Iranian Journal of Forest

Iranian Journal of Forest

Trend Analysis of Vegetation and Monitoring of Ecosystem Health using Remote Sensing (Case Study: Fandoghlo Region)

Document Type : Research Paper

Authors
1 Associate Prof., Dept. of Forest Science and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
2 Prof., Dept. of Forest Science and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
Abstract
Introduction: Natural ecosystems in Iran are under the influence of unprecedented disturbances, including climate changes such as drought, pests and diseases, invasive species, and as a result, vegetation changes. Trend analysis of vegetation and ecosystem health monitoring on a regional scale is necessary to evaluate the changing status of ecosystems and inform the management of environmental disturbance factors. However, these analyses and monitoring are difficult only based on field investigations. Remote sensing provides the possibility of comprehensive evaluations of ecosystems in large areas, and their changes can be observed over time using vegetation indicators in multi-temporal satellite images.
Materials and Methods: This research is based on the time series of Landsat-7 ETM+ and Landsat 8 OLI/TIRS images related to the years 2003 to 2023 and Geographic Information Systems used for analyzing vegetation trends and monitoring the health of the ecosystems in the Fandoghlo region (farthest end of the western Hyrcanian region located in the east of Ardabil). The non-parametric Mann-Kendall (M-K) trend test was used to analyze the spatial-temporal changes of vegetation in the study area.
Results: Based on the results of land cover changes in the study area, the reduction of vegetation cover was found in all natural ecosystems, including forests (-8.5 %), rangeland (-16.5 %), and water bodies (-17.2 %). On the other hand, agriculture (14.7 %) and built-up (30.9 %) areas showed an increase during the study period. Furthermore, the results of the changes in health index (NDVI) during the period 2003-2023 indicate a trend of vegetation browning (decrease) in the Fandoghlo region. In addition, the results of the M-K trend test show that NDVI had a significant linear increasing trend from 2003 to 2011, a non-significant linear increasing trend from 2011 to 2016, and a non-significant linear decreasing trend from 2016 to 2023.
Conclusion: In this study, the analysis of the trend of vegetation cover and health index based on NDVI can provide valuable indications about some processes that are relevant for ecological assessments such as the simplification and destruction of ecosystems or the intensification of agricultural activities. Considering the declining trend of natural ecosystems and the threat to health, it is recommended to implement conservation programs in line with nature to increase the vegetation cover and its health.
Keywords

