Generic models of the biomass of larches (Larix spp.) and stone pines (Pinus L. subsection Cembrae Loud.) for laser sensing in climatic gradients of Eurasia
DOI:
https://doi.org/10.12775/EQ.2021.033Keywords
hydrothermal gradients, biomass components, laser sensing of trees, allometric models, average January temperature, annual precipitationAbstract
In the context of growing global urbanization and climate fluctuations, understanding the development of forest ecosystems in terms of their ability to remove atmospheric carbon is of increasing interest. Its content in the atmosphere continues to increase due to the burning of fossil fuels and deforestation. Airborne laser scanning technology has become widely used in assessing the biomass of trees by remotely registering such taxation indicators of trees, such as the width of the crown and the height of the tree. There are many allometric biomass models of different tree species in different climatic conditions, but allometric models for estimating biomass by remote methods are presented by single works.
The authors use the base of harvest data of 138 sample trees of larch (Larix spp.) and 93 ones of stone pines (Pinus L. subsection Cembrae Loud.) growing on Eurasia with measured indicators of tree height, crown width, as well as the biomass of the trunk, foliage, branches and roots. For all components of aboveground biomass, a positive relationship with the crown width and the tree height was established.
The results obtained give an vision of how the structure of the biomass of equal-sized trees of such species as larch and stone pine can differ, whether this structure can change in the climatic gradients of Eurasia, what can be the contribution of climate variables to the explanation of the variability of tree biomass, and what are the potential possibilities of laser technology for recognizing tree species at the level of individual trees
References
Åkerblom M., Raumonen P., Mäkipää R. & Kaasalainen M., 2017, Automatic tree species recognition with quantitative structure models. Remote Sensing of Environment 191: 1–12.
Atkinson M.D., Jervis A.P. & Sangha R.S., 1997, Discrimination between Betula pendula, Betula pubescens, and their hybrids using near-infrared reflectance spectroscopy. Canadian Journal of Forest Research 27: 1896-1900.
Baskerville G.L., 1972, Use of logarithmic regression in the estimation of plant biomass. Canadian Journal of Forest Research 2(1): 49-53.
Belov S.V. & Artsybashev E.S., 1957, Study of the reflectivity of wood species. Botanical Journal [Botanicheskiy Zhurnal] 42(4): 517-534 (in Russian).
Blanchette D., Fournier R.A., Luther J.E. & Côté J.-F., 2015, Predicting wood fiber attributes using local-scale metrics from terrestrial LiDAR data: A case study of Newfoundland conifer species. Forest Ecology and Management 347: 116-129.
Burger H., 1939, Der Kronenaufbau gleichalteriger Nadelholzbestände. Mitteilungen der Schweizerischen Anstalt für das forstliche Versuchswesen 21(1): 5-57.
Calders K., Adams J., Armston J., Bartholomeus H., Bauwens S., Bentley L.P., Chave J., Danson F.M., Demol M., Disney M., Gaulton R., Moorthy S.M.K., Levick S.R., Saarinen N., Schaaf C., Stovall A., Terryn L., Wilkes P. & Verbeeck H., 2020, Terrestrial laser scanning in forest ecology: expanding the horizon. Remote Sensing of Environment 251: 112102.
Campbell S.A. & Borden J.H., 2005, Bark reflectance spectra of conifers and angiosperms: implications for host discrimination by coniferophagous bark and timber beetles. The Canadian Entomologist 137: 719–722.
Cannell M.G.R., 1982, World forest biomass and primary production data. Academic Press, London, 391 pp.
Colombo S.J., 2008, Ontario’s forests and forestry in a changing climate / Climate change research report; CCRR-12. Ontario Forest Research Institute, Ontario Ministry of Natural Resources, 22 pp.
Dai X., 2021, Novel methods for estimating aboveground biomass. Master Thesis (Forestry). University of New Brunswick, 123 pp. DOI: 10.13140/RG.2.2.13216.30721
Dalponte M., Bruzzone L. & Gianelle D., 2008, Fusion of hyperspectral and LiDAR remote sensing data for classification of complex forest areas. IEEE Transactions on Geoscience and Remote Sensing 46: 1416–1427.
