Author: Matt Hallas
Often when I'm teaching an ENVI class or in communications with a customer I am asked the question, “Where can I get data?!” My typical response in the past was to list sites such as GLOVIS and OpenTopo where a wide-array of free data, usually at low spectral resolution, is available to for download. Next, I would rattle off the various commercial satellite organizations. While free data is truly a wonderful thing, often you need your data faster and at a higher spatial resolution than what is available for free.
Companies like DigitalGlobe and Airbus Defense and Space are at the forefront of the commercial imagery sector, with the well-known WorldView and Pleiades constellations, respectively. These constellations are just the tip of the iceberg when it comes to high resolution imagery with extremely short revisit times. WorldView-3 boasts a revisit time of less than one day with roughly 1.24 meter pixel resolution and Pleiades-1 has a similar revisit time with 2-meter pixel resolution for the multispectral bands. Wouldn’t it be nice to have a one-stop-shop for all of your data needs, where you can search what's available based on the area of interest, data type or sensor, and then acquire your data quickly so you can start your work ASAP? Enter the IntelliEarth Marketplace.
Harris boasts a truly massive library of data, as well as on-demand access to LiDAR, SAR, raster and vector data through the IntelliEarth Marketplace. Through this online marketplace, you can purchase data in its raw form or have it processed as much or as little as you would like. Many people want a product that has been just orthorectified. We've got that covered. Other folks want even more sophisticated visual/simulation dataset models. No problem, just look to our Services page! There is a large amount of data available immediately for download while other products need to be ordered. After placing your order, you'll receive it in anywhere from a couple of hours to a few days. The IntelliEarth Marketplace gives you access to:
The variety and most notably the quality of the data products provided through this online marketplace really help to differentiate the store from other options. What's more is that only the IntelliEarth Marketplace provides immediate download of worldwide 30cm+ resolution imagery from DigitalGlobe and Airbus Defense and Space.
The best place to start when you are looking for data is to check out the Data Store webpage, where you will see the variety of data types and products available. Once you know whether or not you want a DEM, raster data, perhaps some LiDAR data, or whatever, then view the available products found here. After selecting ‘More Information’ about your desired product you will see the sensors available for that data type, as well as various options for downloading/ordering what you need. This screenshot shows the various sensors available for ‘Satellite Imagery’.
Once you have perused the large library of datasets available through the marketplace and acquired the data, you need a tool to work with your information. ENVI and IDL have the ability to ingest and analyze all of the data available through the store (and much more, of course). So not only do you have access to the most recent imagery possible, you have the analytical tools required to get the job done.
This harmony between data and analytics is at the forefront of the recent acquisition of Exelis Inc. by the Harris Corporation. With the new Harris your problem is fully supported from beginning to end. You'll get access to the best possible data products through the IntelliEarth Marketplace, as well as best-in-class analytic and feature extraction tools contained within ENVI and IDL. We’re excited to work more closely with customers on the problem of data acquisition, and continue our great track record of helping our customers get the most out of their data as possible. Take some time to explore this marketplace and see what problems can be solved with our wide assortment of products available. As always don't hesitate to reach out to me with questions - email@example.com.
Categories: ENVI Blog | Imagery Speaks
Tags: IDL, ENVI, GIS, Academic, Feature Extraction, Image Analysis, Image Processing, Spectral Analysis, multispectral imagery, hyperspectral imagery, DEM, NDVI, geospatial, GEOINT, LiDAR, Visualization, data analysis, geospatial data, data, Data Store, IntelliEarth Marketplace
Short Wave Infrared Data allows an image analyst to "see" beyond what human eye can perceive. This blog assumes a basic knowledge of absorption features and SWIR data. Follow this link if you would like a refresher on SWIR data. The range of light that humans can perceive is roughly 400-700 nanometers, an incredibly small range when you look at the whole scope of the electromagnetic spectrum. Analyzing the reflectance of photos in the near infrared and short wave infrared ranges is at the heart of photogrammetry and spectroscopy. We can use SWIR data to understand the health of our plants, but also potentially identify disease vectors, prime harvest time, ensure proper fertilization, etc.
For decades researchers have understood the importance of collecting near-infrared data when analyzing the health of vegetation, but it is only since about the mid-1990s that the research community began working heavily with SWIR data to understand how the surface reflectance can indicate vegetation health, carbon content, nitrogen content, etc. The analysis of SWIR data can be of great use in the fields of agriculture and forestry.
