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Geo Sense - UAV service, unmanned remote sensing
1. Utilizing Hyperspectral Imaging System in unmanned
aerial vehicle (UAV) for Agricultural/Palm Oil Analysis
Geo
Sense
Sdn.
Bhd.
79A,
Jalan
Seri
Impian
1
T06-‐03,
Jln
Centry
Square
Taman
Impian
Emas
Block
2320
81300
Johor
Bahru
63000
Cyberjaya
ismaili@geosense.com.my
www.geosense.com.my
2. Geo
Sense
Sdn.
Bhd.
Brief
Background
• Establish
May
2006
• MSC
Status
–
in
web
GIS
and
aerial
mapping
• Pioneering
Civilian
UAV
applicaTons
/
Services
• Skudai,
Johor
Bahru
Base
Company
&
Cyberjaya
• CollaboraTon
with
UTM,
IMREC
and
Inst.
Sustainable
Agri
of
Cardoba
Spain
• Since
2007
-‐
R&D
in
unmanned
aerial
mapping
and
remote
sensing
• Recepient
Anugerah
Perdana
Menteri
APICTA
ICT
eGov.
Catergory
in
2007
• Vision,
to
become
leading
tech
company
in
civilian
UAV
applicaTon
3. Sources of Aerial Imagery
Light Aircraft
Imaging Altitude10k – 30k feet
Satellite
500-800 km Cost
RC AIrcarft / UAV
Altitude 500 – 2000 ft
• erial Camera System
A
• adiometric resolution
R
• 0 cm – 60 cm resolution
2
(avg 30 in resolution)
Cloud Issues • erial mapping survey
A
• PSAR (SAR) / LIDAR
I • atural color (RGB)
N
• - 15 cm resolution
7
• urveillance / Monitoring
S
200 meter
90 km 3 km
300 meter
30 km 3 km
18. Case
Study
1
-‐
UAV
Mapping
by
Geo
Sense
–
Semporna,
Sabah
(5
sq
km)
Visual
Using
UAV
PopulaTon:
~
130K
Mail
Volume:
~
200
per
day
PO:
Semporna
Post
Office
Mail
delivery:
limited
Address:
Using
kampung,
schools
&
PO
Box
(710
units)
Bank:
Maybank
&
BSN
19. House
Numbering
and
Address
Assignment
by
Pos
Malaysia
DigiTzing
using
GIS
KAMPUNG
BANGAU-‐BANGAU
2,436
houses
ADDRESS
SAMPLE
Cikgu
Ahmad
No2,
KampungBangauBangau
PERKAMPUNGAN
AIR
1
91300
Semporna,
Sabah
659
houses
PERKAMPUNGAN
AIR
2
Lat
4.4883
N
Long
118.6050
E
3,313
houses
Approximately
6,000
new
delivery
points
idenTfied
20. Base Map Preparation – Using High Res Aerial Image from UAV
High
resoluTon
UAV
Images
Image
digitalizing
Asset
Data
in
Base
Map
Base
Map
from
high
res
images
21. 3D
/
DTM
–
OrthoracTficaTon
Process
Sojware
upgrade
24. Scope
of
Work
&
Delivery
UAV
capturing
images
Image
process
&
digitalizing
The
big
mosaic
(sTtched)
image
Image
presentaTon
&
potenTal
uTlizaTon
Big
Poster
Tiles
for
quick
viewing
Online
Tles
visualizaTon
Image
registraTon
(GIS)
IntegraTon
with
-‐
Project
GIS
Project
Management
-‐
Project
monitoring
and
System
Online
visualizaTon
Quick
distribuTon
–
images
reporTng
system
-‐
Structure
modeling
Online
archiving
system
store
in
DVD
-‐
Decision
support
with
WBS
system
26. Quanta
Lab
Geo
Sense
Geo
Sense,
is
Malaysian
MSC
Status
company,
that
is
using
unmanned
aerial
vehicle
(UAV)
for
aerial
mapping
and
remote
sensing.
Geo
Sense
is
collaboraTng
with
Dr.
Pablo
J.
Zarco
Tejada
the
Director
of
Laboratory
for
Research
in
QuanTtaTve
Remote
Sensing,
under
the
InsTtute
of
Sustainable
Agricultural
in
Cardoba,
Spain
in
uTlizing
UAV
for
advance
remote
sensing
for
agricultural
purposes.
Geo
Sense
27. CollaboraTon
of
experts
between
Quantalab
and
Geo
Sense
Sdn.
Bhd.
Profile
Pablo
J.
