Peer-Reviewed Articles
1121 Spectral Characteristics of Forest Vegetation
in Moderate Drought Condition Observed by
Laboratory Measurements and Spaceborne
Hyperspectral Data
Kyu-Sung Lee, Min-Jung Kook, Jung-Il Shin, Sun-Hwa Kim, and Tae-Geun Kim
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Although there have been several studies on the spectral
characteristics related to leaf water content, it remains
unclear whether the spectral property of leaves can be
extended to the canopy-level. In this study, we attempt to
compare the spectral characteristics of forest vegetation in
moderate drought condition observed by laboratory measurement
and satellite hyperspectral image data. Spectral
reflectance data were measured from detached pine needles
and oak leaves in the laboratory with a spectroradiometer.
Canopy reflectance spectra of the same species were collected
from temperate forest stands with dense canopy
conditions using EO-1 Hyperion imaging spectrometer data
obtained during the moderate drought season in 2001, and
then compared with those obtained in the normal precipitation
season of 2002. The relationship between leaf-level
spectral reflectance and leaf water content was the clearest
at the shortwave infrared (SWIR) regions. However, the
canopy-level spectral characteristics of forest stands did not
quite correspond with the leaf-level reflectance spectra.
Further, four water-related spectral indices (WI, NDWI, MSI,
and NDII) developed mainly with leaf-level reflectance were
not very effective to be used with the canopy-level
reflectance in dense forest condition. Forest canopy spectra
under moderate drought status may be more influenced by
canopy foliage mass, rather than by canopy moisture level.
1129 Removal of Noise by Wavelet Method
to Generate High Quality Temporal Data
of Terrestrial MODIS Products
Xiaoliang Lu, Ronggao Liu, Jiyuan Liu, and Shunlin Liang
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Time-series terrestrial parameters derived from NOAA/AVHRR,
SPOT/VEGETATION, TERRA, or AQUA/MODIS data, such as
Normalized Difference Vegetation Index (NDVI), Leaf Index
Area (LAI), and Albedo, have been extensively applied to
global climate change. However, the noise impedes these
data from being further analyzed and used. In this paper,
a wavelet-based method is used to remove the contaminated
data from time-series observations, which can effectively
maintain the temporal pattern and approximate the “true” signals. The method is composed of two steps: (a), timeseries
values are linearly interpolated with the help of
quality flags and the blue band, and (b), time series are
decomposed into different scales and the highest correlation
among several adjacent scales is used, which is more robust
and objective than the threshold-based method. Our objective
was to reduce noise in MODIS NDVI, LAI, and Albedo timeseries
data and to compare this technique with the BISE
algorithm, Fourier-based fitting method, and the Savitzky-Golay filter method. The results indicate that our newly
developed method enhances the ability to remove noise
in all three time-series data products.
1141 Estimating Grassland Biomass Using
SVM Band Shaving of Hyperspectral Data
J.G.P.W. Clevers, G.W.A.M. van der Heijden, S. Verzakov, and M.E. Schaepman
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In this paper, the potential of a band shaving algorithm
based on support vector machines (SVM) applied to hyperspectral
data for estimating biomass within grasslands is
studied. Field spectrometer data and biomass measurements
were collected from a homogeneously managed grassland
field. The SVM band shaving technique was compared with
a partial least squares (PLS) and a stepwise forward selection
analysis. Using their results, a range of vegetation indices
was used as predictors for grassland biomass. Results from
the band shaving showed that one band in the near-infrared
region from 859 to 1,006 nm and one in the red-edge region
from 668 to 776 nm used in the weighted difference vegetation
index (WDVI) had the best predictive power, explaining
61 percent of grassland biomass variation. Indices based on
short-wave infrared bands performed worse. Results could
subsequently be applied to larger spatial extents using
a high-resolution airborne digital camera (for example,
Vexcel’s UltraCamTM).
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1149 Estimating Crop Yield from Multi-temporal
Satellite Data Using Multivariate Regression
and Neural Network Techniques
Ainong Li, Shunlin Liang, Angsheng Wang, and Jun Qin
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Accurate, objective, reliable, and timely predictions of crop
yield over large areas are critical to helping ensure the
adequacy of a nation’s food supply and aiding policy
makers on import/export plans and prices. Development of
objective mathematical models of crop yield prediction
using remote sensing is highly desirable. In this study, we
develop a new methodology using an artificial neural
network (ANN) to estimate and predict corn and soybean
yields on a county-by-county basis, in the “corn belt” area
in the Midwestern and Great Plains regions of the United
States. The historical yield data and long time-series NDVI
derived from AVHRR and MODIS are used to develop the
models. A new procedure is developed to train the ANN
model using the SCE-UA optimization algorithm. The performance
of ANN models is compared with multivariate
linear regression (MLR) models and validation is made
on the model’s stability and forecasting ability. The new
algorithms can effectively train ANN models, and the
prediction accuracy can be as high as 85 percent.
