Comprehensive analysis of gap formation in the canopy of an old-growth broadleaved forest

封面

如何引用文章

全文:

详细

We performed a quantitative and qualitative assessment of the dynamics of gap formation in the canopy of intact old-growth polydominant broadleaved forest in a permanent sample area in the Kaluga Zaseki Nature Reserve. Digital elevation models were obtained from aerial survey data of the forest in 2018 and 2021, from which gap diagrams of several elevation classes were constructed. The resulting schematics were expertly analyzed using orthophotomosaic survey data and gap areas were estimated. We conducted a sample ground survey of gaps and regression analysis of the relationship between relative gap area and stand species composition from the primary enumeration data. It was shown that the phenophase at the time of the survey can significantly change the estimate of gap areas, and the height of the stand in the gap cannot serve as a reliable indicator of its age. It was also found that aerial photography reveals a more complex gap structure than ground-based surveys.

全文:

受限制的访问

作者简介

A. Portnov

Institute of Physicochemical and Biological Problems in Soil Science of RAS – separate subdivision PSCBR RAS

编辑信件的主要联系方式.
Email: alekseyportnow@gmail.com
俄罗斯联邦, Institutskaya, 2, Pushchino, 142290

M. Shashkov

Karaganda Buketov University

Email: alekseyportnow@gmail.com
哈萨克斯坦, Universitetskaya, 28, Karaganda, 100024

V. Shanin

Institute of Physicochemical and Biological Problems in Soil Science of RAS – separate subdivision PSCBR RAS

