Spatiotemporal modeling of surface urban heat island and the influence of land cover changes in land surface temperature in Cagayan de Oro City, Misamis Oriental, Mindanao, Philippines
Paper Details
Spatiotemporal modeling of surface urban heat island and the influence of land cover changes in land surface temperature in Cagayan de Oro City, Misamis Oriental, Mindanao, Philippines
Abstract
Rapid urbanization and morphological transformations in Cagayan de Oro City have substantially intensified the Surface Urban Heat Island (SUHI) effect. This research quantifies the spatiotemporal evolution of the city’s thermal environment from 2016 to 2024 using an empirical, multi-tiered geospatial framework. Sentinel-2A multispectral data mapped six land cover classes, validated by a simple random sampling of 300 independent points, while Landsat 8 TIRS data retrieved kinetic Land Surface Temperature (LST) via single-channel emissivity modeling. Spatial statistics expose a profound terrestrial transition, primarily driven by a 27.15% contraction of forest canopy alongside a 441.40% surge in Bare Ground and a 5.20% expansion of Built-up areas. This physical restructuring catalyzed a 2.26°C escalation in the mean basin LST. Bivariate analyses confirmed the Normalized Difference Vegetation Index (NDVI) exerts a stronger suppressive influence on LST variance (R2= 0.4174) than the warming effect of the Normalized Difference Built-up Index (R2= 0.3190). Translating radiometry into ecological vulnerability, the Urban Thermal Field Variance Index (UTFVI) revealed an aggressive geographic diffusion of thermal stress. Furthermore, Contribution Index (CI) assessments determined Forests are the sole remaining thermal sink, with the overall Landscape Effect Index (LI) degrading to 0.981, signaling an inverted thermal regime. Getis-Ord Gi* hotspot analysis anchored these patterns, demonstrating 67.5% of administrative units have coalesced into 99% Confidence Hot Spots. Mitigating this self-reinforcing SUHI effect demands a shift toward spatial de-clustering, strict zone to protect cold spots, and extensive green infrastructure deployment.
Abdi AM. 2019. Decadal land-use/land-cover and land surface temperature change in Dubai and implications on the urban heat island effect: A preliminary assessment. EarthArXiv.
Agrawal Y, Pandey H, Tiwari PS. 2023. Analytical study of relation between land surface temperature and land use/land cover using spectral indices: A case study of Chandigarh. Journal of Geomatics 17(2). DOI:10.58825/jog.2023.17.2.65.
Avdan U, Jovanovska G. 2016. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors 2016, 1–8. DOI: 10.1155/2016/1480307.
Cagayan de Oro City Government. 2024. Enhanced Local Climate Change Action Plan (ELCCAP) 2022–2031. City Planning and Development Office.
Cetin M, Ozenen Kavlak M, Anil Senyel Kurkcuoglu M, Ozturk GB, Cabuk SN, Cabuk A. 2024. Determination of land surface temperature and urban heat island effects with remote sensing capabilities: The case of Kayseri, Türkiye. Natural Hazards 120. DOI: 10.1007/s11069-024-06431-5.
Dadole JT, Companion KS, Albiento EE, Masalig R. 2025. Spatio-temporal analysis of land use and land cover change and its impact on land surface temperature in Cagayan de Oro City. Journal of World Development 6, 52.
European Space Agency (ESA). 2022. Sentinel-2 MSI technical guide. ESA Sentinel Online.
Faisal AA, Kafy AA, Al Rakib A, Akter KS, Jahir DMA, Sikdar MS, Rahman MM, et al. 2021. Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan Area. Environmental Challenges 4, 100192. DOI: 10.1016/j.envc.2021.100192.
Getis A, Ord JK. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis 24(3), 189–206. DOI: 10.1111/j.1538-4632.1992.tb00261.x.
Guerri G, Crisci A, Messeri A. 2021. Thermal hot-spot and cool-spot analysis using Landsat 8 LST data related to 2015–2019 summer daytime periods. Remote Sensing 13(3), 538. DOI: 10.3390/rs13030538.
Guillevic PC, Biard JC, Hulley GC, Privette JL, Hook SJ, Olioso A, Radocinski RG, et al. 2014. Validation of land surface temperature products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements. Remote Sensing of Environment 154, 19–37. DOI:10.1016/j.rse.2014.08.013.
Kafy AA, Al Rakib A, Akter KS, Rahaman ZA, Faisal AA, Mallik S, Ali MY, et al. 2021. Monitoring and forecasting the impact of urbanization on the thermal environment of Addis Ababa City, Ethiopia. Environmental Challenges 4, 100170.
Le HT, Pham VT, Tran XB, Nguyen VT, Vu XC, Tong TH, Le VP. 2024. Mapping impervious surface change from remote sensing and GIS data: A case study in Ho Chi Minh City, Vietnam. Ecological Questions 35(3). DOI: 10.12775/EQ.2024.030.
Li ZL, Tang BH, Wu H, Ren H, Yan G, Wan Z, Sobrino JA. 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment 131, 14–37.
Liping C, Yujun S, Saeed S. 2018. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques: A case study of a hilly area, Jiangle, China. PLOS ONE 13(7), e0200493.
Malik A, Kalotra G. 2024. Land surface temperature and urban heat island: A case study of Faridabad City using RS and GIS techniques. International Journal for Multidisciplinary Research 6(1).
PAGASA. 2023. Climatological data for Lumbia Airport, Misamis Oriental (1991–2023). Department of Science and Technology.
Sekertekin A. 2020. Performance of land surface temperature retrieval algorithms: A case study of Landsat 8 TIRS. Environmental Science and Pollution Research 27(12), 12345–12356.
UN-Habitat. 2022. Achieving sustainable urban development: Cagayan de Oro City. UN-Habitat Philippines.
USGS. 2020. Landsat 8–9 Operational Land Imager/Thermal Infrared Sensor Level-2, Collection 2. U.S. Geological Survey. DOI: 10.5066/P9OGBGM6.
Zhang Y, Sun L, Hu L. 2017. Estimating the contribution of different land use/land cover types to urban heat island intensity. Remote Sensing 9(1), 17.
John Oliver R. Abian*, Peter D. Suson, Jaime Q. Guihawan, Hilly Ann Roa-Quiaoit, Elizabeth Edan M. Albiento, 2026. Spatiotemporal modeling of surface urban heat island and the influence of land cover changes in land surface temperature in Cagayan de Oro City, Misamis Oriental, Mindanao, Philippines. J. Biodiv. Environ. Sci., 28(6), 17-29.
Copyright © 2026 by the Authors. This article is an open access article and distributed under the terms and conditions of the Creative Commons Attribution 4.0 (CC BY 4.0) license.