Assessment of the FCD model for estimating canopy density in Zagros forests using Landsat 8 and Sentinel 2 images
Subject Areas : Environmental managment
Ahmad abbasiwand
1
,
Hormoz Sohrabi
2
*
1 - MSc. Graduated, Forest Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran.
2 - Associate Professor, Forest Science and Engineering, Tarbiat Modares University, Tehran, Iran. *(Corresponding Author)
Keywords: Forest Canopy Density, Zagros Forests, Sentinel 2, Landsat 8.,
Abstract :
Background and Objective: One of the important criteria for studying, assessing changes, and monitoring forests is the canopy cover. Considering the role of Zagros forests in water and soil conservation and the significance of canopy cover in these forests, timely mapping canopy cover and cover changes are of special importance.
Material and Methodology: this research selected a total of 27 study areas throughout the Zagros growth region. A total of 2637 plots of 400 meters were examined, and the actual percentage of canopy cover was extracted. To estimate the canopy cover for Landsat 8 and Sentinel-2 satellites, data from the calculation of four integrated indices were combined, and the Forest Canopy Density (FCD) map was calculated. The model's performance was evaluated using statistical error metrics. Pearson correlation was used to assess the correlation between Landsat 8 and Sentinel 2 derived FCD indices and measures canopy cover.
Findings: The results showed that AVI and SI, had a direct relationship, while BI and TI had an inverse relationship with the canopy cover in all regions. Moreover, the highest coefficient of determination among all regions was obtained for the Sentinel-2 satellite (86.64%) in the densely forested area of northern Zagros. The lowest statistical error values, including RMSE (17.41%), RMSE% (31.57%), Bias (1.57%), Bias% (2.70%), MAE (12.60%), and MAE% (24.28%), were all obtained for the Sentinel-2 satellite and areas with dense canopy cover. Additionally, the statistical error metrics in northern Zagros showed lower values compared to central and southern Zagros, indicating the model's efficiency in densely forested areas.
Discussion and Conclusion: In general, it was not possible to achieve canopy estimation with high accuracy by the FCD model. The results obtained from the Sentinel-2 satellite performed better than the Landsat 8 satellite. Overall, this model is not suitable for the estimation of canopy covers of sparser semi-dense forests in Zagros.
1. Aghabarati, A.; Marvie Mohajer, MR.; Etemad, V.; Sefidi, K. (2018) Structural characteristics of coppice forest stands in Fandoghloo Forest, Ardebil Province Province. Forest Research and Development, 4(2), 223-239. (In Persian)
2. Nazariani, N., Fallah, A.; Hamidi, S. K.; Varamesh, S. (2022). Estimation of quantitative characteristics of Zagros forests using data mining nonparametric algorithms (case study: Olad Ghobad Watershed, Koohdasht, Lorestan). Forest Research and Development, 8(3), 249-263. (In Persian)
3. Ghanbari Motlagh, M.; Kiadaliri, M.; Halimi, M. (2023) Investigating spatiotemporal changes in greenness of Zagros Oak forests in response to drought, Journal of Renewable Natural Resources Research, 13(2), 131-143. (In Persian)
4. Azizi, A.; Montazeri, Z. (2018) Effects of microtopography on the spatial pattern of woody species im West Iran, Arabian Journal of Geosciences, 2018, 11-24.
5. Sohrabi, H.; Taheri Sarteshnizi, M.J. (2013) Fitting probability distribution functions for modeling diameter distribution of oak species in pollarded northern Zagros forests (Case study: Armardeh-Baneh). Iranian Journal of Forest, 4(4), 333-343. (In Persian)
6. Erfani Fard, S. Y.; Zobeiri, M.; Feghhi, J.; & Namiranian, M. (2007). Estimation of crown cover on aerial photographs using shadow index (case study: Zagros Forests, Iran). Iranian Journal of Forest and Poplar Research, 15(3), 288-278. (In Persian)
7. Beygiheidarlou, H.; Karamat Mirshekarlou, A.; Sasanifar, S.; & Khezryan, B. (2023). Forest cover density mapping of Zagros forests using Landsat-9 imagery and hemispherical photographs. Forest Research and Development, 9(1), 47-65. (In Persian)
8. Anand, A.; Singh, S.K.; Kanga, S. (2018) Estimating the change in forest cover density and predicting NDVI for west Singhbhum using linear regression. International Journal for Environmental Rehabilitation and Conservation 9, 193-203.
