BATHYMETRIC LIDAR, Bathymetric Light Detection and Ranging (LiDAR) was designed to penetrate the water column and reach the stream bottom

1. Laser Remote Sensing
Final Presentation
Department of Geomatics
National Cheng Kung University
Tainan - Taiwan
Date 2017/06/28
Presenter : Iva Nurwauziyah

2. 3
BATHYMETRIC
LIDAR

3. BATHYMETRIC LIDAR
“Bathymetric
Light Detection
and Ranging
(LiDAR) was
designed to
penetrate the
water column and
reach the stream
bottom”
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4

4. 1
5
PAPER 1
5

5. BATHYMETRY LiDAR SYSTEM MOUNTED IN A UAVs
6
Unmanned Aerial Vehicles (UAVs):
High Spatial Resolution
Low-Cost
Without a human pilot onboard

6. TECHNICAL SPECIFICATION UAV-BASED HYPERSPECTRAL SENSOR
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NAME WEIGHT
SPECTRAL
BAND
DATA
COLLECTION
TYPE
REFERENCE
OCI™ -1000 180 g VIS-NIR Push-broom
http://www.bayspec.com/spectroscop
y/oci-
uav-hyperspectral-camera/
Headwall”s Nano-
Hyperspec sensor
680 g 380-2500 nm Push-broom http://www.headwallphotonics.com
microHSI 450 g~2.6 kg 400-2400 nm Push-broom http://www.nova-sol.com/
Ocean Opyics USB4000 190 g 350-1000 nm Push-broom http://oceanoptics.com/
SOC710-GX Airborne
Hyperspectral Imager
1.25 kg 400-1000 nm Push-broom https://surfaceoptics.com/
Table 1. Technical Specification for Some Currently Available UAVs-based Hyperspectral Sensor

7. OVERVIEW OF RECENT ACHIEVMENT FOR UAV
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 Bathymetric LiDAR system is an alternative for observing the
water-surface slope.
 Bathymetric LiDAR have been applied to qualitatively derive
sediment size and distribution.
 UAVs-based platform has shown the better performance than
conventional sensors for insufficient light (e.g., night time)
situation on a broad scale.

8. CONCLUSSION
9
 UAVs can provide new opportunities for scientists and
practitioners to revolutioneze measurement campaigns,
which has been difficult when relying only in-situ
observation.
 Unprecedented datasets acquired with UAVs can
definetely contribute to a better understanding of
fluvial processes in riverine environments.

9. 1
10
PAPER 2
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10. OBJECTIVES
“To evaluate the usefulness of
WorldView-2 imagery and LiDAR data
for the derivation of water depth and
classification of submerged and
emergent habitats”
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11

11. METHODS
12
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WorldView-2 LiDAR
Pre-Processing:
- Radiance Calibration
- Atmospheric Correction
- Pan-sharpen
Supervised
Classification
Bare Earth
DEM
Submerged
Habitat Mask
Derive
Bathymetry

12. STUDY AREA
13
13
Pages Creek is located in
southestern North Carolina,
United States

13. RESULT
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Measured Versus Calculated Water Depth
R2 = 0.8036

14. RESULT
15
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Derived Bathymetry for the Study Area

15. CONCLUSSION
16
 The LiDAR data increased habitat map
accuracy; especially in the low density
cordgrass and black needlerush classes.

16. 1
17
PAPER 3
17

17. OBJECTIVES
“To test the six algorithms for single-
wavelength bathymetric waveform
processing”
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18. METHODS
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Echo Detection Algorithms
Mathematical Approximation
Algorithms
Deconvolution Algorithms
1
2
3

19. METHODS
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Echo Detection Algorithms
1
Peak Detection (PD)
𝑘 = 𝑓𝑖𝑛𝑑 𝑑𝑖𝑓𝑓 𝑠𝑖𝑔𝑛 𝑑𝑖𝑓𝑓 𝑤 < 0 + 1
Average Square Different Function (ASDF)
𝑅 𝑡 =
𝑚=1
𝑁
𝑊𝑇 𝑚𝜏 − 𝑊𝑅(𝑚𝜏 + 𝑡)

20. METHODS
21
21
Mathematical Approximation Algorithms
2
Gaussian Decomposition (GD)
𝑓𝑐 =
1
𝑁
𝑊𝑅 𝑡 −
𝑖=1
𝑁
𝛼𝑖 𝑒
(𝑡−𝜇 𝑖)2
𝜎𝑖
2
Quadrilateral Fitting (QF)
𝑓𝑐 =
1
𝑁
𝑊𝑅 𝑡 −
𝑖=1
𝑁
𝛼𝑖 𝑒
(𝑡−𝜇 𝑖)2
𝜎𝑖
2
− 𝑓𝑞(𝑡)

21. METHODS
22
22
Deconvolution Algorithms
3
Richardson-Lucy Deconvolution (RLD)
𝑃 𝑖+1
𝑡 = 𝑃 𝑖
𝑡 .
𝑊𝑅(𝑡)
𝑃𝑖 𝑡 ∗ 𝑊𝑇(𝑡)
∗ 𝑊𝑇(𝑡)
Wiener filter Deconvolution (WD)
𝐹 𝑓 =
𝑊𝑇(𝑓) 2
𝑊𝑇(𝑓) 2 + 𝐾

22. THE OPTECH AQUARIUS DATASET
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The Optech Aquarius dataset of test area (about 250 m x 250 m)
(a) The point cloud and some examples waveforms; and (b) The camera photo

23. RESULT
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Algorithms RMSED (m) Cm Sr (%) Fr (%) R2 Tc (s)
Echo Detection
PD 4.5158 0.73917 75.39 12.11 0.97356 96.2
ASDF 4.4318 0.65631 70.79 6.09 0.98298 146.2
Mathematical
Approximation
GD 4.5457 0.78179 73.55 7.03 0.97489 844.3
QF 4.4906 0.77974 75.84 5.04 0.98388 1370.0
Deconvolution
RLD 3.9222 0.83422 81.95 5.28 0.98533 1567.6
WD 3.9599 0.70648 76.65 4.37 0.98152 109.6
Table 2. Performance Assesment of the Six Waveform Processing Algorithms

24. CONCLUSSION
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 The Richardson-Lucy Deconvolution (RLD) method had
a superior performance in terms of high detection rate
and low errors in the retrieved depth.
 The attenuation coefficient, noise level, water depth,
and bottom reflectance had significant influences on the
measurement error of the retrieved depth.

25. RESUME
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Paper 1
dsfdfd
Paper 2
We can know the
usefulness of
LiDAR data to
Paper 3
Is the color of
blood, and because
of this it has
historically been
associated with
sacrifice, danger
and courage.

26. REFERENCES
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 Halls, J., Costin, K. 2016. Submerged and Emergent Land Cover and Bathymetric
Mapping of Estuarine Habitats Using WorldView-2 and LiDAR Imagery. Remote
Sensing. 8, 718.
 Rhee, DS., Kim, YD., Kang, Boosik., and Kim, D. 2017. Applications of Unmanned
Aerial Vehicles in Fluvial Remote Sensing: An Overview of Recent Achievements.
KSCE Journal of Civil Engineering. 1-15
 Wang, C., Li, Q., Liu, Y., Wu, G., Liu, P., and Ding, X. 2015. "A Comparison of Waveform
Processing Algorithms for Single-wavelength." ISPRS Journal of Photogrammetry
and Remote Sensing 22-35.

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THANK YOU FOR YOUR LISTENING!
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