aims (i) to generate an LST map in Rizal from the 

years 1993, 1997, 2014 and 2020; (ii) to calculate the NDVI and NDBI values of the said years; and (iii) to 

determine and interpret the relationship of LST to NDVI and NDBI in the study area. Investigating abiotic 

dynamics in areas that have yet to be studied can potentially provide novel insights, in a regional aspect, into the 

processes driving socio-economic change and environmental impact, which, in turn, can contribute to sustainable 

development strategies. With these set objectives, research gaps may be bridged by elucidating the 

understanding of biological processes, particularly regarding abiotic factors such as vegetation and temperature. 

2. Data and Methods 

2.1. Study Area 

Rizal (see Figure 1), a rapidly growing province situated in Region IV-A within the northern-central 

region of CALABARZON in Luzon, is recognized as one of the Philippines' distinguished first-class provinces. 

It is specifically located west of Metro Manila and borders Bulacan to the north, Quezon to the east, and Laguna 

to the southeast. Notably, it stretches along the northern banks of Laguna de Bay, the largest lake in the 

Philippines. Characterized by a rugged terrain, Rizal Province is nestled within the western slopes of the 

southern section of the Sierra Madre Mountain range and spans a total area of 1,182.65 square kilometers. The 

latitudinal and longitudinal extent of Rizal are delineated by these coordinates: 14°28’69.79”N-14°89’24.46”N 

and 121°09’51.58”E-121°46’65.31”E. 

 

Figure 1. Map of the study area, Rizal, Philippines. 

The province consists of 1 city and 13 municipalities, with 8 of these municipalities classified as first-class, 

including Angono, Binangonan, Cainta, Pililla, Rodriguez, San Mateo, Tanay, and Taytay. In contrast, there are 

2 second-class municipalities, Morong and Teresa, while Cardona and Baras represent the third and fourth class 

categories, respectively. This demarcation encompasses a total of 189 barangays, collectively shaping the 

province's administrative boundaries. Serving as capital city of Rizal, Antipolo plays a pivotal role in anchoring 

this vibrant province. As per the 2020 census data from the National Statistics Office of the Philippines, Rizal 

Province is now home to a substantial population, ranking as the fourth most populous province in the country. 

Over the course of 117 years, it has experienced a remarkable demographic transformation, with its population 

burgeoning from 50,095 residents in 1903 to a significant 3,330,143 individuals by 2020. This remarkable growth 

signifies an increase of 3,280,048 people, reflecting an expansion rate of 3.07%. Notably, this surge translates to 

an additional 445,916 individuals compared to the 2015 population count of 2,884,227. 

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74 

Classified under the Koppen climate system as Type 1, the study area features two distinct seasons: a dry 

season spanning November to April and a wet season from May to October. This climate type is conducive to 

both industrial and agricultural development. Moreover, its strategic location makes Rizal an attractive prospect 

for business ventures and settlements, including the establishment of ecotourism destinations, thereby drawing 

potential investments to the region. Additionally, its proximity to Metropolitan Manila renders it susceptible to 

the effects of rapid urbanization, given its suitability for urban development. 

2.2. Data Acquisition 

As a means to detect changes in LST in the study area, remotely sensed imageries obtained from the 

Landsat Collection 1-Level 1 (specifically, Landsat 4-5 ™ C1-Level 1 and Landsat 8 OLI/TIRS C1 Level 1) 

accessible via the USGS EarthExplorer were used. This resource offers an extensive catalog of satellite and aerial 

images that are readily accessible for comprehensive data analysis. The process of satellite imagery selection 

took into account several critical criteria to ensure maximum accuracy and reliability. These criteria 

encompassed the necessity for minimal Land and Scene Cloud cover, both of which were stipulated to be below 

10%, as well as the requirement that the imagery be captured during daylight hours. 

Table 1. Details of Landsat 5 ™ C1- Level 1 and 8 OLI / TIRS C1 Level Imagery Utilized for Classification. 

