Month: December 2015

Harmful Algal Blooms – Part 6: Finally, a Project

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This semester, I’ve been working with Dr. Kenton Ross, the national science advisor at NASA DEVELOP, to understand the spectral properties of Alexandrium monilatum. I am using aspects of this work as my term project for GIS 295 and GIS 255.

In Harmful Algal Blooms – Part 5: Trouble in Data Land, I discussed the challenge of completing a mapping project when the data has lost its geospatial reference.  However, we were able to obtain approximate locations for each hyperspectral scene by estimating pixel size and lining up the time field in the hyperspectral data  with the time field in the the flight path data set. You can see the scene center lines in black on the image below.

Scene Center lines

The water in this map looks unusual. NDVI is a band ratio index used to indicate vegetation. I used ((Landsat 8 band 5 – band 4)/ (band 5+band 4)) to mask out the land  in ArcGIS. I then clipped out the unmasked water and used false color imagery (bands 6, 5 and 4) to enhance different features of the water.  The water on the right side looks thick and yellow because it is very turbid.

Once I knew where each scene was located, I analyzed each hyperspectral image individually. This is York scene 5.

20150817_YK5

The first thing I noticed was that any detail was hard to see. That’s because one side is in shadow. So, I clipped out the black edges and the bright, overly illuminated right side of the image. I also reduced my number of bands from 283 to 13 carefully selected bands in order to reduce noise and maximize the spectral signal. The resulting image is in the slide below.

Slide7

The deep red swirl is the algal bloom.

I used bands 146 (710 nm), 128 (665 nm) and 85 (559 nm) to create a NIR, red, green false color composite to highlight areas with high chlorophyll.  That’s the second image in the slide.

High chlorophyll should appear red in these images, but the areas of intense blooms appeared bright yellow-green. This is because there is also a red pigment in the blooms that gives them their color.

The third image is estimated chlorophyll. I used the  ratio of band 146 (710 nm) over band 128 (665 nm) as a proxy for chlorophyll-a. Red and yellow indicate high chlorophyll, while dark blue indicates low chlorophyll. You can see that despite the red color, the algal bloom has plenty of chlorophyll.

What about the third image? I used ENVI to run an unsupervised classification. This means that ENVI sorted the pixels in the image into five groups according to their spectral properties. I then used class statistics to obtain the spectral signature of each group. That’s the big image on the left of the slide.

I repeated this procedure on seven images from the York River, four images from the James River, and one image from Mobjack Bay. The signatures in the York River were distinctive enough that we believe we are on the right track to find the spectral signature of Alexandrium monolatum.

For my GIS 295 class project, I created an web app which shows the imagery and spectral signatures at each scene. If you are a member of Northern Virginia Community College GIS group, you can access the app here. Everyone else will have to wait until we are ready to make the app public.

References:

Moses, Wesley J.; Gitelson, Anatoly; Perk, Richard L.; Gurlin, Daniela; Rundquist, Donald C.; Leavitt, Bryan C.; Barrow, Tadd M.; and Brakhage, Paul, “Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data” (2012). Papers in Natural Resources. Paper 313. http://digitalcommons.unl.edu/natrespapers/313

Tobler, Steno and Geologic Maps

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Waldo Tobler is a geographer at my alma mater, UC Santa Barbara. He is known for Tobler’s Law or the “first law of geography” which states “Everything is related to everything else, but near things are more related to each other than distant things.”

My classmate Sunil Bharuchi recently published a discussion of Tobler’s Law on his blog, GIS 295 Web GIS. He included this image, which explains spatial auto correlation.

Spatial autocorrelation measures how well a set of spatial features and their values are clustered together in space. A spatial feature is a point, line or polygon that identifies the geographic location of a real world object; this object could be a building, a forest, a rock unit or a lake.

According to Tobler’s law, spatial features will be clustered next to more similar spatial features – this is illustrated in the first image above. But, is this always true? Sunil’s post got me thinking.

