Minnesota Land Cover Mapping Project

Joe Knight, Marv Bauer, Lian Rampi, Keith Pelletier Remote Sensing and Geospatial Analysis Lab

Department of Forest Resources University of Minnesota

Thanks to Marv, Keith, Lian, and Leif Olmanson for contributing slides.

Outline

- Brief history of MN land cover mapping

- Challenges funding mapping work

- Overview of lidar and OBIA benefits

- Methods for 2013-14 project

- Project status, early results

MN Land Cover Mapping

Statewide (RSGAL):

– 1990, 2000

Metro only (RSGAL)

– 1986, 1991, 1998, 2002, 2007, 2011

Minneapolis, St. Paul, Woodbury only (RSGAL)

– 2009

National Land Cover Data (MRLC)

– 2001, 2006, 2011

2000 Land Cover Level 3

0 % Impervious 100

Legend

92% accuracy

2011 Land Cover Level 2

Upland

County

0 % Impervious 100

Woodbury 2000 Land Cover Level 3

Woodbury 2011 Land Cover Level 2

Urban Tree Cover Woodbury Land Cover

Funding Challenges

- Federal agencies generally not interested

- State agencies interested, but lack funds and not coordinated

- LCCMR: political, relevance, uncertain future

Methods for 2013-14 Project

- Using statewide lidar, derived products, and ancillary data

- Object-based classification

- Ecoregion-based

Point Cloud for the Minnesota State Capitol area

Optical image for the Minnesota State Capitol area

Digital Surface Model for the Minnesota State Capitol area

Digital Elevation Model (DEM) for the Minnesota State Capitol area

Return

LIDAR-Derived Products DEM

Maximum

Vegetation Height

Mean

Topographic Position Index (TPI)

Compound Topographic Index (CTI) Slope

d = ( z - z(min)) / (z(max) - z(min) )

Buildings

What do you see?

Predominant Land-use?

Predominant Land-use?

Shape

Height

Tone/Color

Texture

Size

Association

Location

Pattern

Shadow

Knowledge-based Workflow

Segmentation

Integration

Classification

Image Objects

Morphology

Worldview-2 image, Richfield, MN

Selected class objects shown in blue

Objects Have Characteristics

• Color: What are the spectral values and how do they vary? • Size: How big or small is the object? • Shape: Roundness, Length/Width Ratio, etc. • Texture: Contrast, Homogeneity • Context: How does the object relate to its neighbors? Neighbors, Relative Location, Sub Objects, Super Objects • The big picture: How do the characteristics interrelate?

Landsat 8 Mosaic false color, Summer 13-14 Landsat 8 Mosaic false color, Fall 13-14

Optical Data

Optical Data

TCMA false color, Summer 13-14

TCMA false color, Summer Fall 13-14

Summer 13-14

Fall 13-14

Normalized Difference Vegetation Index (NDVI)

NIR - Red / NIR + Red

Optical Data

Lidar Data

Lidar derived layers :

• Digital Elevation Model (DEM) • Digital Surface Model (DSM) • Digital Terrain Model (DTM) • Normalized Digital Surface Model (nDSM) • Normalized Digital Terrain Model (nDTM) • Compound Topographic Index (CTI) • Slope • Building footprints

Other Data

• Major roads

• City streets

• Updated NWI

• Airport runway

• Railroads

Object Based Image Analysis

Design of a customized ruleset with the Cognition Network Language (CNL) within the Definiens eCognition

Hybrid approach:

• Segmentation

• Classify “easy” classes with ruleset

• Random Forest Classifier (Random Trees in eCognition)

Random Forest Classifier

N e

xam

ple

s

....…

....…

Take the majority

vote

M features

Figure from Oznur Tastan

Classes

(Very) Preliminary Results

Do you always want to use OBIA?

0 % Impervious 100 Landsat image

Summary

• The integration of OBIA and lidar is expanding the applicability and accuracy of image-based analysis.

• The technology and data are mature.

• We are optimistic that the statewide map will be of high quality.