This presentation covers the principles of remote sensing and reflectance profiling and explains how the concept of spectral signature is utilized in entomology research

1. REMOTE SENSING AND REFLECTANCE PROFILING IN
ENTOMOLOGY
Presented by:
G.Satish
RAM/18-48
Dept. of Entomology
CREDIT SEMINAR
PROFESSOR JAYASHANKAR TELANGANA STATE
AGRICULTURAL UNIVERSITY,HYDERABAD.

2. Structure of the presentation
▪ What is remote sensing ?
▪ What is reflectance profiling ?
▪ Principles & process of remote sensing
▪ Spectral signature
▪ Remote sensing techniques used in entomology
▪ Case studies
▪ Conclusion

3. Definition
• Remote sensing is the science and art of obtaining
information about an object, area or phenomenon
through the analysis of data acquired by a device
that is not in contact with the object under
investigation. (Lillesand et al., 2004)

4. Reflectance profiling
• Reflectance profiles represent the radiometric energy
reflected by an object in a series of spectral bands.
• An acquired reflectance profile is always relative to
and determined by an object in a series of spectral
bands.
a) The radiometric energy source used to elicit a
reflectance profile.
b) The spectral and spatial sensitivity of the sensor used
to acquire the reflectance data.
c) calibration and processing steps involved in
photogrammetric process.

5. Principles of remote sensing.
• Detection and discrimination of objects or surface
features means detecting and recording of radiant
energy reflected or emitted by objects or surface
material.
• This depends on the property of
material(structural,chemical and physical),surface
roughness,angle of incidence,intensity and
wavelength of radiant energy.
(Aggarwal, 2004)

6. Stages in remote sensing
I. Emission of electromagnetic radiation or
EMR(sun/self emission)
II. Transmission of energy from the source to the
surface of the earth,as well as absorption and
scattering.
III. Interaction of EMR with the earth's surface :
reflection and emission.
IV. Transmission of energy from the surface to remote
sensor.
V. Sensor data output.
(Aggarwal, 2004)

7. Process of remote sensing

8. • The fundamental objective in remote sensing is to
differentiate objects on the basis of a combination
of spectral features extracted from reflectance
profiles, and this endeavor is based on two
fundamental assumptions:
➢ It is possible to control for environmental
heterogeneity(i.e.,through calibration).
➢ A given object is associated with unique reflectance
profile features, such that even very similar objects
can be distinguished from all the other objects
belonging to different categories.

9. TYPES OF REMOTE SENSING BASED ON SENSORS
• Passive remote sensing :
❖ It uses sun as a source of
electro magnetic energy.
❖ Passive sensors detect energy
that is naturally emitted or
reflected by the object or
surrounding areas.
❖ Eg : Cameras , Infra red
sensors, imaging
spectrometers.

10. • Active remote sensing :
❖ It uses its own source of
electromagnetic energy.
❖ The sensor emits radiation which is
directed toward the target/object
and return energy is measured by
the sensor.
❖ Eg : RADAR,LIDAR

11. TYPES OF REMOTE SENSING BASED ON PLATFORMS
Ground based platforms.
Short range systems (50-100 m)
Medium range systems (150-250 m)
Long range systems (upto 1 km)
Air borne platforms.
Space-borne platforms.

12. Ground based platform
• Instruments that are ground based and often used
to measure the quantity and quality of light coming
from the sun or for close range of characterisation
of objects.
GER 1500 SPECTROPHOTOMETER

13. Ground based
• Spectro radiometry is the technique of measuring the
spectrum of radiation emitted by a source.
• This is achieved by diffraction grating technique
within the spectroradiometers which split the
radiation entering into system into its constituent
wavebands.
• A suitable detector is then used to quantify the
radiation of each wave length.

14. Air borne
• Air borne platforms were the sole non-ground
based platforms for early remote sensing work.
• Downward or sideward looking sensors are
mounted on an aircraft to obtain images of earth's
surface.It has the capability of offering very high
spatial resolution images.

15. Space borne platform.
• In space borne remote sensing, sensors are
mounted on-board a space craft(space shuttle or
satellite) orbiting the earth.
• Located at a high altitude of 36,000 km.
• Advantages :
• Large area coverage.
• Frequent and repetitive cover of an area of interest.
• Semi automated computerised processing and
analysis.