Dalponte M., 2018, itcSegment: Individual tree crowns segmentation. R package version 0.8. https://CRAN.R-project.org/package=itcSegment.
Danilin I.M., Medvedev Е.М. & Melnikov S.R., 2005, Laser location of earth and forest: Textbook. Krasnoyarsk, V.N. Sukachev Institute of forest, Russian Academy of sciences, Siberian branch, 182 pp. (In Russian).
Dengler A., 1937, Kronengrösse, Nadelmenge und Zuwachsleistung von Altkiefern. Zeitschrift für Forst- und Jagdwesen 69: 321-336.
Falster D.S., Duursma R.A., Ishihara M.I., Barneche D.R., FitzJohn R.G., Vårhammar A., Aiba M., Ando M., Anten N., Aspinwall M.J., Baltzer J.L., Baraloto C., Battaglia M., Battles J.J., Bond-Lamberty B., van Breugel M., Camac J., Claveau Y., Coll L., Dannoura M., Delagrange S., Domec J.-C., Fatemi F., Feng W., Gargaglione V., Goto Y., Hagihara A., Hall J.S., Hamilton S., Harja D., Hiura T., Holdaway R., Hutley L.S., Ichie T., Jokela E.J., Kantola A., Kelly J.W.G., Kenzo T., King D., Kloeppel B.D., Kohyama T., Komiyama A., Laclau J.-P., Lusk C.H., Maguire D.A., le Maire G., Mäkelä A., Markesteijn L., Marshall J., McCulloh K., Miyata I., Mokany K., Mori S., Myster R.W., Nagano M., Naidu S.L., Nouvellon Y., O'Grady A.P., O'Hara K.L., Ohtsuka T., Osada N., Osunkoya O.O., Peri P.L., Petritan A.M., Poorter L., Portsmuth A., Potvin C., Ransijn J., Reid D., Ribeiro S.C., Roberts S.D., Rodríguez R., Saldaña-Acosta A., Santa-Regina I., Sasa K., Selaya N.G., Sillett S.C., Sterck F., Takagi K., Tange T., Tanouchi H., Tissue D., Umehara T., Utsugi H., Vadeboncoeur M.A., Valladares F., Vanninen P., Wang J.R., Wenk E., Williams R., Ximenes F. de Aquino, Yamaba A., Yamada T., Yamakura T., Yanai R.D. & York R.A., 2015, BAAD: a Biomass And Allometry Database for woody plants. Ecology 96: 1445–1445.
Fu L., Sun W. & Wang G., 2017, A climate-sensitive aboveground biomass model for three larch species in northeastern and northern China. Trees 31: 557–573.
Fu W. & Wu Y., 2011, Estimation of aboveground biomass of different mangrove trees based on canopy diameter and tree height. Procedia Environmental Sciences 10: 2189–2194.
Goodman R.C., Phillips O.L. & Baker T.R., 2014, The importance of crown dimensions to improve tropical tree biomass estimates. Ecological Applications 24(4): 680–698.
Goodwin N.R., Coops N.C. & Culvenor D.S., 2006, Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sensing of Environment 103: 140–152.
Grote R., Kiese R., Grünwald T., Ourcival J.-M. & Granier A., 2011, Modelling forest carbon balances considering tree mortality and removal. Agricultural and Forest Meteorology 151: 179–190.
Hartig R., 1896, Wachstumsutersuchungen an Fichten. Forstlich-Naturwissenschaftliche Zeitschrift 5(1, 3): 1-15, 33-45.
Hayashi R., 2014, Evaluation of airborne LiDAR as a tool for obtaining sustainable forest management of Maine's forests. Electronic Theses and Dissertations. Paper 2223, 64 pp. http://digitalcommons.library.umaine.edu/etd.
Hennessy A., Clarke K. & Lewis M., 2020, Hyperspectral classification of plants: a review of waveband selection generalisability. Remote Sensing 12: 113.
