2024-03-29T10:39:45Z
https://soar-ir.repo.nii.ac.jp/oai
oai:soar-ir.repo.nii.ac.jp:00021956
2022-12-14T04:20:00Z
1595:1850
Mapping growing stock volume and biomass carbon storage of larch plantations in Northeast China with L-band ALOS PALSAR backscatter mosaics
Gao, Tian
Zhu, J. J.
Yan, Q. L.
Deng, S. Q.
Zheng, X.
Zhang, J. X.
Shang, G. D.
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 29 Jun 2018, available online: http://www.tandfonline.com/10.1080/01431161.2018.1479793.
Reliable spatial information on growing stock volume (GSV) and biomass is critical for creating management strategies for plantation forests. This study developed empirical models to map the GSV and biomass of larch plantations (LPs) in Northeast China (1.25 million km(2) total area) by integrating L-band synthetic aperture radar (SAR) data with ground-based survey data. The best correlation model was used to map the GSVs and biomasses of LPs. The total GSV and biomass carbon storage were estimated at 224.3 +/- 59.0 million m(3) and 113.0 +/- 29.7 x 10(12) g C with average densities of 85.1 m(3) ha(-1) and 42.9 10(6) g x C ha(-1), respectively, over a total area of 2.64 million ha. The saturation effect of SAR was determined beyond 260 m(3) ha(-1), which was expected to influence the estimations for a small proportion of the study area. The accuracy of the estimations has limitations mainly due to the uncertainties in the GSV inventories, discrimination of natural larch and the SAR dataset. Based on the mapping results of the GSVs of LPs, a planning strategy for multipurpose management was tentatively proposed. This study can inform policies and management practices to assure broader and sustainable benefits from plantation forests in the future.
Article
INTERNATIONAL JOURNAL OF REMOTE SENSING.39(22):7978-7997(2018)
TAYLOR & FRANCIS LTD
2018-06-29
eng
journal article
AM
http://hdl.handle.net/10091/00022711
https://soar-ir.repo.nii.ac.jp/records/21956
https://doi.org/10.1080/01431161.2018.1479793
10.1080/01431161.2018.1479793
0143-1161
AA00234335
INTERNATIONAL JOURNAL OF REMOTE SENSING
39
22
7978
7997
https://soar-ir.repo.nii.ac.jp/record/21956/files/16K18716_02.pdf
application/pdf
1.3 MB
2021-02-22