One researcher, Greg Asner, helped improve the understanding of how light interacts with vegetation and how we can use optical imagery to better understand plant health. His paper, Biophysical and Biochemical Sources of Variability in Canopy Reflectance (Asner 1998), helped form a basis of understanding how plants interact with light. He took this further with another paper focused on SWIR data as it applies to vegetation titled: A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation (Asner and Lobell, 2000). We will dive into just one aspect of this paper and visualize a finding made by Asner and Lobell using ENVI; delineating between senescent vegetation and soil using an absorption feature only present in the SWIR spectral region.
The SWIR range is roughly 1400-3000 nanometers. Historically this range of the EM spectrum has been used for material detection and identification in the fields of minerology, geology and land management. In the late 1990's work improved on identifying specific ranges within the SWIR that can be used to determine vegetative health, water content, carbon content, etc. The figure you see below contains reflectance spectra for water and lignin + cellulose in the visible, near-infrared and short-wave-infrared regions. As you can see, many of the identifiable absorption features are found in the SWIR spectral region.
The visible range of the EM spectrum can be used to identify the pigment of your vegetation. Healthy leafy-plants appear green because of a higher concentration of Chlorophyll a and b, stressed and senescent vegetation appears more yellow because of an increase presence of carotenoids, and vegetation can appear more red because of anthocyanins in newly-forming leaves, which increases with senescence. A great example of using pigment to identify vegetation health and senescence is autumn, when billions of leaves change color from a vibrant green to a yellow/red hue as they decay before dying all-together. Although understanding the pigment of your plant is essential, we cannot identify water content, nitrogen content or carbon content using only the visible range. In the figure below the second plot shows the reflectance of a green leaf, yellow leaf and a red leaf; you can clearly see how the profiles differ depending on the chlorophyl, carotenoid and anthrocyanin content.
(Plot courtesy of D.A. Sims, J.A. Gamon, Remote Sensing of Environment1)
The SWIR range of the EM spectrum allows the user to see certain absorption features very clearly due to certain materials only displaying absorption features in that range, which allows you to determine vegetation health as well as delineate between soil and decaying canopy. As we stated above you can identify the relative health of a plant based off of how green/yellow/red the leaves appear. Looking at a spectral profile comparing decayed leaf spectra and soil in the visible range it is relatively difficult to see the difference in surface reflectance. When you include portions of the SWIR range in the spectral profile the surface reflectance values are clearly different. The top plot shows the mean leaf hemispherical reflectance and trasmittance properties collected from sites in the US and Brazil of a woody plant species, and the bottom plot shows reflectance and transmittance for herbaceous species. The plot below shows the increase in absorption features and spectral features present in the SWIR compared to the Visible and NIR range.
(Plot courtesy of G. Asner, Remote Sensing of Environment2)
According to some of the research performed by Dr. Asner and his colleagues, "soil spectra collected by Asner (1998) had a distinctive absorption feature centered near 2,200 nm, which results from combinations and overtones of hydroxyl absorption in the clay lattice structure of soils that dominate many arid and semi-arid environments." (Asner, 2000). What this means in layman’s terms is that the absorption feature we see in soil surface reflectance at 2,200 nm is due to the presence of clay, and this fact can make it very easy to delineate between soils and decaying canopy. Decaying canopy will have lignin and cellulose while soil will not, and conversely the soil spectra will contain the clay absorption while the decaying canopy spectra will not. Look at the video below to fully understand this concept.
ENVI can easily display spectra from libraries as well as from your own pixel data. Identifying these spectral features and then understanding what causes them is essential to any advanced image analysis workflow. This is but a small demonstration of the power of SWIR data focusing on a single absorption feature. For more information be sure to peruse the many papers published on SWIR data as it applies to vegetation and agriculture.
I will be going into much greater detail regarding SWIR data as it pertains to ENVI and vegetation analysis during a webinar at 9 am MST Thursday, December 3rd 2015. Sorry if you missed the live webinar but you can follow this link to see a recording of the Webinar focused on using DigitalGlobe’s SWIR product within ENVI.
1 - Sims, Daniel A., and John A. Gamon. "Relationships between Leaf Pigment Content and Spectral Reflectance across a Wide Range of Species, Leaf Structures and Developmental Stages." Remote Sensing of Environment 81.2-3 (2002): 337-54. Web. 24 Nov. 2015.
2 - Asner, Gregory P. "Biophysical and Biochemical Sources of Variability in Canopy Reflectance." Remote Sensing of Environment 64.3 (1998): 234-53. Web. 24 Nov. 2015.
