ارزیابی همبستگی شاخص‌های گیاهی با متغیرهای هواشناسی و بیولوژیکی با استفاده از سامانه گوگل ارث انجین

نوع مقاله : مقاله کامل علمی پژوهشی

نویسنده

نویسنده مسئول، استادیار گروه علوم و مهندسی آب، مجتمع آموزش عالی میناب، دانشگاه هرمزگان، ایران.

چکیده

پوشش گیاهی طبیعی جزء حیاتی مدل‌های تغییر اکوسیستم و اقدامات حفاظتی است. پژوهش حاضر به بررسی پویایی مکانی-زمانی شاخص گیاهی تفاضلی نرمال شده (NDVI) و شاخص پوشش گیاهی بهبود یافته (EVI) با شاخص‌های جوی مانند بارش(P) و خشکسالی(MSPI)؛ شاخص بیولوژیکی مانند تبخیر و تعرق(ET) و رطوبت خاک (SM)؛ و شاخص خاک، پارامتر دمای سطح زمین(LST) به کمک تصاویر ماهواره‌ای در سامانه گوگل ارث انجین طی بازه زمانی 1378- 1400 در استان هرمزگان می‌پردازد.
مواد و روش‌کار: به منظور ارزیابی شاخص‌های گیاهی با متغیرهای اقلیمی و بیولوژیکی از تصاویر سامانه گوگل ارث انجین استفاده شد. شاخص‌های NDVI و EVI، تبخیر و تعرق و دمای سطح زمین از تصاویر ماهواره‌ای Terra 16 روزه و رطوبت سطح خاک (0-10سانتی متر) از نقشه‌های خاک (Open Land Map)، طی بازه زمانی 12/10/1378 الی 12/10/1400 با برنامه نویسی توابع برای منطقه به‌صورت ماهانه استخراج شد. بارش و شاخص خشکسالی MSPI به کمک آمار 22 ساله ایستگاه‌ها تهیه شد. با روش زمین آمار کریجینگ نقشه‌هم‌بارش در نرم‌افزارArcGIS تهیه شد. با روش PCA خشکسالی شاخص در نقاط مختلف با نرم‌افزار SPSS محاسبه گردید. به منظور درک بهتر تغییرات زمانی شاخص‌های گیاهی آنومالی آنها طی دو دهه استخراج شد. همبستگی و روند تغییرات پارامترها با روش اسپیرمن و من-کندال محاسبه شد.
یافته‌ها: ضــریب همبستگی اسپیرمن و روندیابی روش من-کندال بین دو شاخصNDVI وEVI در سطح ۹9 درصد به‌ترتیب 84/۰ و 67/۰ معناداراست. پراکنش زمانی شاخص‌های گیاهی نشان داد تغییرات سالانه و ماهانه آنها با میزان کشت پاییزه مطابقت دارد. طبق اقلیم گرم و خشک استان بیشتر بارش‌ها مربوط به دی تا اسفندماه است. ازاین رو شاخص‌ها طی ماه‌های دی، اسفند و فروردین بیشترین مقادیر را داشته‌اند. این بازه منطبق با فصل کشت و برداشت محصولات کشاورزی است و گیاهان به عالی-ترین مرحله رشد خود رسیده است. نتایج نشان داد تغییرات مکانی شاخص‌های گیاهی منطبق برمناطق پربارش و حاشیه روخانه‌ها و اراضی کشاورزی و باغی است. رابطه معکوس و معنادار بین شاخص‌های پوشش گیاهی و دمای سطح زمین بیانگر افزایش پوشش گیاهی همراه با کاهش دما و برعکس است. همپوشانی مناسب پهنه‌های بارش و شاخص خشکسالی MSPI با توزیع مکانی شاخص-های گیاهی و دمای سطح زمین و رطوبت خاک بیانگر نقش تعیین کننده بارش در مناطق خشک و نیمه‌خشک است. نتایج آنومالی شاخص‌ها نشان داد طی بازه 1378-1389به‌دلیل وجود خشکسالی‌های مکرر و شدید همبستگی مثبت ضعیفی وجود دارد. در دهه‌ی دوم 1389-1400 به دلیل کاهش شدت خشکسالی، شاخص‌ها همبستگی قوی معناداری از خود نشان دادند. طبق نمودار شاخص خشکسالی MSPI مناطق غربی، مرکزی و بخش‌هایی در شرق با بیشترین مقادیر خشکسالی و کمبود بارش، پایین‌ترین مقدار شاخص-های گیاهی را به خود اختصاص داده‌اند.
نتیجه‌گیری: گستردگی وسعت منطقه سبب تمرکز بیشتر شاخص‌های پوشش گیاهی در برخی مناطق شده است. بیشترین مقادیر NDVI و EVI در گستره‌ی حاشیه رودخانه‌ها و منابع آبی است. این مناطق منطبق با مراکز کشاورزی در دشت‌های رودان، میناب، شمیل و تخت و بخش‌هایی از حاجی‌آباد که از نظر جغرافیایی مناطق شمال‌شرقی و بخش‌هایی از شمال است. افزایش هر یک از شاخص‌های گیاهی بیانگر افزایش وسعت و فراوانی آنها است. ولی کاهش هریک به دلیل چند وجهی بودن ارتباط گیاه با عوامل جوی، خاکی و کاربری معرف ناهمگن شدن منطقه است. روند پویایی پوشش گیاهی و ارتباط آنها با سایر عوامل باید در دراز مدت مورد مطالعه قرار گیرد تا یک الگو ایجاد شود. این مطالعه تاحد امکان و با توجه دردسترس بودن یک مجموعه داده انجام شده است. مطالعات بلندمدت را می‌توان با استخراج متغیرهای تجربی در سطح منطقه‌ای و به منظور تاثیر رشد جمعیت برمنطقه نیز انجام داد. یافته‌های این مطالعه می‌تواند به مطالعات آتی مرتبط با پوشش گیاهی و ارتباط آن با سایر عوامل، حفاظت از طبیعت و تخصیص منابع کمک کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of the correlations of plant indices with atmospheric and biological variables using the Google Earth Engine

