2. STUDY AREA AND DATA
The area selected for this Study is the Lake Mai-Ndombe (about
380km North-east of the capital, Kinshasa) and its surroundings
in Democratic Republic of the Congo. This area has good
coverage of forest with cropland and grassland in patches.
Three epochs satellite data; 1990, 2000, and 2010 were used in
the analysis and their details are as follows:
i) Landsat 5 Thematic Mapper (TM): Path 180/Row 062,
dated 1995 February 19.
ii) Landsat 7 Enhanced Thematic Mapper Plus (ETM+): Path
180/Row 062, dated 2002 May 5.
iii) ALOS AVNIR-2: Path 278/Frame 3650, dated 2009 July 4.
Landsat 5 TM and Landsat 7 ETM- have 7 bands and spatial
resolution bands 1 to 5 and 7 are 30m. ALOS AVNIR-2 has 4
bands with spatial resolution 10m.
Analyzed area was equal to one ALOS AVNIR-2 scene. This
area was covered by 4 scenes of ALOS Phased Array type L-
band Synthetic Aperture Radar (PALSAR) (Path 600/Row 714,
dated 2009 October 4; Path 601/Row 713 & 714, dated 2009
July 4; Path 602/Row 713, dated 2009 September 12.
3. METHODOLOGY
3.1 Semi-automated Cloud/Shadow, and Haze
Identification and Removal
Among these analyzed satellite data, ALOS AVNIR-2 and
Landsat ETM+ had cloud/shadow, and ALOS AVNIR-2 and
Landsat TM had haze. The adopted methodology for
identification and removal of cloud/shadow, and haze is
summarized in below workflow (Figure 1).
Optical Satellite Image
Y Y v Y
Sun Azimuth angle | Cloud area | Water body | Tassel Cap |
& Average Cloud
Ger [sem
Shadow Distance
Haze signal to
be subtracted, A
Shadow area
v
Combined Cloud
& Shadow area
PALSAR Dehazed Image
Satellite Image
y
Cloud/Shadow, and
Haze removed Image
Figure 1. General Workflow for Identification and Removal of
Cloud/Shadow, Haze.
3.1.1 Cloud/Shadow Identification:
i) Cloud identification: Instead of employing TRRI and CSI
index, cloud area was extracted using Unsupervised
Classification for 3 visible bands (for instance, bands 1, 2,
and 3 for both Landsat and AVNIR-2) and then recoding the
representing classes (which is generally one or all from 28th
to 30th). The Unsupervised Classification was also tried using
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
4 bands data; however, the result was not synchronized as that
with 3 visible bands.
ii) Shadow identification: This was carried out by
incorporating its direction and estimating the average distance
from cloud. Shadow direction was known using the Sun
Azimuth angle of the image, which is generally provided with
associated metadata. Average distance of shadow from cloud
was estimated by measuring two or more cases on image.
Both cloud and shadow areas were extended for proper
representation. Then, both were combined to one.
3.1.2 Haze Identification and Removal:
i) Haze identification: For this, Haze component of Tassel
Cap (TC) transformation was used. The clear and hazy area
was separated using equation (1) and pixel with value greater
than mean TC was designated as Haze area (Richter, 2011).
Te = (x:* DNprup) + (X: * DN pp) 1)
Where,
DN... : DN value in Blue band
DN,zp : DN value in Red band
X1 : weighing coefficients for Blue band
X: : weighing coefficient for Red band
The index coefficient for Landsat 5 TM was used that
presented by Crist et al. (1986). And, for ALOS AVINIR-2,
such coefficient being un-available, the same coefficient that
for Landsat5 TM was employed and result is promising one.
Cloud/Shadow, and water body area was not included in the
haze area. Water body was estimated using Normalized
Difference Vegetation Index (NDVI) method followed by
some manual editing.
ii) Haze removal: First, using the Pixel values of Blue and
Red bands for clear area, slope angle (a) was calculated,
which was used to estimate haze levels map or Haze
Optimized Transform (HOT) using equation (2).
HOT - DN yu; * sina — DN,» * Cosa (2)
Then, for bands; Blue, Green, and Red (that is, < 800mm) of
both ALOS AVNIR-2 and Landsat TM, the haze signal to be
subtracted, A for each HOT level and for each AVNIR-2
band was calculated. Dehazing was done by subtracting the
A from original DN for haze area (Richter, 2011).
3.1.3 Cloud/Shadow Removal: From the above dehazed
image, cloud/shadow pixels were replaced with free pixels of
same image with the help of PALSAR image by nearest
neighborhood. For each cloud/shadow pixel, the algorithm first
finds the corresponsive pixel in PALSAR image. Then, it
searches nearest similar pixels on iteration basis. At iteration |,
immediate surrounding pixels are searched, then at iteration 2,
pixels surrounding the iteration 1 are searched, and so on. For
instance, for a center pixel, the searching ring at Iteration 2 and
Iteration 4 is presented in Figure 2.
