Land Cover Land Cover ClassificationClassification

Defining the pieces that make Defining the pieces that make up the puzzleup the puzzle

What is a Classified ImageWhat is a Classified Image

Image has been processed to put each Image has been processed to put each pixel into a categorypixel into a category

Result is a vegetation map, land use map, Result is a vegetation map, land use map, or other map grouping related featuresor other map grouping related features

Categories are defined by the intended Categories are defined by the intended use of the mapuse of the map

Can be few or many categories, depending Can be few or many categories, depending on the purpose of the map and available on the purpose of the map and available resourcesresources

Land cover classification stepsLand cover classification steps

Define why you want a classified image, Define why you want a classified image, how will it be used?how will it be used?

Decide if you really need a classified imageDecide if you really need a classified image Define the study areaDefine the study area Select or develop a classification scheme Select or develop a classification scheme

(legend) (legend) Select imagery Select imagery Prepare imagery for classificationPrepare imagery for classification Collect ancillary dataCollect ancillary data Choose classification method and classifyChoose classification method and classify Adjust classification and assess accuracyAdjust classification and assess accuracy

How will you use the classified How will you use the classified information? information?

Are statistics okay or is mapped output Are statistics okay or is mapped output necessary?necessary?

Is it going to be used primarily as a visual Is it going to be used primarily as a visual tool?tool?

Will it be used as input to a model or for some Will it be used as input to a model or for some other numerical analysis?other numerical analysis?

Are the results going to be used as a Are the results going to be used as a management tool? management tool?

Do you really need to create a classified Do you really need to create a classified image?image?

Define the study areaDefine the study area

Reaching consensus among participants Reaching consensus among participants can be challengingcan be challenging

Often need to balance practical issues with Often need to balance practical issues with desired output. For example, is it worth desired output. For example, is it worth purchasing and processing extra imagery to purchasing and processing extra imagery to include a small portion of the study area?include a small portion of the study area?

Should adjacent areas be included? Can Should adjacent areas be included? Can adjacent areas be included using a buffer or adjacent areas be included using a buffer or by deliberate selection.by deliberate selection.

Choosing a classification Choosing a classification schemescheme

A classification scheme defines the legendA classification scheme defines the legend There are several classification schemes available:There are several classification schemes available:

Classification nameClassification name URLURL

Anderson Anderson http://landcover.usgs.gov/pdf/anderson.http://landcover.usgs.gov/pdf/anderson.pdfpdf

National Land Cover DataNational Land Cover Data http://landcover.usgs.gov/classes.asphttp://landcover.usgs.gov/classes.asp

FAO Land Cover Classification FAO Land Cover Classification SystemSystem

http://www.africover.org/LCCS.htmhttp://www.africover.org/LCCS.htm

Classification schemes can be hierarchical or non-Classification schemes can be hierarchical or non-hierarchicalhierarchical Other attributes can be mapped including:Other attributes can be mapped including:

Vegetation structureVegetation structure Land cover disturbanceLand cover disturbance Vegetation age (for example, primary and secondary)Vegetation age (for example, primary and secondary) Distribution of taxaDistribution of taxa Land useLand use Crown closureCrown closure

Other classification commentsOther classification comments

Each class should be well defined and Each class should be well defined and documented so users of the final map know what documented so users of the final map know what the individual classes representthe individual classes represent

Rules for dealing with mixed classes, such as Rules for dealing with mixed classes, such as transition and mosaic vegetation should be transition and mosaic vegetation should be developeddeveloped

A minimum mapping unit can be defined to A minimum mapping unit can be defined to explicitly define the smallest feature that will be explicitly define the smallest feature that will be mapped as a single classmapped as a single class

Defining classes is often an iterative process. A Defining classes is often an iterative process. A balance must be struck between what is desired balance must be struck between what is desired based on the maps purpose and the classes that based on the maps purpose and the classes that can be accurately and economically delimitedcan be accurately and economically delimited

Above all, be consistent and document your Above all, be consistent and document your methodsmethods

Select imagerySelect imagery

Selecting appropriate imagery is usually a Selecting appropriate imagery is usually a subjective task and experience is very helpfulsubjective task and experience is very helpful

Image choice is often limited by budget and Image choice is often limited by budget and image availabilityimage availability

Spatial detail of the final classified map is Spatial detail of the final classified map is limited by the input imagerylimited by the input imagery

After assessing your imagery alternatives it After assessing your imagery alternatives it may be necessary to redefine your goals, may be necessary to redefine your goals, study area, and classification schemestudy area, and classification scheme

Preprocessing – Radiometric Preprocessing – Radiometric correctionscorrections

Goal is to calculate ground reflectance (ratio Goal is to calculate ground reflectance (ratio of the intensity of light reflected from a of the intensity of light reflected from a surface over the intensity of incident light)surface over the intensity of incident light)

Satellite measures radiance at the sensor not Satellite measures radiance at the sensor not surface reflectancesurface reflectance

Due to irregularities in the earths surface and Due to irregularities in the earths surface and atmospheric scattering and absorption it is atmospheric scattering and absorption it is impossible to directly measure surface impossible to directly measure surface reflectancereflectance

Many of the “easy” correction methods are Many of the “easy” correction methods are not very effectivenot very effective

Radiometric correction is becoming more Radiometric correction is becoming more accessible to novice usersaccessible to novice users

Preprocessing – Geometric Preprocessing – Geometric correctionscorrections

Goal is to warp the image to match a Goal is to warp the image to match a mapmap

Geometric processing includes:Geometric processing includes: Systematic corrections – removes Systematic corrections – removes

distortions from the sensor and movement distortions from the sensor and movement of the satellite and earthof the satellite and earth