نویسنده [English]

  • Maryam Heydarzadeh
Corresponding Author Assistant Prof., Dept. of Water Sciences and Engineering, Faculty of Agriculture, Minab Higher Education Center, University of Hormozgan, Iran.
چکیده [English]

Background and purpose: Natural vegetation is a vital component of ecosystem change models and conservation measures. The current study investigates the spatio-temporal dynamics of Normalized Differential Vegetation Index (NDVI) and Improved Vegetation Index (EVI) with atmospheric indices such as precipitation (P) and Multivariate Standardized Precipitation Index (MSPI); biological index such as evapotranspiration (ET) and soil moisture (SM); and soil index, land surface temperature parameter (LST) with the help of satellite images in Google Earth Engine system during 2000-2021 in Hormozgan province.
Materials and methods: Google Earth Engine system images were used to evaluate plant indices with climatic and biological variables. NDVI and EVI indices, evaporation and transpiration and earth surface temperature from 16-day Terra satellite images and soil surface moisture (0-10 cm) from Open Land Map, during the period from 2000-2021 were extracted monthly by programming functions for the region. Rainfall and MSPI drought index were prepared with the help of 22-year statistics of the stations. A precipitation map was prepared using the Kriging geostatistics method in ArcGIS software. Using the PCA method, the drought index was calculated in different places with SPSS software. In order to better understand the changes in time, their anomaly plant indices were extracted over two decades. Correlation and change trend of parameters were calculated by Spearman and Mann-Kendall method.

