DOI: https://doi.org/10.14710/geoplanning.11.1.71-84

Tracking the Temporal Changes in Land Surface Temperature, Vegetation, and Built-up Patterns in Rizal Province, Philippines using Landsat Imagery

Pauline Angela Sobremonte-Maglipon  -  Department of Biological Sciences, College of Science, University of Santo Tomas, España, Manila, 1008, Philippines , Philippines
Anne Olfato-Parojinog  -  Department of Biological Sciences, College of Science, University of Santo Tomas, España, Manila, 1008, Philippines , Philippines
King Joshua Almadrones-Reyes  -  Research Center for the Natural and Applied Sciences University of Santo Tomas, España, Manila, 1008, Philippines , Philippines
James Eduard Limbo-Dizon  -  Research Center for the Natural and Applied Sciences University of Santo Tomas, España, Manila, 1008, Philippines , Viet Nam
*Nikki Heherson A Dagamac  -  Department of Biological Sciences, College of Science, University of Santo Tomas, España, Manila, 1008, Philippines , Philippines

Citation Format:
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.

Fulltext View|Download
Keywords: LST, NDVI, NDBI, Remote Sensing

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
Geographic Information System for Spatial Planning in Indonesia and Its Support for Smart City Development Comparison of Land Cover Change Prediction Models: A Case Study in Kedungkandang District, Malang City Mapping Landslide Vulnerability using Machine Learning Approach along the Taba Penanjung-Kepahiang Road, Bengkulu Province More recent articles
Most cited articles
THE DEVELOPMENT OF MARINE SPATIAL PLANNING AND ITS APPLICATION FOR FLOATING FISH NET CULTURE MODEL SPASIAL STATISTIK KEPEMILIKAN SEPEDA MOTOR DI KECAMATAN BANYUMANIK, KOTA SEMARANG EVALUASI CITRA WORLDVIEW-2 UNTUK PENDUGAAN KEDALAMAN PERAIRAN DANGKAL PULAU KELAPA-HARAPAN MENGGUNAKAN ALGORITMA RASIO BAND Geometric Accuracy Assessment for Shoreline Derived from NDWI, MNDWI, and AWEI Transformation on Various Coastal Physical Typology in Jepara Regency using Landsat 8 OLI Imagery in 2018 Evaluation of MODIS-Derived LST Products with Air Temperature Measurements in Cyprus More cited articles

  1. Almadrones-Reyes, K. J., & Dagamac, N. H. A. (2023). Land-use/land cover change and land surface temperature in Metropolitan Manila, Philippines using Landsat imagery. GeoJournal, 88(2), 1415-1426. https://doi.org/10.1007/s10708-022-10701-9">[Crossref]

  2. Avdan, U., & Jovanovska, G. (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors, 1-8. https://doi.org/10.1155/2016/1480307">[Crossref]

  3. Bala, R., Prasad, R., Yadav, V. P., & Sharma, J. (2018). A comparative study of land surface temperature with different indices on heterogeneous land cover using Landsat 8 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 389-394. https://doi.org/10.5194/isprs-archives-XLII-5-389-2018">[Crossref]

  4. Barsi, J. A., Schott, J. R., Palluconi, F. D., Helder, D. L., Hook, S. J., Markham, B. L., Chander, G., & O'donnell, E. M. (2003). Landsat TM and ETM+ thermal band calibration. Canadian Journal of Remote Sensing, 29(2), 141-153. https://doi.org/10.5589/m02-087">[Crossref]

  5. Buchori, I., Rahmayana, L., Pangi, P., Pramitasari, A., Sejati, A. W., Basuki, Y., & Bramiana, C. N. (2022). In situ urbanization-driven industrial activities: the Pringapus enclave on the rural-urban fringe of Semarang Metropolitan Region, Indonesia. International Journal of Urban Sciences, 26(2), 244-267. https://doi.org/10.1080/12265934.2021.1925141">[Crossref]

  6. Chase, T. N., Pielke Sr, R. A., Kittel, T. G. F., Nemani, R. R., & Running, S. W. (2000). Simulated impacts of historical land cover changes on global climate in northern winter. Climate Dynamics, 16, 93-105. https://doi.org/10.1007/s003820050007">[Crossref]

  7. Chen, L., Li, M., Huang, F., & Xu, S. (2013). Relationships of LST to NDBI and NDVI in Wuhan City based on Landsat ETM+ image. In 2013 6th International Congress on Image and Signal Processing (CISP), (2), 840-845. IEEE

