Tracking Turkeys

November 25, 2015November 26, 2015 ~ slubkin ~ Leave a comment

It’s a sad day for Meleagris gallopavo, the American wild turkey (also domesticated turkey). Turkeys all over the U.S. are wearing disguises and hiding today in the hopes that they won’t be the main attraction at the Thanksgiving feast. So, where are these turkeys hiding?

The BISON (Biodiversity Information Serving Our Nation) database contains occurrence data for millions of species. I decided to see if I could track down those sneaky turkeys. My search returned 324,274 results.

Normally, when I search BISON, I get point data. But, turkeys are so common in the U.S. that I got a heatmap.

As you can see, the turkeys are hiding in Wisconsin.

My older kids use to make turkey jokes. They seemed to feel that turkeys aren’t highly intelligent birds. But, the decision to hide in Wisconsin shows that turkeys are much smarter than we think.

The current weather map shows that it’s pretty cold in Wisconsin- not cold enough for a turkey to freeze, but cold enough that someone hunting a wild turkey for Thanksgiving dinner would be seriously tempted to give up, go home and drink hot chocolate.

But, this post isn’t really about clever turkeys or cold weather or even Thanksgiving dinner. It’s definitely not about geology although I think I could probably find a geological reason for turkeys to gather in Wisconsin. It’s about data.

Today data is everywhere. It’s easy to go online and find data for anything from the number of wild turkeys in each state (based on publications, not actual counts) to the weather to each state’s most Googled Thanksgiving dish (according to the New York Times, it is brownberry stuffing in Wisconsin). It’s just as easy to use the abundance of data to support any argument you feel like making. Today, I’m arguing that turkeys are Packers fan.

Source: http://www.businessinsider.com/nfl-teams-popular-by-state-2015-9

Happy Thanksgiving!

11/26 updated to repair image links.

200 Years of Geological Mapping

November 25, 2015December 7, 2015 ~ slubkin ~ Leave a comment

This is a geologic map of Britain. It is a screen shot of the British Geologic Survey’s “Geology of Britain” viewer.

I chose to show bedrock and surface geology because that’s what William Smith showed when he produced the first geologic map of Britain two hundred years ago in 1815.

Unlike many of the English men who made great scientific contributions, William Smith was not nobility – or even well off. He was the son of a farmer. As a young man, he became an apprentice to a surveyor. He eventually went to work for the Somersetshire Coal Canal Company.

While working in the mines, Smith noticed that individual layers of rock on the sides of the pit were always arranged in the same recognizable relative order. He also noticed that some layers were identifiable by the fossils they contained, and that these fossils were also always in a predictable order. He was inspired to see if the relationship between the layers of rock or strata, their positions and the fossils they contain was consistent throughout Britain.

As William Smith studied the rocks of England, he drew cross-sections showing relationships and maps showing location. Eventually his work evolved into the first national geologic map. It measured 6 feet by 8.5 feet and showed the rocks of Britain at the a scale of 5 miles per inch.

Smith’s map isn’t so different from the BGS map created using GPS units and satellite data.

627px-Geological_map_Britain_William_Smith_1815 — By William Smith (1769-1839) {Public Domain], via Wikimedia Commons

Two hundred years ago, one man created a map by walking through Britain. Since then, geologic maps have been created for every part of the Earth. Thanks to William Smith, mapping is an intergal part of the training of every geologist.

As a geology student, I learned to map in the field by carefully measuring and plotting geologic contacts, folds and faults on a topographic base map. It wasn’t always easy to distinguish between the greyish-brown of one unit and the brownish-grey of another or determine my location based on map contours. Yet, I eventually learned to make a map that could be interpreted to tell the geologic history of a small area. William Smith didn’t have a topographic base map. How did he do it?

Smith’s map is more than the distribution of rocks. It is a first edition volume of Britain’s geologic history. Since 1815, that volume has been edited and revised hundreds of times, but William Smith is remembered and honored as the original author.

You learn more about William Smith and his maps and play with an interactive William Smith mapping app here.

Edited some typos (12/7/15)

Mapathon at George Mason University

November 23, 2015 ~ slubkin ~ Leave a comment

On Friday, November 20, 11-year-old Arielle and I attended a GIS week map-off at George Mason University. Students from George Mason and Northern Virginia University competed against students from George Washington University while using Open Street Map.