Email: alekseyportnow@gmail.com
俄罗斯联邦, Institutskaya, 2, Pushchino, 142290

参考

  1. Бобровский М.В., Ханина Л.Г. Заповедник “Калужские засеки” // Оценка и сохранение биоразнообразия лесного покрова в заповедниках Европейской России / Под ред. Л.Б. Заугольновой. М.: Научный мир, 2000. С. 104–124.
  2. Бобровский М.В. Козельские засеки (эколого-исторический очерк). Калуга: Изд-во Н. Бочкаревой, 2002. 92 с.
  3. Комаров А.В., Ершов Д.В., Тихонова Е.В. Информативность спектральных и морфометрических признаков оконной структуры полога древостоя на основе спутниковых данных // Лесоведение. 2021. № . 3. С. 227–239. https://doi.org/10.31857/S0024114821030074
  4. Коротков В.Н. Новая парадигма в лесной экологии // Биологические науки. 1991. № 8. С. 7–20.
  5. Портнов А.М., Быховец С.С., Дин Е.С., Иванова Н.В., Фролов П.В., Шанин В.Н., Шашков М.П. Количественная оценка размеров окон в пологе старовозрастного широколиственного леса наземными и дистанционными методами // Материалы Седьмой Национальной научной конференции с международным участием “Математическое моделирование в экологии”. Пущино: ИФХиБПП РАН – ФИЦ ПНЦБИ РАН, 2021. С. 99–102.
  6. Смирнова О.В. Популяционная организация биоценотического покрова лесных ландшафтов // Успехи современной биологии. 1998. Т. 118. № . 2. С. 148–165.
  7. Широков А.И. Использование метода парцеллярного анализа для оценки структурного биоразнообразия лесных сообществ // Лесоведение. 2005. № 1. С. 19–27.
  8. Barton I., Király G., Czimber K., Hollaus M., Pfeifer N. Treefall gap mapping using Sentinel-2 images // Forests. 2017. Vol. 8. No 11. ID426. https://doi.org/10.3390/f8110426
  9. Bastin J.F., Berrahmouni N., Grainger A., Maniatis D., Mollicone D., Moore R., Patriarca C., Picard N., Sparrow B., Abraham E.M., Aloui K., Atesoglu A., Attore F., Bassüllü Ç., Bey A., Garzuglia M., García-Montero L.G., Groot N., Guerin G., Laestadius L., Lowe A.J., Mamane B., Marchi G., Patterson P., Rezende M., Ricci S., Salcedo I., Diaz A.S.-P., Stolle F., Surappaeva V., Castro R. The extent of forest in dryland biomes // Science. 2017. Vol. 356. No 6338. P. 635–638. https://doi.org/10.1126/science.aam6527
  10. Branson S., Wegner J.D., Hall D., Lang N., Schindler K., Perona P. From Google Maps to a fine-grained catalog of street trees // ISPRS Journal of Photogrammetry and Remote Sensing. 2018. Vol. 135. P. 13–30. https://doi.org/10.1016/j.isprsjprs.2017.11.008
  11. Chen Q., Gao T., Zhu J., Wu F., Li X., Lu D., Yu F. Individual tree segmentation and tree height estimation using leaf-off and leaf-on UAV–LiDAR data in dense deciduous forests // Remote Sensing. 2022. Vol. 14. No 12. ID2787. https://doi.org/10.3390/rs14122787
  12. Condit R. Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama and a Comparison with Other Plots. Berlin: Springer, 1998. 211 p.
  13. Crowther T.W., Glick H.B., Covey K.R., Bettigole C., Maynard D.S., Thomas S.M., Smith J.R., Hintler G., Duguid M.C., Amatulli G., Tuanmu M.N., Jetz W., Salas C., Stam C., Piotto D., Tavani R., Green S., Bruce G., Williams S.J., Wiser S.K., Huber M.O., Hengeveld G.M., Nabuurs G.J., Tikhonova E., Borchardt P., Li C.F., Powrie L.W., Fischer M., Hemp A., Homeier J., Cho P., Vibrans A.C., Umunay P.M., Piao S.L., Rowe C.W., Ashton M.S., Crane P.R., Bradford M.A. Mapping tree density at a global scale // Nature. 2015. Vol. 525. P. 201–205. https://doi.org/10.1038/nature14967
  14. Dandois J.P., Ellis E.C. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision // Remote Sensing of Environment. 2013. Vol. 136. P. 259–276. https://doi.org/10.1016/j.rse.2013.04.005
  15. Feldmann E., Drößler L., Hauck M., Kucbel S., Pichler V., Leuschner C. Canopy gap dynamics and tree understory release in a virgin beech forest, Slovakian Carpathians // Forest Ecology and Management. 2018. Vol. 415–416. P. 38–6. https://doi.org/10.1016/j.foreco.2018.02.022
  16. Fox T.J., Knutson M.G., Hines R.K. Mapping forest canopy gaps using air-photo interpretation and ground surveys // Wildlife Society Bulletin. 2000. Vol. 28. No 4. P. 882–889. https://doi.org/10.2307/3783843
  17. Gardner T.A., Barlow J., Araujo I.S., Avila-Pires T.C., Bonaldo A.B., Costa J.E., Esposito M.C., Ferreira L.V., Hawes J., Hernandez M.I.M., Hoogmoed M.S., Leite R.N., Lo-Man-Hung N.F., Malcolm J.R., Martins M.B., Mestre L.A.M., Miranda-Santos R., Overal W.L., Parry L., Peters S.L., Ribeiro M.A., da Silva M.N.F., Motta C.D.S., Peres C.A. The cost-effectiveness of biodiversity surveys in tropical forests // Ecology Letters. 2008. Vol. 11. No 2. P. 139–150. https://doi.org/10.1111/j.1461-0248.2007.01133.x
  18. Getzin S., Wiegand K., Schöning I. Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles // Methods in Ecology and Evolution. 2012. Vol. 3. No 2. P. 397–404. https://doi.org/10.1111/j.2041-210X.2011.00158.x
