Skip to main content

Advertisement

SpringerLink
Log in

Spatio and Temporal Analysis of Indonesia Land Surface Temperature Variation During 2001–2020

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

The temperature increase characterizes global warming that occurs. Land Surface Temperature (LST) is an important indicator in climate science to assess the temperature condition of a place. This research aimed to examine the trend and variation in land surface temperature in the Indonesia archipelago by applying the cubic spline method and multivariate regression. Indonesia’s territory was divided into five main islands, 21 super-regions with 189 sub-regions using 105-pixels (95 km) of longitude and latitude distance. The data for each sub-region were downloaded from NASA Moderate Resolution Imaging Spectroradiometer from 2001 to 2020. Overall, Indonesia has had a stable LST with a total average increase of 0.009 °C (95% confidence interval: −0.041,0.059 °C). The variation differed by island; a significant increase in Sumatra and Kalimantan, a significant decrease in Java and Bali and Sulawesi, and a slight decrease in Papua. For future investigation, the variation in LST on a larger island, namely a continent, must be investigated. Additional factors, such as Normalized Difference Vegetation Index, land use and land cover, might also be beneficial.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The data and the code that support the findings of this study are available in IndoLST at: https://drive.google.com/drive/folders/1cRCv1y9DJwRvCwSQmp8XGdPynstENPCd?usp=sharing.

References

  • Aik, D. H. J., Ismail, M. H., & Muharam, F. M. (2020). Land use/land cover changes and the relationship with land surface temperature using Landsat and MODIS imageries in Cameron Highlands, Malaysia. Land, 9(10), 372. https://doi.org/10.3390/land9100372

    Article  Google Scholar 

  • Berezin, Y., Gozolchiani, A., Guez, O., & Havlin, S. (2012). Stability of climate networks with time. Scientific Reports, 2(1), 1–8. https://doi.org/10.1038/srep00666

    Article  Google Scholar 

  • Case, M., Ardiansyah, F., & Spector, E. (2007). Climate change in Indonesia: implications for humans and nature. Climate change in Indonesia: Implications for humans and nature.

  • DAAC, O. (2018). MODIS and VIIRS Land Products Global Subsetting and Visualization Tool. ORNL DAAC, Oak Ridge, Tennessee, USA.

  • Fadlin, F., Kurniadin, N., & Prasetya, F. V. A. S. (2020). Analisis Indeks Kekritisan Lingkungan di Kota Makassar Menggunakan Citra Satelit LANDSAT 8 OLI/TIRS. Jurnal Geodesi Dan Geomatika (ELIPSOIDA), 3(01), 55–63.

    Article  Google Scholar 

  • Frederick, W. H., & Worden, R. L. (1993). Indonesia: A Country Study (Vol. 550, Issue 39). Government Printing Office.

  • Gillespie, A. R. (2014). Land Surface Emissivity. Encyclopedia of remote sensing, 939. https://doi.org/10.1007/978-0-387-36699-9_79

  • Gosling, S. N., Dunn, R., Carrol, F., Christidis, N., Fullwood, J., Gusmao, D. D., & Warren, R. (2011). Climate: Observations, projections and impacts. Climate: Observations, projections and impacts.

  • Jepson, P. (2012). Birding Indonesia: A birdwatcher's guide to the world's largest archipelago. Tuttle Publishing.

  • Klhk, R. I. (2019). Statistik Lingkungan Hidup dan Kehutanan Tahun 2018 (Environmental and Forestry Statistics in 2018). Pusat Data dan Informasi Kementerian Lingkungan Hidup dan Kehutanan RI, Jakarta.

  • Klhk, R. I. (2018). Status Hutan dan Kehutanan Indonesia 2018. Kementerian Lingkungan Hidup dan Kehutanan Republik Indonesia Isi.

    Google Scholar 

  • Kurnia, E., & Jaya, N. S. (2008). Satellite-Based Land Surface Temperature Estimation of Bogor Municipality, Indonesia. Indonesian Journal of Electrical Engineering and Computer Science, 2, 701–708.

    Google Scholar 

  • Mardia, K. V., Kent, J. T., Bibby, J. M. (1979). Multivariate analysis, 10th edn. In Birbaum, Z.W., Likacs, E. (eds). Academic Press, Inc.

  • Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F., & Hansen, M. C. (2014). Primary forest cover loss in Indonesia over 2000–2012. Nature Climate Change, 4(8), 730–735. https://doi.org/10.1038/nclimate2277

    Article  Google Scholar 

  • Maru, R., Baharuddin, I. I., Umar, R., Rasyid, R., Uca, U., Sanusi, W., & Bayudin, B. (2015). Analysis of the heat island phenomenon in Makassar, South Sulawesi, Indonesia. American Journal of Applied Sciences, 12(9), 1. https://doi.org/10.3844/ajassp.2015.616.626

    Article  Google Scholar 

  • McAlpine, C. A., Johnson, A., Salazar, A., Syktus, J., Wilson, K., Meijaard, E., & Sheil, D. (2018). Forest loss and Borneo’s climate. Environmental Research Letters, 13(4), 044009. https://doi.org/10.1088/1748-9326/aaa4ff

  • Mimura, N. (2013). Sea-level rise caused by climate change and its implications for society. Proceedings of the Japan Academy, Series B, 89(7), 281–301. https://doi.org/10.2183/pjab.89.281

    Article  Google Scholar 

  • Oktavianingrum, S., Pin, T. G., & Shidiq, I. P. A. (2020). The Effect of Land Cover Changes on Land Surface Temperature in Tangerang Selatan on 2005, 2008, 2013, and 2018. In IOP Conference Series: Earth and Environmental Science (Vol. 412, No. 1, p. 012029). IOP Publishing. https://doi.org/10.1088/1755-1315/412/1/012029

  • Pearce, A., Faskel, F., & Hyndes, G. (2006). Nearshore sea temperature variability off Rottnest Island (Western Australia) derived from satellite data. International Journal of Remote Sensing, 27(12), 2503–2518. https://doi.org/10.1080/01431160500472138

    Article  Google Scholar 

  • Team, R. C. (2018). R: A language and environment for statistical computing; 2018. Accessed 2 Feb 2019

  • Rahmad, R., Nurman, A., & Pinem, K. (2019, May). Impact of NDVI change to spatial distribution of land surface temperature (A study in Medan city, Indonesia). In 1st International Conference on Social Sciences and Interdisciplinary Studies (ICSSIS 2018) (pp. 167–171). Atlantis Press.

  • Rejekiningrum, P. (2014). Dampak perubahan iklim terhadap sumberdaya air: Identifikasi, simulasi dan rencana aksi. Jurnal Sumberdaya Lahan, 8(1), 1–15.

    Google Scholar 

  • Rohde, R., Muller, R., Jacobsen, R., Perlmutter, S., Rosenfeld, A., Wurtele, J., & Mosher, S. (2013). Berkeley Earth Temperature Averaging Process, Geoinfor. Geostat.-An Overview, 1, 2. Geoinformatics Geostatistics an Overview, 1(2), 20–100. https://doi.org/10.4172/2327-4581.1000103

    Article  Google Scholar 

  • Rossati, A. (2017). Global warming and its health impact. The International Journal of Occupational and Environmental Medicine, 8(1), 7. https://doi.org/10.15171/ijoem.2017.963

    Article  Google Scholar 

  • Sam, K., Koane, B., Sam, L., Mrazova, A., Segar, S., Volf, M., & Novotny, V. (2020). Insect herbivory and herbivores of Ficus species along a rain forest elevational gradient in Papua New Guinea. Biotropica, 52(2), 263–276. https://doi.org/10.1111/btp.12741

    Article  Google Scholar 

  • Smit, B., Pilifosova, O., Burton, I., Challenger, B., Solomon, S., Plattner, G. K., & Sathiyarajah, R. (2010). World Development Report 2010: Development and Climate Change. Journal of Economic Literature, 48, 786.

    Google Scholar 

  • Venables, W. N., & Ripley, B. D. (2002). Random and mixed effects. In Modern applied statistics with S (pp. 271–300). Springer.

  • Verstappen, H. T. (2010). Indonesian landforms and plate tectonics. Indonesian Journal on Geoscience, 5(3), 197–207. https://doi.org/10.17014/ijog.5.3.197-207

    Article  Google Scholar 

  • Wahba, G. (1990). Spline models for observational data. Society for Industrial and Applied Mathematics.