  8. Dalling, J. W. (2008). Pioneer species. Encyclopedia of Ecology, 181–184.

  9. Doygun, H., & Alphan, H. (2006). Monitoring urbanization of Iskenderun, Turkey, and its negative implications. Environmental Monitoring and Assessment, 114, 145-155. https://doi.org/10.1007/s10661-006-2524-0">[Crossref]

  10. Ekwurzel, B., Boneham, J., Dalton, M. W., Heede, R., Mera, R. J., Allen, M. R., & Frumhoff, P. C. (2017). The rise in global atmospheric CO2, surface temperature, and sea level from emissions traced to major carbon producers. Climatic Change, 144(4), 579-590. https://doi.org/10.1007/s10584-017-1978-0">[Crossref]

  11. Fang, J., Zhu, J., Wang, S., Yue, C., & Shen, H. (2011). Global warming, human-induced carbon emissions, and their uncertainties. Science China Earth Sciences, 54, 1458-1468. https://doi.org/10.1007/s11430-011-4292-0">[Crossref]

  12. Gebre, S. L., Cattrysse, D., Alemayehu, E., & Van Orshoven, J. (2021). Multi-criteria decision making methods to address rural land allocation problems: A systematic review. International Soil and Water Conservation Research, 9(4), 490–501. https://doi.org/10.1016/j.iswcr.2021.04.005">[Crossref] 

  13. Gaur, A., Eichenbaum, M. K., & Simonovic, S. P. (2018). Analysis and modelling of surface Urban Heat Island in 20 Canadian cities under climate and land-cover change. Journal of environmental management, 206, 145-157. https://doi.org/10.1016/j.jenvman.2017.10.002">[Crossref]

  14. Guha, S., Govil, H., & Mukherjee, S. (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India. Journal of Applied Remote Sensing, 11 (3), 036020-036020. https://doi.org/10.1117/1.JRS.11.036020">[Crossref]

  15. He, J. F., Liu, J. Y., Zhuang, D. F., Zhang, W., & Liu, M. L. (2007). Assessing the effect of land use/land cover change on the change of urban heat island intensity. Theoretical and applied climatology, 90, 217-226. https://doi.org/10.1007/s00704-006-0273-1">[Crossref]

  16. Hilario, F., de Guzman, R., Ortega, D., Hayman, P., & Alexander, B. (2009). El Niño Southern Oscillation in the Philippines: impacts, forecasts, and risk management. Philippine Journal of Development, 36(1), 9.

  17. Jiménez-Muñoz, J. C., Sobrino, J. A., Gillespie, A., Sabol, D., & Gustafson, W. T. (2006). Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sensing of Environment, 103(4), 474-487. https://doi.org/10.1016/j.rse.2006.04.012">[Crossref]

  18. Kasperson, J. X., Kasperson, R. E., & Turner, B. L. (1995). Regions at risk: Comparisons of threatened environments. United Nations Univ. Press, Tokyo.

  19. Khandelwal, S., Goyal, R., Kaul, N., & Mathew, A. (2018). Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 87-94. https://doi.org/10.1016/j.ejrs.2017.01.005">[Crossref]

  20. Kumar, K. S., Bhaskar, P. U., & Padmakumari, K. (2012). Estimation of land surface temperature to study urban heat island effect using Landsat ETM+ image. International Journal of Engineering Science and Technology, 4(2), 771-778.

  21. LaGro, J. A. (2005). Land-use classification. Encyclopedia of Soils in the Environment, 7(3), 321-328. 

  22. Lasco, R. D., Pulhin, F. B., & Banaticla, M. R. N. (2005). Potential carbon sequestration projects in the Philippines. Environmental Forestry Program. University of the Philippines Los Banos, College of Forestry and Natural Resources, College, Laguna.

  23. Limbo-Dizon, J. E., & Dagamac, N. H. A. (2023). Assessment of coastal change detection on an urban coastline: A case study in metropolitan Manila, Philippines. In IOP Conference Series: Earth and Environmental Science, 1165(1), 12-15. IOP Publishing.

  24. Malik, M. S., Shukla, J. P., & Mishra, S. (2019). Relationship of LST, NDBI and NDVI using Landsat-8 data in Kandaihimmat watershed, Hoshangabad, India.

