MEASUREMENTS OF FOREST BIOMASS CHANGE USING L- AND P-BAND SAR BACKSCATTER

被引:0
|
作者
Huuva, Ivan [1 ]
Fransson, Johan E. S. [1 ]
Persson, Henrik J. [1 ]
Wallerman, Jorgen [1 ]
Ulander, Lars M. H. [2 ]
Blomberg, Erik [2 ]
Soja, Maciej J. [2 ]
机构
[1] Swedish Univ Agr Sci, Dept Forest Resource Management, SE-90183 Umea, Sweden
[2] Chalmers Univ Technol, Dept Space Earth & Environm, Gothenburg, Sweden
关键词
Biomass; forestry; L-band; P-band; radar; modeling; INTENSITY; IMAGES;
D O I
暂无
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Three-year forest above-ground biomass change were measured using L-and P-band Synthetic Aperture Radar (SAR) backscatter. The SAR data were collected in the airborne BioSAR 2007 and BioSAR 2010 campaigns over the hemiboreal Remningstorp test site in southern Sweden. Regression models for biomass were developed using reference biomass maps created using airborne laser scanning data and field measurements. The results from regression analysis show that using HV backscatter (or VH) in a model with above-ground biomass and backscatter change on either natural logarithmic or square root, and decibel scale, respectively, explained most of the variation in the biomass change, both for L- and P-band. In the case of L-band, the two best cases showed R-2 values of 66%, when comparing two SAR images acquired 2007 and 2010. For P-band using the same models, the best cases showed R-2 values of 62%. In summary, the results look promising using L- and P-band backscattering for mapping biomass change.
引用
收藏
页码:5818 / 5821
页数:4
相关论文
共 50 条
  • [41] Potential of P-Band SAR Tomography in Forest Type Classification
    Ho Tong Minh, Dinh
    Ngo, Yen-Nhi
    Le, Thu Trang
    REMOTE SENSING, 2021, 13 (04) : 1 - 16
  • [42] Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data
    Santi, Emanuele
    Paloscia, Simonetta
    Pettinato, Simone
    Cuozzo, Giovanni
    Padovano, Antonio
    Notarnicola, Claudia
    Albinet, Clement
    REMOTE SENSING, 2020, 12 (05)
  • [43] Analysis of forest biomass variation in the Amazon and its' influence on the response of P-band SAR polarimetric data
    dos Santos, JR
    de Araujo, LS
    Freitas, CDC
    Soler, LD
    Gama, FF
    Dutra, LV
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY IV, 2003, 4879 : 252 - 259
  • [44] Evaluating P-Band TomoSAR for Biomass Retrieval in Boreal Forest
    Blomberg, Erik
    Ulander, Lars M. H.
    Tebaldini, Stefano
    Ferro-Famil, Laurent
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (05): : 3793 - 3804
  • [45] PREDICTION OF HEMI-BOREAL FOREST BIOMASS CHANGE USING ALOS-2 PALSAR-2 L-BAND SAR BACKSCATTER
    Huuva, Ivan
    Persson, Henrik J.
    Wallerman, Jorgen
    Ulander, Lars M. H.
    Fransson, Johan E. S.
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 3326 - 3329
  • [46] Performance simulation of spaceborne P-band SAR for global biomass retrieval
    Hallberg, B
    Smith, G
    Olofsson, A
    Ulander, LMH
    IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET, 2004, : 503 - 506
  • [47] BIOMASS: A P-Band SAR Earth Explorer Core Mission Candidate
    Heliere, F.
    Lin, C. C.
    Fois, F.
    Davidson, M.
    Thompson, A.
    Bensi, P.
    2009 IEEE RADAR CONFERENCE, VOLS 1 AND 2, 2009, : 598 - 603
  • [48] SCINTILLATION IMPACTS ON THE BIOMASS P-BAND SPACE-BASED SAR
    Rogers, Neil
    Quegan, Shaun
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 6400 - 6403
  • [49] Theoretical Modeling and Analysis of L- and P-band Radar Backscatter Sensitivity to Soil Active Layer Dielectric Variations
    Du, Jinyang
    Kimball, John S.
    Moghaddam, Mahta
    REMOTE SENSING, 2015, 7 (07): : 9450 - 9472
  • [50] CHANGE DETECTION UNDER THE FOREST IN MULTITEMPORAL FULL-POLARIMETRIC P-BAND SAR IMAGES USING PAULI DECOMPOSITION
    Rosa, Rafael A. S.
    Fernandes, David
    Barreto, Thiago L. M.
    Wimmer, Christian
    Nogueira, Joao B., Jr.
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 6213 - 6216