Huang H. & Mayer H., 2007, Extraction of 3D unfoliaged trees from image sequences via a generative statistical approach. Conference: Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14, Proceedings: 1-10.
Hyyppä J., Hyyppä H., Leckie D., Gougeon F., Yu X. & Maltamo M., 2008, Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal Remote Sensing 29(5): 1339-1366.
Jaaskelainen T., Silvennoinen R., Hiltunen J. & Parkkinen J.P.S., 1994, Classification of the reflectance spectra of pine, spruce, and birch. Applied Optics 33: 2356-2362.
Jensen J.R., 2005, Introductory digital image processing. Third edition. Upper Saddle River, New Jersey, Prentice Hall, 526 pp.
Jing L., Hu B., Noland T. & Li J., 2012, An individual tree crown delineation method based on multi-scale segmentation of imagery. ISPRS Journal of Photogrammetry and Remote Sensing 70: 88–98.
Jucker T., Caspersen J., Chave J., Antin C., Barbier N., Bongers F., Dalponte M., van Ewijk K.Y., Forrester D.I., Heani M., Higgins S.I., Holdaway R.J., Iida Y., Lorimer C., Marshall P.M., Momo S., Moncrieff G.R., Ploton P., Poorter L., Rahman K.A., Schlund M., Sonké B., Sterck F.J., Trugman A.T., Usoltsev V.A., Vanderwel M.C., Waldner P., Wedeux B., Wirth C., Wöll H., Woods M., Xiang W., Zimmermann N. & Coomes D.A., 2017, Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Global Change Biology 23: 177-190.
Kajihara M., 1981, Сrown form, crown structure and the relationship between crown dimensions and leaf fresh weight of hinoki (Chamaecyparis obtusa). Bulletin of the Kyoto Prefectural University Forests 25: 11-28.
Kajihara M., 1980, Сrown structure of sugi (Cryptomeria japonica) and the relationship between crown dimensions and leaf fresh weight. Bulletin of the Kyoto Prefectural University Forests 24: 49-63.
Kapustinskaite T.K. & Rusečkas J.J., 1982, Tables for determining the leaf surface and weight of various parts of a tree in forest phytocenoses. Lithuanian Forestry Research Institute, Kaunas, Lithuania, 11 pp. (in Russian).
Kern K.G., 1962, Die Beziehungen zwischen einigen Kronenkennwerten und dem Nadeltrockengewicht bei Fichte und Tanne. Allgemeine Forst- und Jagdzeitung 133: 13 -18.
Knight T.C., Ezell A.W., Shaw D.R., Byrd J.D. & Evans D.L., 2004, Identifying loblolly pine and four common competing hardwood species using multispectral reflectance analysis, [in:] K.F. Connor (ed.), Proceedings of the 12th biennial southern silvicultural research conference. Gen. Tech. Rep. SRS–71. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 336-342.
Lau A., Calders K., Bartholomeus H., Martius C., Raumonen P., Herold M., Vicari M., Sukhdeo H., Singh J. & Goodman R.C., 2019, Tree biomass equations from terrestrial LiDAR: a case study in Guyana. Forests 10: 527.
Lefsky M.A., Cohen W.B., Parker G.G. & Harding D.J., 2003, Lidar remote sensing for ecosystem studies. AIBS Bulletin 52(1): 19-30.
Leontiev V.L., 1950, On the estimation of the biomass of Haloxylon Bunge. Botanical Journal [Botanicheskiy Zhurnal] 35(6): 637-645. (in Russian).
Lines E.R., Zavala M.A., Purves D.W. & Coomes D.A., 2012, Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition. Global Ecology and Biogeography 21: 1017–1028.
Liu R., Yang X., Gao R., Hou X., Huo L., Huang Z. & Cornelissen J.H.C., 2021, Allometry rather than abiotic drivers explains biomass allocation among leaves, stems and roots of Artemisia across a large environmental gradient in China. Journal of Ecology 109(2): 1026-1040.
