نشریه علمی - پژوهشی مرتع و آبخیزداری

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی طبیعت، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران

10.22059/jrwm.2025.399863.1846

چکیده

با توجه به تغییرات گسترده در تنوع زیستی و اهمیت حیاتی آن در حفظ پایداری و عملکرد اکوسیستم‌ها، ارزیابی دقیق و مستمر شاخص‌های تنوع گیاهی امری ضروری است. از آنجا که نمونه‌برداری‌های میدانی به‌دلیل محدودیت‌های زمانی، مکانی و اقتصادی در بسیاری از مناطق دشوار و پرهزینه است، استفاده از داده‌های سنجش از دور به‌عنوان منبعی قابل اتکا، کارآمد و مقرون به‌صرفه برای بررسی و پایش تنوع زیستی مورد توجه فزاینده‌ای قرار گرفته است. هدف این پژوهش، ارزیابی قابلیت داده‌های ماهواره‌ای سنتینل- 2 در برآورد شاخص‌های تنوع زیستی گیاهی در مراتع نیمه‌استپی است. برای این منظور، ۸ سایت نمونه‌برداری با در نظر گرفتن شرایط مدیریتی، پوشش گیاهی و ویژگی‌های اکولوژیکی انتخاب شد و در هر سایت سه ماکروپلات به ابعاد 30*30 مترمربعی مستقر گردید. نمونه‌برداری از پوشش گیاهی به روش تصادفی- سیستماتیک و با استفاده از پلات‌های 2*2 مترمربعی در امتداد سه ترانسکت انجام گرفت. پس از محاسبه شاخص‌های تنوع گیاهی شامل تنوع آلفا، بتا و عملکرد، رابطه میان آن‌ها و شاخص‌های پوشش گیاهی حاصل از داده‌های سنتینل- 2 مورد بررسی و تحلیل آماری قرار گرفت. تحلیل داده‌ها با استفاده از روش‌های رگرسیون خطی و آزمون همبستگی در محیط نرم‌افزار R انجام شد. نتایج به‌طور مشخص نشان می‌دهد که شاخص‌های پوشش گیاهی مشتق از تصاویر ماهواره‌ای سنتینل- 2 قادر به پیش‌بینی مؤلفه‌های مختلف تنوع زیستی مراتع نیمه‌استپی هستند. شاخص EVI بیشترین همبستگی را با تنوع آلفا (R2=0.20, P-value=0.02) و تنوع عملکردی (غنای عملکردی) (R2=0.34, P-value=0.001) نشان داد، درحالی‌که شاخص NDVI بیشترین همبستگی را با تنوع بتا (شاخص شباهت و فاصله Bray-curtis) (R2=0.21, P-value=0.01) داشت. شاخص‌های دیگر مانند MSAVI2، AVI و SAVI نیز همبستگی مثبت و معنی‌دار با مؤلفه‌های مختلف تنوع نشان دادند، هرچند ضریب همبستگی آنها نسبت به شاخص‌های اصلی کمتر بود.

کلیدواژه‌ها

عنوان مقاله [English]

Assessment of Sentinel-2 Satellite Data Capability for Estimating Vegetation Diversity Indices in Semi-Steppe Rangelands of Chaharmahal and Bakhtiari Province

نویسندگان [English]

  • Leila Mahmoudzadeh
  • Pejman Tahmasebi
  • Ataollah Ebrahimi
  • Samaneh Sadat Mahzooni Kachapi

Department of Nature engineering, Faculty of Natural Resources and Earth Sciences, University of Shahrekord, Shahrekord, Iran

چکیده [English]

Considering the widespread changes in biodiversity and its vital importance in maintaining ecosystem stability and functionality, precise and continuous assessment of plant diversity indices is essential. Due to temporal, spatial, and economic constraints, field sampling is often difficult and costly in many regions. Therefore, remote sensing data have increasingly gained attention as a reliable, efficient, and cost-effective source for biodiversity assessment and monitoring. This study aims to evaluate the capability of Sentinel-2 satellite data in estimating plant biodiversity indices in semi-steppe rangelands. To this end, eight sampling sites were selected based on management conditions, vegetation cover, and ecological characteristics, and three 30*30 m2 macroplots were established at each site. Vegetation cover sampling was performed using a systematic-random method with 2*2 m2 plots along three transects. After calculating plant diversity indices including alpha diversity, beta diversity, and functional diversity, the relationships between these indices and vegetation indices derived from Sentinel-2 data were examined and statistically analyzed. Data analysis was conducted using linear regression and correlation tests in the R software environment. The results clearly demonstrate that vegetation indices derived from Sentinel-2 satellite imagery are capable of predicting different components of biodiversity in semi-steppe rangelands. Among the indices, EVI showed the strongest correlation with alpha diversity (R²=0.20, P-value=0.02) and functional diversity (functional richness) (R²=0.34, P-value=0.001), whereas NDVI exhibited the highest correlation with beta diversity (Bray-Curtis Similarity and distance indices index) (R²=0.21, P-value=0.01). Other indices such as MSAVI2, AVI, and SAVI also revealed positive and significant correlations with various biodiversity components, although their correlation coefficients were lower than those of the primary indices.

