Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 4, pp. 115-130

Predictive analysis of forest cover in the Middle Volga Region based on time series and climate scenarios

O.N. Vorobyov 1 , S.A. Lezhnin 1 , E.A. Kurbanov 1 , A.B. Yahyayev 2 , D.M. Dergunov 1 , L.V. Tarasova 1 , A.V. Yastrebova 1 
1 Volga State University of Technology, Yoshkar-Ola, Russia
2 Western Caspian University, Baku, Azerbaijan
Accepted: 20.06.2024
DOI: 10.21046/2070-7401-2024-21-4-115-130
In this study, a comprehensive predictive analysis of Middle Volga Region’s forest cover was conducted from 2021 to 2050, utilizing data from the NDVI (Normalized Difference Vegetation Index), average monthly Land Surface Temperature (LST), and Precipitation (Pr). A temporal convolutional network model was employed for data predicting. A multiple linear regression model was subsequently developed to examine the relationship between NDVI, LST, and Pr within the study area. The model was then used to simulate the dynamics of NDVI in response to three climate change scenarios from the Intergovernmental Panel on Climate Change (IPCC), which are based on representative concentration pathways (RCPs) for greenhouse gases. To assess the spatial-temporal trends of the indicators within QGIS (Quantum Geographic Information System), the non-parametric Mann-Kendall correlation test was applied. The NDVI analysis, covering all three IPCC scenarios, revealed a generally increasing trend in forest cover within the Middle Volga Region by 2050, with the RCP8.5 (high emissions) scenario demonstrating the most pronounced growth. The central and eastern areas of the study region displayed the highest NDVI spatial trends. LST trends indicated an upward trajectory by mid-21st century across all scenarios, while precipitation trends, particularly under the RCP8.5, showed a noticeable decrease. The observed trends indicate a reduction in the time interval between drought seasons from 10 to 4–6 years, which poses significant risks for the forest plantations in the Middle Volga. The models and trends obtained provide valuable insights for long-term sustainable forest management planning in the Middle Volga Region.
Keywords: forest ecosystems, predictive analyses, IPCC, RCP, MODIS, NDVI, LST, Pr
Full text

References:

  1. Alekseev A. S., Chernikhovskii D. M., Analysis of relations between the landscape morphometric characteristics and forest productivity (using the example of Leningrad oblast), Contemporary Problems of Ecology, 2020, Vol. 13, No. 7, pp. 730–741, DOI: 10.1134/S1995425520070021.
  2. Bartalev S. A., Stytsenko F. V., Khvostikov S. A., Loupian E. A., Methodology of post-fire tree mortality monitoring and prediction using remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 6, pp. 176–193 (in Russian), DOI: 10.21046/2070-7401-2017-14-6-176-193.
  3. Vorobyov O. N., Kurbanov E. A., Polevshchikova Yu. A., Lezhnin S. A., Spatio-temporal analysis of forest cover dynamics in Middle Volga region on the base of satellite data, Yoshkar-Ola: Volga State University of Technology, 2019, 200 p. (in Russian).
  4. Vorobyov O. N., Kurbanov E. A., Sha J. et al., Trend analysis of MODIS time series vegetation indices to assess the impact of droughts on forest stands in the Middle Volga from 2000 to 2020, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 181–194 (in Russian), DOI: 10.21046/2070-7401-2022-19-4-181-194.
  5. Gubayev A. V., Lezhnin S. A., Vorobyov O. N., Kurbanov E. A., Dergunov D. M., Tarasova L. V., QGIS Forecast_CSFM&RS for predicting seasonal parameters, Certificate of state registration of software No. 2023684190 (RU), Reg. 14.11.2023 (in Russian).
  6. Elsakov V. V., The remote sensing data in European North productivity estimation, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 1, pp. 71–79 (in Russian).
  7. Elsakov V. V., Shchanov V. M., Current changes in vegetation cover of Timan tundra reindeer pastures from analysis of satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 128–142 (in Russian), DOI: 10.21046/2070-7401-2019-16-2-128-142.
  8. Ershov D. V., Burtseva V. S., Gavriluk E. A. et al., Recognizing the recent succession stage of forest ecosystems in Pechora-Ilych Nature Reserve with thematic satellite products, Lesovedenie, 2017, No. 5, pp. 3–15 (in Russian), DOI: 10.7868/S0024114817050011.
  9. Zuev V. V., Korotkova E. M., Pavlinsky A. V., Climate-related changes in the vegetation cover of the taiga and tundra of Western Siberia in 1982–2015 according to satellite observations, Issledovanie Zemli iz kosmosa, 2019, No. 6, pp. 66–76 (in Russian), DOI: 10.31857/S0205-96142019666-76.