Findings: Spearman's correlation coefficient and Man-Kendall's trending method between NDVI and EVI indices are significant at the 99% level of 0.84 and 0.67, respectively. The time distribution of plant indices showed that their annual and monthly changes are consistent with the amount of autumn cultivation. According to the hot and dry climate of the province, most of the rains are from January to March. The indices had the highest values during the months of January, March and April. This period corresponds to the season of planting and harvesting agricultural products, and the plants have reached their highest growth stage. Results showed the spatial changes of the plant indices correspond to the high rainfall areas and the edges of rivers and agricultural and garden lands. The appropriate overlap of precipitation zones and MSPI drought index with the spatial distribution of plant indices, ground surface temperature, and soil moisture indicates the determining role of precipitation in arid and semi-arid regions. The anomaly results showed that there is a weak positive correlation during the period of 2011-2000 due to frequent and severe droughts. In the second decade of 2011-2021, due to the decrease in the severity of the drought, the indicators showed a significantly strong correlation. According to the MSPI drought index, the western, central and eastern regions with the highest amounts of drought and lack of rainfall have the lowest amount of plant indices.
Conclusion: The expansion of the area has caused more concentration of vegetation indicators in some areas. The highest values of NDVI and EVI are in the area along the banks of rivers and water sources. These areas correspond to agricultural centers in the plains of Rodan, Minab, Shamil and Takht and parts of Haji Abad, which are geographically in the north-eastern regions and parts of the north. Vegetation dynamic trends and their relationship with other factors should be studied in the long term to establish a pattern. This study has been done as much as possible and according to the availability of a data set. Long-term studies can be done by extracting experimental variables at the regional level and in order to influence the population growth on the region. The findings of this study can help future studies related to vegetation and its relationship with other factors, nature protection and resource allocation.

کلیدواژه‌ها [English]

  • Land surface temperature (LST)
  • NDVI index
  • EVI index
  • soil moisture (SM)
  • Google Earth Engine (GEE)

