Determination of the threshold and sensitivity assessment of the urban ecosystems Vegetation Index in the face of climate shocks the case study of Gorgan city

Document Type : Research article

Authors

Department of Environmental Education, College of Environment, University of Tehran, Tehran, Iran

10.22059/jurbangeo.2023.353153.1774

Abstract

ABSTRACT
Today, climate change and its obvious negative effects on ecosystems have caused concern. This research seeks to test whether vegetation changes are sensitive to climate shocks and also how the ecosystem recovery process is through this index. In this regard, by using the GEE platform, Java coding, GIS and statistical analysis, vegetation and Palmer indices were calculated and based on time series climate data, vegetation and climate changes were presented. The results of Palmer's drought index show that during the statistical period (1985-2020) the study area is facing drought or is moving towards drought. Also, the results indicate the longest period of drought in the region from 2013 to 2020. Totaly from 420 evaluated months, the NDVI index is below the change threshold in 70 months. Among these, 31 months of the study period is below the acceptable threshold in green and non-reservoir seasons, which is ecologically worrying. The distribution of the vegetation index based on hexagons in 1985 and 2005 had a normal and almost normal distribution; But in 2020, the graph deviated from the normal state and skewed towards the vegetation cover index under stress or even thin covers. According to the analysis of the indicators, it is predicted that the Gorgan region is on the border of such ecological developments and the historical ecosystem of the region is moving towards new ecosystems or being in a new equilibrium state with climatic conditions and human disturbances
Extended Abstract
Introduction
Today, climate change and its obvious negative effects on terrestrial ecosystems have caused great concern to humans. These changes are effective on vegetation performance, plant distribution patterns, and have economic and environmental consequences. Therefore, it is important to know the behavioral pattern of vegetation changes against climate changes. Reviewing the studies of scientists in the world shows many researchers have used the NDVI index to study temporal and spatial changes in vegetation and its relationship with the climatic index of precipitation in different parts of the world. Studies have shown that NDVI follows precipitation with different time scales. Surveys showed that there are very few studies on determining the threshold of changes in the vegetation cover index in the face of climate shocks. Determining these thresholds can provide a suitable solution for evaluating the state of the ecosystem, the consequences of climate shocks and the reversibility or disturbance in the ecosystem. This study was conducted with the aim of improving our understanding of the dynamics of vegetation in the forest city of Gorgan during 1985-2020 against climatic stresses.
 
Methodology
The current research is a comparative and monitoring research and seeks to test whether changes in vegetation cover are sensitive to climate shocks and also how the ecosystem recovery process is through this index. To achieve the gole, first, NDVI index was selected among the optimal vegetation indices and its calculation process was done as a time series in the GEE system. In parallel with those climate shocks, the main elements including temperature, precipitation and storm were calculated during the historical process of 35 years and the average and standard deviation statistical indicators were calculated for them and the trend of changes in the thresholds was determined. The results of climate plots and climate changes show that in the years before 1985, 2005 and 2020, drastic changes have occurred in climatic elements and climatic factors. Therefore, these years can be considered as the periods when the climate shock happened.. Next, the region was divided into 436 hexagons and the NDVI index for each of the hexagons was calculated and modeled for the years 1985, 2005 and 2020 as selected years affected by climate shocks. In conclusion, to analyze the trend of changes in the time series of the vegetation index and compare the behavior of its changes with climatic indices, the Palmer index was calculated.
 
