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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
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
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
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
Landsat
SPOT
SPOT (Pan)
IKONOS
SPOT (Pan)
Quickbird
IRS (Pan)
Digital Aerial Cameras
ModRes
C-CAP
Spectral Resolution
• 4 Band Imagery• Near Infrared, Spectral Derivatives, NIR
Vegetation
• Middle Infrared• Natural Color as ancillary data
Leaf On, Tide controlled Leaf Off, no tide control
• 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
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)
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
•eCognition:
Multi-resolution
Segments (Baatz & Schape, 2000)
Worldview2
Landsat
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
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)
Imagery Considerations
Change Detection
Mean NIR – Class MeanDate 1 Land Cover
High : 106.826
Low : -75.1246
Date 1 Date 2
Segments
Recap: High Resolution Change Mapping