Geo-referencing – Uses control points to Geo-referencing – Uses control points to warp the image to a map but in warp the image to a map but in mountainous terrain distortions are still mountainous terrain distortions are still problematicproblematic

Ortho-rectification – Corrects for distortions Ortho-rectification – Corrects for distortions caused by terrain by using a DEMcaused by terrain by using a DEM

Collect ancillary dataCollect ancillary data

If additional data is available that can If additional data is available that can improve the classification it should be improve the classification it should be included. Some possible datasets include:included. Some possible datasets include: DEMs and their derived datasets (slope DEMs and their derived datasets (slope

and aspect) and aspect) Climate data such as rainfall and Climate data such as rainfall and

temperaturetemperature Vector overlays such as roads, rivers, and Vector overlays such as roads, rivers, and

populated places populated places In many cases adequate datasets are not In many cases adequate datasets are not

useful because they are too coarse, too useful because they are too coarse, too expensive, or simply not availableexpensive, or simply not available

Incorporating ancillary data is not always Incorporating ancillary data is not always straightforward and depends on the selected straightforward and depends on the selected classification methodclassification method

Manual vs. Automated Manual vs. Automated ClassificationClassification ManualManual

Uses photo interpretation methods to delineate classesUses photo interpretation methods to delineate classes Uses visual cues such as tone, texture, shape, size, Uses visual cues such as tone, texture, shape, size,

shadows, and locationshadows, and location Uses heads-up digitizingUses heads-up digitizing Very subjectiveVery subjective

AutomatedAutomated Uses computer algorithms to group clusters based on Uses computer algorithms to group clusters based on

similar characteristicssimilar characteristics Rules for classifying usually developed subjectively but Rules for classifying usually developed subjectively but

these are applied objectivelythese are applied objectively

HybridHybrid Mixture of automated and manual methodsMixture of automated and manual methods Can be done by manually editing an automated Can be done by manually editing an automated

classification resultclassification result

Supervised vs. Unsupervised Supervised vs. Unsupervised ClassificationClassification

SupervisedSupervised The analyst provides information, usually in the form of The analyst provides information, usually in the form of

samples from the image, to train the algorithm to define samples from the image, to train the algorithm to define classesclasses

Requires prior information about the land cover Requires prior information about the land cover

UnsupervisedUnsupervised The computer uses an algorithm to group similar pixels The computer uses an algorithm to group similar pixels

into classesinto classes User provides parameters such as the number of classes to User provides parameters such as the number of classes to

produce, and rules defining how classes should be merged produce, and rules defining how classes should be merged and split as the algorithm runsand split as the algorithm runs

Usually an iterative algorithmUsually an iterative algorithm The user must associate each class with a particular land The user must associate each class with a particular land

cover type cover type

HybridHybrid Uses unsupervised methods to create training data and Uses unsupervised methods to create training data and

then use a supervised algorithm for the final classificationthen use a supervised algorithm for the final classification

The Sea of AlgorithmsThe Sea of Algorithms

There are dozens of algorithms that can be usedThere are dozens of algorithms that can be used Algorithm selection depends on what is available, Algorithm selection depends on what is available,

the image type, available training data, ancillary the image type, available training data, ancillary layers available, and experiencelayers available, and experience

Claims suggesting superior accuracy from one Claims suggesting superior accuracy from one algorithm over another should be viewed with a algorithm over another should be viewed with a gain of saltgain of salt

Some popular algorithms include:Some popular algorithms include: ISODATA unsupervised classificationISODATA unsupervised classification Supervised statistical classificationSupervised statistical classification

ParallelepipedParallelepiped Minimum distanceMinimum distance Maximum likelihoodMaximum likelihood Mahalanobis distanceMahalanobis distance

Artificial neural net Artificial neural net Binary decision treeBinary decision tree Image segmentation (not really a classification tool)Image segmentation (not really a classification tool)

Thematic MapperNominal Resolution: 30 meters

Band Spectrum Wavelength UtilityBand 1 blue-green 0.45 - 0.52 m separation of soil and vegetationBand 2 green 0.52 - 0.60 m reflection from vegetationBand 3 red 0.63 - 0.69 m chlorophyll absorptionBand 4 near infrared 0.76 - 0.90 m delineation of bodies of waterBand 5 mid infrared 1.55 - 1.75 m vegetative moistureBand 6 far infrared 10.4 - 12.5 m hydrothermal mappingBand 7 mid infrared 2.08 - 2.35 m plant heat stress

NDVI = (Near Infrared - Red)

(Near Infrared + Red)

Normalized Difference Vegetation Index (NDVI)

• Identify several training sites for each category (Numerous small sites are preferable to a few large ones.)• Identify several training sites for each category (Numerous small sites are preferable to a few large ones.)

• The size of each site generally should be 10 to 40 acres.• The size of each site generally should be 10 to 40 acres.

• Training areas for each category should contain a total of at least 100 pixels.• Training areas for each category should contain a total of at least 100 pixels.

• Select representative sites across the image.• Select representative sites across the image.

• Training areas (all sites for each category) must be uniform. (The histogram should be unimodal.)• Training areas (all sites for each category) must be uniform. (The histogram should be unimodal.)

Guidelines for Training AreasGuidelines for Training Areas

B and 4

Ban

d 5

0

025

5

255DN

DN

Parallelepiped C lassification

B and 4

Ban

d 5

0

025

5

255DN

DN

M in im um D istance C lassification

Unc la sse d p ixe ls a ssig ne d o n b a sis o f Euc lid e a n d ista nc e to c e ntro id s