Arielle has attended several mapathons and is quite good with Open Street Map, so we chose the intermediate project. We digitized buildings on imagery from villages in Indonesia that are located near active volcanoes.

I felt it was important that Arielle understand why we are tracing squares on a map. I asked Arielle, “Why does this matter?”. She understood the difference between imagery and maps and that it is important to know what was at a location before a natural disaster in order to estimate damage and direct rescue efforts.

The event was sponsored by Missing Maps, NOVA Community College ASPRS club, George Mason University ASPRS club, Peace Corps, National Geographic, and MapGive.

Are you a better mapper than my 6th grader?

New York: Why the Height of a Skyscraper Depends on Location

November 23, 2015November 26, 2015 ~ slubkin ~ 4 Comments

Take a look at the New York City skyline. It’s unique and recognizable because of its skyscrapers. New York City is home to some of the tallest buildings in the world.

Suppose you are interested the height of skyscrapers in New York City. You could make a list of building heights, like this list from Wikipedia.

The Freedom Tower, One World Trade Center (1,776 ft.)
423 Park Avenue (1,400 ft.)
Empire State Building (1,250 ft.)
Bank of America Tower (1,200 ft.)
Chrysler Building (1,046 ft.)
The New York Times Building (1,046 ft.)
One57 (1,005 ft.)
Four World Trade Center (978 ft.)
70 Pine Street (952 ft.)
The Trump Building, 4o Wall Street (927 ft.)

This list tells me that the Empire State Building is the third tallest building in the City. It tells me that Freedom Tower is about 750 feet taller than the Chrysler Building. But, what does that look like?

I’d get a better idea of what this means with a bar graph. Or. I could use an image like this (also from Wikipedia):

This gives me a much better idea of how building heights compare. But, this information is still limited.

What if I want to know where these buildings are? What if I care about their locations? I will need a map.

I used the Building Footprints shapefile from NY OpenData to create this map of buildings with a roof height over 500 feet tall in NYC. Only 177 out of 1,082,433 buildings in NYC are over 500 feet tall. Those buildings are indicated in red.

The most interesting thing about this map is that all these very tall buildings are clumped in two locations: Midtown and the Financial District. You can see these clumps in this photograph:

Here’s a closer look:What is going on? Did New York City specifically zone these locations to have tall buildings? Is this meant to preserve the skyline? Or, is it intended to show the importance of the Financial District?

The answers can also be found in a map. The location of New York’s skyscrapers is all about geology. As you can see in my hideously ugly geological map (colors courtesy of USGS’s New York Geological Map downloaded as a shapefile), the island of Manhattan has a different type of surface rock than the surrounding area. This bedrock is a metamorphic rock called the Manhattan schist (in pale lavender).

The Manhattan schist formed more than 400 million years ago when a volcanic island arc (similar to today’s Japan) crashed into the eastern side of the continent of Laurentia forming a huge mountain range known as the Taconic Orogeny. The high temperatures and pressures associated with mountain building caused the clay minerals in the mud that accumulated of the coast of Laurentia to transform to more resistant minerals such as biotite, muscovite and quartz.

Throughout most of Manhattan, this erosion-resistant bedrock is covered with large amounts of unconsolidated sediments. But, this exceptionally hard rock lies very close to the surface in Downtown and Midtown Manhattan. Because this rock is so strong, it makes the perfect foundation for a skyscraper.

What about other cities? There are only two structures over 500 feet tall in Washington DC: the Hughes Memorial radio tower (761 ft) and the Washington Monument (555 ft). Why doesn’t Washington DC have tall skyscrapers?

While there are some strong metamorphic rocks in Northwest DC, most of DC is built on much softer sedimentary rocks. These rocks cannot support a skyscraper.

So, maps can help us understand where things are, but they can also help us understand why they are where they are. In New York, the height of a building depends on location and location depends on geology.

(Many of the other tallest buildings in the U.S. are located in Chicago. Chicago is weird. You can learn more about the challenges of building skyscrapers in a swamp here. )

This post was inspired by Episode 1 of Making North America on PBS.

Geology Matters: Watersheds in Fairfax County

November 16, 2015November 26, 2015 ~ slubkin ~ Leave a comment

On November, 11th, Greg Bacon, an analyst at Fairfax County GIS, came to talk to the GIS 295 class about his work and the data available online at the Fairfax County Geoportal website.

The website contains information about public services, land records, land development, transportation (a big deal in Northern Virginia), safety, amenities, elections, and wildlife – basically, all the county information that your average citizen or business might need. At the very very bottom of the page, you will find watersheds.

A watershed is all of the land that drains into a particular body of water. Watersheds occur at all scales. The United States is divided into two great watersheds by the Continental Divide, a line of high peaks stretching from the Andes through the Rockies that divides land that drains into the Pacific Ocean from land that drains into the Atlantic Ocean. You can see the continental divide on the map below.

The Rocky Mountains of the U.S. formed through a series of continental plate collisions. The most recent is the Laramide Orogeny that occurred between 80 and 55 million years ago. At that time, the Rockies were about 6,000 meters above sea level. Today, the highest peak is Mount Elbert at 4,401 meters.

While the Continental Divide determines which ocean water ultimately ends up in, there are many ways for water to get to those oceans. The U.S. Geological Survey (USGS) uses hydrologic unit codes (HUC) at six different scales (2,4,6,8,10,12) to designate the area of land that uses a particular water body as a path to the ocean. In this map, you can see HUC 2 or regional divisions. Unlike other watershed models, the USGS’s model is based entirely on water drainage – not state or local administrative boundaries.

Fairfax County is in the HUC 2-02 watershed boundary, or mid-Atlantic region. If I zoom in, I can see that Fairfax County is also in HUC 4-207. Drainage in this region flows into the Potomac River. If Fairfax County was further north, water would flow into the Susquehanna. If it were further South, water would flow into the lower Chesapeake. The upper Chesapeake watershed is to the east.

At the HUC 6 level, most of Fairfax flows into the Middle Potomac. However, the rivers used to get to the mid-Potomac differ. That causes the area to be divided into the Anacostia-Occoquan and the Catoctin district (northwest).

The area continues to be subdivided until the HUD 12 level which is based on local creeks and lakes – a resolution that is very similar to the Fairfax County dataset. The Fairfax County system continues to subdivide regions to the level of individual creeks and streams, but one must click at the “more info” tab on the mapped watersheds to learn about these subdivisions.

There is one important difference between the Fairfax County watershed designations and the USGS system. The Fairfax County watershed boundaries end at the county line. The USGS boundaries do not. That is because the USGS watersheds are based on geology. The Fairfax County watersheds are based on administrative boundaries.

I looked up the Cob Run and Bull Run watersheds on the Fairfax County website. This area is located in southwestern Fairfax County between Loudon and Prince William Counties and covers 64 miles of land that drain into tributaries of the Occoquan Reservoir. The website says that 14 square miles in Loudon County also drains into this watershed. This matters.

We all need clean water. Yet, the conveniences of everyday life (manufacturing, transportation, agriculture) create pollution that is carried into our drinking water. In Fairfax County, all drinking water comes from the Potomac or the Occoquan.

All water that falls or accumulates in a river’s watershed goes into that river. This includes the rain that falls on the roads and mixes with the oil and gas that cars leave behind It includes chemical-laden runoff from industry and farms. It even includes the water that washed off your neighbor’s yard after he sprayed his azaleas. Although, this water is cleaned and treated, small “safe” amounts of contaminants remain.

Fairfax County, like all counties, must consider water pollution when planning for the future. The County must answer questions like “How will expanding Route 28 increase runoff into Bull Run?”, “How much of that water will end up in our drinking supply?”, and “How will that water be treated?” What happens when Manassas doesn’t care because commuters are sick of sitting in traffic and the creek is across a county line? Or, when an administrator uses the map without reading the metadata? Wouldn’t it be better to stick with the USGS system which is based on the way the Earth actually works and then figure out the overlap?

As a geologist, I know that a watershed is all of the land that drains into a particular body of water. I’m uncomfortable with the Fairfax County map; anyone want to offer reassurance?

