Abstract:
The mountain cloud forest of Taiwan can be delimited
from other forest types using a map of the ground
fog frequency. In order to create such a frequency map from
remotely sensed data, an algorithm able to detect ground fog
is necessary. Common techniques for ground fog detection
based on weather satellite data cannot be applied to fog occurrences
in Taiwan as they rely on several assumptions regarding
cloud properties. Therefore a new statistical method
for the detection of ground fog in mountainous terrain from
MODIS Collection 051 data is presented. Due to the sharpening
of input data using MODIS bands 1 and 2, the method
provides fog masks in a resolution of 250 m per pixel. The
new technique is based on negative correlations between optical
thickness and terrain height that can be observed if
a cloud that is relatively plane-parallel is truncated by the
terrain. A validation of the new technique using camera data
has shown that the quality of fog detection is comparable to
that of another modern fog detection scheme developed and
validated for the temperate zones. The method is particularly
applicable to optically thinner water clouds. Beyond a cloud
optical thickness of ? 40, classification errors significantly
increase.
