XIX-B8, 2012
ake in China, was
1). Poyang Lake
n part of Jiangxi
level throughout
in this area is an
migratory birds,
rable habitat for
r schistosomiasis
al changes in the
> difficult to map
Is and emergence
control from the
icult to formulate
ges in this region
ighly pathogenic
'elieved to be the
? changes in land
‚OCESSING
Level-1 products
cts (MOD09GA)
study area were
ral bands of TM
nd corresponding
ed.
d on October 28,
ge to a published
nd control points
117°0'0"E
117?0'0"E
MODIS
12 | 2004/8/8
13 | 2004/9/12 |
29 | 2004/9730 |
/31 [2004/10/30
(GCPs) and the nearest neighbor resampling produced an
average RMSE of 0.25 pixel. Each of image-to-image
registration between the georegistered base image and the other
eight TM images produced an average RMSE of less than 0.25
pixel using a first-order polynomial fit with more than 30 GCPs.
Brightness values of these geometrically corrected TM images
were converted to the ground reflectance through the
atmospheric correction using ACORN. The c-correction method
(Teillet et al., 1982), a semiempirical function primarily based
on cosines of the incident and reflected illumination angles, was
applied to minimize the topographic effects. In contrast, only a
map reprojection was performed to the time-series MODIS data.
This is because MODO9GA products were geometrically
accurate when compared with the TM data, and they have
already been atmospherically corrected.
Field data of the land cover conditions in the study area were
collected in December, 2007 and April and May, 2008. In order
to comprehensively include all land cover types throughout the
study area, the locations of the field record collection were
determined with the reference of remotely sensed images in
Google Earth. We recorded the GPS coordinates at more than
350 centers of 30 m by 30 m squares covered with single land
cover types. Land cover types and existence of land cover
changes were also recorded at each location. The field records
were classified into four EM land cover classes: (1) green
vegetation (GV); (2) non-photosynthetic vegetation (NPV),
soils, and impervious surfaces (N/S/I); (3) bright water (W1);
and (4) dark water (W2). For each TM image, 240 records
consisting of 60 records per EM land cover class were selected
from all field records. We chose the field records for image
considering the land cover changes by referring the information
on land cover changes obtained through the interviews with the
local residents and researchers. Therefore, all sets of field
records were different for every TM images. These field records
for each TM image were utilized as the reference in collecting
the candidate image EM spectra and in the class accuracy
assessments of the land cover fractions (LCFs) derived through
the MESMA of blended reflectance.
5. METHODS
5.1 Examination of input data combinations for ESTARFM
The agreement between observed reflectance and the reflectance
fused with blending techniques is dependent on the combination
of input data as Watts et al. (2011) demonstrated. In order to
investigate the input data combinations that could achieve
highest agreement between the observed and blended
reflectance, this research tested all of the possible combinations
of the TM and corresponding MODIS images on the TM
Observation dates. Testing all possible input combinations
revealed what data combination was appropriate to blend the
relectance in which particular season, particularly when the
water level changed rapidly.
The agreement was assessed based on the mean values of the
average absolute difference (AAD) between observed and
blended reflectance for each band. Since no additional TM data
acquired during the studied period was available and the
ESTARFM required a prior and a posterior moderate-resolution
remotely sensed data, the blended data were generated for the
seven TM observation dates: October 28, November 29, and
December 15 in 2004, and March 5, August 12, September 13,
and September 29 in 2005. The input data combinations
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
followed the chronological order: the prior data were acquired
before the target date and the posterior data were acquired after
the target date.
5.2 Investigation in the applicability of MESMA to blended
data
The applicability of MESMA to the blended reflectance was
assessed from three perspectives: (1) percentage of modeled
pixels; (2) agreement of image dominant land cover classes (EM
classes with highest LCFs in pixels) and reference dominant
classes; and (3) AAD between the LCFs derived from observed
TM data and those from blended data. The blended reflectance
generated with the input data combinations that achieved
highest agreement between the observed and blended
reflectance on the seven TM observation dates were utilized as
the blended input data for MESMA.
To establish the approach for EM library development, we made
the three comparisons in unmixing the observed blended
reflectance using five different EM libraries shown in Table 2.
These six cases were set in order to examine whether the
candidate EM spectra collected from blended reflectance data
need to be included in the candidate spectral library for the
MESMA of blended reflectance. Comparisons were made
between Case 1 and 2, Case 3 and 4, and Case 5 and 6 utilizing
all observed and blended input data on seven target dates.
We utilized the VIPER Tools, a plug-in software under ITT
ENVI, in the application of MESMA (Roberts et al., 2007). 40
spectra for each EM land cover class of every images were
randomly selected from the image pixels at the locations of 60
field record. The candidate EM libraries were constructed for
each input by combining all EM spectra for each case in Table
2. 20 optimal EM spectra (five per land cover class) were
chosen from each EM library. using the EM average RMSE
(EAR) produced by a spectrum that was used to model all other
EM spectra in the same class (Dennison and Roberts, 2003).
one spectrum of photogrammetric shade was added to account
for the spectral variation of reflectance data. When a series of
SMA models were applied to every pixel of each reflectance
data, two-, three, and four-EM models were applied in this
study. The combinations of EM land cover classes used in this
study are shown in Table 3. Maximum RMSE (2.596
reflectance), maximum fraction (10546 reflectance), minimum
fraction (-5% reflectance), and maximum shade fraction (50%
reflectance) restrictions were applied to the SMA models
(Michishita et al., 2012). In the selection of optimal models for
every pixels, we empirically determined the RMSE threshold
between two- and three-EM models as 0.2%, that between
three- and four-EM models as 0.4%, and that between two- and
Reflectance EM spectra
Comp. |Case| used for collected from
unmixing Observed Blended
1 1 | Observed Prior and posterior -
2 Blended Prior and posterior -
2 3 | Observed | Prior, target, and posterior -
4 Blended Prior and posterior Target
3 5 | Observed - Target
6 Blended - Target
Table 2. Case settings in the applicability investigation of
MESMA