Remote Sensing in the Cloud – Deriving chlorophyll concentration using HICO IPS

Continuing our review of the algorithms currently implemented in the cloud-based HICO Image Processing System (HICO IPS), here we provide an overview of the two algorithms utilized for deriving estimates of chlorophyll concentration in oceanic water.


Objective – Implement multiple algorithms for estimating chlorophyll concentration, as well as a methodology for evaluating the difference between these algorithms.

Algorithms – Derive estimates of surface chlorophyll-a concentration using one of two ocean color algorithms, OC4 (O’Reilly et al. 2000) or OCI (Hu et al. 2012); or for comparison, the difference between these two algorithms (OC4 – OCI). These algorithms first perform spectral resampling of the HICO hyperspectral data to the multispectral SeaWiFS bands on which the algorithms are based.

Inputs – User specified HICO scene, with optional region-of-interest; optional NDWI land/water mask, with user adjustable NDWI threshold; and specification of desired chlorophyll algorithm.

HICO IPS Chesapeake Bay

Output – Surface chlorophyll-a concentration (mg/m^3) depicted using a blue-red color ramp where blue represents low chlorophyll concentration and red represents high concentration. If the NDWI land/water mask was selected, then chlorophyll concentrations are only calculated and mapped for the water pixels (as is logical).

HICO IPS Chesapeake Bay Chlorophyll

Try it out today for yourself:


Related posts

Introducing the HICO Image Processing System

Calculating a land/water mask using HICO IPS

Evaluating water optical properties using HICO IPS

Characterizing shallow coastal environments using HICO IPS



Hu C, Lee Z, Franz BA (2012) Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, Journal of Geophysical Research, vol. 117(C1), 25 pp.

O’Reilly JE, Maritorena S, O’Brien MC, et al. (2000) SeaWiFS postlaunch calibration and validation analyses, Part 3, NASA Technical Memorandum 2000-206892, vol. 11, 49 pp.

Remote Sensing in the Cloud – Calculating a land/water mask using HICO IPS

Last year we launched the HICO Image Processing System (HICO IPS) – a prototype web application for on-demand remote sensing data analysis in the cloud.


To demonstrate the capabilities of this system, we implemented a collection of coastal remote sensing algorithms to produce information on water quality, water depth and benthic features using example imagery from the HICO instrument on the International Space Station.

As the HICO IPS approaches its one year anniversary, and continues its excellent performance, we’d like to take a moment to highlight each of the algorithms currently implemented in the system.

Here we begin with an overview of the land/water mask utilized in the HICO IPS.

Objective – Implement an automated algorithm for classifying land versus water, thereby masking land pixels from further analysis and allowing subsequent processing steps to focus on just water pixels.

Algorithm – Generates a binary mask differentiating land from water using the Normalized Difference Water Index (NDWI; McFeeters 1996). This algorithm can be implemented on its own, or as a pre-processing step in other algorithm workflows.

Inputs – User specified HICO scene, with optional region-of-interest; and user adjustable NDWI threshold, where -1.0 ≤ NDWI ≤ 1.0, land ≤ threshold < water, and default threshold = 0.0.

HICO IPS Christchurch

Output – Binary land/water mask (0 = land; 1 = water), where land is displayed in the online map using a black mask and water remains unchanged.

HICO IPS Christchurch mask

Reference – McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, vol. 17(7), 1425-1432.

Try it out today for yourself:


Related posts

Introducing the HICO Image Processing System

Deriving chlorophyll concentration using HICO IPS

Evaluating water optical properties using HICO IPS

Characterizing shallow coastal environments using HICO IPS

What’s New in ENVI 5.3

As the geospatial industry continues to evolve, so too does the software. Here’s a look at what’s new in ENVI 5.3, the latest release of the popular image analysis software from Exelis VIS.


