Determining the Regeneration Dynamics of Burned Forest Areas Using Satellite Images and Climate Parameters

Abstract

Forest fires significantly impact ecosystems by reducing biological diversity and sustainability. Observing the regeneration process of burned areas and identifying factors influencing this process, monitoring the regeneration status, determining the spread of invasive species, and understanding the impact on wildlife and its evolution contribute to assessing the consequences of this disaster. However, on-site monitoring of burned areas is a time-consuming and challenging process. Therefore, in this study, the regeneration processes of burned forest areas and the factors influencing these processes were investigated using data from remote sensing systems. In this context, the regeneration processes of areas affected by the forest fire in Antalya Kumluca in 2016 were examined. Landsat-8 satellite images of the study area were obtained with the assistance of Google Earth Engine (GEE). NBR (Normalized Burn Ratio) showing the severity of the burn and NDVI (Normalized Difference Vegetation Index) indicating the vitality status of the forest were calculated using these images. In addition, parameters such as wind speed, soil moisture, precipitation amount, Land Surface Temperature (LST), and air temperature were obtained from data provided by remote sensing systems through GEE. Multiple regression analysis was conducted to identify the parameters affecting the regeneration process.

Keywords

• Remote Sensing
• Forest Fires
• NDVI
• NBR
• Regeneration Process

References

1. Aalen, O. O. (1989). A linear regression model for the analysis of life times. Statistics in medicine, 8(8), 907925.
2. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific data, 5(1), 1-12.
3. Aguilar, A. (2005). Remote sensing of forest regeneration in highland tropical forests. GIScience & remote sensing, 42(1), 66-79.
4. Akça, Ş. (2023). Hava Lidar Verisi Üzerinde K-Ortalamalar ve Bulanık C-Ortalama ile Bina Çıkarımı. Türkiye Lidar Dergisi, 5(2), 45-51.
5. Akosman, E. N., Makineci, H. B. Sentinel-2A Verileriyle Trabzon İli 2019-2020 Yılları Arasında Ortaya Çıkan Sınıflandırma Farklarının Çeşitli Algoritmalarla Değerlendirilmesi. Türkiye Uzaktan Algılama Dergisi, 5(2), 78-88.
6. Aliyazıcıoğlu, Ş., Öztürk, K. F., Günen, M. A. (2023). Analysis of Gümüşhane-Trabzon highway slope static and dynamic behavior using point cloud data. Advanced Lidar, 3(2), 70-75.
7. Avdan, U., Kucuk Matci, D., Kaplan, G., Yigit Avdan, Z., Erdem, F., Demirtas, I. Mızık, E.T. . (2021). Evaluating the Atmospheric Correction Impact on Landsat 8 and Sentinel-2 Data for Soil Salinity Determination. Geodetski list, 75(3), 255-240.
8. Basara, A. C., Tabar, M. E., Gulsun, S., Sisman, Y. (2022). Monitoring urban sprawl in Atakum district using CORINE data. Advanced Geomatics, 2(2), 4956.

