1

Apr

2015

Squaring the Circle: Coming to Terms With the Earth Being Round

Author: Peter DeCurtins

Viewing WGS84 Data On A Flat Screen Provides A Lot To Think About

Sometimes when thinking about issues related to mapping and cartography, it helps to refer to a globe. I love globes. I've always liked scale models, and what could be more impressive than a miniature replica of our entire planet? We don't see the old-fashion, real-world kind of globes around as much as we used to. These days, everyone can pull up a virtual globe whenever they wish on their desktop, tablet or phone.  They are useful because they allow us to better visualize geographic spaces.

 

Image from the Minnesota Historical Society, CC BY-SA 3.0

Hellenistic astronomy established the physical fact that the Earth is spherical in shape in the 3rd century BC. History credits the ancient Greek philosopher Crates with being the first to devise a globe to represent the Earth not long after. Two millennia may have passed since then, but globes have lost none of their utility, simply because a globe is the only way to represent the earth without distorting either the shape or the size of large features.

A geographic coordinate system allows us to index the world -- that is to be able to specify any location on the planet with a set of coordinate values. Any point on the surface of a globe can be labelled with two coordinate values, latitude and longitude being the most common example. The key to remember is that we are discussing a point on the two-dimensional surface of a three-dimensional object such as a sphere or an ellipsoid. So latitude and longitude are angular coordinates, values that carry with them angular units, such as degrees. Latitude specifies an angle that determines how far north or south of the equator a given location is, and longitude indicates the angle that the location intersects on a circle that traverses the world in the east-west plane. We often helpfully inscribe our globes with a crisscrossed grid of constant latitude and longitude lines (parallels and meridians) called the graticule.

 

 Image: Public Domain

Globes really do make things so easy to visualize the big picture. But of course, we don't typically perceive the world at such scale, but rather locally. Needless to say, our common everyday experience provides a framework that makes it appealing to model the world -- or at least our little region of it -- as being flat. The map is a human invention that well precedes the arrival of the globe. There is a star chart painted on the walls of the Lascaux caves that has been dated to 16,500 BC. More down to earth, geographic maps of territory existed in even the most ancient times. Traditionally, maps whether they be rendered on cave walls, parchment, paper or computer screens, are flat. For a whole lot of reasons, it's easier that way. The word "map" itself comes from the Latin term Mappa mundi, mappa having the meaning of napkin or cloth, and mundi the world. The word "map" thereby became a shorthand term referring to any two-dimensional representation of the world.

 

Image: Public Domain

Maps are incredibly powerful tools, but among all the many advantages and utilities they afford, there is a downside.  Any representation of the three-dimensional globe on a flat map is going to introduce some amount of distortion which invariably will increase as the area the map represents grows larger. One of the most crucial arts practiced by the geographer is the selection of an appropriate map projection to best accommodate the purpose a given map is designed to serve. A good geographer will choose a map projection that minimizes distortion of relevant geographic characteristics for an appropriate scale and extent. And whereas a geographic coordinate system is used to index a location on the globe, a projected coordinate system will be used to find locations on a map. On the globe, coordinates specified angular values, but map coordinates represent linear quantities, associated with units such as meters or nautical miles.

 

 Images by Stefan Kühn, CC BY-SA 3.0

The gold standard for geographic coordinate systems would seem to be the World Geodetic System, the latest revision of which goes by the shorthand WGS 84. This is the reference coordinate system used by the Global Positioning System, and is regularly used in applications that span cartography, geodesy and navigation. It is commonly represented by the European Petroleum Survey Group code EPSG:4326. It is this coordinate system that denominates features on global raster data sets such as Google Earth, Celestia and NASA World Wind. It is very easy to encounter data geospatially registered with this coordinate system.

It is very easy to forget that the simple act of viewing such data on a computer screen is in effect a distortion-inducing projection of the data and its geographic, spherically based coordinate system. Actually it's the simplest projection that there is, the plate carrée or geographic projection, which has been around for almost as long as globes have been.  Basically, the projection lets longitude represent the x ordinate values and latitude the values along the y axis. The graticule thus forms a regular lattice of constantly spaced vertical straight lines mapping meridians perpendicularly crossed by constantly spaced horizontal lines representing parallels. This projection distorts both area and shape, rendering it of little use for most navigation or mapping applications. However, the advantage of such a particularly simple relationship between an x,y map coordinate - think image pixel - and a geographic location on Earth has made this geographic projection a standard of sorts for global raster data sets (such as those utilized by the previously mentioned Google Earth, Celestia and World Wind). Just keep in mind when dealing with a projection like this that the Earth is round; it's just convenient to sometimes imagine it as flat.