Here is a geologic map of Yosemite National Park. Which of the images above does it look like?

Map of Yosemite National Park.svg
“Map of Yosemite National Park” by General_geologic_map_of_Yosemite_area.png: en:United States Geological Survey derivative work: Grandiose – This file was derived from  General geologic map of Yosemite area.png: . Licensed under CC BY-SA 3.0 via Commons.

I’ve spent years looking at geologic maps, so I told Sunil “image three looks more like geology.” But, does that mean Tobler is wrong?

Not at all.

Nicolas Steno (Niels Stensen, 1638-1686) was a Danish scientist and bishop who made important contributions to the fields of anatomy, paleontology, crystallography and geology. Steno’s principles of statigraphy explain the formation of sedimentary rock and are still used by geologists to determine the history of a rock unit. There are three principles:

  1. The Principle of Superposition: When sediments are deposited, the sediment that is deposited first is at the bottom while sediment that is deposited later is at the top. Therefore, the lower sediments are older.
  2. The Principle of Original Horizontality: Sediment is originally deposited in horizontal layers.
  3. The Principle of Original Continuity: Sediment is deposited in continuous sheets that only stop when they meet an obstacle or taper off because of distance from the source.

Doesn’t the Principle of Original Horizontality sound a lot like Tobler’s Law? Then why don’t geological maps look like the first picture on Sunil’s image?

First of all, sedimentary rock isn’t the only type of rock on Earth.Steno’s principles do not apply to igneous and metamorphic rock.

Second, the Earth is an active planet. Plate tectonics causes sedimentary layers to bend, break and even overturn. Igneous rocks intrude into existing rock from below the Earth’s surface or erupt from above. These processes mean that geologic units are often very complex and the resulting spatial patterns reflect that complexity.

Yosemite USA.JPG
“Yosemite USA” by GuyFrancis – Own work. Licensed under CC BY-SA 3.0 via Commons.

James Hutton (1726-1997) was a Scottish physician and geologist who is known as the founder of modern geology. He was the first to suggest that the Earth is continually being formed and that based on the rates of geologic processes, the Earth must be much,much older than the accepted estimate of a few thousand years. He is also known for the Law of Cross-cutting Relationships.

Law of Cross-cutting Relationships: If a fault or other body of rock cuts through another body of rock, then that intrusion must be younger in age than the rock that it cuts or displaces.

It is this Law of Cross-cutting Relationships that helps us interpret geological units and create geological maps.

Can you figure out the temporal relationships in this cross section?

geology
From Earth: Portrait of a Planet, 4th Edition (2011) by Stephen Marshak.

So, how does Tobler’s Law fit in? It depends on scale. If you are standing on an outcrop of sandstone, chances are good that the rock surrounding you will also be sandstone – especially if you are in the tectonically quieter center of a continent. But, If you are mapping Yosemite park using one kilometer pixels, you will find a lot more variation in neighboring areas.

 

Harmful Algal Blooms – Part 5: Trouble in Data Land

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If you’ve read Harmful Algal Blooms, Part 4, you know that I had developed a plan to obtain the spectral signature of the Alexandrium monilatum, a toxic dinoflagellate that causes harmful algal blooms in the Chesapeake Bay watershed, from the hyperspectral data that was collected August 17, 2013. I wanted to use spectral signatures to map the extent of harmful algal blooms in the James and York Rivers. However, lots of data doesn’t always mean good data.

The hyperspectral data was collected using a sensor that was mounted on a NASA airplane. The angular cone of visibility detected by a sensor at a given time is called the Instantaneous Field of View (IFOV). The size of the IFOV determines the resolution or minimum size of a pixel.

In this image from Natural Resources Canada, area A is the IFOV and area B is the area on Earth’s surface that that can be seen at a given time (B=A*C).

