Outcomes

DEM Artifacts: Stream networks & watersheds derived using ArcGIS’s HYDROLOGY routines are only as good as the DEMs used.

- Both DEM examples below have problems… - Lidar and SRTM DEM products are free of such problems - Lidar DEMs often produces unique problems of their own

USGS 1:24,000 quad-based DEM The National Elevation Dataset (NED)

Layering or

pancake effects

Lidar DEMs:

Raw LiDAR data contain return signals from

• Human-made objects (buildings, telephone poles, and power lines) • Vegetation (Trees, shrubs, grasses) • Birds (Barber & Shortrudge 2004, Stoker et al. 2006).

Therefore, it is crucial to filter or extract bare earth points from LiDAR data.

• Various filter methods have been developed to classify or separate raw LiDAR data into ground and non-ground data.

• None of automated filter processes is 100% accurate so far (Romano, 2004). Manual editing of the filtering results are still needed (Chen, 2007).

• Efforts are still needed to improve the performance of filter algorithms.

Corduroy

• A common Lidar artifact due to vertical misalignment in scan lines. • Most prominent when gridding data at a resolution that

approaches the spacing of the individual scan lines.

Where to find NED & SRTM DEM data

http://www2.jpl.nasa.gov/srtm/

http://seamless.usgs.gov/

National Elevation Dataset (NED)

Where to find Lidar DEMs for IOWA

ftp://ftp.igsb.uiowa.edu/gis_library/projects/Lidar/Lidar_Blocks/Lidar_LAS_ASCII.html

Lidar DEMs: No best method of interpolation Deterministic Methods – do not take into account a model of the spatial processes within the data

• IDW

- Assumes each input point has a local influence that diminishes with distance

- Works well for dense, evenly-distributed Lidar sample points (A.K.A. postings).

- If postings are sparse or unevenly distributed results may not sufficiently represent the desired surface.

- IDW’s weighted averaging cannot make estimates that are outside the range of minimum & maximum sample point values…

- Some topographical features (e.g., ridges & valleys) are likely to be lost unless adequately sampled.

• SPLINE - Fits a minimum-curvature surface “exactly through” the measured values at the sample location.

- Can estimate values that are below the minimum or above the maximum values in the sample data.

- Produce smooth surfaces, but with less recognizable characteristic features like peaks, ridges and valleys (Podobnikar, 2005).

Geostatistical Methods – Factors in both (1) the distance & (2) the degree of SAC among samples.

• KRIGING - Essentially a weighted average technique, but its weights depend not only on the distances between

sample points and estimation locations but also on mutual distances among sample point pairs.

- Does better than IDW, especially when sample points are sparse (Zimmerman et al. 1999, Lloyd and Atkinson, 2006)

Sampling Patterns -- Systematic ADVANTAGES • Fixed X, Y intervals • Simplest to plan • No in-field subjective judgments required • Can be adapted to avoid spatial

autocorrelation (range param.) DISADVANTAGES • May be statistically inefficient

Equal sampling undersampling

• May be hard to stay on track Rough terrain private land restrictions

• May introduce BIAS in measured variable

Pattern may coincide with grid Oversampling misinterpretation

Sampling Patterns -- Random ADVANTAGES • X & Y coords each chosen in separate

random process

• Satisfies one assumption of linear regression… each point on landscape has equal

chance of being sampled

• Don’t have to sample in any order Can reduce pt-pt travel time.

DISADVANTAGES • Does noting to reduce over/under

sampling in areas of high variation

• Can complicate field crew training

• Seldom chosen for sampling over large areas

• May not be spatially independent: SAC

Sampling Patterns -- Cluster ADVANTAGES • Can place systematically or randomly

Cluster centers Point within a cluster

• Speeds up ground sampling time

• Often used in off-road natural resource surveys USFS & DNR

• Good for understanding SAC in the measured variable

DISADVANTAGES • Still prone to over/under sampling in

areas of high variation

• Must be careful to avoid those measurements (in later analyses) that are spatially autocorrelated (SAC) • Sample colinearity SLR/OLS

violation

Sampling Patterns -- Adaptive ADVANTAGES • Can be designed to avoid over/under

sampling Increase sample density where

feature of interest is more variable • Also good for understanding SAC in the

measured variable DISADVANTAGES

• Must be careful to avoid those measurements (in later analyses) that are spatially autocorrelated (SAC) • Sample colinearity • Ordinary least-squares regression • GLM

Systematic Sample Plot Layout NW corner N 4649564 E 462618

SW corner N 4649381 E 462618

East Boundary E 462775

Say we want 20 sample points placed systematically within this area….

There are many options, but we want 4 E-W lines (spaced from N-S) with 5 plots/line.

Also, we want to stay away from the edges to avoid bias 20 meter (~1 chain).

• E-W distance: 462775 – 462618 = 157 m – 40 m buffer 117 m E-W • N-S distance: 4649564 – 4649381 = 183 m – 40 m buffer 143 m N-S • EW Spacing between plots on a line is 117/4 = 29.25 m • NS Spacing between lines is 143/3 = 47.6 m

• Coordinates for the first plot in the NW are: N = 4649564 – 20 = 4649544 E = 462619 + 20 = 462638

• All plots on the same line will have the same northing • First plot on the west will start 20 m in from the west boundary, and all successive

plots eastward will have easting values 29.25 m greater than the previous plot. • Each line will have a northing value of 47.6 m less than the previous line.

Systematic Plot Coordinates The order of the Descriptor, Northing, Easting (DNE) isn't important for PFO, but once an order is selected, it must be the same for all waypoints in the file.

