PE&RS August 2014 - page 708

Clark, M.L. and Roberts, D.A. 2012. Species-Level Differences
in Hyperspectral Metrics among Tropical Rainforest Trees
as Determined by a Tree-Based Classifier,
Remote Sensing,
4(6): 1820-1855.
Congalton, R.G. and Green, K. 1999 Assessing the accuracy of
remotely sensed data: principles and practices. in
New York:
Lewis.
Congalton, R.G. and Green, K. 2008 Assessing the Accuracy of
Remotely Sensed Data: Principles and Practices, CRC Press,
London, 208 pp.
Cook, B., Corp, L., Nelson, R., Middleton, E., Morton, D.,
McCorkel, J., Masek, J., Ranson, K., Ly, V. and Montesa-
no, P. 2013. NASA Goddard’s LiDAR, Hyperspectral and
Thermal (G-LiHT) Airborne Imager,
Remote Sensing,
5(8):
4045-4066.
Delalieux, S., Auwerkerken, A., Verstraeten, W., Somers, B.,
Valcke, R., Lhermitte, S., Keulemans, J. and Coppin, P.
2009. Hyperspectral Reflectance and Fluorescence Imaging
to Detect Scab Induced Stress in Apple Leaves,
Remote Sens-
ing,
1(4): 858-874.
Galvão, L.S. 2011 Crop type discrimination using hyperspec-
tral data. Chapter 17. pp. 397-422, in Hyperspectral Remote
Sensing of Vegetation, P.S. Thenkabail, et al., Eds., ed: CRC
Press- Taylor and Francis group, Boca Raton, London, New
York, 781 p.
Gitelson, A.A. 2013. Remote estimation of crop fractional veg-
etation cover: the use of noise equivalent as an indicator of
performance of vegetation indices,
International Journal of
Remote Sensing,
34(17): 6054-6066.
Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J. and
Strachan, I.B. 2004. Hyperspectral vegetation indices and
novel algorithms for predicting green LAI of crop canopies:
Modeling and validation in the context of precision agricul-
ture,
Remote Sensing of Environment,
90(3): 337-352.
Hecker, C.A., Smith, T.E.L., Ribeiro da Luz, B. and Wooster,
M.J. 2013 Chapter 3: Thermal infrared spectroscopy in the
laboratory and field in support of land surface remote sens-
ing. In C. Kuenzer and S. dech (eds.), Thermal Infrared
remote Sensing: Sensors, methods, Applications, Remote
Sensing and Digital Image Processing 17, DOI 10.1007/978-
94-007-6639-6_3, @ Springer Science+Business Media Dor-
drecht 2013.
Hook, S.J., Johnshon, W.R. and Abrams, M.J. 2013 Chapter
5: NASA’s Hyperspectral Thermal Emission Spectrometer
(HyTES) In C. Kuenzer and S. dech (eds.), Thermal Infrared
remote Sensing: Sensors, methods, Applications, Remote
Sensing and Digital Image Processing 17, DOI 10.1007/978-
94-007-6639-6_3, @ Springer Science+Business Media Dor-
drecht 2013.
Mariotto, I., Thenkabail, P.S., Huete, A., Slonecker, E.T.
and Platonov, A. 2013. Hyperspectral versus multispectral
crop-productivity modeling and type discrimination for the
HyspIRI mission,
Remote Sensing of Environment,
139(0):
291-305.
Marshall, M.T. and Thenkabail, P. 2014. Biomass modeling
of four water intensive crops using hyperspectral narrow-
bands. ,
Photogrammetric Engineering and Remote Sensing
,
80(8): 757-772.
Middleton, E.M., Ungar, S.G., Mandl, D.J., Ong, L., Frye, S.W.,
Campbell, P.E., Landis, D.R., Young, J.P. and Pollack, N.H.
2013. The Earth Observing One (EO-1) Satellite Mission:
Over a Decade in Space
IEEE Selected Topics in Applied
Earth Observations and Remote Sensing.
6 (2): 427-438.
Miphokasap, P., Honda, K., Vaiphasa, C., Souris, M. and
Nagai, M. 2012. Estimating Canopy Nitrogen Concentration
in Sugarcane Using Field Imaging Spectroscopy,
Remote
Sensing,
4(6): 1651-1670.
Mirzaie, M., Darvishzadeh, R., Shakiba, A., Matkan, A.A.,
Atzberger, C. and Skidmore, A. 2014. Comparative analysis
of different uni- and multi-variate methods for estimation
of vegetation water content using hyper-spectral measure-
ments,
International Journal of Applied Earth Observation
and Geoinformation,
26(0): 1-11.
Mundt, J., Streutker, D.R. and Glenn, N.F. 2006. Mapping
sagebrush distribution using fusion of hyperspectral and
lidar classifications. ,
Photogrammetric Engineering and Re-
mote Sensing
72: 47-54.
Nielsen, A. 2001. Spectral Mixture Analysis: Linear and
Semi-parametric Full and Iterated Partial Unmixing in
Multi- and Hyperspectral Image Data,
International Jour-
nal of Computer Vision,
42(1-2): 17-37.
Ortenberg, F. 2011 Hyperspectral sensor characteristics: Air-
borne, Spaceborne, Hand-held, and truck mounted: Integra-
tion of hyperspectral data with Lidar. Pp. 39-68. in Hyper-
spectral Remote Sensing of Vegetation, Pp. 561-578 in P.
S. Thenkabail, J. G. Lyon, and A. Huete, Eds. Boca Raton,
London, New York: CRC Press/Taylor and Francis Group,
2011, ch.23, 561-578 pp.
Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J.,
Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M.,
Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J.C. and
Trianni, G. 2009. Recent advances in techniques for hyper-
spectral image processing,
Remote Sensing of Environment,
113, Supplement 1(0): S110-S122.
Poças, I., Cunha, M. and Pereira, L.S. 2012. Dynamics of
mountain semi-natural grassland meadows inferred from
SPOT-VEGETATION and field spectroradiometer data,
In-
ternational Journal of Remote Sensing,
33(14): 4334-4355.
Pu, R. and Bell, S. 2013. A protocol for improving mapping
and assessing of seagrass abundance along the West Central
Coast of Florida using Landsat TM and EO-1 ALI/Hyperi-
on images,
ISPRS Journal of Photogrammetry and Remote
Sensing,
83(0): 116-129.
Qi, J. 2011. Hyperspectral remote sensing in global change
studies (P. S. Thenkabail, et. al., editors), CRC Press-Taylor
and Francis group, New York, N.Y., 561-578, 781 pp.
Ribeiro da Luz, B. and Crowley, J.K. 2010. Identification of
plant species by using high spatial and spectral resolution
thermal infrared (8.0–13.5 μm) imagery,
Remote Sensing of
Environment,
114(2): 404-413.
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