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. Shavers1 , Lawrence V. Stanislawski1

1 U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Email: eshvers@usgs.gov, lstan@usgs.gov

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

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)

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)

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

6+

Low topographic relief agricultural region

Methods

7+

NHD agreement with elevation-derived

channels

Methods

8+

Elevation-derived channels:omissions

Methods

9+

Elevation-derived channels:

commission errors

Methods

10+

Elevation-derived channels:

commission errors

Methods

11+ LAS point cloudMethods

Stream permanence

12+

3 m DEM

Methods

13+

Return intensity

Methods

14+

Topographic Position Index

Methods

15+

Point drop out

265 m 345 m

Methods

16+ Methods

NAIP analysis

17+Object Based Image Analysis

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

0.25 km

2.0 km

Methods

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)

19+ Preliminary Results

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

20+ Preliminary Results

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

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 %

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

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.

Thanks

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