  25. Maruna, M., Crnčević, T., & Milojević, M. P. (2019). The institutional structure of land use planning for urban forest protection in the post-socialist transition environment: Serbian experiences. Forests, 10(7), 560. https://doi.org/10.3390/f10070560">[Crossref]

  26. Meeus, S. J., & Gulinck, H. (2008). Semi-urban areas in landscape research: a review. Living Reviews in Landscape Research, 2. https://doi.org/10.12942/lrlr-2008-3">[Crossref]

  27. Morris, K. I., Chan, A., Morris, K. J. K., Ooi, M. C. G., Oozeer, M. Y., Abakr, Y. A., Nadzir, M. S. M., Mohammed, I. Y., & Al-Qrimli, H. F. (2017). Impact of urbanization level on the interactions of urban area, the urban climate, and human thermal comfort. Applied Geography, 79, 50-72. https://doi.org/10.1016/j.apgeog.2016.12.007">[Crossref]

  28. Murdiyarso, D., & Skutsch, M. (Eds.). (2006). Community forest management as a carbon mitigation option: case studies. CIF

  29. Naikoo, M. W., Islam, A. R. M. T., Mallick, J., & Rahman, A. (2022). Land use/land cover change and its impact on surface urban heat island and urban thermal comfort in a metropolitan city. Urban Climate, 41, 101052. https://doi.org/10.1016/j.uclim.2021.101052">[Crossref]

  30. Nazeer, M., Nichol, J. E., & Yung, Y. K. (2014). Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. International journal of remote sensing, 35(16), 6271-6291. https://doi.org/10.1080/01431161.2014.951742">[Crossref]

  31. Nowak, D. J., Rowntree, R. A., McPherson, E. G., Sisinni, S. M., Kerkmann, E. R., & Stevens, J. C. (1996). Measuring and analyzing urban tree cover. Landscape and Urban Planning, 36(1), 49–57. https://doi.org/10.1016/s0169-2046(96)00324-6">[Crossref]

  32. Peng, S-S., Piao, S., Zeng, Z., Ciais, P., Zhou, L., Li, L. Z. X., Myneni, R. B., Yin, Y., & Zeng, H. (2014). Afforestation in China cools local land surface temperature. Proceedings of the National Academy of Sciences, 111(8), 2915-2919. https://doi.org/10.1073/pnas.1315126111">[Crossref]

  33. Peng, X., Wu, W., Zheng, Y., Sun, J., Hu, T., & Wang, P. (2020). Correlation analysis of land surface temperature and topographic elements in Hangzhou, China. Scientific Reports, 10(1), 10451. https://doi.org/10.1038/s41598-020-67423-6">[Crossref]

  34. Phelan, K., Hurley, J., & Bush, J. (2018). Land-use planning’s role in Urban Forest Strategies: Recent local government approaches in Australia. Urban Policy and Research, 37(2), 215–226. https://doi.org/10.1080/08111146.2018.1518813">[Crossref]

  35. Poursanidis, D., Chrysoulakis, N., & Mitraka, Z. (2015). Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping. International Journal of Applied Earth Observation and Geoinformation, 35, 259-269. https://doi.org/10.1016/j.jag.2014.09.010">[Crossref]

  36. Qin, Z., Karnieli, A., & Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18), 3719-3746. https://doi.org/10.1080/01431160010006971">[Crossref]

  37. Ramachandra, T. V., Bharath, A. H., & Sowmyashree, M. V. (2015). Monitoring urbanization and its implications in a mega city from space: Spatiotemporal patterns and its indicators. Journal of Environmental Management, 148, 67-81. https://doi.org/10.1016/j.jenvman.2014.02.015">[Crossref]

  38. Regmi, R. K. (2017). Urbanization and related environmental issues of Metro Manila. Journal of Advanced College of Engineering and Management, 3, 79-92. https://doi.org/10.3126/jacem.v3i0.18906">[Crossref]

  39. Saha, S., Saha, A., Das, M., Sarkar, R., & Das, A. (2021). Analyzing spatial relationship between land use/land cover (LULC) and land surface temperature (LST) of three urban agglomerations (UAs) of Eastern India. Remote Sensing Applications: Society and Environment, 22, 100507. https://doi.org/10.1016/j.rsase.2021.100507">[Crossref]

  40. Schneider, K., & Mauser, W. (1996). Processing and accuracy of Landsat Thematic Mapper data for lake surface temperature measurement. International Journal of Remote Sensing, 17(11), 2027-2041. https://doi.org/10.1080/01431169608948757">[Crossref]

  41. Sobrino, J. A., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. International Journal of Remote Sensing, 21(2), 353-366. https://doi.org/10.1080/014311600210876">[Crossref]