Results and discussion
The results of climate change monitoring based on the Palmer index showed that during the statistical period the study area is facing drought in most years. The most severe climatic fluctuations and drought in the region were recorded in 2018 and in the months of October to December. The longest period of drought has also prevailed in the region from 2013 to 2020. During this period, rainfall, temperature and storm fluctuations have the most changes. The results of drought monitoring show that in 270 months, the region is facing climatic drought stress, 57 months of the study period, the region is facing severe and very severe drought stress. The results of the time series of the NDVI vegetation index showed that, out of the 420 evaluated months, 70 months of the year the NDVI index is below the change threshold, 31 of which are in the green and non-accumulating seasons, the seasons when the vegetation is expected to be at its maximum. Placing below the acceptable range means crossing the ecological thresholds and challenges the recovery and restoration of the ecosystem, also the ecological performance will be affected at this point. Based on the assessment of the Palmer index, from 2014 to 2019, the situation of the Palmer index is in the extreme drought range. Also, since 2015, i.e. with a one-year time delay, NDVI index has experienced the lower limit of the equilibrium threshold of vegetation cover. These conditions are also valid for the years 2008, 2009, 2002 and 1997. In general, it can be said that the vegetation cover index is dependent on climatic changes and fluctuations and shows high sensitivity to changes. The important point in this section is that in the years when the NDVI index changes are at the lower limit of the threshold, we witness the most climate shocks and temperature changes, the occurrence of severe storms and precipitation fluctuations. The distribution of the vegetation index based on hexagons in 1985 and 2005 have a normal distribution; but in 2020, the graph has deviated from the normal state and skewed towards the vegetation cover index under stress or even thin covers. The visual interpretation done on the vegetation cover index in 1985 confirms the condition of the vegetation cover in the southern and western limits of the region in a state with suitable dense and pasture vegetation and forest cover on the edges. However, in 2005 and 2020, this cover has been changed and mainly turned into agricultural land and poor rangeland. In such a way that in 2020, the situation of the region has revealed the critical state of vegetation. The vegetation cover index in the central areas of the city has also reached from a relatively favorable situation in 1985 to a critical situation with almost no dense and stress-free vegetation cover in 2020. The results of the present studies are consistent with the studies of Visentr Serrano et al. in 2013 and confirm the relationship between NDVI vegetation and climate change. In addition, the results of the studies are consistent with the studies of Alwesabi 2012, Xiai & Moody, 2005 and Yan et al. 2001. In such a way that the present study and the aforementioned studies all confirm the influence of the vegetation index on climate fluctuations and precipitation with a one-year time difference.
 
 
 
Conclusion
In general, the threshold is defined as a border with different conditions. After crossing the thresholds, the stability and positioning of the NDVI in the equilibrium range is often difficult, and the ecosystem is constantly spending energy to restore itself or to position itself in a new stability state. The result of the mentioned disorders is the reduction of resilience and resistance in the region, which leads the ecosystem to alternative states or crossing the threshold or being in a new equilibrium state. The results showed that the areas where green vegetation is concentrated and denser are less affected by climatic stresses and show more resilience. However, the areas that have become spots and islands due to destruction in the urban areas are more affected by climatic stress and destruction and show less tolerance against the destruction factors. The results help managers to focus their management plans for the preservation and maintenance of urban green spaces as well as forest and pasture ecotones on the edge of the city by knowing the thresholds.
 
Funding
There is no funding support.
 
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
 We are grateful to all the scientific consultants of this paper.
 

