Hyperspectral Data Dimensionality Reduction and
the Impact of Multi-seasonal Hyperion EO-1
Imagery on Classification Accuracies of
Tropical Forest Species
Manjit Saini, Binal Christian, Nikita Joshi, Dhaval Vyas, Prashanth Marpu, and N.S.R Krishnayya
Abstract
Synchronizing hyperspectral data acquisition with phenolog-
ical changes in a tropical forest can generate comprehensive
information for their effective management. The present
study was performed to identify a suitable dimensionality
reduction method for better classification and to evaluate the
impact of seasonality on classification accuracy of tropical
forest cover.
EO
-1 Hyperion images were acquired for three
different seasons (summer (April), monsoon (October), and
winter (January)). Spectral signatures of pure patches of
Teak, Bamboo, and mixed species covers are significant-
ly different across the three seasons indicating distinctive
phenology of each cover. Kernel Principal Component
Analysis (k-
PCA
) is more suitable for dimensionality reduc-
tion for these covers. The three vegetation covers classified
using images of three seasons achieved the best classifi-
cation accuracies using k-
PCA
with maximum likelihood
classifier 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.
Introduction
The Importance of Tropical Forests
Tropical forests constitute about half of the world’s forests
and have the intrinsic property of being extremely rich in
terms of species richness and diversity (Bradshaw
et al
., 2009;
Gibson
et al
., 2011). They store 40 to 50 percent of carbon in
terrestrial vegetation and are responsible for one third of the
global terrestrial primary productivity (Beer
et al
., 2010). Over
the past century tropical forests have been suffering from
exceptional rates of changes as they are destroyed by human
activities and climate change (Achard
et al
., 2004; Féret and
Asner, 2013; Morris, 2010). The global character of tropical
deforestation and its consequences on climate change and
biodiversity make it an important emerging global concern
that increasingly transcends individual nations and their
boundaries (Fuller, 2006). Tropical forest destruction is likely
to continue in the future, causing an extinction crisis among
tropical forest species (Bradshaw
et al
., 2009). Therefore,
comprehensive information on the spatial distribution and
composition of existing plant species is fundamental to de-
sign effective strategies for conservation and management of
increasingly fragmented tropical forests (Gillespie
et al
., 2008;
Rodriguez
et al
., 2007). Hence, strong preference has been
given to acquire updated data on vegetation cover changes
regularly or annually so as to better assess the environment
and ecosystem (Knight
et al
., 2006). Unfortunately, such
information cannot be obtained exclusively from traditional
survey techniques, due to logistical difficulties and the costs
involved. For a subcontinent like India, survey for mapping
vegetation and other land covers using conventional tech-
niques is too complex and demands a huge amount of human
resource and time (Roy and Joshi, 2002). Quite the opposite,
forest vegetation mapping using remotely sensed observations
is efficient and cost effective. Currently hyperspectral remote
sensing is fast emerging as a key technology for advanced and
improved understanding, classification, modeling, and moni-
toring of complex forest vegetation.
The Importance of Phenological Variation in Species Discrimination
Vegetation phenology can provide an useful signal for classi-
fying forest cover. Seasonal phenological changes are mainly
caused by inter-annual climatic variability and are reflected
through an increase or decrease in green biomass (Pettorelli
et al
., 2005). Phenological changes significantly influence
spectral reflectance curves. Changes in vegetation spectral
response caused by phenology can conceal long term changes
in the landscape (Hobbs, 1989; Lambin and Ehrlich, 1996).
An understanding of vegetation phenology is prerequisite to
inter-annual studies and predictive modeling of land surface
responses to climate change (Myneni
et al
., 1997; Vina
et al
.,
2004). However, the phenology and interactions of tropi-
cal forests with environmental, climate, and anthropogenic
factors are not well perceived. Synchronizing hyperspectral
data acquisition with phenological changes in tropical trees
is a daunting task. At times, it is practically not feasible.
Identifying appropriate endmembers for classifying tropical
trees with diverse phenologies is an important aspect to look
Manjit Saini, Binal Christian, Nikita Joshi, Dhaval Vyas, and
N.S.R Krishnayya with the Ecology Laboratory, Department
of Botany, Faculty of Science, The M.S. University of Baroda,
Gujarat, India (
).
Prashanth Marpu is with the Institute Center for Water and
Environment (iWATER), Masdar Institute of Science and
Technology, PO Box 54224, Abu Dhabi, United Arab Emirates.
Photogrammetric Engineering & Remote Sensing
Vol. 80, No. 8, August 2014, pp. 773–784.
0099-1112/14/8007–773
© 2014 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.80.8.773
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
August 2014
773