Satellite             Sensor             Acquisition date   Landsat Product Identifier Source 

Landsat 5 
Multispectral Scanner and the 

Thematic Mapper 
05-04-1993 

LT05_L1TP_116050_1993050
4_20170119_01_T1 

https://earthexplorer.
usgs.gov/ 

Landsat 5 
Multispectral Scanner and the 

Thematic Mapper 
10-22-1997 

LT05_L1TP_116050_1997 
1022_20161229_01_T1 

https://earthexplorer.
usgs.gov/ 

Landsat 8 
Operational Land Imager and 

Thermal Infrared Sensor 
02-07-2014 

LC08_L1TP_116050_2014 
0207_20170426_01_T1 

https://earthexplorer.
usgs.gov/ 

Landsat 8 
Operational Land Imager and 

Thermal Infrared Sensor 
12-24-2020 

LC08_L1TP_116050_2020 
1224_20210310_01_T1 

https://earthexplorer.
usgs.gov/ 

Over a period spanning 27 years (1993, 1997, 2014, and 2020), a total of four remotely sensed images were 

procured (see Table 1). These specific years were chosen due to their alignment with the additional criteria 

established for quality assurance. Furthermore, to demarcate the boundaries of Rizal province within the 

remotely sensed imagery, an Administrative Boundary dataset from the DIVA-GIS database was layered and 

clipped. This comprehensive approach allowed satellite imageries that met stringent quality standards to be 

obtained, facilitating an accurate and reliable analysis of the study area's LST. The schematic diagram 

summarizing the major steps in data acquisition and processing is seen in Figure 2. 

 
Figure 2. Methodological Framework for Assessing the Land Surface Temperature in Rizal 

2.3. Data Processing 

Prior to LST analyses of the remotely sensed data acquired, the images had also undergone image 

preprocessing through QGIS 3.20 Odense. Atmospheric correction was employed since it is important to 

minimize the differences in surface reflectance (SR), which will enable to conduct a direct comparison between 

https://doi.org/10.14710/geoplanning.11.1.71-84
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
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75 

variations of image dates and different sensors (Nazeer et al., 2014). The Semi-automatic Classification Plugin 

was utilized, wherein Landsat data to Top of the Atmosphere (TOA) reflectance and brightness temperature, 

with DOS1 Atmospheric correction. Prior to the Supervised Classification of the LULC categories, an adjustment 

in brightness and contrast of the remotely sensed images is made through ArcGIS for more accurate 

classification. In addition, the area of the Rizal province was delineated with the use of the Administrative 

Boundary shapefile acquired through the DIVA-GIS database and extracted by mask through ArcGIS 

geoprocessing tools. 

2.4. Land Surface Temperature Analysis 

Considering that the data acquired from 1993-2014 are from Landsat 5 TM and data from 2020 is from 

Landsat 8 OLI, the description, wavelength, and resolution of each Landsat corresponds to the different 

designation of band number, hence, varying equations were used to retrieve certain variables, values of both 

indices, and the LST of each respective year. This may be due to the complexity that Landsat 8 brings, seeing 

that it is the first of its kind, it has Thermal Infrared Sensors (TIRS) as well as an Operational Land Imager 

(OLI) incorporating spectral bands of 1 to 11 with bands of 10 and 11 being the thermal bands. On the other 

hand, Landsat 5 uses a Landsat Thematic Mapper (TM) sensor, and out of its 7 spectral bands, band 6 was used 

for the conversion of spectral radiance so as to acquire the LST (Poursanidis et al., 2015). 