مراجع

1.Zhou, X. Y., Shi, H. D., & Wang, X. R. (2014). Impact of climate change and human activities on vegetation coverage in the Mongolian Plateau, Arid Zone Res. 31, 604-610.
2.Gordo, O., & Sanz, J. J. (2009). Long-Term Temporal Changes of Plant Phenology in the Western Mediterranean. Glob. Change Biol. 15, 1930-1948. https://doi.org/ 10.1111/ j.1365-2486. 2009. 01851.x.
3.Jin, X., Zhang, Y., Schaepman, M., Clevers, J., & Su, Z. (2008). Impact of elevation and aspect on the spatial distribution of vegetation in the qilian mountain area with remote sensing data. In: XXIth ISPRS Congress, Beijing, 3 July 2008 - 11 July 2008. International Society for Photogrammetry and Remote Sensing, 1385-1390. https://doi.org/10. 5167/uzh-77426.
4.Suding, K. N., Farrer, E. C., King, A. J., Kueppers, L., & Spasojevic, M. J. (2015). Vegetation change at high elevation: Scale dependence and interactive effects on niwot ridge, Plant Ecol. Divers.8, 713-725.
5.Taloor, A. K., Singh Manhas, S. D., & Kothyari, C. G. (2021). Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data. Appl. Comput. Geosci.
9, 100051. doi:10.1016/j.acags.2020. 100051.
6.Sharma, S., Zhang, M., Anshika, Gao,J. G., Zhang, H., & Kota, S. H. (2020). Effect of restricted emissions during COVID-19 on air quality in India. Science of the Total Environment.728, 867-878. doi:10.1016/j.scitotenv. 2020.138878.
7.Alam, A., Bhat, S., Kotlia, B. S. Ahmad, B., Ahmad, S., Taloor, A. K., & Farooq Ahmad, H. (2018). Hybrid tectonic character of the Kashmir basin: response to comment on “Coexistent pre-existing extensional and subsequent compressional tectonic deformation in the Kashmir basin Hybrid tectonic character of the Kashmir basin: response to comment on”Coex, Quaternary International. 468, 284-289. doi:10.1016 /j.quaint.2018.02.010.
8.Kannaujiya, S., Gautam, P. K., Chauhan, P., Roy, P. N. S., Pal, S. K., & Taloor,A. K. (2021). Contribution of seasonal hydrological loading in the variation of seismicity and geodetic deformation in Garhwal region of Northwest Himalaya. Quaternary International, 575, 62-71.
9.Kothyari, G. C., Joshi, N., Taloor, A. K., Kandregula, R. S., Kotlia, B. S., Pant,C. C., & Singh, R. K. (2019). Landscape evolution and deduction of surface deformation in the Soan Dun, NW Himalaya, India. Quaternary International, 507, 302-323.
10.Sarkar,T., Kannaujiya, S., Taloor, A. K., Champati Ray, P. K., & Chauhan, P. 2020a. Integrated study of GRACE data derived interannual groundwater storage variability over water stressed Indian regions. Groundw. Sustain. Dev. 10, 364-376. doi:10.1016/j.gsd.2020. 100376.
11.Couteron, P., Hunke, P., Bellot, J., Estrany, J., Martínez-Carreras, N., Mueller, E. N., Papanastasis, V. P., Parmenter, R. R., & Wainwright, J. (2014). characterizing patterns, In Patterns of Land Degradation in Drylands, Springer, New York, NY, USA, 211-245.
12.Wu, C., Peng, D., Soudani, K., Siebicke, L., Gough, C. M., Arain, M. A., Bohrer, G., Lafleur, P. M., Peichl, M., Gonsamo, A., Xu, S., Fang, B., & Ge, Q. (2017). Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites,Agr. Forest Meteorol. 233, 171-182.
13.Alademomi, A. S., Okolie, C. J., Daramola, O. E., Agboola, R. O., & Salami, T. J. (2020). Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment. Quaest. Geogr.39 (3), 87-109. doi:10.2478/quageo-2020-0025.
14.Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors. 7 (11), 2636-2651. doi:10.3390/s7112636.
15.Hinojo-Hinojo, C., & Goulden, M. L. (2020). Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production. Remote Sens. 12 (9), 1391-1405.
16.Restrepo-Coupe, N., Huete, A., Davies, K., Cleverly, J., Beringer, J., Eamus, D., van Gorsel, E., Hutley, L. B., & Meyer, W. S. (2016). MODIS Vegetation Products as Proxies of Photosynthetic Potential along a Gradient of Meteorologically and Biologically Driven Ecosystem Productivity. Biogeosciences, 13, 5587-5608.
17.Cavalaris, C., Megoudi, S., Maxouri, M., Anatolitis, K., Sifakis, M., Levizou, E., & Kyparissis, A. (2021). Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery. Agronomy, 11 (8), 1473-1486.
