Tropical highland regions are susceptible to climate change and natural disasters due to their geographical location and hilly terrain. The objectives of this study are to determine land use land cover (LULC) changes in the Cameron Highlands and analyse the climatic parameters of the Cameron Highlands. This study integrates LULC analysis using remote sensing techniques and 10-year climatic parameters data to evaluate the impact of climate variability on the sustainability of the Cameron Highlands. Based on the validation results, the overall accuracy of LULC was 95.42% in 2016, 96.60% in 2018, and 97.40% in 2020. The results show an 18% rise in agriculture, a 16% increase in urban growth, and an 8.14% decline in forest coverage in Tanah Rata and Ringlet, Cameron Highlands, from 2016 to 2020. The Mann -Kendall and Sen slope indicated a statistically significant increasing trend in rainfall (Kendall's Tau, z = 0.102, p < 0.0001 and Sen value= 0.131, p < 0.001, respectively) and temperature (Kendall's Tau, z = 0.151, p < 0.001 and Sen value = 0.294, p < 0.001, respectively) from 2012 to 2021, increasing the area's susceptibility towards climate change impact and natural disasters. This study highlights the vulnerability of the Cameron Highlands to natural disasters, emphasizing the crucial need for efficient land management in slope areas.

  • The increase in rainfall in Cameron Highlands suggests a potential rise in surface runoff and greater vulnerability to flooding and landslides.

  • The increase in temperature can lead to prolonged drought conditions, adversely affecting agricultural yields and productivity in Cameron Highlands.

  • Changes in land use land cover show the decline in forest and an increase in agriculture, increasing the risk of landslides and floods

Rapid changes in climate variables frequently cause significant variations in rainfall patterns, which then cause various types of hazards, such as landslides, erosion, and floods (Nasidi et al. 2021). Flood tragedies become worse and more unpredictable in this situation. Unusual heavy rainfall patterns influenced by climate variability and land use, such as roads, buildings, and other constructions in slope areas may further exacerbate natural disasters. High-magnitude rainfall events are anticipated to control rainfall erosivity and may even increase with intensifying storms (Gonzalez-Hidalgo et al. 2012). Heavy rainfall in mountainous areas has been proven to be a consistent cause of slope failure in most past landslides (Kadamb & Savoikar 2022). The chance of soil erosion and landslides can increase in the events of heavy rainfall with higher erosivity combined with a slope gradient of 45% (Abdullah et al. 2019). The most important factors of rainfall are its intensity, volume, duration, and frequency, all of which are frequently impacted by climate change. With an increase in rainfall amounts, duration, or frequency, rainfall erosivity is likely to rise as well, leading to saturation and ponding. This will gradually boost the raindrops' capacity to detach from soil particles. Rainfall is sensitive to rising greenhouse gas emissions, which significantly affects the ecosystem services and sustainability of the ecosystem.

Globally, rainfall patterns evolved during the past century in both time and place, and future increases in the tropical and subtropical regions are expected (Kang et al. 2009). Tropical countries are critical areas subjected to highly intense rain due to their geographical location, high humidity, temperature, and monsoon effect (Zafirah et al. 2017). Malaysia has witnessed warming and unpredictable rainfall, especially over the past 20 years (Saimi et al. 2020). The northeast monsoon, which lasts from November to March, is the wettest time of the year for most of Malaysia's states, including Pahang. Severe flooding incidents have been a feature of this monsoon season (Zafirah et al. 2017).

Land use land cover (LULC) analysis using satellite images through remote sensing and Geographic Information System is widely used to analyse and map spatiotemporal changes in the environment (Deb & Tarafdar 2019). LULC is important in continuous monitoring of environmental changes due to economic and social development, such as agricultural activities, residential settlement, commercial development, and urban growth (Aik et al. 2021). Negative consequences of LULC in the environment are associated with biodiversity loss through deforestation, increasing runoff, and water pollution during periods of highly intense rain and floods, which subsequently affects food and water security (Mishra et al. 2021). The changes in LULC may not necessarily imply land degradation, but they might increase susceptibility to natural disasters in certain geographical locations.

Malaysia's highlands have undergone various rapid developments, including the construction of new highways, dams, logging, mining, agriculture, tourism, settlement, and other activities. Due to uncontrolled rapid development and inefficient management, the physical and cultural environment has seen severe adverse effects in recent decades (Chan 1997). Due to numerous unlawful land clearings that breach the forest reserve and riverside regions, as well as unsustainable development methods, Cameron Highlands has faced substantial environmental issues (Razali et al. 2018). Cameron Highlands is identified as an environmentally sensitive area that is rich in flora and fauna, serving as the primary water catchment area for Jelai River and Pahang River.

Many studies have reported on the impact of changing land use and heavy rain on landslide vulnerability (Karsli et al. 2009; Reichenbach et al. 2014; Chen et al. 2019; Nguyen et al. 2020). Cameron Highlands has experienced an increasing number of landslides from 1966 to 2020 in Tanah Rata, Ringlet, Kuala Terla, Tringkap, Brinchang, and Kg. Raja, where cases of flooding, mudflows, and erosion were reported from 1991 to 2021 (Chan 1997; Weng & Nying 2017; Alnaimat et al. 2018; Muaz Abu Mansor Maturidi et al. 2020; Leh & Mokhtar 2021; Rahim et al. 2021).