Luo Y., Wang X., Ouyang Z., Lu F., Feng L. & Tao J., 2020, A review of biomass equations for China's tree species. Earth System Science Data 12(1): 21-40.
Machimura T., Fujimoto A., Hayashi K., Takagi H. & Sugita S., 2021, A novel tree biomass estimation model applying the pipe model theory and adaptable to UAV-derived canopy height models. Forests 12: 258.
Martin M.E., Newman S.D., Aber J.D. & Congalton R.G., 1998, Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment 65: 249–254.
Mas J.F. & Flores J.J., 2008, The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing 29: 3617–3663.
Masaitis G., Mozgeris G., Augustaitis A., 2013, Spectral reflectance properties of healthy and stressed coniferous trees. iForest: e1-e7. http://www.sisef.it/iforest/contents?id=ifor0709-006
Næsset E. & Økland T., 2002, Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment 79: 105–115.
Næsset E., Gobakken T., Holmgren J., Hyyppä H., Hyyppä J., Maltamo M., Nilsson M., Olsson H., Persson A. & Derman U., 2004, Laser scanning of forest resources: the Nordic experience. Scandinavian Journal of Forest Research 19: 482-489.
Neuville R., Bates J.S. & Jonard F., 2021, Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning. Remote Sensing 13: 352.
Ohmann L.F., Grigal D.F. & Brander R.B., 1976, Biomass estimation for five shrubs from northeastern Minnesota. USDA Forest Service, North Central Forest Experiment Station. Research paper NC-133, 11 pp. https://www.nrs.fs.fed.us/pubs/rp/rp_nc133.pdf
Oki K., Shan L., Saruwatari T., Suhama T. & Omasa K., 2006, Evaluation of supervised classification algorithms for identifying crops using airborne hyperspectral data. International Journal of Remote Sensing 27(10): 1993–2002.
Ørka H.O., Næsset E. & Bollands°as O.M., 2009, Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sensing of Environment 113: 1163–1174.
Poorter H., Jagodzinski A.M., Ruiz-Peinado R., Kuyah S., Luo Y., Oleksyn J., Usoltsev V.A., Buckley T.N., Reich P.B. & Sack L., 2015, How does biomass allocation change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytologist 208(3): 736-749.
Pozdnyakov L.K., Protopopov V.V. & Gorbatenko V.M., 1969, Biological productivity of forests in Middle Siberia and Yakutia. Knizhnoe Izdatelstvo, Krasnoyarsk, 120 pp. (in Russian).
Puttonen E., Litkey P. & Hyyppä J., 2010, Individual tree species classification by illuminated—shaded area separation. Remote Sensing 2: 19-35.
Puttonen E., Jaakkola A., Litkey P. & Hyyppä J., 2011, Tree classification with fused mobile laser scanning and hyperspectral data. Sensors 11(5): 5158–5182.
Rehling F., Sandner T.M. & Matthies D., 2021, Biomass partitioning in response to intraspecific competition depends on nutrients and species characteristics: a study of 43 plant species. Journal of Ecology. Preprint. DOI:10.1111/1365-2745.13635.
Schepaschenko D., Shvidenko A., Usoltsev V.А., Lakyda P., Luo Y., Vasylyshyn R., Lakyda I., Myklush Y., See L., McCallum I., Fritz S., Kraxner F. & Obersteiner M., 2017, A dataset of forest biomass structure for Eurasia. Scientific Data 4 (No 170070): 1-11.
Shlyakhter I., Rozenoer M., Dorsey J. & Teller S., 2001, Reconstructing 3D tree models from instrumented photographs. IEE Computer Graphics and Applications 21(3): 53–61.
Sobhan I., 2007, Species discrimination from a hyperspectral perspective. Doctoral Thesis. International Institute for Geoinformation Science & Earth Observation, Enschede, the Netherlands, 164 pp.
Tolkach I.V. & Sayevich F.K., 2016, Spectral and brightness characteristics of the main forest-forming species on images of the scanner Leica ADS100. Proceedings of Belarusian State Technological University 1: 24-27. (in Russian).