Subjects


 
Asghari, S.H., Dizajghoorbani Aghdam, S., & Esmali, A. (2015). Investigation te Spatial Variability of some Soil Physical Quality Indices in Fandoghlou Region of Ardabil Using Geostatistics. Journal of Water and Soil, 28(6), 1271-1283. https://doi.org/10.22067/jsw.v0i0.33460. (In Persian)
Assal, T.J., Anderson, P.J., & Sibold, J. (2016). Spatial and temporal trends of drought effects in a heterogeneous semi-arid forest ecosystem. Forest Ecology and Management, 365, 137–151. https://doi.org/10.1016/j.foreco.2016.01.017
Buras, A., Rammig, A., & Zang, C.S. (2021). The European Forest Condition Monitor: Using Remotely Sensed Forest Greenness to Identify Hot Spots of Forest Decline. Frontiers in Plant Science, 12, 689220. https://doi.org/10.3389/fpls.2021.689220
Dastigerdi, M., Nadi, M., Raeini Sarjaz, M., & Kiapasha, K. (2022). Vegetation trend analysis using NDVI time series of Modis satellite in the northeast of Iran. Journal of Water and Soil Conservation, 29(1), 135-150. (In Persian). https://doi.org/10.22069/jwsc.2022.20208.3554
Delegido, J., Verrelst, J., Meza, C.M., Rivera, J.P., Alonso, L., & Moreno, J. (2013). A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy, 46, 42-52. https://doi.org/10.1016/j.eja.2012.12.001
Evangelides, C., & Nobajas, A. (2020). Red-Edge Normalised Difference Vegetation Index (NDVI705) from Sentinel-2 imagery to assess post-fire regeneration.  Remote Sensing Applications: Society and Environment, 17, 100283. https://doi.org/10.1016/j.rsase.2019.100283
Forbes, C.T., Chandler, M.A., Bhattacharya, D., Carroll Steward, K., Blake, J., Johnson, V., Morrow, M., Mason, W., & DeGrand, T. (2020). Fostering climate literacy with global climate models in secondary science classrooms: Insights from a collaborative partnership. In Teaching Climate Change in the United States. J.A. Henderson and A. Drewes, Eds., Routledge Advances in Climate Change Research, Routledge, pp. 29-43. http://dx.doi.org/10.4324/9780367179496-3
Freer-Burton, J.K.A., Kay, F.P., Anderson, G.H., & Radloff, M.E. (2022). What are the main indicators of forest health in Riccarton Bush and how can they be assessed and monitored?. School of Earth and Environment, University of Canterbury, GEOG309: Research for Resilient Environments and Communities October 21, 2022.
Hamed, K.H. (2009). Exact distribution of the Mann–Kendall trend test statistic for persistent data. Journal of Hydrology, 365(1–2), 86-94. https://doi.org/10.1016/j.jhydrol.2008.11.024
Huang, C., Yang, Q., & Huang, W. (2021). Analysis of the Spatial and Temporal Changes of NDVI and Its Driving Factors in theWei and Jing River Basins. International Journal of Environmental Research and Public Health, 18, 11863. https://doi.org/10.3390/ijerph182211863
IPBES. (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio, H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G.F., Midgley, P., Miloslavich, Z., Molnár, D., Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany, 56 p. https://doi.org/10.5281/zenodo.3553579 
Jahdi, R., Arabi, M., & Bussotti, F. (2020). Effect of environmental gradients on leaf morphological traits in the Fandoghlo forest region (NW Iran). iForest, 13, 523-530. https://doi.org/10.3832/ifor3391-013
Kashi Zenouzi, L., Ahmadi, H., & Nazari Samani, A. (2016). Using Statistical Hydrogeomorphology Method for Estimating Sediment Yield of Watersheds (Case study: Zonouz Chay and Zilber Chay watersheds). Journal of Watershed Management Research, 6(12), 166-174. (In Persian). http://jwmr.sanru.ac.ir/article-1-567-en.html
Keivan Behjou, F. (2012). Measuring damage to residual shrubs due to recreational activity in Fandoghlou forest. Iranian Journal of Forest, 4(3), 231-242. (In Persian)
Langner, A., Miettinen, J., Stibig, H.J. (2016).  Monitoring forest degradation for a case study in Cambodia—comparison of Landsat 8 and Sentinel-2 imagery. In: Proceedings of ESA Living Planet Symposium held 9-13 May 2016 in Prague, Czech Republic. Edited by L. Ouwehand. ESA-SP Volume 740, ISBN: 978-92-9221-305-3, p.200
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(8), 1799-1809. https://doi.org/10.1111/2041-210X.13025
Lechner, A.M., Foody, G.M., & Boyd, D.S. (2020). Applications in Remote Sensing to Forest Ecology and Management. One Earth, 2(5), 405-412. https://doi.org/10.1016/j.oneear.2020.05.001
Liu, S., Xie, Y., Fang, H., Du, H., & Xu, P. (2022). Trend Test for Hydrological and Climatic Time Series Considering the Interaction of Trend and Autocorrelations. Water, 14(19), 3006. https://doi.org/10.3390/w14193006
Maroufzade, E., & Attarod, P. (2021). Are variations of forest vegetation consistent with trends of meteorological parameters in the northern Zagros region of Iran?. Iranian Journal of Forest, 12(4), 449-466. (In Persian). https://doi.org/10.22034/ijf.2021.127780
Meddens, A.J.H., & Hicke, J.A. (2014). Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado, USA. Forest Ecology and Management, 322, 78–88. https://doi.org/10.1016/j.foreco.2014.02.037
Meng, J., Li, S., Wang, W., Liu, Q., Xie, S., & Ma, W. (2016). Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images. Remote Sensing, 8, 719. https://doi.org/10.3390/rs8090719
Murfitt, J., He, Y., Yang, J., Mui, A., & De Mille, K. (2016). Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images. Remote Sensing8, 256. https://doi.org/10.3390/rs8030256