کلیدواژه‌ها [English]

  • Biodiversity
  • Enhanced Vegetation Index (EVI)
  • Linear Regression
  • Remote sensing
  • Semi-Steppe Rangelands
Adam, E. M., Mutanga, O., Rugege, D., & Ismail, R. (2012). Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP. International Journal of Remote Sensing, 33(2), 552-569.‏
Ahmad, A., Ordoñez, J., Cartujo, P., & Martos, V. (2020). Remotely piloted aircraft (RPA) in agriculture: A pursuit of sustainability. Agronomy, 11(1), 7.
Balvanera, P., Pfisterer, A. B., Buchmann, N., He, J. S., Nakashizuka, T., Raffaelli, D., & Schmid, B. (2006). Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecology letters, 9(10), 1146-1156.
Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., Narwani, A., Mace, G. M., Tilman, D., Wardle, D. A., Kinzig, A. P., Daily, G. C., Loreau, M., Grace, J. B., Larigauderie, A., Srivastava, D. S., & Naeem, S. (2012). Biodiversity loss and its impact on humanity. Nature, 486(7401), 59-67.‏
Cerrejón, C., Valeria, O., Marchand, P., Caners, R. T., & Fenton, N. J. (2021). No place to hide: Rare plant detection through remote sensing. Diversity and Distributions, 27(6), 948-961.
Chrysafis, I., Korakis, G., Kyriazopoulos, A. P., & Mallinis, G. (2020). Predicting tree species diversity using geodiversity and Sentinel-2 multi-seasonal spectral information. Sustainability, 12(21), 9250.‏
Cooper, M. (2023). An inverse latitudinal gradient in species richness of forest red millipedes chersastus attems, 1926 and centrobolus cook, 1897. International Journal of Engineering Science Invention Research & Development, 10(2), 5-23.
Craven, D., Eisenhauer, N., Pearse, W. D., Hautier, Y., Isbell, F., Roscher, C., Bahn, M., Beierkuhnlein, C., Bönisch, G., Buchmann, N., Byun, C., Catford, J. A., Cerabolini, B. E. L., Cornelissen, J. H. C., Craine, J. M., Luca, E. D., Ebeling, A., Griffin, J. N., Hector, A., Hines, J., Jentsch, A., Kattge, J., Kreyling, J., Lanta, V., Lemoine, N., Meyer, S. T., Minden, V., Onipchenko, V., Polley, H. W., Reich, P. B., Ruijven, J. V., Schamp, B., Smith, M. D., Soudzilovskaia, N. A., Tilman, D., Weigelt, A., Wilsey, B., & Manning, P. (2018). Multiple facets of biodiversity drive the diversity- stability relationship. Nature ecology & evolution, 2(10), 1579-1587.‏
Cui, B., Zhao, Q., Huang, W., Song, X., Ye, H., & Zhou, X. (2019). A new integrated vegetation index for the estimation of winter wheat leaf chlorophyll content. Remote Sensing, 11(8), 974.
Fulford, R. S., Russell, M., Myers, M., Malish, M., & Delmaine, A. (2022). Models help set ecosystem service baselines for restoration assessment. Journal of Environmental Management, 317, 115411.‏
Garcia, N., Campos, J. C., Silva, D., Alírio, J., Duarte, L. B., Arenas-Castro, S., Pôças, I., Loureiro, A., Teodoro, A. C., Sillero, N. (2024). Biodiversity dataset and atlas of the special area of conservation Montesinho/Nogueira, Portugal. Biodiversity Data Journal, 8, 12: e118854.
Gillespie, T. W. (2005). Predicting woody‐plant species richness in tropical dry forests: a case study from south Florida, USA. Ecological Applications, 15(1), 27-37.
González‐Megías, A., María Gómez, J., & Sánchez‐Piñero, F. (2007). Diversity‐habitat heterogeneity relationship at different spatial and temporal scales. Ecography, 30(1), 31-41.
Gyamfi-Ampadu, E., Gebreslasie, M., & Mendoza-Ponce, A. (2021). Evaluating multi-sensors spectral and spatial resolutions for tree species diversity prediction. Remote Sensing, 13(5), 1033.‏
Harrison, S., Vellend, M., & Damschen, E. I. (2011). ‘Structured' beta diversity increases with climatic productivity in a classic dataset. Ecosphere, 2(1), 1-13.‏
