DEM Artifacts: Layering or pancake effects

Transcription

1 Outcomes

2 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 Layering or pancake effects USGS 1:24,000 quad-based DEM The National Elevation Dataset (NED)

3 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.

4 Where to find NED & SRTM DEM data National Elevation Dataset (NED)

5 Where to find Lidar DEMs for IOWA ftp://ftp.igsb.uiowa.edu/gis_library/projects/lidar/lidar_blocks/lidar_las_ascii.html

6 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)

7 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

8 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

9 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

10 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

11 Systematic Sample Plot Layout NW corner N E SW corner N E East Boundary E 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: = 157 m 40 m buffer 117 m E-W N-S distance: = 183 m 40 m buffer 143 m N-S EW Spacing between plots on a line is 117/4 = m NS Spacing between lines is 143/3 = 47.6 m Coordinates for the first plot in the NW are: N = = E = = 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 m greater than the previous plot. Each line will have a northing value of 47.6 m less than the previous line.

12 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 Y X COL m Comma delimited TEXT format for ingest with PFO ROW 47.6 m Repeat 5 X s

13 ArcGIS Ingest of Excel Coordinate File

14 ArcGIS Ingesting of Excel Coordinate File Make sure XLS columns match Arc s X, Y Fields Right Click on file & select Display XY Data which pops this open. Set projection

15 ArcGIS Ingesting of Excel Coordinate File As a SHAPEFILE

16 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

17 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

18 Get Today s Data From the Class Website

19 Grad Presentations Next 2 Weeks 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

20 Generate Fixed # of Random Points/Polygon Have table open with polygons selected Puts 25 points in whatever polygon record(s) you select regardless of polygon size

21 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 Then

22 5 Random Points/Acre in Forest Click

23 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 * Minimum point spacing = {2*sqrt(4356/PI)} / 3.28 = 22.71m (aka LINEAR UNIT) 1 10 acre 1 10 acre Then go to Create Random Points

24 25% sample with 1/10 ac circular plots What s wrong with this?

25 Buffer in from Polygon Edge By Plot Radius Don t want 1/10 th acre plot centers to be < 11.35m from an edge. Select the polygon(s) to buffer in the Attribute Table

26 Generate Points in Buffered Polygon

27 Generate Points in the Buffered Polygon

28 Start Here

29 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.

30 LANDSAT/ Data for today s exercise

31 Creating a Viewshed using a DEM srtm-dem_30m.img View_Point_Top.shp Qutput_raster 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.

32 Prepare Viewshed for Export to KML Edit your viewshed output so that the Not Visible locations have no color

33 Viewshed from the Red Point

34 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.

35 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

36 Exporting GIS Data to a KML

37 3D Image Rendering (aka image drape) 30 meter SRTM DEM 30 meter Landsat-5 ( ) Quad Cities Area

38 3D Image Rendering

39 3D Image Rendering ArcScene Read Landsat image & DEM same way as in ArcGIS Then, you can play with this to change view angle

40 3D Image Rendering ArcScene srtm-dem_30m.img

41 3D Image Rendering ArcScene

42 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

43 Create Flyby Animation Open animation editor toolbar Animation Toolbar

44 Animation Controls

45 Exporting a FLYBY to an AVI file Audio Video Interleave