The Future of Weather Observation is Coming Soon! GOES-R

Author: Joey Griebel

If all goes according to plan, next month the GOES-R weather satellite will be launching and the next generation of weather forecasting, solar activity monitoring, and lightening detection will be here. This advanced satellite will change how quickly and accurately we are able to monitor and predict hazardous weather and help give those in harm’s way the time needed to prepare and evacuate. The GOES-R satellite will include:

Advanced Baseline Imager (ABI) – an advanced imager that has 3 times more channels, 4 times better resolution , and 5 times faster than before. All of this leads to better observation of severe storms, fire, smoke, aerosols, and volcanic ash.

Geostationary Lightening Mapper - The lightening mapper will allow mapping of lightning strikes on ground, as well as lightning in the atmosphere. Researchers have found that an increase in lightning activity may be a sign of tornadoes forming, thus providing the data to detect tornadoes faster.

Space Weather Observation - GOES-R will work with NOAA instruments to gather information on radiation hazards from sun that can interfere with communication and navigation systems, damage satellites, threaten power utilities.


Harris Corporation has been supporting NOAA with some aspects on the GOES-R Satellite construction and will be providing the Ground System Support, as well as the 16.4 Meter triband antenna needed to stay in touch with it.

Now what does this have to do with ENVI? Once GOES-R is operational and collecting data, ENVI will be working to support the Harris Weather groups WXconnect systems for data validation and visualization, supporting the ABI data directly in ENVI, as well as working with NOAA to help continue to create advanced products in the future. It is an exciting time for NOAA with this milestone launch and an exciting time to be working with ENVI to get to work the advanced data that will be coming down!

Below are the baseline products, as well as some future products that can be expected.




Advanced Baseline Imager (ABI)

Absorbed Shortwave Radiation: Surface

Aerosol Detection (Including Smoke and Dust)

Aerosol Particle Size

Aerosol Optical Depth (AOD)

Aircraft Icing Threat

Clear Sky Masks

Cloud Ice Water Path

Cloud and Moisture Imagery

Cloud Layers/Heights

Cloud Optical Depth

Cloud Liquid Water

Cloud Particle Size Distribution

Cloud Type

Cloud Top Height

Convective Initiation

Cloud Top Phase


Cloud Top Pressure

Currents: Offshore

Cloud Top Temperature

Downward Longwave Radiation: Surface

Derived Motion Winds

Enhanced "V" / Overshooting Top Detection

Derived Stability Indices

Flood/Standing Water

Downward Shortwave Radiation: Surface

Ice Cover

Fire/Hot Spot Characterization

Low Cloud and Fog

Hurricane Intensity Estimation

Ozone Total

Land Surface Temperature (Skin)

Probability of Rainfall

Legacy Vertical Moisture Profile

Rainfall Potential

Legacy Vertical Temperature Profile

Sea and Lake Ice: Age


Sea and Lake Ice: Concentration

Rainfall Rate / QPE

Sea and Lake Ice: Motion

Reflected Shortwave Radiation: TOA

Snow Depth (Over Plains)

Sea Surface Temperature (Skin)

SO2 Detection

Snow Cover

Surface Albedo

Total Precipitable Water

Surface Emissivity

Volcanic Ash: Detection and Height

Tropopause Folding Turbulence Prediction

Geostationary Lightning Mapper (GLM)

Upward Longwave Radiation: Surface

Lightning Detection: Events, Groups & Flashes

Upward Longwave Radiation: TOA

Space Environment In-Situ Suite (SEISS)

Vegetation Fraction: Green

Energetic Heavy Ions

Vegetation Index

Magnetospheric Electrons & Protons: Low Energy


Magnetospheric Electrons & Protons: Med & High Energy

Solar & Galactic Protons

Magnetometer (MAG)

Geomagnetic Field

Extreme Ultraviolet and X-ray Irradiance Suite (EXIS)

Solar Flux: EUV

Solar Flux: X-ray Irradiance

Solar Ultraviolet Imager (SUVI)

Solar EUV Imagery


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Categories: ENVI Blog | Imagery Speaks





Just an analyst looking at the world

Author: James Lewis

In most cases as a geospatial analyst I use ENVI and IDL to look at the world in a rather flat and two dimensional aspect when it comes to data analytics. However, LiDAR, drones, advancements in photogrammetry, and temporal analytics within ENVI have given me the ability to analyze data in new ways that were previously too time consuming, difficult, or simply not available.  

