Investigation of land surface temperature heterogeneity in municipal landfills by satellite images

Abstract

With the increasing population and urbanization, the amount of municipal solid waste (MSW) is increasing day by day. As a result, problems such as odor, fire, and intense biogas formation originate from landfills. In order to detect and solve these problems, landfills should be monitored regularly. Geographic Information Systems (GIS) and Remote Sensing offer fast and practical solutions for the regular monitoring of landfills compared to field studies. In this study, Kömürcüoda landfill on the Anatolian side of Istanbul is monitored throughout 2022 with open source Landsat8/9 and Sentinel-2 satellite images. In this context, the surface temperature heterogeneity of the landfill was mapped by generating Land Surface Temperature (LST) images for the landfill from the Landsat thermal band. Points with statistically significant high - low LST values were determined with Hot Spot Analysis. The average annual LST for 2022 was calculated as 25.5 °C. It was observed that LST had the highest values during the summer season and the lowest values during the winter season. Additionally, it has been determined that there are persistent hot spots and cold spots in the landfill. This study presents a simple methodology using open source satellite data to monitor LST and detect LST abnormalities on landfills.

Keywords

• Waste management
• land surface temperature
• landfill
• GIS
• satellite imagery

References

1. M. C. Höke, and S. Yalcinkaya, “Municipal solid waste transfer station planning through vehicle routing problem-based scenario analysis,” Waste Management & Research, Vol. 39(1), pp. 185-196, 2021. [CrossRef]
2. S. Yalcinkaya, “A spatial modeling approach for siting, sizing and economic assessment of centralized biogas plants in organic waste management,” Journal of Cleaner Production, Vol. 255, Article 120040, 2020. [CrossRef]
3. S. Yalcinkaya, and O. S. Kirtiloglu, “Application of a geographic information system-based fuzzy analytic hierarchy process model to locate potential municipal solid waste incineration plant sites: A case study of Izmir Metropolitan Municipality,” Waste Management & Research, Vol. 39(1), pp. 174-184, 2021. [CrossRef]
4. T. Bayram, Y. A. Arhun, and S. Tirink, “An evaluation of solid waste management in Turkey,” Black Sea Journal of Engineering and Science, Vol. 2(3), pp. 88-91. [CrossRef]
5. A. Sağlık, Y. S. Domaç, Ş. N. Reyhan, F. Avcı, F. Kartal, and D. Şenkuş, “Improvement and analysis of solid waste landfills example of Çanakkale Onsekiz Mart University,” Academia Journal of Nature and Human Sciences, Vol. 7(1), pp. 105-125, 2021.
6. S. Yalçınkaya, F. Doğan, and H. İ. Kaleli, “Investigation of waste fires and spatial accessibility of fire Stations in Izmir, Turkey,” Urban Academy, Vol. 15(2), pp. 727-741, 2022. [CrossRef]
7. J. Kret, L. D. Dame, N. T. Tutlam, R. DeClue, S. Schmidt, K. Donaldson, R. D. Lewis, S. E. Rigdon, S. Davis, A. Zelicoff, C. King, Y. Wang, S. Patrick, and F. Khan, “A respiratory health survey of a subsurface smoldering landfill,” Environmental Research, Vol. 166, pp. 427-436, 2018. [CrossRef]
8. H. Şenol, E. A. Elibol, Ü. Açıkel, and M. Şenol, “Türkiye’de biyogaz üretimi için başlıca biyokütle kaynakları,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Vol. 6(2), pp. 81-92, 2017. [Turkish] [CrossRef]