Given the frequent recorded natural disasters in the area, our study focuses on two critical elements in climate variability assessments, such as LULC and climatic parameters. Notably, there is limited study of the interplay between changing LULC and climate parameters, specifically focusing on rainfall, temperature, and relative humidity in Cameron Highlands, especially in the specific regions where residents reside, namely, Ringlet and Tanah Rata. By examining these two key elements and delving further into resident-populated areas in Ringlet and Tanah Rata, our research aims to fill this gap and provide a deeper understanding of the interconnected impacts of human-induced land use changes and climate variability on the vulnerability of highland areas in Cameron Highlands to natural disasters. The objectives of this study are: (1) to determine the LULC changes in Cameron Highlands in 2016, 2018, and 2020 and (2) to analyse the climate parameters of Cameron Highlands from 2012 to 2021. The findings of this study, which demonstrate the connection between changing land use and climate change, can be applied as guidelines for effective land management in slope areas and may be particularly useful in upland areas.

Study area

Cameron Highlands is situated in the high-elevation centre of the Titiwangsa Range of the Peninsular Malaysia and lies at latitude 4°28′N and longitude 101°22′E at the mountainous region in Pahang state, Malaysia (Figure 1). Cameron Highlands is the smallest district in Pahang compared to the other 10 districts (Lipis, Jerantut, Raub, Bentong, Temerloh, Bera, Maran, Kuantan, Pekan, and Rompin). Cameron Highlands has a total area 71,227 ha of land, which is divided into three prime sub-districts Hulu Telom, Ringlet, and Tanah Rata. It is one of Malaysia's most well-known retreat areas, with an average temperature between 17 and 20 °C all year and approximately 74% of the whole area is at an elevation of more than 1,000 m. Malaysia experiences more rain during the first monsoon, which occurs between November and January, than during the second, which occurs between April and May. The two inter-monsoon seasons, lasting for 2–3 months between these two monsoons, are known for being Malaysia's hottest and driest days (Matori et al. 2012).
Figure 1

Study area – districts of Cameron Highlands, Pahang.

Figure 1

Study area – districts of Cameron Highlands, Pahang.

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Remote sensing image and climate data

The elevation data obtained from the United States Geological Survey (USGS) were utilized to map the geographical features of the study location of Cameron Highlands, with the data collected by the Shuttle Radar Topography Mission (SRTM) to create a digital elevation model (DEM) of the Earth's surface. The DEM was created using dual radar antennas to obtain interferometric radar data, which were then transformed into digital topographic data at a resolution of 1 arc sec (Farr et al. 2007; Su et al. 2015). The SRTM instrument captured data with a resolution of roughly 30 m (Farr et al. 2007).

The climate parameters were obtained from the Malaysian Meteorological Department (MET) for meteorological stations in Cameron Highlands, latitude 4°28′ N and between 101°22′ E at an elevation of 1,545 m. The parameters were average monthly precipitation (mm); minimum, maximum, and mean temperature (°C); and relative humidity (%) from 2012 to 2021.

Climate data analysis

Datasets obtained from the Malaysian MET, which include 24 h temperature, relative humidity, and rainfall for 2012 to 2021, were statistically analysed using SPSS V27 and Microsoft Excel. Data were tested using the normality test. Based on the Skewness, Kurtosis, Kolmogorov–Smirnov, and Shapiro–Wilk test for temperature, relative humidity, and rainfall data, these data did not show a normal distribution. Based on this basis, using non-parametric tests is the most effective approach than the parametric test. The Kruskal–Wallis test was run to determine the significant differences between climate parameters (rainfall, relative humidity, and temperature) and years. The Spearman Rho correlation was conducted to determine the degree of relationship between the three climate parameters, while the regression model was done to predict how rainfall is expected to be influenced by temperature and relative humidity. The dependent variable was the rainfall, while the independent variables were the temperature and relative humidity, as indicated by this regression equation:
(1)
where y = the outcome variable, b0 = intercept of the line, b1 = coefficient of the first predictor (X1), b2 = coefficient of the second predictor (X2), and X1 and X2 are the selected independent variables.
The Mann–Kendall (MK) test (Mann 1945; Kendall 1975) and Sen slope (Sen 1968) are non-parametric trend tests commonly used to analyse meteorological data due to their robustness, unaffected by missing data, and the length of the time series (Nguyen et al. 2022). These tests were conducted using XLSTAT 2019. The MK trend test was used to examine the presence of a consistent trend of rainfall, temperature, and relative humidity. The main advantage of using non-parametric statistical tests is that they are better suited for data with abnormal distributions (Saimi et al. 2020). A positive Z value indicates an increasing trend, while a negative value indicates a decreasing trend (Mozejko 2012). The MK trend test does not depend on the actual distribution of the data and is less susceptible to outliers because it is based on the ranks of the observations rather than their actual values (Deb & Tarafdar 2019). The MK trend test is based on the null hypothesis (H0) that no trend exists, and the other is the alternative (H1) hypothesis, which expresses a significant upward or declining trend on the basis of 5% significance level (Jiqin et al. 2023). The MK equation is given as follows:
(2)
where n is the numbers of data points, Xj and Xi are annual values in years j and i, and sign (XjXi) is calculated using the equation:
(3)
The Sen slope estimator has also been used as an improved approach from the MK trend to determine the magnitude of change for all the relevant climate change parameters. In hydro-meteorological time series, the Sen slope estimator has been widely employed (Saimi et al. 2020). The Sen slope's equation used is given as follows:
(4)
where i = 1 to n − 1, j = 2 to n, Yj and Yi are data values at time j and i (j > i), respectively. If there are n values of Yj in the time series, estimates of the slope will be N = n(n − 2)/2. The slope of the Sen estimator is the mean slope of such slopes' N values. The Sen's slope is expressed as follows:
(5)

The positive Qi indicates an increasing trend, while the negative Qi values tell us that there is a negative trend in the temporal data. The unit of Sen's slope (Qi) is the slope magnitude per year.

