Review Jurnal Penginderaan Jauh Kelautan

1. Assessment of Planet Scope
Images for Benthic Habitat and
Seagrass Species Mapping in A
Complex Optically Shallow Water
Environment
(P. Wicaksono & W. Lazuardi, 2018)
Anisa Aulia Sabilah
C552190011

2. Q1

3. INTRODUCTION
Mapping and
monitoring of
benthic habitat
and seagrass
biodiversity on
a detailed-
scale is a vital
component in
management
and monitoring
of coastal area
Several
issues
encountered
of benthic
habitat when
using
the existing
satellite
images, i.e.,
the delay
between
image
acquisition
and field
survey
Accuracy
difference of
several
researches is
partially
affected by
the
difference in
the seagrass
species
classification
scheme
The variation
of seagrass
reflectance is
due to the
variation of
biomass,
atmospheric
condition,
sunglint and
water column
condition
Planet Scope’s
with very high
spatial (3m) and
temporal
resolution
(almost daily), it
is possible to
obtain an image
with the same
recording date
as the field
survey and high
temporal
monitoring effort

4. STUDY AREA
Karimunj
awa
Island
Kemuj
an
Island
Menjan
gan
Besar
Island
Menjan
gan
Kecil
Island
Goson
g
Island
Cilik
Island

5. IMAGE DATA
Two PlanetScope images at the 3B level
(17 May and 15 August 2017)
Surface
area 150
million
km2/day
Visible
Bands (Red,
Green, Blue,
NIR)
3 m spatial
resolution
12-bit
radiometric
resolution
1 day
temporal
resolution
These images were the best images recorder closest to the date of field
survey.
Presently, Planet Scope made the surface reflectance (SR) product available,
and thus the orthorectified images no longer need atmospheric correction.

6. FIELD DATA
Field survey were conducted
in 8-13 April 2017 and 11-16
August 2017
Benthic habitat and seagrass
data collected using photo-
transect method
Each photo was given UTM
coordinate using Garmin 78s
Global Positioning System
(GPS)
Location survey based on the
variation and
representativeness
The locations of photo-
transect survey for April &
August were not similar
Photo-transect samples were
analysed using Coral Point
Count Excel (CPCE) Program

7. METHODS
Resampled: Spectral Angle Mapper (SAM), Spectral
Information Divergence (SID), and Linear Spectral
Unmixing (LSU).
Classifying: Maximum Likelihood (ML). Support Vector
Machine (SVM), and Classification Tree Analysis (CTA).
Seagrass species spectral
0
1
Atmospheric correction
Sunglint correction
Image corrections
0
2
Principal Component Analysis (PCA)
Minimum Noise Fraction (MNF)
Image transformation
0
3
Per-pixel classification
Object-Based Image Analysis (OBIA)
Benthic habitat mapping
0
4

8. RESULTS
Seagrass species mapping
Seagrass species mapping of 17
May 2017 image
Comparison of May and August
2017 image
Benthic habitat mapping
Benthic habitat mapping of 17
May 2017 image
Comparison of May and
August 2017 image
A B
1 1
2 2

9. Benthic Habitat
Mapping
of 17 May 2017

10. Comparison of May &
August 2017 Benthic
Habitat Image

11. Seagrass Species Mapping
of 17 May 2017 Image

12. Comparison of May & August 2017
Seagrass Species Image

13. DISCUSSION
The use of BOA
reflectance
image without
additional image
transformation
and correction
delivered
promising
accuracy.
In this study, apllied different
classification algorithm and
suggested that for benthic
habitat and seagrass species
mapping, machine-learning
CTA delivered the best
accuracy.
Planet Scope image is not
without issue. The noise
level is high and low SNR.
This issue was encountered
during the process of
sunglint correction.
For seagrass
species mapping,
the accuracy was
also relatively
good with more
than 70% OA for
five classes
species.
The accuracy of
Planet Scope
image was
relatively good
for mapping
benthic habitat
at five classes
complexity was
50%.
The radiometric
quality are low
on homogeneous
clusters of pixels,
especially for
mixed species
class such as
CrHu, ThCr, and
EaThCr.
Planet Scope image is a new
image with many
advantages, including high
temporal and high spatial
resolution.

14. CONCLUSION
03
Planet Scope image has a serious issue which has low SNR and limits the application of
sunglint correction. Mapping benthic habitat and seagrass species in water with high sunglint
will be very challenging and difficult. This is a serious issue that must be addressed by Planet
engineers for future Planet Scope satellites development.
02
The accuracy of seagrass species mapping (74.31%) of Planet Scope
image are comparable to those from Quickbird, IKONOS, WorldView-2,
and Rapideye. Indeed, the accuracy of benthic habitat mapping
(50.00%) is lower than the previous satellites.
01
Planet Scope satellite obtain images at almost
daily basis, hence allow us to obtain remote-
sensing image very close to the date of field
survey.

15. Thank You 