Najafi, A., Azizi Ghalati, S., & Mokhtari, M.H. (2017). Assessment Kernel Support Vector Machines in Classification of Landuses (Case Study: Basin of Cheshmeh kileh-Chalkrod). Journal of Watershed Management Research, 8(15), 92-101. (In Persian). http://jwmr.sanru.ac.ir/article-1-846-en.html
O'Connell, R.M., Rao, D.S., Chaudhuri, A.A., & Baltimore, D. (2010). Physiological and pathological roles for microRNAs in the immune system. Nature Reviews Immunology, 10(2), 111-22. https://doi.org/10.1038/nri2708
O’Laughlin, J., & Cook, P.S. (2003). Inventory-based forest health indicators: Implications for national forest management. Journal of Forestry, 101, 11–17. http://dx.doi.org/10.1093/jof/101.2.11
Parsons, J.J. (1976). Forest to pasture: development or destruction?. Revista de Biología Tropical, 24(1), 121-38.
Pause, M., Schweitzer, C., Rosenthal, M., Keuck, V., Bumberger, J., Dietrich, P., Heurich, M., Jung, A., & Lausch, A. (2016). In Situ/Remote Sensing Integration to Assess Forest Health—A Review. Remote Sensing, 8, 471. https://doi.org/10.3390/rs8060471
Qu, W., Jin, Z., Zhang, Q., Gao, Y., Zhang, P., & Chen, P. (2022). Estimation of Evapotranspiration in the Yellow River Basin from 2002 to 2020 Based on GRACE and GRACE Follow-On Observations. Remote Sensing, 14, 730. https://doi.org/10.3390/rs14030730
Rita, A., Camarero, J.J., Nolè, A., Borghetti, M., Brunetti, M., Pergola, N., Serio, C., Vicente-Serrano, S.M., Tramutoli, V., & Ripullone, F. (2020). The impact of drought spells on forests depends on site conditions: the case of 2017 summer heat wave in southern Europe. Global Change Biology, 26, 851–863. https://doi.org/10.1111/gcb.14825
Rodrigues, J.M.R., Pellizari, V.H., Mueller, R., Baek, K., Jesus, E.C., Paula, F.S., Mirza, B., Hamaoui, G.S., Tsai, S.M., Feigl, B., Tiedje, J.M., Bohannan, B.J., & Nüsslein, K. (2012). Conversion of the Amazon Rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proceedings of the National Academy of Sciences, 110, 988–993. https://doi.org/10.1073/pnas.1220608110
Rostamikia, Y., Tabari Kouchaksaraei, M., Asgharzadeh, A., & Rahmani, A. (2017). Effect of Growth Promoting Rhizobacteria on growth and nutrient elements of common hazelnut (Corylus avellana L.) seedlings in Ardabil Fandoqlou nursery. Iranian Journal of Forest and Poplar Research, 25(1), 116-126. https://doi.org/10.22092/ijfpr.2017.109781. (In Persian)
Sanjaya, R.S., Anggraini, M.F., & Pratama, M.Z. (2020). Peat forest health analysis on landsat 8 OLI / TIRS imagery using NDVI method in Kotawaringin Timur Regency. Sociae Polites: Majalah Ilmiah Sosial Politik, 21(2), 209-217.  http://dx.doi.org/10.33541/sp.v21i3.2257
Sarparast, M., Ownegh, M., & Sepehr, A. (2020). Investigation the driving forces of land-use change in northeastern Iran: Causes and effects. Remote Sensing Applications: Society and Environment, 19, 100348. https://doi.org/10.1016/j.rsase.2020.100348
Sims, D.A., & Gamon, J.A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2–3), 337–354. https://doi.org/10.1016/S0034-4257(02)00010-X
Teimouri, R., Ghorbani, R., Karbasi, P., & Sharifi, E. (2023). Investigation of land use changes using the landscape ecology approach in Maragheh City, Iran. Journal of Environmental Studies and Sciences, 13, 271–284. https://doi.org/10.1007/s13412-023-00822-z
Thanh, N.P, & Kappas, M. (2017). Comparison of Random Forest, k-Nearest neighbor, and support Vector Machine Classifiers for Land Cover classification using Sentinel-2 imagery. Sensors, 18, 18. https://doi.org/10.3390/s18010018
Trumbore, S., Brando, P., & Hartmann, H. (2015). Forest health and global change. Science, 349(6250), 814-818. http://dx.doi.org/10.1126/science.aac6759
Tuominen, J., Lipping, T., Kuosmanen, V., & Haapanen, R. (2009). Remote Sensing of Forest Health. Geoscience and Remote Sensing. InTech. Available at: http://dx.doi.org/10.5772/8283.
Valizadeh Kamran, K., Sadegih, M., & Hejazi, S.A. (2023). Modeling Land Changes forest Using by LCM in Fandoqhlo Forest Area (Ardabil). Journal of Civil and Environmental Engineering, 52(4), 172-104. http://dx.doi.org/10.22034/JCEE.2021.43502.1984. (In Persian)
Woodcock, C.E., Loveland, T.R., Herold, M., & Bauer, M.E. (2020). Transitioning from change detection to monitoring with remote sensing: A paradigm shift. Remote Sensing of Environment238, 111558. https://doi.org/10.1016/j.rse.2019.111558
Xue, J.R., & Su, B.F. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 1353691. https://doi.org/10.1155/2017/1353691
Zhan, Y., Fan, J., Meng, T., Li, Z., Yan, Y., Huang, J., Chen, D., & Sui, L. (2021). Analysis on vegetation cover changes and the driving factors in the mid-lower reaches of Hanjiang River Basin between 2001 and 2015. Open Geosciences, 13(1), 675-689. https://doi.org/10.1515/geo-2020-0259 
Zhou, Y., Fan, J., & Wang, X. (2020). Assessment of varying changes of vegetation and the response to climatic factors using GIMMS NDVI3g on the Tibetan Plateau. PLoS ONE, 15(6), e0234848. https://doi.org/10.1371/journal.pone.0234848
Zhu, C., Zhang, X., Zhang, N., Hassan, M.A., & Zhao, L. (2018). Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data. Remote Sensing10, 360. https://doi.org/10.3390/rs10030360
Volume 16, Issue 2 - Serial Number 2
Summer 2024
Pages 291-310

  • Receive Date 22 August 2023
  • Revise Date 06 February 2025
  • Accept Date 25 February 2024