Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido, Japan
Introduction
Because trees are recognized as a carbon reservoir, precise and convenient methods for forest biomass estimation are required for adequate carbon management under the increasing CO2 concentration in the atmosphere. Traditionally, carbon densities have been assessed using field-based inventory plots, which are valuable but also expensive, time consuming and inherently limited in geographic representativeness (Asner, 2009). Remote sensing is considered to be a promising solution for large-scale forest biomass evaluation, and an airborne light detection and ranging (LiDAR) technique, in particular, has been developed for this purpose (Naesset, 1997). This technique can provide 3D information on land surfaces by measuring the distance between the laser ranging sensor installed on an airplane and points on the land surface and can yield aboveground carbon maps over thousands of hectares per day of flying, when combined with field calibration plots. The LiDAR-based maps can be used in a baseline analysis to initiate a long-term monitoring program that will subsequently rely primarily on low-cost satellite data and analysis methods (Asner, 2009).
Many LiDAR studies have evaluated the timber volume or biomass of forests with a relatively sparse canopy or plantation with simple age and size structures with considerable success (Boudreau et al., 2008, Hayashi et al., 2013, Tsuzuki et al., 2006, Yone et al., 2002, Yone et al., 2005, Zhao et al., 2009). On the other hand, quantification has been less successful in the case of natural forests with high aboveground biomass because of the difficulty in predicting the state of the forest below the canopy layer (Yone, 2008). However, several studies have succeeded in estimating forest biomass by simply relating the LiDAR canopy height to ground observations on forest biomass or volume (Asner, 2009, Asner et al., 2009, Lefsky et al., 2002). This approach has been recommended for dense natural forests to reduce the effort and expense required to develop global biomass estimates from satellite LiDAR data (Asner, 2009, Lefsky et al., 2002).
The aim of this study was to clarify the relationship between LiDAR canopy height and the timber volume/biomass of cool-temperate forests in northern Hokkaido, Japan. For this purpose, we conducted LiDAR observations covering the total area of the Teshio Experimental Forest (225 km2) of Hokkaido University and compared the results with ground surveys and previous studies.
Section snippets
Study site and ground surveys
The study site was located in the Teshio Experimental Forest, Hokkaido University (44°54′–45°06′N, 141°56′–142°10′E) in northernmost Hokkaido, Japan. The site has the characteristics of a mid-latitude cool-temperate climate, and it lies in the transition zone between temperate and sub-boreal forest ecosystems. About 90% of the studied forest is naturally regenerated forest and the other 10% is conifer plantation. The dominant tree species are Abies sachalinensis (F. Schmidt) Mast., Picea
Results and discussion
DSM or DTM showed good agreement between the two LiDAR observation in 2002 and 2004 in spite of the different sensor specifications and phenological stage of the forest trees and the understory (r2 = 0.99 for DSM and DTM), although the DTM in 2004 tended to be slightly higher than that in 2002 compared with the relationship between DSMs of the two years (slope of the regression line = 1.0058 for DSM and 1.0119 for DTM) (Fig. 2). Change in DSM by tree growth is considered to be little and within the
Acknowledgements
This research was part of the “CC-LaG experiment” project, which was carried out through the collaboration of Hokkaido University, the National Institute for Environmental Studies, and Hokkaido Electric Power Co., Inc. This work was partly supported by JSPS KAKENHI Grant Numbers 23255009, 25241002, 26292076 and 21114008. This study would not have been possible without the assistance of graduate students of Agriculture, Engineering, and Environment Science of Hokkaido University, and the
References (27)
- et al.
Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec
Remote Sens. Environ.
(2008) - et al.
Seasonal and interannual variations in carbon dioxide exchange of a temperate larch forest
Agric. For. Meteorol.
(2007) - et al.
Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data
Int. J. Appl. Earth Obs. Geoinform.
(2012) - et al.
Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the western cascades of Oregon
Remote Sens. Environ.
(1999) Estimating timber volume of forest stands using airborne laser scanner data
Remote Sens. Environ.
(1997)- et al.
Investigating RaDAR–LiDAR synergy in a North Carolina pine forest
Remote Sens. Environ.
(2007) - et al.
Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers
Remote Sens. Environ.
(2009) Tropical forest carbon assessment: integrating satellite and airborne mapping approaches
Environ. Res. Lett.
(2009)- et al.
Environmental and biotic controls over aboveground biomass throughout a tropical rain forest
Ecosystems
(2009) DEM generation from laser scanner data using adaptive TIN models
Int. Arch. Photogramm. Remote Sens.
(2000)
Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests
Ann. For. Sci.
Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica
J. Geophys. Res.
Forest biomass mapping with airborne LiDAR in Yokohama City
J. Jpn. Soc. Photogramm.
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