2.5. Retrieval of Land Surface Temperature Calculation from Landsat 5 

Retrieval of LST initiated with obtaining the Digital Number (DN) image from band 6. The DN was 

converted to top-of-atmosphere (TOA) radiance also known as the spectral radiance, which is denoted by the 

symbol 𝐿𝜆., 𝐿𝑚𝑖𝑛, and 𝐿𝑚𝑎𝑥𝜆 (which is obtained from the following calculation Equation 1) represent the detected 

scaled spectral radiance to the minimum and maximum quantized calibrated pixel value, denoted by 𝑄𝐶𝐴𝐿, in 

digitized numbers, both of these spectral radiances are in W/(m2 ster m) (Barsi et al., 2003; Qin et al., 2001). 

𝐿𝜆 =  
(𝐿𝑚𝑎𝑥𝜆−𝐿𝑚𝑖𝑛𝜆)

(𝑄𝐶𝐴𝐿𝑀𝐴𝑋 − 𝑄𝐶𝐴𝐿𝑀𝐼𝑁)(𝑄𝐶𝐴𝐿 − 𝑄𝐶𝐴𝐿𝑀𝐼𝑁)
+ 𝐿𝑚𝑖𝑛𝜆……(Eq.1) 

Spectral radiance is, then, converted into temperature in Kelvin. This is denoted by 𝑇 which represents 

the effective at-satellite brightness temperature of TM6 in the calibration constants 1 and 2, which are 

represented by the symbols 𝐾1 and 𝐾2. The calibration constant values for Landsat 5 TM are as follows: 𝐾1 = 

60.776 and 𝐾2 = 1260.56 (Schneider & Mauser, 1996). Once 𝑇 is obtained, considering that the unit of 

temperature is Kelvin, it must be converted into degree Celsius by subtracting 273.15 (Equation 2): 

𝑇 =
𝐾2

𝐼𝑛(
𝐾1
𝐿𝑇

)+1
− 273.15………………………(Eq.2) 

2.6. Retrieval of Land Surface Temperature Calculation from Landsat 8 

Similar to that of Landsat 5 TM, the initial step is to input a band, however, for Landsat 8 OLI the band 

number inserted into the application is band 10 for thermal infrared 1. The Equation 3, below represents the 

first conversion formula for Landsat 8 to obtain the top of atmospheric spectral radiance. 

𝐿𝜆 = 𝑀𝐿 ∗ 𝑄𝑐𝑎𝑙 + 𝐴𝐿 − 𝑂𝑖 ………………(Eq.3) 

The radiance multiplicative band number is denoted by ML. It is then multiplied to the pixel values or the 

DN of the quantized and calibrated standard product. 𝐴𝐿, on the other hand, is the additive rescaling factor that 

is distinct for each band. Lastly, 𝑂𝑖 is the correction value for band 10 which is 0.29. Once spectral radiance was 

converted into reflection, the step that followed was the conversion of atmosphere brightness temperature (𝐵𝑇) 

by the use of the equation below, which utilized the same thermal constants, only with varying values due to the 

difference in band number used (Equation 4). The calibration constant values for Landsat 8 OLI band 10 are as 

follows: 𝐾1 = 774.89 mW and 𝐾2 = 1321.08 (Guha et al., 2017). 

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BT =
𝐾2

𝐼𝑛(
𝐾1
Lλ

)+1
− 273.15………………..(Eq.4) 

The two indices utilized in the study were the NDVI and NDBI, both of which are derived from three 

reflectance bands, namely: visible, Near Infra-Red, and Short-Wave Infra-Red.  In order to acquire the respective 

values of each index, computations were made possible by using the equation seen in Table 2. 