Hooper, D. U., Adair, E. C., Cardinale, B. J., Byrnes, J. E. K., Hungate, B. A., Matulich, K. L., Gonzalez, A., Duffy, J. E., Gamfeldt, L., & O’Connor, M.L. (2012). A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature, 486, 105-108.
Huang, L., Wang, J., Fang, Y., Zhai, T., & Cheng, H. (2021). An integrated approach towards spatial identification of restored and conserved priority areas of ecological network for implementation planning in metropolitan region. Sustainable Cities and Society, 69, 102865.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213.
Immitzer, M., Neuwirth, M., Böck, S., Brenner, H., Vuolo, F., & Atzberger, C. (2019). Optimal input features for tree species classification in Central Europe based on multi-temporal Sentinel-2 data. Remote Sensing, 11(22), 2599.‏
Kang, S., Niu, J., Zhang, Q., Zhang, X., Han, G., & Zhao, M. (2020). Niche differentiation is the underlying mechanism maintaining the relationship between community diversity and stability under grazing pressure. Global Ecology and Conservation, 24, e01246.
Kishore, B. S. P. C., Kumar, A., Saikia, P., & Khan, M. L. (2024). Alpha and beta diversity mapping in Indian tropical deciduous forests using high-fidelity imaging spectroscopy. Advances in Space Research, 73(2), 1413-1426.
Koomen, E., & Stillwell, J. (2007). Modelling land-use change: theories and methods. Modelling land-use change: Progress and applications, 90, 1-22.
‏Lass, L. W., & Prather, T. S. (2004). Detecting the locations of Brazilian pepper trees in the Everglades with a hyperspectral sensor. Weed Technology, 18(2), 437-442.
Lima, T. A., Beuchle, R., Langner, A., Grecchi, R. C., Griess, V. C., & Achard, F. (2019). Comparing Sentinel 2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon. Remote sensing, 961(11), 1-21.
Liu, X., Frey, J., Munteanu, C., Still, N., & Koch, B. (2023). Mapping tree species diversity in temperate montane forests using Sentinel-1 and Sentinel-2 imagery and topography data. Remote Sensing of Environment, 292, 113576.‏
Llerena Gordillo, S. A., & Kurbatova, A. (2022). NDVI-alpha diversity relationship in tropical montane cloud forest of Ecuador. 10.25750/1995-4301-2022-3-058-067.
Loarie, S. R., Joppa, L. N., & Pimm, S. L. (2007). Satellites miss environmental priorities. Trends in Ecology & Evolution, 22(12), 630-632.
Ma, X., Mahecha, M. D., Migliavacca, M., van der Plas, F., Benavides, R., Ratcliffe, S.,  Kattge, J., Richter, R., Musavi, T., Baeten, L., Barnoaiea, I., Bohn, F. J., Bouriaud, O., Bussotti, F., Ioppi, A., Domisch, T., Huth, A., Jaroszewicz, B., Joswig, J., Pabon-Moreno, D. E., Papale, D., Selvi, F., Vaglio Laurin, G., Valladares, F., Reichstein, M., & Wirth, C. (2019). Inferring plant functional diversity from space: the potential of Sentinel-2. Remote Sensing of Environment, 233, 111368.
MacArthur, R. H. (1965). Pattern in species diversity. Biol. Rev. 40, 510-533.
Magurran, A. E. (2016). How ecosystems change. Science, 351(6272), 448-449.
Martin-Gallego, P., Aplin, P., Marston, C., Altamirano, A., & Pauchard, A. (2020). Detecting and modelling alien tree presence using Sentinel-2 satellite imagery in Chile’s temperate forests. Forest Ecology and Management, 474, 118353.‏
Mesdaghi, M. (2005). Ecology. Jihad Daneshgahi Publications, Mashhad, 1, 187. (In Persian)
Mutowo, G., Mutanga, O., & Masocha, M. (2018). Evaluating the applications of the near-infrared region in mapping foliar N in the miombo woodlands. Remote Sensing, 10(4), 505.‏
Overcast, I., Ruffley, M., Rosindell, J., Harmon, L., Borges, P. A. V., Emerson, B. C., Etienne, R. S., Gillespie, R., Krehenwinkel , H., Mahler, D. L., Massol, F., Parent, C. E., Patiño, J., Peter, B., Week, B., Wagner, C., Hickerson, M. J., & Rominger, A. (2021). A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities. Molecular Ecology Resources, 21(8), 2782-2800.‏