9. Başaran, N., MATCI, D. K., Avdan, U. (2022). Using multiple linear regression to analyze changes in forest area: the case study of Akdeniz Region. International Journal of Engineering and Geosciences, 7(3), 247263.
10. Cai, W., Yang, J., Liu, Z., Hu, Y., Weisberg, P. J. (2013). Post-fire tree recruitment of a boreal larch forest in Northeast China. Forest Ecology and Management, 307, 20-29.
11. Capolupo, A., Monterisi, C., Tarantino, E. (2020). Landsat Images Classification Algorithm (LICA) to automatically extract land cover information in Google Earth Engine environment. Remote Sensing, 12(7), 1201.
12. Chu, T., Guo, X., Takeda, K. (2016). Remote sensing approach to detect post-fire vegetation regrowth in Siberian boreal larch forest. Ecological Indicators, 62, 32-46.
13. Çömert, R., Matcı, D. K., Emir, H., Avdan, U. (2017). Nesne Tabanlı Sınıflandırma ile Yanmış Orman Alanlarının Tespiti. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 17(4), 27-34.
14. Çömert, R., Matci Küçük, D., Avdan, U. (2019). Object Based Burned Area Mapping With Random Forest Algorithm. International Journal ofEngineering and Geosciences, 4(2), 78-87.
15. Diaz-Delgado, R., Lloret, F., Pons, X. (2003). Influence of fire severity on plant regeneration by means of remote sensing imagery. International Journal of Remote Sensing, 24(8), 1751-1763.
16. Digavinti, J., Manikiam, B. (2021). Satellite monitoring of forest fire impact and regeneration using NDVI and LST. Journal of Applied Remote Sensing, 15(4), 042412-042412.
17. Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., . . . Hoell, A. (2015). The climate hazards infrared precipitation with stations— a new environmental record for monitoring extremes. Scientific data, 2(1), 1-21.
18. Gilbert, K. M., Shi, Y. (2023). Land use/land cover change detection and prediction for sustainable urban land management in Kigali City, Rwanda. Advanced Land Management, 3(2), 62-75.
19. Gilbert, K. M., Shi, Y. (2024). Using GlobeLand30 data and cellular automata modeling to predict urban expansion and sprawl in Kigali City. Advanced Remote Sensing, 4(1), 46-57.
20. Güngör, R., Yilmaz, O. S., Sanli, F. B., Ates, A. M. (2022). Investigation of spatial change in Lake Surface with Google Earth Engine: Example of Marmara Lake. Advanced Remote Sensing, 2(1), 8-15.
21. Johnstone, J. F., Rupp, T. S., Olson, M., Verbyla, D. (2011). Modeling impacts of fire severity on successional trajectories and future fire behavior in Alaskan boreal forests. Landscape Ecology, 26, 487500.
22. Kazemi Garajeh, M., Haji, F., Tohidfar, M., Sadeqi, A., Ahmadi, R., Kariminejad, N. (2024). Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine. Scientific reports, 14(1), 5469.
23. Keeley, J. E., Pausas, J. G. (2022). Evolutionary ecology of fire. Annual Review of Ecology, Evolution, and Systematics, 53, 203-225.
24. Kucuk Matci, D. (2022). Monitoring and estimating spatial-temporary land use changes of the Aegean region with remotely sensed data. Environmental Science and Pollution Research, 1-10.
25. KumlucaBelediyesi. (2019). COĞRAFYA.
26. Kuplich, T. M. (2006). Classifying regenerating forest stages in Amazonia using remotely sensed images and a neural network. Forest Ecology and Management, 234(1-3), 1-9.
27. Lemesios, I., Petropoulos, G. P. (2024). Vegetation regeneration dynamics of a natural mediterranean ecosystem following a wildfire exploiting the LANDSAT archive, google earth engine and geospatial analysis techniques. Remote Sensing Applications: Society and Environment, 34, 101153.
28. Lopes, L. F., Dias, F. S., Fernandes, P. M., Acacio, V. (2024). A remote sensing assessment of oak forest recovery after postfire restoration. European Journal of Forest Research, 143(3), 1001-1014.
29. Maki neci, H. B., Arıkan, D. (2024). Seyfe lake seasonal drought analysis for the winter and summer periods between 2017 and 2022. Remote Sensing Applications: Society and Environment, 34, 101172.
30. Mallinis, G., Mitsopoulos, I., Chrysafi, I. (2018). Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GIScience & remote sensing, 55(1), 1-18.
31. Matarira, D., Mutanga, O., Naidu, M. (2022). Google Earth Engine for Informal Settlement Mapping: A Random Forest Classification Using Spectral and Textural Information. Remote Sensing, 14(20), 5130.
32. Matci, D. K., Avdan, U. (2020). Comparative analysis of unsupervised classification methods for mapping burned forest areas. Arabian Journal of Geosciences, 13(15), 1-13.
33. Munoz Sabater, J. (2019). ERA5-Land monthly averaged data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS)[data set]. In.
34. Mutanga, O., Kumar, L. (2019). Google earth engine applications. In: Multidisciplinary Digital Publishing Institute.
35. Ocer, N. E., Kaplan, G., Erdem, F., Kucuk Matci, D., Avdan, U. (2020). Tree extraction from multi-scale UAV images using Mask R-CNN with FPN. Remote Sensing Letters, 11(9), 847-856.
36. OGM. (2017). Orman İstatistikleri. In: Yayin.
37. Petrie, M., Wildeman, A., Bradford, J. B., Hubbard, R., Lauenroth, W. (2016). A review of precipitation and temperature control on seedling emergence and establishment for ponderosa and lodgepole pine forest regeneration. Forest Ecology and Management, 361, 328-338.
38. Polat, N., Memduhoğlu, A., Akça, Ş. (2022). Determining the change in burnt forest areas with UAV: The example of Osmanbey campus. Advanced UAV, 2(1), 11-16.
39. Rasul, A. O., Hameed, H. M., Ibrahim, G. R. F. (2021). Dramatically increase of built-up area in Iraq during the last four decades. Advanced Remote Sensing, 1(1), 1-9.
40. Roy, D. P., Boschetti, L., Trigg, S. N. (2006). Remote sensing of fire severity: assessing the performance of the normalized burn ratio. IEEE Geoscience and Remote Sensing Letters, 3(1), 112-116.
41. Shafiq, M., Mahmood, S. (2022). Spatial assessment of forest cover change in Azad Kashmir, Pakistan. Advanced GIS, 2(2), 62-69.
42. Şimşek, F. F. (2024). Optik ve radar görüntüleri ile aşiri gradyan artirma algoritmasi kullanilarak tarimsal ürün desen tespiti. Geomatik, 9(1), 54-68.
43. Stankova, N., Avetisyan, D. (2024). Postfire Forest Regrowth Algorithm Using Tasseled-Cap-Retrieved Indices. Remote Sensing, 16(3), 597.
44. Tucker, C. J. (1978). Red and photographic infrared linear combinations for monitoring vegetation. USGS. (2015). Landsat 8 band designations.
45. Yalçin, M., Boz, İ. (2007). Kumluca İlçesinde Seralarda Üreticilerin Kullandiklari Bilgi Kaynaklari. Bahçe, 36(1), 1-10.
46. Yiğit, A. Y., Uysal, M. (2020). Automatic road detection from orthophoto images. Mersin Photogrammetry Journal, 2(1), 10-17.