High Spatial Resolution Land Cover Development for the Coastal United States Eric Morris (Presenter) Chris Robinson The Baldwin Group at NOAA Office for Coastal Management Nate Herold NOAA Office for Coastal Management

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High Spatial Resolution Land Cover Development for the

Coastal United States

Eric Morris (Presenter)Chris Robinson

The Baldwin Group at NOAA Office for Coastal Management

Nate HeroldNOAA Office for Coastal Management

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Coastal Change Analysis Program (C-CAP)

• 25% of contiguous U.S., authoritative source for coastal landcover

(30m moderate res and 1-5m higher res)

• Coastal expression of the NLCD (National Land Cover Database)

• NLCD is 90%+ C-CAP in coastal areas

• Standard data and methods

• Inventory of intertidal areas, wetlands

and adjacent uplands

• Updated every five years

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High Resolution C-CAP Land Cover

“Our goal is to provide consistent, accurate, nationally relevant data at a

spatial scale more appropriate for support of increasingly detailed, site-

specific, management decisions.”

• Since 2006, direct response to customer demands

– Uses the C-CAP Nat’l framework for producing local level data

– Selected based on need and data availability

• Developed through partnerships with private industry

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Why Map at a Higher Resolution?

Small geography of interest•Islands, counties, watersheds•management reserves

Extraction of land cover components•Impervious Surfaces•Invasive species•Specific habitats

Site specific issue•Local level analysis

< 1m

1m to 5m

5m to 10m

10m to 30m

Site SpecificMapping

Application SpecificMapping



SPOT (Pan)


SPOT (Pan)


IRS (Pan)

Digital Aerial Cameras



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Spectral Resolution

• 4 Band Imagery• Near Infrared, Spectral Derivatives, NIR


• Middle Infrared• Natural Color as ancillary data

Leaf On, Tide controlled Leaf Off, no tide control

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• Accuracy• Scale

– Usually lower res

• Vintage– Usually older

Why needed?– Spectral data insufficient – Features are subdued at

the time of acquisition

• Sources – National Wetland

Inventory (USFWS)– SSURGO Soils (USDA)– Lidar

Ancillary Data

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Lidar - Derivatives Bare Earth DEM•Slope•Curvature•Wetlands and other vegetation types

Digital Surface Model•Used with Bare Earth DEM•Normalized Digital Surface Model (nDSM)

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Image Processing Considerations

High spatial res. ≠ easier = detailIncreased spectral classes per thematic Class•Traditional (Pixel based) Classifiers•Noise and poor accuracy

Segmentation•Network of

homogenous areas • Image Objects



Segments (Baatz & Schape, 2000)



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Hierarchical Approach (vs. All at Once)

Major distinctions first

•Woody vs. Herbaceous

•Forest vs. Scrub

Automated Classification

•Classification and Regression Tree Analysis (CART)

– Rule Set/Tree output

– A lot of training data

Spatial Modeling

•Regional recursive rules Ex: If Object = Forest (class 10) and nDSM < 4m

Then Scrub/Shrub (class 12)

Manual Edits for unique & rare features

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Change Detection Process

Steps•Baseline data•Identify areas (i.e. via Change Mask)

•Collect training data•Classify change area•Insert into baseline map

• Map “Change Only” areas• Instead of post classification

• Object based approach

• Methods guided by available imagery• (Niemeyer et al., 2008), (Duro et al.,2013)

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Imagery Considerations

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Change Detection

Mean NIR – Class MeanDate 1 Land Cover

High : 106.826

Low : -75.1246

Date 1 Date 2


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Recap: High Resolution Change Mapping

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Eric [email protected]