 Image by Strebe, CC BY-SA 3.0

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26

Mar

2015

Utilizing ENVI and the Airbus Defense and Space Constellation for Precision Agriculture

Author: Sky Rubin

Editor's note: Sky Rubin, our guest blogger from Airbus Defense and Space, talks about how Airbus DS data and imagery works with ENVI image analysis software from Exelis VIS. Check out this webinar for more information on the partnership between the two companies and how you can benefit.

 

In the past two years, I have seen a dramatic increase in the demand for in-season crop monitoring using Earth observation satellites. Driving the growth of the agriculture industry is the need to increase crop yield and reduce its inputs. For this reason, agri-businesses are now working with Airbus Defense and Space to setup custom monitoring programs for their areas of interest. The programs vary from intensive monitoring on a daily/weekly basis to monthly/bi-monthly monitoring. Airbus Defense and Space’s new constellation of satellites (Pléiades 1A, Pléiades 1B, SPOT 6, and SPOT 7 and UK-DMC2) is a perfect tool to satisfy their needs. It guarantees fast, fresh and frequent coverage of agricultural areas with an unmatched reliability and range of resolutions, allowing intra-parcel insight.

Clients often mention that the most important factor when choosing imagery for Precision Agriculture is the actual availability of the data at the right point in time during the crop growth cycle. Airbus Defense and Space excels at this with an impressive rate of new collections. Our success rate is almost 9 out of 10, which means that 9 new imagery requests out of 10 are collected on-time and according to specification on a global basis. Our experience in satellite tasking dates back to 1982. Ever since then, a team of world-class tasking experts gets up every morning to ensure that your area is covered on-time and on-spec. They carefully conduct feasibility studies and closely follow up on open tasking requests. They constantly adjust priorities and do not hesitate to use satellite capacity even in difficult areas whenever there is a chance to capture the desired target. All of that fine-tuning is in our DNA, and more than any technical feature, this is the secret of our reliability.

Using the right satellite resource is essential to reduce satellite data input costs and to maximize return on investment. Depending on the application, imagery with of range of resolutions up to 22m may be sufficient. Very high resolution, 50cm imagery is always available, but may not be necessary depending on the final application. Teaming with our customers,understanding precisely what their needs are, allows for Airbus Defense and Space to come back with the right imagery source for their application- the one that will be the best match between collection time, budget and information depth required.

The majority of companies I have worked with in the agricultural vertical are using Exelis products such as ENVI.  ENVI Processing Modules like QUAC (surface reflectance model) can be used to process imagery and create derivative works such as crop yield maps, fertilizer recommendation, water needs maps etc.  Airbus Defense and Space data is very robust as our new sensors are 12 bit. This means we have 4,096 values per pixel, twice as many values per pixel as our competition. The increased spectral range is ideal for multispectral analysis with ENVI. Airbus Defense and Space also has worked with Exelis to educate the market on our JPG2000 Lossless imagery format, which can save up to 50% in terms of file size, speeding up processing and delivery times without information loss.

With DMCii, Airbus Defense and Space is also able to provide large area coverage of the Continental U.S. during the growing season for a U.S. Government client.  This data is available for commercial purchase. As the 22m DCMii imagery is some of the least expensive satellite imagery in the commercial market, clients often purchase DMCii data to be used in conjunction with free Landsat data. The 600km swathand ease of use with Landsat data makes DMC data very attractive to those who need large area coverage at 22m. 

We work with partners to provide 22m images collected every three days, which are then used for irrigation management. Without utilizing DMCii, such an imagery program would be not be possible due to the size of the areas of interest and the limited revisit provided by Landsat Satellites.  A sensor with a very large swath width and a high temporal revisit is required to collect large regions every 3 days.  The DMCii satellite swath width is 600km. For comparison, the State of Colorado is 605km in width.  The large 600km swath combined with the high temporal revisit makes DMCii the ideal choice for large area monitoring.

Within the agriculture vertical, the goal of Airbus Defense and Space is to help our clients meet the needs of a growing planet by helping increase global food production via the use of precision agriculture and remote sensing.