9. Azizi, Z.; Khalili, Z.; Soltani, A. (2018) The effect of physiographic factors on sudden oak trees death, case study area: barz and Shvrs Watershed, Geospatial Eng. J.; 9 (3): 19-25. (In Persian)
10. Phan, T.N.; Kuch, V.; Lehnert, L.W. (2020) Land Cover Classification using Google Earth Engine and Random Forest Classifier the Role of Image Composition. Remote Sensing 12 (15), 2411.
11. Taefi Feijani, M.; Azadnejad, S.; Moradi, M. (2021). Improvement of the forest canopy density model based on the addition of the FCC index and the average kernel implementation. Journal of Space Science and Technology, 14(2), 27-36. (In Persian)
12. Witharana, C.; Lynch, H.J. (2016) An object-based image analysis approach for detecting penguin guano in very high spatial resolution satellite images. Remote Sensing 8 (5), 375.
13. Taefi Feijani, M.; Alomphamadi, A. (2016) The authors of the article evaluate the FCD model in order to estimate density classes of crown - forest cover, Geomatics Conference, Tehtan, Iran. (In Persian)
14. Mohamed, M.A. (2021) An Assessment of Forest Cover Change and Its Driving Forces in the Syrian Coastal Region during a Period of Conflict, 2010 to 2020. Land, 10 (2), 191.
15. Taefi Feijani, M.; Azadnejad, S. (2020) Introducing a new local FCD model based on local adaptive thresholdingwith the aim of estimating forest canopy density over large areas. Scientific- Research Quarterly of Geographical Data (SEPEHR), 29(114), 37-49. (In Persian)
16. Mirzaeizadeh, V.; niknezhad, M; hojati, S.M. (2015). Estimation of forest canopy density using FCD. Ecology of Iranian Forest. 3(5), 63-75. (In Persian)
17. Shahvali Kouhshour, A.; Pir Bavaghar, M.; Fatehi, P. (2012). Forest cover density mapping in sparse and semi dense forests using forest canopy density model (Case Study: Marivan forests). Journal of RS and GIS for Natural Resources, 3(3), 73-83. (In Persian)
18. Rikimaru, A.; Roy, P.S.; Miyatake, S. (2002) Tropical Forest cover density mapping. International Society for Tropical Ecology 43(1): 39- 47.
19. Danoedoro, P.; and Gupita, D.D. (2022). Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery. International Journal on Advanced Science, Engineering, and Information Technology, 12, (3): 881-891
20. Vafaei, S.; Maleknia, R.; Naghavi, H.; Fathizadeh, O. (2022) Estimation of Forest Canopy Using Remote Sensing and Geostatistics (Case Study: Marivan Baghan Forests), Journal of Environment and Sciences Technology, 24(1): 71-82. (In Persian)
21. Sharma, R.; Singh, T. (2018) Forest Canopy Density Assessment Using High Resolution LISS-4 Data in Yamunanagar District, Haryana. International Archives of the Photogrammetry, Remote sensing and spatial Information Sciences, 42, 5.
22. Aguswan, Y.; Gumiri, S.; Sukarna, R. M.; Permana, I. (2022) Mapping Degraded Area for Tropical Peatland Revegetation Using Forest Canopy Density Model Landsat 8 OLI-TIRS in Central Kalimantan, Indonesia. Environment and Natural Resources Journal, 20 (4), 426-437.
23. Pakkhesal, E.; Bonyad, A. E. (2013). Classification and delineating natural forest canopy density using FCD model (Case study: Shafarud area of Guilan). Iranian Journal of Forest and Poplar Research, 21(1), 99-114. (In Persian)
24. Mondal, I.; Thakur, S.; Juliev, M.; Kumar De, T. (2021) Comparative analysis of forest canopy mapping methods for the Sundarban biosphere reserve, West Bengal, India. Environment, Development and Sustainability, 2021, 1-26.
25. Delfan, E., Naghavi, H., Maleknia, R., & Nouredini, A. (2022). Comparing the Capability of Sentinel 2 and Landsat 8 Satellite Imagery in Land Use and Land Cover Mapping Using Pixel-based and Object-based Classification Methods. Desert Ecosystem Engineering, 8(25), 1-12. (In Persian)