Land-cover classification and forest biophysical retrieval from SAR

被引:0
|
作者
Dobson, MC [1 ]
Bergen, K [1 ]
机构
[1] Univ Michigan, Radiat Lab, Ann Arbor, MI 48109 USA
关键词
radar; remote sensing; land-cover classification; tree height; basal area;
D O I
暂无
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Orbital SAR data can be used to generate high accuracy classifications of landcover. The existing single-frequency and singly polarized SAR systems provide marginal accuracy when used alone unless a multitemporal data set is acquired. Multi-frequency archival data yield good results that are robust over a number of ecoregions for a very simple land-cover categorization. SAR data complements electro-optical data. Fusion of these two spectral regimes provides superior classification. The next generation of orbital SAR systems now in construction and design will provide multi-polarized and multi-frequency capabilities that will significantly improve land-cover classification. The retrieval of forest biophysical properties (average tree height, basal area, aboveground biomass and timber volume) can be accomplished using orbital SAR data. The best results use a multi-frequency SAR with polarimetric capability at a long wavelength (such as L-band). LightSAR should yield biophysical estimates that are comparable in accuracy to those obtained by traditional field methods.
引用
收藏
页码:98 / 106
页数:3
相关论文
共 50 条
  • [1] Unsupervised land-cover classification of interferometric SAR images
    Dammert, PBG
    Kuhlmann, S
    Askne, J
    [J]. IGARSS '98 - 1998 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, PROCEEDINGS VOLS 1-5: SENSING AND MANAGING THE ENVIRONMENT, 1998, : 1805 - 1808
  • [2] Exploiting SAR Tomography for Supervised Land-Cover Classification
    D'Hondt, Olivier
    Haensch, Ronny
    Wagener, Nicolas
    Hellwich, Olaf
    [J]. REMOTE SENSING, 2018, 10 (11)
  • [3] Statistical Convolutional Neural Network for Land-Cover Classification From SAR Images
    Liu, Xinlong
    He, Chu
    Zhang, Qingyi
    Liao, Mingsheng
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2020, 17 (09) : 1548 - 1552
  • [4] Land-cover Classification in SAR Images using Dictionary Learning
    Aktas, Gizem
    Bak, Cagdas
    Nar, Fatih
    Sen, Nigar
    [J]. SAR IMAGE ANALYSIS, MODELING, AND TECHNIQUES XV, 2015, 9642
  • [5] An assessment of the effectiveness of a random forest classifier for land-cover classification
    Rodriguez-Galiano, V. F.
    Ghimire, B.
    Rogan, J.
    Chica-Olmo, M.
    Rigol-Sanchez, J. P.
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2012, 67 : 93 - 104
  • [6] Land-cover categories versus biophysical attributes to monitor land-cover change by remote sensing
    Lambin, E
    [J]. OBSERVING LAND FROM SPACE: SCIENCE, CUSTOMERS AND TECHNOLOGY, 2000, 4 : 137 - 142
  • [7] Targeted Land-Cover Classification
    Marconcini, Mattia
    Fernandez-Prieto, Diego
    Buchholz, Tim
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (07): : 4173 - 4193
  • [8] Land-cover Classification Based on SAR Data Using Superpixel and Cosine Similarity
    Mao, Xueyue
    Lu, Yilong
    Xiao, Xiao
    [J]. PROCEEDINGS OF THE 2020 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL ELECTROMAGNETICS (ICCEM 2020), 2020, : 92 - 94
  • [9] A SAR process model for land-cover mapping
    Bugden, JL
    Andrey, J
    Howarth, PJ
    [J]. CANADIAN JOURNAL OF REMOTE SENSING, 2004, 30 (02) : 195 - 204
  • [10] Multitemporal INSAR in land-cover classification
    Engdahl, ME
    Hyyppä, J
    [J]. PROCEEDINGS OF THE THIRD INTERNATIONAL SYMPOSIUM ON RETRIEVAL OF BIO- AND GEOPHYSICAL PARAMETERS FROM SAR DATA FOR LAND APPLICATIONS, 2002, 475 : 245 - 250