18.Lanfredi, M., Coppola, R., Simoniello, T., Coluzzi, R., Imbrenda, V., & Macchiato, M. (2015). Early identification of land degradation hotspots in complex bio-geographic regions. Remote Sensing,7 (6), 8154-8179.
19.Shewangzaw, M. (2014). Vegetation dynamics analysis using normalized differences vegetation index as indicator of restoration or degradation, south wollo zone, northern Ethiopia. ADDIS ABABA University, p: 71.
20.Kyparissis, A., & Levizou, E. (2022) Climatic Drivers of the Complex Phenology of the Mediterranean Semi-Deciduous Shrub Phlomis fruticosa Based on Satellite-Derived EVI.Plants, 11 (5), 571-584. https:// doi.org/ 10.3390/plants11050584.
21.NASA. MODIS-specifications. Weitere Details. https://modis.gsfc.nasa.gov/ data/dataprod /mod13.12p.
22.Lu, D., Mausel, P., Brondızio, E., & Moran, E. (2004). Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest ecology and management, 198 (1), 149-167.
23.Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International journal of remote sensing, 10 (6), 989-1003.
24.Wessels, K. J., Van Den Bergh, F., & Scholes, R. J. (2012). Limits to detectability of land degradation by trend analysis of vegetation index data. Remote sensing of Environment, 125, 10-22.
25.Gholampoor, M., Khosroshahi, M., & Barkhordari, J. (2009). Determination of desert domains of Hormozgan province using geomor phological criteria.15 (4), 485-492. [In Persian]
26.Najafi Shabankareh, K., Khosroshahi, M., & Gholampoor, M. (2008). Determination of the geographical domain of Hormozgan province desert area in vegetation view. Iranian journal of Range and Desert Research. 15 (1), 97-113.
27.Zhe, M., & Zhang, X. (2021). Time-lag effects of NDVI responses to climate change in the Yamzhog Yumco Basin, South Tibet. Ecol. Indic. 124, 420-431. doi:10.1016/ j. ecolind.2021.107431.
28.Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 104 (2), 133-146. doi:10.1016/ j.rse.2005.11.016.
29.Gan, R., Zhang, Y. Q., Shi, H., Yang, Y. T., Eamus, D., Cheng, L., Chiew, F. H. S., & Yu, Q. (2018). Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems. Ecohydrology, doi:10.1002/eco.1974.
30.Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q., & Yang, Y. (2019). Coupled estimation of 500m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sens. Environ. 222, 165-182.
31.McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C., Peters-Lidard, C. D., & Verdin, J. P. (2017). A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data, 4 (12), 1-19. DOI: 10.1038/sdata.2017.12.
32.Ranjbar, A., Valia, A., Mokarramb, M., & Taripanahc, F. (2020). Analyzing of the spatio-temporal changes of vegetation and its response to environmental factors in north of Fars province, Iran. Iranian Journal of Remote Sensing & GIS. 11 (4), 61-82. doi: 10.52547/gisj.11.4.61. [In Persian]
33.Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetaryscale geospatial analysis for everyone. Remote Sens Environ.202, 18-27.
34.Mirabbasi, R., Anagnostou, E. N., Fakheri-Fard, A., Dinpashoh, Y., & Eslamian, S. (2013). Analysis of meteorological drought in northwest Iran using the joint deficit index. Journal of Hydrology, 492, 35-48. [in Persian]
35.Bazrafshan, J., Hejabi, S., & Rahimi, J. (2014). Drought monitoring using the multivariate standardized precipitation index (MSPI). Water Resour. Manage. 28 (4), 1045-1060.
36.Raziei, T., Bordi, I., & Pereira, L. S. (2011). An application of GPCC and NCEP/NCAR datasets for drought variability analysis in Iran. Water Resour. Manage. 25 (4), 1075-1086.
37.Wilks, D. S. (2011). Statistical methods in the atmospheric sciences, 3rd Ed., Academic, London.  
38.Eskandari Damaneh, H., Sayadi, Z., & Khoorani, A. (2021). Evaluation of spatiotemporal changes and correclations of aerosol optical depth, NDVI and climatic data over Iran. 28 (4), 772-786. doi:10.22092/ ijrdr. 2021.125252.
39.Mirahsani, M. S., Salman Mahiny, A., Soffianian, A., Mohamadi, J., Modarres, R., Modares, R., & Pourmanafi, S. (2019). Evaluation of Trend in Vegetation Variations using Time Series Images and Mann-Kendall test over Gavkhuni Basin. Journal of Environmental Studies, 45 (1), 99-114. doi: 10.22059/jes.2019.260567.1007699.
