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DENG SONGQIU

Academic OrganizationTEL0265-77-1642
Education and Research OrganizationInterdisciplinary Cluster for Cutting Edge Research Institute of Mountain ScienceFAX0265-77-1642
PositionAssociate ProfessorMail Addressdeng@shinshu-u.ac.jp
Address8304, Minamiminowa-Village, Kamiina-County 399-4598Web sitehttps://www.shinshu-u.ac.jp/faculty/agriculture/lab/finfo/

Profile

Research Field
Forest measurement and planning
Keywords:Forest measurement , Remote sensing , Laser scanning , Smart precision forestry

Research

Books, Articles, etc.
Articles
Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
Remote Sensing of Environment,270(112845):1-20 2022
Author:Duncanson L., Kellner J., Armston J., Dubayah R., Minor D., Deng S., et al


Individual tree canopy detection and species classification of conifers by deep learning
Japanese Journal of Forest Planning,55(1):3-22 2021(Aug. 20)
Author:Hayashi Yusuke;Deng Songqiu;Katoh Masato;Nakamura Ryosuke
Abstract:

Yusuke Hayashi, Songqiu Deng, Masato Katoh and Ryosuke Nakamura: Individual tree canopy detection and species classification of conifers by deep learning. Jpn. J. For. Plann. 55: 3~22, 2021 Recently, machine leaning (ML) and deep learning (DL) have been used to grasp tree species at the single tree level. However, the traditional methods need much experience and work time of analyzer, and it was so difficult to re-use the model to new data. Therefore, we applied a one of DL method Mask R−CNN to the UAV−based ortho image and UAV− and ALS−based shape characteristics (canopy height model (H), slope model (S)), and tried to build a model that can full-automatically delineate individual tree crown and classify species at a new site. We created three data sets (RGB, RGB+H, RGB+S) from the multi-period data of the Shinshu University’s campus forest, and built original models to detect and classify the dominant coniferous tree species: red pine (Pinus densiflora), larch (Larix kaempferi), cypress (Chamaecyparis pisifera). Using these models, Individual tree crowns and tree species were estimated at four sites located in Ina City and Minamiminowa Village, Nagano Prefecture. As a result, it became clear that the RGB+S model had the most generalization of the models with a detection rate of 0.905 and a classification accuracy of 0.955, and that it was highly reusability for new sites. In the future, it is necessary to build a model that does not depend on environmental conditions more, and optimization of DL method, improvement of learning efficiency, accumulation of data, etc. are issues.




Terrestrial laser scanning-derived canopy interception index for predicting rainfall interception
Ecohydrology,13:1-15 2020(Apr. 20)
Author:Yu Y., Gao T., Zhu J., Wei X., Guo Q., Su Y., Li Y., Deng S., Li M.


Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements
FOREST ECOSYSTEMS,6 2019
Author:Liang, Xinlian; Wang, Yunsheng; Pyorala, Jiri; Lehtomaki, Matti; Yu, Xiaowei; Kaartinen, Harri; Kukko, Antero; Honkavaara, Eija; Issaoui, Aimad E. I.; Nevalainen, Olli; Vaaja, Matti; Virtanen, Juho-Pekka; Katoh, Masato; Deng, Songqiu;


Mapping growing stock volume and biomass carbon storage of larch plantations in Northeast China with L-band ALOS PALSAR backscatter mosaics
INTERNATIONAL JOURNAL OF REMOTE SENSING,39(22):7978-7997 2018(Jun. 29)
Author:Gao, Tian; Zhu, J. J.; Yan, Q. L.; Deng, S. Q.; Zheng, X.; Zhang, J. X.; Shang, G. D.


DETECTING FORESTS DAMAGED BY PINE WILT DISEASE AT THE INDIVIDUAL TREE LEVEL USING AIRBORNE LASER DATA AND WORLDVIEW-2/3 IMAGES OVER TWO SEASONS
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,XLII-3/W3:181-184 2017(Oct. 19)
Author:Takenaka, Y; Katoh, M; Deng, S; Cheung, K


DEVELOPMENT OF SMART PRECISION FOREST IN CONIFER PLANTATION IN JAPAN USING LASER SCANNING DATA
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,XLII-3/W3:95-100 2017(Oct. 19)
Author:Katoh, M; Deng, S; Takenaka, Y; Cheung, K; Oono, K; Horisawa, M; Hyyppä, J; Yu, X; Liang, X; Wang, Y


TREE SPECIES CLASSIFICATION OF BROADLEAVED FORESTS IN NAGANO, CENTRAL JAPAN, USING AIRBORNE LASER DATA AND MULTISPECTRAL IMAGES
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,XLII-3/W3:33-38 2017(Oct. 19)
Author:Deng, S; Katoh, M; Takenaka, Y; Cheung, K; Ishii, A; Fujii, N; Gao, T


Aboveground net primary productivity of vegetation along a climate-related gradient in a Eurasian temperate grassland: spatiotemporal patterns and their relationships with climate factors
ENVIRONMENTAL EARTH SCIENCES,76(1):56 2017(Jan. 03)
Author:Gao, Tian; Xu, Bin; Yang, Xiuchun; Deng, Songqiu; Liu, Yuechen; Jin, Yunxiang; Ma, Hailong; Li, Jinya; Yu, Haida; Zheng, Xiao; Yu, Qiangyi


Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms
REMOTE SENSING,8(12):1034 2016(Dec. 19)
Author:Deng, Songqiu; Katoh, Masato; Yu, Xiaowei; Hyyppa, Juha; Gao, Tian


Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,52:155-165 2016(Oct.)
Author:Gao, Tian; Zhu, Jiaojun; Deng, Songqiu; Zheng, Xiao; Zhang, Jinxin; Shang, Guiduo; Huang, Liyan

Research Grants
Grants‐in‐aid for Scientific Research(Research Representative)
2022 - 2024 , レーザセンシングによる広葉樹林の精密な立木幹材積算定技術の開発 , 基盤研究(C)
2020 - 2022 , 空と陸の次世代レーザセンシング統合による立木の高精度な品等区分技術の開発 , 基盤研究(B)
2019 - 2021 , ドローンレーザと多波長センサによる単木レベルでの広葉樹林資源量解析技術の開発 , 若手研究
2017 - 2019 , 次世代レーザセンシングによる高精度な広葉樹天然林の樹種別資源量の算定方法の開発 , 基盤研究(B)
2016 - 2018 , 航空LiDARデータと高分解能画像による単木レベルでの森林資源解析システムの構築 , 若手研究(B)