PHOTOGRAMME TR I C ENG I NE ER I NG & REMOT E SENS I NG
The official journal for imaging and geospatial information science and technology
August 2014 Volume 80 Number 8
H I GHL I GHT ART I C L E
Prasad S. Thenkabail, Murali Krishna Gumma, Pardhasaradhi Teluguntla,
and
Irshad A.
Mohammed
I NT ERV I EW
SPEC I AL I SSUE FOREWORD
Prasad S. Thenkabail
PE ER - REV I EWED ART I C L ES
Mohamad Awad, Ihab Jomaa,
and
Fatima Arab
An effective, low cost, and fast method for monitoring the changes in the forest cover,
detecting diseases in forests, and mapping different forest species.
Caiyun Zhang
A synergy of hyperspectral and LiDAR systems for automated vegetation mapping in a
complex wetland Florida Everglades.
Nilda Sánchez, Maria Piles, José Martínez-Fernández, Mercè Vall-llossera, Luca Pipia,
Adriano Camps, Albert Aguasca, Fernando Pérez-Aragüés,
and
Carlos M. Herrero-
Jiménez
The potential of merging optical and thermal hyperspectral airborne data with microwave
observations for estimating surface soil moisture at very high spatial resolution.
Michael Marshall
and
Prasad Thenkabail
Ground-based spectroradiometric and aboveground fresh biomass data for four major
world crops studied in the Central Valley of California to identify hyperspectral
narrowbands sensitive to biomass using empirically-based modeling techniques.
Manjit Saini, Binal Christian, Nikita Joshi, Dhaval Vyas, Prashanth Marpu,
and
Krishnayya Nadiminti
EO-1 Hyperion data was used to classify three distinct forest species during 3 seasons
(monsoon, winter, summer) and the best classification accuracies were achieved using
kernel principal component analysis through maximum likelihood classifier (kPCA-ML)
for the monsoon season with overall accuracies of 83 to 100 percent for single species, 74
to 81 percent for two species, and 72 percent for three species respectively.
Helge Aasen, Martin Leon Gnyp, Yuxin Miao,
and
Georg Bareth
Introducing the software HyperCor for automated preprocessing and calculation of
correlation matrices from hyperspectral field spectrometry and the multi-correlation
matrix strategy for the retrieval of hyperspectral vegetation indices to estimate rice
biomass in the tillering, stem elongation, heading, and across all growth stages.
Ilia Parshakov, Craig Coburn,
and
Karl Staenz
A new method for the automatic labeling of classified imagery using Z-Score distance is
for class label assignment of Landsat-5 TM imagery using Hyperion hyperspectral data.
COLUMNS
ANNOUNCEMENTS
Call for Papers
,
DEPARTMENTS
“Hyperspectral Hyperion
Images and Spectral
Libraries of Agricultural
Crops” is the theme
of this month’s special
issue. Global Image on
the cover page shows
the location of ~ 64,000
Hyperion sensor (onboard
Earth Observing-1 or
EO-1;
gov/ satellite) acquired
images during years 2000-2013. Each image is 7.5 km
by 180 km, 242 bands, and 10 nm narrow bandwidth
acquiring data in 400-2500 nm spectral range. Images
are freely available at:
/
Cover page also shows a sample Hyperion image
data cubes for an area within the Krishna river basin,
India. Typical crop spectra derived from Hyperion
images for some of the leading world crops, at certain
phenological growth stages, are depicted in bottom
left. Hyperspectral signatures of crops shown with
photos in the background are gathered using a hand-
held spectroradiometer. For details read the Highlight
article in this issue.
Cover page credits: Dr. Prasad S. Thenkabail, U.S.
Geological Survey (USGS), Dr. Murali Krishna Gumma,
International Center for Research in the Semi-arid
Tropics (ICRISAT), Dr. Pardhasaradhi Teluguntla, U.S.
Geological Survey (USGS) and Bay Area Environmental
Research Institute (BAERI), and Mr. Irshad A.
Mohammed, ICRISAT. Contact:
or
.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
August 2014
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