16. • When radiation falls on earth's surface three major
types of interactions occur :
➢ a part is reflected
➢ another part is absorbed
➢ remaining part is transmitted.
• The energy balance is expressed as EI=ER+EA+ET.
Interactionof energy with earth’s surface(Lillesandand Kiefer ,1993)

17. Energy incident upon object
• In remote sensing there is much emphasis on
reflected radiation
• The reflectance characters of earth surface features
are often expressed quantitatively in terms of Rλ
called spectral reflectance.
• Spectral reflectance=
Energy reflected by object

18. Spectral signature
• Spectral reflectance, is the ratio of reflected energy
to incident energy as a function of wavelength.
• Various materials of the earth’s surface have
different spectral reflectance characteristics.
• The values of the spectral reflectance of objects
averaged over different , well defined wavelength
intervals comprise the spectral signature of the
objects or features by which they can be
distinguished.
• So, spectral signature is a term used for unique
spectral response ,which is characteristic of a
terrain feature.

19. • The spectral reflectance is dependent on wavelength,
it has different values at different wavelengths for a
given terrain feature. The reflectance characteristics
of the earth’s surface features are expressed by
spectral reflectance, which is given by:
• R(λ) = [ER(λ) / EI(λ)] x 100
Where,
• R(λ) = Spectral reflectance (reflectivity) at a particular
wavelength.
• ER(λ) = Energy of wavelength reflected from object
• EI(λ) = Energy of wavelength incident upon the object
• The plot between R(λ) and λ is called a spectral
reflectance curve

20. Typical spectral reflectance curves for vegetation, soil
and water
Vegetation
Dry soil
(5% w ater)
Wet soil
% w ater)
(20
Clear lake w ater
Turbid river w ater
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
Wavelength (micrometers)
SPOT XS Multispectral Bands
Landsat TM Bands
5 7
1 2 3
1 2 3 4
60
40
20
0
Middle Infrared
ReflectedInfrared

21. Typical spectral reflectance curve of healthy vegetation depending
on different absorption peaks
Wavelength(nanometers)
Reflectance(%)

22. Reflectance curve of healthy vs stressed vegetation
Wavelength(nanometers)
Reflectance(%)

23. ▪ Remote sensing of biotic stress is based on the
assumption that stress interferes with photosynsthesis
and physical structure of the plant at tissue and canopy
level, and thus affects the absorption of light energy and
alters the reflectance spectrum.(Riley, 1989)
▪ Thus remote sensing instruments that measure and
record changes in electromagnetic radiation may provide
a better means to objectively quantify the stress than
visual assessment methods.

24. Remote sensing techniques used in Entomology
Riley JR. 1989
• Photography and videography from aircraft
and the ground.
• Satellite borne photography.
• Multi/hyperspectral scanning
• Thermal imaging
• Ground based and air borne radar.

25. Hyperspectral remote sensing
Also known as image spectroscopy, is a relatively newtechnology
that utilises sensors operating in hundreds of narrowcontiguous
spectral bands, which offerspotential to improve the assessment
of crop diseases and pests.
In this system radiation from any specified target has
been obtained continuously making it possible to gain
detailed information on the materials.
Detects plant health through analysis of spectral
signatures.
Utilised in studies for detection of stress caused by insects,
or to discriminate damage caused by various pests in a crop.

26. Importance of hyperspectral remote sensing
Relationships between spectral characteristics
and symptoms of infestations will be
adequately investigated based on ground
studies, before the development of the
remote sensing algorithms and management
schemes,thus helps to play an effective role in
crop pest management.

27. Multispectral Hyperspectral
Separated spectral bands no spectral gap
Broadband RS : Wider band width Narrowband RS : Narrowband width(10-
20nm)
Coarserrepresentationof spectral
signature
Complete representationof spectral
signature

28. Thermal imaging :
The branch of remote sensing that deals with the
acquisition,processing and interpretation of data acquired
primarily in the thermal infrared portion of EM
spectrum.(Roselyne and Ahmed, 2014).
Principle: collects the thermal infra red regions within the IR
radiation from 8-12μm which emitted from earth’s surface by
thermal sensors into an image.

29. ➢ Thermal imaging offers a new approach to detect
the insects in grain storage.
1.Controlpanel
2.Microwaveapplicator
3.Conveyor
4.Thermal camera
5.Data acquisition
system

30. Radar
• Radar has a more obvious connection with
entomology because it has been effectively used
in direct observations of insects(Riley, 1980).
• Following the pioneering work done
demonstration by Schaefer(1969) on radar
studies of locust,moth and butterfly migration in
Sahara,application of this radar technique has
led to spectacular advances in the study of long-
distance migration.

31. why radar...?
• Many insect species engage in high altitude
wind-borne migration,often several hundred
meters above the ground.
• Direct observation of high-flying insects
migrants is very difficult ,especially at night,
but the remote sensing capabilities of
entomologicalradar provide a solution to this
seemingly intractable problem.

