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Hi-Res Hi-Res Landcover Landcover Pete Kollasch, Iowa

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Hi-Res Landcover. Pete Kollasch, Iowa DNR. You are here. It’s about resolution. HRLC 1m. It’s about resolution. 2002 15m. It’s about resolution. 2009 2m. It’s about resolution. 2002 15m. What is it?. Statewide Land Cover file 1 meter spatial resolution - PowerPoint PPT Presentation

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Page 1: Hi-Res Landcover

Hi-Res Hi-Res LandcoverLandcover

Pete Kollasch, Iowa DNR

Page 2: Hi-Res Landcover

You are here

Page 3: Hi-Res Landcover

It’s about It’s about resolutionresolution

HRLC 1m

Page 4: Hi-Res Landcover

It’s about It’s about resolutionresolution

2002 15m

Page 5: Hi-Res Landcover

It’s about It’s about resolutionresolution

2009 2m

Page 6: Hi-Res Landcover

It’s about It’s about resolutionresolution

2002 15m

Page 7: Hi-Res Landcover

What is it?What is it?

• Statewide Land Cover file

• 1 meter spatial resolution– Compare to previous 15m 1986, 1990, 2002

• Derived from aerial imagery and lidar data– Previous all derived from Landsat imagery

• Interpreted to Summer 2009 NAIP

• County files ~ 50% complete now– 100% complete by end of 2013

Page 8: Hi-Res Landcover

NeedNeed

• 2002 – most recent landcover product– More current data desired

• In May 2003 – Landsat 7 partial failure– With only Landsat 5: difficult to obtain

sufficient satellite imagery coverage

• Interest in higher resolution product

Page 9: Hi-Res Landcover

OpportunityOpportunity

• Annual NAIP imagery available– From 2004 through 2011

• 4 band spring leaf-off imagery available– 2007 Northwest by Sanborn– 2009 West & 2010 East by ASI

• Lidar elevation data becoming available

Page 10: Hi-Res Landcover

Criteria for ProcessCriteria for Process

• Reliable enough to produce compatible products from a wide range of input quality

• That can be completed in a finite period

Page 11: Hi-Res Landcover

IssuesIssues

• Aerial Photography has high spectral variability– Collection date (esp. NAIP)– Multiple cameras / collections– Internal variability – hotspots, etc.– Suggests the use of “flattening” technologies

• Need sufficient spectral content

Page 12: Hi-Res Landcover

TechnologiesTechnologies

• Multitemporal• Lidar Normalized Elevation• Common Land Units• Segmentation• Knowledge-based classification• Classification & Regression Tree• •

Page 13: Hi-Res Landcover

TechnologiesTechnologies

• Multitemporal • Lidar Normalized Elevation • Common Land Units X• Segmentation X• Knowledge-based classification X• Classification & Regression Tree X• Independent Component Analysis • Classical Unsupervised

Page 14: Hi-Res Landcover

HRLC HistoryHRLC History

• Initial research began in 2002– Initial results were not very successful

• Meetings: DNR, UI, ISU, UNI late 2008

• Procedure design 2009

• Began receiving enough data in late 2009

• 2010 to present– Preprocessing, Interpretation, Postprocessing– Final results began emerging early 2012

Page 15: Hi-Res Landcover

InputsInputs

• Multitemporal Aerial Imagery– 2007/2009/2010 Four band spring imagery– 2009 NAIP imagery– 2008 NAIP imagery

• LiDAR normalized elevation layer– First return minus Bare earth

Page 16: Hi-Res Landcover

InputsInputs

• Multitemporal Aerial Imagery– 2007/2009/2010 Four band spring imagery– 2009 NAIP imagery– 2008 NAIP imagery

• LiDAR normalized elevation layer– First return minus Bare earth

• What issues do they solve?

Page 17: Hi-Res Landcover
Page 18: Hi-Res Landcover

PreprocessingPreprocessing

• Aerial Imagery Stack– 10 bands (4 + 3 + 3)– Areas of consistent spectral character– ICA (primary flattening technology)

• Add lidar normalized elevation band

• Unsupervised / Supervised Classification– 250 clusters

Page 19: Hi-Res Landcover
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InterpretationInterpretation

• ERDAS Class Grouping Tool

• Initially 2 tier– Grouping / Checking

• Later 3 tier– Grouping / Checking / Final Check– Final check by a single interpreter for

consistency

Page 21: Hi-Res Landcover
Page 22: Hi-Res Landcover

PostprocessingPostprocessing

• Fuzzy Recode (another flattener)

• Erode edges of tiles, sequence

• Mosaic tiles together

• Lidar normalized elevation filtering

• Shadow conversion around structures

• Eliminate objects < 10 pixels

• Reconstruct entire mosaic

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CountiesCounties

• Clip county to rectangle with 100m buffer– If there be holes, wait for enough data to fill

• Raster edit steps– General rule: if not possible, change it– Affects only a small percent of space, but

makes a big difference in the look– 2 sets of eyes

• Prep for NRGIS library– (4 bit, color table, names, round to .5 m)

Page 28: Hi-Res Landcover
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HRLC Data AccessHRLC Data Access

• NRGIS library, by FTP– By county: HRLC_2009_xx.img– http://www.igsb.uiowa.edu/nrgislibx/

• Map Service available

Page 30: Hi-Res Landcover

ThanksThanks

• Thejashwini Ramarao• Matt Swanson• Kathryne Clark• Matt Gosse• Sarah Porter• Cody Hackney • ISU, UI, UNI remote

sensing personnel

• Jim Giglierano• Chris Ensminger• Daryl Howell• Casey Kohrt• Chris Kahle• and many more …

Page 31: Hi-Res Landcover

Questions?Questions?

• Pete Kollasch– [email protected]– 319-335-1578