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Methodology
and Algorithm
Algorithm
Description
There are
many methodologies used in forest fires detections. Some of them are very
simple and basic; others are very complicated and involve complex issues.
However, there are a limited number of methods that is commonly used. Most
notable is the Kaufman algorithm, which has been developed to achieve more
accurate measurements.
A number of remote sensing systems, e.g., AVHRR, use this algorithm or a
modified version of it. In the Kaufman algorithm, each and every pixel has
to undergo 6 “tests.” When a pixel passes through all of the tests, we
can call it a forest fire. For explanation purposes, we’re going to
elaborate on these tests specifically in the content of AVHRR. However, it
is crucial to understand that Kaufman algorithm is not solely limited for
the use of AVHRR remotely sensored data. These tests are as follows:
·
Detection Test: Fires are detected when band 3 (3.7 micrometers) is saturated or very
close to saturation by the income radiation from the fires. Band 3 is
called saturated when the AVHRR sensor receives a thermal signal of around
320 K.
·
Warm Background test: The temperature reading in band 3 should be greater
than the reading band 14 (11 micrometers) plus 15 K. If a pixel makes this
condition true, then it means that the background is not what saturates
band 3.
·
High Reflective Cloud test: It is common for the clouds to have similar
readings to those of fires. This is because clouds highly reflect solar
energy radiation. Thus, we have to exclude any cloud cover from the data
set. We can say for sure that there the reason for band 3 saturation is
not cloud cover only if the temperature reading in band 4 should be
greater than 245 K.
·
High Reflective Background test: It also usual for bare soil (i.e., sandy beach) to
give high band 3 temperature readings. We only can differentiate between
fire and bare soil by the visible band 1. Band 1 gets high visible
reflectance from bare soils, unlike forest fires, which give much lower
visible reading. Thus, when band 1 is less than 0.25, it means the pixel
is not bare soil.
·
Glitter test: Sometimes, water surfaces under sun glint can have similar spectral
response patter to this of forest fire. But, fires give different readings
for band 1 and band 2, more than 0.01. So, to exclude water surfaces under
glint condition, we subtract the reflectance of band 1 and band 2. If the
difference is more than 0.01, the pixel is not water surface.
·
Visual inspection: The tests above don’t include every fire and exclude every other
unrelated earth object. Unrealistic detection is till present, while some
highly probable fires are still lost. The visual inspection is needed to
cancel dubious cases (e.g., large uniform zone taken as fire). In the
examining of 4000 images, the visual inspection led to the rejection of
10% of the images, because environmental problems were marked as fire.
Limitations
In addition to the environmental problems, there are
some types of problems and limitations. Some of them are as follows:
1
Cloud
cover can be so huge that it covers a significant part of the study area.
This becomes troublesome, because all the area covered is completely
discarded from the study as non-accessible.
2
The
fire temperature relative to its extent might be insufficient to mark the
pixel as fire.
3
The
orbit of the satellites may drift, which produces different reading from
one year to another.
4
The
images are corrected from distortion caused by atmospheric objects, e.g.,
aerosols.
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