32. Advantages of RADAR
• Its unique capacity to detect insects simultaneously
at a range of altitudes that can reach more than
1km above ground level.
• The large sampling volume that it provides relative
to traditional sampling methodologies(Chapman et
al. 2002)
• Insects are unaffected by flying through the radar
beam, this method provides an unparallel
oppurtunity to investigate aspects of insect
migration behavior, such as orientaton in high flying
migrants.(Riley and Reynolds, 1986)

33. Vertically looking radar
schematic elevationview of vertical lookingradar beam

34. Radar equipment
The VLR emits a circularly symmetric, vertically looking beam in which the plane
of linear polarisation is continuously rotated by mechanically turning the upward-
pointing wave-guide feed about the vertical.
This produces nutation,conical-scan motion similar to that of spinning top that
has started to wobble as it slows down.
Targets flying through the beam are simultaneously detected within 15 altitude
bands(range gates),each 45m in depth ,separated by non sampling intervals of
26m; these range gates are located between 150 and 1166m above the radar.
Insects and other targets that pass through the radar beam bounce back a signal
that is detected by the receiver.
Signals captured within the 15 range gates are recorded for 5-minute periods
once every 15 minutes,24 hours a day giving almost continuous coverage.

35. sampling regime of the vertical lookingradar

36. • Target identification :
• Identification procedures currently used by VLR rely
on three potential types of information embedded
in the radar signals:
a) Maximum and minimum radar reflectivity.
b) Estimates of bodymass
c) Wing beat frequency
• These applications of radar-based remote sensing
have provided fascinating quantitative insight into
the diurnal rhythms and long-term dynamics of
migrating and dispersing insects across a wide range
of orders.

37. Areas of application of remote
sensing
• Agriculture
• Forestry
• Geology
• Hydrology
• Sea ice
• Land cover & land use
• Oceans & coastal monitoring
• Mapping

38. Agriculture
• Scope :
• Crop acreage estimation
• Crop modeling for yield and production forecast or
estimation.
• Crop and orchard monitoring
• Benefits :
• Timely availability of crop statistics for decision
making and planning.
• Crop growth monitoring.
• Soil status monitoring.
• Regular reports regarding total area under cultivation.

39. Forestry
• Scope :
• Satellite image based forest resource mapping and
updation.
• Forest change detection.
• Forest resource inventory’
• GIS database development.
• Benefits :
• Availability of baseline information.
• Planning for afforestation strategies.
• Futuristic resource planning.
• Sustainability of environment.

40. Case studies

41. Use of ground based hyperspectral remote sensing for detection of
stress in cotton caused by leaf hopper
Mean reflectance spectra of cotton plants infested by leafhopper at different levels
of infestation (Prabhakar et al. 2011)

42. Wave length (nm)
correlation
coefficient
376 nm
496 nm
691 nm
1457 nm
715 nm
1124 nm
(Prabhakar et al. 2011)
Linear correlationbetween cotton leaf hopper infestationgrades and canopy spectral reflectance

43. • Using two or more of these bands in different
combinations , several indices were formulated
and tested through regression analysis.
• Pest severity grade was taken as the independent
variable and vegetation index as independent
variable.
• High R2 were obtained for the following four
leafhopper indices.
• LHI 1 : R691 /R761 (R2 =0.771,p≤0.001)
• LHI 2 : R1124 –R691 /R1124 +R691 (R2 =0.801)
• LHI 3 : R761 – R691 / R761 + R715 (R2 =0.812)
• LHI 4 : R761 – R691 / R550 – R715 (R2 =0.825)
(Prabhakar et al. 2011)

44. Coefficient of determination and probability of leafhopper indices
Index R2 p R2 p R2 p
LHI-1 0.558 <0.01 0.726 <0.01 0.771 <0.01
LHI-2 0.850 <0.01 0.741 <0.01 0.801 <0.01
LHI-3 0.521 <0.01 0.768 <0.01 0.812 <0.01
LHI-4 0.461 <0.01 0.777 <0.01 0.825 <0.01
(Prabhakar et al. 2011)
Field 1 Field 2 Field 3

45. Characterisationof brown plant hopper damage on
rice crops through hyperspectral remote sensing
under field conditions
Prasannakumar etal., 2014

46. Variations in the mean reflectance in relation to variable BPH
infestations
Prasannakumar etal., 2014

47. Correlation coefficient (r) between reflectance spectra of rice
plants at wave bands and different BPH infestation levels
Prasannakumar etal., 2014

48. Coefficient of determination R-square and
probability of BPH Indices
F value R square P
BPHI-1 149.63 0.71 <0.0001
BPHI-2 172.02 0.74 <0.0001
BPHI-3 203.05 0.7 <0.0001
Prasannakumar etal., 2014