  19. Hansen M.C., Potapov P.V., Moore R., Hancher M., Turubanova S.A., Tyukavina A., Thau D., Stehman S.V., Goetz S.J., Loveland T.R., Kommareddy A., Egorov A., Chini L., Justice C.O., Townshend J.R.G. High-resolution global maps of 21st-century forest cover change // Science. 2013. Vol. 342. No 6160. P. 850–853. https://doi.org/10.1126/science.1244693
  20. Hobi M.L., Ginzler C., Commarmot B., Bugmann H. Gap pattern of the largest primeval beech forest of Europe revealed by remote sensing // Ecosphere. 2015. Vol. 6. No 5. P. 1–15. https://doi.org/10.1890/ES14-00390.1
  21. Jonckheere I., Nackaerts K., Muys B., Coppin P. Assessment of automatic gap fraction estimation of forests from digital hemispherical photography // Agricultural and Forest Meteorology. 2005. Vol. 132. No 1–2. P. 96–114. https://doi.org/10.1016/j.agrformet.2005.06.003
  22. Koh L.P., Wich S.A. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation // Tropical Conservation Science. 2012. Vol. 5. No 2. P. 121–132. https://doi.org/10.1177/194008291200500202
  23. Koukoulas S., Blackburn G.A. Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS // International Journal of Remote Sensing. 2004. Vol. 25. No. 15. P. 3049–3072. https://doi.org/10.1080/01431160310001657786
  24. Li W., Fu H., Yu L., Cracknell A. Deep learning based oil palm tree detection and counting for high-resolution remote sensing images // Remote Sensing. 2017. Vol. 9. No 1. ID22. https://doi.org/10.3390/rs9010022
  25. Loarie S.R., Joppa L.N., Pimm S.L. Satellites miss environmental priorities // Trends in Ecology & Evolution. 2007. Vol. 22. No 12. P. 630–632. https://doi.org/10.1016/j.tree.2007.08.018
  26. Lukina N.V., Geraskina A.P., Gornov A.V., Shevchenko N.E., Kuprin A.V., Chernov T.I., Chumachenko S.I., Shanin V.N., Kuznetsova A.I., Tebenkova D.N., Gornova M.V. Biodiversity and climate-regulating functions of forests: current issues and research prospects // Forest Science Issues. 2021. Vol. 4. No 1. P. 1–60. https://doi.org/10.31509/2658-607x-202141k-60
  27. McCarthy J. Gap dynamics of forest trees: a review with particular attention to boreal forest // Environmental Reviews. 2001. Vol. 9. No 1. P. 1–59. https://doi.org/10.1139/a00-012
  28. Mohan M., Silva C.A., Klauberg C., Jat P., Catts G., Cardil A., Hudak A.T., Dia M. Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest // Forests. 2017. Vol. 8. No 9. ID340. https://doi.org/10.3390/f8090340
  29. Nijland W., Coops N.C., Macdonald E.S., Nielsen S.E., Bater C.W., Stadt J.J. Comparing patterns in forest stand structure following variable harvests using airborne laser scanning data // Forest Ecology and Management. 2015. Vol. 354. P. 272–280. https://doi.org/10.1016/j.foreco.2015.06.005
  30. Otero V., Van De Kerchove R., Satyanarayana B., Martínez-Espinosa C., Fisol M.A.B., Ibrahim M.R.B., Sulong I., Mohd-Lokman H., Lucas R., Dahdouh-Guebas F. Managing mangrove forests from the sky: forest inventory using field data and unmanned aerial vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia // Forest Ecology and Management. 2018. Vol. 411. P. 35–45. https://doi.org/10.1016/j.foreco.2017.12.049
  31. Pajares G. Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs) // Photogrammetric Engineering and Remote Sensing. 2015. Vol. 81. No 4. P. 281–330. https://doi.org/10.14358/PERS.81.4.281
  32. Paneque-Gálvez J., McCall M., Napoletano B., Wich S., Koh L. Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas // Forests. 2014. Vol. 5. No 6. P. 1481–1507. https://doi.org/10.3390/f5061481
  33. Puliti S., Orka H.O., Gobakken T., Naesset E. Inventory of small forest areas using an Unmanned Aerial System // Remote Sensing. 2015. Vol. 7. No 8. P. 9632–9654. https://doi.org/10.3390/rs70809632
  34. Rango A., Laliberte A., Herrick J.E., Winters C., Havstad K., Steele C., Browning D. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management // Journal of Applied Remote Sensing. 2009. Vol. 3. No 1. ID033542. https://doi.org/10.1117/1.3216822
  35. Ross C.W., Loudermilk E.L., Skowronski N., Pokswinski S., Hiers J.K., O’Brien J. LiDAR voxel-size optimization for canopy gap estimation // Remote Sensing. 2022. Vol. 14. No 5. ID1054. https://doi.org/10.3390/rs14051054
  36. Saatchi S.S., Harris N.L., Brown S., Lefsky M., Mitchard E.T., Salas W., Zutta B.R., Buermann W., Lewis S.L., Hagen S., Petrova S., White L., Silman M., Morel A. Benchmark map of forest carbon stocks in tropical regions across three continents // Proceedings of the National Academy of Sciences of the United States of America. 2011. Vol. 108. No 24. P. 9899–9904. https://doi.org/10.1073/pnas.1019576108