IFOV2

Area B, the area that can be sensed at any time, depends on many factors, including the altitude of the plane and the angle of the sensor. This illustration from Natural Resources Canada illustrates the effect of angle of view.

IFOV 1

 

During the August 17 data collection flight, the part of the sensors internal navigation system that measures the plane’s attitude or angle failed. This meant that we could not determine pixel size. It also meant that location data was not available for the hyperspectral data.

GIS stands for Geographic Information Systems. The term “geographic” refers to location.  Without geographic coordinates, we could not accurately place our data on a map. What could we do?

Normally, one would georectify the data using by lining up ground control points. Ground control points are known locations on the ground. they must be small, unchanging and easy to recognize. But, how do you find ground control points in a picture of water?

YK6

Fortunately, we had a .kml file of the flight path, which listed geographic coordinates and times, and a few images with features other than water like this image with a large Navy dock.

20150817_YK7

We were able to use measurements of the dock to calculate pixel size. We calculated the pixels to be about 2 meters long (a long track) and 3 meters wide (across track).

Dr. Kenton Ross, the national science adviser for NASA DEVELOP,  was also able to use time signatures from the flight path file to determine where the plane was at a given time. He matched these times to the time variable of the hyperspectral images and was able to estimate approximate geographic coordinates for each of the images. The seven hyperspectral image sites for the York River are shown below.

Path

However, I still had to give up a big part of my project design. When planning the project, I had forgotten one very important fact: water flows. Unlike ground control points, water does not stay in place over time.

On the image above, you can see a black squiggle. This is the path of the data flow cruise. It overlaps two  of the hyperspectral images shown above in space. However, since the chlorophyll samples were not collected at the exact same time (although it was within a few hours) as the hyperspectral images, the two data sets do not overlap in time. Because there is a time difference, the water moved. This might not make a big difference at 30 meter resolution, but at 2 to 3 meters, it could be a big deal.

There was another problem. The shape file with the locations lost its headers during processing. The ASCII file had to be edited in order to move the locations into ENVI.

Stay tuned for the final post of this series, Harmful Algal Blooms, Part 6, to learn how I (hopefully) resolve these issues and finish my project.

Harmful Algal Blooms – Part 4: What is a Spectral Signature?

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The human eye is a light sensor. We can see because the objects around us emit or reflect light at wavelengths that our eyes can detect.

Remote sensing detectors work in the same way. Sunlight is reflected from Earth’s surface. Satellite sensors detect that light and create images. But, satellite and aerial sensors are a lot more sensitive than our eyes.

69904main_RemoteSnsg-fig2

Our eyes can only detect light with wavelengths from about 390 nanometers to 700 nanometers. This range is called visible light and is shown as a rainbow in the NASA image below. But, satellites can detect a much wider range of wavelengths, depending on the sensor. Landsat satellites detect light from visible blue (450 nm)  to the thermal infrared (1251 nm).ems_length_final

All objects reflect, absorb and transmit light. But, some types of materials reflect and absorb certain wavelengths of light in very characteristic ways. So, these types of materials can be distinguished from each other based on the differences in reflectance, or the differences in the pattern of wavelengths that are detected by the sensor. The pattern is called a spectral signature. The following image from NASA shows spectral signatures for some common Earth materials.

(If you click on the picture, it will take you to a NASA site that explains light and remote sensing in more detail.)

spectral-signatures.png

Harmful algal blooms also have spectral signatures. This is the spectral signature of Cochlodinium polykrikoides, one of the species that causes HABs in Virginia (Simon and Shanmugam, 2012).

Cochlodinium

C. polykrikoides is known to absorb light at 555 nanometers and reflect it at 678 nanometers.  These wavelengths correspond to the high and low peaks in the image above.

There is a lot of research about C. polykrikoides, but very little is known about the spectral signature of Alexandrium monilatum. I decided that I would use the hyperspectral data that was collected on our Golden Day of Data Collection, August 17, 2015 to identify blooms of C. polykrikoides and I would try to decode the spectral signature of Alexandrium blooms.

