A Better Overlay

I’m currently working on a 3D flyover for a client, inevitably I do this task in Google Earth, but there are many things I don’t like about how the software functions, so, I’ve created some cartographic patches to make it more tolerable to view.  Touring from one point to the next or down a path are very smooth, and that’s my main reason for using GE, so I’ve taken full advantage of that.  But from there, I’ve tried to customize the rest.  The first patch to take care of was the imagery.  Google’s source has a huge cloud over the property, so I brought in a tiled NAIP overlay.  The resolution isn’t terribly high, but that’s not necessary for the altitude of our flyover.

The second issue I had to deal with was showing protected lands polygons surrounding the focus property.  A simple polygon overlay in GE produces pretty dismal results, as there are no applied shadows to show shaded relief and the property boundaries blur and become completely distracting at oblique views.  So, my trick here was to build the protected lands polygons into the imagery tiles using ArcMap.  I brought in NAIP imagery on the bottom, followed by the polygons, overlayed by an 85% transparent hillshade, but only showing the hillshade where it overlapped the polygons using Advanced Drawing Options in ArcMap.  I didn’t want to double-up the shadows, and this little tool worked great.  If you wanted to have some sense of the imagery on the protected lands as well, you could add a second layer of NAIP just below the hillshade and set that to 60%-70% transparent.

Open Source GIS – gdal2tiles

I’ve been starting to try to wrap my head around Open Source GIS tools lately, thinking about how we can use them for our projects.  In this process, I’ve done a decent amount of research, lots of downloading and installing, troubleshooting installs, and figuring out how all of these puzzle pieces fit together.  I come from a liberal arts education background and have always loved GIS because of its combination of science and art (heavy on the art).  So, naturally, the past couple of weeks have been a whirlwind of understanding, black boxes of mystery have slowly opened up and revealed themselves.  Within one of these boxes was the gdal2tiles tool from the OSGeo4W install.  It quickly and easily takes large georeferenced images (jpg, png, tif, etc) and chops them up into manageable tile sizes at standard on-line mapping zoom levels.  So instead of drawing and re-drawing a 50mb image of the Bay Area every time you pan to a new location , this allows you to draw small, seamless tiles, pieces of the whole image as you pan around.  My test here is a 10-meter hillshade of the SF Bay Area at seven different zoom levels.  Lots to build off of, lots of ideas going now, steamrolling ahead into the OS Geo world.

California Protected Areas Database v1.2

GreenInfo Network has worked diligently over the past few years compiling all federal, state, county, city, special district, and non-profit protected areas into one database for the state of California.  The California Protected Areas Database (CPAD) v1.2 now contains 48 million acres of land in 47,000 parcels from over 800 agencies.  You can download and review the data by visiting www.calands.org.  And, the data can be seen and queried in all their interactive glory at the ParkInfo and California Department of Parks and Recreation sites.  Hats off to Jason Jones, CPAD Project Manager for all of his hard work!

Click on the map below to Zoomify.

My Tracks for Android

Google released the My Tracks application for Android phones a little while back, and I’ve been testing it out every chance I get.  A couple screen shots and links below show a day of hiking around Big Basin State Park near Davenport, CA, and a day skiing at Alpine Meadows near Lake Tahoe.  I was immediately impressed that  it does an amazing job of accurately tracking your movement, having used both recreational Garmin units and gigantic, clunky, Trimble units in the past.  Granted, I used the Trimble unit 8 years ago, so this is not a fair comparison.  But I was able to start recording tracks and toss the phone into my jacket pocket and forget about it for the day.  It kept a good signal both days, and we put in just over 5hrs of skiing this past weekend.