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1159 Increasing Gross Primary Production
(GPP) in the Urbanizing Landscapes
of Southeastern Michigan
Tingting Zhao, Daniel G. Brown, and Kathleen M. Bergen
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In order to understand the impact of urbanizing landscapes
on regional gross primary production (GPP), we analyzed
changes in land-cover and annual GPP over an urban-rural
gradient in ten Southeastern Michigan counties between 1991
and 1999. Landsat and AVHRR remote sensing data and
biophysical parameters corresponding to three major landcover
types (i.e., built-up, tree, and crop/grass) were used to
estimate the annual GPP synthesized during the growing
season of 1991 and 1999. According to the numbers of
households reported by the U.S. Census in 1990 and 2000, the area settled at urban (>1 housing unit acre-1), suburban (0.1
to 1 housing units acre-1), and exurban (0.025 to 0.1 housing
units acre-1) densities expanded, while the area settled at
rural (<0.025 housing units acre-1) densities reduced. GPP in
this urbanizing area, however, was found to increase from
1991 to 1999. Increasing annual GPP was attributed mainly to
a region-wide increase in tree cover in 1999. In addition, the
estimated annual GPP and its changes between 1991 and 1999
were found to be spatially heterogeneous. The exurban
category (including constantly exurban and exurban converted
from rural) was associated with the highest annual GPP as well
as an intensified increase in GPP. Our study indicates that lowdensity
exurban development, characterized by large proportions
of vegetation, can be more productive in the form of GPP
than the agricultural land it replaces. Therefore, low-density
development of agricultural areas in U.S. Midwest, comprising
significant fractions of highly productive tree and grass
species, may not degrade, but enhance, the regional CO2 uptake from the atmosphere.
1169 Estimation of Regional Evapotranspiration by
TM/ETM+ Data over Heterogeneous Surfaces
Shaomin Liu, Guang Hu, Li Lu, and Defa Mao
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Evapotranspiration is an important part in surface energy
balance and water balance. Compared with other methods
(micrometeorological, climatological, or hydrological
method), the remote sensing model has obvious superiority
to estimate regional evapotranspiration over heterogeneous
surfaces. In this study, based on Landsat TM/ETM+ data and
meteorological data, evapotranspiration in Beijing area on
17 April 2001, 12 April 2002, 06 July 2004, 06 May 2005,
and 22 May 2005 were calculated by an estimation model of
regional evapotranspiration. Comparisons of energy balance
components (net radiation, soil heat flux, sensible and latent
heat flux) with measured fluxes were made integrating the
remotely sensed fluxes by the footprint model. Results show
that latent heat flux estimates (adjusted for closure) with
errors (MBE±RMSE) 26.47±42.54 Wm-2, sensible heat flux
error of -8.56±23.79 Wm-2, net radiation error of
25.16±50.87 Wm-2 and soil heat flux error of 10.68±22.81
Wm-2. The better agreement between the estimates and the
measurements indicates that the remote sensing model is
appropriate for estimating regional evapotranspiration over
heterogeneous surfaces. Furthermore, the spatial distribution
of evapotranspiration in Beijing area was analyzed.
1179 River Floodplain Vegetation Scenario
Development Using Imaging Spectroscopy
Derived Products as Input Variables in a
Dynamic Vegetation Model
M.E. Schaepman, G.W.W. Wamelink, H.F. van Dobben, M. Gloor, G. Schaepman-Strub,
L. Kooistra, J.G.P.W. Clevers, A. Schmidt, and F. Berendse
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River floodplains are becoming increasingly subject to
multifunctional land-use. In this contribution, we are
linking imaging spectrometer derived products with a
dynamic vegetation model to improve the simulation
and evaluation of scenarios for a river floodplain in the
Netherlands. In particular, we are using airborne HyMap
imaging spectrometer data to derive Leaf Area Index (LAI),
spatial distribution of Plant Functional Types (PFT), and
model dominant species abundances as input for the
ecological model. We use the dynamic vegetation model
(DVM) SMART2-SUMO to simulate vegetation succession
under scenarios of changing abiotic conditions and management
regimes. SMART2 is a soil chemical model
whereas SUMO describes plant competition and resulting
vegetation succession. We validate all remote sensing
derived products and the DVM calibration independently
using extensive field sampling. We conclude that the
dynamic vegetation models can be successfully initialized
using imaging spectrometer data at currently unprecedented
accuracy. However, all efforts undertaken for
validation in this contribution may significantly exceed
capacities for national or continental scale application of
the proposed method.