Spatiotemporal monitoring and assessment of green oak leaf-roller outbreaks in Zagros forests using Sentinel-2 data and the BFAST algorithm

1. Kitaibekova, S., Toktassynov, Z., Sarsekova, D., Mohammadi Limaei, S. & Zhilkibayeva, E. Assessment of forest ecosystem services in Burabay National Park, Kazakhstan: A case study. Sustainability 15, 4123 (2023).

   Google Scholar 

2. Ritchie, H. The world has lost one-third of its forest, but an end of deforestation is possible. Our World in Data https://ourworldindata.org/world-lost-one-third-forests (2021).

3. Beygi Heidarlou, H., Karamat Mirshekarlou, A., Lopez-Carr, D. & Borz, S. A. Conservation policy and forest transition in Zagros forests: Statistical analysis of human welfare, biophysical, and climate drivers. For. Policy Econ. 161, 103177 (2024).

   Google Scholar 

4. McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).

   Google Scholar 

5. Asaro, C., Koch, F. H. & Potter, K. M. Denser forests across the USA experience more damage from insects and pathogens. Sci. Rep. 13, 3666 (2023).

   Google Scholar 

6. Hernández-Blanco, M. et al. Ecosystem health, ecosystem services, and the well-being of humans and the rest of nature. Glob. Change Biol. 28, 5027–5040 (2022).

   Google Scholar 

7. Beygi Heidarlou, H., Banj Shafiei, A., Erfanian, M., Tayyebi, A. & Alijanpour, A. Effects of preservation policy on land use changes in Iranian Northern Zagros forests. Land Use Pol. 81, 76–90 (2019).

   Google Scholar 

8. Pimentel, C. S. et al. Multiple factors associated with forest decline in the context of control measures for the pinewood nematode. European Eur. J. Plant Pathol. 1–16 (2025).

9. Vîrteiu, A.-M., Boldea, M., Molnar, L., Grozea, I. & Sala, F. Assessment of green oak leaf-roller (Tortrix viridana, Lepidoptera: Tortricidae) activity using pheromonal traps. Res. J. Agric. Sci. 57, 277–286 (2025).

   Google Scholar 

10. Gholami, H. et al. Patterns of genetic variation among host-plant associated populations of the green oak leaf roller moth, Tortrix viridana (Lepidoptera: Tortricidae) in oak forests of northwestern Iran. J. Entomol. Soc. Iran 42, 67–80 (2022).

    Google Scholar 

11. Andreieva, O., Martynchuk, I., Zhytova, O., Vyshnevskyi, A. & Zymaroieva, A. Features of forecasting of leaf-eating insects distribution in the forests of Zhytomyr Polissia. Sci. Horiz. 24, 68–76 (2021).

    Google Scholar 

12. Zargaran, M. R., Mousavi Mirkala, S. R., Banj Shafiei, A. & Ramezani kakroudi, E. Survey on biology of Tortrix viridana L. in laboratory and field conditions and its distribution in West-Azerbaijan. For. Res. Dev. 1, 31–42 (2015).

    Google Scholar 

13. Balla, A. et al. The threat of pests and pathogens and the potential for biological control in forest ecosystems. Forests 12, 1579 (2021).

    Google Scholar 

14. Tyukavina, A. et al. Global trends of forest loss due to fire from 2001 to 2019. Front. Remote Sens. 3, 825190 (2022).

    Google Scholar 

15. Meshkova, V., Stankevych, S., Koshelyaeva, Y., Korsovetskyi, V. & Borysenko, O. Climate-induced shift in the population dynamics of Tortrix viridana L. in Ukraine. Forests 16, 1005 (2025).

    Google Scholar 

16. Tăut, I. et al. Climate-driven decline of oak forests: Integrating ecological indicators and sustainable management strategies. Sustainability 17, 9197 (2025).

    Google Scholar 

17. Zhi, J. et al. Rapid large-scale monitoring of pine wilt disease using sentinel-1/2 images in GEE. Forests 16, 981 (2025).

    Google Scholar 

18. Bárta, V., Lukeš, P. & Homolová, L. Early detection of bark beetle infestation in Norway spruce forests of Central Europe using sentinel-2. Int. J. Appl. Earth Obs. Geoinf. 100, 102335 (2021).

    Google Scholar 

19. Bright, B. C., Hudak, A. T., Meddens, A. J., Egan, J. M. & Jorgensen, C. L. Mapping multiple insect outbreaks across large regions annually using Landsat time series data. Remote Sens. 12, 1655 (2020).

    Google Scholar 

20. Verbesselt, J., Hyndman, R., Newnham, G. & Culvenor, D. Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 114, 106–115 (2010).

