SPATIAL AND TEMPORAL ANALYSIS OF LAND SURFACE TEMPERATURE CHANGE ON NEW BRITAIN ISLAND

Rafika Minati Devi, Tofan Agung Eka Prasetya, Diah Indriani

Abstract

Land Surface Temperature (LST) is a parameter to estimate the temperature of the Earth’s surface and to detect climate change. Papua New Guinea is a tropical country with rainforests, the greatest proportion of which are located on the island of New Britain. Hectares of rainforests have been logged and deforested because of infrastructure construction. This study aims to investigate the change in land surface temperatures on the island from 2000 to 2019. The temperature data were taken from National Aeronautics and Space Administration (NASA) Terra satellites and were analysed using two statistical models: spatial and temporal. The spatial model used multivariate regression, while the temporal one used autoregression (AR). In this study, a cubic spline fitted curve was employed because this has the advantage of being smoother and providing good visuals. The results show that almost all the sub-regions of New Britain have experienced a significant increase in land surface temperature, with a Z value of 7.97 and a confidence interval (CI) of 0.264 – 0.437. The study only investigated land surface temperature change on New Britain Island using spatial and temporal analysis, so further analysis is needed which takes into account other variables such as vegetation and land cover, or which establishes correlations with other variables such as human health.

Keywords

Land Surface Temperature, New Britain Island, Climate change, Cubic Spline

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References

Ahmed, T., Scholz, M., Al-Faraj, F., & Niaz, W. (2016). Water-Related Impacts of Climate Change on Agriculture and Subsequently on Public Health: A Review for Generalists with Particular Reference to Pakistan. International Journal of Environmental Research and Public Health, 13(1051), 1–16. https://doi.org/10.3390/ijerph13111051

Alamgir, M., Sloan, S., Campbell, M. J., Engert, J., Kiele, R., Porolak, G., … Laurance, W. F. (2019). Infrastructure Expansion Challenges Sustainable Development in Papua New Guinea. PLOS ONE, 14(7), e0219408. https://doi.org/10.1371/journal.pone.0219408

Australian Academy of Science. (2015). The Science of Climate Change: Questions and Answers. Canberra.

Ayuningtyas, V. A. (2015). Pengolahan Data Thermal (TIRS) Citra Satelit Landsat 8 untuk Temperatur Suhu Permukaan (Studi Lokasi : Kabupaten Banyuwangi) [Thermal Data Processing (TIRS) Landsat 8 Satellite Imagery for Surface Temperatures (Location Study: Banyuwangi Regency)]. 1–8. Retrieved from http://eprints.itn.ac.id/1484/1/JURNAL 1125008.pdf

Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2015). Time Series Analysis: Forecasting and Control (5th ed.; D. J. Balding, N. A. C. Cressie, G. M. Fitzmaurice, G. H. Givens, H. Goldstein, G. Molenberghs, … S. Weisberg, eds.). New Jersey: John Wiley & Sons, Inc.

Bryan, J. E., & Shearman, P. L. (2015). The State of the Forests of Papua New Guinea 2014: Measuring Change Over Period 2002-2014. Port Moresby: University of Papua New Guinea.

Busygin, B., & Garkusha, I. (2013). Technology Mapping of Thermal Anomalies in the City of Dnipropetrovs’k, Ukraine, with Application of Multispectral Sensors. In G. Pivnyak, O. Beshta, & M. Alekseyev (Eds.), Energy Efficiency Improvement of Geotechnical Systems: International Forum on Energy Efficiency (pp. 151–160). Boca Raton: CRC Press.

Choudhury, D., Das, K., & Das, A. (2019). Assessment of Land Use Land Cover Changes and its Impact on Variations of Land Surface Temperature in Asansol-Durgapur Development Region. Egyptian Journal of Remote Sensing and Space Science, 22(2), 203–218. https://doi.org/10.1016/j.ejrs.2018.05.004

CSIRO, & Australian Bureau of Meteorology. (2011). Climate Change in the Pacific : Scientific Assessment and New Research. Volume 2 : Country Reports (Vol. 2). https://doi.org/10.1108/S0732-1317(2011)0000020013

Fu, P., & Weng, Q. (2016). A Time Series Analysis of Urbanization Induced Land Use and Land Cover Change and its Impact on Land Surface Temperature with Landsat imagery. Remote Sensing of Environment, 175, 205–214. https://doi.org/10.1016/j.rse.2015.12.040

Fyfe, J. C., Gillett, N. P., & Thompson, D. W. J. (2010). Comparing Variability and Trends in Observed and Modelled Global-Mean Surface Temperature. Geophysical Research Letters, 37(16), 2–5. https://doi.org/10.1029/2010GL044255

International Organization for Migration. (2015). Assessing The Evidence: Migration, Environment and Climate Change in Papua New Guinea. https://doi.org/10.1163/ej.9789004163300.i-1081.143

Izenman, A. J. (2013). Modern Multivariate Statistical Techniques (2nd ed.; G. Casella, S. Fienberg, & I. Olkin, eds.). New York: Springer New York.

Kantar, Y. M. (2016). Estimating Variances in Weighted Least-Squares Estimation of Distributional Parameters. Mathematical and Computational Applications, 21(2). https://doi.org/10.3390/mca21020007

Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., Goldberg, A. (2010). Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations. Journal of Climate, 23(3), 618–633. https://doi.org/10.1175/2009JCLI2900.1

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 Sciences, 21, 87–94. https://doi.org/10.1016/j.ejrs.2017.01.005

Korada, N., Sekac, T., Jana, S. K., & Pal, D. K. (2018). Delineating Drought Risk Areas Using Remote Sensing and Geographic Information Systems– A Case Study of Western Highlands Province, Papua New Guinea. European Journal of Engineering Research and Science, 3(10), 103–110. https://doi.org/10.24018/ejers.2018.3.10.937

Lahan, M., Verave, R., & Irarue, P. (2015). A Preliminary Reconnaissance Geothermal Mapping in West New Britain Province , Papua New Guinea. Proceedings World Geothermal Congress 2015, 19–25.