Table 2. Retrieval of Indices Values 

Index Equation Bands used in Landsat 5 Bands used in Landsat 8 

Normalized Difference 
Vegetation Index (NDVI) 

NIR − RED Band 4 – Band 3 Band 5 – Band 4 

NIR + RED Band 4 + Band 3 
 

Band 5 + Band 4 

Normalized Difference Built- up 
Index (NDBI) 

NIR – RED Band 5 – Band 4 Band 6 – Band 5  
NIR + RED Band 5 + Band 4 Band 6 + Band 5 

It is of utmost importance to retrieve the two indices as it is the required values used to calculate the 

proportion of vegetation. A method in quantifying the vegetation proportion from Wang et al. (2015), suggests 

that soil and vegetation NDVI values be included so as to fit to a more global context. However, considering the 

varying NDVI values of different geographical regions, in the case of the study it was best to simply use the 

minimum and maximum values of DN out of the NDVI image seeing that, generally, the NDVI value itself was 

quantified from the reflectivity of the spectral radiance (Avdan & Jovanovska, 2016). 

 𝑃𝑣   = (
𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼𝑚𝑖𝑛

𝑁𝐷𝑉𝐼𝑚𝑎𝑥−𝑁𝐷𝑉𝐼𝑚𝑎𝑥
)

2

…………..…….(Eq.5) 

Another aspect required to obtain an estimation of the LST is the land surface emissivity as it is used to 

indicate the emitted radiance (Jimenez-Munoz et al., 2006). The equation for the land surface emissivity is 

denoted by 𝜀 (Equation 6). The value 0.004 presented is considered the surface roughness constant (Sobrino & 

Raissouni, 2000). On the other hand, since the obtained NDVI denotes that the majority of the land surface is 

covered by soil, therefore giving an NDVI value of 0 to 0.2, an emissivity value of 0.986 is designated in the 

equation (Avdan & Jovanovska, 2016; Sobrino et al., 2004; Guha et al., 2017). 

𝜀 = 0.004 ∗ 𝑃𝑣 + 0.986…..…………….(Eq.6) 

The determination of LST, in Celsius, is conditionally quantified by using the equation below; Equation 7 

(Stathopoulou & Cartalis, 2007). The symbol ρ represents the wavelength of the emitted radiance, which is given 

an assigned value of 10.8 for band 10 in Landsat 8. 

𝑇𝑠 =
𝐵𝑇

{1+[
𝜆𝐵𝑇

𝜌
]Inε}

   ………………………….(Eq.7) 

3. Results and Discussion 

Four spatial distribution maps of the LST in Rizal were generated from ArcGIS. The various thermal 

signatures indicated in each of the LST maps are results of the presence of different land cover classes 

constituting distinct thermal properties. Fluctuations of temperature for these years are apparent. However, one 

aspect is shared among the retrieved data: relatively high temperatures are mostly centralized in the 

municipalities neighboring Metro Manila, where bare land, settlements, and population density are at their 

highest, causing higher concentrations of surface temperature. The spatial distribution of LST across the 

province of Rizal reveals that cities located in the Northern, Eastern, and Southern parts experience 

comparatively lower temperatures, whereas cities specifically in the Western section of the region have higher 

temperatures. The descriptive statistic exhibiting the values of LST, NDVI, and NDBI is presented in Table 3. 

These values, obtained from Landsat 5 TM and Landsat 8 OLI and TIRS satellites, range between 15.18°C and 

40.31°C across the entire dataset. Based on 1200 points selected from the calibrated satellite imagery, the average 

LST for 1993, 1997, 2014, and 2020 is 29.34°C, 23.91°C, 25.59°C, and 24.03°C, respectively. 

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Figure 3.  Land Surface Temperature Maps of (a) 1993, (b) 1997, (c) 2014, (d) 2020. 

As shown in Figure 3a, the year with the highest surface temperature is 1993. This may be attributed to 

several biological phenomena that have taken place in Rizal during that particular year. Alongside this, it can 

also be observed that the Pearson r value for buildup was at its highest. By 1997, the LST visibly decreased in 

response to the significant decline of barren lands and settlements. This is succeeded by a significant rise in LST 

for the year 2014. However, in the most recent LST map, the data exhibits yet another decline in surface 

temperature values. Furthermore, it is from this year that the Pearson r value for buildup was second to the 

lowest, followed by the Pearson r value of 1997. Though almost having the same average LST values from 2014, 