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24

Mar

2015

Taking the Next Step in Learning

Author: Amanda O'Connor

Finding time to take formal training is like trying to milk a parakeet, no one wins. Even with training budgets, goals on performance reviews, and a genuine desire to improve one’s skillset, oftentimes meetings, deadlines, and other job commitments keep people from fulfilling this growth need. I wanted to use this blog as an opportunity to talk about the resources at your disposal to learn more about ENVI and IDL. We have standard training events and onsites, but there are other ways when training has to be more catch-as-catch-can where you can still meet at least some of your objectives.

VIS Training Resources:

1)Formal training courses. This is the gold standard of what Exelis VIS can provide training wise, these are 1-4day courses offered at our regional offices taught by Exelis VIS personnel. 

These courses are available as a part of the software maintenance you pay for ENVI and IDL, so there is NO ADDITIONALCOST for these classes aside from time away and travel if needed.  These courses include:

·        Exploring ENVI

·        Spectral Analysis

·        Spectral Processing Exploitation and Dissemination (PED)

·        ENVI LiDAR

·        ENVI Boot Camp (Geared toward Defense and Intelligence users)

·        Extending ENVI with IDL

·        Introto IDL

·        Scientific Programming with IDL

The course calendar can be found here. These are our public courses, meaning anyone can register.  If you see a course and it doesn’t fit your schedule, let us know, the calendar isn’t written on stone tablets per se and we may be able to add a course to fit your needs.

2) Onsite training. These are courses delivered for a fee at your location. The content can be any of the above courses, a combination of the above, or something that meets your specific needs. We have a lot of material to choose from or can develop custom content. As these courses are delivered for a fee, we typically find the breakeven point, if you were to have people who would otherwise be traveling to us for a no cost class, to be around 4students, depending on your location. So it can be a cost effective solution if you are not near our regional offices (Boulder, CO and Herndon, VA for the US).

3) Webinars. We do webinars roughly once a month covering a specific topic. Most recently we did one with Airbus Defense and Space on Agricultural use of Imagery. The main level webinar directory will easily help you find topics that can help you get up to speed with ENVI or IDL in about an hour. There are “What’s New Webinars” that can help you learn new tools that have recently been released in ENVI 5.2/IDL 8.4, webinars on specific domains, and technical webinars on understanding the quality of the products that you are creating.

4) Short Videos. This includes our White Board Blogger series, short product how-to demonstrations, recorded Google Hangouts and more! I expect this area of the website to grow with more and more how-to materials. Today’s consumer wants to be able to sit down and in 10 minutes know how to do something tangible, at least that’s what I want when I Google “fixing toilet”. We’ll be investing more time to add this type of content, so don’t be shy about letting us know what’s important to you.

 5) The Docs Center. The doc center not only contains all of the ENVI and IDL documentation in a web browser, but helpfully points you to many online tutorials and sample code so you can learn in a self paced manner. These tutorials come with data, so it’s easy to get started and have a self paced learning experience. 

6) Over the Shoulder Training.This is where an Exelis VIS trainer or consultant comes to your site and helps you work through your data or a specific problems. We charge this as a time and materials services, but it can be a tremendous bang for your buck to have someone be able to troubleshoot in your specific environment and your concept of operations.

Well that’s all I have for the moment. I certainly welcome your feedback on other ways for us to communicate how to grow your knowledge and skills with ENVI and IDL. Feel free to email me at Amanda.Oconnor@exelisinc.com.

Follow me on twitter @asoconnor.

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19

Mar

2015

Developing Custom Processing Algorithms with ENVI + IDL - It's So Easy!

Author: Joe Peters


Next week I will be teaching a class here in our Boulder, Colorado, office called Extending ENVI with IDL. Many people may not be aware that our geospatial image analysis software (ENVI) is built on a powerful programming language called IDL. IDL is used across disciplines to extract meaningful information and to create visualizations from complex numerical data. Predecessor versions of IDL were written for analysis of data from NASA missions such as Mariner and the International Ultraviolet Explorer. These predecessor versions of IDL were developed in the1970s at the Laboratory for Atmospheric and Space Physics (LASP) at the University of Colorado, Boulder. The first official version of IDL was released in 1977, by Research Systems Inc. (RSI). IDL has come a long way since it was first created in the 1970s. Our engineers are constantly adding new functionality to IDL to keep it modern, flexible, and user-friendly.

Because IDL has its roots as a programming language geared towards scientists and engineers working with multidimensional arrays of scientific data, it lends itself very naturally to working with raster data from remote sensing systems. In 1994, RSI released the first version of ENVI. Since then, ENVI and IDL have been like inseparable best friends. Whatever IDL can do, ENVI can too. ENVI has an API written in IDL that can be used to create custom algorithms, custom workflows, and for batch processing of remotely sensed data.