LULC mapping process utilizing Landsat 8

Satellite images were employed to classify LULC in the study area. The supervised classification method was specifically applied to categorize the LULC from satellite imageries. Analysis and classification of the satellite images, along with area calculations and accuracy assessment, were conducted using ArcMap 10.8 software. The mapping process workflow is illustrated in Figure 2.
Figure 2

The flowchart of the LULC classification process.

Figure 2

The flowchart of the LULC classification process.

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Data acquisition

A specific day's Landsat imagery from 2016, 2018, and 2020, obtained from the USGS, was chosen for analysis. July 2016, April 2018, and April 2020 were selected as the respective months for these years due to the availability of cloud-free images. After downloading the desired satellite images, they were prepared for subsequent processing.

In addition to the primary data derived from Landsat imagery for LULC classification, Google Earth was utilized as the secondary data. The LULC data obtained were cross-verified with Google Earth, a widely utilized platform offering high-resolution satellite imagery. This validation process enhances the accuracy of the LULC data and contributes to the overall robustness of the analysis.

Data preparation

Landsat 8 data were used for the classification of LULC, obtained from the USGS Earth Explorer online portal (http://earthexplorer.usgs.gov/). The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments are installed in the Landsat 8 satellite (Wulder et al. 2016). The instruments gather information from different parts of the electromagnetic spectrum, such as the visible, near-infrared, short-wave infrared, and thermal infrared regions, producing a 30-m resolution LULC data (Roy et al. 2014).

The datasets contained in TIF files were downloaded and extracted from the zipped format. Subsequently, these datasets were imported into ArcMap, the central component within Esri's ArcGIS geospatial processing tools, specifically version 10.8. This software, recognized for satellite image processing, was employed to create a false-colour composite (FCC) and undertake further pre-processing and processing steps integral to the LULC mapping process (Juliev et al. 2019).

Image pre-processing

Image pre-processing is an integral step in satellite image interpretation, aiming to enhance both the visual interpretation and spectral separability of Earth surface characteristics. In addition, it aims to furnish enhanced inputs for automated image processing algorithms (Maini & Aggarwal 2010). The processes involved in image pre-processing encompass layer stacking, image correction, and the extraction of the study area.

Landsat 8 OLI/TIRS is equipped with 11 bands, and the specific characteristics of each band in the satellite image are detailed in Table 1. Given that the bands are initially separate in raw images, bands 2, 3, 4, 5, and 6 of Landsat 8 were combined by stacking them to generate a unified image using ArcMap (Srivastava et al. 2012). Each band was then allocated to a distinct channel (red, green, and blue), resulting in various true-colour and FCC combinations. Following this, the study area's subset of the composite image underwent image processing, with the composite image identifying five distinct classes: ‘Agriculture,’ ‘Bare land,’ ‘Forest,’ ‘Urban,’ and ‘Water bodies.’ Table 2 presents detailed descriptions of these classes.

Table 1

Descriptions of the bands in Landsat 8′s OLI/TIRS satellite images

Landsat 8 (OLI/TIRS)DescriptionWavelength (μm)
Band 1 Coastal/aerosol 0.433–0.453 
Band 2 Visible blue 0.450–0.515 
Band 3 Visible green 0.525–0.600 
Band 4 Visible red 0.630–0.680 
Band 5 Near infrared 0.845–0.885 
Band 6 Short wavelength infrared 1.56–1.66 
Band 7 Short wavelength infrared 2.10–2.30 
Band 8 Panchromatic 0.50–0.68 
Band 9 Cirrus 1.36–1.39 
Band 10 Long wavelength infrared 10.3–11.3 
Band 11 Long wavelength infrared 11.5–12.5 
Landsat 8 (OLI/TIRS)DescriptionWavelength (μm)
Band 1 Coastal/aerosol 0.433–0.453 
Band 2 Visible blue 0.450–0.515 
Band 3 Visible green 0.525–0.600 
Band 4 Visible red 0.630–0.680 
Band 5 Near infrared 0.845–0.885 
Band 6 Short wavelength infrared 1.56–1.66 
Band 7 Short wavelength infrared 2.10–2.30 
Band 8 Panchromatic 0.50–0.68 
Band 9 Cirrus 1.36–1.39 
Band 10 Long wavelength infrared 10.3–11.3 
Band 11 Long wavelength infrared 11.5–12.5 
Table 2

Description of land cover categories for the classification of the study area

Land cover typesDescription
Water bodies Areas encompass various water features, such as rivers, lakes, and reservoirs. 
Urban This category includes settled regions with concrete structures for residential and industrial, along with the associated network of roads. 
Agriculture This class encompasses both agricultural land and cropland, representing areas dedicated to cultivation. 
Bare land This classification comprises terrain lacking crops, featuring barren rock, or sandy sections found along rivers or streams. 
Forest Regions covered by native tree species, exhibiting no obvious signs of human activity, and where ecological processes remain undisturbed. 
Land cover typesDescription
Water bodies Areas encompass various water features, such as rivers, lakes, and reservoirs. 
Urban This category includes settled regions with concrete structures for residential and industrial, along with the associated network of roads. 
Agriculture This class encompasses both agricultural land and cropland, representing areas dedicated to cultivation. 
Bare land This classification comprises terrain lacking crops, featuring barren rock, or sandy sections found along rivers or streams. 
Forest Regions covered by native tree species, exhibiting no obvious signs of human activity, and where ecological processes remain undisturbed. 

