Iranian Journal of Forest

Iranian Journal of Forest

Monitoring the Degradation of Northern Zagros Forests Using the BFAST Algorithm (Case Study: Part of the Sarvabad Forests)

Document Type : Research Paper

Authors
Sabri Rasooli 1
Amir Eslam Bonyad 2
Sasan Vafaei 3
1 4. Ph.D. candidate., Dept. of Forestry, Faculty of Natural Resources, University of Guilan, Someh sara, I. R. Iran
2 Prof., Dept. of Forestry, Faculty of Natural Resources, University of Guilan, Sowmeh Sara I.R. Iran
3 Remote Sensing Specialist, Forestry and Utilization Office, Natural Resources and Watershed Management of Iran organization, Tehran, I. R. Iran
10.22034/ijf.2026.545596.2068
Abstract
Introduction: Zagros forests are among the most important forest ecosystems in the country in terms of ecology and economy, which unfortunately have always been affected by destructive factors and land use changes. One of the appropriate solutions to determine the extent and trend of these changes is to use satellite data time series and the BFSAST model, because this model can simultaneously separate the trend of long-term, seasonal, and sudden changes. The capabilities of this algorithm in studying the country's ecosystems, especially the Zagros forests, have rarely been used and reported. Therefore, the purpose of this research is to use the BFAST algorithm to monitor and evaluate changes in a part of the Zagros oak forests in Sarvabad County.
Material and Methods: In this study, the dynamics of vegetation cover in a part of the forests of Sarvabad County in Kurdistan Province, as an example of the Zagros forests, was studied and analyzed from 2001 to 2020 (20 years). Using time series analysis of the Normalized Difference Vegetation Index (NDVI) of Landsat 5, 7, and 8 images and the BFAST algorithm, changes in the vegetation of the region were examined. First, using the maximum likelihood classification, the forest class was separated from other land uses in the region on the September 2001 image. Then, the historical period and the threshold was determined. After that, the NDVI index of the forest class was separated from other classes of the 20-year time series. Finally, the time series stack of the forest class NDVI index was used for subsequent analyses. A total of 174 Landsat satellite images from different sensors were processed. To evaluate the accuracy of the BFASTSpatial model in detecting disturbance and destruction of forest areas, 409 points were randomly selected.
Results: In this study, through trial and error, the first 5 years of the research period were considered as the historical period. The BFASTSpatial algorithm chart shows a breakpoint and sudden drop in 2014. The change detection also showed that 589 hectares (4.6 percentage) of the region's forest area was lost during the study period. For the degraded forest land map with the threshold of -0.025, the BFASTSpatial model shows the change with the user's accuracy of 87.1 percent, the producer's accuracy of 85.9 percent, and the overall accuracy of 86 percent.
Conclusion: The results show that the BFASTSpatial model, using time series analysis and determining the degraded pixels of oak forests in the study area with an accuracy of over 85 percentage, has a good ability to determine the geographical boundaries of degraded lands and can be considered as a tool in monitoring forest environments.
Keywords
Landsat
Threshold
Historical period
time series
Zagros
Subjects
 
