Fast forward to present day (when nothing is free) and I can’t help but get excited about the recent launch of SPOT 6. Once the SPOT constellation is complete (with the launch of SPOT 7 in 2013), we will have access to multispectral imagery with “high resolution and large-area coverage” – with a one-day revisit time!  Some .jpg images are already available for download ftp://ftp.astrium-geo.com/spot6/BoraBora_SPOT6_2012.jpg .

I’ve zoomed-in to an area in this image of Bora Bora to what looks like a very nice resort destination. I think I may need to visit sometime.

As a refresher, impervious surfaces are paved or hardened surfaces that do not allow water to pass through, and also refers to the general inability of a surface to allow water to percolate through.   Impervious surfaces are mainly artificial structures–such as pavements (roads, sidewalks, driveways and parking lots) that are covered by impenetrable materials such as asphalt, concrete, brick, and stone–and also rooftops fall into this category. Soils compacted by urban development are also highly impervious.

In ENVI, one of the easiest methods to identify impervious surfaces is through the Feature Extraction module, which uses an object based image analysis method to find and extract feature from an image. ENVI uses all spectral, texture, and spatial values in the image to find contiguous regions of similar values to group into segments.  I have access to all of the attributes in the image – for every segment outline, ENVI calculates spectral, textural, and spatial attributes that I can view, threshold against, and set parameters for to build a set of rules that define my feature or features.  In my example I selected the average of band 3 attribute, which is a brightness threshold, as most of the impervious surfaces in the image I was using were bright white. I also used the texture mean attribute, as the texture or roughness of the roads, driveways, and roofs is going to be very different from the texture of the surrounding vegetation and fields. Finally, I added an area parameter, to rule out perhaps any very small features or to isolate features of a certain size.  These three parameters worked quite well to identify the impervious surfaces in that three-band NAIP image as you can see in the picture below.

However, if you have access to some of the other data that I mentioned in the webinar, such as Landsat or ASTER, you can take advantage of the near infrared band of data and add that to your analysis.  Using the near infrared and red bands of data, ENVI can calculate a vegetation index within the feature extraction workflow. What that means is that you can easily threshold against any vegetation in the image, allowing you to focus on the rest of the non-vegetated features that you’re interested in.

Have any of you tried thresholding for or against vegetation to improve the quality of your results?

Remote sensing capabilities have improved dramatically in the last 40 years. The following is a brief overview of the history of earth observing multispectral sensors and how improvements in instrument design and spectral resolution have increased the application areas and types of analysis that can be performed using satellite imagery. The discussion starts at the beginning of the NASA Landsat program in the 1970s and extends through present day with commercial sensors operated by companies such as GeoEye and DigitalGlobe.

NASA launched Landsat 1 in 1972, which carried both a camera system and the first MSS sensor, a multispectral instrument with four bands (green, red and two in the near-infrared) with approximately 60m spatial resolution (resampled from a larger 68m x 83m field of view). With this launch began a revolution in the field of remote sensing. Although limited by today’s standards, Landsat MSS opened many doors for image analysis of the Earth’s surface. Importantly, based on the longevity and fidelity of the Landsat program, as of this year there are now 40 years of image data available for analysis.

NASA went on to launch Landsat 2 and 3, in 1975 and 1978 respectively, with MSS sensors containing nearly identical spectral and spatial characteristics as the MSS on Landsat 1. The only exception was that the MSS on Landsat 3 included a fifth band for measuring the thermal-infrared. In terms of spatial scale, the 60m resolution of these MSS instruments equates to a pixel size approximately equivalent to half of a professional soccer field. Many important analysis techniques were initiated in these early years. For example, the normalized difference vegetation index, which provides a relative measure of live green vegetation and is still used widely today, originated during the early years of the NASA Landsat and NOAA AVHRR programs.

The Landsat program next progressed to Landsat 4 and 5, launched in 1982 and 1984 respectively, which carried both the MSS sensor and a TM sensor. The newer TM sensor included a total of seven bands, six bands with 30m resolution (blue, green, red, one near-infrared, and two in the mid-infrared) and one band with 120m resolution (thermal-infrared). Landsat 6 was launched in 1993 but unfortunately failed to reach orbit. The most recent Landsat satellite, Landsat 7, was launched in 1999. This satellite carries the ETM+ sensor, which includes eight bands, the same seven bands as the TM sensor, with the addition of a panchromatic band at 15m resolution and an improved spatial resolution for the thermal-infrared band at 60m. Leveraging the 15m scale of the panchromatic band, the ETM+ sensor can be used to represent a ground area equivalent to a tennis court or moderately sized house, which is significantly improved from Landsat 1. This increase in spatial resolution and the accompanying greater number of bands achieved throughout the life of the Landsat program has enabled scientists to address an ever increasing array of Earth remote sensing applications, from agriculture and forestry to coral reefs and urban development.

There are plans for a Landsat Data Continuity Mission, to be launched in 2013, as well as numerous other government satellite programs, both within the U.S. and internationally; however, development in recent years has also moved into the commercial realm. Beginning with the IKONOS satellite [GeoEye], which was launched in 1999, satellite imagery was no longer limited to just government programs but now available from a commercial provider. IKONOS contains five total bands, four multispectral bands at 3.2m resolution (blue, green, red, near-infrared) and one panchromatic band at 0.8m resolution. Additional options now available on the market include: GeoEye-1 [GeoEye] (launched 2008; four multispectral bands at 1.6m and one panchromatic at 0.4m); QuickBird [DigitalGlobe] (launched 2001; four multispectral bands at 2.4m and one panchromatic at 0.6m); WorldView-1 [DigitalGlobe] (launched 2007; one panchromatic band at 0.5m); and WorldView-2 [DigitalGlobe] (launched 2009; eight multispectral bands at 1.8m and one panchromatic at 0.5m). This remarkable progression in resolution is enabling another leap forward in what can be accomplished using satellite imaging, literally putting global imagery in the palm of your hand.