Lamo, X. de, Arnell, A., Pollini, B., Salvaterra, T., Gosling, J., Ravilious, C., & Miles, L. (2018). Using Spatial Analysis to Support REDD + Land-Use Planning in Papua New Guinea: Strengthening Benefits for Biodiversity , Ecosystem Services and Livelihoods. Cambridge, UK: UNEP-WCMC.

Leufen, L. H., & Schädler, G. (2019). Calculating The Turbulent Fluxes in The Atmospheric Surface Layer with Neural Networks. Geoscientific Model Development, 12(5), 2033–2047. https://doi.org/10.5194/gmd-12-2033-2019

Luintel, N., Ma, W., Ma, Y., Wang, B., & Subba, S. (2019). Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal. Atmospheric and Oceanic Science Letters, 12(5), 305–312. https://doi.org/10.1080/16742834.2019.1625701

Mansouri, E., Feizi, F., Jafari Rad, A., & Arian, M. (2018). Remote-Sensing Data Processing with the Multivariate Regression Analysis Method for Iron Mineral Resource Potential Mapping: A Case Study in the Sarvian Area, Central Iran. Solid Earth, 9(2), 373–384. https://doi.org/10.5194/se-9-373-2018

Mavrakou, T., Polydoros, A., Cartalis, C., & Santamouris, M. (2018). Recognition of Thermal Hot and Cold Spots in Urban Areas in Support of Mitigation Plans to Counteract Overheating: Application for Athens. Climate, 6(16), 1–12. https://doi.org/10.3390/cli6010016

Me-Ead, C., & McNeil, R. (2019). Pattern and Trend of Night Land Surface Temperature in Africa. Scientific Reports, 9(1), 18302. https://doi.org/10.1038/s41598-019-54703-z

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (2nd ed.; D. J. Balding, N. A. C. Cressie, G. M. Fitzmaurice, G. H. Givens, H. Goldstein, G. Molenberghs, … S. Weisberg, eds.). New Jersey: John Wiley & Sons, Inc.

Oyoshi, K., Akatsuka, S., Takeuchi, W., & Sobue, S. (2014). Hourly LST Monitoring with the Japanese Geostationary Satellite MTSAT-1R over the Asia-Pacific Region. Asian Journal of Geoinformatics, 14(3), 1–13.

Pal, A., & Prakash, P. (2017). Practical Time Series Analysis. Birmingham: Packt Publishing Ltd.

Paolella, M. S. (2019). Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH (1st ed., Vol. 52). Hoboken, NJ: John Wiley & Sons Inc.

Public Health Institute. (2016). Climate Change 101 : Climate Science Basics. In Public Health Institute/Center for Climate Change and Health (pp. 1–10). Retrieved from http://climatehealthconnect.org/wp-content/uploads/2016/09/Climate101.pdf

Samanta, S. (2009). Assessment of Surface Temperature Using Remote Sensing Technology. Papua New Guinea Journal of Research, Science and Technology, 1, 12–18.

Sharma, I., Tongkumchum, P., & Ueranantasun, A. (2018). Modeling of Land Surface Temperatures to Determine Temperature Patterns and Detect their Association with Altitude in the Kathmandu Valley of Nepal. Chiang Mai University Journal of Natural Sciences, 17(4), 275–288. https://doi.org/10.12982/CMUJNS.2018.0020

Suwanwong, A., & Kongchouy, N. (2016). Cubic Spline Regression Model and Gee for Land Surface Temperature Trend Using Modis in the Cloud Forest of Khao Nan National Park Southern Thailand During 2000-2015. Journal of Engineering and Applied Sciences 11, 11, 2387–2395.

Tampubolon, T., Abdullah, K., San, L. H., & Yanti, J. (2016). The Identification of Geothermal with Geographic Information System and Remote Sensing in Distric of Dolok Marawa. AIP Conference Proceedings, 1712, 030011-1-030011–030016. https://doi.org/10.1063/1.4941876

Tharmalingam, T., & Vijayakumar, V. (2019). Linear Kernel with Weighted Least Square Regression Co-efficient for SVM Based Tamil Writer Identification. International Journal of Recent Technology and Engineering, 8(2), 586–591. https://doi.org/10.35940/ijrte.B1629.078219

Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (4th ed.). New York: Springer New York.

Wan, Z., & Li, Z.-L. (2010). MODIS Land Surface Temperature and Emissivity. In B. Ramachandran, C. O. Justice, & M. J. Abrams (Eds.), Land Remote Sensing and Global Environmental Change (pp. 563–577). https://doi.org/10.1007/978-1-4419-6749-7_25

Weatherdon, L. V., Magnan, A. K., Rogers, A. D., Sumaila, U. R., & Cheung, W. W. L. (2016). Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3(48), 1–21. https://doi.org/10.3389/fmars.2016.00048

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), 1–17. https://doi.org/10.3390/rs9121254

Yang, Y. Z., Cai, W. H., & Yang, J. (2017). Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China. Remote Sensing, 9(5), 1–19. https://doi.org/10.3390/rs9050410

Zhang, X., Shi, X., Sun, Y., & Cheng, L. (2017). Multivariate Regression with Gross Errors on Manifold-valued Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 444–458. https://doi.org/10.1109/TPAMI.2017.2776260

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