Harmful Algal Blooms – Part 2: Monitoring Harmful Algal Blooms

November 15, 2015December 7, 2015 ~ slubkin ~ 2 Comments

In Harmful Algal Blooms – Part 1, I discussed what a harmful algal bloom is and why we care about blooms. I wrote about the dangers that HABs pose to public health and the economy and explained why it is important to monitor and study HABs.

Members of the Virginia Harmful Algal Bloom Task Force use a combination of fixed stations, continuous sampling, and periodic dataflow cruises to monitor water quality in the Chesapeake Bay watershed. This map shows monitoring stations in Virginia.

Sorce: Virginia Institute of Marine Science

Data for each of these stations is available at the Virginia Estuarine and Coastal Observing System (VECOS) website. It looks as if Virginia’s rivers are well-covered, but if you go to the site and take a look at this data, you will notice that many of these sites are no longer active due to funding cuts.

Current monitoring consists of fixed stations and periodic dataflow cruises operated by the Virginia Institute of Marine Science and Old Dominion University. Most of this monitoring focuses on the James River and the York River. Sanitation districts, like the Hampton Roads Sanitation District, do automated continuous sampling in their service areas. This monitoring is on-going, but it doesn’t provide a complete picture of HAB activity.

The first obstacle is that processing all these samples takes time. Harmful algal bloom species have to be separated out of water samples that contain hundreds or thousands of other microorganisms through a complex series of DNA tests. Information isn’t available until weeks or months after a bloom occurs. By then, it may be too late to determine what factors contributed to the bloom.

The second problem is that using dataflow cruises for real-time monitoring is expensive. So, boat cruises are restricted to areas of high concern. This means that blooms in other parts of the Chesapeake Bay watershed may be overlooked.

This makes it difficult to get the environmental and water quality information needed to understand and predict HAB occurrences. It also makes it difficult to get real time information about HAB activity in the Chesapeake Bay watershed.

What if there was a less expensive option?

Last summer, I participated in the NASA DEVELOP program at the Patrick Henry Building in Richmond. Our team, Cassandra Morgan and I, worked on a method to monitor harmful algal blooms using satellite data.

Sara Lubkin (NASA DEVELOP), Todd Egerton (ODU), Wolfgang Vgelbein (VIMS), Kimberly Reece (VIMS), Cassandra Morgan (NASA DEVELOP)

Satellites can take pictures of HABs like this Landsat 8 true color image.

While, you can see the bloom in this picture, it’s hard to determine exactly which areas are in the bloom. However, phytoplankton uses chlorophyll-A to harvest the energy of the sun. VIMS and ODU detect harmful algal blooms by measuring levels of chlorophyll-A. What if we could detect chlorophyll-A in the water using remote sensing? We could then use chlorophyll measurements as a proxy for HABs.

NASA’s MODIS Aqua satellite collects information about water, including chlorophyll-A levels. Chlorophyll-A maps are available at no charge from NOAA CoastWatch’s East Coast Node.

This is a MODIS chlorophyll map for the same day. MODIS

You can see that there are high levels of chlorophyll in the James River, Upper Chesapeake, Potomac and Mobjack Bay.

The problem is that MODIs aqua chlorophyll products have a 1.4 kilometer resolution. So, they don’t give a lot of detail – especially in narrow rivers like the York.

Landsat 8 has a 30 meter resolution. But, there are no publicly available Landsat chlorophyll-A products. It was Cassandra and my job to create this product.

We started by downloading Landsat 8 images (Path 14, Row 34) from May through September 2011-2014. We looked for images without too much cloud cover. We masked out the land and the clouds and filtered these images through a 1.4 km moving window to match the MODIS resolution. We chose the day with the best overlap between MODIS and Landsat and joined MODIS the chlorophyll-a values to each smoothed, masked Landsat band. We also added bathymetric measurements

Once we had this data in one table, we were able to export it to “R” and run a series of regressions. We tested 78 separate equations. The best five equations were used to create tools using ArcGIS model builder (r-squared values .57 to .62). Here is an image of chlorophyll in the Bay created with one of those tools. ChlJuly3

We were now able to show chlorophyll-A estimates at 30 meter resolution.

Cassandra and I created a preliminary model for chlorophyll-A during our summer term. This term, NASA DEVOLOP teams at Langley Research Center and Wise County are testing the model on additional dates and validating the equations using VECOS water quality data. They are also creating easy-to-use ArcGIS tools that will allow VIMS and ODU to quickly assess the extent of algal blooms in the Chesapeake Bay.

