Spatiotemporal analysis of land surface temperature and land cover change: Assessing the impact of urbanization and vegetation dynamics

Authors

  • Muhammad Annas Fathoni Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, West Java 16424, Indonesia

DOI:

https://doi.org/10.61511/srsd.v2i1.2025.1752

Keywords:

GEE, land cover change, LST, Sukabumi, spatiotemporal

Abstract

Background: Land cover change refers to changes in the surface cover of an area over time due to natural and human factors. The urbanization of Cibadak District, near the toll exit, contrasts with the rural Cikidang District, resulting in different dynamics of land surface temperature (LST) and land cover change. This study focuses on the observed temperature increase in both districts from 2013 to 2023, aiming to analyze the relationship between land cover change and LST variation. Methods: This study used a spatiotemporal analysis method, with land cover as the independent variable and LST as the dependent variable. Clustered purposive sampling was used. Land cover was validated using Google Earth imagery, while LST was validated with air temperature data from BMKG. Landsat 8 imagery was processed using the Google Earth Engine (GEE) platform to create spatiotemporal maps of land cover and LST. The relationship between the two variables was analyzed through cross-sectional spatial analysis and statistical calculations, including Spearman correlation and multiple linear regression. Findings: From 2013 to 2023, the average increase in LST in land cover was 7.76°C. The analysis showed that vegetated land cover (forest and garden) showed temperatures between 24-32°C, while bare land had temperatures between 32-36°C, with bare land exceeding 40°C in 2023. The statistical results showed a strong positive correlation between land cover changes and LST increases. The correlation coefficient between 2013-2018 was 0.8117 (R² = 0.6588), and between 2018-2023, it was 0.7925 (R² = 0.6560). Conclusions: This study revealed a significant increase in LST in both study sites from 2013 to 2023, with land cover changes playing a key role in this trend. Urban areas with less vegetation contribute to higher temperatures, while vegetated areas help mitigate temperature increases. Novelty/Originality of this article: This study uniquely combines spatiotemporal analysis and statistical methods to assess the impact of land cover change on LST dynamics.

References

Achmadi, P. N., Dimyati, M., Manesa, M. D. M., & Rakuasa, H. (2023). Model Perubahan Tutupan Lahan Berbasis CA-Markov: Studi Kasus Kecamatan Ternate Utara, Kota Ternate. Jurnal Tanah Dan Sumberdaya Lahan, 10(2), 451–460. https://doi.org/10.21776/ub.jtsl.2023.010.2.28

Afasel, D., Purnamasari, R., & Edwar, E. (2023). Klasifikasi Tutupan Lahan Menggunakan Supervised Machine Learning Pada Citra Satelit Menggunakan Google Earth Engine. eProceedings of Engineering, 9(6). https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/18996

Arvidson, T. (2002). Personal correspondence, landsat 7 senior systems engineer. Landsat Project Science Office, Goddard Space Flight Center.

BPS. (2014). Kabupaten Sukabumi Dalam Angka Tahun 2014. Badan Pusat Statistik Indonesia.

BPS. (2019). Kabupaten Sukabumi Dalam Angka Tahun 2019. Badan Pusat Statistik Indonesia.

BPS. (2024). Kabupaten Sukabumi Dalam Angka Tahun 2024. Badan Pusat Statistik Indonesia.

Damayanti, A., Khairunisa, F. I., & Maulidina, K. (2023). Impacts of Land Cover Changes on Land Surface Temperature using Landsat Imagery with the Supervised Classification Method. Aceh International Journal of Science and Technology, 12(1), 116–125. https://doi.org/10.13170/aijst.12.1.30834

Desfandi, M., & Ruliani, R. (2022). Identifikasi Perubahan Lahan Hutan Menjadi Lahan Pertanian Di Desa Paya Dedep Kecamatan Jagong Jeget Kabupaten Aceh Tengah. Jurnal Pendidikan Geosfer, 7(2), 168-179. https://doi.org/10.24815/jpg.v7i2.23722

Ermida, S. L., Soares, P., Mantas, V., Göttsche, F. M., & Trigo, I. F. (2020). Google earth engine open-source code for land surface temperature estimation from the landsat series. Remote Sensing, 12(9), 1471. https://doi.org/10.3390/rs12091471

Ghozali, I. (2018). Aplikasi Analisis Multivariate dengan Program IBM SPSS 25. Badan Penerbit Universitas Diponegoro.

