Global, zonal and focal operations, map algebra

Task: compute summaries, use map algebra to analyze, modify and derive new maps, and explain difference between operations on

discrete and continuous data

1.Compute area and zonal statistics us in g r . repor t

Landuse 96_28m res=12 landuse96_28m res=30

These results are relevant because both outputs have within .01% of land use but the cell counts are very different.

2 Zonal Statistics Elevation_streets Avg_elevation_zipcode Mode-landuse_zipcode

3. Land use is spread

between four

categories of seven and

primarily in the 2nd and

3rd category

Lu_divers lu_divers report

This area does not appear to be very diverse but I would need to further evaluate the data to find if this is because of how

the data is categorized or due to lack of diversity in the actual land use classification.

MEA 582, Assignment4

Geospatial Analysis

Global, zonal focal operations,

map algebra

Due: Tues, Feb 8th

2011

Brenda McLuskie

Student ID: bhmclusk

bhmclusk@ncsu.edu

704 708-4880

4. Using

neighborhood

operator,

How would

neighborhood size

influence results?

Elev_srtm_30m Elev_srtm_30m_sm5

The size (5) determines how many cells are used to average the each cell value. Using a higher value allows

for greater smoothing of the data because more cells are used in the average.

5. Patch multiple

layers into a single

raster maps

Composite map,

contains:

Major roads,

facility

Lakes

elevation

Custom colors

6. Map algebra

Ndvi1 Ndvi2

Integer Floating Point

Floating point

values require

special handling

because of the

decimal point.

Division of integers

creates truncated

integers where

division of float

values creates

accurate float values

7.SRTM and NED

elevation differences

Original ned Original srtm Difference Difference with

color change

This gives the data range for the custom color table.

Is the srtm mostly higher or lower than elev_ned?

The srtm elevation is higher in range and mean. I used Univar because it shows the statistical differences. I

would use the images if I wanted to know geographical area differences.

Strm Map with legend and streets

Strm_ned_difference Strm_ned_difference no nulls

8.Working with if

commands creating maps

of urban areas

These two maps are the same because the If commands are different methods to do the same thing..

r.mapcalc "urban1_30m=if(landclass96==1,1,0) + if(landclass96==2,2,0)"

creates a output where values or 1 is changed to 1 and values on 2 are changed to 2 and if the values are not 1 or 2

then are changed to 0

r.mapcalc "urban2_30m=if(landclass96==1 || landclass96==2,landclass96,0)"

d.rast urban2_30m

creates an output where values of 1 and 2 are left as their original values all other values are changed to 0

9. Land class 96 where

lakes are /1000

10. Landclass 96 where

where LULC codes are

>1(not nulls)

11. Masks Elevation with mask Elevation without mask

r.mask urban maskcats=55 g.remove rast=MASK

12. Elevation

3d_tilt_plane

13. Create subsets Ortho elevation elevation with mask ortho with mask

14. r-stats by zipcode

for land use

r.stats

The high developed zip code is the

27603 but 59% of the zipcodes are

no_data so accuracy is a concern.

15. Working with

relative coordinates

Elev_srtm_30

Elev_srtm_30_smooth

Elev_strm_30m Elev_strm_30m_smooth

Appro ach

I used Grass ins t ruc t ions to co mplete these ta sks .

Resu l t s

Th e r esu l t s are that modi fi cat ion to ras t er d ata u s in g masks , IF s t a t ement s and method o f d at a management may al t er your

resu l t s . Nul l s in the d at a must a l so be consid ered .

Discuss ion and Con clusio n

In the end i t i s impor tan t t o no t on ly cons ider the graphical ou tpu t bu t a l so kn ow your d at a . The impor t ance o f

unders t anding the d ata t yp e and e f fects o f methodo logy can skew output and r esu l t in poor deci s ion making.