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

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 Regional Monitoring Moderate Resolution High Resolution Ultra High Resolution Site Specific Mapping Application Specific Mapping High Resolution Landsat SPOT SPOT (Pan) IKONOS SPOT (Pan) Quickbird IRS (Pan) Digital Aerial Cameras Mod Res C-CAP

Spectral Resolution 4 Band Imagery Near Infrared, Spectral Derivatives, NIR Vegetation Middle Infrared Natural Color as ancillary data Leaf On, Tide controlledLeaf 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 = detail Increased 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) Impervious Surface Automated or Manual Extraction Automated Classification Distinguish general land cover Classification and Regression Tree Analysis (CART) – Rule Set/Tree output – A lot of training data Spatial Modeling Wetland vs. Upland – Class specific rule sets 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)

Recap: High Resolution Change Mapping

Imagery Considerations

Change Detection Mean NIR – Class Mean Date 1 Land Cover Date 1Date 2 Segments

Questions Eric Morris Chris Robinson Nate Herold