Ajin, R.S., Loghin, A.M., Jacob, M.K., Vinod, P., & Krishnamurthy, R. (2016). The risk assessment study of potential forest fire in Idukki Wildlife Sanctuary using RS and GIS techniques. International Journal of Advanced Earth Science and Engineering, 5(1), 308-318. https://doi.org/10.23953/cloud.ijaese.201
Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2), 949–981. https://doi.org/10.3390/rs5020949
Azizi, Gh., Miri, M., Mohammadi, H., & Pourhashemi, M. (2015). Analysis of relationship between forest decline and precipitation changes in Ilam Province. Iranian Journal of Forest and Poplar Research, 23(3), 502-515. (In Persian). https://doi.org/ 0.22092/ijfpr.2015.105655
Bueno, I.T., McDermid, G.J., Silveira, E.M.O., Hird, J.N., Domingos, B.I., & Acerbi Júnior, F.W. (2020). Spatial agreement among vegetation disturbance maps in tropical domains using Landsat time series. Remote Sensing, 12(18), 2948. https://doi.org/ 10.3390/rs12182948
Chandra, J.P. (2011). Performance evaluation of vegetation indices using remotely sensed data. International Journal of Geomatics and Geosciences, 2(1), 231–240.
De Beurs, K.M., & Henebry, G.M. (2010). Spatio-temporal statistical methods for modelling land surface phenology.  In: Hudson, I., Keatley, M. (eds) Phenological Research. (pp. 177-208). Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3335-2_9
De Jong, R., de Bruin, S., de Wit, A., Schaepman, M.E., & Dent, D.L. (2011). Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2), 692-702. https://doi.org/ 10.1016/j.rse.2010.10.011
DeVries, B., Decuyper, M., Verbesselt, J., Zeileis, A., Herold, M., & Joseph, S. (2015). Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series. Remote Sensing of Environment, 169, 320–334. https://doi.org/ 10.1016/j.rse.2015.08.020
Dutrieux, L.P., Verbesselt, J., Kooistra, L., & Herold, M. (2015).  Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia. ISPRS Journal of Photogrammetry and Remote Sensing, 107, 112–125. https://doi.org/ 10.1016/j.isprsjprs.2015.03.015
Fensholt, R., Sandholt, I., & Rasmussen, M.S. (2004). Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91(3-4),490–507. https://doi.org/ 10.1016/j.rse.2004.04.009
Gao, Y., Solórzano, J.V., Quevedo, A.,  & Loya-Carrillo, J.O. (2021). How BFAST trend and seasonal model components affect disturbance detection in tropical dry forest and temperate forest. Remote Sensing, 13(11), 2033. https://doi.org/ 10.3390/rs13112033
Gholamnia, M., Khandan, R., Bonafoni, S., & Sadeghi, A. (2019). Spatiotemporal Analysis of MODIS NDVI in the Semi-Arid Region of Kurdistan (Iran). Remote Sensing, 11(14), 1723. https://doi.org/ 10.3390/rs11141723
Harris, A., Carr, A.S., & Dash, J. (2014). Remote sensing of vegetation cover dynamics and resilience across southern Africa. International Journal of Applied Earth Observation and Geoinformation, 28(1), 131-139. https://doi.org/ 10.1016/j.jag.2013.11.014
Hashemi Dareh Badami, S., Nouraeisefat, I., Karimi, S., & Nazari, S. (2015). Development Trend Analysis of Urban Heat Island Regarding Land Use/Cover Changes Using Time Series of Landsat Images. Journal of Rs and Gis for Natural Resources, 6(3), 15-28. (In Persian).
Hosseinzadeh, J., Namiranian, M., Mohajer, M.M., & Amiri, G.Z. (2004). Structure of less degraded oak forests in Illam Province (Southwest Iran).‌ Iranian Journal of Natural Resources, 57(1), 75-90. (In Persian).
Huang, S., Tang, L., Hupy, J.P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of forestry research, 32(1), 1-6. https://doi.org/10.1007/s11676-020-01155-1
Lambert, J., Denux, J.P., Verbesselt, J., Balent, G., & Cheret, V. (2015). Detecting clear-cuts and decreases in forest vitality using MODIS NDVI time series. Remote Sensing, 7(4), 3588–3612. https://doi.org/ 10.3390/rs70403588
Lambert, J., Drenou, C., Denux, J.P., Balent, G., & Cheret, V. (2013). Monitoring forest decline through remote sensing time series analysis. GISCience & Remote Sensing, 50(4), 437–457. https://doi.org/ 10.1080/15481603.2013.820070
LeVine, D., & Crews, K. (2019). Time series harmonic regression analysis reveals seasonal vegetation productivity trends in semi-arid savannas. International Journal of Applied Earth Observation and Geoinformation, 80, 94–101. https://doi.org/ 10.1016/j.jag.2019.04.007
Liu, S., Li, W., Qiao, W., Wang, Q., Hu, Y., & Wang, Z. (2019). Effect of natural conditions and mining activities on vegetation variations in arid and semiarid mining regions. Ecological Indicators, 103, 331–345. https://doi.org/ 10.1016/j.ecolind.2019.04.034
Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401. https://doi.org/10.1080/0143116031000139863
Lu, M., Pebesma, E., Sanchez, A., & Verbesselt, J. (2016). Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 227–236. https://doi.org/ 10.1016/j.isprsjprs.2016.03.007
Mashhadi, N., & Alganci, U. (2022). Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey. ISPRS International Journal of Geo-Information, 11(11), 573. https://doi.org/ 10.3390/ijgi11110573
