Aboveground biomass estimation in linear forest objects: 2D- vs. 3D-data

https://doi.org/10.17221/106/2018-JFSCitation:Lingner S., Thiessen E., Hartung E. (2018): Aboveground biomass estimation in linear forest objects: 2D- vs. 3D-data. J. For. Sci., 64: 523-532.
download PDF

Wood-chips of linear forest objects (hedge banks and roadside plantings) are used as sustainable energy supply in wood-chip heating systems. However, wood yield of linear forest objects is very heterogeneous and hard to estimate in advance. The aim of the present study was to compare the dry mass estimation potentials of two different non-destructive data: (i) Canopy area (derived from aerial images) and mean age at stump level (2D), (ii) volume of vegetation cover based on structure from motion (SfM) via unmanned aerial vehicle (3D). These two types of data were separately used to predict reference dry mass (ground truth) in eleven objects (5 hedge banks and 6 roadside plantings) in Schleswig-Holstein, Germany. The predicting potentials were compared afterwards. The reference dry mass was ascertained by weighing after harvesting and drying samples to constant weight. The model predicting reference dry mass using canopy area and mean age at stump level achieved a relative root mean square error (RMSE) of 52% (42% at larger combined plot sizes). The model predicting reference dry mass using SfM volume achieved a relative RMSE of 30% (16% at larger combined plot sizes). This result indicates that biomass is better described by volume of vegetation cover than by canopy area and age.

Dandois Jonathan P., Ellis Erle C. (2010): Remote Sensing of Vegetation Structure Using Computer Vision. Remote Sensing, 2, 1157-1176  https://doi.org/10.3390/rs2041157
Díaz-Varela Ramón, de la Rosa Raúl, León Lorenzo, Zarco-Tejada Pablo (2015): High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials. Remote Sensing, 7, 4213-4232  https://doi.org/10.3390/rs70404213
Eigner J. (1982): Bewertung von Knicks in Schleswig-Holstein. Laufener Seminarbeiträge No. 5/1982: 110–117.
Fritz A., Kattenborn T., Koch B. (2013): UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS – TREE STEM MAPPING IN OPEN STANDS IN COMPARISON TO TERRESTRIAL LASER SCANNER POINT CLOUDS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W2, 141-146  https://doi.org/10.5194/isprsarchives-XL-1-W2-141-2013
Hyde Peter, Nelson Ross, Kimes Dan, Levine Elissa (2007): Exploring LiDAR–RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR. Remote Sensing of Environment, 106, 28-38  https://doi.org/10.1016/j.rse.2006.07.017
Isensee E., Stübig D.K., Lubkowitz C. (2000): Bergung und Aufbereitung von Knick- und Schwachholz. Landtechnik – Agricultural Engineering, 55: 346–347.
Stefan Lingner, Eiko Thiessen, Kerrin Müller, Eberhard Hartung (2018): Dry Biomass Estimation of Hedge Banks: Allometric Equation vs. Structure from Motion via Unmanned Aerial Vehicle. Journal of Forest Science, 64, 149-156  https://doi.org/10.17221/152/2017-JFS
Mantau U. (2012): Holzrohstoffbilanz Deutschland, Entwicklungen und Szenarien des Holzaufkommens und der Holzverwendung 1987 bis 2015. Hamburg, Universität Hamburg: 65.
Miller J., Morgenroth J., Gomez C. (2015): 3D modelling of individual trees using a handheld camera: Accuracy of height, diameter and volume estimates. Urban Forestry & Urban Greening, 14: 932–940.
Ministerium für Energiewende, Landwirtschaft, Umwelt und ländliche Räume des Landes Schleswig-Holstein (2017): January 20: Durchführungsbestimmungen zum Knickschutz. Kiel, Ministerium für Energiewende, Landwirtschaft, Umwelt und ländliche Räume des Landes Schleswig-Holstein: 19.
Muukkonen P., Heiskanen J. (2005): Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data. Remote Sensing of Environment, 99, 434-447  https://doi.org/10.1016/j.rse.2005.09.011
Muukkonen P., Heiskanen J. (2007): Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories. Remote Sensing of Environment, 107, 617-624  https://doi.org/10.1016/j.rse.2006.10.011
Ploton Pierre, Pélissier Raphaël, Proisy Christophe, Flavenot Théo, Barbier Nicolas, Rai S. N., Couteron Pierre (2012): Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecological Applications, 22, 993-1003  https://doi.org/10.1890/11-1606.1
Popescu Sorin C., Zhao Kaiguang, Neuenschwander Amy, Lin Chinsu (2011): Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sensing of Environment, 115, 2786-2797  https://doi.org/10.1016/j.rse.2011.01.026
Segura Milena, Kanninen Markku, Suárez Damaris (2006): Allometric models for estimating aboveground biomass of shade trees and coffee bushes grown together. Agroforestry Systems, 68, 143-150  https://doi.org/10.1007/s10457-006-9005-x
Seidel Dominik, Busch Gerald, Krause Benjamin, Bade Claudia, Fessel Carola, Kleinn Christoph (2015): Quantification of Biomass Production Potentials from Trees Outside Forests—A Case Study from Central Germany. BioEnergy Research, 8, 1344-1351  https://doi.org/10.1007/s12155-015-9596-z
Snavely Noah, Seitz Steven M., Szeliski Richard (2008): Modeling the World from Internet Photo Collections. International Journal of Computer Vision, 80, 189-210  https://doi.org/10.1007/s11263-007-0107-3
Tao W., Lei Y., Mooney P. (2011): Dense point cloud extraction from UAV captured images in forest area. In: Proceedings of the 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM 2011), Fuzhou, June 29–July 1, 2011: 389–392.
Turner Darren, Lucieer Arko, Watson Christopher (2012): An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds. Remote Sensing, 4, 1392-1410  https://doi.org/10.3390/rs4051392
Verscheure P. (1998) Energiegehalt von Hackschnitzeln – Überblick und Anleitung zur Bestimmung. Versuchsbericht 1998/14. Freiburg im Breisgau, Forstliche Versuchs- und Forschungsanstalt Baden-Württemberg, Abteilung Arbeitswirtschaft und Forstbenutzung: 13.
Walther R., Bernath K. (2009): Energieholzpotenziale ausserhalb des Waldes. Studie im Auftrag des Bundesamtes für Umwelt (BAFU) und des Bundesamtes für Energie (BFE). Luzern, Interface Institut für Politikstudien, Zollikon, Ernst Basler + Partner AG: 82.
Wickham H. (2009): ggplot2: Elegant Graphics for Data Analysis. New York, Springer-Verlag: 213.
Wood Simon N. (2011): Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73, 3-36  https://doi.org/10.1111/j.1467-9868.2010.00749.x
Zarco-Tejada P.J., Diaz-Varela R., Angileri V., Loudjani P. (2014): Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 55, 89-99  https://doi.org/10.1016/j.eja.2014.01.004
Zianis D., Muukkonen P., Mäkipää R., Mencuccini M. (2005): Biomass and Stem Volume Equations for Tree Species in Europe. Helsinki, Finnish Society of Forest Science, Finnish Forest Research Institute: 63.
download PDF

© 2022 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti