LCLU practicals - Home - Earth Onlineearth.esa.int/landtraining07/D3PA-Caetano.pdf · Sobreiro. Sep. 5, 2007 D3PA LCLU Practicals Mário Caetano 18 0 200 400 600 800 1000 1200 12

LCLU practicalsMário Caetano

September 5th, 2007Practicals D3PA

Sep. 5, 2007 D3PA LCLU Practicals Mário Caetano 2

Exercise1


Areas that are in their major part constituted by waterWater10

Areas with 10% - 30% trees and with < 50% of herbaceous vegetation

Transitional-woodland forest9

Areas with 10% - 30% trees and with > 50% of herbaceous vegetationAgro-forestry8

Areas with >30% trees in which both broadleaf and needleleaf types are between 30% - 70%Mixed forest7

Areas with >30% trees in which >70% are of the needleleaf typeNeedleaf forest6

Areas with >30% trees in which >70% are of the broadleaf typeBroadleaf forest5

Areas with < 10% trees and that are not Urban / Bare soil, Sparse vegetation and Cropland

Other natural vegetation4

Areas with < 10% trees and with > 70% of herbaceous vegetationCropland3

Areas with < 10% trees and with 30% - 70% of bare soilSparse vegetation2Areas with < 10% trees and with > 70% of bare soilUrban / Bare soil1DescriptionClass nameCode


Definition of the mapping approach

Map format = raster

Map format = vector

MMU = pixel size of input satellite data

Feature selection > Image classification > accuracy assessment

The steps required to information extraction depend on the defined mapping approach:

MMU > pixel size of input satellite data

Feature selection > Image classification > post-processing > accuracy assessment

upscaling

Spatial unit of analysis = image pixelFeature selection > Image classification > post-processing > accuracy assessment

Generalisation + Raster to vector conversionSpatial unit of analysis = object

Image segmentation > Feature selection > Image classification > post-processing > accuracy assessment

GeneralisationGenerate the objects


Sparse herbaceous vegetation9Pine tree crown8Cork tree crown7Herbaceous vegetation6Shadow5Eucalyptus crown4Bare soil 3Shallow water2Deep water1Class nameCode


Tree

Shrub

Herbaceous veg.

Map of landscape units

Pixel 30 m (e.g. Landsat)


Forest

Shrubland


Map of landscapeunits

Tree

Shrub

Herbaceous veg.


Floresta

Mato


Map of landscapeunits

Tree

Shrub

Herbaceous veg.


Tree crown

Shade

IKONOS 4 m (e.g. Landsat)

Tree

Shrub

Herbaceous veg.


Surface elementsmap

Tree

Shrub

Herbaceous veg.


IKONOS

Surface ElementsMap

Objects

SEM + objects Landscape UnitsMap


671346308614125451519104313712

Surface element – our decision

Surface element – your decision

Sample ID (ID)


Segmentation

SEM+

Objects LUM

Forest = crowns + shade + herbaceous veg. + bare soil


Water AgricultureBare

soil/urbanEucaliptus

forest

Segmentation

SEM+

Objects

LUM

Classes


Pinus pineaforest

Quercussuber forest Mix forest

Agroforestryareas

Segmentation

SEM+

Objects

LUM

Classes


Thematic information extraction from satellite images

12

3456

7

8 * mandatory

Geographical stratification

Image segmentation

Ancillary data integration

Post-classification processing

Definition of the mapping approach *

Feature identification and selection

Classification

Accuracy assessment

**

*


Eucalipto

Pinheiro bravoOutras

Folhosas Pinheiro Bravo

Pinheiro Manso

Sobreiro

Eucalipto

Pinheiro Manso

Eucapiltusglobulus

Sobreiro


0

200

400

600

800

1000

1200

1 2 3 4

Deep water Shallow water

Bare soil Eucalyptus tree crown

Shadow Herbaceous vegetation

Cork tree crown Pine tree crown

Sparce herbaceous vegetation

Spectral class means IKONOS


In course resolution images the mixed pixels are mainly due to co-existence in the same pixel of different classes.

The problem of mixed pixels exist in coarse and fine resolution images:

The mixed pixel problem

MERIS FR

In fine resolution images the mixed pixels are mainly due to co-existence in the same pixel of different components (e.g., houses, trees).

IKONOS


Meaningless segmentationMeaningful segmentation


SEM+Objects



12

3456

7

8 * mandatory


Image segmentation





Classification

Accuracy assessment

**

*


3. Image segmentationA type of segmentation that is very common is the multi-resolution segmentation, because of its ability to deal with the range of scales within a single image.

Super-objects

Sub-objects


Source: Jensen (1996)

Hard classificationDecision rules

0 – 30 -> Water30 - 60 -> Forest wetland

60 - 90 -> Upland forest

6. Classification

Fuzzy classification Decision rules are defined as membership functions for each class.

Membership functions allocates to each pixel a real value between 0 and 1, i.e. membership grade.

But, wow can we represent the sub-pixel information?


Exercise2



12

3456

7

8 * mandatory


Image segmentation





Classification

Accuracy assessment

**

*


Water areas (lakes, rivers, ocean, etc)Water8Areas with > 80% of bare soilBarren7

Areas with shrubs, herbaceous and other vegetationOther natural vegetation6

Needleleaf tree areas with >40% crown coverNeedleleaf forest5Broadleaf tree areas with >40% crown coverBroadleaf forest4

Croplands with no vegetation (fallow lands)Agriculture without vegetation3

Croplands with vegetation (cultivated lands)Agriculture with vegetation2

Areas with a built up density >80%Urban1DescriptionClass nameCode


The number of mixed pixels in an image varies mainly with:

Landscape fragmentation

Sensor’s spatial resolution

The mixed pixel problem6. Classification

MERIS FR pixels


34263400239933824374231523082300327852194109

Surface element – our decision

Surface element – your decision

Sample ID (ID)


0,000

0,050

0,100

0,150

0,200

0,250

0,300

0,350

0,400

1 2 3 4 5 6 7 8 9 10 11 12 13

Urban Agriculture with vegetation

Agriculture without vegetation Broadleaf forest

Needleleaf forest Other natural vegetation

Barren Water

Spectral class means MERIS


9. Accuracy assessment

The most widely used method for accuracy assessment may be derived from a confusion or error matrix.

Accuracy assessment allows users to evaluate the utility of a thematic map for their intended applications.

The confusion matrix is a simple cross-tabulation of the mapped class label against the observed in the ground or reference data for a sample set.


0,880,860,450,881,000,960,711,001,00PA2202222422425214222Total

1,00191980,911110170,74509371360,922622450,9226124141,00151530,825133124221,0022221UATotal87654321

MapR

efer

ence

Samples deterministically selected


0,630,670,290,720,380,650,220,940,88PA0,03860158729884781139824Total1,0010610680,04521022521101170,7429012214311919460,8637432150,6184149519140,962612530,42217393261054092220,4448214110211UATotal87654321

Map

Ref

eren

ceSamples randomly selected


Agriculture without vegetation

Urban


Other natural vegetation

Needleleaf forest

Documents

LCLU practicals - Home - Earth Onlineearth.esa.int/landtraining07/D3PA-Caetano.pdf · Sobreiro. Sep. 5, 2007 D3PA LCLU Practicals Mário Caetano 18 0 200 400 600 800 1000 1200 12