Zarco
has
been
Course
Director
and
Dr
Pablo
J.
Zarco
Tejada
Teaching
Assistant
within
the
Departments
of
Environmental
Science,
and
Earth
and
Space
Ph.D.
in
Earth
and
Space
Science,
York
Science
(
York
University
,
Canada
),
and
Land,
Air,
University
(Canada),
2000
and
Water
Resources
(LAWR),
University
of
M.Sc.
in
Remote
Sensing,
Image
California
Davis
,
in
courses
related
to
Processing
and
ApplicaTons.
Dept.
of
Environmental
Science
and
Remote
Sensing.
He
Applied
Physics,
Electronic
and
has
also
collaborated
in
other
courses
at
University
Mechanical
Engineering
(APEME),
of
California
,
Davis
in
Precision
Agriculture
and
University
of
Dundee
(Scotland
,
UK),
Environmental
Remote
Sensing:
1997
Since
2008,
Dr
Pablo
has
been
uTlizing
UAV
for
B.S.
Agricultural
Engineering
(Cordoba
,
agri.
remote
sensing.
Spain)
Geo
Sense
28. The
collaboraTon
will
offer
Malaysia
users
to
access
to
the
latest
technology
in
agricultural
monitoring
and
analysis
using
UAV
for
quicker
respond
at
lower
cost
compare
with
convenTonal
methods.
The
UAV
with
mulT
spectral
camera
enable
to
meet
any
on
demand
request
for
urgent
requirement
in
any
agricultural
respond
and
analysis,
eg
to
quickly
get
the
assessment
over
agri.
epidemics
in
paddy
field
and
mapping
DOA
IntegraTng
farming
area
without
the
need
to
wait
for
satellite
images
or
convenTonal
airplane.
Geo
Sense
29. Sample
of
analysis
from
UAV
hyperspec.
Sensor
operate
by
Quantalab
in
Spain.
30. ExisTng
Geo
Sense
UAV
Agricultural
/
Crop
Monitoring
/Precision
farming
Without
hyperspec
sensor
–
limited.
Infrared
Imagery
Crop
Analysis
31. Sample
of
large
agricultural
area
(track
record)
–
1000
hectares
olive
farm
in
Cardova,
Spain
.
32. Hyperspectral
imagery
acquired
with
an
UAV
plaOorm
over
orchard
crops
Imagery
acquired
at
40
cm
resoluTon
and
260
bands
in
the
400-‐900
nm
region
@
5
nm
FWHM
Hyperspectral
imagery
acquired
from
an
UAV
plaoorm
and
the
Micro-‐Hyperspec™
Imaging
Spectrometer
from
Headwall
Photonics.
Imagery
acquired
at
550
m
AGL
over
an
orange
orchard
where
stress
detecTon
experiments
are
conducted
by
QuantaLab
at
the
InsTtute
of
Sustainable
Agriculture
(IAS),
NaTonal
Research
Council
(CSIC),
Spain.
33. Hyperspectral
Image
OrthorecQficaQon
AStude
data
acquired
with
an
AHRS
system
onboard
the
UAV
Image
orthorecTficaTon
is
conducted
using
aqtude
data
acquired
with
an
AHRS
instrument
synchronized
with
the
hyperspectral
imager.
Commercial
sojware
and
IAS-‐CSIC
algorithms
are
applied
in
the
laboratory
ajer
each
flight
campaign.
34. Image
CalibraQon
and
Atmospheric
CorrecQon
Spectral
calibraTon
of
the
hyperspectral
instrument
is
conducted
at
IAS-‐CSIC
using
Hg-‐Ar
calibraTon
lamps.
Radiometric
calibraTon
coefficients
are
developed
in
the
opTcs
laboratory
at
IAS-‐CSIC
using
a
radiometrically
calibrated
integraTng
sphere.
Image
calibraTon
and
atmospheric
correcTon
to
obtain
surface
reflectance
are
conducted
from
field-‐measured
data
and
aerosol
opTcal
depth
measured
at
the
Tme
of
flight.
Radiance
and
reflectance
imagery
are
produced
ajer
calibraTon
algorithms
are
applied
in
QuantaLab
IAS-‐CSIC
Laboratory.
Imagery
acquired
at
40
cm
resoluTon,
260
bands
in
the
400-‐900
nm
region
(5
nm
FWHM).