Image correction, encompassing both radiometric and geometric correction, was applied to optimize the clarity and comprehension of satellite images for analysis. Radiometric correction improves the brightness magnitude of satellite images to enhance visibility. Meanwhile, geometric correction focuses on refining spatial images by mitigating geometric distortions caused by altitude and sensor variations. All Landsat 8 satellite images utilized in this study were acquired at Level-1, indicating prior orthorectification and geometric correction to the satellite images (Srivastava et al. 2012).

Image classification

The supervised classification method was employed as the chosen approach for the image classification of LULC. This method entails categorizing pixels within an image through a specific algorithm. The algorithm classifies satellite imaging pixels by considering spectral reflectance properties that are either similar or identical and produces a numerical representation of different land cover types (Lillesand et al. 1980).

In this study, the supervised classification approach utilized the maximum likelihood (ML) algorithm within ArcMap to categorize LULC. The ML classification is known as one of the widely recognized and applied algorithms in supervised classification for LULC analysis (Brahmabhatt et al. 2000; Jensen 2005; Bayarsaikhan et al. 2009; Zubair Iqbal 2018). Its fundamental concept is the probability function, assuming a normal distribution of training data for each class in every band (Basukala et al. 2017). Following Bayes' theorem, the ML classification method primarily specifies the regular distribution of cells within the class samples throughout the multidimensional space (Srivastava et al. 2012).

The classification procedure consists of three stages: training sample selection, classification, and accuracy assessment. Initially, a numerical representation of the spectral characteristics for each feature type of land cover is created using sample sites known as training sites. Subsequently, the compilation of constructed training sites defines the spectral attributes of different land cover types. Each pixel in the dataset is then labelled based on its closest resemblance to these spectral categories through statistical comparison (Ramachandran & Reddy 2017).

Accuracy assessment

The evaluation of the categorized images was assessed through the ArcGIS software after generating the LULC classification. The initiation of locating randomly generated spots began by utilizing the classes within the classified image, with the suitable category being manually assigned. Google Earth Pro images provided the reference data for this accuracy assessment. The accuracy evaluation of the supervised classification involves utilizing a confusion matrix to illustrate the precision of the LULC classification (Foody 2002). Within the matrix, the user-defined classes are depicted in the columns based on the reference value, with the rows aligning to the producer-assigned values obtained from the categorized image. Incorrectly identified pixels were indicated by off-diagonal cells, highlighting a discrepancy between the reference and classified data. In addition, the confusion matrix provides information about commission and omission errors within the LULC categorization process. The producer's accuracy is estimated to indicate the commission error, whereas the user's accuracy denotes the omission error (Seyam et al. 2023).

Trend analysis of climate parameters in Cameron Highlands

The MET recorded the highest annual rainfall at 3,197 mm in 2021 in Cameron Highlands (Figure 3). The average annual rainfall from 2012 until 2021 is 2,585.9 mm year−1. The highest days of rainfall (278) were recorded in 2017. There were significant differences in rainfall between years (Kruskal–Wallis, H = 598.247, p < 0.05). Rainfall was found to have a positive significant relationship with temperature (Spearman rho, r = 0.757, p < 0.01), indicating that Cameron Highlands would have greater rainfall and increasing temperatures in the coming years (Table 3). This increasing pattern was also consistent with the research by Weng & Nying (2017). Findings from Weng & Nying (2017) suggested that during the next 10 and 100 years, maximum rainfall may increase significantly. The implication of such a pattern could lead to the risk of climate change impacts, natural disasters, or flooding. Heavy rain causes soil detachment, transportation, erosion, and landslides because it raises the pore water pressure in the gap of the soil (Zafirah et al. 2017; Kahar et al. 2022). The region has had more flood disasters in recent years (Leh & Mokhtar 2021), supporting the vulnerability of the area. Repetitive erosion, flooding, and landslide caused huge economic loss and a large number of fatalities in hilly and mountainous areas, such as Cameron Highland (Mohd et al. 2019).
Table 3

Spearman Rho's correlation analysis between temperature, relative humidity, and rainfall

TemperatureRelative humidityRainfall
Temperature Correlation coefficient –0.213* 0.757* 
 p-Value  0.000 0.000 
Relative humidity Correlation coefficient –0.213* 0.146* 
 p-Value 0.000  0.000 
Rainfall Correlation coefficient 0.757* 0.146* 
 p-Value 0.000 0.000  
TemperatureRelative humidityRainfall
Temperature Correlation coefficient –0.213* 0.757* 
 p-Value  0.000 0.000 
Relative humidity Correlation coefficient –0.213* 0.146* 
 p-Value 0.000  0.000 
Rainfall Correlation coefficient 0.757* 0.146* 
 p-Value 0.000 0.000  

Note: *Correlation is significant at the 0.01 level (two tailed).

Figure 3

Trend of the 2012–2021 annual rainfall pattern in Cameron Highlands.

Figure 3

Trend of the 2012–2021 annual rainfall pattern in Cameron Highlands.

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The temperature ranged from 17.93 to 18.84 °C, with an average temperature of 18.27 °C. The annual temperature showed the highest temperature in 2016 (18.80 °C), as shown in Figure 4. The maximum temperature and the minimum temperature in 2016 (Figure 5) resulted in the highest temperature in 2016. Significant differences were found between temperature and years (Kruskal–Wallis test, H = 965.303, p < 0.05). In contrast, the weak negative relationship between temperature and relative humidity (Spearman rho, r = –0.213, p < 0.01) implies that there are other factors affecting the relationship.
Figure 4

Trend of the 2012–2021 annual temperature pattern in Cameron Highlands.