3 - Asner, Gregory P., and David B. Lobell. "A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation." Remote Sensing of Environment 74.1 (2000): 99-112. Web. 24 Nov. 2015.
Tags: IDL, ENVI, GIS, NASA, Remote Sensing, Image Analysis, Image Processing, Spectral Analysis, climate change, USGS, Satellite Images, geospatial imagery, NOAA, NDVI, GEOINT, vegetation analysis, infrared, environmental monitoring, Visualization, geospatial data, Hyperspectral, Geospatial Analysis, earth observation, multispectral, UAS, vegetation, SWIR, Short-Wave Infrared
Several months ago I published a short blog detailing our new online training option known as computer based training (CBT). Since many companies are dealing with tighter travel budgets, we decided to start offering training on the latest and greatest innovations in the ENVI and IDL product lines from the comfort of your office or home. While nothing can replace having an extremely knowledgeable instructor right there for hands on help, CBT's allow users to take a wide variety of training courses from anywhere with an internet connection (if you will be without an internet connection these CBT's can be saved to disk). This will save you countless hours of travel as well as the time it takes to sift through material you may not need. Since our CBTs are short modules, you'll have the flexibility to pick and choose what you want to explore. The training gives you the flexibility to quickly hone and learn that skills you need for the task at hand.
Currently we have three CBT's on our training page that can be found here. The first training is great for those of you that are new to ENVI software and remote sensing in general. Once you have gotten yourself acquainted with the basics of the application, the next two CBT's offer more comprehensive trainings and discussions on important topics: Working with SWIR data; and, identifying and detecting materials using Hyperspectral Imagery.
The only thing required to access this online training is a login to the exelisvis.com website. Once you have received this login you will be ready to start training! In order to make the training more applicable to the real world, we provide a training dataset for each CBT so if you have an ENVI license you can follow along with the CBT step-by-step. We are constantly updating the CBT page with new training modules, so be sure to check back regularly to see if there is a topic pertinent to your workflow, or if you are just curious about how to work with a certain data type within ENVI/IDL.
As always please do not hesitate to shoot me questions or comments at firstname.lastname@example.org.
Tags: IDL, ENVI, Remote Sensing, hyperspectral imagery, Training, HSI, Computer Based Training, CBT, SWIR, Online Training
These past few days I attended the ENVI Analytics Symposium and had the distinct pleasure of listening to some thought-provoking and inventive presentations from people in the geospatial industry trying to solve the big problems facing us today. These problems come in the form of data bottlenecks that will only be made worse with the continued deployment of new sensors, to locating and attempting to quantify human rights violations using satellite imagery. We have so much information at our fingertips at this point but people are struggling to pick and choose what is the important data that can help solve a complex issue, and what is simply taking up space on our storage devices.
One issue that has me pondering about the future of our society is that of global security. The National Security Strategy for 2015 was published in February of this year and the forward by President Barack Obama highlights a major shift in the idealogies of global governments, "Moreover, we must recognize that a smart national security strategy does not rely solely on military power."
When one thinks about national security they probably picture F-16s, Kilo-class nuclear submarines and quantifiable military strength. We are shifting the paradigm to realize that a strong national security strategy incorporates the idea that the climate, education, healthcare, and diplomatic strength of our country is an integral part of what makes up our total national security. This point was brought up by a man who knows a thing or two about our national strategy, former head of the National Geospatial Agency, Vice Admiral Robert Murrett (Ret.).
Vice Admiral Robert Murrett (Ret) moderated"The Role of Analytics in Global Security Issues" panel at the 2015ENVI Analytics Symposium.
After delivering the keynote address, Vice Admiral Murrett then led a series of panel discussions that helped to extract the big issues facing global securities. The panelists, Dr. Andrew Marx with the Claremont Graduate University, Dr. John Irvine with the Charles Stark Draper Laboratory, and Dr. Alex Philp of Adelos, Inc., all work in the realm of global security and had some fascinating insight.
Dr. Marx's work focuses on monitoring human rights violations throughout the world using medium-resolution imagery sensors such as Landsat. By developing a baseline average of what a pixel "looks like" over a number of years, his research team has been able to identify the location of SCUD missile attacks in Syria with 90% accuracy. Identifying the location of human rights violations as soon as possible can help with convictions of war crimes as well as the distribution of aide and support the affected regions.