Guntara, I. (2016). Analisis Urban Heat Island untuk Pengendalian Pemanasan Global di Kota Yogyakarta Menggunakan Penginderaan Jauh. Fakultas Geografi UMS.

Handayani, H. (2007). Identifikasi Perubahan Kapasitas Panas Kawasan Perkotaan Dengan Menggunakan Citra Landsat TM/ETM (Studi Kasus :Kodya Bogor). FMIPA IPB.

Healey, N. C., Taylor, J. L., & Auch, R. F. (2023). Assessment of public and private land cover change in the United States from 1985–2018. Environmental Research Communications, 5(6). https://iopscience.iop.org/article/10.1088/2515-7620/acd3d8/meta

Hooijer, A., & Vernimmen, R. (2021). Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics. Nature communications, 12(1). https://www.nature.com/articles/s41467-021-23810-9

Ibochi, A. A., & Richard, J. U. (2020). Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image. Engineering & Technology Review, 1(1), 1-10. http://dx.doi.org/10.47285/etr.v1i1.34

Julianto, F. D., Putri, D. P. D., & Safi’i, H. H. (2020). Analisis Perubahan Vegetasi dengan Data Sentinel-2 menggunakan Google Earth Engine (Studi Kasus Provinsi Daerah Istimewa Yogyakarta). Jurnal Penginderaan Jauh Indonesia, 2(2), 13-18. https://journal.its.ac.id/index.php/jpji/article/view/262

Karandikar, A., & Agrawal, A. (2023). Performance analysis of change detection techniques for land use land cover. International Journal of Electrical and Computer Engineering (IJECE), 13(4), 4339. https://doi.org/10.11591/ijece.v13i4.pp4339-4346

Kunz, A. (2017). Misclassification and kappa-statistic: theoretical relationship and consequences in application. Ludwig-Maximilians-Universitat Munchen Institut fur Statistik.

Latue, P. C. (2023). Analisis Spasial Temporal Perubahan Tutupan Lahan di Pulau Ternate Provinsi Maluku Utara Citra Satelit Resolusi Tinggi. Buana Jurnal Geografi, Ekologi dan Kebencanaan, 1(1), 31–38. https://doi.org/10.56211/buana.v1i1.339

Madakarah, N. Y., Wibowo, A., Manessa, M. D. M., & Ristya, Y. (2019). Variations of Land Surface Temperature and Its Relationship with Land Cover and Changes in IPB Campus, Dramaga Bogor 2013-2018. In E3S Web Of Conferences, 125, 01004. https://doi.org/10.1051/e3sconf/201912501004

Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union, 12(3), 131-140. https://iguonline.in/journal/Volume_12-3.html

Mansour, S., Al-Belushi, M., & Al-Awadhi, T. (2020). Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy, 91. https://doi.org/10.1016/j.landusepol.2019.104414

Mebankerlang, N. (2023). Impact of land use land cover change on the land surface temperature: A case study of Shillong. Indian Scientific Journal Of Research In Engineering And Management. https://ijsrem.com/download/impact-of-land-use-land-cover-change-on-the-land-surface-temperature-a-case-study-of-shillong/

Narulita, S., Zain, A. F. M., & Prasetyo, L. B. (2016). Geographic Information System (GIS) application on urban forest development in Bandung City. Procedia Environmental Sciences, 33, 279-289. https://doi.org/10.1016/j.proenv.2016.03.079

Nawangwulan, N. H., Sudarsono, B., & Sasmito, B. (2013). Analisis pengaruh perubahan lahan pertanian terhadap hasil produksi tanaman pangan di Kabupaten Pati tahun 2001–2011. Jurnal Geodesi UNDIP, 2(2). https://doi.org/10.14710/jgundip.2013.2444

Nugroho, S. A., Wijaya, A. P., & Sukmono, A. (2016). Analisis Pengaruh Perubahan Vegetasi Terhadap Suhu Permukaan Di Wilayah Kabupaten Semarang Menggunakan Metode Penginderaan Jauh. Jurnal Geodesi UNDIP, 5(1), 253–263. https://doi.org/10.14710/jgundip.2016.10597

Pal, S., & Ziaul, S. K. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 125–145. https://doi.org/10.1016/j.ejrs.2016.11.003

Pavan, P., Varma, B. S. S., Asish, K., & Suneetha, M. (2023). Detection of Land Cover Changes using Satellite Image Classification Technique. 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), 1499–1505. https://doi.org/10.1109/ICSSIT55814.2023.10060992