Figure 4

Trend of the 2012–2021 annual temperature pattern in Cameron Highlands.

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Figure 5

Minimum and maximum 24-h temperature (°C) in Cameron Highlands from 2012 to 2021.

Figure 5

Minimum and maximum 24-h temperature (°C) in Cameron Highlands from 2012 to 2021.

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Based on Figure 6, the relative humidity was the lowest in 2019 (85.242%), coinciding with the lowest temperature in 2019 (Figure 4). Malaysia normally experienced a relative humidity of not less than 80% all year round (Hamzah et al. 2017). A significant positive relationship was found between relative humidity and rainfall (Spearman rho, r = 0.146, p < 0.01), but the relationship is weak. Other climate variables, such as wind speed and vapour pressure, may affect the rainfall (Ilaboya & Igbinedion 2019).
Figure 6

Annual relative humidity (%) in Cameron Highlands from 2012 to 2021.

Figure 6

Annual relative humidity (%) in Cameron Highlands from 2012 to 2021.

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The regression analysis shows the R2 of 0.400 between rainfall, temperature, and relative humidity (Table 4). This translates into 40% of rainfall was associated with temperature and relative humidity. Using the intercept based on the constant, b0 value of 4.015 and the gradient of the regression line (b-value) of temperature (0.704), and relative humidity (–0.100), the regression equation derived was:
(6)
Table 4

Regression analysis between rainfall, temperature, and relative humidity in Cameron Highlands from 2012 to 2021

ModelEquationConstant b0b1R2p-Value
 y = 4.015 + 0.704 T – 0.100 RH 4.015  0.4 0.000 
Temperature   0.704   
Relative humidity   –0.1   
ModelEquationConstant b0b1R2p-Value
 y = 4.015 + 0.704 T – 0.100 RH 4.015  0.4 0.000 
Temperature   0.704   
Relative humidity   –0.1   

Note: Regression is significant at the 0.05 level (5% significance level).

The b-values show that there is a positive relationship between the temperature and rainfall, while there is a negative relationship between rainfall and relative humidity. The positive relationship implies that as the temperature increases by 1 °C, rainfall increases by 0.704 mm. In contrast, rainfall decreases by 0.100 mm as relative humidity increases by 1%. This finding indicates that increasing rain and higher temperatures may affect the main agricultural activities in Cameron Highland involving horticulture, tea, floriculture, and fruit cultivation. This outcome may subsequently affect the productivity and returns of farmers and agro-related businesses in Cameron Highlands (Gasim et al. 2009). More rainfall and an increase in temperature may affect water safety and food security issues with subsequent economic implications (Khyber et al. 2021; Majeed et al. 2021). This study provides a 10-year monitoring trend for future forecasting of climate change impacts in tropical regions.

The positive Kendall's Tau value (Z) indicates an increasing trend in temperature and rainfall (Table 5). Both the MK test and the Sen slope (Table 5) revealed a positive gradient, indicating that Cameron Highlands would have greater temperature and rainfall in the coming years. This finding suggests that with the impact of climate change, major changes to climatic variables, such as precipitation, air temperature, and relative humidity, are expected (Hamzah et al. 2017; Natarajan & Vasudevan 2020). The increasing trend of the temperature could be linked to the 0.6 °C increase in the Earth's average temperature in the latter part of the 20th century (IPCC 2013). Global warming and precipitation rates will not be dispersed equally over the globe (Tabari 2020), and the impact of climate change is evident, with the world average temperature climbing up to 1.2 °C. The different trends in rainfall are influenced by the topographical characteristics of a location, changes in atmospheric circulation driven by human activities, and the effects of land use and land cover on weather and climate processes at local, regional, and global scales (Mishra et al. 2023).

Table 5

Mann–Kendall test and Sen slope estimator for climatic parameters in Cameron Highlands

ParametersKendall's Taup-ValueSen's slopeTrend
Temperature (°C) 0.151 <0.0001 0.294 Upward trend 
Relative humidity (%) –0.194 <0.0001 –0.069 Downward trend 
Rainfall (mm) 0.102 <0.0001 0.131 Upward trend 
ParametersKendall's Taup-ValueSen's slopeTrend
Temperature (°C) 0.151 <0.0001 0.294 Upward trend 
Relative humidity (%) –0.194 <0.0001 –0.069 Downward trend 
Rainfall (mm) 0.102 <0.0001 0.131 Upward trend 

On the other hand, the negative value specifies a decreasing trend in relative humidity based on the negative score of the MK and the Sen slope estimator value (Table 5). Jamaludin et al. (2015) also reported the decreasing trend of relative humidity in Cameron Highlands, which were attributed to seasonal factors. Relative humidity plays an important role in controlling water vapour lost from leaf surfaces during photosynthesis and respiration of various agricultural crops in Cameron Highlands, such as tea plantations, coffee, vegetables, and fruit cultivation.

Elevation and land use land change in Cameron Highlands

The elevation map of Cameron Highlands in Figure 7 shows that the eastern side of Cameron Highlands has the lowest elevation, reaching around 200 m above sea level. Conversely, the highest elevation areas are in the north-western and southern parts of Cameron Highlands, exceeding 2,000 m above the sea level. Moreover, the elevation gradually increases towards the north of Cameron Highlands. Specifically, the portion of Cameron Highlands with a high elevation of more than 1,400 m covers approximately 33,034 ha or 34% of the region. In contrast, the lower elevation areas, with elevations less than 1,000 m, cover around 22,833 ha or 24% of Cameron Highlands. These highlight the significance of the higher elevation areas, which play a crucial role in shaping the geography and climate of Cameron Highlands.
Figure 7

Elevation map of Cameron Highlands, colour coded by elevation with blue representing low elevations and red representing high elevations. The yellow border demarcates the boundaries of Cameron Highlands, while the black border delineates the areas of Ringlet and Tanah Rata within the region.

