The World Health Organization has reported numerous fatalities, primarily among urban residents, during El Niño events. This study employed remote sensing technology to investigate the influence of the El Niño Southern Oscillation on temperature. The objective was to analyze the effect of ENSO on temperature across different land cover types using Landsat satellite data. Pre-processing was applied to the satellite data before converting numerical values into surface temperatures. The findings revealed that RS technology effectively captured the impact of varying ENSO intensity levels on surface temperatures. ENSO strength influenced temperature variations in the study areas. During El Niño events, urban areas exhibited higher land surface temperatures compared to vegetation, wetlands, and water bodies, a pattern consistent during La Niña. Specifically, there was a 2.5 °C temperature increase in the urban land cover area during El Niño events between 2016 and 1997. Water bodies, vegetation, and wetlands experienced respective temperature increases of 0.17 °C, 0.17 °C, and +0.7 °C during ONI value 1 events between 2016 and 1997. These findings are crucial for local authorities, providing spatial information on hot spots to enhance vigilance against potential El Niño temperatures.

Climate change presents significant problems to Malaysia, impacting its ecology and socio-economic structure through modified precipitation patterns, heightened flooding, coastal erosion, and agricultural susceptibility. This review methodically evaluates the utilization of remote sensing (RS) and geographic information systems (GISs) in comprehending these effects, employing a systematic approach following the PRISMA protocol. Through the examination of peer-reviewed papers from 2010 to 2024, we underscore the progress in remote sensing (RS) and geographic information system (GIS) technologies and their contribution to improving the spatial analysis of climate change impacts. Our research indicates considerable regional variations in the effects of climate change, highlighting the need for customized adaptation and mitigation efforts. The research Illustrates how advancements In remote sensing (RS) and geographic information systems (GISs) have enhanced forecasting skills and real-time surveillance, offering essential insights into ecosystem susceptibilities and socio-economic threats. This analysis underscores the necessity for cohesive policy strategies that leverage remote sensing and geographic information system insights to tackle the complex difficulties posed by climate change in Malaysia while pushing for additional research on the amalgamation of artificial intelligence and big data analytics to bolster climate resilience.

Malaysia’s location in Southeast Asia exposes it to various weather patterns influenced by El Niño–Southern Oscillation (ENSO), monsoons, the Madden–Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD). To overcome the limitations of previous studies due to insufficient spatial information, this study utilizes remote sensing (RS) data from Landsat and MODIS satellites, along with the Oceanic Niño Index (ONI), to analyze the spatial distribution of temperature affected by El Niño–Southern Oscillation (ENSO). This study employs radiometric and atmospheric corrections on remote sensing (RS) data, converting them to surface temperature data. Our analysis reveals a correlation coefficient of 0.73 (MODIS) and 0.71 (Landsat) between the ONI and RS temperature data. During El Niño events, Landsat recorded temperature increases of 0–1.6 °C, while MODIS showed increases of 2.2–2.8 °C. The spatial information obtained assists in identifying affected areas and facilitating the implementation of mitigation measures by the government. By utilizing RS data, this research enhances our understanding of the ENSO–temperature relationship, surpassing previous limitations and providing valuable insights into climate dynamics.

Transformation of land cover vegetation toward urban areas causes the temperature at urban higher to compare to suburban and rural areas, namely urban heat island (UHI) effect. The UHI has a negative impact, such a stroke heat, air pollution, green gasses emission, and electric consumption. UHI studies at a tropical country still limited due to the containment of cloud cover. Besides that, studies only focus on big cities which have residents above than 2 million. The outcome this studied important to enhance our knowledge of urban heat effect at small-medium cities and guidelines to policymaker and urban planner to discover there has effectively taken to decrease the effect of urban heat at the hot spot area. The main goal of this research about to discovered influence of urban growth and selected urban index, namely the Normalized Difference Built Index (NDBI) to LST. NDBI is an index which denotes intensity of urban built up. In the first step, we generate the LST and NDBI from Landsat 8 OLI at year 2018 and Landsat 5 TM for the year 2011 and 1991. Second, we applied the unsupervised classification of Landsat 8 OLI and Landsat 5 TM to generate the land cover maps for the years 1991, 2011, and 2018. Third of our method to examine the relationship between Land surface temperature (LST) and NDBI. The higher value NDBI is a hot spot, and the low value is a cold spot. In the last step, we applied for Change Detection analysis using GIS to examine the land cover change between 1991 and 2018. Our results show the higher the value of NDBI and LST at the centre of the city and the lowest value at vegetation land cover. The transformation of land cover vegetation to urban increase at countryside area and out-of-town and significantly increase of distribution of UHI. On another hand, the shows positive relationships between LST and NDBI. The output of the study provides a guideline for policymakers and town designers to develop to toward city zero carbon, sustainable and health.