Keywords

References

  1. Alwesabi, M. (2012). MODIS NDVI satellite data for assessing drought in Somalia during the period 2000-2011. Student Thesis Series INES.
  2. Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., & Parsian, S. (2020). Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350.
  3. Azzari, G., & Lobell, D. B. (2017). Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring. Remote Sensing of Environment, 202, 64–74.
  4. Badapalli, P. K., Kottala, R. B., Madiga, R., & Mesa, R. (2021). Land suitability analysis and water resources for agriculture in semi-arid regions of Andhra Pradesh, South India using remote sensing and GIS techniques. International Journal of Energy and Water Resources, 7(4),1–16.
  5. Bahram, M., Põlme, S., Kõljalg, U., Zarre, S., & Tedersoo, L. (2012). Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran. New Phytologist, 193(2), 465–473.
  6. Bullock, E. L., Woodcock, C. E., & Olofsson, P. (2020). Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis. Remote Sensing of Environment, 238, 110968.
  7. Chander, G., Markham, B. L,. & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903.
  8. Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., Dong, J., Qin, Y., Zhao, B., & Wu, Z. (2017). A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 104–120.
  9. 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.
  10. De Keersmaecker, W., Lhermitte, S., Tits, L., Honnay, O., Somers, B., & Coppin, P. (2015). A model quantifying global vegetation resistance and resilience to short‐term climate anomalies and their relationship with vegetation cover. Global Ecology and Biogeography, 24(5), 539–548.
  11. DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. W., & Lang, M. W. (2020). Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240, 111664.
  12. Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A. K., & Cheng, W. H. (2018). Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment, 205, 253–275.
  13. Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang, J., & Zhang, W. (2020). Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510.
  14. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
  15. Guha, S., Govil, H., Taloor, A. K., Gill, N., & Dey, A. (2022). Land surface temperature and spectral indices: A seasonal study of Raipur City. Geodesy and Geodynamics, 13(1), 72–82.
  16. Hao, B., Ma, M., Li, S., Li, Q., Hao, D., Huang, J., Ge, Z., Yang, H., & Han, X. (2019). Land use change and climate variation in the three gorges reservoir catchment from 2000 to 2015 based on the Google Earth Engine. Sensors, 19(9), 2118.
  17. Hossain, M. L., & Li, J. (2021). NDVI-based vegetation dynamics and its resistance and resilience to different intensities of climatic events. Global Ecology and Conservation, 30, e01768.
  18. Huang, Q., Long, D., Du, M., Zeng, C., Qiao, G., Li, X., Hou, A., & Hong, Y. (2018). Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River. Remote Sensing of Environment, 219, 115–134.
  19. Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index
  20.  (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6.
  21. Isbell, F., Craven, D., Connolly, J., Loreau, M., Schmid, B., Beierkuhnlein, C., Bezemer, T. M., Bonin, C., Bruelheide, H., & De Luca, E. (2015). Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature, 526(7574), 574–577.
  22. Jahani, A., & Saffariha, M. (2021). Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques. Scientific Reports, 11(1), 1–13.
  23. Jones, H. G., & Vaughan, R. A. (2010). Remote sensing of vegetation: principles, techniques, and applications. edition 1.publisher: Oxford university press.
  24. Kakooei, M., & Baleghi, Y. (2020). A two-level fusion for building irregularity detection in post-disaster VHR oblique images. Earth Science Informatics, 13(2), 459–477.
  25. Krakauer, N. Y., Lakhankar, T., & Anadón, J. D. (2017). Mapping and attributing normalized difference vegetation index trends for Nepal. Remote Sensing, 9(10), 986.
  26. Kumar, B. P., Babu, K. R., Anusha, B. N., & Rajasekhar, M. (2022). Geo-environmental Monitoring and Assessment of Land Degradation and Desertification in the Semi-arid regions using Landsat 8 OLI/TIRS, LST, and NDVI approach. Environmental Challenges, 8, 100578.
  27. Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., & Wang, S. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209, 227–239.
  28. Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Little, B. (2015). A scalable satellite-based crop yield mapper. Remote Sensing of Environment, 164, 324–333.
