Forest biomass resources and utilization in China

2012 ◽  
Vol 11 (39) ◽  
Author(s):  
Jianfeng ZHANG
2020 ◽  
Vol 3 (1) ◽  
pp. 40
Author(s):  
Yusuke Matsuoka ◽  
Hiroaki Shirasawa ◽  
Uichi Hayashi ◽  
Kazuhiro Aruga

To promote sustainable timber and forest biomass utilization, this study estimated technically feasible and economically viable availability considering forest regenerations. This study focuses on five prefectures, namely, Aomori, Iwate, Miyagi, Akita, and Yamagata, and considers the trade between these prefectures. The data used in this study include forest registration (tree species and site index) and GIS data (information on roads and subcompartment layers) from the prefectures for private and communal forests. Additionally, this study includes GIS data (subcompartment layers, including tree species) from the Forestry Agency of Japan for national forests as well as 10-m-grid digital elevation models (DEMs) from the Geographical Survey Institute. As a result, supply potentials of timber and forest biomass resources were estimated at 11,388,960 m3/year and 2,277,792 m3/year, respectively. Then, those availabilities were estimated at 1,631,624 m3/year and 326,325 m3/year. Therefore, the rate of availabilities to supply potentials was 14.3%. Since timber production, and wood chip usage from thinned woods and logging residues in 2018 were 4,667,000 m3/year and 889,600 m3/year, respectively, the rates of timber and forest biomass resource availabilities to those values were 35.0% and 36.7%, respectively. Furthermore, the demand was estimated at 951,740 m3/year from 100,000 m3/year with the generation capacity of 5 MW. The rate of forest biomass resource availability versus the demand was 34.2%. The rates were increased to 64.1% with an additional regeneration subsidy, 173.3% with the thinning subsidy, and 181.5% with both subsidies. Thus, the estimated availability with both subsidies met the demand sufficiently in this region.


2011 ◽  
Vol 37 (6) ◽  
pp. 596-611 ◽  
Author(s):  
Hans-Erik Andersen ◽  
Jacob Strunk ◽  
Hailemariam Temesgen ◽  
Donald Atwood ◽  
Ken Winterberger

2014 ◽  
Vol 103 (3) ◽  
pp. 431-438 ◽  
Author(s):  
Yeong Mo Son ◽  
Sun Jeoung Lee ◽  
Sowon Kim ◽  
Jeong Sun Hwang ◽  
Raehyun Kim ◽  
...  

2013 ◽  
Vol 29 (1) ◽  
pp. 81-89
Author(s):  
Jin-A Lee ◽  
Jae-Heun Oh ◽  
Du-Song Cha

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 71
Author(s):  
Yusuke Matsuoka ◽  
Hiroaki Shirasawa ◽  
Uichi Hayashi ◽  
Kazuhiro Aruga

To utilize timber and forest biomass resources for bioenergy, technically feasible and economically viable timber and forest biomass resources should be estimated accurately considering their long-term availability. This study focuses on five prefectures, namely, Aomori, Iwate, Miyagi, Akita, and Yamagata, and considers trade between these prefectures. The annual availability of timber and forest biomass resources, such as small-diameter or defect stem logs, rather than logging residues in Japan was estimated as supply potential from profitable forests where expected revenues surpassed all costs, from planting to final harvest. As a result, the supply potential and annual availability of timber were estimated at 11,388,960 m3/year and 1,631,624 m3/year, whereas those of forest biomass resources were estimated at 2,277,792 m3/year and 326,325 m3/year, respectively. Therefore, the rate of annual availability to supply potential was 14.3%. Since timber production and wood chip usage from thinned woods and logging residues in 2018 were 4,667,000 m3/year and 889,600 m3/year, the rates of annual availability for timber and forest biomass resources to those values were 35.0% and 36.7%, respectively. Furthermore, the demand was estimated at 951,740 m3/year from 100,000 m3/year with a generation capacity of 5 MW. The rate of forest biomass resource availability to demand was 34.2%. A thinning subsidy was provided for operational site areas larger than 5 ha in Japan. The rates from subcompartments and aggregated forests with a thinning subsidy increased to 91.4% and 190.3%, respectively. Thus, the estimated availability from aggregated forests with a thinning subsidy met the demand sufficiently in this region.


Author(s):  
Chaitanya B. Pande ◽  
Kanak N. Moharir ◽  
Sudhir Kumar Singh ◽  
Abhay M. Varade ◽  
Ahmed Elbeltagi ◽  
...  

2011 ◽  
Vol 26 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Hans-Erik Andersen ◽  
Jacob Strunk ◽  
Hailemariam Temesgen

Abstract Airborne laser scanning, collected in a sampling mode, has the potential to be a valuable tool for estimating the biomass resources available to support bioenergy production in rural communities of interior Alaska. In this study, we present a methodology for estimating forest biomass over a 201,226-ha area (of which 163,913 ha are forested) in the upper Tanana valley of interior Alaska using a combination of 79 field plots and high-density airborne light detection and ranging (LiDAR) collected in a sampling mode along 27 single strips (swaths) spaced approximately 2.5 km apart. A model-based approach to estimating total aboveground biomass for the area is presented. Although a design-based sampling approach (based on a probability sample of field plots) would allow for stronger inference, a model-based approach is justified when the cost of obtaining a probability sample is prohibitive. Using a simulation-based approach, the proportion of the variability associated with sampling error and modeling error was assessed. Results indicate that LiDAR sampling can be used to obtain estimates of total biomass with an acceptable level of precision (8.1 ± 0.7 [8%] teragrams [total ± SD]), with sampling error accounting for 58% of the SD of the bootstrap distribution. In addition, we investigated the influence of plot location (i.e., GPS) error, plot size, and field-measured diameter threshold on the variability of the total biomass estimate. We found that using a larger plot (1/30 ha versus 1/59 ha) and a lower diameter threshold (7.6 versus 12.5 cm) significantly reduced the SD of the bootstrap distribution (by approximately 20%), whereas larger plot location error (over a range from 0 to 20 m root mean square error) steadily increased variability at both plot sizes.


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