  42. Stathopoulou, M., & Cartalis, C. (2007). Daytime urban heat islands from Landsat ETM+ and Corine land cover data: An application to major cities in Greece. Solar Energy, 81(3), 358-368. https://doi.org/10.1016/j.solener.2006.06.014">[Crossref]

  43. Stempihar, J. J., Pourshams-Manzouri, T., Kaloush, K. E., & Rodezno, M. C. (2012). Porous asphalt pavement temperature effects for urban heat island analysis. Transportation Research Record, 2293(1), 123-130. https://doi.org/10.3141/2293-15">[Crossref]

  44. Sun, F., Liu, M., Wang, Y., Wang, H., & Che, Y. (2020). The effects of 3D architectural patterns on the urban surface temperature at a neighborhood scale: Relative contributions and marginal effects. Journal of Cleaner Production, 258, 120706. https://doi.org/10.1016/j.jclepro.2020.120706">[Crossref]

  45. Tejada, S. Q., Tuddao Jr, V. B., Juanillo, E., & Brampio, E. (2014). Drought conditions and management strategies in the Philippines. Country Report, 2-5.

  46. Tiangco, M., Lagmay, A. M. F., & Argete, J. (2008). ASTER‐based study of the night‐time urban heat island effect in Metro Manila. International Journal of Remote Sensing, 29(10), 2799-2818. https://doi.org/10.1080/01431160701408360">[Crossref]

  47. Tinoy, M. M., Novero, A. U., Landicho, K. P., Baloloy, A. B., & Blanco, A. C. (2019). Urban effects on land surface temperature in Davao City, Philippines. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 433-440. https://doi.org/10.5194/isprs-archives-XLII-4-W19-433-2019">[Crossref]

  48. Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119-132. https://doi.org/10.1016/j.isprsjprs.2017.01.001">[Crossref]

  49. Uddin, A. S. M. S., Khan, N., Islam, A. R. M. T., Kamruzzaman, M., & Shahid, S. (2022). Changes in urbanization and urban heat island effect in Dhaka city. Theoretical and Applied Climatology, 147(3-4), 891-907. https://doi.org/10.21203/rs.3.rs-442136/v1">[Crossref]

  50. United Nations. (2015). General Assembly Resolution A/RES/70/1. Transforming Our World, the 2030 Agenda for Sustainable Development.

  51. United Nations Framework Convention on Climate Change (UNFCCC). Paris Agreement. 2015. Available online: https://unfccc.int/sites/default/files/english_paris_agreement.pdf.

  52. United Nations Human Settlements Programme (UN HABITAT). New Urban Agenda. A/RES/71/256. United Nations, 2017. Available online: http://habitat3.org/wp-content/uploads/NUA-English.pdf 

  53. Verheye, W. H. (1997). Land use planning and national soils policies. Agricultural Systems, 53(2-3), 161-174. https://doi.org/10.1016/S0308-521X(96)00064-9">[Crossref]

  54. Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo, J. M. (1997). Human domination of Earth's ecosystems. Science, 277(5325), 494-499.

  55. Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A., & Zhao, S. (2015). An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote sensing, 7(4), 4268-4289. https://doi.org/10.3390/rs70404268">[Crossref]

  56. Yang, L., Qian, F., Song, D-X., & Zheng, K-J. (2016). Research on urban heat-island effect. Procedia Engineering, 169, 11-18. https://doi.org/10.1016/j.proeng.2016.10.002">[Crossref]

  57. Yuan, F., & Bauer, M. E. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3), 375-386. https://doi.org/10.1016/j.rse.2006.09.003">[Crossref]

  58. Zhou, W., Huang, G., & Cadenasso, M. L. (2011). Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and urban planning, 102(1), 54-63. https://doi.org/10.1016/j.landurbplan.2011.03.009">[Crossref]

  59. Zhou, W., Qian, Y., Li, X., Li, W., & Han, L. (2014). Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landscape Ecology, 29, 153-167. https://doi.org/10.1007/s10980-013-9950-5">[Crossref]

  60. Zhou, X., & Chen, H. (2018). Impact of urbanization-related land use land cover changes and urban morphology changes on the urban heat island phenomenon. Science of the Total Environment, 635, 1467-1476. https://doi.org/10.1016/j.scitotenv.2018.04.091">[Crossref]


Last update:

No citation recorded.

Last update: 2024-07-17 18:06:30

No citation recorded.