  29. Lu, Q., Zhao, D., Wu, S., Dai, E., & Gao, J. (2019). Using the NDVI to analyze trends and stability of grassland vegetation cover in Inner Mongolia. Theoretical and Applied Climatology, 135(3), 1629–1640.
  30. McDowell, N., Pockman, W. T., Allen, C. D., Breshears, D. D., Cobb, N., Kolb, T., Plaut, J., Sperry, J., West, A. & Williams, D. G. (2008). Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought?. New Phytologist, 178(4), 719–739.
  31. Midekisa, A., Holl, F., Savory, D. J., Andrade-Pacheco, R., Gething, P. W., Bennett, A., & Sturrock, H. J. W. (2017). Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PloS One, 12(9), e0184926.
  32. Mirmosavei, S., & Kareimei, H. (2013). Effect of drought on vegetation cover using MODIS sensing images case: Kurdistan Province. Geography and Development Iranian Journal, 11(31), 57–76.
  33. Mutanga, O., & Kumar, L. (2019). Google earth engine applications. In Remote Sensing, 11(5), 591- 611.
  34. Nila, M., Salma, U., Beierkuhnlein, C., Jaeschke, A., Hoffmann, S., & Hossain, M. L. (2019). Predicting the effectiveness of protected areas of Natura 2000 under climate change. Ecological Processes, 8(1), 1–21.
  35. Palmer, W. C. (1965). Meteorological drought (Vol. 30). US Department of Commerce, Weather Bureau.
  36. Park, T., Ganguly, S., Tømmervik, H., Euskirchen, E. S., Høgda, K.-A., Karlsen, S. R., Brovkin, V., Nemani, R. R., & Myneni, R. B. (2016). Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environmental Research Letters, 11(8), 84001.
  37. Pinzon, J. E., & Tucker, C. J. (2014). A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sensing, 6(8), 6929–6960.
  38. Poorazimy, M., Shataee, S., Attarchi, S., & Mohammadi, J. (2017). Estimation of aboveground biomass using Alos-Palsar data in Hyrcanian forests (Case study: ShastKalateh, Gorgan). Forest and Wood Products, 70(3), 479–488.
  39. Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017). Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Frontiers in Earth Science, 5, 17.
  40. Taloor, A. K., Manhas, D. S., & Kothyari, G. C. (2021). Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data. Applied Computing and Geosciences, 9, 100051.
  41. Teluguntla, P., Thenkabail, P. S., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., Yadav, K., & Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 325–340.
  42. Vicente-Serrano, S. M., Beguería, S., Lorenzo-Lacruz, J., Camarero, J. J., López-Moreno, J. I., Azorin-Molina, C., Revuelto, J., Morán-Tejeda, E., & Sanchez-Lorenzo, A. (2012). Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interactions, 16(10), 1–27.
  43. Vicente-Serrano, S. M., Gouveia, C., Camarero, J. J., Beguería, S., Trigo, R., López-Moreno, J. I., Azorín-Molina, C., Pasho, E., Lorenzo-Lacruz, J., & Revuelto, J. (2013). Response of vegetation to drought time-scales across global land biomes. Proceedings of the National Academy of Sciences, 110(1), 52–57.
  44. Wang, Y., Ma, J., Xiao, X., Wang, X., Dai, S., & Zhao, B. (2019). Long-term dynamic of Poyang Lake surface water: A mapping work based on the Google Earth Engine cloud platform. Remote Sensing, 11(3), 313.
  45. Weishou, S., Di, J., Hui, Z., Shouguang, Y., Haidong, L., & Naifeng, L. (2011). The response relation between climate change and NDVI over the Qinghai-Tibet plateau. Journal of the World Academy of Science,. Engineering and Technology, 59, 2216–2222.
  46. Xiao, J., & Moody, A. (2005). Geographical distribution of global greening trends and their climatic correlates: 1982–1998. International Journal of Remote Sensing, 26(11), 2371–2390.
  47. Yun-Hao, C., Xiao-Bing, L., & Feng, X. (2001). NDVI changes in China between 1989 and 1999 using change vector analysis based on time series data. Journal of Geographical Sciences, 11(4), 383–392.
  48. Zhang, Q., Kong, D., Singh, V. P., & Shi, P. (2017). Response of vegetation to different time-scales drought across China: Spatiotemporal patterns, causes and implications. Global and Planetary Change, 152, 1–11.
  49. Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., & Gong, P. (2021). Progress and trends in the application of Google Earth and Google Earth Engine. Remote Sensing, 13(18), 3778.
  50. Zhou, Y., Dong, J., Xiao, X., Liu, R., Zou, Z., Zhao, G., & Ge, Q. (2019). Continuous monitoring of lake dynamics on the Mongolian Plateau using all available Landsat imagery and Google Earth Engine. Science of the Total Environment, 689, 366–380.
  51. Zurqani, H. A., Post, C. J., Mikhailova, E. A., Schlautman, M. A., & Sharp, J. L. (2018). Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 69, 175–185.
Geographical Urban Planning Research (GUPR)
Volume 11, Issue 2
August 2023
Pages 97-114

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