the Pearson r value for NDBI decreased by 0.179. In comparison, the Pearson r value for NDVI has increased 

by 0.186. The results obtained are not limited to this alone. Correlations among LST, NDVI, and NDBI were 

additionally graphed (see Fig. 4 and Fig. 5). Based on the normalized difference indices, it can be observed that 

a decrease in vegetation and an increase in buildups lead to a rise in surface temperature. The end-to-end results 

showed an overall increase in the vegetation cover from 1993-2020 in correlation with the decrease in LST 

simulated in the study. These observations are in coherence with similar studies (Kumar et al., 2012; Zhou et al., 

2014; Khandelwal et al., 2017; Tran et al., 2017; Peng et al., 2020; Saha et al., 2021). 

3.1. NDVI vs LST Relationship 

The NDVI is the quantification of the amount and vigor of vegetation present at the land surface. This 

index is related to vegetation to the utmost degree seeing that it can immensely reflect the near-infrared portion 

of the spectrum (Kumar et al., 2012). NDVI is susceptible to seasonal changes; therefore, its marginal variations 

can easily affect and modify the LST of an area. The relationship between LST and NDVI was determined to be 

inversely proportional through correlation analysis. In line with this, graphs were generated to further indicate 

the association between the two. As exhibited in Figure 4, LST increases as NDVI value decreases. This can be 

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supported by the negative Pearson correlation values of -0.754, -0.181, -0.568, and -0.353 for the 1993-2020 

datasets, thus, this evidently designates how LST is negatively correlated with NDVI, further reinforcing the 

statement that areas with the highest amount of vegetation tend to have lower surface temperatures. 

 

Figure 4. LST vs NDVI 2020 Correlation Graph of (a) 1993, (b) 1997, (c) 2014, (d) 2020 

3.2. NDBI vs LST Relationship 

One of the most established indices used in the processing and quantifying satellite data, especially that of 

monitoring the presence of build-up or settlements in each area, is NDBI (Kumar et al., 2012). The pixel values 

of NDBI range between negative (-) 1 to positive (+) 1, with greater values indicating highly concentrated build- 

ups. In this study, the relationship between NDBI and LST was assessed using the same methods for NDVI. 

However, in this analysis, the values of the Pearson correlation coefficient were all positive values, indicating 

that there is a strong and directly proportional correlation between the two variables, which corroborates the 

premise that as build-up increases, so does the surface temperature (Fig. 5).  

 
Figure 5. LST vs NDBI 2020 Correlation Graph of (a) 1993, (b) 1997, (c) 2014, (d) 2020 

This statement is further reaffirmed, as the acquired results indicate that the highest LST values are found 

within built-up areas. From this, it can be deduced that densely urbanized areas generate higher surface 

temperatures. Hence, it is considered to be a primary factor in UHI, as previously confirmed by similar studies 

such as that of Malik et al. (2019) and Almadrones-Reyes & Dagamac (2022). The Pearson r value for the years 

1993, 1997, 2014, and 2020 are as follows: 0.799, 0.428, 0.757, and 0.571. Though there may be visible fluctuations 

in these values, it overall indicates that there is a decline in buildup throughout the years. 

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There are numerous environmental parameters that heavily affect the modification and the transition of 

landscapes, and one such parameter is temperature. It is considered one of the major drivers of the productivity 

of vegetation in an area, which is directly interconnected with the concepts underlying NDVI and NDBI. In the 

case of this study, LST is strongly influenced by several factors such as solar incident radiation, angle of incidence 

of solar radiation, air temperature, and the expanse of vegetation; not to mention topography which encompasses 

several terrain conditions namely elevation, slope, and aspect. These are all linked to land surface properties like 

soil moisture and surface roughness (Khandelwal et al., 2017; Peng et al., 2020). 

