نوع مقاله : مقاله پژوهشی

نویسندگان

1 دکتری گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 استاد گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری ، ساری، ایران

3 پژوهشگر پسادکترای گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری

چکیده

تغییر اقلیم تأثیر قابل ملاحظه‌ای بر محیط‌زیست دارد و منجر به حساسیت متفاوت پوشش گیاهی به‌عوامل آب و هوایی در مقیاس های مکانی- زمانی مختلف می شود. آگاهی از وضعیت پوشش گیاهی به‌دلیل کاربرد در برنامه‌ریزی‌های خرد و کلان در حال حاضر یکی از ارکان مهم در تولید اطلاعات است .با توجه به پرهزینه و زمان‌بر بودن استفاده از روش‌های مبتنی بر مشاهدات، امروزه فناوری سنجش از دور به‌عنوان راهکار جدید در بهبود این روش‌ها مطرح شده است. در پژوهش پیش‌رو هدف بررسی اثر عوامل اقلیمی بر روند پوشش گیاهی جنگل فریم در استان مازندران با استفاده از تصاویر سنتینل 2 و تعیین مناسب‌ترین شاخص برای این منطقه است. به‌منظور مدل ‌سازی از فاکتورهای اقلیمی (درجه حرارت و بارندگی) مربوط به منطقه به‌‌دست آمده از نزدیک‌ترین ایستگاه هواشناسی مربوط، استفاده شد. بعد از پیش‌پردازش و پردازش‌ تصاویر سنتینل 2 ارزش‌های رقومی متناظر از باندهای طیفی استخراج و به‌عنوان متغیرهای مستقل در نظر گرفته شد. رابطه درجه حرارت و بارندگی با شاخص‌های پوشش گیاهی با ضریب همبستگی 0.43 و 0.56 و میزان AIC و BIC به‌ترتیب (565 و 3209) و (739  و 3383) به‌دست آمد. همچنین نتایج نشان داد بیش‌ترین اثرگذاری در رابطه با هر دو فاکتور درجه حرارت و بارندگی مربوط به شاخص پوشش گیاهی تفاضلی (DVI) است، که نشان‌دهنده کارائی بالای این شاخص در منطقه است. با توجه به نتایج فوق، می‌توان بیان کرد که شاخص مذکور به‌منظور بررسی تأثیر متغیرهای اقلیمی بر جنگل مورد مطالعه، انطباق و همبستگی مناسبی دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling the Most Appropriate Vegetation Indicators under the Influence of Climatic Factors using Sentinel 2 Images - Case study: Farim Forest

نویسندگان [English]

  • Seyedeh Kosar Hamidi 1
  • Asghar Fallah 2
  • Nastaran Nazaryani 3

1 1. PhD, Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resource University, Sari, Mazandaran, Iran

2 Professor, Department of Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

3 3. Postdoctoral researcher, Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resource University, Sari, Mazandaran, Iran

چکیده [English]

