Quantitative analysis of factors influencing damage to old-growth hemiboreal stands as a result of a catastrophic windthrow, based on remote sensing and merged data

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

The consequences of a catastrophic windthrow in a old-growth hemiboreal stands of the Kologrivsky Forest Reserve were investigated. The degree of damage to tree stands was assessed by interpretation of the Sentinel-2 satellite images. Data from the GBIF portal, SRTM global terrain height models, and tree stand heights were used for the quantitative analysis of factors affecting the presence of wind damage. It was found that tree stands on an area of 277.9 hectares (40.5% of the entire massif) were damaged by windthrow. The results of the analysis of height models and regression models showed that spruce stands are more vulnerable to wind damage, as well as stands of greater height or those growing at higher elevation on the ground.

Негізгі сөздер

Авторлар туралы

N. Ivanova

Institute for Problems of Mathematics, Russian Academy of Sciences

Email: Natalya.dryomys@gmail.com

Institute of Mathematical Problems of Biology, Russian Academy of Sciences

Ресей, 142290 Pushchino

М. Shashkov

Karaganda University

Email: Natalya.dryomys@gmail.com
Қазақстан, 100028 Karaganda

А. Lebedev

Russian State Agrarian University – Moscow Agricultural Academy named after. K.A. Timiryazeva; Kologrivsky Les Nature Reserve

Email: Natalya.dryomys@gmail.com
Ресей, 127434 Moscow; 157440 Kologriv

V. Shanin

Federal Research Center “Pushchino Scientific Center for Biological Research, Russian Academy of Sciences”

Хат алмасуға жауапты Автор.
Email: Natalya.dryomys@gmail.com

Institute of Physicochemical and Biological Problems of Soil Science, Russian Academy of Sciences

