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Field Data Collection and GPS

Field data collection Visiting and characterizing representative

sitesUsed for classification (training data),

information extraction, and validation (accuracy assessment)

Collecting field data for remote sensing

Accurate field data permit us to match areas in the imagery to corresponding areas on the ground

Field data must be suitable for specific task

Field data must be appropriate for the scale (and resolution) of the remotely sensed imagery(!!!)

Field data must be associated with accurate spatial location

Field reconnaissance

Matching cover types to imagery

Dunes

Reed stands

Nominal dataQualitative/categorical designation of features

E.g., Forest, cropland, turbid water, etc.

Biophysical dataMeasurements of physical characteristics

E.g., Height, diameter, and volume of trees in a forest plot

Texture, color, and mineralogy of the soil surfaceEtc.

Site characteristicsCharacters of the area that may be relevant to

classification or other applications (e.g., topography)

Types of field data that may be needed

Ideally occurs at least three times during a projectBefore the interpretation of imagery starts

Develop an understanding of study areaDuring the interpretation process

Address uncertainties in classification/interpretation

After interpretation – detect and resolve problemsAssess accuracy before publishing maps and

reports

Field visits for information collection

Match the classification scheme (explicit definitions of types/personnel training)

Sites spread throughout the study area (capture as much variability as possible)

All land cover/use classes are representedAppropriate sampling design

Guidelines for collecting field data

Field visits: on the ground

Field visits: from the air

Can sometimes use high resolution data instead of going to the field (e.g. Google Earth)

Field visits: virtual trips

Can be biased or unbiased depending on your purposeBiased – useful for building models for

classification, etc., but not useful for statistical generalization about the entire area

Unbiased – can be used to generalize from a sample to a larger area or population

Field data collection

Must know the probability of visiting any pixel

Simple random samplingSites distributed randomly throughout the

study areaStratified random sampling

Sites distributed based on the areas of classes within the study area

Systematic samplingSites are distributed based on certain fixed

criteria such as distance (e.g., a site every 100 m along a transect)

Unbiased (random) sampling

Fully Random SampleDistribute sample points randomly across the

entire map area without regard to the various types.

Statistically robust but labor intensive.Usually not very efficient!

Simple random sampling design

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Stratified Random SamplingInsures that all of the types on your map get

sampledAn efficient way to gather enough data to do

accuracy assessmentInsures that rare types get sampledCan gather less sites for types that are very

accurate (e.g. open water)Unbiased – can calculate the probability of

sampling any pixel

Stratified Sample Design

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Systematic Sample Design

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-- Samples regularly spaced but origin of design is randomly chosen-- Unbiased

Can be used for training data, understanding the study site, etc.

Can’t determine the probability of visiting a particular pixelE.g., sampling places that are easy to

access (along roads, on public land, etc.)Looking for “good” examples of typesUsing available incomplete data

Biased Sampling

Include site location information (point, line, or area (polygon) and information to extend these beyond the field site if possible.

Consider spatial uncertainty on the ground and in the imagery!

Accurately record relevant ground characteristics.

Field data should …

Field data – points

You must be able to extrapolate to some area around the point based on your field notes

You must recognize that there may be spatial error in the point location

Usually a good idea to include notes describing location of point relative to features that might be visible on the imagery (roads, land cover boundaries, etc.)

If you collect points

CR 4-1b Facing South Firehole T21N, R108, S 20 11.3

Lines

They must represent some area that you can find when you are back in the lab.

Requires unbiased sampling and good field notes.

Line transects should be long enough to capture variability in the area

If you use line sampling

Field data – areas (polygons)

You must accurately characterize what is inside each polygon.

You must recognize spatial uncertainty in the polygon boundaries.

You must collect areas large enough to find with some certainty on the image (generally at least 3 pixels x 3 pixels)

If you collect areas (polygons)

Uses 3 or more satellites (4 or more for accuracy) to “trilaterate” (not triangulate) to a position

Requires relatively unobstructed view of the sky

Accuracy varies depending on equipment and conditions

GPS

GPS receiversRecreational, mapping, or survey grade

instruments have different accuracyRecreational: ~5 - 10 m accuracy; $100 - $600Mapping: ~ 1 - 3 m accuracy; $1000 - $3000Survey: 1 cm accuracy or better; > $10,000

On-screen digitizing on an image in the field

Annotations on topographic mapsScale should match the application

Collecting location information in the field

Recreational grade: Garmin, etc., -- good enough for many RS applications. Pay attention to accuracy and data collection modes

Mapping grade: Trimble, etc.. High end data collection modes. Best choice for most RS applications

Survey grade: Trimble, etc. – most accurate. Rigorous time consuming data collection. Usually not required for RS, but might be for some applications.

Choosing GPS Equipment

Remember that field data collection for remote sensing is different than for many traditional ecological studies.

You must think about spatial uncertainty!

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