1. BOT / GEOG / GEOL 4111 / 5111

2. Field data collection Visiting and characterizing representative sites Used for classification (training data), information extraction, and validation (accuracy assessment)

3. 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

6. Nominal data Qualitative/categorical designation of features E.g., Forest, cropland, turbid water, etc. Biophysical data Measurements of physical characteristics E.g., Height, diameter, and volume of trees in a forest plot Texture, color, and mineralogy of the soil surface Etc. Site characteristics Characters of the area that may be relevant to classification or other applications (e.g., topography)

7. Ideally occurs at least three times during a project Before the interpretation of imagery starts Develop an understanding of study area During the interpretation process Address uncertainties in classification/interpretation After interpretation – detect and resolve problems Assess accuracy before publishing maps and reports

8. 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 represented Appropriate sampling design

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

12. Can be biased or unbiased depending on your purpose Biased – 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

13. Must know the probability of visiting any pixel Simple random sampling Sites distributed randomly throughout the study area Stratified random sampling Sites distributed based on the areas of classes within the study area Systematic sampling Sites are distributed based on certain fixed criteria such as distance (e.g., a site every 100 m along a transect)

14. Distribute sample points randomly across the entire map area without regard to the various types. Statistically robust but labor intensive. Usually not very efficient!

15. ° ° ° ° ° ° ° ° ° ° ° ° ° 0 1 2 3 03 2 1 A B C

16. Insures that all of the types on your map get sampled An efficient way to gather enough data to do accuracy assessment Insures that rare types get sampled Can gather less sites for types that are very accurate (e.g. open water) Unbiased – can calculate the probability of sampling any pixel

17. ° ° ° ° ° ° ° ° ° ° ° ° ° 0 1 2 3 03 2 1 ° ° ° °° ° ° ° ° ° ° ° ° ° ° ° °

18. °°°°°°°°°°°° 0 1 2 3 03 2 1 °°°°°°°°°°°° °°°°°°°°°°°° °°°°°°°°°°°° °°°°°°°°°°°° °°°°°°°°°°°° °°°°°°°°°°°° °°°°°°°°°°°° °°°°°°°°°°°° -- Samples regularly spaced but origin of design is randomly chosen -- Unbiased

19. Can be used for training data, understanding the study site, etc. Can’t determine the probability of visiting a particular pixel E.g., sampling places that are easy to access (along roads, on public land, etc.) Looking for “good” examples of types Using available incomplete data

20. 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.

22. 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.)

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

24. 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

26. 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)

27. 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

28. GPS receivers Recreational, mapping, or survey grade instruments have different accuracy Recreational: ~5 - 10 m accuracy; $100 - $600 Mapping: ~ 1 - 3 m accuracy; $1000 - $3000 Survey: 1 cm accuracy or better; > $10,000 On-screen digitizing on an image in the field Annotations on topographic maps Scale should match the application

29. 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.

30. Remember that field data collection for remote sensing is different than for many traditional ecological studies. You must think about spatial uncertainty!