aerial imaging and lidar point cloud fusion for low-order

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+ Aerial Imaging and Lidar Point Cloud Fusion for Low-Order Stream Identification Ethan J. Shavers 1 , Lawrence V. Stanislawski 1 1 U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Email: [email protected], [email protected]

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Page 1: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

+Aerial Imaging and Lidar Point Cloud Fusion for

Low-Order Stream Identification

Ethan J. Shavers1 , Lawrence V. Stanislawski1

1 U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Email: [email protected], [email protected]

Page 2: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

2+ Outline Introduction Objectives and Challenges Methods Preliminary Results Conclusions and Future Work

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

Page 3: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

3+ Introduction Weighted Flow Accumulation model and NHD Identify matching and mismatching features in both datasets Coefficient of Line Correspondence (CLC) metric

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

𝐢𝐢𝐢𝐢𝐢𝐢 =𝑆𝑆𝑆𝑆𝑆𝑆 π‘œπ‘œπ‘œπ‘œ 𝑑𝑑𝑑𝑑𝑑 𝑙𝑙𝑑𝑑𝑙𝑙𝑙𝑙𝑑𝑑𝑑 π‘œπ‘œπ‘œπ‘œ 𝑑𝑑𝑑𝑑𝑑 π‘†π‘†π‘šπ‘šπ‘‘π‘‘π‘šπ‘šπ‘‘π‘šπ‘šπ‘™π‘™π‘™π‘™ π‘™π‘™π‘šπ‘šπ‘™π‘™π‘‘π‘‘π‘™π‘™ π‘šπ‘šπ‘™π‘™ π‘π‘π‘œπ‘œπ‘‘π‘‘π‘‘ π‘‘π‘‘π‘šπ‘šπ‘‘π‘‘π‘šπ‘šπ‘™π‘™π‘‘π‘‘π‘‘π‘‘π‘™π‘™

𝑆𝑆𝑆𝑆𝑆𝑆 π‘œπ‘œπ‘œπ‘œ 𝑑𝑑𝑑𝑑𝑑 𝑙𝑙𝑑𝑑𝑙𝑙𝑙𝑙𝑑𝑑𝑑 π‘œπ‘œπ‘œπ‘œ π‘šπ‘šπ‘™π‘™π‘™π‘™ π‘™π‘™π‘šπ‘šπ‘™π‘™π‘‘π‘‘π‘™π‘™ π‘šπ‘šπ‘™π‘™ π‘π‘π‘œπ‘œπ‘‘π‘‘π‘‘ π‘‘π‘‘π‘šπ‘šπ‘‘π‘‘π‘šπ‘šπ‘™π‘™π‘‘π‘‘π‘‘π‘‘π‘™π‘™

(Stanislawski et al., 2015)

Page 4: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

4+ Introduction Headwater Stream length as a percentage of total stream length

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

(Nadeau and Rains, 2007)

Page 5: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

5+ Challenge and Objectives

Challenge Regular NHD validation and updating Low order stream modeling inaccuracy

Objectives Automate low-order stream identification in low topographic relief

humid regions Identify conditions that allow for stream classification

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

Page 6: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

6+

Low topographic relief agricultural region

Methods

Page 7: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

7+

NHD agreement with elevation-derived

channels

Methods

Page 8: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

8+

Elevation-derived channels:omissions

Methods

Page 9: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

9+

Elevation-derived channels:

commission errors

Methods

Page 10: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

10+

Elevation-derived channels:

commission errors

Methods

Page 11: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

11+ LAS point cloudMethods

Stream permanence

Page 12: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

12+

3 m DEM

Methods

Page 13: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

13+

Return intensity

Methods

Page 14: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

14+

Topographic Position Index

Methods

Page 15: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

15+

Point drop out

265 m 345 m

Methods

Page 16: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

16+ Methods

NAIP analysis

Page 17: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

17+Object Based Image Analysis

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

0.25 km

2.0 km

Methods

Page 18: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

18+ Preliminary Results

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

Lidar derivatives: DEM (TPI and profile curvature), intensity, and density of returns

NAIP: Οƒ(blue)* blue/ NIR (below)

Page 19: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

19+ Preliminary Results

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

Page 20: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

20+ Preliminary Results

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

Page 21: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

21+ Preliminary Results

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

Panther Creek intermittent PerennialMatch lines 36.74 40.67Model lines 22.02 37.69

59 % 93 %

Forked Creek intermittent PerennialMatch lines 22.11 37.43Model lines 5.45 29.23

25 % 78 %

Page 22: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

22+ Conclusions and Future Work Lidar derivatives and NAIP data can be used to extract streams Classification as ratio of model match Ground-truthing Dynamic weighting may be required for automation

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

Page 23: Aerial Imaging and Lidar Point Cloud Fusion for Low-Order

23+ References

UCGIS 2018 Symposium and CaGIS AutoCarto, Madison WI, May 22-24, 2018

Nadeau, T. and Rains, M. C. (2007), Hydrological Connectivity Between Headwater Streams and Downstream Waters: How Science Can Inform Policy. JAWRA Journal of the American Water Resources Association, 43: 118-133. doi:10.1111/j.1752-1688.2007.00010.x

Stanislawski, L. V., Buttenfield, B. P., & Doumbouya, A. (2015). A rapid approach for automated comparison of independently derived stream networks. Cartography And Geographic Information Science, (5), 435.

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