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.
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.
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 Optical Depth
Cloud Liquid Water
Cloud Particle Size Distribution
Cloud Top Height
Cloud Top Phase
Cloud Top Pressure
Cloud Top Temperature
Downward Longwave Radiation: Surface
Derived Motion Winds
Enhanced "V" / Overshooting Top Detection
Derived Stability Indices
Downward Shortwave Radiation: Surface
Fire/Hot Spot Characterization
Low Cloud and Fog
Hurricane Intensity Estimation
Land Surface Temperature (Skin)
Probability of Rainfall
Legacy Vertical Moisture Profile
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)
Total Precipitable Water
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
Magnetospheric Electrons & Protons: Low Energy
Magnetospheric Electrons & Protons: Med & High Energy
Solar & Galactic Protons
Extreme Ultraviolet and X-ray Irradiance Suite (EXIS)
Solar Flux: EUV
Solar Flux: X-ray Irradiance
Solar Ultraviolet Imager (SUVI)
Solar EUV Imagery
Categories: ENVI Blog | Imagery Speaks
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
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
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.
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?
Tags: SAR, ENVI SARscape, utilies
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
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.
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:
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:
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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