  10. Im S. T., Kharuk V. I., Lee V. G., Migration of the northern evergreen needleleaf timberline in Siberia in the 21st century, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 1, pp. 176–187 (in Russian), DOI: 10.21046/2070-7401-2020-17-1-176-187.
  11. Kolomyts E. G., Sharaya L. S., NDVI vegetation index as a photosynthetic potential indicator in boreal forests of the Volga basin, Lesovedenie, 2020, No. 4, pp. 301–313 (in Russian), DOI: 10.31857/S0024114820040075.
  12. Kurbanov E. A., Vorob’ev O. N., Retrospective analysis of vegetation cover loss in Republics of Mari El and Chuvashia after flooding of Cheboksarskaya dam from Landsat/MSS data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 1, pp. 127‒137 (in Russian), DOI: 10.21046/2070-7401-2021-18-1-127-137.
  13. Lezhnin S. A., Gubaev A. V., Vorobev O. N. et al., Forecasting the state of forest ecosystems in the Middle Volga region using self-learning models, Forest ecosystems under climate change: biological productivity and remote monitoring, 2022, No. 8, pp. 106‒118 (in Russian), DOI: 10.25686/10.25686.2022.48.81.010.
  14. Mamedaliyeva V. M., Changes in forested areas of the north-eastern region of Azerbaijan revealed by satellite images, Lesnoi zhurnal, 2020, No. 1, pp. 88‒97 (in Russian), DOI: 10.37482/0536-1036-2022-1-88-97.
  15. Miklashevich T. S., Bartalev S. A., Plotnikov D. E., Interpolation algorithm for the recovery of long satellite data time series of vegetation cover observation, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 6, pp. 143‒154 (in Russian), 10.21046/2070-7401-2019-16-6-143-154.
  16. Terekhin E. A., Spatial analysis of tree vegetation of abandoned arable lands using their spectral response in forest-steppe zone of Central Chernozem Region, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 5, pp. 142‒156 (in Russian), DOI: 10.21046/2070-7401-2020-17-5-142-156.
  17. Terekhin E. A., Estimation of forest disturbance in the forest-steppe zone at the beginning of the XXI century using satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 3, pp. 164‒175 (in Russian), DOI: 10.21046/2070-7401-2020-17-2-134-146.
  18. Terekhov A. G., Vitkovskaya I. S., Abayev N. N., Dolgikh S. A., Long term trends in vegetation in Tien-Shan and Dzungarian Alatau from eMODIS NDVI C6 data (2002–2019), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 6, pp. 133‒142 (in Russian), DOI: 10.21046/2070-7401-2019-16-6-133-142.
  19. Chen Z., Liu H., Xu C. et al., Modeling vegetation greenness and its climate sensitivity with deep-learning technology, Ecology and Evolution, 2021, Vol. 11(12), pp. 7335–7345, DOI: 10.1002/ece3.7564.
  20. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker, D. Qin, G.-K. Plattner et al. (eds.), Cambridge: Cambridge Univ. Press, 2013, 1535 p.
  21. Collins W. J., Bellouin N., Doutriaux-Boucher M. et al., Development and evaluation of an Earth-System model — HadGEM2, Geoscientific Model Development, 2011, Vol. 4(4), pp. 1051–1075, DOI: 10.5194/gmd-4-1051-2011.
  22. Cong X., Du S., Li F., Ding Y., Study of mesoscale NDVI prediction models in arid and semiarid regions of China under changing environments, Ecological Indicators, 2021, No. 131, Article 108198, pp. 1–18, DOI: 10.1016/j.ecolind.2021.108198.
  23. Decuyper M., Chávez R. O., Lohbeck M. et al., Continuous monitoring of forest change dynamics with satellite time series, Remote Sensing of Environment, 2022, Vol. 269, Article 112829, DOI: 10.1016/j.rse.2021.112829.
  24. Fernández-Manso A., Quintano C., Fernández-Manso O., Forecast of NDVI in coniferous areas using temporal ARIMA analysis and climatic data at a regional scale, Intern. J. Remote Sensing, 2011, Vol. 32(6), pp. 1595–1617, DOI: 10.1080/01431160903586765.
  25. Frazier R. J., Coops N. C., Wulder M. A. et al., Analyzing spatial and temporal variability in short-term rates of post-fire vegetation return from Landsat time series, Remote Sensing of Environment, 2018, Vol. 205, pp. 32–45, DOI: 10.1016/j.rse.2017.11.007.
  26. Frieler K., Lange S., Piontek F. et al., Assessing the impacts of 1.5 °C global warming — simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geoscientific Model Development, 2017, Vol. 10(12), pp. 4321–4345, DOI: 10.5194/gmd-10-4321-2017.