Extended Abstract
Introduction:
Climate change has a consider impact on the environment and has led to different sensitivity of vegetation to weather factors at different spatial-temporal scales. Knowledge of the state of vegetation due to its use in micro and macro planning is currently one of the important pillars in the production of information, considering the high cost and time-consuming use of methods based on ground station observations for Estimating the relative heat of cities using air temperature measurement on the one hand. and providing data with relatively high spatial resolution and capable of measuring ground surface parameters on the other hand, nowadays remote sensing technology as a new solution It has been proposed to improve these methods. Quantitative relationship between vegetation pattern and climatic elements is one of the most important applications of remote sensing in the global and regional scale. Forecasting the amount of vegetation is necessary and essential for planning its exploitation and protection.
Materials & Methods:
In the present research, the aim is to investigate the effect of climatic factors on the vegetation trend of Frame forest using Sentinel 2 images and to determine the most suitable index for this area in Mazandaran province. In order to model the climatic factors (temperature and precipitation) related to the region obtained from the nearest weather station related to the city of Farim together with the climatic data of the city of Farim have been used in such a way that the changes in height from the surface The sea was used. After the pre-processing and processing of the Sentinel 2 images, the corresponding digital values were extracted from the spectral bands and consider as independent variables. ENVI software was used for image processing and STATISTICA and R software were used for modeling. The obtained data are divided randomly into training and testing data, so that 70% of the data was used for training and the rest was used for testing or evaluating the model. Mean square error, correlation, size of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the presented models. The models with the highest correlation value and the lowest error value of the criterion, the mean square error, the Akaike information evaluation criterion and the Bayesian evaluation criterion were selected as the best models for evaluating the studied variables.
Results & Discussion:
The relationship between temperature and precipitation with vegetation indices was obtained with a correlation coefficient of 0.43 and 0.56 and AIC and BIC values (565 and 3209) and (739 and 3383) respectively. Also, the results showed that the most effective in relation to both temperature and precipitation factors is related to the Differential Vegetation Index (DVI), which indicates the high efficiency of this index in the region. The analysis of the effects of temperature on the vegetation index in the region indicated that with the increase in temperature, only the differential vegetation indices, the normalized green differential vegetation index and the green differential vegetation index increase, and there is a negative relationship with the vegetation index. It has been normalized. Precipitation is considered one of the most important factors affecting vegetation, the fluctuation and change of precipitation from year to year always affects vegetation. The results of the effects of precipitation on vegetation indices show that differential vegetation index, differential green vegetation index, normalized differential green vegetation index, non-linear vegetation index and normalized difference vegetation index have a greater impact on precipitation in have an area in forest ecosystems, changes in climatic factors may have different effects on forest trees.
 Conclusion:
One of the solutions in this field is to investigate the relationship between climatic variables and tree characteristics. Obtaining information about the state of forest vegetation is very important, and this study tried to investigate its relationship with climatic variables in addition to investigating vegetation indicators. On the other hand, satellite data is a suitable tool for investigating forest ecosystems, because it has a good ability to investigate vegetation at a relatively low cost and provides the possibility of continuous monitoring of land surface coverage. According to the above results, it can be stated that climatic factors are among the influencing factors on vegetation indicators in the study area in this research. Vegetation, through the balance of environmental factors, causes the protection and stability of the environment. Due to the importance of vegetation, many researchers have taken steps to understand the growth and spatial patterns of vegetation in different regions; In line with the current research, it is suggested to investigate the effect of climatic factors on the vegetation of the studied areas in different geographical directions. In addition, if information is available, other climatic factors such as relative humidity, wind speed, evaporation, transpiration, and images with higher accuracy should be used in order to achieve results that are more accurate.

کلیدواژه‌ها [English]

