3. METHODS
3.1 Resolution merge
Resolution merging and data fusion techniques are commonly
used for combining high-resolution single band images with
colour images mainly for interpretation purposes. This is
specifically useful when a satellite sensor system simultaneously
acquires several spectral bands in different resolutions. In the
case of SPOT-5, the multispectral bands 1-3 are imaged with
10m [FOV while the SWIR-band has 20m IFOV. All bands in
the SPOT-5 multispectral products have 10 m pixel size. In
parallel with the multispectral data, SPOT-5 is capable of
simultaneously acquiring panchromatic data with 2.5 m or 5m
resolution. Resolution merging methods are needed, which can
utilise the SPOT-5 multiresolution data in an efficient manner
without deteriorating data quality, both for visual interpretation,
and for classification and change detection.
A variety of resolution merging techniques are available and
described by several authors. The most common techniques are
implemented in standard image processing software packages
(IHS , PCA and Brovey transforms) The techniques can roughly
be categorised into a limited number of main types (Pohl, C.
1999. Hill, J., Diemer, 1999, Bretschneider 2004).
Transformation based methods are replacing the low-resolution
“intensity” image with the high-resolution single band data.
Examples of this are the IHS and PC transformations and is
commonly used because the availability in standard image
processing systems. Also examples for forestry applications are
reported (Fritz, 1999).
Addition and multiplication techniques are weighting a part of
the panchromatic signal into the multispectral bands having a
high correlation with the higher resolution panchromatic band.
Filter fusion techniques are adding only the high frequency part
of the high-resolution channel, by multiplication or addition,
into the multispectral channels. Variants of this are the HPF,
LMM and LMVM methods (de Béthune 1998a, 1998b,
Netzband, 1998) and the SFIM method (Liu, 2000)
Wavelet decomposition fusion techniques are introducing the
transformed high-resolution information into the multispectral
image with different methods.
For merging of the combined 10/20m resolution multispectral
SPOT-5 data with the 2.5 or 5 m resolution panchromatic band
with the purpose of enhancing the dataset for both interpretation
and image analysis applications, the merging method should
work on all spectral bands, including the SWIR-band, which is
of high importance for boreal forest applications. The spectral
properties should not be changed be the merging process.
These requirements eliminate the addition/multiplication and
transformation techniques. We have selected a modified filter
fusion methods, which can be easily implemented before a more
complex wavelet methods. The technique used is a normalised
difference version of the high frequency modulation method
(HFM) given by:
p Pr pe
Pept” Ge
AST — AS: ;
Where H denotes high resolution, L = low resolution or low
- pass filtered version of the image and a is a gain factor defining
the strength of the introduced high frequency component from
the panchromatic image. The size of the low pass filter is
defined by the ratio between multispectral pixel size and
panchromatic pixel size.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part Bl. Istanbul 2004
3.2 Clear-cut mapping method
The SWIR band is the single most important spectral band for
boreal forest applications. It contains information correlated to
the density, timber volume, and tree height of the conifer forest.
NDVI has very low correlation to the biomass of the conifer
forest. In comparison with the red band (XS2), the number of
digital levels within the forest is normally much higher in the
SWIR band (standard deviation 8.7 vs. 22.8 in the SPOT-5
scene used). This is of great importance when mapping dark
boreal forest in low illumination conditions.
The clear cut mapping method developed by Metria and used
operationally by NBF is a single band image difference method
using the SWIR band when present in both scenes used for
change detection, otherwise the red band may also be used but
with less dynamic range in the resulting difference image.
The SWIR bands from the old and new images are
radiometrically matched using a linearised histogram matching
method based on the histograms of the matched.
Radiometric matching between the SWIR bands of the new and
the old image is performed using only an area of interest
defined by the forest mask from the digital topographic map,
excluding areas with clouds or cloud shadows. A linearised
histogram matching method is used between the percentiles
15% —-85% of the 2 images. By using only the forest mask,
variations in agricultural and other areas are removed from the
matching and by cutting off the ends of the histogram, seasonal
variations and changes from forestry activities are minimised.
By using cubic convolution resampling when the difference
image is calculated, satellite scenes with different resolutions
are easily handled. This would not be the case if nearest
neighbour resampling was used, as this would have introduced
false pixel border effects from the lowest resolution image used.
The resulting single band difference image will have the
smallest pixel size of the input images.
3.3 Mapping of seed trees left on felled area
Seed trees are left on clear felled areas to enable the
regeneration of new forest. The alternative is to replant within 3
years after the felling. NBF and the Regional Forest Boards are
responsible for the legal supervising of the regeneration of new
forest. If any areas are regrowing poorly, there is a demand for
extra planting activities in order to fulfil the legal. NBF has a
need for monitoring of recently clear-cut areas for the detection
of indicators for activities to promote the regeneration of new
forest is of interest from the NBF. Methods for detection of
seed-trees left, soil scarification activities and if possible also
detection of failed regrowth are of interest.
A method for mapping of seed-trees left on new clear-cut areas
was tested. The purpose of this method is to enable the planning
and prioritising of field visits to the felled areas. There are no in
situ data present at the time of the satellite image mapping. A
simple thresholding method was evaluated for the purpose of
mapping seed-tree density. The SWIR band of the newest scene
was found to be most correlated to seed-tree density.
The mapping was performed on SPOT-5 20m SWIR band data
(10 m pixels) by simple thresholding in the SWIR band of the
2002 scene limited to the new clear cuts performed between
1999 and 2002. The 2.5 m merged colour image could be used
as a reference for the development and calibration of the
method. An alternative would have been simultaneously
acquired aerial photos. In situ measurements were not feasible,
as we had no advance knowledge where the areas were
localised. The seed-trees could be visual interpretation in the
merged images. Panchromatic 5 m data could also be used.
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