  27. Herzen J., Lässig F., Piazzetta S. G. et al., Darts: user-friendly modern machine learning for time series, J. Machine Learning Research, 2022, No. 23, pp. 1–6, DOI: 10.48550/arXiv.2110.03224.
  28. Kendall M. G., Rank correlation methods, Oxford: Charles Griffin, 1955. 196 p.
  29. Lai S., El-Adawy A., Sha J. et al., Towards an integrated systematic approach for ecological maintenance: Case studies from China and Russia, Ecological Indicators, 2022, Vol. 140, Article 108982, DOI: 10.1016/j.ecolind.2022.108982.
  30. Lan H., Stewart K., Sha Z. et al., Data gap filling using cloud-based distributed Markov chain cellular automata framework for land use and land cover change analysis, Inner Mongolia as a case study, Remote Sensing, 2022, Vol. 14(3), Article 445, DOI: 10.3390/rs14030445.
  31. LeCun Y., Bengio Y., Hinton G., Deep learning, Nature, 2015, Vol. 521(7553), pp. 436–444, DOI: 10.1038/nature14539.
  32. Mann H. B., Nonparametric tests against trend, Econometrica, 1945, Vol. 13, pp. 245–259, DOI: 10.2307/1907187.
  33. Na R., Na L., Du H. et al., Vegetation greenness variations and response to climate change in the arid and semi-arid transition zone of the Mongolian plateau during 1982–2015, Remote Sensing, 2021, Vol. 13(20), Article 4066. DOI:10.3390/rs13204066.
  34. Pahlavani P., Askarian O. H., Bigdeli B., A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation, Earth Observation and Geomatics Engineering, 2017, Vol. 1(2), pp. 82–99, DOI: 10.22059/eoge.2017.220342.1006.
  35. Reddy D. S., Prasad P., Prediction of vegetation dynamics using NDVI time series data and LSTM, Modeling Earth Systems and Environment, 2018, Vol. 4(5), pp. 409–419, DOI: 10.1007/s40808-018-0431-3.
  36. Talukdar S., Singha P., Mahato S. et al., Land-use land-cover classification by machine learning classifiers for satellite observations — a review, Remote Sensing, 2020, Vol. 12(7), Article 1135, DOI: 10.3390/rs12071135.
  37. Tang Z., Xia X., Huang Y. et al., Estimation of national forest aboveground biomass from multi-source remotely sensed dataset with machine learning algorithms in China, Remote Sensing, 2022, Vol. 14(21), Article 5487, DOI: 10.3390/rs14215487.
  38. Tian M., Wang P., Khan J., Drought forecasting with vegetation temperature condition index using ARIMA models in the Guanzhong plain, Remote Sensing, 2016, Vol. 8, Issue 9, Article 690, DOI: 10.3390/rs8090690.
  39. Tian L., Tao Y., Fu W. et al., Dynamic simulation of land use/cover change and assessment of forest ecosystem carbon storage under climate change scenarios in Guangdong Province, China, Remote Sensing, 2022, No. 14(10), Article 2330, DOI: 10.3390/rs14102330.
  40. Touhami I., Moutahir H., Assoul D. et al., Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia, Acta Oecologica, 2022, Vol. 114, Article 103804, DOI: 10.1016/j.actao.2021.103804.
  41. van Duynhoven A., Dragicevic S., Assessing the impact of neighborhood size on temporal convolutional networks for modeling land cover change, Remote Sensing, 2022, Vol. 14(19), Article 4957, DOI:10.3390/rs14194957.
  42. Yli-Heikkilä M., Wittke S., Luotamo M. et al., Scalable crop yield prediction with Sentinel 2 time series and temporal convolutional network, Remote Sensing, 2022, Vol. 14(17), Article 4193, DOI: 10.3390/rs14174193.
  43. Yuan J., Bian Z., Yan Q. et al., An approach to the temporal and spatial characteristics of vegetation in the growing season in Western China, Remote Sensing, 2020, Vol. 12(6), Article 945, DOI: 10.3390/rs12060945.
  44. Zhang C., Sargent I., Pan X. et al., Joint deep learning for land cover and land use classification, Remote Sensing of Environment, 2019, Vol. 221, pp. 173–187, DOI: 10.1016/j.rse.2018.11.014.
  45. Zhou Z., Ding Y., Shi H. et al., Analysis and prediction of vegetation dynamic changes in China: Past, present and future, Ecological Indicators, 2020, Vol. 117, Article 106642, pp. 1–11, DOI: 10.1016/j.ecolind.2020.106642.