Ресей, 142290 Pushchino

Әдебиет тізімі

  1. Ulanova N.G. The effects of windthrow on forests at different spatial scales: a review // Forest Ecology and Management. 2000. V. 135. P. 155–167. https://dx.doi.org/10.1016/S0378-1127(00)00307-8
  2. Gardiner B. Wind damage to forests and trees: a review with an emphasis on planted and managed forests // Journal of Forest Research. 2021. V. 26. № 4. P. 248–266. https://doi.org/10.1080/13416979.2021.1940665
  3. Seidl R., Schelhaas M.-J., Lexer M.J. Unraveling the drivers of intensifying forest disturbance regimes in Europe // Global Change Biology. 2011. V. 17. P. 2842–2852. https://dx.doi.org/10.1111/j.1365-2486.2011.02452.x
  4. Baumann M., Ozdogan M., Wolter P.T. et al. Landsat remote sensing of forest windfall disturbance // Remote Sensing of Environment. 2014. V. 143. P. 171–179. https://doi.org/10.1016/j.rse.2013.12.020
  5. Kislov D.E., Korznikov K.A. Automatic windthrow detection using very-high-resolution satellite imagery and deep learning // Remote Sensing. 2020. V. 12. № 7. Art. 1145. https://doi.org/10.3390/rs12071145
  6. Lazecky M., Wadhwa S., Mlcousek M. et al. Simple method for identification of forest windthrows from Sentinel-1 SAR data incorporating PCA // Procedia Computer Science. 2021. V. 181. P. 1154–1161. https://doi.org/10.1016/j.procs.2021.01.312
  7. Olmo V., Tordoni E., Petruzzellis F. et al. Use of Sentinel-2 satellite data for windthrows monitoring and delimiting: the case of “Vaia” storm in Friuli Venezia Giulia region (North-Eastern Italy) // Remote sensing. 2021. V. 13. Art. 1530. https://dx.doi.org/10.3390/rs13081530
  8. Phillips S.J., Anderson R.P., Schapire R.E. Maximum entropy modeling of species geographic distributions // Ecological Modelling. 2006. V. 190. P. 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  9. Poggio L., de Sousa L.M., Batjes N.H. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty // SOIL. 2021. V. 7. P. 217–240. https://doi.org/10.5194/soil-7-217-2021
  10. Nature Map Explorer 2021. https://explorer.naturemap.earth/ (1.11.2022).
  11. Santoro M., Cartus O., Carvalhais N. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations // Earth System Science Data. 2021. V. 13. P. 3927–3950. https://doi.org/10.5194/essd-13-3927-2021
  12. Farr T.G., Rosen P.A., Caro E. et al. The shuttle radar topography mission // Reviews of Geophysics. 2007. V. 45. Art. RG2004. https://doi.org/10.1029/2005RG000183
  13. Lang N., Jetz W., Schindler K. et al. A high-resolution canopy height model of the Earth 2022. https://langnico.github.io/globalcanopyheight/
  14. Potapov P., Hansen M.C., Pickens A. et al. The global 2000–2020 land cover and land use change dataset derived from the Landsat archive: first results // Frontiers in Remote Sensing. 2022. V. 3. Art. 856903. https://doi.org/10.3389/frsen.2022.856903
  15. Heberling J.M., Miller J.T., Noesgaard D. et al. Data integration enables global biodiversity synthesis // Proceedings of the National Academy of Sciences of the United States of America. 2021. V. 118. № 6. Art. e2018093118. https://doi.org/10.1073/pnas.2018093118
  16. Кологривский лес: Экологические исследования. М: Наука, 1986. 128 с.
  17. Хорошев А.В., Немчинова А.В., Кощеева А.С. и др. Ландшафтные и сукцессионные факторы соотношения неморальных и бореальных свойств травяного яруса в заповеднике “Кологривский лес” // Вестник Московского гос. ун-та. Серия 5: География. 2013. № 5. С. 11–18.
  18. Иванов А.Н., Буторина Е.А., Балдина Е.А. Многолетняя динамика коренных южно-таежных ельников в заповеднике “Кологривский лес” // Вестник Московского гос. ун-та. Серия 5: География. 2012. № 3. С. 74–79.
  19. Ivanova N.V., Shashkov M.P. Tree stand assessment before and after windthrow based on open-access biodiversity data and aerial photography // Nature Conservation Research. 2022. V. 7. Suppl. 1. P. 52–63. https://dx.doi.org/10.24189/ncr.2022.018
  20. Chen D., Huang J., Jackson T.J. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands // Remote Sensing of Environment. 2005. V. 98. P. 225–236. https://doi.org/10.1016/j.rse.2005.07.008
  21. Абатуров Ю.Д., Письмеров А.В., Орлов А.Я. и др. Коренные темнохвойные леса южной тайги (резерват “Кологривский лес”). М.: Наука, 1988. 220 с.
  22. Лебедев А.В., Чистяков С.А. Долговременные наблюдения на пробных площадях в древостоях ядра заповедника “Кологривский лес” // Вклад ООПТ в экологическую устойчивость регионов. Современное состояние и перспективы: Мат-лы конф. Кологрив: ГПЗ “Кологривский лес”, 2021. С. 31–43.
  23. Ivanova N.V. Factors limiting distribution of the rare lichen species Lobaria pulmonaria (in forests of the Kologriv Forest Nature Reserve) // Biology Bulletin. 2015. V. 42. № 2. P. 145–153. https://doi.org/10.1134/S1062359015020041
  24. Иванова Н.В., Терентьева Е.В. Состояние популяций охраняемого лишайника Lobaria pulmonaria в лесах северо-востока Костромской области // Вестник Томского гос. ун-та. Биология. 2017. № 38. C. 149–166. https://doi.org/10.17223/19988591/38/9
  25. GBIF.org (16 April 2022) GBIF Occurrence Download. https://doi.org/10.15468/dl.qzgpn2 (1.11.2022)
  26. Jarvis A., Reuter H.I., Nelson A. et al. Hole-filled seamless SRTM data V4, International Centre for Tropical Agriculture (CIAT). 2008. http://srtm.csi.cgiar.org (29.12.2022).
  27. Potapov P., Li X., Hernandez-Serna A. et al. Mapping global forest canopy height through integration of GEDI and Landsat data // Remote Sensing of Environment. 2020. V. 253. Art. 112165. https://doi.org/10.1016/j.rse.2020.112165
  28. R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
  29. Ribeiro P.J. Jr., Diggle P.J. A package for geostatistical analysis // R-NEWS. 2001. V. 1. № 2. P. 15–18.
  30. QGIS Development Team, 2022. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org.
  31. Taylor R.A., Dracup E., MacLean D.A. et al. Forest structure more important than topography in determining windthrow during Hurricane Juan in Canada’s Acadian Forest // Forest Ecology and Management. 2019. V. 434. P. 255–263. https://dx.doi.org/10.1016/j.foreco.2018.12.026
  32. Ruel J.-C. Understanding windthrow: silvicultural implications // The Forestry Chronicle. 1995. V. 71. № 4. P. 434–445. https://dx.doi.org/10.5558/tfc71434-4
  33. Бобровский М.В., Стаменов М.Н. Влияние катастрофического ветровала 2006 года на структуру и состав лесной растительности заповедника “Калужские засеки” // Лесоведение. 2020. № 6. С. 523–236. https://doi.org/10.31857/S0024114820050022
  34. Богачев А.В. Лесотаксационные исследования. М.: ВНИИЛМ, 2007. 344 с.
  35. Pretzsch H. The effect of tree crown allometry on community dynamics in mixed-species stands versus monocultures. A review and perspectives for modeling and silvicultural regulation // Forests. 2019. V. 10. № 9. Art. 810. https://doi.org/10.3390/f10090810
  36. Ovaskainen O., Meyke E., Lo K. et al. Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology // Scientific Data. 2020. V. 7. Art. 47. https://doi.org/10.1038/s41597-020-0376-z
  37. Буйволов Ю.А., Минин А.А., Черногаева Г.М. Летопись природы – вызовы и возможности // Использование и охрана природных ресурсов в России. 2022. № 1. С. 46–55.
  38. Roslin T., Antão L., Hällfors M. et al. Phenological shifts of abiotic events, producers and consumers across a continent // Nature Climate Change. 2021. V. 11. P. 241–248. https://doi.org/10.1038/s41558-020-00967-7
  39. Shashkov M.P., Bobrovsky M.V., Shanin V.N. et al. Data on 30-year stand dynamics in an old-growth broad-leaved forest in the Kaluzhskie Zaseki State Nature Reserve, Russia // Nature Conservation Research. 2022. V. 7. Suppl. 1. P. 24–37. https://dx.doi.org/10.24189/ncr.2022.013

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