  • Weigand, M., Wurm, M., Dech, S., & Taubenböck, H. (2019). Remote sensing in environmental justice research—A review. ISPRS International Journal of Geo-Information, 8(1), 20. https://doi.org/10.3390/ijgi8010020

    Article  Google Scholar 

  • Wold, S. (1974). Spline functions in data analysis. Technometrics, 16(1), 1–11.

    Article  Google Scholar 

  • Wolff, N. H., Masuda, Y. J., Meijaard, E., Wells, J. A., & Game, E. T. (2018). Impacts of tropical deforestation on local temperature and human well-being perceptions. Global Environmental Change, 52, 181–189.

    Article  Google Scholar 

  • Wongsai, N., Wongsai, S., & Huete, A. R. (2017). Annual seasonality extraction using the cubic spline function and decadal trend in temporal daytime MODIS LST data. Remote Sensing, 9(12), 1254. https://doi.org/10.3390/rs9121254

    Article  Google Scholar 

  • Wu, X., Lu, Y., Zhou, S., Chen, L., & Xu, B. (2016a). Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environment International, 86, 14–23. https://doi.org/10.1016/j.envint.2015.09.007

    Article  Google Scholar 

  • Wu, X., Lu, Y., Zhou, S., Chen, L., & Xu, B. (2016b). Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environment International, 86, 14–23. https://doi.org/10.1016/j.envint.2015.09.007

    Article  Google Scholar 

  • Wüst, S., Wendt, V., Linz, R., & Bittner, M. (2017). Smoothing data series by means of cubic splines: Quality of approximation and introduction of a repeating spline approach. Atmospheric Measurement Techniques, 10(9), 3453–3462.

    Article  Google Scholar 

  • Yananto, A., & Dewi, S. (2017). Analysis of El Niño event in 2015 and the impact to the increase of hotspots in Sumatera and Kalimantan Region OF Indonesia. UNEJ e-Proceeding, 1, 168–173.

    Google Scholar 

  • Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., & Asseng, S. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences, 114(35), 9326–9331. https://doi.org/10.1073/pnas.1701762114

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge Professor Don McNeil for his invaluable assistance during this research. This research was supported by Thailand's Education Hub for ASEAN Countries (TEH-AC), Prince of Songkla University graduate school research grant and Centre of Excellence in Mathematics, commission on higher Education, Thailand.

Author information

Authors and Affiliations

  1. Statistics Department, Faculty of Mathematics and Science, Syiah Kuala University, Banda Aceh, Aceh, Indonesia

    Munawar Munawar

  2. Health Departement, Faculty of Vocational Studies, Universitas Airlangga, Surabaya, East Java, Indonesia

    Tofan Agung Eka Prasetya

  3. Centre of Excellence in Mathematics, Commission On Higher Education (CHE), Ministry of Education, Ratchathewi, Bangkok, Thailand

    Rhysa McNeil

  4. Ungku Aziz Centre for Development Studies Universiti Malaya, Kuala Lumpur, Malaysia

    Rohana Jani

  5. School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand

    Suhaimee Buya

  6. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Suhaimee Buya

Authors
  1. Munawar Munawar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tofan Agung Eka Prasetya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rhysa McNeil
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rohana Jani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Suhaimee Buya
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MM and TAEP obtained the data and performed it statistically. RM and RJ contributed to statistical analyses, discussion, and interpretation results. SB focused on the R command and created the figures. All authors contributed to writing and editing the manuscript.

Corresponding author

Correspondence to Munawar Munawar.

Ethics declarations

Conflict of interest

This research is an original research, hence there is no potential conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

See Table

Table 2 Indonesia super-region seasonal pattern
Full size table

2.

Appendix 2

See Table

Table 3 Indonesia Seasonal Adjusted Time Series
Full size table

3.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Munawar, M., Prasetya, T.A.E., McNeil, R. et al. Spatio and Temporal Analysis of Indonesia Land Surface Temperature Variation During 2001–2020. J Indian Soc Remote Sens 51, 1393–1407 (2023). https://doi.org/10.1007/s12524-023-01713-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-023-01713-0

Keywords

Search

Navigation