  • Precipitation
  • Remote sensing
  • Sentinel 2
  • Temperature
  • Vegetation indicators
  1. Abdolalizadeh, Z.; Ghorbani, A.; Mostafazadeh, R.; Moameri, M., 2020. Rangeland canopy cover estimation using Landsat OLI data and vegetation indices in Sabalan rangelands, Iran, Arabian Journal of Geosciences, 13, 245 (2020).
  2. Ahmadi, B.; Ghorbani, A.; Safarrad, T.; Sobhani, B., 2015. Evaluation of surface temperature in relation to land use/cover using remote sensing Data. Journal of RS and GIS for Natural Resources (Journal of Applied RS & GIS Techniques in Natural Resource Science), 6(1), 66-77.
  3. Amiri, M.; and Pourghasemi, H.R., 2022. Mapping the NDVI and monitoring of its changes using Google Earth Engine and Sentinel-2 images. In Computers in Earth and Environmental Sciences, 127-136.
  4. Askarizadeh, D.; Arzani. H.; Jaffari, M.; Bazrafshan, J.; Prentice, I.C., 2018. Surveying of the past, present and future of vegetation changes in the central Alborz ranges in relation to climate change. RS & GIS for Natural Resources, 9(3), 1-18.
  5. Bajwa, S. G.; Mozaffari. M., 2007. Effect of N availability on vegetative index of cotton canopy: A spatial regression approach. Trans. ASABE, 50(5), 1883-1892.
  6. Balew, A.; Korme, T., 2020. Monitoring land surface temperature in Bahir Dar city and its surrounding using Landsat images. The Egyptian Journal of Remote Sensing and Space Science. doi:https://doi.org/10.1016/j.ejrs.2020.02.001.
  7. Barati, S.; Rayegani, B.; Saati, M.; Sharifi, A; Nasri, M., 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyptian Journal of Remote Sensing and Space Science, 14(1), 49-56.
  8. Bastiaanssen, W.G.M.;Ahmad, M.D.; Chemin, Y., 2002. Satellite Surveillance of Evaporative Depletion across the Indus Basin. Water Resource Research, (38)12, 1-9.
  9. Bayat, M, Knoke, T, Heidari, S. Hamidi, S.K. Burkhart, H. and Jaafari, A. 2022. Modeling Tree Growth Responses to Climate Change: A Case Study in Natural Deciduous Mountain Forests. Forests, DOI: 
  10. 3390/f13111816
  11. Bell, G.E.; Howell, B.M.; Johnson, G.V.; Solie, J.B.; Raun, W.R.; Stone, M.L., 2004. A comparison of measurements obtained using optical sensing with turf growth, chlorophyll content, and tissue nitrogen. Horticultural Science, 39(5), 1130-1132.
  12. Chen, J.M., 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3), 229-242.
  13. Chu, H.; Venevsky, S.; Wu, C.; Wang, M., 2019. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of the Total Environment, 650, 2051-2062.
  14. Farajzadeh, M.; Fathnia, A.; Alijani, B.; Ziaian, P., 2012. Evaluation of the effect of climatic factors on vegetation growth in dense grasslands of Iran using AVHRR images. Natural Geography Researches (Geographical Researches), 43 (75), 1-13.
  15. Farajzadeh Asl, M.; Gwaidel Rahimi, Y.; Osivand, F., 2019. Modeling the changes in vegetation greenness index with atmospheric precipitation in Zagros region. Natural Geography Quarterly, 11 (14), 1-17.
  16. Feng, W.; Wu, Y.; He, L.; Ren, X.;   Wang, Y.; Hou, G.; Wang, Y.; Liu, W.; Guo, T., 2019. An optimized non‑linear vegetation index for estimating leaf area index in winter wheat. Precision Agriculture, 20, 1157-1176.
  17. Goodarzi, M.; Pourhashemi, M.; Azizi, Z., 2019. Investigation on Zagros forests cover changes under the recent droughts using satellite imagery. Journal of Forest Science, 65 (1), 9-17.
  18. Goldsmith, F.B., 1991. Monitoring for Conservation and Ecology. Chapman & Hall, United Kingdom, 275p.
  19. Govil, H.; Guha, S.; Diwan, P.; Gill, N.; Dey, A., 2020. Analyzing linear relationships of LST with NDVI and MNDISI using various resolution levels of Landsat 8 OLI and TIRS data. In: Data Management, Analytics and Innovation. Springer, 171-184. https://doi.org/110.1007/1978-1981-1032-9949-2020, 1008_1013.
  20. Guan, Q.; Yang, L.; Guan, W.; Wang, F.; Liu, Z.; Xu, C.H., 2018. Assessing vegetation response to climatic variations and human activities: spa-tiotemporal NDVI variations in the Hexi Corridor and surrounding areas from 2000 to 2010. Theor Appl Climatol, 135, 1179–1193. https: //doi.org/ 10.100 7/s00704-018-2437-1.