The ability of ENVI to be extended using IDL is, in my opinion, the single most powerful aspect of ENVI. The ENVI API has a quick learning curve and once you figure it out, the sky is the limit to what you can do with your data. This includes powerful visualizations of geographic data, such as the one I created below using the CONTOUR function.

One of the more powerful aspects of the ENVI API is the ability to implement custom algorithms.This allows users to apply an algorithm that they have read about in scientific literature or to experiment with their own image analysis algorithms. Let’s go ahead and take a look at how one might implement a custom algorithm in ENVI using IDL. In this example, we will use the Tasseled Cap Transformation (TCT) for Landsat 8. The TCT was originally designed for the Landsat Multi-Spectral Scanner (MSS), which was launched in 1972. Subsequent adaptations of the TCT have been published in scientific literature for the Landsat Thematic Mapper (TM), the Landsat Enhanced Thematic Mapper (ETM) and the Landsat Enhanced Thematic Mapper Plus (ETM+) sensors. The ability to perform the TCT on Landsat MSS, TM, ETM and ETM+ images is available out-of-the-box with ENVI. However,the TCT algorithm for the Landsat Operational Land Imager (OLI) sensor was only recently published and has not yet made it into the ENVI Toolbox. But, because the algorithm is now available in the scientific literature, we can apply this algorithm to Landsat 8 data by using the ENVI API. This algorithm takes a Landsat 8 image and calculates a new image file containing 6 bands, with each band containing unique information about a scene, such as albedo (brightness), vegetation health (greenness), and water content (wetness).

The example code below can be implemented in ENVI + IDL to perform a TCT on Landsat 8 data, as described in an article titled, Derivations of a tasseled cap transformation based on Landsat 8 at-satellite reflectance:

; Copyright (c) 1988-2015, Exelis Visual Information Solutions, Inc. All rights reserved.

pro tasseledcapidl


; Set compile options - this is a standard compatibility line that is recommended for use in all IDL code

compile_opt IDL2

 

; Use the current session of ENVI

e=envi(/current)

 

; Path and filename to a pre-calibrated Landsat 8 image file

; Input file should be radiometrically calibrated and optionally atmospherically corrected

inputRaster = e.OpenRaster('C:\Data\Naples\LC80160422015028LGN00_RadCal_MS_Subset.dat')

 

; Subset the input raster - only these 6 bands are needed for the TCT calculation

subsetRaster=ENVIsubsetRaster(inputRaster, BANDS=[1,2,3,4,5,6])


; Set up output raster file

outputRaster = ENVIRaster(URI=outputURI, INHERITS_FROM=subsetRaster)


; Create tiles - creating tiles allows ENVI + IDL to iterate through the tiles, which is a good idea

; when you want to minimize the impact of image processing on your computer's resources

tiles = subsetRaster.CreateTileIterator(MODE='spectral')


; Iterate through tiles 

FOREACH tile, tiles DO BEGIN

; band 1 

b1 = tile[*,0]

; band 2

b2 = tile[*,1]

; band 3

b3 = tile[*,2]

; band 4

b4 = tile[*,3]

; band 5

b5 = tile[*,4]

; band 6

b6 = tile[*,5]

dims= size(tile,/DIMENSIONS)

data = make_array(dims,/FLOAT)

 

; Use IDL to do the TCT calculations

; Calculate brightness

data[*,0] = (b1 * 0.3029) + (b2 * 0.2786)+ (b3*0.4733) + (b4*0.5599) + (b5*0.508) + (b6*0.1872)

; Calculate greenness

data[*,1] = (b1 * (-0.2941)) + (b2 * (-0.243))+ (b3 * (-0.5424)) + (b4 * 0.7276) + (b5 * 0.0713)

; Calculate wetness

data[*,2] = (b1 * 0.1511) + (b2 * 0.1973)+ (b3 * 0.3283) + (b4 * 0.3407) + (b5 * (-0.7117)) + (b6*(-0.4559))

; Calculate TCT4 (Haze)

data[*,3] = (b1 * (-0.8239)) + (b2 * 0.0849)+ (b3 * 0.4396) + (b4 * (-0.058)) + (b5 * 0.2013) + (b6*(-0.2773))

; Calculate TCT5

data[*,4] = (b1 * (-0.3294)) + (b2 * 0.0557)+ (b3 * 0.1056) + (b4 * 0.1855) + (b5 * (-0.4349)) + (b6 *0.8085)