I had a plan. I would map the chlorophyll measurements from the data cruises on the Landsat image to identify the areas with the most chlorophyll. I believed this would correspond to the blooms. I would join these areas to the hyperspectral images in order to obtain spectral signatures. Then, I could map the extent of each bloom.

It was a plan, but you know they say about plans….

Stay tuned for Harmful Algal Blooms Part 5: Trouble in Data Land.

 

 

Harmful Algal Blooms – Part 3: A Golden Day of Data Collection

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In my post, Harmful Algal Blooms – Part 2, I wrote about the challenges involved in monitoring harmful algal blooms (HABs). I also wrote about working with NASA DEVELOP last summer to develop a method to track harmful algal blooms using remote sensing data. We hoped to develop a tool that would allow HAB researchers to quickly identify algal hotspots.

One of our big challenges was finding dates for which there was both good satellite data and good ground data. We found one such date, July 3, in 2013.

Why only one day? NASA’s MODIS Aqua satellite monitors the Chesapeake Bay on a daily basis. But, Landsat covers the area only once every 16 days. If that day is cloudy, there might be very little overlap between the Landsat and MODIS Aqua images.

Here is a Landsat true color image for Path 13, Row 34 for June 17, 2013 downloaded from USGS’s EarthExplorer website:

LC80140342013168LGN00.jpg

Here is the MODIS image for the same day:

June17

As you can see, having plenty of satellite imagery doesn’t mean that we have good data about conditions in the Chesapeake Bay. And, unfortunately this happens a lot. What we really needed to  complete our project was a Golden Day: a day with clear skies where there was a boat cruise and Landsat coverage in addition to daily MODIS Aqua data. A Golden Day like that would allow us to verify the model.

The “Golden Day of Data Collection” occurred on August 17, 2015. On that day, there was a large bloom of Alexandrium monilatum in the York River and a possible bloom of Cochlodinium polykrikoides on the James River. As Landsat 8 passed above the Alexandrium bloom, the Virginia Institute of Marine Science used a boat to monitor chlorophyll in the water.  You can see the boat path (red squiggle) on the Landsat image below.  StudyArea

The MODIS Aqua imagery for the same day shows high levels of Chlorophyll in the Chesapeake Bay and its tributaries:

Aug17

Since our term with DEVELOP was over, the “Golden Day of Data Collection” didn’t help our project. However, the new team received plenty of information to verify our work. You can learn about their work  here.

Hyperspectral Data

But, the story doesn’t end with good verification data. One of my frustrations with working with Landsat data was that Landsat 8 is multispectral. It’s sensors measure 11 bands of reflectance ranging from 0.43 to 12.51 micrometers. But, these are wide bands and many species of bioluminescent phytoplankton like Alexandrium monilatum and Cochlodinium polykrikoides emit, absorb, and reflect light in very narrow ranges of  wavelengths.

While multispectral sensor bands are wide, hyperspectral sensors divide the same range of wavelengths into dozens, hundreds or even thousands of much thinner slices or bands. This image from Wikipedia explains the concept visually:

MultispectralComparedToHyperspectral

On the Golden Day, a NASA test flight equipped with a hyperspectral sensor passed overhead and obtained hyperspectral imagery of the area. The sensor was able to measure 283 bands of reflectance ranging from .35 to 10.50 micrometers. This means that the sensor could measure the very specific wavelengths I was interested in.

The true color images look like this:

YK6

 

A pixel in this image is about 2 meters by 3 meters.

Because the images show water from a high altitude, they aren’t at all very exciting too look at. However, having this type data  was very exciting to me. I volunteered to work with the hyperspectral data during the fall term. This work is my project for GIS 255 and GIS 295 and I will describe my project (and the frustrations of working with the hyperspectral data) in future posts.

Go to: Harmful Algal Blooms, Part 4: What is a Spectral Signature?