  21. Guha, S.; Govil, H., 2020. An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment. Development and Sustainability, doi: 10.1007/s10668-020-00657-6.
  22. Hamidi, S.K.; Fallah, A.; Bayat, M.; Hosseini yekani. S.A., 2019. Investigating the diameter and height models of beech trees in uneven age forest of northern Iran (Case study: Forest Farim), Iranian Forest Ecology, 3 (11): 373-386.
  23. Hamidi, S.K.; Fallah, A.; Bayat, M.; Hosseini yekani, S.A., 2017. Determining the Forest Volume Growth using Permanent Sample Plots (Case Study: Farim Forest, Jojadeh District). Ecology of Iranian Forest, 4 (8): 1-8.
  24. Hamidi, S.K.; Weiskittel, A.; Bayat M.; Fallah. A., 2021. Development of individual tree growth and yield model across multiple contrasting species using nonparametric and parametric methods in the Hyrcanian forests of northern Iran. European Journal of Forest Research. https://doi.org/10.1007/s10342-020-01340-1.
  25. Hamidi, S.K.; Eric, Z.; Bayat, M.; Fallah, A., 2021. Analysis of Plot-level Volume Models Developed from Data Mining Applied to an Uneven-aged, Mixed Forest. Annals of forest science.
  26. Hamidi, S.K.; Fallah, A.; Bayat, M.; de Luis, M., 2021.The effects of climate variables (temperature and precipitation) on growth characteristics of trees (case study: Farim forest). Forest Research and Development, 6(4): 593-607. doi: 10.30466/jfrd.2020.120877.
  27. Hamidi, S.K.; de Luis, M.; Bourque, Ch. P.‑; Bayat, M.; Serrano‑Notivoli. R., 2022. Projected biodiversity in the Hyrcanian Mountain Forest of Iran: an investigation based on two climate scenarios. Biodiversity and Conservation, https://doi.org/10.1007/s10531-022-02470-1.
  28. Heink, U.; Kowarik, I., What are indicators? On the definition of indicators in ecology and environmental planning. Ecol. Ind 2010, 10, 584-593.
  29. Heydarian Agakhani, M.; Tamertash, R.; Jafarian, Z.; Turkesh Esfahani, M.; Tatian, M. R., 2016. Forecasting the effects of climate change on the potential distribution of Amygdalus scoparia species using consensus modeling in Central Zagros. Remote sensing and geographic information system in natural resources, 8(3), 1-14.
  30. Hu, S.; Wang, F.Y.; Zhan, C.S.; Zhao, R.X.; Mo, X.G.; Liu, L.M., 2019. Detecting and attributing vegetation changes in Taihang Mountain, China. Journal Mt science, 16 (2), 337-350. https://doi.org/10.1007/s11629-018-4995-1
  31. IPCC, Climate Change 2007. The Physical Science Basis. Contribution of I to the Fourth Assessment Report  of  the  Intergovernmental  Panel on  Climate  Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 p.
  32. Janizadeh, S.; Avand, M.; Jaafari, A.; Phong, TV.; Bayat, M.; Ahmadisharaf, ; Prakash, I.; Pham, BT.; Lee, S., 2019. Prediction success of machine learning methods for fash food susceptibility mapping in the Tafresh Watershed. Iran Sustain, 11 (19), 5426.
  33. Kalbi, S.; Fallah, A.; Shataee, Sh.; Bettinger, P.; Yousefpour, R., 2019. Growth and yield models for uneven-aged forest stands man-aged under a selection system in northern Iran. Eurasian Journal Forest Science, 7 (3), 321-333.
  34. Lian, X.; Jiao, L.; Liu, Z.; Jia, Q.; Zhong, J.; Fang, M.; Wang, W., 2022. Multi-spatiotemporal heterogeneous legacy effects of climate on terrestrial vegetation dynamics in China. GIS cience & Remote Sensing, 1-20.
  35. Liang, E.Y.; Shao, X.M.; He, J.C., 2005. Relationships between tree growth and NDVI of grassland in the semiarid grassland of north China. International Journal of Remote Sensing 26(13), 2901–2908.
  36. Lillesand, T. M.; Kiefer, R. W., 1987. Remote Sensing and Image Processing. John Wiley & Sons, Inc, New York, NY, USA.
  37. Mohammadi, A.; Qazavi, R.; Mirzai, R.; Naseri, H., 2019. Examining the pattern of vegetation changes using MODIS sensor images and its relationship with precipitation. Pasture and Watershed, Journal of Natural Resources of Iran, 72 (3), 852-843.