  37. Shashkov M.P., Bobrovsky M.V., Shanin V.N., Khanina L.G., Grabarnik P. Ya., Stamenov M.N., Ivanova N.V. 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. Vol. 7 (Suppl. 1). P. 24–37. https://doi.org/10.24189/ncr.2022.013
  38. Simard M., Pinto N., Fisher J.B., Baccini A. Mapping forest canopy height globally with spaceborne lidar // Journal of Geophysical Research. 2011. Vol. 116. No G4. ID G04021. https://doi.org/10.1029/2011JG001708
  39. Smirnova O.V., Bobrovsky M.V., Khanina L.G., Braslavskaya T. Yu., Starodubtseva E.A., Evstigneev O.I., Korotkov V.N., Smirnov V.E., Ivanova N.V. Nemoral Forests // O.V. Smirnova, M.V. Bobrovsky, L.G. Khanina (Eds.): European Russian forests: Their current state and features of their history. Plant and Vegetation 15. Dordrecht: Springer, 2017. P. 333–476. https://doi.org/10.1007/978-94-024-1172-0_5
  40. Sturm J., Santos M.J., Schmid B., Damm A. Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought // Global Change Biology. 2022. Vol. 28. No 9. P. 2956–2978. https://doi.org/10.1111/gcb.16136
  41. Sylvain J.-D., Drolet G., Brown N. Mapping dead forest cover using a deep convolutional neural network and digital aerial photography // ISPRS Journal of Photogrammetry and Remote Sensing. 2019. Vol. 156. P. 14–26. https://doi.org/10.1016/j.isprsjprs.2019.07.010
  42. The mosaic-cycle concept of ecosystem / ed. by H. Remmert. Berlin, Heidelberg, NewYork: Springer-Verlag, 1991. 168 p.
  43. Tyrrell L.E., Crow T.R. Structural characteristics of old-growth hemlock-hardwood forests in relation to age // Ecology. 1994. Vol. 75. No 2. P. 370–386. https://doi.org/10.2307/1939541
  44. Vepakomma U. Spatial contiguity and continuity of canopy gaps in mixed wood boreal forests: Persistence, expansion, shrinkage and displacement // Journal of Ecology. 2012. Vol. 100. No 5. 1257–1268. https://doi.org/10.1111/j.1365-2745.2012.01996.x
  45. Veselov V.M., Pribylskaya I.R., Mirzeabasov O.A. World Data Center (RIHMI-WDC), Roshydromet. 2021. http://aisori-m.meteo.ru/waisori
  46. Vierling K.T., Vierling L.A., Gould W.A., Martinuzzi S., Clawges R.M. LiDAR: shedding new light on habitat characterization and modeling // Frontiers in Ecology and the Environment. 2008. Vol. 6. No 2. P. 90–98. https://doi.org/10.1890/070001
  47. Wang Z., Waser L.T., Ginzler C. A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds // Forestry: An International Journal of Forest Research. 2015. Vol. 88. No 4. P. 429–440. https://doi.org/10.1093/forestry/cpv012
  48. Watt A.S. Pattern and process in the plant community // Journal of Ecology. 1947. Vol. 35. No 1–2. P. 1–22.
  49. Whitehead K., Hugenholtz C.H. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges // Journal of Unmanned Vehicle Systems. 2014. Vol. 2. No 3. P. 69–85. https://doi.org/10.1139/juvs-2014-0006
  50. Whitehead K., Hugenholtz C.H., Myshak S., Brown O., LeClair A., Tamminga A., Barchyn T.E., Moorman B., Eaton B. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: scientific and commercial applications // Journal of Unmanned Vehicle Systems. 2014. Vol. 2. No 3. P. 86–102. https://doi.org/10.1139/juvs-2014-0007
  51. Wulder M.A., Hall R.J., Coops N.C., Franklin S.E. High spatial resolution remotely sensed data for ecosystem characterization // BioScience. 2004. Vol. 54. No 6. P. 511–521. https://doi.org/10.1641/0006-3568(2004)054[0511: HSRRSD]2.0.CO;2
  52. Zhang J., Huang S., Hogg E.H., Lieffers V., Qin Y., He F. Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data // Biogeosciences. 2014. Vol. 11. No 10. P. 2793–2808. https://doi.org/10.5194/bg-11-2793-2014
  53. Zhang J., Nielsen S.E., Mao L.F., Chen S.B., Svenning J.-C. Regional and historical factors supplement current climate in shaping global forest canopy height // Journal of Ecology. 2016. Vol. 104. No 2. P. 469–478. https://doi.org/10.1111/1365-2745.12510

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. Projected areas of crowns of trees of the first tier of different species according to archival data (count of 1986–1988). The lower limit of the box is the 1st quartile, the upper is the 3rd; the thick horizontal line indicates the 2nd quartile (median value); “whiskers” show the range of values

下载 (101KB)
索引源数据
3. Fig. 2. Histograms of the distribution of normalized pixel heights for two different-time surveys. Vertical lines show height thresholds for distinguishing different classes of windows

下载 (69KB)
索引源数据
4. Fig. 3. Plan of a permanent trial plot for June 4, 2021, dividing the territory into forest canopy (height > 20 m) and renewal windows of different height classes (a); plan of a permanent trial plot with division of woody vegetation into 3 classes (b).

下载 (116KB)
索引源数据

版权所有 © Russian Academy of Sciences, 2024

##common.cookie##