Measho, S., Chen, B., Trisurat, Y., Pellikka, P., Guo, L., Arunyawat, S., Tuankrua, V., Ogbazghi, W., & Yemane, T. (2019). Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea. Remote Sensing, 11(6), 724. https://doi.org/ 10.3390/rs11060724
Mirahsani, M., Salman Mahiny, A., Soffianian, A., Moddares, R., Jafari, R., & Mohammadi, J. (2018). Evaluation of Vegetation Supply Water Index through Time-Series Images of MODIS Products in Drought Monitoring over Gavkhuni Basin. Iranian Journal of Applied Ecology, 6(4) ,31-47. (In Persian). https://doi.org/ 10.29252/ijae.6.4.31
Mirmosavei, S., & kareimei, H. (2013). Effect of Drought on Vegetation Cover Using MODIS Sensing Images Case: Kurdistan Province. Geography and Development, 11(31), 57-76. (In Persian). https://doi.org/ 10.22111/gdij.2013.794
Mohammadi Sarvaleh, F., Pir bavaghar, M., & Shabanian, N. (2013). Application of artificial neural network for forest fire risk mapping based on physiographic, human and climate factors in Sarvabad, Kurdistan province. Iranian Journal of Forest and Range Protection Research, 11(2), 97-107. (In Persian).  https://doi.org/ 10.22092/ijfrpr.2014.8431
Potter, C. (2019). Changes in vegetation cover of Yellowstone National Park estimated from MODIS greenness trends, 2000 to 2018. Remote Sensing in Earth Systems Sciences, 2, 147–160. https://doi.org/ 10.1007/s41976-019-00019-5
Pourhashemi, M., & Sadeghi, S.M.M. (2020).  A Review on Ecological Causes of Oak Decline Phenomenon in Forests of Iran. Ecology of Iranian Forest, 8(16), 148-164. (In Persian). https://doi.org/10.52547/ifej.8.16.148
Rodriguez, P.S., Schwantes, A.M., Gonzalez, A., & Fortin, M.J. (2024). Monitoring changes in the enhanced vegetation index to inform the management of forests. Remote Sensing, 16(16), 2919.  https://doi.org/10.3390/rs16162919
Schmidt, M., Lucas, R., Bunting, P., Verbesselt, J., & Armston, J. (2015). Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. Remote Sensing of Environment, 158, 156–168. https://doi.org/ 10.1016/j.rse.2014.11.015
Shiravand, H., Khaledi, S., Behzadi, S., & sanjabi, H.A. (2020). Monitoring and Assessing the Changes in the Coverage and Decline of Oak Forests in Lorestan Province using Satellite Images and BFAST Model. Journal of Applied Researches in Geographical Sciences, 20(57), 265-280. (In Persian). https://doi.org/ 10.29252/jgs.20.57.265
Smith, V., Portillo-Quintero, C., Sanchez-Azofeifa, A., & Hernandez-Stefanoni, J.L. (2019). Assessing the accuracy of detected breaks in Landsat time series as predictors of small-scale deforestation in tropical dry forests of Mexico and Costa Rica. Remote Sensing of Environment, 221, 707–721. https://doi.org/ 10.1016/j.rse.2018.12.020
Vafaei, S., Darvishsefat, A.A., & Pir Bavaghar, M. (2013). Monitoring and predicting land use changes using LCM module (Case study: Marivan region). Iranian Journal of Forest, 5(3), 323-336. (In Persian).
Vasios, G.K., Alexoudaki, E., Kaloveloni, A., & Troumbis, A.Y. (2025). Landsat Time Series Analysis with BFAST for Detecting Degradation of Thyme Shrublands by Fire on Lemnos Island. Fire, 8(8), 335. https://doi.org/10.3390/fire8080335
Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1), 106–115. https://doi.org/ 10.1016/j.rse.2009.08.014
Verbesselt, J., Zeileis, A., & Herold, M. (2012). Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, 123, 98–108. https://doi.org/10.1016/j.rse.2012.02.022
Vu, Q.M., Le, Q.B., Frossard, E., & Vlek, P.L.G. (2014). Socio-economic and biophysical determinants of land degradation in Vietnam: An integrated causal analysis at the national level. Land Use Policy, 36. 605-617. https://doi.org/ 10.1016/j.landusepol.2013.10.012
Watts, L.M., & Laffan, S.W. (2014). Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region. Remote Sensing of Environment, 154, 234–245. https://doi.org/ 10.1016/j.rse.2014.08.023
Wu, L., Li, Zh., Liu, X., Zhu, L., Tang, Y., Zhang, B., Xu, B., Liu, M., Meng, Y., & Liu, B. (2020). Multi-type forest change detection using BFAST and monthly Landsat time series for monitoring spatiotemporal dynamics of forests in subtropical wetland. Remote Sensing, 12(2), 341. https://doi.org/ 10.3390/rs12020341
Zand, M., Miri, M., Razie, T., & Norouzi, A.A. (2021). Investigating the effects of meteorological drought on canopy level dieback of oak forests of Lorestan Province, western Iran. Journal of Climate Research, 12(46), 1-12. (In Persian).
Zhang, L., & Wu, C. (2017). Advance and future development of change detection for multi-temporal remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10), 1447–1459. (In Chinese). https://doi.org/10.11947/j.AGCS.2017.20170340
Zhao, Q., & Qu, Y. (2024). The retrieval of ground ndvi (normalized difference vegetation index) data consistent with remote-sensing observations. Remote Sensing, 16(7), 1212. https://doi.org/10.3390/rs16071212
Iranian Journal of Forest
Volume 18, Issue 1 - Serial Number 1
Summer 2026
Pages 147-163

  • Receive Date 29 September 2025
  • Revise Date 30 November 2025
  • Accept Date 05 January 2026