Landsat classification methodology
After an initial classification
carried out with MODIS MOD43B4 imagery a higher
was obtained by classification of Landsat ETM+. The
Enhanced Thematic Mapper Plus (ETM+) on board of Landsat-7 provides 15-meter resolution “panchromatic” data
and six bands in the visible, near-IR and mid-IR
spectral regions at a resolution of 30 metres.
Using the Worldwide
Reference System (WRS),
the global notation system for Landsat data,
is located between: Path 157 to 161 and row 068
to 078, comprising 37 Landsat scenes. For each
scene 3 to 5 images are available, dated between
1999 and 2003.
classification of Landsat images was performed using
to the following steps:
- Pre-processing: The entire data
from different sources and available in different
formats, was first imported and layer stacked.
Single multilayer images were conformed including
the Landsat bands to be used in the classification
(1,2,3,4,5 and 7). Also images were unified (reprojecting
or rectifying when needed) to a common projection.
Standard Universal Transverse Mercator sectors
38-39 South is used as the uniform projection system
for this project.
- Prioritisation of images
used for classification: For each Landsat
scene two to four different images
are available. In most cases they were acquired
in different seasons, providing valuable seasonality
information. The criteria used for selecting images
a. Scene quality in terms of cloud freeness
b. Season requirements. When Landsat
images for both dry and wet season are available the decision
which one to use was made upon peculiarities of
the vegetation in that certain area.
c. Date. Trying to use the latest image available.
classification: A supervised maximum likelihood
algorithm was used within Erdas Imagine 8.6.
The training sample was obtained using a digitised
version of Faramalala’s vegetation map,
the initial Modis classification and the Conservation
International deforestation map. Also Landsat
images from a different season were used in order
to provide seasonality information (NDVI analysis
and visual interpretation). The scheme used for
this classification tried to separate 11 broad
classes to be subsequently refined in a GIS environment.
analysis: Although Landsat images were selected
a minimal cloud cover, Madagascar’s
east coast has frequent cloud cover. Of the Landsat
images 35% (mainly centred in north east part of
the country) used for classification have cloud
problems. Clouds and their associated shadows provide
incorrect reflectance values of the features in
the earth surface and disrupt the classification.
Therefore pixels covered by clouds or shadows were
excluded from classification.
First a cloud mask was developed for each image by running an unsupervised classification
looking for 250 classes (ISODATA algorithm, 6 itineration, 0.950 convergence
threshold). Classes representing either clouds or shadows were identified and
the classified images recoded to conform a binary mask. Manual modification was
carried out wherever misclassifications were identified. These binary images
were used to mask out clouds and cloud shadows from the images prior classification.
In each scene with cloud problems successive Landsat images were classified to
replace cloud holes until the scene was complete or no more data was available.
As a result cloud problems were minimised using the entire set of Landsat images
- Post classification
processing: A 3 x 3 pixel
majority filter was applied to the output images
in order to smooth the classification. Finally,
classified images were assembled to conform a classified