I can use tools like the photogrammetry suite to create dense point clouds over areas that it would be difficult to attain the same fidelity, or even fly over with airborne platforms. As small satellites come online they will give us coverage and revisit times unlike anything we’ve had before!

What will we do with all that data?  

Utilizing the spatiotemporal tools in ENVI, we can gain information from days, weeks, months, or years’ worth of collects that classic change detection analytics would not support.

    Point cloud drone collect   Temporal analysis            


LiDAR data has become more available for analyst, and we need to be able to exploit it beyond looking at a bunch of points. Using the ENVI LiDAR tools, you can quickly and easily build robust elevation models to be used for everthing from creating an ortho-mosaic to a line-of-sight analysis. We can also extract 3D models for modeling and simulation, or building scenes for decsion making.


Collada models from ENVI LiDAR

DSM and building vectors from LiDAR point cloud

I can also take advantage of IDL to build out custom tools that enable me to become a more efficient analyst. For example, I can create a tool to ingest a point cloud that gives me multiple products, where before I would have needed to create them individually. I also won’t have to rewrite my code over again when I’m ready to move to the enterprise thanks to the new ENVI task system.

Custom tool combing multiple task

ENVI is not only allowing analyst to respond to various problem sets more efficiently, but also to ENVI enables analysts to do things that just weren’t possible a few years ago. Though the exploitation and types of data is forever changing at a rapid pace, ENVI is leading the charge to remain an industry standard for analytics.

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Categories: ENVI Blog | Imagery Speaks






Could satellites be the secret to detecting water leaks?

Author: Cherie Muleh

As a Channel Manager for Harris Geospatial, I look after our distributors in Australia, Latin America, and Southeast Asia. Distributors sell our products in their respective regions, handle support, training, and also uncover new uses for our products. Esri Australia's Principal Consultant Remote Sensing and Imagery, Dipak Paudyal, is driving new insights with data, and has started investigating a new way that satellites could help detect water leaks.

Water utility companies routinely face millions of dollars in lost revenue with wasted water and leaks in their pipeline infrastructure. In a recent blog, Dipak explores if there is a way for satellite data and location analytics to help preserve water loss and also enable utility companies to better identify cracks in their system. As Dipak notes, “…water utilities that are willing to think outside of the box and investigate new technologies such as SAR imagery will be guaranteed to stay ahead of the game.”

Analyzing all of this data requires the use of specialty tools like ENVI SARscape to help users transform raw SAR data into an easy-to-interpret images for further analysis. Check out Dipak's blog and let us know what do you think? Can we help the water industry better map their resources with this type of technology?

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Choosing the right type of imagery for your project

Author: Jon Coursey

Satellite Imagery is used for a wide range of projects, but often times it is tough to figure out just which type of data is needed. In the world of remote sensing, there are many different satellite sensors, all with different resolutions and multispectral capabilities. In this blog, we will explore some basics of remote sensing, what all is available, and common applications of the data.

Remote Sensing 101

As great as a natural color image can look, it is not collected in the same way as, say, the camera on your iPhone. The process of collecting imagery is most commonly referred to as remote sensing, which basically means objects on earth are detected and classified without direct contact. This blog will focus on passive sensing, as this is what is used for natural color and multispectral images (except radar). Passive sensors gather information via radiation that is either emitted or reflected by whatever object the satellite is trying to capture. This data will be captured with whatever spectral bands a sensor can acquire (generally red, green, blue, and near infrared), then sent down to a ground station for processing. From here, the natural color or multispectral image you might have ordered would be put together.