These tools will save time and money by allowing VIMS and ODU to better target their monitoring efforts. The tools will also allow researchers to collect information about environmental factors associated with HABs.

To learn more about NASA DEVELOP and this project, check out our video or our story map.

Next Week: Harmful Algal Blooms – Part 3: The Golden Day of Data Collection

Harmful Algal Blooms – Part 1: Who Cares About Harmful Algal Blooms?

November 9, 2015December 6, 2015 ~ slubkin ~ 1 Comment

It’s a beautiful day in Virginia. The sun is shining, the birds are singing and the water is a beautiful shade of brownish green…

The “lovely” color of the water is a harmful algal bloom. An algal bloom is an overgrowth of phytoplankton – tiny, photosynthetic organisms that live in the water. A harmful algal bloom or HAB is an algal bloom that has some kind of negative effect on other organisms.

In Virginia, harmful algal blooms occur between May and September when the water is warm and full of nutrients like phosphorous and nitrogen – the same nutrients used to fertilize plants. Some of these nutrients come from industry and sewage treatment, but many nutrients are washed into the water when rain falls on farms and yards. Warm, wet summers are great for plants and for harmful algal blooms.

Algal blooms discolor the water and produce noxious odors. They also block the sunlight that is needed by underwater plants and keep filter feeders like oyster from being able to obtain food. When the phytoplankton die and decompose, they remove large quantities of dissolved oxygen from the water and create anoxic dead zones. The result is massive fish kills because fish, oysters, clams, crabs, and other organisms can’t get the oxygen they need.

Source: Chesapeake Bay Foundaton — Source: Chesapeake Bay Foundation

Scientists in Virginia are especially concerned about two species: Cochlodinium polykrikoides and Alexandrium monilatum. Cochlodinium polykrikoides is native to Virginia. Alexandrium monilatum is an invasive species that wasn’t found north of Florida until 2007. It now occurs regularly in Virginia’s rivers.

Both these species produce red tides, like the bloom in the picture below. They also produce toxins that can kill fish and cause birth defects in shellfish. These toxins may also cause illness in humans.

Image from Virginia Institute of Marine Science (VIMS) — Source: Virginia Institute of Marine Science (VIMS)

HABs are a public health problem. They are also an economic problem because a large bloom can have a big impact on Virginia’s fishing industry and tourist industry. So, Virginia has a Harmful Algal Bloom Task Force. The task force includes representatives from the Virginia Institute of Marine Science (VIMS), the Marine Resource Commission, the Department of Environmental Quality (DEQ), Old Dominion University (ODU) and the Virginia Department of Health. These agencies work together to monitor Virginia’s waters for harmful algal blooms and to respond to bloom events. VIMS and ODU do research on harmful algal bloom species and the biological and environmental conditions that contribute to bloom growth.

HABmap — Virginia Harmful Algal Bloom Surveillance Map

But, harmful algal blooms aren’t unique to Virginia. Large blooms and dead zones have also been recorded in the Great Lakes, the Gulf of Mexico, the St. Lawrence Estuary, the Oregon Coast, Monterey Bay, the Baltic Sea and Florida. The total economic impact of harmful algal blooms in the U.S. is estimated at $100 million per year.

Last year, Congress reauthorized and expanded the Harmful Algal Blooms and Hypoxia Research and Control Act of 1998. This act provides funding for the study and monitoring of HABs in U.S. waters.

As climate change causes waters to warm, harmful algal blooms are expected to increase in both frequency and severity. More and more water will be affected. So, who cares about harmful algal blooms? We all should care.

Next week: Harmful Algal Blooms – Part 2: Monitoring Harmful Algal Blooms

Edited on 11/20 to add map.

Using Beetles to Learn About Past Climates

November 1, 2015December 7, 2015 ~ slubkin ~ Leave a comment

How many species are there on Earth? Nobody really knows, but one study estimated the number to be about 8.7 million and most of these species are insects.

The largest group of insects are the beetles. Beetles make up about 40% of insects and 30% of animal life. Why are there so many beetles?