Figure 7

Elevation map of Cameron Highlands, colour coded by elevation with blue representing low elevations and red representing high elevations. The yellow border demarcates the boundaries of Cameron Highlands, while the black border delineates the areas of Ringlet and Tanah Rata within the region.

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In addition, the elevation map indicates that Tanah Rata has a higher elevation of 1,800 m above the sea level, while Ringlet has a lower elevation of 1,000 m above the sea level. This suggests that Ringlet is a basin, surrounded by higher elevation areas. As a result, Ringlet could be more susceptible to natural disasters such as floods and landslides. This study is also proven according to the report in the Cameron Highlands District Local Plan, which states that 25.6% of land has a steep slope, or steeper than 25 degree (JPBD Pahang 2016). Erosion is almost certainly a concern in the Cameron Highlands, given that they are in the highlands and areas with slopes. Due to the steep slopes and other anthropogenic changes to the environment, most cropland areas in the hilly zone are vulnerable to erosion (Jankauskas et al. 2012). Different terrain cover (vegetation), discharge volume, and soil loss rates have an impact on the soil agrochemical characteristics of erosion and sloping land surfaces. The topography has a significant impact on erosion (Jarašiūnas & Kinderienė 2015).

The LULC map in Figure 8 was created using the Landsat 8 imagery with a 30-m resolution and supervised classification between three periods of 2016, 2018, and 2020, focusing only on the southern part of Cameron Highland, in Tanah Rata and Ringlet. The dominant land cover types in Ringlet and Tanah Rata in Cameron Highlands were agriculture and forest. Agriculture covers about 34% of Ringlet and Tanah Rata, while forest covers approximately 54%. The areas of bare land and urban cover only around 3 and 7%, respectively, whereas water bodies cover less than 1%. In Ringlet, urban areas are primarily located in the south and southeast regions, with agriculture being the dominant land use. Meanwhile, Tanah Rata is mainly covered by urban land use, which contrasts with Ringlet. The dominant land use of agriculture and forest in Ringlet and Tanah Rata is aligned with the research by Abidin et al. (2022), which can highlight the importance of these land uses in the area. Tanah Rata and Ringlet are among the main townships and agrotourism and ecotourism-promoted areas in the southern part of Cameron Highland compared to the other main areas of Brinchang, Kea Farm, Tringkap, and Kuala Terla (Gasim et al. 2009; Leh & Mokhtar 2021).
Figure 8

LULC maps of Ringlet and Tanah Rata, Cameron Highland in 2016, 2018, and 2020 derived from Landsat 8 data using the classification process outlined in Figure 2.

Figure 8

LULC maps of Ringlet and Tanah Rata, Cameron Highland in 2016, 2018, and 2020 derived from Landsat 8 data using the classification process outlined in Figure 2.

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The confusion matrix reveals the overall accuracy of the LULC classification system in producing the LULC of Ringlet and Tanah Rata from Landsat 8, with values of 95.42% in 2016, 96.60% in 2018, and 97.40% in 2020. The producer's accuracy values for year 2016, 2018, and 2020 are 95.42, 96.58, and 97.38%, respectively. Correspondingly, the user's accuracy values are 95.54, 96.81, and 97.56% for 2016, 2018, and 2020, respectively. The detailed confusion matrices for 2016, 2018, and 2020 are shown in Tables 68, respectively. It is noteworthy that ground truthing points using GPS were unavailable for validating the classification results, leading to the utilization of Google Earth for deriving the confusion matrix and assessing accuracy as an alternative method in the absence of ground truthing.

Table 6

Confusion matrix of 2016 LULC classification

LULC 2016AgricultureBare landForestUrbanWater bodiesTotalUser (%)
Agriculture 94 108 87.04 
Bare land 90 94 95.74 
Forest 101 102 99.02 
Urban 94 98 95.92 
Water bodies 100 100 100.00 
Total 100 101 101 100 100 502 95.54 
Producers accuracy (%) 94.00 89.11 100.00 94.00 100.00 95.42  
LULC 2016AgricultureBare landForestUrbanWater bodiesTotalUser (%)
Agriculture 94 108 87.04 
Bare land 90 94 95.74 
Forest 101 102 99.02 
Urban 94 98 95.92 
Water bodies 100 100 100.00 
Total 100 101 101 100 100 502 95.54 
Producers accuracy (%) 94.00 89.11 100.00 94.00 100.00 95.42  
Table 7

Confusion matrix of 2018 LULC classification

LULC 2018AgricultureBare landForestUrbanWater bodiesTotalUser (%)
Agriculture 97 110 88.18 
Bare land 90 91 98.90 
Forest 101 102 99.02 
Urban 95 97 97.94 
Water bodies 100 100 100.00 
Total 100 99 101 100 100 500 96.81 
Producers accuracy (%) 97.00 90.91 100.00 95.00 100.00 96.58  
LULC 2018AgricultureBare landForestUrbanWater bodiesTotalUser (%)
Agriculture 97 110 88.18 
Bare land 90 91 98.90 
Forest 101 102 99.02 
Urban 95 97 97.94 
Water bodies 100 100 100.00 
Total 100 99 101 100 100 500 96.81 
Producers accuracy (%) 97.00 90.91 100.00 95.00 100.00 96.58  
Table 8

