From: Subject: General Phase 2 Landcover Classification Process for Iowa Gap - 2/2001 Date: Tue, 2 Oct 2007 14:33:31 -0500 MIME-Version: 1.0 Content-Type: multipart/related; type="text/html"; boundary="----=_NextPart_000_0000_01C80501.345793C0" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2900.3138 This is a multi-part message in MIME format. ------=_NextPart_000_0000_01C80501.345793C0 Content-Type: text/html; charset="utf-8" Content-Transfer-Encoding: quoted-printable Content-Location: http://www.iowagap.iastate.edu/lcproces.htm =EF=BB=BF General Phase 2 Landcover Classification Process for = Iowa Gap - 2/2001

General Phase 2 Land Cover Classification Process for Iowa Gap -=20 2/2001

Preprocessing

Use Phase 1 land cover grid extents to make a polygon clip coverage = in=20 Arc/Info. In Imagine, subset each input TM scene with the clip coverage. = Check=20 that each referenced TM scene lines up exactly at the pixel level with = the Phase=20 1 image; adjust if necessary within the map model. Recode the Phase 1 = image=20 classes to 1 - 6 or 1 - 7, depending on presence of clouds. This allows = for=20 correct overlaying of subsequent classifications.

Wetlands

Mapjoin NWI county basin model results to cover the scene. Recode = text to=20 numeric field for importing into Imagine. Model aggregates wetlands into = temporary, seasonal, semi-permanent, permanent and water.

Mapjoin original NWI county data to cover the scene. Recode text to = numeric=20 field for importing into Imagine. Original data is used to add back = forested,=20 shrub and saturated wetlands.

For north and south state edge scenes, mapjoin adjoining state's = original NWI=20 quads that overlap Iowa's border. Recode text to numeric field for = importing=20 into Imagine. Original data is used to write herbaceous, shrub and = forested=20 wetlands onto Phase 1 land cover.

Convert the model results and original NWI data from an Arc/Info = coverage to=20 an Imagine .img file. Align the pixels to the Phase 1 land cover image = by=20 changing the map model. Overlay basin NWI data, then original NWI data, = then=20 Minnesota or Missouri NWI data, if applicable, onto Phase 1 land = cover.

Clip land cover to Iowa border plus Minnesota or Missouri NWI quad=20 boundaries, if applicable.

Clouds

If there is a cloud class from Phase 1, determine which dates = available are=20 cloud-free for those areas.

Create a mask of the cloud and cloud shadow areas from the to-date = land cover=20 for each cloud-free date.

Run unsupervised classification on the cloud mask(s) with 80 classes. = Use=20 field data, DOQs and CIR bands to assign alliances to classes.

Overlay final alliances onto to-date land cover or recode general = classes=20 into corresponding classes in the to-date land cover.

Forest

Use to-date land cover to mask out all but trees in the earliest date = available (April or early May). Make sure skip factor=3D1, rerun = statistics.

2530

Run tasseled cap transformation on early tree mask; keep first three = bands.=20 Run unsupervised classification on the tree mask with 80 classes.

2630

Instead of the tasseled cap process, display the early tree mask in = the=20 viewer (bands 4,5,6) for the CIR effect. Use the spectral profiler to = get=20 several profiles each of upland deciduous forest, cedar forest and pine = forest.=20 In the graph, check the values for band 5 to see if there is enough = difference=20 (100 vs 75) between the deciduous and evergreen sites. If so, use the = decid.gmd=20 model and set the minimum cut off value for deciduous trees to just = above the=20 evergreen value in band 5. The model result will have the deciduous = forest=20 pixels ranked 1. Overlay that on the to-date land cover; the pixels left = in the=20 Phase 1 woody class will be the evergreens and mixed forest.

Use those remaining pixels to make a mask of the April tree mask. Do = an=20 unsupervised classification on all 6 bands with 50 classes.

Use CIR bands displayed under the classification to pick out the = evergreens=20 and evergreen/deciduous mixes. Use sample sites to check evergreens. = Tree=20 classes left after evergreens are done are upland deciduous; there may = be some=20 deciduous trees left because the cutoff for deciduous forest in the = model was an=20 estimate.

Overlay the forest classes onto the to-date land cover.

Woodland

Found in the herbaceous class in 2630 in the early Spring date = (40691) using=20 bands 3,4,5 once the obvious grass and shrub alliances were assigned.=20 Unsupervised classification was run with 50 classes.

Grasslands

Use to-date land cover to mask out all but herbaceous in early and = late=20 Spring dates (April and May) or early Spring and Fall dates (April and = October).=20 The choice of which to use depends on the degree of separation between = warm and=20 cool season sites when sampled with the spectral profiler. Use May for = cool vs=20 warm grass; if no May date exists, use the October date.

Run tasseled cap transformation on the two dates and keep the first = three=20 bands of each. Layer stack the 6 bands together and rerun the statistics = for=20 skip =3D 1. Run unsupervised classification on the herbaceous mask with = 80=20 classes.

Use CIR bands displayed under the classification to pick out cool and = warm=20 season grasses. Use sample sites to find shrubland, woodland, savanna, = and check=20 grasses. Use DOQs to find grass/other mixes.

Overlay grassland, shrub and grass/mix alliances onto the to-date = land=20 cover.

Urban

Use to-date land cover to mask out all but artificial in a late = spring or=20 early Fall date (October). It is better to have contrast with growing=20 vegetation.

2530

Run principal components analysis on the two dates, keep the first = three=20 bands from each and combine the two images into one, 6 band image. Run=20 unsupervised classification on the artificial mask with 20 classes.

2630

Use the DOQ and CIR bands displayed for the Fall date (October) and = find=20 areas of mostly pavement and mostly vegetation. Use the spectral = profiler in=20 these sites to see which 3 bands provide the best separation between = high and=20 low vegetation. Subset out the 3 best bands and run an unsupervised=20 classification on that image with 20 classes.

Use DOQs to differentiate between artificial/high and artificial/low=20 vegetation in the resultant classified image.

Overlay artificial/high and artificial/low vegetation alliances onto = the=20 to-date land cover.

Cropland, Barren, Water

These Phase 1 classes will not be further delineated unless there is = time at=20 the end of the project to subdivide the Barren class.


Return to the Iowa=20 Gap Home Page.

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