Qian, L. S., Chen, J. H., Deng, T., & Sun, H. (2020). Plant diversity in Yunnan: Current status and future directions. Plant diversity, 42(4), 281-291.‏
Rocchini, D., Perry, G. L., Salerno, M., Maccherini, S., & Chiarucci, A. (2006). Landscape change and the dynamics of open formations in a natural reserve. Landscape and urban planning, 77(1-2), 167-177.
Rocchini, D., Ricotta, C., & Chiarucci, A. (2007). Using satellite imagery to assess plant species richness: The role of multispectral systems. Applied Vegetation Science, 10(3), 325-331.‏
Rocchini, D., Hernández-Stefanoni, J. L., & He, K. S. (2015). Advancing species diversity estimate by remotely sensed proxies: a conceptual review. Ecological informatics, 25, 22-28.
Schroeder, P. J., & Jenkins, D. G. (2018). How robust are popular beta diversity indices to sampling error? Ecosphere, 9(2), e02100.
Singh, P., Pandey, P. C., Petropoulos, G. P., Pavlides, A., Srivastava, P. K., Koutsias, N., Kwal Deng, K. A., & Bao, Y. (2020). Hyperspectral remote sensing in precision agriculture: Present status, challenges, and future trends. In Hyperspectral remote sensing, Elsevier‏, 121-146.
Tahmasebi, P., Moradi, M., & Omidipour, R. (2017). Plant Functional Identity as the Predictor of Carbon Storage in Semi-Arid Ecosystems. Plant Ecology & Diversity, 10(2-3), 139-151.
Team, R. C. (2020). R: A Language and Environment for Statistical Computing. Foundation for Statistical Computing, Vienna, Austria. 1-12.
‏Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349.
Vellend, M. (2010). Conceptual synthesis in community ecology. The Quarterly review of biology, 85(2), 183-206.
Verrelst, J. C. V., Gustau, M. M., Jordi, P. R., Juan, V., Frank, Jan, G. P. W. C., & José, M. (2015). How essential biodiversity variables and remote sensing can help national biodiversity monitoring. Global Ecology and Conservation, 10, 43-59.
Wang, R., Gamon, J. A., Schweiger, A. K., Cavender-Bares, J., Townsend, P. A., Zygielbaum, A. I., & Kothari, S. (2018). Influence of species richness, evenness, and composition on optical diversity: A simulation study. Remote Sensing of Environment, 211, 218-228.
Wang, D., Qiu, P., Wan, B., Cao, Z., & Zhang, Q. (2022). Mapping α-and β-diversity of mangrove forests with multispectral and hyperspectral images. Remote Sensing of Environment, 275, 113021.‏
Whittaker, R. H., & Levin, S. A. (1977). The role of mosaic phenomena in natural communities. Theor. Pop. Biol. 12, 117-139.
Wookey, P. A., Aerts, R., Bardgett, R. D., Baptist, F., Bråthen, K. A., Cornelissen, J. H. C., Gough, L., Hartley, I. P., Hopkins, D. W., Lavorel, S., & Shaver, G. R. (2009). Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Global Change Biology, 15(5), 1153-1172.
Zhang, X., Liao, C., Li, J., & Sun, Q. (2013). Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 21, 506-512.‏
Zhang, Y., She, J., Long, X., & Zhang, M. (2022). Spatio-temporal evolution and driving factors of eco-environmental quality based on RSEI in Chang-Zhu-Tan metropolitan circle, central China. Ecological Indicators, 144, 109436.‏
Zhang, Y., Wang, Y., & Liu, X. (2024). Late Holocene vegetation diversity change and potential response to climate variations on the northern Qinghai-Tibetan Plateau. Quaternary International, 695, 45-54.
‏Zhao, Y. P., Wang, Z. W., WENDU, R., Zhao, Y. J., & Bai, Y. F. (2022). Remotely sensed monitoring method of grassland plant functional diversity and its relationship with productivity based on Sentinel-2 satellite data. Chinese Journal of Plant Ecology, 46(10), 1234.