  • New data formats and sensors. ENVI 5.3 now provides support to read and display imagery from Deimos-2, DubaiSat-2, Pleiades-HR and Spot mosaic tiles, GeoPackage vectors, Google-formatted SkySat-2, and Sentinel-2.
  • Spectral indices. In addition to the numerous indices already included in ENVI (more than 60), new options include the Normalized Difference Mud Index (NDMI) and Modified Normalized Difference Water Index (MNDWI).
  • Atmospheric correction. The Quick Atmospheric Correction (QUAC) algorithm has been updated with the latest enhancements from Spectral Sciences, Inc. to help improve algorithm accuracy.
  • Digital elevation model. Users can now download the GMTED2010 DEM (7.5 arc seconds resolution) from the Exelis VIS website for use in improving the accuracy of Image Registration using RPC Orthorectification and Auto Tie Point Generation.
  • Point clouds. If you subscribe to the ENVI Photogrammetry Module (separate license from ENVI), then the Generate Point Clouds by Dense Image Matching tool is now available for generating 3D point clouds from GeoEye-1, IKONOS, Pleiades-1A, QuickBird, Spot-6, WorldView-1,-2 and -3, and the Digital Point Positioning Data Base (DPPDB).
  • LiDAR. The ENVI LiDAR module has been merged with ENVI and can now be launched directly from within the ENVI interface.
  • Geospatial PDF. Your views, including all currently displayed imagery, layers and annotations in those views, can now be exported directly to geospatial PDF files.
  • Spatial subset. When selecting files to add to the workspace, the File Selection tool now includes options to subset files by raster, vector, region of interest or map coordinates.
  • Regrid raster. Users can now regrid raster files to custom defined grids (geographic projection, pixel size, spatial extent and/or number of rows and columns).
  • Programming. The latest ENVI release also includes dozens of new tasks, too numerous to list here, that can be utilized for developing custom user applications in ENVI and ENVI Services Engine.

To learn more about the above features and improvements, as well as many more, read the latest release notes or check out the ENVI help documentation.

ENVI 5.3

Application Tips for ENVI 5 – Exporting a Geospatial PDF

This is part of a series on tips for getting the most out of your geospatial applications. Check back regularly or follow HySpeed Computing to see the latest examples and demonstrations.

Objective: Utilize ENVI’s print and export options to generate a Geospatial PDF.

Geospatial PDF

Scenario: This tip utilizes a Landsat-8 scene of California’s Central Valley to demonstrate the steps for creating a Geospatial PDF using two different options: (1) using Print Layout; and (2) using Chip View to Geospatial PDF.

Geospatial PDFs allow you to easily share your geospatial output in standard PDF format while still enabling users to measure distances and identify locations in geographic coordinates, but without need for any specialized GIS or remote sensing software.

Option 1 – Print Layout

  • The Print Layout option requires ENVI 5.0 or later and works only on Windows platforms. It also requires that you launch ENVI in 32-bit mode and have a licensed ArcGIS application on the same system.
  • If you’re looking for the ENVI 32-bit mode (as opposed to the now standard 64-bit mode), it is typically found in either the ‘32-bit’ or ‘ENVI for ArcGIS’ subdirectory of the ENVI directory located under Start > All Programs.
  • Now, using your data of choice, prepare the active View in ENVI as you would like it to appear in the Geospatial PDF. In our example, we simply use a color infrared image of our example Landsat-8 scene. However, if desired, your output can include multiple layers and even annotations.
  • Once you are satisfied with the View, go to File > Print…, and this will launch the Print Layout viewer where you can make further adjustments to your output before exporting it to Geospatial PDF.
  • Note: If the File > Print… option doesn’t produce the desired output in Print Layout (which doesn’t directly support all file types, georeferencing formats or annotation styles), then you can also use File > Chip View To > Print… as another option. The Chip View To option creates a screen capture of whatever is in the active View, so it can accommodate anything you can display in a View, but with the tradeoff that there is slightly less functionality in the Print Layout format options.
  • In our example, for instance, the File > Print… option didn’t support the Landsat-8 scene when opened using the ‘MTL.txt’ file, but instead of using the Chip View To option, as a different workaround we resaved the scene in ENVI format to retain full functionality of Print Layout.
  • Once in the Print Layout viewer, you can apply different ArcMap templates, adjust the zoom level and location of the image, and edit features in the template. Here we made a few edits to the standard LetterPortrait.mxt template as the basis for our output.

ENVI Print Layout

  • To output your results to a Geospatial PDF, select the Export button at the top of the Print Layout viewer, enter a filename, and then select Save.
  • Note that Print Layout can also be used to Print your output using the Print button.
  • You have now created a Geospatial PDF of your work (see our example: CA_Central_Valley_1.pdf). Also, see below for tips on viewing and interacting with this file in Adobe Reader and Adobe Acrobat.