Confusion matrix of 2020 LULC classification

LULC 2020AgricultureBare landForestUrbanWater bodiesTotalUser (%)
Agriculture 100 110 90.91 
Bare land 88 89 98.88 
Forest 101 102 99.02 
Urban 98 99 98.99 
Water bodies 100 100 100.00 
Total 100 99 101 100 100 500 97.56 
Producers accuracy (%) 100.00 88.89 100.00 98.00 100.00 97.38  
LULC 2020AgricultureBare landForestUrbanWater bodiesTotalUser (%)
Agriculture 100 110 90.91 
Bare land 88 89 98.88 
Forest 101 102 99.02 
Urban 98 99 98.99 
Water bodies 100 100 100.00 
Total 100 99 101 100 100 500 97.56 
Producers accuracy (%) 100.00 88.89 100.00 98.00 100.00 97.38  

Comparing the LULC areas in Ringlet and Tanah Rata between 2016, 2018, and 2020 shows notable changes during this period, with increases in agriculture and urban areas. As shown in Table 9, the agricultural area in the region grew from 1,517.24 ha in 2016 to 1,800.48 ha in 2020, representing an 18% increase over this period. Similarly, the urban area in Ringlet and Tanah Rata also experienced growth, increasing from 324.21 ha in 2016 to 376.43 ha in 2020, which is a rise of 16%. In addition, the area of bare land in Ringlet and Tanah Rata decreased from 230.43 ha in 2016 to 118.20 ha in 2020 implying the progressive LULC during this period.

Table 9

Comparative areas of LULC in Ringlet and Tanah Rata for 2016, 2018, and 2020, corresponding to Figure 8's LULC maps generated from Landsat 8 data using the classification process

LULC classificationArea year 2016 (ha)Area year 2018 (ha)Area year 2020 (ha)
Agriculture 1,517.24 1,744.74 1,800.48 
Bare land 230.43 161.97 118.20 
Forest 2,795.57 2,627.55 2,568.12 
Urban 324.21 333.52 376.43 
Water bodies 34.61 33.83 37.99 
LULC classificationArea year 2016 (ha)Area year 2018 (ha)Area year 2020 (ha)
Agriculture 1,517.24 1,744.74 1,800.48 
Bare land 230.43 161.97 118.20 
Forest 2,795.57 2,627.55 2,568.12 
Urban 324.21 333.52 376.43 
Water bodies 34.61 33.83 37.99 

The produced LULC using the Landsat 8 imagery in this study aligned with the significant expansion of natural lands to urbanization and agriculture from 1966 to 2019 in these areas (Gasim et al. 2009; Rozimah & Khairulmani 2016; Sholagberu et al. 2016; Aik et al. 2020). The tendency of growing urbanization was intended to satisfy Cameron Highlands' tourism needs. Furthermore, the pressure to open more agricultural land can be driven by the rising demand for agricultural products. Agriculture plays a crucial role in the economy, serving as the backbone of developing nations, where it often serves as the primary source of income (Razali et al. 2018). Even so, the documented involvement of the Cameron Highlands District in unlawful land exploration for agricultural purposes demonstrates the need for comprehensive agricultural land reform, involving collaboration among all stakeholders, including farmers, as well as both state and federal governments (Razali et al. 2018).

In contrast to the growth in agriculture, Table 9 also shows a decline in forest in Ringlet and Tanah Rata. From 2,795.57 ha in 2016, the forest area decreased to 2,568.12 ha in 2020, representing a decrease of 8% over this period. The similar declining trend in forest areas was reported from the year 1947 to 2019 in Cameron Highlands, implying a consistent decline of forested areas throughout the years (Gasim et al. 2009; Salleh & Ghaffar 2010; Zin & Ahmad 2014; Rozimah & Khairulmani 2016; Ul Mustafa et al. 2019; Aik et al. 2020). The reduction in forest area could be attributed to factors, such as deforestation, urbanization, and the expansion of agricultural activities. The forest is sacrificed for development upon the discovery of land for cropland practice and other different sorts of development (Ul Mustafa et al. 2019). Moreover, deforestation increases the number of unstable slopes, slope ruptures caused by road and highway construction, and the number of houses built in the sensitive area (Reichenbach et al. 2014). The observed trend is of great concern due to the crucial role of forests in regulating the local climate, preserving biodiversity, and providing essential ecosystem services, including soil protection and water retention (Sonter et al. 2017).

Implications of land use land change activities and associated climate variability in Cameron Highlands

The landslides in Cameron Highlands have been a persistent issue, causing damage to property, infrastructure, and natural resources, with the disasters being frequently reported (Rahim et al. 2021). According to the National Slope Master Plan 2009–2023, the Cameron Highlands District experienced at least 33 slope disasters and landslides between 1961 and 2007, with 515 locations identified as susceptible to landslides, including both active and inactive sites. The estimated loss for remediation work per incident was approximately RM1.35 million. Land use, particularly urbanization, the establishment of plantations, uncontrolled development in slope areas, and the clearing of forests for development, can be blamed for many of the landslides in the area (Nhu et al. 2020).

The changing land use to agriculture has made the area of Cameron Highlands more susceptible to environmental contamination and occasional natural disasters, such as landslides and floods. Using land for agriculture in sloped locations encourages landslides after heavy rains and causes slope failure. The changes in land use can change the soil surface's properties (Reichenbach et al. 2014), and reduced soil shear strength can result in mass soil failure via landslides on steeper hillslopes (Muaz Abu Mansor Maturidi et al. 2020). Moreover, according to the report from Local Plan Cameron Highlands (JPBD Pahang 2016), about 34% of the agricultural land in the Cameron Highlands District is covered by a rain protection structure to boost agricultural productivity. This keeps plants from being directly exposed to rain, minimizing soil erosion. Nevertheless, without a competent drainage system, surface runoff from a high-capacity rain-protective structures can trigger a higher amount of runoff, especially during periods of heavy rain (Hamdan et al. 2014).