Table 3. Descriptive Statistics of LST, NDVI, and NDBI 

LST 

Year Minimum Maximum Mean SD 

1993 15.1795 40.3119 29.3395 3.7753 

1997 12.8362 34.4621 23.9105 1.8889 

2014 18.1954 36.8866 25.5940 2.7182 

2020 16.7025 35.2090 24.0299 2.6917 

Table 4. Descriptive Statistics of NDVI 

NDVI 

Year Minimum Maximum Mean SD Pearson R 
1993   -0.3953 0.7333 0.3565 0.1876 -0.7539 

1997 -0.3704 0.7692 0.5488 0.1569 -0.1815 

2014 -0.1874 0.5895 0.3616 0.1143 -0.5677 

2020 -0.1937 0.6072 0.3914 0.1228 -0.3527 

Table 5. Descriptive Statistics of NDBI 

NDBI 

Year Minimum Maximum Mean SD Pearson R 

1993 -0.4074 0.4958 0.0456 0.1650 0.7981 

1997 -0.4203 0.3797 -0.1097 0.0953 0.4284 

2014 -0.4188 0.1826 -0.1762 0.0920 0.7567 

2020 -0.3982 0.2038 -0.2195 0.0844 0.5710 

It can be regarded that areas with relatively high settlements and patches of barren lands have garnered 

higher LST values. This assertion aligns with the results produced by this study (see Fig. 3). This may be 

attributed to the lack of moisture from the soil surface in view of the natural disturbances that took place prior to 

1993, such as a series of El Niño events between the years of 1986-1992, which induced severe stress on water 

resources (Hilario et al., 2009).  Another aspect that highlights the increase in surface temperature is the high 

amount of solar radiation received in the region. It is also worth noting that during the same period as the natural 

disturbances, the urban core areas of Rizal, which are San Mateo, Cainta, Taytay, Angono, and Binangonan, have 

significantly contributed to the intensification of the UHI effect by means of increasing the distribution of hotspots 

through the establishments of several medium scale residential areas, including subdivisions (Regmi, 2017). In 

these highly concretized areas, NDVI values are at their lowest. The incremental consumption of energy, increase 

in built-up surfaces of concrete, asphalt, and the like, as well as a significant decline in both vegetation and water 

surfaces, all constitute the increase in LST (Kumar et al., 2012), as it is very much susceptible to various heat 

discharges coming from increased surface coverage of urban development. 

This is in alignment with the research conducted by Olfato-Parojinog et al., which primarily focused on 

tracing the trends on the land use and land cover (LULC) in the province of Rizal. Their work that sheds light on 

the pattern of changes in LULC, and this study, set in the same study area and utilized data collected during the 

same years that ensures a directly comparable temporal and spatial context, corroborate each other's findings, 

providing robust evidence for LULC-LST dynamics. Just as Olfato-Parojinog et al. highlighted the LULC 

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changes, our findings also emphasize the importance of LST as a key indicator of the various modifications in 

LULC classes through time. The consistent patterns observed between this study and the work of Olfato-

Parojinog et al.46 affirm the broader relevance of LULC-induced effects on thermal regimes, it becomes evident 

that the changes in LULC is directly correlated with changes in LST consistently exhibited similar trends. 

Extreme lack of precipitation, coming in the form of insufficiency of rainfall as well as protracted drought, 

gives off direct and indirect effects not only on the ecological system but also to humans. The El Niño events that 

occurred in the years 2010 and 2014 have caused both meteorological as well as agricultural droughts. 

Meteorological droughts entail a deficiency in precipitation that lasts for a few months or so. Alterations in the 

pattern of atmospheric circulation bring about this phenomenon, which is primarily prompted by occurrences 

such as El Niño-Southern Oscillation (ENSO) causing anomalous modification in sea surface temperature. On the 

other hand, agricultural drought is centered on deficiencies pertaining to soil water, therefore, resulting in low 

soil moisture and higher LST. These two droughts have significantly contributed to reduced growth of vegetation 

and decline of crop productivity, further aggravating reduced humidity, not to mention higher rates of soil 

evaporation- resulting in drier environments. 