40.Nohegar, A., Heydarzadeh, M., Eydoon, M., & Pannahi, M. (2016). Assessment of drought and its impact on surface and groundwater resources (Case study: River basin Minab). Researches in Earth Sciences. 7 (3), 28-43. [In Persian]
41.Muradyan, V., Tepanosyan, G., Asmaryan, S., Saghatelyan, A., & Dell’Acqua, F. (2019). Relationships between NDVI and climatic factors in mountain ecosystems: a case study of Armenia. Remote Sens. Appl. Soc. Environ. 14, 158-169. doi: 10.1016/j. rsase.2019.03.004.
42.Stoddard, L. A., Smith, S. D., & Box, T. W. (1975). Range-condition Analysis. In Range Management; McGraw-Hill Book Co.: New York, NY, USA.
43.Eghdami, H., Azhdari, G., Lebailly, P., & Azadi, H. (2019). Impact of Land Use Changes on Soil and Vegetation Characteristics in Fereydan, Iran. Agriculture. 9, 58; doi: 10.3390/ agriculture9030058.
44.Hormozgan province land development strategic document. (2018). Management and Planning Organization of Hormozgan Province, p 32. [In Persian]
45.Robock, A. (2003). Hydrology Soil Moisture. Encyclopedia of Atmospheric Sciences. Elsevier. p. 987-993. doi.org/ 10.1016/B0-12-227090-8/ 00169-X.
46.Abadeh, M., & Khosroshahi, M. (2021). Assessment and drought monitoring using Standardized Precipitation (SPI) and Standardized Precipitation Evapotranspiration (SPEI) Indices in Hormozgan province. Iranian journal of Range and Desert Research. 28 (4), 718-732. [In Persian]
47.Xu, X.K., Chen, H., & Zhang, F. (2007). Temporal and spatial change of vegetation cover in the Northwest of China and factors analysis influencing on vegetations variation, Environmental Science, 28 (1), 41-47.
48.Yang, G. H., Bao, A. M., Chen, X., Liu, H. L., Huang, Y., & Dai, S. Y. (2009). Study of the vegetation cover change and its driving factors over Xinjiang during 1998-2007, Journal of Glaciology and Geocryology, 31 (3), 436-445.
49.Naderi, M., Sheikh, V., Komaki, C. B., Bahrehmand, A., Ghanghermeh, A.A., & Siroosi, H. (2022). Detection and Prediction of Land Use Changes Using Modeling Approach within a GIS Environment (Case Study: Hablehroud Watershed). Journal of Water and Soil Conservation, 29 (2), 113-134. (In Persian)
50.Entezari, A., Zandi, R., & Khosravian, M. (2019). 'Evaluation of spatial variations of vegetation and surface temperature using Landsat and midsize images, case study: Fars Province, 1967-2017', Watershed Engineering and Management, 11 (4), 929-940. doi: 10.22092/ijwmse.2018.122914.1528.
51.Ferreira, S. L., & Duarte, D. H. S. (2019). Exploring the relationship between urban form land surface temperature and vegetation indices in a subtropical megacity. Urban Climate. 27, 105-123.
52.Fathizad, H., Tazeh, M., Kalantari, S., & Shojaei, S. (2017). The investigation of spatiotemporal variations of Land Surface temperature based on land use changes using NDVI in southwest if Iran. Journal of African Earth Sciences. 134, 249-256.
53.Zhijia, G., Xingwu, D., Yandong, S., Ya, L., & Xi, P. (2018). Spatiotemporal variation in vegetation coverage and its response to climatic factors in the Red River Basin, China, Ecological Indicators, 93, 54-64.
54.Faramarzi, M., Heidarizadi, Z., Mohamadi, A., & Heydari, M. (2018). Detection of vegetation cover changes using normalized difference vegetation index in semi-arid rangeland in western Iran. Journal of Agricultural Science and Technology. 20, 51-60. [In Persian]
55.Agam, N., Kustas, W. P., Anderson, M.C., Li, F., & Neale, C. M. U. (2007). A vegetation index-based technique for spatial sharpening of thermal imagery. Remote Sens. Environ. 107 (4), 545-558. doi: 10.1016/j.rse.2006.10.006.
56.Mukherjee, S., Joshi, P. K., & Garg, R. D. (2015). Evaluation of LST downscaling algorithms on seasonal thermal data in humid subtropical regions of India. Int. J. Remote Sens. 36 (10), 2503-2523. doi:10.1080/ 01431161.2015.1041175.
مجله پژوهش‌های حفاظت آب و خاک
دوره 30، شماره 2
تیر 1402
صفحه 1-26

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