In addition, land use and land cover changes involving declining forest cover can have several implications such as increasing temperature due to exposed bare lands and increasing runoff due to the loss of forest trees, and in the case of high precipitations, this condition may lead to erosion, landslide, and flood (Leh & Mokhtar 2021). Deforestation can result in increased pressure on the soil and the destabilization of slopes (Reichenbach et al. 2014), where slope and terrain roughness have a substantial impact on the vulnerability and probability of landslides (Karsli et al. 2009). Moreover, deforestation can lead to reduced absorption capacity due to saturation, which can cause a higher amount of surface runoff to result in more floods. Continuous deforestation may also affect the agrotourism and ecotourism activities in the area. The comfort of visitors who come to the Cameron Highlands to experience the region's natural coolness is impacted by the rise in temperature. Continuing the same activities in the longer term will be detrimental to the sustainability of the area.

In addition to the change in land use, heavy rainfall can cause landslides and floods in Cameron Highlands. As the annual rainfall in Cameron Highlands from 2012 to 2021 showed an increasing trend (Figure 3 and Table 5), it can potentially result in a higher amount of surface runoff, which can lead to more frequent landslides and floods. Xu & Zhang (2010) also reported that landslides can be influenced by increasing rainfall, which was predicted to occur over time, especially in areas experiencing rapid land use changes. Moreover, the produced LULC from the Landsat 8 imagery showed that the area covered by water bodies in Cameron Highlands showed an increasing trend during this period. The LULC map from Landsat 8 showed that the area of Ringlet and Tanah Rata covered by water bodies increased from 34.61 ha in 2016 to 37.99 ha in 2020 although it decreased to 33.83 ha in 2018, as shown in Table 9. This increasing trend of the water body area can also indicate an increase in rainfall and water runoff in Ringlet and Tanah Rata, which could potentially increase the vulnerability to flooding and landslides in the area.

The observed increase in temperature patterns, as detailed in Table 3, Figure 5, and Table 5, has the potential to lead to prolonged drought conditions. These conditions can adversely affect agricultural yields and productivity, causing shifts in harvesting times. In addition to these climatic factors, Cameron Highlands has faced significant ecological disturbances. Illegal land clearings, encroaching into forest reserves and riverbank areas, coupled with unsustainable development practices, such as farming on steep slopes, utilizing rain shelters, installing irrigation pipes along roadsides and slopes, and the excessive use of pesticides and fertilizers, have contributed to issues like soil erosion and a high rate of sedimentation in riverbeds (Razali et al. 2018). This condition can alter land surface characteristics, influencing both latent heat flow and surface temperature.

The increasing agriculture, decreasing forest cover, natural high-elevation area, and increasing rainfall and temperature patterns in highland areas are early warning signs for its plausible vulnerability towards climate change impacts, and understanding LULC changes and climatic trends is important as an early detection approach for future planning and management in highland regions. These preliminary assessments in highland areas contribute to the fundamental understanding of climate change impact in highland areas in tropical regions. Changes in land use and the observed pattern and trend in climate parameters may cause an implication towards sustainability and ecosystem services, such as water quality, dynamic changes in rainfall and temperature patterns, agriculture, and resilience towards natural disasters. Any locality's land use pattern is a result of its natural and socioeconomic variables, as well as how much human activity has influenced those factors over time and space (Ul Mustafa et al. 2019). Further action is required to withstand the dynamic changes by raising awareness (Rahman 2018) and adaptation resilience among highland communities (Palomo 2017).

This article explores the LULC changes in Cameron Highlands from 2015 to 2021 based on Landsat and meteorological data. Given the shape of the terrain and the records of past landslides, floods, and mudflows, this area is prone to natural disasters. Converting the land cover for agriculture is necessary for the livelihood of the locals in the area; however, at the same time, it is detrimental to the environment. The trend of 10-year meteorological data from 2012 to 2021 showed increasing rainfall and temperature while decreasing relative humidity. The fluctuations of these data are expected, given the multiple factors affecting these parameters.

The focus of this study is the pattern of the last 10 years. Our study serves as a relevant early warning indicator for continuous monitoring, offering a preliminary baseline pattern for further studies. The main drawback of this study is the short period selected, which spans only 10 years. For a more robust analysis, the duration should extend beyond 30 years, which will also allow a specific focus on climate change. Future research should incorporate additional temporal and spatial variability factors in assessing LULC and climatic parameter variability. Overall, the changes in land use and meteorological parameters are suggested to impact agricultural production and local tourism in Cameron Highlands.

To mitigate the impact of land use change on agriculture, a combination of innovative approaches is highly suggested. Hydroponic technologies offer a sustainable solution by utilizing mineral-rich water instead of soil, enabling efficient crop production in controlled environments and addressing the scarcity of fertile land. Forest farming, involving the cultivation of plants beneath a forest canopy, is another viable strategy. This method manages multiple levels in the forest structure, promoting sustainable harvests of non-timber forest products, including culinary and medicinal herbs, roots, mushrooms, and florist products. In addition, effective disaster management, encompassing improved communication and cooperation, is crucial in reducing or preventing the impact of land use change. Disaster management organizations must effectively organize, coordinate, and implement measures in operating at the local, district, and state levels. Encouraging the development of community action plans aligns with the 2015–2030 Disaster Risk Reduction objectives, serving as a proactive step forward.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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