    Google Scholar 

21. Wu, L. et al. Multi-type forest change detection using BFAST and monthly Landsat time series for monitoring spatiotemporal dynamics of forests in subtropical wetland. Remote Sens. 12, 341 (2020).

    Google Scholar 

22. van Asch, M. & Visser, M. E. Phenology of forest caterpillars and their host trees: The importance of synchrony. Annu. Rev. Entomol. 52, 37–55 (2007).

    Google Scholar 

23. Hosseinzadeh, J., Aazami, A. & Mohammadpour, M. Influence of topography on Brant’s oak decline in Meleh-Siah forest, Ilam province. Iran. J. For. Poplar Res. 23, 190–197. https://doi.org/10.22092/ijfpr.2015.102384 (2015).

    Google Scholar 

24. Shiravand, H., Khaledi, S., Behzadi, S. & Sanjabi, H. A. Monitoring and assessing the changes in the coverage and decline of oak forests in Lorestan province using satellite images and BFAST model. J. Appl. Res. Geogr. Sci. 20, 265–280. https://doi.org/10.29252/jgs.20.57.265 (2020).

    Google Scholar 

25. Lambert, J., Drenou, C., Denux, J.-P., Balent, G. & Cheret, V. Monitoring forest decline through remote sensing time series analysis. GISci. Remote Sens. 50, 437–457 (2013).

    Google Scholar 

26. Ibañez-Álvarez, M. et al. Satellite-based monitoring of primary production in a Mediterranean islet post black rat eradication. Remote Sens. 14, 101 (2021).

    Google Scholar 

27. Conte, A. L. et al. Oak decline in the Mediterranean basin: A study case from the southern Apennines (Italy). Plant Sociol. 56, 69–80 (2019).

    Google Scholar 

28. Senf, C., Seidl, R. & Hostert, P. Remote sensing of forest insect disturbances: Current state and future directions. Int. J. Appl. Earth Obs. Geoinf. 60, 49–60 (2017).

    Google Scholar 

29. Lemmo, S. L., Kerhoulas, L. P., Sherriff, R. L. & Beckmann, J. J. Drought effects on tree mortality and regeneration in Northern California. For. Ecol. Manage. 563, 121969 (2024).

    Google Scholar 

30. Fassnacht, F. E. et al. Review of studies on tree species classification from remotely sensed data. Remote Sens. Environ. 186, 64–87 (2016).

    Google Scholar 

31. Kennedy, R. E., Yang, Z. & Cohen, W. B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms. Remote Sens. Environ. 114, 2897–2910 (2010).

    Google Scholar 

32. Anderegg, W. R. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).

    Google Scholar 

33. Koch, T. L. et al. Assessment of tree species specific phenology metrics from Sentinel-2 data to complement in situ monitoring. Ecol. Indic. 180, 114299 (2025).

    Google Scholar 

34. Meng, X., Xie, S., Sun, L., Liu, L. & Han, Y. Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data. Int. J. Digit. Earth 16, 2574–2598 (2023).

    Google Scholar 

35. Meigs, G. W., Kennedy, R. E. & Cohen, W. B. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sens. Environ. 115, 3707–3718 (2011).

    Google Scholar 

36. Molnár, T., Móricz, N., Hirka, A., Csóka, G. & Kern, A. Retrospective analysis of a large-scale Gypsy Moth outbreak in Hungary combining multi-source satellite and in situ data. Forests 16, 1472 (2025).

    Google Scholar 

37. Hartmann, H. & Trumbore, S. Understanding the roles of nonstructural carbohydrates in forest trees: From what we can measure to what we want to know. New Phytol. 211, 386–403 (2016).

    Google Scholar 

38. Jactel, H. et al. Drought effects on damage by forest insects and pathogens: A meta-analysis. Glob. Change Biol. 18, 267–276 (2012).

    Google Scholar 

39. Gooshbor, L., Bavaghar, M. P., Amanollahi, J. & Ghobari, H. Monitoring infestations of oak forests by Tortrix viridana (Lepidoptera: Tortricidae) using remote sensing. Plant Prot. Sci. 52 (2016).

40. Netherer, S. et al. Do water-limiting conditions predispose N orway spruce to bark beetle attack?. New Phytol. 205, 1128–1141 (2015).

    Google Scholar 

41. Dargahian, F., Pourhashemi, M. & Lotfinasabasl, S. Investigating the occurrence of drought in the forest ecosystem of Zagros due to its coincidence with the decline of oak forests. Iran. J. For. Range. Prot. Res. 22, 285–308 (2025).

    Google Scholar 

42. Senf, C., Pflugmacher, D., Hostert, P. & Seidl, R. Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe. ISPRS. J. Photogramm. Remote. Sens. 130, 453–463 (2017).