Usoltsev V.A., 2016, Single-tree biomass of forest-forming species in Eurasia: Database, climate-related geography, weight tables. Ural State Forest Engineering University, Yekaterinburg, 336 pp. https://elar.usfeu.ru/handle/123456789/5696
Usoltsev V.A., 2020, Single-tree biomass data for remote sensing and ground measuring of Eurasian forests: digital version. The second edition, enlarged. Ural State Forest Engineering University; Botanical Garden of Ural Branch of RAS, Yekaterinburg. https://elar.usfeu.ru/bitstream/123456789/9647/2/Base1_v2_ob.pdf
Usoltsev V.A., Shobairi S.O.R. & Chasovskikh V.P., 2019a, Comparing of allometric models of single-tree biomass intended for airborne laser sensing and terrestrial taxation of carbon pool in the forests of Eurasia. Natural Resource Modeling 32: e12187.
Usoltsev V.A., Zukow W., Osmirko A.A., Tsepordey I.S. & Chasovskikh V.P., 2019b, Additive biomass models for Quercus spp. single-trees sensitive to temperature and precipitation in Eurasia. Ecological Questions 30(4): 29–40.
Usoltsev V.A., Shobairi O., Tsepordey I.S., Ahrari A., Zhang M., Shoaib A.A. & Chasovskikh V.P., 2020a, Are there differences in the response of natural stand and plantation biomass to changes in temperature and precipitation? A case for two-needled pines in Eurasia. Journal of Resources and Ecology 11(4): 331–341.
Usoltsev V.A., Shobairi O., Tsepordey I.S. & Chasovskikh V.P., 2020b, On some differences in the response of Picea spp. and Abies spp. single-tree biomass structure to changes in temperatures and precipitation in Eurasia. Environment and Ecology 38(3): 300–315.
Van den Berge S., Vangansbeke P., Calders K., Vanneste T., Baeten L., Verbeeck H., Parvathi S., Moorthy K. & Verheyen K., 2021, Biomass expansion factors for hedgerow-grown trees derived from terrestrial LiDAR. BioEnergy Research (Preprint). DOI: 10.1007/s12155-021-10250-y
Veyisov S. & Kaplin V.G., 1976, To the method of biomass estimating in white saxaul of the Eastern Kara-Kum desert. Problems of desert exploitation [Problemy osvoeniya pustyn’] 1: 60–64. (in Russian).
Voss M. & Sugumaran R., 2008, Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach. Sensors 8: 3020–3036.
Westman W.E. & Whittaker R.H., 1975, The pygmy forest region of Northern California: studies on biomass and primary productivity. The Journal of Ecology 63: 493-520.
World Weather Maps, 2007. https://www.mapsofworld.com/referrals/weather
Xiong F., Nie X., Yang L., Wang L., Li J. & Zhou G., 2021, Biomass partitioning pattern of Rheum tanguticum on the Qinghai–Tibet Plateau was affected by water-related factors. Plant Ecology. Preprint. DOI:10.1007/s11258-021-01122-8
Zanotelli D., Montagnani L., Manca G. & Tagliavini M., 2013, Net primary productivity, allocation pattern and carbon use efficiency in an apple orchard assessed by integrating eddy covariance, biometric and continuous soil chamber measurements. Biogeosciences 10: 3089–3108.
Zeng W.S., Duo H.R., Lei X.D., Chen X.Y., Wang X.J., Pu Y. & Zou W.T., 2017, Individual tree biomass equations and growth models sensitive to climate variables for Larix spp. in China. European Journal of Forest Research 136: 233–249.
Zhang C. & Qiu F., 2012, Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery. Photogrammetric Engineering & Remote Sensing 78(10): 1079–1087.
Zhang K. & Hu B., 2012, Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles. Remote Sensing 4: 1741-1757.
Zhen Z., Quackenbush L.J. & Zhang L., 2016, Trends in automatic individual tree crown detection and delineation—Evolution of LiDAR data. Remote Sensing 8(4): 333.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Stats
Number of views and downloads: 446
Number of citations: 0