Fortunately, even though major cities in the Philippines, especially those found in Metro Manila, have 

undergone notable increase in LST may it be due to natural disturbances like drought or anthropogenic activities 

such as urban expansion (Tiangco et al., 2008; Almadrones-Reyes & Dagamac, 2022), effective measures were put 

in place to address the situation in Rizal. The rejuvenation of vegetated areas may be attributed to the Laguna 

Lake Development Authority-Tanay Streambank Rehabilitation Project which is one of the reforestation 

campaigns conducted in Rizal from 2004-2014 that aimed to reforest 70 hectares of private land and establish 25 

hectares of agroforestry land to increase riparian forest cover (Lasco et al., 2005). In line with this, the 

microwatershed occupying a notable portion of Tanay and the flank of the Sierra Madre Mountain Range located 

east of Rodriguez are prominent features found in the Northern and Eastern municipalities of Rizal that also 

substantially induced a cooling effect due to the presence of dense vegetation as measured by the vegetation index 

(Murdiyarso & Skutsch, 2006). The increase in the amount of vegetation due to the enrichment of soil moisture 

demarcates LST through the continuous change of latent heat coming from the surface transferred through the 

atmosphere by way of evapotranspiration. Furthermore, the decrease in patches of bare lands partakes in 

narrowing the discrepancies in radiant surface temperature (Sun et al., 2020). Both aforementioned statements 

give way to changes in the thermal responses and consequentially generate higher NDVI and lower NDBI values 

(Yuan & Bauer, 2007). 

The reforestation program implemented has further improved the state of Rizal, in terms of its LST, as it 

has slowed down the rate of both agricultural and urban extensification, to a degree. Moreover, it has also 

increased the forest cover, which lowered the study area’s Albedo, and, in turn, portions of its absorbed solar 

radiation increased at the surface. Because of this, shortwave radiation, especially during the daytime, is absorbed 

more, eliciting a warming effect. Nevertheless, this is counteracted by a higher amount of latent heat loss by means 

of increased evapotranspiration, which is primarily influenced by vegetation activity and soil moisture status 

(Peng et al. 2014). The positive modifications in the geophysical characteristics of Rizal have somehow rectified 

the previous predicaments from which the region had previously suffered. Evidently, by implementing effective 

and sustainable reforestation initiatives, it is feasible to progressively attain cooler temperatures. In essence, 

through integrating well-planned and environmentally responsible measures to restore forested areas, mitigating 

the effects of UHI, and contributing to the reduction of surface temperature is within the realm of possibility even 

for third-world countries, like the Philippines. 

 As the challenges of urban growth become more concerning throughout time, the significance of 

managing land resources is more pronounced in order to achieve sustainable development (UN, 2015; UNFCC, 

2015; UN HABITAT, 2018). Thus, maximizing the available land resource through sustainable urban planning 

can achieve short- and long-term objectives given the prevailing land quality and socioeconomic factors in the 

locality (Verheye, 1997; Gebre et al., 2021). As the landscape has limited area for greeneries, giving priority for 

residential and industrial land use, the implementation of compact greenfields can minimize the effects of urban 

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heat islands. This can be in the form of establishment of street trees, forest parks, gardens, green walls and roofs, 

and green corridors and networks (Phelan et al., 2018; Maruna et al., 2019). Residential lands are the most 

spatially extensive in the peri-urban province, giving limitations for opportunities for vegetation establishment, 

thus, the government efforts towards urban greening must employ more strengthened and clear land use 

regulations, for the potential and existing tree covers even in private properties. In addition, involving the 

community and addressing their inputs and engagement is vital for strategizing policies and development, 

instilling responsibilities not just for the government, but also for the stakeholders (Phelan et al., 2018). Since 

peri-urban vegetation, such as in the case of the province of Rizal, plays a significant role in adaptation to climate 

change effect mitigation, looking further into the accurate and spatially explicit information on land suitability 

must be employed, involving stabilization of soils, reduction of pollutants, and overall environmental restoration 

(LaGro Jr., 2005; UN, 2015). 