This brings we to my second impression.  After you set your phone to record tracks, the screen will timeout according to your settings, but the phone will continue recording.  This maintains battery life throughout the day, which is the one thing you hope for with Android applications, as the phone’s battery life is less than desirable.  Okay, onto some links and screenshots…

 
I added a couple photos from our hike to the final map, placed at the blue markers

Notice the straight chairlift lines…

Tracks on the phone, stats page, and elevation (we got in 17 runs)

US Unemployment Map 2 – The KML

Tommy added a legend to the KML, and now we have it available for download right here.  What’s best is that he spent some time and put county names, unemployment rates, and some more attribution into the info bubbles.  This data set is powerful stuff, and we see that even though the unemployment rate is currently 7.9%, there are counties like Mackinac County in Northern Michigan that have an unemployment rate of 24.2%.  Staggering numbers…

US Unemployment Map

My former boss, Tommy Albo, imported centroids of unemployment data by county to Google Earth and quickly compiled this pretty amazing visualization.  The numbers come from the Bureau of Labor Statistics and represent unemployment levels as of December, 2008.  Download the original county data or a PDF map from Primary Data Source.  This was all brought to our attention through The Society for Conservation GIS email list, and a post by Gina Clemmer at New Urban Research, Inc.  Thanks to Tommy and Gina!  Enough links?  Okay, the goods are below.  Notice the Southeast, Upper Midwest and the West Coast.

Google Ocean is here

I just got an email from Google Earth Outreach about the release of Google Earth 5.  As speculated over and over, and discussed here just over a year ago, it includes the new bathymetry layer, and it now has ocean depths.  You can grab the update from the Google Earth website.

California Sprawl

A coworker of mine, Jason Jones, created a projected change in population file for the state of California from the year 2000 to 2030.  This was a calculation based on data downloaded from the Natural Resource Ecology Lab.  In the map below, gray patches are varying degrees of current urbanized areas.  White patches are private, rural areas.  These locations will probably not change in the next 20 years.  Shades of red show increasing amounts of population growth.  Overall, lightest pink shows some urban edge infill and far suburbs.  The darkest red is mostly suburban growth, as you can see these colors basically ring around metropolitan areas.  I tossed in public/protected lands to show the ownership landscape and to highlight threatened areas in need of conservation.  An interesting thing to note: if you look at northern central Marin (just north of San Francisco) (sorry about the lack of labels for those unfamiliar with the area), there is a swath of white, rural, that is surrounded by protected areas to the west and projected population growth to the east.  My guess is that this private rural land, a prime location for development in the Bay Area, actually has a ton of conservation easements in place on it.  This diminishes the chances of population growth by 2030.

California LIDAR 2

Okay, so I can’t get over these data quite yet.  One more post.  Oh, and yes, Happy Holidays!  I’m heading to New York this evening — nothing like a Christmas Eve red eye, but I’m excited to see family.  Back to LIDAR for one second.  I wanted to show what the calculation of the DEM with trees minus the DEM without trees (which equals trees, basically) looks like without symbolizing by land cover type.  I originally got this idea from an article discussing LIDAR analysis of gigantic redwoods along the Northern California coast, a project funded by Save the Redwoods League.  So, just below you can see the height of vegetation simply shown with a color ramp.  The darkest red represent the tallest trees, the light blue shows the water or barren land.  This image says so much, it kind of blows my mind.  In the northwest, you can see where the tallest trees follow the natural ravine, along the banks of a stream.  But that square in the center, and the line of trees in the southeast, that could only be created by one thing:  humans and their crazy “property rights.”

So, the next image shows parcel boundaries, and yes, they line right up with the drastic difference in tree height.  This goes to show that just because an area may be forested with identical tree species, there can still be extreme differences in habitat type caused by forest management.

Is there really a difference between looking at these images and just inspecting some aerial imagery?  There is, check it out below.  Zooming to the property line in the southeast, you see the original calculation, just as above, then an aerial image with parcel lines, followed by just an aerial image.  If you look close enough at the aerial, you can make out a property line, but it is definitely not as glaring as the original calculation.  Cheers!  Happy Holidays!

California LIDAR

A search for California LIDAR brought up a NASA Planetary Geodynamics Laboratory link with some very localized LIDAR geotiffs.  I recognized the name Fort Ross, so downloaded these images, and they’re great.  There are four files for each area, a digital elevation model (DEM) with tree heights, a DEM without tree heights, and hillshades of each DEM.  I thought about how I could call out forested areas, and came to the conclusion that I could subtract the DEM without trees from the DEM with trees to just get heights of objects above the ground.  The biggest differences, the highest trees, are shown as dark green, the smaller differences are shown as yellowish grasses and shrubs, and no difference is shown as blue water.  Set transparencies between this layer and both hillshades, and we get a pretty great land cover map.  Click on the image below to see a high resolution version, and further below you will see the DEMs and calculation described above.