Analysis of a Decade of Sardasht Forest Dynamics: Insights from the Integration of Satellite Imagery and Spectral Indices

Subject Areas : natural sources

Hossein Panahi ¹ , Zahra Azizi ² , Hadi Kiadaliri ³ , Seyed Ali Almodaresi ⁴ , Hossein Aghamohammadi ⁵

1 - PhD student of Architecture, Department of Architecture, Technical and Engineering Faculty, Damghan Branch, Islamic Azad University, Damghan, Iran.
2 - Associate Professor, Department of Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Iran, Tehran 1477893855, Iran. *(Corresponding Author)
3 - Associate Professor, Department of Environment Sciences and Forest, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Professor, Department of Geography, Faculty of Humanities, Yazd Branch, Islamic Azad University, Yazd, Iran.
5 - Assistant Professor, Department of Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Iran, Tehran 1477893855, Iran.

Received: 2024-01-09 Accepted : 2024-05-15 Published : 2024-09-22

Keywords: Sardasht, Gram-Schmidt, Detection, Spectral Index.,

Abstract :

Background and Objective: The forests of the Zagros region in western Iran have experienced notable environmental shifts in recent years. Investigating the vertical and horizontal forest structures across different timeframes offers a window into these transformations.

Material and Methodology: Remote sensing methods, alongside satellite imagery, present a cost-effective and accurate means to monitor temporal changes in forested areas. In this study, image integration techniques and spectral indices were employed to assess the scale of these variations.

Findings: Initially, Gram-Schmidt integration was used to enhance the satellite imagery to the fourth order of spatial resolution. Subsequently, changes in the horizontal forest structure of the Sardasht region were examined by evaluating three vegetation indices (NDVI, EVI, and LAI) over a decade (2011, 2015, 2018, and 2021).

Discussion and Conclusion: The findings highlighted the efficacy of image integration in detecting pixels with small tree presence on the forest floor, with NDVI emerging as a particularly suitable index for discerning changes in the forest's horizontal structure.

References:

1. Witzell, J., & Martin, J. A. (2018). Endophytes and forest health. In Endophytes of forest trees (pp. 261–282). Springer.
2. Foster, K. R., Davidson, C., Tanna, R. N., & Spink, D. (2019). Introduction to the virtual special issue monitoring ecological responses to air quality and atmospheric deposition in the Athabasca Oil Sands region: The Wood Buffalo Environmental Association’s Forest Health Monitoring Program. Science of The Total Environment, 686, 345-359.
3. Azizi, Z., & Montazeri, Z. (2018). Effects of microtopography on the spatial pattern of woody species in West Iran. Arabian Journal of Geosciences, 11(10), 1-9.
4. Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365-2401.
5. Lausch, A., Bastian, O., Klotz, S., Leitão, P. J., Jung, A., Rocchini, D., Schaepman, M. E., Skidmore, A. K., Tischendorf, L., & Knapp, S. (2018). Understanding and assessing vegetation health by in situ species and remote-sensing approaches. Methods in Ecology and Evolution, 9, 1799-1809.
6. Lausch, A., Erasmi, S., King, D. J., Magdon, P., & Heurich, M. (2017). Understanding forest health with remote sensing—Part II: A review of approaches and data models. Remote Sensing, 9(2), 129-161. https://doi.org/10.3390/rs9020129
7. • Mirzaei, J., Mohamadi, A., Heidarizadi, Z., Noorolahi, H., & Omidipour, R. (2015). Assessment of land cover changes using RS and GIS: A case study of Zagros forests, Iran. Journal of Materials and Environmental Science, 6(9), 2565-2572.
8. Nasiri, V., Darvishsefat, A., Shirvani, A., & Avatefi Hemat, M. (2019). Monitoring and modeling changes of forest area using logistic regression, Markov, and GeoMod. Journal of Renewable Natural Resources Research, 19(65), 171-189. (In Persian)
9. Shabani, S., Pourghasemi, H. R., & Blaschke, T. (2020). Forest stand susceptibility mapping during harvesting using logistic regression and boosted regression tree machine learning models. Global Ecology and Conservation, 22, e00974.
10. Amani, M., Ghorbanian, A., Mahdavi, S., & Mohammadzadeh, A. (2019). Iranian land cover mapping using Landsat-8 imagery and random forest algorithm. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 77-81.
11. Sevinc, V., Kucuk, O., & Goltas, M. (2020). A Bayesian network model for prediction and analysis of possible forest fire causes. Forest Ecology and Management, 457, 117723.
12. Azizi, Z., Najafi, A., & Sohrabi, H. (2008). Forest canopy density estimation using satellite images. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 8(11), 1127-1130.
13. Bandara, W. G. C., Valanarasu, J. M. J., & Patel, V. M. (2021). Hyperspectral pansharpening based on improved deep image prior and residual reconstruction. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16.
14. Beygi Heidarlou, H., Banj Shafiei, A., Erfanian, M. A., Tayyebi, A., & Alijanpour, A. (2020). Underlying driving forces of forest cover changes due to the implementation of preservation policies in Iranian northern Zagros forests. International Forestry Review, 22(2), 241-256.
15. Babaie Kafaki, S., Moradzadeh Azar, N., Kiadaliri, H., Banj Shafiei, A., & Rahimizadeh, N. (2021). Fire risk mapping using hierarchical analysis and GIS methods along with risk assessment in the Zab-Sardasht forests. Journal of Renewable Natural Resources Research, 11(2), 101-115. (In Persian)
16. Lawley, V., Lewis, M., Clarke, K., & Ostendorf, B. (2016). Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review. Ecological Indicators, 60, 1273-1283.
17. Rui, B., Deng, Q., Liu, Y., Yang, R., & Zhang, H. (2021). Multi-hazard disaster scenario method and emergency management for urban resilience by integrating experiment–simulation–field data. Journal of Safety Science and Resilience, 2(2), 77-89.
18. Rui, B., Song, W., Lovallo, M., Zhang, H., & Telesca, L. (2022). Informational analysis of MODIS NDVI and EVI time series of sites affected and unaffected by wildfires. Physica A: Statistical Mechanics and its Applications, 604, 127911.
19. Becker, A., Russo, S., Puliti, S., Lang, N., Schindler, K., & Wegner, J. D. (2023). Country-wide retrieval of forest structure from optical and SAR satellite imagery with deep ensembles. ISPRS Journal of Photogrammetry and Remote Sensing, 195, 269-286.
20. Pordel, F., Ebrahimi, A. A., & Azizi, Z. (2017). Evaluating spatio-temporal phytomass changes using vegetation index derived from Landsat 8: A case study of Mrajan rangeland, Boroujen. Journal of Rangeland, 2, 166-178.
21. Azizi, Z., Hosseini, A., & Iranmanesh, Y. (2017). Estimating biomass of single oak trees using terrestrial photogrammetry. Journal of Environmental Science and Technology, 19(4), 81-93. (In Persian)