A wide variety of sensors and band combinations are available, and the best options will depend on what you are trying to view, and the size of your project area. Below are a few situations one might face when deciding how to go about selecting the most suitable captures.

What kind of imagery do I need for a base map?

This is generally our most common request. Engineering, architecture, etc. firms will go for images to view ground conditions before building on their project areas. Essentially, they just need an accurate and high resolution picture. In these cases, natural color is the best option.

How much resolution do I need?

This will come down to how large your project area might be, and what is important that you can view with quality. Say your project requires you to obtain an image of California. For this, you wouldn’t want as high of a resolution as if you needed an image of, say, an airport. This would be an extreme amount of detail for such a large area, and would result in enormous file sizes. Additionally, higher resolution sensors typically capture smaller area sizes, so many would be required to fill such an area. And if full coverage even existed, it would be from images accumulated over the course of many months or years. Lower resolution options will give a better overview of an area of this size, and generally use less images to complete the coverage.

One great option is to obtain high resolution imagery of a project site, and lower resolution for the general area. In the images below you will see captures from both the RapidEye & GeoEye sensors. RapidEye captures at 5 meter (being the lower resolution), and GeoEye captures at 0.5 meter resolution. Both show coverage of islands of Hawaii. As you can see, RapidEye still has great detail for a larger city area, but GeoEye has the ability to see specific features within such a city. 

         Figure 1: GeoEye image (0.5 meter resolution)                   Figure 2: RapidEye image (5 meter resolution)


When would I need the multispectral bands?

The most common uses for these are measuring crop health and doing vegetative analysis. Most sensors available at MapMart will be equipped with four bands, which are red, green, blue and near-infrared (or RGB & NIR). An image made from these four bands within the ENVI software environment is below. For crop health, one would measure relative reflectance along the wavelengths of these bands. When crops are not healthy, one will notice a decreased reflectance, mainly in the near-infrared reflectance plateau, but also with the blue and green regions of the spectrum.

Multispectral bands can also be used to detect different characteristics on the earth’s surface. Soil, plants, asphalt, etc. all have different spectral signatures, which would be apparent through these multispectral bands. Depending on how many bands you have available, and where they are along the light spectrum, you can start to see more differences within each surface. For example, an asphalt rooftop may appear as only one color with the standard RGB & NIR bands available. If you were working with shortwave infrared (or SWIR) as well, you would start to see different colors within your image. This is because the differences in the components of asphalt are now being detected.

While many applications of this type of data exist, we have covered the most common ones we are contacted for at MapMart. Fortunately, with such a large archive of satellite imagery already existing, with the opportunity to have more collected, a solution can be found for the most unique type of geospatial project. If you need data or help figuring out what type to use for a project, contact us at MapMart.com.

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Categories: ENVI Blog | Imagery Speaks






Using Attributes to Improve Image Classification Accuracy

Author: Jason Wolfe

In this article I will show an example of how you can improve the accuracy of a supervised classification by considering different attributes.

Supervised classification typically involves a multi-band image and ground-truth data for training. The classification is thus restricted to spectral information only; for example, radiance or reflectance values in various wavelengths. However, adding different types of data to the source image gives a classifier more information to consider when assigning pixels to classes.

Attributes are unique characteristics that can help distinguish between different objects in an image. Examples include elevation, texture, and saturation. In ENVI you can create a georeferenced layer stack  image where each band is a separate attribute. Then you can classify the attribute image using training data.

Creating an attribute image is an example of data fusion in remote sensing. This is the process of combining data from multiple sources to produce a dataset that contains more detailed information than each of the individual sources.

Think about what attributes will best distinguish between the different classes. Spectral indices are easy to create and can help identify different features. Vegetation indices are a good addition to an attribute image if different vegetation types will be classified.

