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Remote sensing, GIS and machine learning in mapping seabed habitats Kristjan Herkül Estonian Marine Institute, University of Tartu Baltic Esri User Conference 2021

Remote sensing, GIS and machine learning in mapping seabed

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Page 1: Remote sensing, GIS and machine learning in mapping seabed

Remote sensing, GIS and machine learning in mapping seabed habitats

Kristjan Herkül

Estonian Marine Institute, University of Tartu

Baltic Esri User Conference 2021

Page 2: Remote sensing, GIS and machine learning in mapping seabed

What?

Seabed substrate

Page 3: Remote sensing, GIS and machine learning in mapping seabed

Plants Animals

Page 4: Remote sensing, GIS and machine learning in mapping seabed

SandbanksReefs

Page 5: Remote sensing, GIS and machine learning in mapping seabed

• Fundamental scientific research

• Applied research

– Environmental impact assessment

– Environmental monitoring

– Maritime spatial planning

– Marine protected areas

– Fish spawning and nursery areas

• EU obligations

– Habitats directive (92/43/EEC)

– Marine strategy framework directive (2008/56/EC)

– Maritime spatial planning directive (2014/89/EU)

Why?

Page 6: Remote sensing, GIS and machine learning in mapping seabed

How?

Page 7: Remote sensing, GIS and machine learning in mapping seabed

RF

GRT

GAM

• Machine laerning algorithms random forest (RF), boosted regression

trees (BRT)

• Semiparametrical generalized additive models (GAM)

Page 8: Remote sensing, GIS and machine learning in mapping seabed

In situ sampling

• Drop camera

• ROV

• Bottom grab samplers

• SCUBA diving

Page 9: Remote sensing, GIS and machine learning in mapping seabed

In situ georeferencing

• Trimble GeoExplorer 6000

• Trimble R1

• SBAS, RTK correction

Page 10: Remote sensing, GIS and machine learning in mapping seabed

Multibeam sonar

• Reson SeaBat 7101-Flow

• 511 equidistant beams

• Swath coverage 150°

• Frequency 240 kHz

• Depth range 0.5–200 m

• Trimble dual antenna GNSS system

Page 11: Remote sensing, GIS and machine learning in mapping seabed

ArcGIS Spatial Analyst: Hillshade

Page 12: Remote sensing, GIS and machine learning in mapping seabed

ArcGIS Spatial Analyst: Slope

Page 13: Remote sensing, GIS and machine learning in mapping seabed
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Page 15: Remote sensing, GIS and machine learning in mapping seabed

• Segmenting (ArcGIS: Create Fishnet)

• Statistics in segments (ArcGIS Spatial Analyst: Zonal Statistics as Table)

Page 16: Remote sensing, GIS and machine learning in mapping seabed

Slope

Slope

Page 17: Remote sensing, GIS and machine learning in mapping seabed
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Page 19: Remote sensing, GIS and machine learning in mapping seabed

R

• Programming language and free software environment for statistical computing and graphics

• RStudio – integrated development environment (IDE) for R

• Plethora of packages for general and specific tasks in data wrangling, statistics, modeling, graphics, spatial analysis, text analysis etc.

Page 20: Remote sensing, GIS and machine learning in mapping seabed

R and ArcGIS

• R package arcgisbinding (r.esri.com)

Page 21: Remote sensing, GIS and machine learning in mapping seabed

• R package sf– Reading Esri File Geodatabase vector layers (no writing )

– Writing shapefiles

– Multitude of vector geoprocessing functions

• R packages raster and stars– Reading/writing GeoTIFFs

– Raster manipulations

– Multitude of raster geoprocessing functions

https://github.com/ryangarnett/cheatsheat

Page 22: Remote sensing, GIS and machine learning in mapping seabed

Optical remote sensing

• Satellite: Sentinel-2

• Airplane: – Estonian Land Board’s orthophotos

– Hyperspectral imager CASI, Hyspex

• Drone: DJI Phantom 4

Page 23: Remote sensing, GIS and machine learning in mapping seabed
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Page 25: Remote sensing, GIS and machine learning in mapping seabed

Kappa = 0.997

Kappa = 0.979

Kappa = 0.985

Kappa = 0.958

Page 26: Remote sensing, GIS and machine learning in mapping seabed

Combining optical and acoustic remote sensing

Page 27: Remote sensing, GIS and machine learning in mapping seabed
Page 28: Remote sensing, GIS and machine learning in mapping seabed

Thank you!