An Object-oriented Classification Approach for Analyzing and Characterizing Urban

Landscape at the Parcel Level

Weiqi Zhou, Austin Troy& Morgan GroveUniversity of Vermont

2006 BES Annual Meeting

Outline

• Background

• Research Objectives

• Methodology

• Results

• Conclusions

Background

Questions & Motivations– What are the ecological and social consequences

associated with urban expansion? The structure of the urban landscape must be first

quantified and understood.

– Land cover data at the parcel level is highly desirable because land management decisions are commonly made at the individual household level.

Background

Data– High-resolution imagery is needed for mapping

detailed urban land cover; Coarse spatial resolution imagery (e.g., MODIS, Landsat

TM) is insufficient; Increased availability of high-resolution satellite imagery,

such as IKONOS and QuickBird, as well as digital aerial imagery

900m2

0.61m panchromatic QuickBird imagery

1m IKONOS data

0.6 m Emerge data

Background

Classification approaches– Visual interpretation is time consuming and

expensive;– Conventional pixel-based methods are inadequate;– Object-oriented classification

Segment images into objects; Incorporate shape, spatial relations, and reflectance

statistics, as well as spectral response; Similar to visual interpretation, but has the advantage of

minimal human interaction.

Objectives

Develop an object-oriented approach for classifying and analyzing the complex mix of vegetation and development in urban landscapes at the parcel level using high-resolution digital aerial imagery and LIDAR data.

Apply this approach to map and inventory the land cover in the Gwynns Falls watershed.

Study sites

Methods

Data collection and preprocessing– Emerge aerial imagery

Color infrared image, collected in 1999, pixel size of .6m

– LIght Detection And Ranging (LIDAR) data Collected first and last returns in 2002; Interpolated into raster data with pixel size of 1m;

– Points from first returns Surface cover DSM– Points from last returns Ground DSM

Surface cover height = surface cover DSM – Ground DSM;

Methods

Data collection and preprocessing– Parcel boundary data

Helped segment objects; Used as functionally meaningful geographic unit by which

to summarize landscape composition.

– Building footprint data

Methods

Image Segmentation– Fractal net evolution approach, embedded in

eCognition;– Segmented the image into 3 levels of objects;

Level 1: objects were considered to be internally homogeneous;

Level 2: classification-based fusion Level 3: Parcel

Level 1 segmentation

Level 1

Level 1

The roof was segmented to 3 object primitives

Level 2 segments

Level 2

Level 1 Details

Level 1

Level 2 details

Level 2

level3

Level 3

Methods

Fuzzy classification– Land cover types of interest

Building Coarse textured vegetation (trees & Shrubs) Fine textured vegetation (herbs & grass) Pavement Bare soil

Methods

Fuzzy classification– Class hierarchy

A class hierarchy was developed, and a number of rules were created to classify each object into one of the 5 classes at the most disaggregated spatial level.

Methods_HierarchyBuildings

NonBuildings

Building footprint

data

Brightness

Context&Texture

LIDAR data

LIDAR data

Context&NDVI

Shaded Fine Vege

Shaded Pavement

Context

Shaded Coarse Vege

Shaded Buildings

Shaded short

Shaded tall

LIDAR data

Fine Vegetation

Coarse Vegetation

NDVIMissing Building

Bare Soil

PavementNonVegetation

Vegetation

NonShaded

Shaded

Results_GF

Results

Reference data Classified data

Building CV FV Pavement Bare soil

Row

Total

User Acc.

(%)

Building 51 2 4 4 0 61 83.6

CV 0 84 0 2 0 86 97.7

FV 0 3 75 1 0 79 94.9

Pavement 2 0 4 68 0 74 91.9

Bare soil 1 0 1 3 45 50 90

Column Total 54 89 84 77 45

Producer Acc. (%) 94.4 94.4 89.3 88.3 100

Accuracy Assessment

Overall Accuracy: 92.3% (n=350)

Results_Parcel_Impervious

Result_parcel_FineVege

Result_parcel_LawnGreenness

Result_parcel_TreeCanopy

Conclusions (1)

The OO classification approach proved to be effective for classifying urban land cover from high-resolution multispectral imagery;

Ancillary data, such as LIDAR can greatly improve the classification;

Conclusion (2)

The OO approach using parcels as pre-defined patches provides a convenient and effective way for integrated research to incorporate biophysical and social factors, especially the research on relationships between household characteristics and structures of urban landscapes.

Acknowledgements

This research was funded by the Northern Research Station, USDA Forest Service and the National Science Foundation LTER program (grant DEB – 042376).

Thanks to a lot of BES people.

Questions?