Pratama, M. R., & Riana, D. (2022). Klasifikasi Penutupan Lahan Menggunakan Google Earth Engine dengan Metode Klasifikasi Terbimbing pada Wilayah Penajam Paser Utara. JUPITER: Jurnal Penelitian Ilmu Dan Teknologi Komputer, 14(2), 637-650. https://doi.org/10.5281./5927/5.jupiter.2022.10

Rakuasa, H., & Pakniany, Y. (2022). Spatial Dynamics of Land Cover Change in Ternate Tengah District, Ternate City. In Forum Geografi, 36(2). https://doi.org/10.23917/forgeo.v36i2.19978

Ramakrishnan, R., Rejuwan, S., Shaik, V. A., Lalit, T., Biju, G., Pillai, R., & Ponnusamy, P. (2023). Classification and Contrast of Supervised Machine Learning Algorithms. https://doi.org/10.1109/AISC56616.2023.10085338

Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(4), 611. https://doi.org/10.4236//ijg.2017.84033

Sasky, P., Sobirin, S., & Wibowo, A. (2017). Pengaruh Perubahan Penggunaan Tanah Terhadap Suhu Permukaan Daratan Metropolitan Bandung Raya Tahun 2000–2016. Prosiding Industrial Research Workshop and National Seminar, 8, 354-361. https://doi.org/10.35313/irwns.v8i3.767

Somae, G., Supriatna, S., Rakuasa, H., & Lubis, A. R. (2023). Pemodelan Spasial Perubahan Tutupan Lahan dan Prediksi Tutupan Lahan Kecamatan Teluk Ambon Baguala Menggunakan CA-Markov. Jurnal Sains Informasi Geografi, 6(1), 10. https://doi.org/http://dx.doi.org/10.31314/jsig.v6i1.1832

Saurabh, K., & Shwetank, S. (2023). Change Detection Analysis of Land Cover Features using Support Vector Machine Classifier. International Journal of Next-Generation Computing, https://doi.org/10.47164/ijngc.v14i2.384

Sugiyono, S. (2008). Metode Penelitian Pendidikan: Pendekatan Kuantitatif dan Kualitatif R&D (6th ed.). Alfabeta.

Tan, K. C., Lim, H. S., MatJafri, M. Z., & Abdullah, K. (2010). Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences, 60(7), 1509-1521. https://doi.org/10.1007/s12665-009-0286-z

Taorui, T., & Shibin, Z. (2022). Land cover change evaluation based on eco-economic balance modeling. Water Science & Technology: Water Supply. https://doi.org/10.2166/ws.2022.419

Utomo, A. W., Suprayogi, A., & Sasmito, B. (2017). Análisis Hubungan Variasi Land Surface Temperature dengan Kelas Tutupan Lahan Menggunakan Data Citra Satelit Landsat (Studi Kasus: Kabupaten Pati). Jurnal Geodesi Undip, 6(2), 71-80. https://doi.org/10.14710/jgundip.2017.16258

Wahyunto, A., Zainal, M., Priyono, Adi., & Sunaryo, S. (2006). Studi Perubahan Penggunaan Lahan di Sub Das Citarik, Jawa Barat dan Das Kaligarang, Jawa Tengah. Prosiding Seminar Nasional Multifungsi Lahan Sawah, 4. https://doi.org/10.20961/region.v17i2.38660

Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., & Erickson, T. A. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248, 112002. https://doi.org/10.1016/j.rse.2020.112002

Wang, S. W., Munkhnasan, L., & Lee, W. K. (2021). Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges, 2, 100017. https://doi.org/10.1016/j.envc.2020.100017

Wibisono, P., Miladan, N., & Utomo, R. P. (2023). Hubungan Perubahan Kerapatan Vegetasi dan Bangunan terhadap Suhu Permukaan Lahan: Studi Kasus di Aglomerasi Perkotaan Surakarta. Desa-Kota: Jurnal Perencanaan Wilayah, Kota, dan Permukiman, 5(1), 148-162. https://jurnal.uns.ac.id/jdk/article/view/63639

Zha, Y., Gao, J., & Ni, S. (2003). Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing, 24(3), 583-594. https://doi.org/10.1080/01431160304987

Zhang, Y., Li, X., Zhang, K., Wang, L., Cheng, S., & Song, P. (2023). A Simple Real LST Reconstruction Method Combining Thermal Infrared and Microwave Remote Sensing Based on Temperature Conservation. Remote Sensing, 15(12), 3033. https://doi.org/10.3390/rs15123033

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2025-02-28

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