  38. Mahdovian, S.; Zinali, B.; Salahi, B., 2022. Monitoring of land use changes and its relationship with surface temperature and vegetation index in southern areas of Ardabil province (Givi Chai catchment area). Remote sensing and geographic information system in natural resources 2021, published online from November.
  39. Motohka, T.; Nasahara, K.N.; Oguma, H.; Tsuchida, S., 2010. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2 (10), 2369-2387.
  40. Naji, T.A., May. 2018. Study of vegetation cover distribution using DVI, PVI, WDVI indices with 2D-space plot. In Journal of Physics. Conference Series, 1003(1), 12-83). IOP Publishing.
  41. Nickolson, S.E.; Davenport, M.L.; Malo, A.L., 1990. A Comparision of  the  Vegetation  Response  to  Precipitation  in  the  Sahel  and  East  Africa  Using  Normalized  Defference  Vegetation  Index from NOAA AVHRR. Climatic Change, 17, 209-241.
  42. Pôças, I.; Cunha, M.; Pereira, L.S; Allen, R.G., 2013. Using Remote Sensing Energy Balance and Evapotranspiration to Characterize Montane Landscape Vegetation with Focus on Grass and Pasture Lands. International Journal of Applied Earth Observation and Geoinformation, 21, 159–172.
  43. Prăvălie, R.; Sîrodoev, I.; Nita, I.A.; Patriche, C.; Dumitraşcu, M.; Roşca, B.; Tişcovschi, A.; Bandoc, G.; Săvulescu, I.; Mănoiu, V.; Birsan, M.V., 2022. NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018. Ecological Indicators, 136, 108- 629.
  44. Roujean, J.L.; Breon, F.M., 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375-384.
  45. Rouse J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W., 1973. Monitoring vegetation system in the Great Plains with ERTS: Proceedings of the Third Earth Resources Technology Satellite-1 Symposium. Washington DC, 309-317.
  46. Sagheb-Talebi, K.; Sajedi, T.; Pourhashemi, M., 2014. Forests of Iran: A Treasure from the Past, a Hope for the Future. Netherlands: Springer Publishing.
  47. Sanaienejad, S. H.; Shah Tahmasbi, A. R.; Sadr Abadi Haghighi, R.; Kelarestani, K., 2008. A study of spectral reflection on wheat fields in Mashhad using MODIS data. Journal of Science and Technology of Agriculture and Natural Resources, 12(45), 11-19.
  48. Scanlon, T.; Albertson, M.; John. D.; Caylor, Kelly. K.; Williams, Chris. A., 2002. Determining land surface fractional cover from NDVI and precipitation time series for a Savanna Ecosystem. Remote Sensing of Environment, 82, 376-388.
  49. Souza, R.; Feng, X.; Antonino, A.; Montenegro, S.; Souza, E.; Porporato, A., 2016. Vegetation response to rainfall seasonality and interannual variability in tropical dry forests. Hydrological Processes, 30 (20), 3583-3595.
  50. Timmer, B.; Reshitnyk, L.Y.; Hessing-Lewis, M.; Juanes, F; Costa, M., 2022. Comparing the Use of Red-Edge and Near-Infrared Wavelength Ranges for Detecting Submerged Kelp Canopy. Remote Sensing, 14(9), p.2241.
  51. Vali, A.; Ranjbar, A.; Mokarram, M.; Taripanah, F., 2019. An investigation of the relationship between land surface temperatures, geographical and environmental characteristics, and biophysical indices from Landsat images. RS & GIS for Natural Resources, 10 (3), 35-58.
  52. Valizadeh Kamran, Kh.; Zand Karimi, A.; Abedi Qashlaghi, H.; Zand Karimi, S., 2014. Investigating climate changes and its effect on vegetation index in Arsbaran forest protected area using remote sensing and GIS (Khoda Afarin case study). National Conference on Climate Change and Engineering Sustainable Development of Agriculture and Natural Resources, 5th of July, Tolo Farzin Science and Industry Company, Hamedan.
  53. Wu, W., 2014. The generalized difference vegetation index (GDVI) for dryland characterization. Remote Sensing, 6(2), 1211-1233.
  54. Yamani, M.; Muzhesi, M.A., 2009. Investigating surface changes and vegetation cover of Siah Kouh desert using remote sensing data, Journal of Geography Research, 11, 4-42 pp.
  55. Yin, G.; Hu, Z.; Chen, X.; Tiyip, T., 2016. Vegetation dynamics and its response to climate change in Central Asia. Journal of Arid Land, 8 (3), 375-388.