The findings from this study highlight the pressing necessity for a more sustainable and holistic 

environmental management strategy within the Rizal Province. As urbanization accelerates, temperature 

fluctuations are magnified, this supports previous studies related to the topic at hand (Yang et al., 2016; Yuan et 

al., 2007; Zhou et al., 2014). The documented increase in vegetation, coupled with a concurrent decrease LST, 

suggests a potentially ameliorating effect on heat islands. However, the direct relationship between LST and the 

NDBI points to the potential worsening of urban heat islands, posing serious challenges to local climate resilience 

and human well-being, if left unchecked. Furthermore, the inverse relationship between LST and the NDVI 

accentuate the ecological implications of urban expansion on vegetation. These shed light on the intricate 

dynamics of land use change, temperature regulation, and ecosystem health within Rizal Province. 

4. Conclusion 

This study sheds light on the dynamic interplay between land cover classes and LST in the Rizal Province. 

Over the years, the region has undergone modifications brought about by natural and anthropogenic-driven 

disturbances that added to the complexity of its already unique landscape. By correlating LST to NDVI and NDBI, 

significant insights have emerged. The observed increase in vegetation cover from 1993 to 2020 has corresponded 

to a notable decrease in LST, pointing out the cooling effect of increased green spaces. Additionally, this highlights 

the role of land cover in influencing temperature patterns. The study's documentation of fluctuating LST, 

attributed to nearly three decades of shifts in vegetation and built-up areas, accentuate the profound impact of 

both anthropogenic and natural factors on heat emissions and ambient temperature. These findings emphasize 

the continuous need for strict adherence of evidence-based land use and environmental policies to ensure a 

sustainable development towards urbanization and facilitate a harmonious coexistence with the environment in 

the Rizal Province, where the intricate relationship among various land cover classifications play a pivotal role in 

the LST of the area. 

Given the significant role in assessing land cover and its influence on LST, future research should delve 

into the socio-economic drivers of the changes in the landscape and integration of the flows of urbanization in the 

province to address the mechanisms of urban expansion. This can be in the form of integration of socio-economic 

data such as population growth, income per capita, and even land tenure system for a deeper understanding of 

underlying conditions causing the landscape changes. Additionally, looking into the qualitative studies of 

management strategies and landscape planning and monitoring can give a clearer insight into the possible 

implementations to sustainable development. Community perceptions can also be assessed on the current land use 

strategies as the engagement of stakeholders is vital in formulating landscape strategies and policies to maintain 

the balance between urban development and environmental conservation. As the changes in the landscape are 

mainly due to infrastructure development, assessing the environmental impacts of these projects in relation to 

ecosystem integrity can give ways to strategize mitigation measures and minimization of urbanization effects. 

Lastly, designing and implementing restoration and rehabilitation projects for degraded landscapes can serve to 

enhance ecosystem resilience. This can be conducted either with the use of remote sensing techniques or ground-

based monitoring. By means of addressing the complex factors driving the changes in vegetation and temperature 

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dynamics, policymakers can develop effective strategies for sustainable development and climate resilience in the 

province. 

5. Acknowledgements 

NHAD acknowledges the DOST- Philippine Council for Agriculture, Aquatic and Natural Resources 

Research and Development (PCAARRD) for the Balik Scientist grant. KJAR and JELD acknowledges Deutscher 

Akademischer Austauschdienst (DAAD) German Academic Exchange Service for the in Country/in Region 

Scholarship. PASM and AOP would like to thank the DOST – Accelerated Science and Technology Human 

Resource Development Program (ASTHRDP) for the Scholarship. The authors declare no conflicts of interest. 

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