Scientists used to believe that beetles had high rates of speciation, but a recent study co-authored by the awesome Dena Smith suggests that beetles might just be really good at avoiding extinction. You can read the paper here.

This resistance to extinction means that many beetle species are very old. Species living now were around millions or even tens of millions of years ago.

Beetle species don’t just live a long time, they are also fussy about where they live. They want the humidity and temperature in their homes to be just right. If it gets too hot or too wet or too cold, they move out and find another home.

Long species durations and specific habitat requirements make fossil beetles very useful for learning about past climates, especially the Pleistocene.

The Pleistocene Epoch is defined as the time period from about 2.6 million years ago to about 11,700 years ago. Like today, the Pleistocene was a period of rapid climate change. During this time there were between 20 and 30 glacial intervals where much of the world’s temperate zones were covered in ice. These glacial intervals were separated by warmer interglacial periods when the ice receded. This map shows the Wisconsinan glaciation 18,000 years ago.

I wanted to see if I could use GIS techniques to reconstruct Pleistocene climate using fossil beetles. I chose two sites for my study: The Titusville Peat in Pennsylvania and Ziegler Reservoir near Snowmass, Colorado. The Titusville site is an ancient peat bog during the mid-Wisconsinan interstadial between 43.5 and 39 thousand years ago (Elias, 1999).

Ziegler Reservoir is located near Snowmass Village, a ski resort in Colorado. In 2010, work began to widen the reservoir, but the remains of a mammoth were uncovered, leading to a paleontological excavation. Numerous mastodons, mammoths, ground sloths, bison and camels as well as insects were recovered from the site. Carbon dating indicates the age of the site ranges from 126 to 77 thousand years old (Elias, 2014).

I obtained the list of species for Titusville from the Paleobiology database and the species from Snowmass from Elias’s 2014 paper about the site.

I looked up species ranges using Global Biodiversity Information Facility (GBIF) and USGS’s Biodiversity Information Serving Our Nation (BISON) databases. These are sites that list museum specimens. Each record includes the longitude and latitude where the specimen was collected.

To determine climate preferences, I used two sources: The global ecological land unit map and Koppen Geiger climate zones

The global ecological land unit map was developed by esri and the USGS. It is a 250 m resolution raster containing information about bioclimate, landcover, lithology, and landforms. Here are my fossil insect species on top of the global ELU map.

The Koppen-Geiger climate classification was first developed in 1884. It has been revised several times, but remains the most widely used climate classification system. The system divides Earth climates into 30 zones with unique temperature, moisture and weather properties.

I used GIS a sequence of spatial joins to connect the ecological and climate data to species. I learned that 43.5- 39,000 years ago, when the Titusville Peat was deposited, Titusville was in Köppen Geiger zone Dfc, which subarctic with cool summer, wet all year. This means that winter temperatures were as low as -40 C (-40F) and summer temperatures as high as 30 C (86F) — much cooler than current climate zone Dfb (humid continental). The bioclimate was cold and wet and the dominant vegetation included needle leaf /evergreen forests.

The Snowmass data was more complicated because it represents almost 50,000 years and the site is on a mountain. On mountains, wind picks up flying insects from warmer, lower elevations and carries them to cooler, higher elevations in a process called orographic lifting.

To compensate for the long time period and the effects of orographic lifting, I divided the beetle assemblage into five time intervals and I removed all the flying beetles from the analysis.

The climate at the Snowmass site was initially similar to today’s climate. Insects suggest Koppen-Geiger Zone Dfc, subarctic with a cool summer.Over the next 2-3 intervals, the climate gradually cooled from Zone Dfc to to zone ET (tundra with no warm season). By the 4th interval, all insects indicated Koppen Geiger zone ET or tundra. But, in the last interval, the prevalence of insects from Zone Dfc indicates warming. As in Titusville, the bioclimate was cold and wet and the dominant vegetation included needle leaf /evergreen forests.

You can see the modern distribution of Dfc and ET in this map.

Using insects to model past ecosystems isn’t a new idea. But, as far as I know, no one else has used GIS to join insects to specific ecological variables for climate reconstruction. I will be presenting the more scientific version of this research at the Geological Society of America Annual Meeting in Baltimore on Tuesday, November 3.

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Month: November 2015