Raw
data
Reflectance
data
35. Hyperspectral
Image
SegmentaQon
of
the
crop
canopy
Object
based
image
analysis
for
automaTc
tree
crown
idenTficaTon
and
stress
detecTon
using
spectral
indices
Hyperspectral
reflectance
image
Object
based
analysis
Stress
map
(object-‐based
analysis)
Interpolated
themaTc
maps
obtained
from
object
based
analysis
conducted
on
hyperspectral
indices
at
the
tree
crown
level.
Stress
maps
are
derived
based
on
photosyntheTc
pigment
concentraTon
and
canopy
density
36. Hyperspectral
Reflectance
from
a
water
body
S p e c t r a l
r e fl e c t a n c e
extracted
from
different
areas
of
a
water
body
47. User
Requirements
• Needs
to
increase
producTvity
by
planTng
more
(new
estate)
&
improve
producTvity
/
yields
– SoluTon;
Maintain
good
tree
condiTons,
by
having
up
to
date
block
/
sectors
/
trees
informaTon
– Healthiness
and
nutrient
status
– Assets,
Land
use,
land
cover
(showing
assets
locaTon,
vegetaTon
and
water
body)
• Nutrient
checking
(leaves
&
soil)
–
up
to
individual
tree
– SoluTon;
Soil
Nutrient
&
Foliar
Variability
Mapping
–
showing
the
availability
of
N,P,K,Mg,B(easier
for
detected
less
nutrient
area).
48. User
Requirements
• Healthiness
oil
palm
trees
map
for
detecTng
stress
trees
and
for
esTmaTng
the
yield.
– SoluTon;
Digital
“stressed”
palms
map.
Maps
showing
healthy
trees,
“stressed”
and
dead
palms
and
development
of
spectral
signature
for
all
palms
condiTon.
• LocaTons
of
the
tree
with
un-‐healthy
condiTon
– SoluTon;
Tree
status.
Showing
tree
maturity
status
(ageing)
– Individual
Oil
Palm
inventory
countswith
precise
GPS
locaTon
Map
(locaTon
each
tree)
49. What
aerial
imagery
tells
• As
evaluaTon
tools
and
diagnosTc
kits,
– PlantaTon
and
forestry
– Inventory
– Healthiness/Stressed/Disease
– Dead
Trees
(Pest/Disease/Water
Stress,
waterlog,
burnt,
etc)
– Species/community
types
– Maturity
• Marine
and
environmental
features
– Inventory
– Community
types
– Changes
detecTon
and
analysis
– Coral/sea
weed
mapping
– Water
quality
(salinity,
turbidity,
pH,
temperature,
etc)
• Physical
Features
– Roads/footpath/track/rivers/streams/topo.
etc
– Area
EsTmaTon=Gross
Area–Vacant
Area=Net
Area
– Boundaries
50. Overview
of
works
proposal
–
design
and
develop
Unmanned
Aerial
Remote
Sensing
Facility
For
Agricultural,
Forestry
and
Palm
Oil
Analysis.
Preparing
UAV
plaoorm
IntegraTng
Micro
Hyperspec
Sensor
for
user
unmanned
remote
Image
capturing
and
image
from
Honeywell
Photonic
(US)
advance
cube.
–
operate
by
Geo
sensing
aerial
vehicle
plaoorm
hyoperspec
sensor
for
UAV
system.
Into
(UAV).
Need
for
stable,
endurance
Sense
&
Quantalab,
Spain
Geo
Sense
UAV
plaoorm.
and
load
(min.
3
kg
load)
system
IntegraTon
work
is
collaboraTon
with
Plaoorm
will
be
provided
by
Geo
Sense
QuantaLab,
Spain
and
Geo
Sense
Online
access
system
–
Design
&
develop
client
web
based
system
for
imaging
database
or
library
mulT
access
via
Internet
/
Image
Analysis
–
system
for
review,
analysis
Intranet
Quantalab,
Spain
and
decision
support
-‐
Geo
Sense
&
partner
-‐
Quantalab
&
Geo
Sense
51.
52. Grant
Plan
ExisTng
Grant
Spin
off
Hand
launch
glider
UAV
Mid
range
UAV
System
Malaysian
IMU
system
Unmanned
aerial
mapping
(20
kg,
payload
1.5kg)
(autopilot
system)
Per
mission
30
min.
endurance,
Per
mission
90
min
1.5
sq
km
per
mission
Min.
3
sq
km
per
mission
6
sq
km
(600
hectares)
per
day.
10
sq
km
(1000
hectares)
per
day
Remote
sensing
on
RGB
compact
camera
Hyperspectral
Imaging
demand
For
UAV
VisualizaTon
Imaging
analysis
Center
for
unmanned
remote
Sensing
for
tropical
agri.