; Calculate TCT6

data[*,5] = (b1 * 0.1079) + (b2 * (-0.9023))+ (b3 * 0.4119) + (b4 * 0.0575) + (b5 * (-0.0259)) + (b6 *0.0252)


; Write to ouput file

outputRaster.SetTile, data, tiles

 

ENDFOREACH


; Add appropriate Band Names to the HDR file

metadata = outputRaster.METADATA

metadata.UpdateItem, 'BAND NAMES', ['Brightness','Greenness','Wetness','TCT4','TCT5','TCT6']


; Save changes to output file

outputRaster.save


; Display output file in ENVI Display

view=e.getview()

layer=view.createlayer(outputRaster)


end


The input file for this example was captured by Landsat 8 over Naples, Florida, on January 28, 2015. The screen capture below from the ENVI display shows a Color Infrared representation of the scene, along with the computed Brightness, Greenness and Wetness bands from the Tasseled Cap Transformation. This is just one example of how the power of IDL can make implementing custom algorithms so easy.

References:

Muhammad Hasan Ali Baig, Lifu Zhang, Tong Shuai & Qingxi Tong (2014) Derivations of a tasseled cap transformation based on Landsat 8 at-satellite reflectance, Remote Sensing Letters, 5:5, 423-431, DOI: 10.1080/2150704X.2014.915434





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12

Mar

2015

What Do Unmanned Aerial Systems and Clouds Have in Common?

Author: Patrick Collins

They both fly.

But seriously folks...with the recent release by the Federal Aviation Administration of a proposed framework of regulations for Unmanned Aerial Systems (UAS), the amount of geospatial data that will be collected by drones throughout the United States is set to increase. Even while some of the proposed rules limit some of the potential uses for UAS technology, the fact of the matter is that these restrictions may soon be overcome by emerging sense-and-avoid technologies and other burgeoning capabilities within the industry.

As more and more data is captured by these systems, there will be a need to store and disseminate it to those who want it, as well as the ability to derive information from it and serve the resulting answers to those requesting it. This is where the cloud comes in.

So a UAS and a cloud walk into a juice bar...

The UAS says "I've got so much data I don't know where to store it all...Hey, you're a cloud, can you help me out?" And the cloud says "I'd love to but I'm just a cloud, what you need is a server".

But seriously folks...the growing ability for servers to store, analyze, and disseminate large amounts of different types of data will allow businesses looking to capitalize on data collected from UAS to effectively manage their data and provide fast, reliable solutions to their customers. Consumers of geospatial data are often not interested in performing manual analysis on the data in order to derive the information they need. They want accurate solutions delivered to them via simple-to-use interfaces that provide the answers they want without bogging them down with unnecessary steps or irrelevant information. This is where the analytics come in.

So a UAS walks into a juice bar and says to the cloud behind the counter "I've got forty gigabytes of LiDAR data, eighteen multispectral and six hyperspectral images taken over the period of twenty-four months, and some historical rainfall and other weather related data over a specific set of agricultural fields. Can you help me understand how to better manage these fields to reduce my overall operating costs and increase my overall yield?" And the cloud says "What you need are some enterprise analytics, I'm just a cloud working at a juice bar".

But seriously folks...without the ability to host and disseminate different data types, as well as reliable algorithms to perform analysis on data and fuse the derived information into understandable solutions, much of the imagery and other geospatial assets captured by UAS will simply collect dust in a database and never see the light of day.

Luckily, businesses have developed enterprise-level dissemination and analysis technology that allow collectors of geospatial datasets the ability to host and derive information products from their data. By leveraging these technologies, UAS experts can turn their passion for data collection into valuable solutions that address problems across industries.

So the next time you're in a juice bar and see a cloud working behind the counter, don't ask them anything about geospatial analysis, after all, they're just a cloud!

Image 1 Reference: "Letecka fotografie z modelu FPVfoto Vit Svajcr 03" by Vít Švajcr Dobré světlo.com - Own work. Licensedunder CC BY-SA 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Letecka_fotografie_z_modelu_FPV_foto_Vit_Svajcr_03.jpg
#/media/File:Letecka_fotografie_z_model u_FPV_foto_Vit_Svajcr_03.jpg

Image 2 Reference: "Carrot Common 068" bySkeezix1000 - Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons -http://commons.wikimedia.org/wiki/File:Carrot_Common_068.JPG#/media/File:Carrot_Common_068.JPG

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