9. Y. Korkmaz, “Biogas and energy production from organic wastes,” SAÜ Fen Edebiyat Dergisi, pp. 489-497, 2012.
10. Turkish Statistical Institute, “Sera Gazı Emisyon İstatistikleri, 1990-2021,” 2023. https://data.tuik.gov.tr/Bulten/Index?p=Sera-Gazi-Emisyon-Istatistikleri-1990-2021-49672 Accessed on Jul 16, 2023.
11. L. G. Papale, G. Guerrisi, D. De Santis, G. Schiavon, and F. Del Frate, “Satellite data potentialities in solid waste landfill monitoring: review and case studies,” Sensors, Vol. 23(8), Article 3917, 2023. [CrossRef]
12. K. Faisal, M. AlAhmad, and A. Shaker, “Remote sensing techniques as a tool for environmental monitoring,” The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, Vol. XXXIX-B8, pp. 513-518, 2012. [CrossRef]
13. H. Abu Qdais, and N. Shatnawi, “Assessing and predicting landfill surface temperature using remote sensing and an artificial neural network,” International Journal of Remote Sensing, Vol. 40(24), pp. 9556-9571, 2019. [CrossRef]
14. L. Fjelsted, A. G. Christensen, J. E. Larsen, P. Kjeldsen, and C. Scheutz, “Assessment of a landfill methane emission screening method using an unmanned aerial vehicle mounted thermal infrared camera - A field study,” Waste Management, Vol. 87, pp. 893-904, 2019. [CrossRef]
15. R. Nazari, H. Alfergani, F. Haas, M. E. Karimi, G. Rabbani Fahad, S. Sabrin, J. Everett, N. Bouaynaya, and R. W. Peters, “Application of satellite remote sensing in monitoring elevated internal temperatures of landfills,” Applied Science, Vol. 10(19), Article 6081, 2020. [CrossRef]
16. N. Karimi, K. T. W. Ng, A. Richter, J. Williams, and H. Ibrahim, “Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands,” Journal of Environmental Management, Vol. 287, Article 112320, 2021. [CrossRef]
17. N. Karimi, K. T. W. Ng, and A. Richter, “Prediction of fugitive landfill gas hotspots using a random forest algorithm and Sentinel-2 data,” Sustainable Cities and Society, Vol. 73, Article 103097, 2021. [CrossRef]
18. A. Grondona, L. P. Gomes, L. M. Schiavo, M. Caetano, and B. J. B. L. Barbosa, “Use of the downscalling method in satellite images for the analysis of heat islands in landfills,” Remote Sensing Applications: Society and Environment, Vol. 26, Article 100702, 2022. [CrossRef]
19. D. Chavan, G. S. Manjunatha, D. Singh, L. Periyaswami, S. Kumar, and R. Kumar, “Estimation of spontaneous waste ignition time for prevention and control of landfill fire,” Waste Management, Vol. 139, pp. 258-268, 2022. [CrossRef]
20. K. Mahmood, F. Faizi, and F. Mushtaq, “Satellite based bio-thermal impact insights into MSW open dumps: a pair-unified proximity scenario,” Geomatics, Natural Hazards and Risk, Vol. 13(1), pp. 667-685, 2022. [CrossRef]
21. Turkish Statistical Institute, “Adrese Dayalı Nüfus Kayıt Sistemi Sonuçları, 2022,” 2023. https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2022-49685 Accessed on Jul 22, 2023.
22. Istanbul Metropolitan Municipality, “Düzenli Depolama,” 2023. http://istac.ssplab.com/tr/temiz-istanbul/evsel-atiklar/duzenli-depolama Accessed on Jul 22, 2023.
23. Istanbul Metropolitan Municipality, “Faaliyet Haritası,” 2023.
24. Istanbul Governorship, “İklim.” http://www.istanbul.gov.tr/iklim-istanbul Accessed on Sep 22, 2023.
25. USGS, “Landsat Satellite Missions,” 2023. https://www.usgs.gov/landsat-missions/landsat-satellite-missions Accessed on Jul 19, 2023.
26. J. Guo, H. Ren, Y. Zheng, S. Lu, and J. Dong, “Evaluation of land surface temperature retrieval from landsat 8/TIRS images before and after stray light correction using the SURFRAD dataset,” Remote Sensing, Vol. 12(6), Article 1023, 2020. [CrossRef]
27. USGS, “EarthExplorer,” 2023. https://earthexplorer.usgs.gov/ Accessed on Jul 19, 2023.
28. ESA, “Open Access Hub,” 2023. https://scihub.copernicus.eu/ Accessed on Jul 21, 2023.
29. USGS, “Landsat 8-9 Collection 2 Level 2 Science Product Guide,” 2023. https://www.usgs.gov/media/files/landsat-8-9-collection-2-level-2-science-product-guide Accessed on Jul 24, 2023.
30. ESRI, “How Hot Spot Analysis (Getis-Ord Gi*) works,” 2023. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm Accessed on Jul 26, 2023.
31. ESRI, “What is a z-score? What is a p-value?” https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/what-is-a-z-score-what-is-a-p-value.htm Accessed on Sep 23, 2023.
32. S. Yalcinkaya, and Y. Ruhbas, “Spatiotemporal analysis framework for identifying emerging hot spots and energy potential from livestock manure in Turkey,” Renewable Energy, Vol. 193, pp. 278-287, 2022. [CrossRef]
33. S. Kartal, and A. Sekertekin, “Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models,” Environmental Science and Pollution Research, Vol. 29(44), pp. 67115-67134, 2022. [CrossRef]