Seasonal Trends in Separability of Leaf Reflectance Spectra for Ailanthus altissima and Four Other Tree Species

被引:24
作者
Burkholder, Aaron [1 ,2 ]
Warner, Timothy A. [2 ]
Culp, Mark [3 ]
Landenberger, Rick [2 ]
机构
[1] NRCS NSSC Geospatial Res Unit, Morgantown, WV 26505 USA
[2] W Virginia Univ, Dept Geol & Geog, Morgantown, WV 26506 USA
[3] W Virginia Univ, Dept Stat, Morgantown, WV 26506 USA
关键词
RANDOM FORESTS; CLASSIFICATION; IMAGERY; DISCRIMINATION; SPECTROSCOPY; INFORMATION; MODEL; SITE;
D O I
10.14358/PERS.77.8.793
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The separability of Ailanthus altissima (tree of heaven) from four native species was investigated using spectral reflectance measurements (350 to 2,500 nm) of leaves collected from 13 May through 24 August 2008. For both the original reflectance and a continuum removed dataset, least angle regression (LARs) and random forest classification were used to identify a single set of optimal wavelengths across all sampled dates, a set of optimal wavelengths for each date, and the dates for which Ailanthus is most separable from other species. Leaf classification accuracy was found to vary with both dates and bands used. Contrary to expectations that early spring would provide the best separability, July and August were also identified as potentially good months for species differentiation. Applying continuum removal generally reduced classification error. Band selection using LABS improved classification accuracy. The optimal spectral bands were selected from across the spectrum, typically including 401 to 431 nm, 1,115 nm, and 1,985 to 1,995 nm.
引用
收藏
页码:793 / 804
页数:12
相关论文
共 58 条
[1]  
*AN SPECTR DEV INC, 1997, ASD TECHN GUID
[2]   Remote sensing of native and invasive species in Hawaiian forests [J].
Asner, Gregory P. ;
Jones, Matthew O. ;
Martin, Roberta E. ;
Knapp, David E. ;
Hughes, R. Flint .
REMOTE SENSING OF ENVIRONMENT, 2008, 112 (05) :1912-1926
[3]   Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR [J].
Asner, Gregory P. ;
Knapp, David E. ;
Kennedy-Bowdoin, Ty ;
Jones, Matthew O. ;
Martin, Roberta E. ;
Boardman, Joseph ;
Hughes, R. Flint .
REMOTE SENSING OF ENVIRONMENT, 2008, 112 (05) :1942-1955
[4]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[5]   Observed and predicted responses of plant growth to climate across Canada [J].
Bunn, AG ;
Goetz, SJ ;
Fiske, GJ .
GEOPHYSICAL RESEARCH LETTERS, 2005, 32 (16) :1-4
[6]   Estimating generalization error on two-class datasets using out-of-bag estimates [J].
Bylander, T .
MACHINE LEARNING, 2002, 48 (1-3) :287-297
[7]   Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada: a rule-based system [J].
Chen, Xianfeng ;
Warner, Timothy A. ;
Campagna, David J. .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2010, 31 (07) :1733-1752
[8]   CANAPI: canopy analysis with panchromatic imagery [J].
Chopping, Mark .
REMOTE SENSING LETTERS, 2011, 2 (01) :21-29
[9]   REFLECTANCE SPECTROSCOPY - QUANTITATIVE-ANALYSIS TECHNIQUES FOR REMOTE-SENSING APPLICATIONS [J].
CLARK, RN ;
ROUSH, TL .
JOURNAL OF GEOPHYSICAL RESEARCH, 1984, 89 (NB7) :6329-6340
[10]   Using vegetation reflectance variability for species level classification of hyperspectral data [J].
Cochrane, MA .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2000, 21 (10) :2075-2087