Option 1 – Chip View to Geospatial PDF

  • The Chip View to Geospatial PDF requires ENVI 5.2 or later, but does not require ArcGIS.
  • This option directly prints whatever is in the active View to a Geospatial PDF, so it has fewer options than the Print Layout option, but can still be very useful for those without an ArcGIS license.
  • As above, prepare the active View in ENVI as you would like it to appear in the Geospatial PDF, including multiple layers and annotations as desired. Here we again simply use a color infrared image of our example Landsat-8 scene, but this time include text annotations and a north arrow added directly to the View.
  • Once you are satisfied with the View, go to File > Chip View To > Geospatial PDF…, enter a filename, and then select OK.
  • Note that the Chip View To option can also be used to export your work to a File, PowerPoint or Google Earth.
  • Congratulations again. You have now created another Geospatial PDF of your work (see our example: CA_Central_Valley_2.pdf).

CA Central Valley 2

Viewing Output in Adobe

  • As mentioned, Geospatial PDFs allow you to measure distances and identify locations in geographic coordinates using a standard PDF format. Geospatial PDFs can be viewed in either Adobe Acrobat or Reader (v9 or later).
  • In Adobe Reader, the geospatial tools can be found under Edit > Analysis in the main menu bar. In Adobe Acrobat, the geospatial tools can be enabled by selecting View > Tools > Analyze in the main menu bar, and then accessed in the Tools pane under Analyze.
  • To measure distance, area and perimeter, select the Measuring Tool.
  • To see the cursor location in geographic coordinates, select the Geospatial Location Tool.
  • And to find a specific location, select the Geospatial Location Tool, right click on the image, select the Find a Location tool, and then enter the desired coordinates.

So now that you’re familiar with the basics of creating Geospatial PDFs, be sure to consider using them in your next project. They’re definitely a powerful way to share both images and derived output products with your colleagues and customers.

2015 ISS R&D Conference – Evolution or Revolution

The 2015 International Space Station Research & Development Conference (ISS R&D) took place recently in Boston, MA from July 7-9.

It was an amazing week of insights and information on the innovations and discoveries taking place on board the ISS, as well as glimpses of the achievements yet to come.

2015 ISS R&D

A highlight of the first day was a conversation with Elon Musk, who mused on his initial commercial forays into space, the state of his transformative company SpaceX, and a view of his vision for the future of space travel, research and exploration.

Core topics discussed at ISS R&D 2015 included everything from biology and human health, to materials development and plant science, to remote sensing and Earth observation, to space travel and human exploration. Here are a few of the top highlights:

  • NASA and its partner agencies have transitioned from assembling an amazingly complex vehicle in space to now utilizing this vehicle for the benefit of humanity.
  • The feat of building and maintaining the International Space Station is often underrated and overlooked, but it’s an incredible achievement, and everyone is encouraged to explore the marvels of what has been, and continues to be, accomplished.
  • We are advancing to a future where space transport will become commonplace, and it is the science, humanitarian, exploration and business opportunities that will be the new focus of ISS utilization.
  • The ISS is an entrepreneur engine, as evidenced in part by the rise of the new space economy. For example, new markets are emerging in the remote sensing domain, with NanoRacks, Teledyne Brown Engineering and Urthecast all making investments in expanding Earth observation from the ISS.
  • The future of the ISS, and its continued operation, is a direct function of the success or failure of what is happening on the ISS right now. The greater the success, the brighter the future.

Throughout the week a question was often asked whether the ISS is evolutionary or revolutionary… and in the end the answer was both!

Interested in learning more about the ISS? Visit the recently launched website to “explore the new era of science in space for life on Earth”.

Also, save the data for next year’s conference, which is taking place July 12-14, 2016 in San Diego, California. See you there!

“Space is now closer than you think.”

From here to there – and everywhere – with Geospatial Cloud Computing

Reposted from Exelis VIS, Imagery Speaks, June 30, 2015, by James Goodman, CEO HySpeed Computing.

In a previous article we presented an overview of the advantages of cloud computing in remote sensing applications, and described an upcoming prototype web application for processing imagery from the HICO sensor on the International Space Station.

First, as a follow up, we’re excited to announce availability of the HICO Image Processing System – a cloud computing platform for on-demand remote sensing image analysis and data visualization.

