Morris highres asprs_pecora_final
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Transcript of Morris highres asprs_pecora_final
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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
Landsat
SPOT
SPOT (Pan)
IKONOS
SPOT (Pan)
Quickbird
IRS (Pan)
Digital Aerial Cameras
ModRes
C-CAP
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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
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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
•eCognition:
Multi-resolution
Segments (Baatz & Schape, 2000)
Worldview2
Landsat
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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
Segments
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Recap: High Resolution Change Mapping