Dr. Philp delivered a fascinating presentation titled the "Internet of Things", which mainly focused on the massive increase in device connectivity that will be attained in the coming 5-10 years. Two things that Dr. Philp brought up in the panel that resonated with me are, "we don't need everything forever" and that eventually "we will run out of time". This makes sense in terms of global security because if analysts are too over-burdened with an overwhelming amount of information they will be less effective at accomplishing their main task. Being able to come up with some sort of "probabilistic interpretation" of our data will be required in order to actually maintain the flow of information into products. The sheer amount of data we will be dealing with in the coming years is truly overwhelming and it will be necessary to filter out the data which is not helpful as early in the workflow as possible.
Dr. John Irvine then piggybacked on this concept to discuss how there needs to be much better coordination across analyses so that when we have discovered something of value, this work is not duplicated or ignored. Overall, these four gentlemen helped to shed light on the many issues which comprise Global Security and the work that will need to be done to assure we have global food and water secutiry, among other factors.
DigitalGlobe has been pushing the boundaries of commercially available satellite imagery for years, and the addition of the WorldView-3 sensor to their satellite constellation has image scientists giddy with excitement. Even though WorldView-3 has been airborne for nearly one year at this point (August 13, 2014 launch date) there is not much information on the web regarding the use of the SWIR bands, we hope to rectify that.
SWIR extends beyond the near-infrared region of the electromagnetic spectrum and refers to non-visible light falling roughly between 1400 and 3000 nanometers. The benefits of collecting reflectance data in these wavelengths are vast, including improved atmospheric transparency, snow and ice distinction, smoke penetration and the identification of man-made materials. For our purposes in this blog we will be highlighting how SWIR data allows an analyst to easily delineate between man-made materials, in our case materials used for roofing.
When you look at an image in true-color you are viewing what the human eye would see from a plane or a helicopter and this is great for spatial context. However, oftentimes the absorption features which define a material will only be apparent in the short wave infrared region of the electromagnetic spectrum, you cannot see these differences in true-color but a sensor collecting data beyond the visible range will detect these differences.
The image below was created with a true color composite of multispectral imagery provided by DigitalGlobe over Fullerton, California. With the MSI it appears as though our two rooftops are made of the same white material. Even when we display a spectral profile of the two rooftops it appears from the multispectral imagery that the pixels simply vary in brightness and contain similar absorption features. If only there was a way we could expand the extent of the x-axis to include more of the electromagnetic spectrum....
The image below is created by displaying SWIR 2 (1570 nm) as Red, SWIR 1 (1210 nm) as Blue and SWIR 8 (2330 nm) as Green, over the same extent as the image above. The pixel size is larger with SWIR data compared to MSI data, but the added coverage of the electromagnetic spectrum in the SWIR data makes up for the pixel size. The first apparent difference in the image below is that these two rooftops are very clearly different colors, and this is due to the different reflectance values seen in the currently displayed SWIR bands. The roof on the right appears purple because those pixels have high reflectance values in the bands being displayed as blue and red, SWIR 1 and SWIR 2 in our case. The roof on the left appears yellow because those pixels have high reflectance values in the bands being displayed as red and green, SWIR 2 and SWIR 8 in our case. The difference in reflectance values in the SWIR 1 band and the SWIR8 band is what is allowing us to view the difference in material-type for these rooftypes. Without a sensor that collects reflectance data beyond 1400 nanometers we would have difficulty identifying the differences in these materials. You can easily tell from the spectral plots of these two roofs that they are very different materials. If we only had the MSI data then we would not have the same spectral detail and thus have a more limited ability to delineate between these man-made materials.
SWIR data allows you to see what the human eye cannot. Oftentimes an object or feature will appear homogeneous with multispectral imagery, but with short wave infrared imagery the features are clearly different. The ability to augment your results by having data covering more of the electromagnetic spectrum will only help you to create better products down the road, and the ENVI suite of tools will help to exploit this added information.
To make this even more relevant many of the images we will be seeing of Pluto and its moons, collected by the New Horizons NASA mission, will be collected within the SWIR range of the electromagnetic spectrum. In fact the first detailed image released was a false-color composite created from SWIR bands which helped to show the presence of large methane-ice deposits on the surface of Pluto. For more information on this mission and how infrared imagery will lead the way in determining the chemical composition of Pluto and its moons go to the New Horizons page on the JHUAPL site.
The figure above is courtesy of NASA-JHUAPL-SwRI
Tags: ENVI, GIS, Feature Extraction, News, Esri UC, Remote Sensing, Image Analysis, Image Processing, geospatial imagery, multispectral imagery, geospatial, Tutorial, environmental monitoring, data processing, Visualization, data analysis, DigitalGlobe, WorldView-3, SWIR, short wave infrared, Pluto, Charon, Methane-Ice, New Horizons
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