HICO IPS - Chesapeake Bay - Chlorophyll

HICO IPS allows users to select specific images and algorithms, dynamically launch analysis routines in the cloud, and then see results displayed directly in an online map interface. System capabilities are demonstrated using imagery collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on the International Space Station, and example algorithms are included for assessing coastal water quality and other nearshore environmental conditions.

This is an application-server, and not just a map-server. Thus, HICO IPS is delivering on-demand image processing of real physical parameters, such as chlorophyll concentration, inherent optical properties, and water depth.

The system was developed using a combination of commercial and open-source software, with core image processing performed using the recently released ENVI Services Engine. No specialized software is required to run HICO IPS. You just need an internet connection and a web browser to run the application (we suggest using Google Chrome).

Beyond HICO, and beyond the coastal ocean, the system can be configured for any number of different remote sensing instruments and applications, thus providing an adaptable cloud computing framework for rapidly implementing new algorithms and applications, as well as making these applications and their output readily available to the global user community.

However, this is but one application. Significantly greater work is needed throughout the remote sensing community to leverage these and other exciting new tools and processing capabilities. To participate in a discussion of how the future of geospatial image processing is evolving, and see a presentation of the HICO IPS, join us at the upcoming ENVI Analytics Symposium in Boulder, CO, August 25-26.

With this broader context in mind, and as a second follow-up, we ask the important question when envisioning this future of how we as an industry, and as a research community, are going to get from here to there?

The currently expanding diversity and volume of remote sensing data presents particular challenges for aggregating data relevant to specific research applications, developing analysis tools that can be extended to a variety of sensors, efficiently implementing data processing across a distributed storage network, and delivering value-added products to a broad range of stakeholders.

Based on lessons learned from developing the HICO IPS, here we identify three important requirements needed to meet these challenges:

  • Data and application interoperability need to continue evolving. This need speaks to the use of broadly accessible data formats, expansion of software binding libraries, and development of cross-platform applications.
  • Improved mechanisms are needed for transforming research achievements into functional software applications. Greater impact can be achieved, larger audiences reached, and application opportunities significantly enhanced, if more investment is made in remote sensing technology transfer.
  • Robust tools are required for decision support and information delivery. This requirement necessitates development of intuitive visualization and user interface tools that will assist users in understanding image analysis output products as well as contribute to more informed decision making.

These developments will not happen overnight, but the pace of the industry indicates that such transformations are already in process and that geospatial image processing will continue to evolve at a rapid rate. We encourage you to participate.

About HySpeed Computing: Our mission is to provide the most effective analysis tools for deriving and delivering information from geospatial imagery. Visit us at

To access the HICO Image Processing System:

Astronaut Photography – Your access to stunning views from space

Astronauts have busy schedules in space – system operations, maintenance, repairs, science experiments – but did you know they also acquire hundreds of photos during each mission?

Reid Wiseman , Astronaut Photography

From stunning views of Earth’s natural features to glimpses of your favorite city at night, and from pure artistry to applied science, these photos offer a remarkable perspective of our planet’s surface as well as a valuable historical record of how and where our planet is changing.

There are now two great resources available for viewing this photography:

Both websites provide access to thousands of photos, are free to use, allow users to search photos or browse by category, and even provide options to download images for your own use (but be sure to read through the conditions of use on both websites).

We’ve spent countless hours browsing through these stunning image collections, and encourage you to take a look for yourself.

We hope you enjoy!

Gateway to Astronaut Photography of Earth

“The Gateway to Astronaut Photography of Earth hosts the best and most complete online collection of astronaut photographs of the Earth from 1961 through the present. This service is provided by the International Space Station program and the JSC Earth Science & Remote Sensing Unit, ARES Division, Exploration Integration Science Directorate.” –

Windows on Earth

“Windows on Earth is an educational project that features photographs taken by astronauts on the International Space Station.  Astronauts take hundreds of photos each day, for science research, education and public outreach.  The photos are often dramatic, and help us all appreciate home planet Earth. The site is operated by TERC, an educational non-profit, in collaboration with the Association of Space Explorers (the professional association of flown astronauts and cosmonauts), the Virtual High School, and CASIS (Center for Advancement of Science in Space).” –

Windows on Earth featured

HICO Image Gallery – Looking beyond the data

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What’s in an image? Beyond the visual impact, beyond the pixels, and beyond the data, there’s valuable information to be had. It just takes the right tools to extract that information.

With that thought in mind, HySpeed Computing created the HICO Image Processing System to make these tools readily available and thereby put image processing capabilities directly in your hands.