    Google Scholar 

43. Beygi Heidarlou, H. et al. Impact of Iran’s forest nationalization law on forest cover changes over six decades: A case study of a Zagros sparse coppice oak forest. Sensors 23, 871 (2023).

    Google Scholar 

44. European Space Agency (ESA) Copernicus sentinel-2. Sentinel-2 MSI: multispectral instrument, level-2A surface reflectance (Harmonized) . <https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED> (2023).

45. Valdivieso-Ros, C., Alonso-Sarria, F. & Gomariz-Castillo, F. Effect of different atmospheric correction algorithms on Sentinel-2 imagery classification accuracy in a semiarid Mediterranean area. Remote. Sens. 13, 1770 (2021).

    Google Scholar 

46. Sudmanns, M., Tiede, D., Augustin, H. & Lang, S. Assessing global Sentinel-2 coverage dynamics and data availability for operational earth observation (EO) applications using the EO-Compass. Int. J. Digit. Earth. 13, 768–784 (2020).

    Google Scholar 

47. El-Alfy, M. A., Alzeny, A. M., El-Tohamy, W. S., Qayoom, I. & Abd El-Hamid, H. T. Integrating Sentinel 2A data for selecting the best methods in observation of biological pigments in coastal water. Ecol. Front. 44, 538–546 (2024).

    Google Scholar 

48. Puliti, S. et al. Above-ground biomass change estimation using national forest inventory data with sentinel-2 and landsat. Remote Sens. Environ. 265, 112644 (2021).

    Google Scholar 

49. Pamungkas, S. In IOP Conference Series: Earth and Environmental Science. 012034 (IOP Publishing).

50. Badruzzaman, A. et al. Satellite imagery pre-processing and feature extraction for the mapping of coastal ecosystems using Google earth engine: A workflow for practitioners. MethodsX, 103516 (2025).

51. Louis, J. et al. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. 3041–3044 (IEEE).

52. Liang, K. et al. A novel method for cloud and cloud shadow detection based on the maximum and minimum values of Sentinel-2 time series images. Remote. Sens. 16, 1392 (2024).

    Google Scholar 

53. Zhou, W. et al. Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data. Front. Plant Sci. 16, 1629146 (2025).

    Google Scholar 

54. Matas-Granados, L. et al. Long-term monitoring of NDVI changes by remote sensing to assess the vulnerability of threatened plants. Biol. Conserv. 265, 109428 (2022).

    Google Scholar 

55. Huang, S., Tang, L., Hupy, J. P., Wang, Y. & Shao, G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. 32, 1–6 (2021).

    Google Scholar 

56. Zhou, M., Li, D., Liao, K. & Lu, D. Integration of Landsat time-series vegetation indices improves consistency of change detection. Int. J. Digit. Earth. 16, 1276–1299 (2023).

    Google Scholar 

57. Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).

    Google Scholar 

58. Kolarik, N., Roopsind, A., Pickens, A. & Brandt, J. A satellite-based monitoring system for quantifying surface water and mesic vegetation dynamics in a semi-arid region. Ecol. Indic. 147, 109965 (2023).

    Google Scholar 

59. Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).

    Google Scholar 

60. Beguería, S., Vicente‐Serrano, S. M., Reig, F. & Latorre, B. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).

    Google Scholar 

61. McKee, T. B., Doesken, N. J. & Kleist, J. In Proceedings of the 8th Conference on Applied Climatology. 179–183 (California).

62. Ghaderpour, E. & Vujadinovic, T. Change detection within remotely sensed satellite image time series via spectral analysis. Remote Sens. 12, 4001 (2020).

    Google Scholar 

63. Ma, J. et al. Analyzing ecological vulnerability and vegetation phenology response using NDVI time series data and the BFAST algorithm. Remote Sens. 12, 3371 (2020).

    Google Scholar 

64. Mendes, M. P., Rodriguez-Galiano, V. & Aragones, D. Evaluating the BFAST method to detect and characterise changing trends in water time series: A case study on the impact of droughts on the Mediterranean climate. Sci. Total Environ. 846, 157428 (2022).

    Google Scholar 

65. Gao, Y., Solórzano, J. V., Quevedo, A. & Loya-Carrillo, J. O. How BFAST trend and seasonal model components affect disturbance detection in tropical dry forest and temperate forest. Remote Sens. 13, 2033 (2021).

    Google Scholar 

66. Pasquarella, V. J. et al. Predicting defoliator abundance and defoliation measurements using landsat-based condition scores. Remote Sens. Ecol. Conserv. 7, 592–609 (2021).

    Google Scholar 

67. Olsson, P.-O., Lindström, J. & Eklundh, L. Near real-time monitoring of insect induced defoliation in subalpine birch forests with MODIS derived NDVI. Remote Sens. Environ. 181, 42–53 (2016).

    Google Scholar 

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