49. Hyperspectral radiometry for the detection and discrimination of damage
caused by sucking pests of cotton
(Ranjitha and Srinivasan , 2014)

50. (Ranjitha and Srinivasan , 2014)

51. (Ranjitha and Srinivasan , 2014)

52. Remote sensingmeasurementfor detectionof
bagworm infestationin oil palm plantation
(Aziz et al., 2012)

53. (Aziz et al., 2012)

54. (Aziz et al., 2012)

55. Descriptive statisticsof spectral reflectance for each level of foliar
damage
Descriptive statistics Class of foliardamage
Light damage Medium damage Serious damage
No. of observations 512 512 512
Minimum 8.9 5.4 6.9
Maximum 62.7 53.0 41.1
Mean 34.1 27.1 21.7
Standarddeviation 23.6 18.7 11.0
Variance 557.0 351.4 121.0
Coefficient of
variation(%)
69.3 69.2 50.6
(Aziz et al., 2012)

56. (Aziz et al., 2012)

57. Integrative insect taxonomy based on morphology,
mitochondrial DNA and hyperspectral reflectance
profiling
• Integrative taxonomy is considered as a reliable taxonomic
approach of closely related and cryptic species by integrating
different sources of taxonomic data.
• In order to infer the boundaries of seven species of
evacanthine leafhopper genus Bundera Distant,1908 , an
integrated analysis based on morpholpgy, mitochondrial
DNA and hyperspectral reflectance profiling was conducted
(Wang et al. 2015)

58. • In this study, comparision and integration of
three taxonomic procedures took place :
I. Classification based on traditional insect
morphology.
II. Classification based on the cytochrome c
oxidase subunit I (COI) mitochondrial
DNA(mtDNA) and 16S ribosomal DNA (rDNA)
gene analyses.
III. Reflectancebased classification.

59. Average reflectance profiles of the seven species included in
this study
Wang et al. 2015

60. Reflectance- based identificationof parasitised
host eggs and adult Trichogrammaspecimens
Nansen et al. 2014

61. Reflectance based assessment of spider mite “bio-
response” to maize leaves and plant potassium
content in different irrigation regimes
Irrigation regime
High
Medium
Low
1.2
1.4
1.6
1.8
2.0
2.2
2009
2010
C.Nansen et al., 2013

62. Average reflectance from 405 to 907 nm
600 700 800 900
500
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
10
20
30
40
50
Relative reflectance
F-value
P < 0.001
C.Nansen et al., 2013

63. Analysis of variance of reflectance at 740nm and of potassium
content in maize plants across classes of spider-mite bio-
response
Spider mite bio-response class
5
4
3
2
1
0.60
0.65
0.70
0.75
0.80
1.2
1.4
1.6
1.8
2.0
Reflectance at 740 nm
Potassium content
a
a
b
c
c
A
B
B
B
C
C.Nansen et al., 2013

64. High altitude migration of the diamondback moth
Plutella xylostella to the U.K. : a study using radar,
aerial netting and ground trapping
Chapmanet al., 2002
Mean aerial density of Plutella xylostella detected in the firstrange
gate of the vertically lookingradar

65. Density-height profile of larger insects(body mass>10 mg)
detected by the VLR in each of the 15 range gates during the
first wave of P.Xylostella immigrations
Chapmanet al., 2002

66. Mean aerial density of Plutella xylostella targets detected in
first range gate of the vertical looking radar and GM daily
catches of P.xylostella in insect survey light traps
Chapmanet al., 2002

67. Can early season landsat images improves locust
habitat monitoring in the Amudarya river dalta of
Uzbekistan
Study
area
(Latchininskyand Sivanpillai,2008)

68. Classified landsat images depicting the spatial distribution of reed
beds in the Amudarya river delta and adjoining area
(Latchininskyand Sivanpillai,2008)

69. Inferences
• Using iterative image classification and
reference data,a reed distribution map was
generated with an overall accuracy of 74%.
• Kappa agreement=0.686.
• Landsat data were able to correctly identify
87% of the reed beds.
(Latchininskyand Sivanpillai,2008)

70. Conclusion
Observations of
insects
Monitoring of
environmental
factors
Detection of
effects that
insects
produce

71. Merits
▪ Remote sensing provides an alternative cost
effective method to obtain detailed spatial
information for entire crop fields at frequent
intervals during the cropping season.
▪ Additionally, remote sensing can be used repeatedly
to collect sample measurements non-destructively
and non-invasively.

72. • Real-time data processing is a requirement
for successful integration of remote sensing
into pest management systems.
• Multi disciplinary approach is needed to
develop calibration and data processing
procedures to enable classifications with high
sensitivity and robustness

73. THANK YOU