The HICO IPS is a prototype web application for on-demand remote sensing image analysis in the cloud. It’s available through your browser, so it doesn’t require any specialized software, and you don’t have to be a remote sensing expert to use the system.

HICO, the Hyperspectral Imager for the Coastal Ocean, operating on the International Space Station from 2009-2014, is the first space-based imaging spectrometer designed specifically to measure the coastal environment. And research shows that substantial amounts of information can be derived from this imagery.

To commemorate the recent launch of the HICO IPS and celebrate the beauty of our coastal environment, we’ve put together a gallery highlighting some of the stunning images acquired by HICO that are available in the system.

We hope you enjoy the images, and encourage you to explore the HICO IPS web application to try out your own remote sensing analysis.

HICO IPS: Chesapeake Bay Chla

To access the HICO Image Processing System:

For more information on HICO:

Innovations and Innovators in Space – Elon Musk to speak at upcoming ISS R&D Conference 2015

Join us at ISS R&D 2015 – the International Space Station Research & Development Conference taking place in Boston, MA from July 7-9 – to connect with game-changing scientists and other experts who are driving innovation through space research.

This year’s featured keynote speaker is Elon Musk – transformative entrepreneur and space visionary – who will be taking the stage on Tuesday July 7 to share “his thoughts on enabling a new era of innovators through space exploration and the International Space Station.”

Elon Musk Keynote Speaker - ISS R&D 2015

Core topics to be discussed at ISS R&D 2015 include Biology and Medicine, Human Health in Space, Commercialization and Nongovernment Utilization, Materials Development, Plant Science, Remote Sensing/Earth and Space Observation, Energy, STEM Education, and Technology Development and Demonstration.

Are you new to space research? If so, see how space can you elevate your research! There’s a New User Workshop being held on Monday July 6 before the conference begins to introduce interested users to the benefits of conducting research in microgravity and utilizing the ISS for Earth observation.

For more information on the conference:

We look forward to seeing you there.

Sunglint Correction in Airborne Hyperspectral Images Over Inland Waters

Announcing recent publication in Revista Brasileira de Cartographia (RBC) – the Brazilian Journal of Cartography. The full text is available open-access online: Streher et al., 2014, RBC, International Issue 66/7, 1437-1449.

Title: Sunglint Correction in Airborne Hyperspectral Images Over Inland Waters

Authors: Annia Susin Streher, Cláudio Clemente Faria Barbosa, Lênio Soares Galvão, James A. Goodman, Evlyn Marcia Leão de Moraes Novo, Thiago Sanna Freire Silva

Abstract: This study assessed sunglint effects, also known as the specular reflection from the water surface, in high-spatial and high-spectral resolution, airborne images acquired by the SpecTIR sensor under different view-illumination geometries over the Brazilian Ibitinga reservoir (Case II waters). These effects were corrected using the Goodman et al. (2008) and the Kutser et al. (2009) methods, and a Kutser et al. (2009) variant based on the continuum removal technique to calculate the oxygen absorption band depth. The performance of each method for reducing sunglint effects was evaluated by a quantitative analysis of pre- and post-sunglint correction reflectance values (residual reflectance images). Furthermore, the analysis was supported by inspection of the reflectance differences along transects placed over homogeneous masses of waters and over specific portions of the scenes affected and non-affected by sunglint. Results showed that the algorithm of Goodman et al. (2008) produced better results than the other two methods, as it approached zero amplitude reflectance values between homogenous water masses affected and non-affected by sunglint. The Kutser et al. (2009) method also presented good performance, except for the most contaminated sunglint portions of the scenes. When the continuum removal technique was incorporated to the Kutser et al. (2009) method, results varied with the scene and were more sensitive to atmospheric correction artifacts and instrument signal-to noise ratio characteristics.

Keywords: coral reefs; remote sensing; field spectra; scale; ecology; biodiversity; conservation hyperspectral remote sensing, specular reflection, water optically active substances, SpecTIR sensor

Figure 5. Deglinted SpecTIR hyperspectral of Ibitinga reservoir (São Paulo, Brazil) images and resultant reflectance profiles after correction by the methods of: (a) Goodman et al. (2008); (b) Kutser et al. (2009); and (c) modified Kutser et al. (2009).

Streher et al. 2015 Fig 5 Deglint