Remote sensing, GIS and machine learning in mapping seabed ...

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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

What?

Seabed substrate

Plants Animals

SandbanksReefs

• 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?

How?

RF

GRT

GAM

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

trees (BRT)

• Semiparametrical generalized additive models (GAM)

In situ sampling

• Drop camera

• ROV

• Bottom grab samplers

• SCUBA diving

In situ georeferencing

• Trimble GeoExplorer 6000

• Trimble R1

• SBAS, RTK correction

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

ArcGIS Spatial Analyst: Hillshade

ArcGIS Spatial Analyst: Slope

• Segmenting (ArcGIS: Create Fishnet)

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

Slope

Slope

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.

R and ArcGIS

• R package arcgisbinding (r.esri.com)

• 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

Optical remote sensing

• Satellite: Sentinel-2

• Airplane: – Estonian Land Board’s orthophotos

– Hyperspectral imager CASI, Hyspex

• Drone: DJI Phantom 4

Kappa = 0.997

Kappa = 0.979

Kappa = 0.985

Kappa = 0.958

Combining optical and acoustic remote sensing

Thank you!