Here are some other attributes to experiment with:

  • Texture measures (occurrence and co-occurrence)
  • Synthetic aperture rader (SAR) data
  • Color transforms (hue, saturation, intensity, lightness)

Including elevation data with your source imagery helps to identify features with noticeable height such as buildings and trees. If point clouds are available for the study area, you can use ENVI LiDAR to create digital surface model (DSM) and digital elevation model (DEM) images. Use Band Math to subtract the DEM from the DSM to create a height image, where pixels represent absolute heights in meters. In the following example, the brightest pixels represent trees. You can also see building outlines.

Example height image at 1-meter spatial resolution

Locating coincident point cloud data for your study area (preferably around the same date) can be a challenge. Luckily I found the perfect set of coincident data for a classification experiment.


The National Ecological Observatory Network (NEON) provides field measurements and airborne remote sensing datasets for terrestrial and aquatic study sites throughout the world. Their Airborne Observation Platform (AOP) carries an imaging spectrometer with 428 narrow spectral bands extending from 380 to 2510 nanometers with a spectral sampling of 5 nanometers. Also onboard is a full-waveform LiDAR sensor and a high-resolution red/green/blue (RGB) camera. For a given observation site, the hyperspectral data, LiDAR data, and high-resolution orthophotography are available for the same sampling period.

I acquired a sample of NEON data near Grand Junction, Colorado from July 2013. My goal was to create an urban land-use classification map using an attribute image and training data. For experimentation and to reduce processing time, I only extracted the RGB and near-infrared bands from the hyperspectral dataset and created a multispectral image with these bands. I used ENVI LiDAR to extract a DEM and DSM from the point clouds. Then I created a height image whose pixels lined up exactly with the multispectral image.

I created an Enhanced Vegetation Index (EVI) image using the Spectral Indices tool. Finally, I combined the RGB/NIR images, the relative height image, and the EVI image into a single attribute image using the Layer Stacking tool.

Next, I used the Region of Interest (ROI) tool to collect samples of pixels from the multispectral image that I knew represented five dominant classes in the scene: Water, Asphalt, Concrete, Grass, and Trees. I used a NEON orthophoto to help verify the different land-cover types.

I ran seven different supervised classifiers with the multispectral image, then again with the attribute image. Here are some examples of Maximum Likelihood classifier results:

Maximum Likelihood classification result from the NEON multispectral image

Notice how the classifier assigned some Building pixels to Asphalt throughout the image. 

Here is an improved result using the attribute image. Those pixels are correctly classified as Building now.

Maximum Likelihood classification result using the attribute image

A confusion matrix and accuracy metrics can help verify the accuracy of the classification.

Confusion matrix calculated from the attribute image classification

The following table shows how the Overall Accuracy value is higher with the attribute image when using different supervised classifiers:

  Multispectral image  Attribute Image 
 Mahalanobis Distance  72.5  83.8
 Minimum Distance  57.69  95.22
 Maximum Likelihood  91.3  98.91
 Parallelepiped  57.18  95.8
 Spectral Angle Mapper  54.97  61.79
 Spectral Information Divergence  62.1  66.93
 Support Vector Machine  85.03  99.16

The accuracy of a supervised classification depends on the quality of your training data as well as a good selection of attributes. In some cases, too many attributes added to a multispectral image can make the classification result worse, so you should experiment with what works best for your study area. Also, some classifiers work better than others when considering different spatial and spectral attributes. Finally, you may need to normalize the different data layers in the attribute image if their pixel values have a wide variation.

New tutorials will be available in ENVI 5.4 for creating attribute images and for defining training data for supervised classification.


National Ecological Observatory Network. 2016. Data accessed on 28 July 2016. Available on-line at http://www.neonscience.org from National Ecological Observatory Network, Boulder, CO, USA.

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Categories: ENVI Blog | Imagery Speaks


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