Excel Format for ingest with ArcGIS Comma delimited TEXT format for ingest with PFO

COL ∆ 29.25 m

ROW ∆ 47.6 m

Repeat 5 X’s

Y X

ArcGIS Ingest of Excel Coordinate File

ArcGIS Ingesting of Excel Coordinate File

Right Click on file & select “Display XY Data” …which pops

this open.

Set projection…

Make sure XLS columns match Arc’s X, Y Fields

ArcGIS Ingesting of Excel Coordinate File

…As a SHAPEFILE

HawthsTools: Available for ArcGIS 10?

In short….NO. What did HawthsTools do??

Provide tools to easily allow the user to Creating Random Sample Points…

• Within a single polygon (simple random sampling)

• Within multiple polygons (stratified random sampling) or from a subset of polygons from the full set.

• Could also specified a “Minimum Distance Between Points”

• If you were doing a forest inventory and didn't want the sample points to be any closer to each other than 1 chain (66 feet), you would specify a minimum distance of 20 meters which very close to 1 chain.

There were 3 options for generating sample points

• Same number of sample points in each polygon • Proportional (to polygon area) distribution of points • Distribute pre-determined number of points/polygon

• Based on attribute specifying the number of points

HawthsTools & The ArcGIS 10 Facsimile

HawthsTools is now formally discontinued, but ARC10 has a couple options:

Requires surface probability raster layers to use properly…

OR

Get Today’s Data From the Class Website…

WEEK14 Tuesday Cooley, Rayma Belyaeva, Anna Iverson, Eve Thursday Konrady, Steven Kuntz, Cody Luby, Elizabeth WEEK15 Tuesday Madden, James Tuttle, Ross Sandoval, Claudette Thursday Final Exam Review

Grad Presentations Next 2 Weeks…

Generate Fixed # of Random Points/Polygon

Puts 25 points in whatever polygon record(s) you select…regardless of polygon size

Have table open with polygons selected

Then

Proportional Random Sampling Points If you want to have 5 sample points/acre in “Forest”…

- First, using “Add Field” create “FOR5” in attribute table - Leave as “short integer”

- Select FOR5 column and all the “FOREST” records

- Then, use the “Field Calculator” [Acres] * 5

Because the FOR5 column is INTEGER, the result is truncated

5 Random Points/Acre in Forest

Click

Generate a specific % Sample – Random Plots Now, you want a 25% sample of the GRASS areas using

random 𝟏

𝟏𝟎 acre circular plots …without double sampling

- First, Add Field “Grass25” …as an integer

- Select “Grass25” column and all “Grass” records - Use Field Calculator to get number of points…

- [Acres] * 10 * 0.25

- Minimum point spacing = {2*sqrt(4356/PI)} / 3.28 = 22.71m (aka LINEAR UNIT)

Then go to… Create Random Points

𝟏

𝟏𝟎 acre

𝟏

𝟏𝟎 acre

25% sample with 1/10 ac circular plots

What’s wrong with this?

Buffer in from Polygon Edge By Plot Radius

Don’t want 1/10th acre plot centers to be < 11.35m from an edge.

Select the polygon(s) to buffer in the Attribute Table

Generate Points in Buffered Polygon

Generate Points in the Buffered Polygon

Start Here

Viewshed Analysis

• A “viewshed” is created from a DEM by using an algorithm that estimates the difference of elevation from one cell (the viewpoint cell) to the next (the target cell).

• To determine the visibility of a target cell, each cell between the viewpoint

cell and target cell is examined for line of sight. • If cells of higher value are between the viewpoint and target cells the line

of sight is blocked. • If the line of sight is blocked then the target cell is determined to not be

part of the viewshed. • If it is not blocked than it is included in the viewshed.

LANDSAT/

Data for today’s exercise…

Creating a “Viewshed” using a DEM

srtm-dem_30m.img

View_Point_Top.shp

To pick you own viewpoint, open an edit session on either of the “point” files (top or bottom) and move the point. Then, rerun the VIEWSHED function.

Qutput_raster

Prepare Viewshed for Export to KML

Edit your viewshed output so that the “Not Visible” locations have “no color”

Viewshed from the Red Point

Exporting GIS data to a KML file

Keyhole Markup Language (KML) …is an XML (eXtensible Markup Language) notation for expressing geographic annotation and visualization within internet-based, two-dimensional maps and three-dimensional Earth browsers. KML was developed for use with Google Earth, which was originally named Keyhole Earth Viewer.

Exporting GIS data to a KML file

Can export a whole Map Document or a single GIS Layer

Save the DEM, the POINTS, and your Viewshed to a MAP DOCUMENT Then……

Exporting GIS Data to a KML

3D Image Rendering (aka… image drape)

30 meter SRTM DEM 30 meter Landsat-5 (2012-10-05)

Quad Cities Area

3D Image Rendering

3D Image Rendering ArcScene

Read Landsat image & DEM same way as in ArcGIS

Then, you can play with this to change view angle

3D Image Rendering ArcScene

srtm-dem_30m.img

3D Image Rendering ArcScene

3D Image Rendering ArcScene

First, use this to “set observer” position…. …e.g.

Then, click this to start flying around...don’t get lost!!

Mouse ↕ = altitude Mouse ↔ = bearing

Left click = faster Right click = slower – reverse

Create Flyby Animation Open animation editor toolbar

Animation Toolbar

Animation Controls

Exporting a FLYBY to an AVI file

Audio Video Interleave