As a parting thought, consider this… with today’s commercial satellites, it is now possible to say that you are a pixel. And if you’re lying down somewhere, such as on the beach, you’re about 4 pixels. It’s amazing technology.

As one would expect, ENVIUS is anchored by topics that explore methods for advanced spectral target detection and feature extraction from geospatial imagery. However, this year’s conference is the first to offer in-depth attention to delivering image analytics in an online, on-demand environment. I’m eagerly anticipating the dialog between image scientists and enterprise developers as the challenges of bringing complex image analysis workflows to the web environment are discussed.

Will cloud, web apps, big data and server side processing be the buzzwords of this year’s symposium?

If you are eligible to access classified information, you could find out. But hurry up, because today is the last day to register.

Event Date: 6 September 2012

Location:  Benjamin Banneker Room, National Geospatial-Intelligence Agency, New Campus East

Classification: TS//SI//TK

Registration: http://www.tinyurl.com/ENVIUS2012

Fee: There is no fee for attending ENVIUS 2012

The instruction typically takes one of two forms.  An evening course located on campus where the hands on instruction of remote sensing is done in a lab, or an online course where the technical work is done at home using student licenses.  With many of my clients, they are reporting increased enrollment and many have reached capacity and begun wait lists.  Penn State University is one such institution that has been making geospatial education available online.  Since 1999 PSU has been enabling busy professionals to learn new skills and thought processes surrounding GIS and Image Analysis.  As more programs are created, it will only increase the revolution in geospatial technology that is happening all around us.

Are you a geospatial professional adding remote sensing to your skill set?  How has remote sensing or image analysis aided your job?

Geospatial technology and imagery are now pervasive in our society. From the GPS device on your car’s dashboard and your local weather report to national maps of drought conditions and global analysis of the earth’s environment, geospatial data is playing an increasingly central role in our lives. As the importance and availability of geospatial data continues to grow, so too does the need to process greater volumes of data at faster rates to provide output in a timely manner. The result is an accompanying need for increased utilization of high-performance computing to meet these processing demands, an area where GPU computing is certain to play a significant role.

So you have some data – in fact you have a hard-drive filled to its storage capacity with more imagery than your computer can seemingly process in a year – and you can’t wait that long to get results. Let’s look at some example software options of how you can start using GPU computing to accelerate your image processing workflow.

One option is to utilize commercial software packages that inherently employ GPU processing as part of their architecture. For example, in the field of graphic design, Adobe Photoshop versions CS4 and later use GPU computing to speedup certain functions. An equivalent in the geospatial field is the GXL GeoImaging Accelerator (PCI Geomatics), which provides GPU-enabled acceleration for applications such as orthorectification, image mosaic creation, and pan-sharpening. However, widespread integration of GPU capabilities in commercial geospatial software is not yet fully realized, and thus many algorithms and processing options still await acceleration.

As an alternative, users can select to employ a high-level programming language to generate their own applications. For example, GPULib (Tech-X) provides a library of GPU-accelerated IDL functions that can be used to customize ENVI (Exelis VIS). Note that IDL is the language on which ENVI is built and also the foundation for developers to create custom modules that integrate directly with ENVI. Similarly, the Parallel Computing Toolbox (Mathworks), as well as Jacket (AccelerEyes), can be used to speedup MATLAB code. Although not explicitly considered a geospatial software tool, MATLAB (MathWorks) has extensive capabilities in scientific computing, including modules designed specifically for image processing and mapping.

For the experienced programmer, there is the option to go directly to the source and develop specialized software using one of the two dominant parallel computing architectures available for GPU development: CUDA and OpenCL. CUDA was developed by NVIDIA explicitly for leveraging the compute capabilities of NVIDIA GPU cards, whereas OpenCL is an open framework that can be used for both NVIDIA and AMD GPUs. Both are excellent choices for developing custom GPU software, but both also require a reasonable level of comfort with programming as well as an understanding of GPU nuances to get the most out of the acceleration potential.

Essentially, we’re still at the growth end of the GPU curve, but the field is progressing rapidly and GPU computing is quickly gaining momentum. It is going to be exciting to see how this field evolves.

The World Resources Institute plans to launch a new version of Global Forest Watch – a web-enabled service that focuses on forest change using 16-day temporal data (provided by NASA satellites) at 500×500 meter resolution. These web tools will enable interactive selection of areas of interest and near real-time forest change information.

Now for the modern-day twist: drum roll… Users can request an email alert if there is a change above a set threshold over their area of interest! The system also enables investors to check a palm oil supplier for their environmental compliance status. What’s more is the vision to eventually supply real-time information, enabling authorities to catch illegal loggers red-handed. Now that would be something!

How do you envision the world of remote sensing will change as web-enabled services are implemented? Do you have ideas or examples of capabilities that would impact your world?

What remote sensing memories does the Landsat 40th anniversary bring up for you?

I’ve always felt an oddly personal connection to the deforestation in the Amazonian rainforest, especially considering that I’ve never even been there.  I think it’s because back when “deforestation” was becoming a household word, I actually had a chance to see it happening via the magic of remote sensing.

Take a ride with me, if you will, on the way back machine to the . . . mumble . . . mumble . . . 1980s.  A gallon of gas cost 97 cents, the Berlin wall hadn’t yet come down, and Ronald Reagan was president.  I was working my first real job at the remote sensing lab in the Geological Sciences department at the University of Washington.  One of the biggest projects the lab was working on had to do with detecting deforestation in the Brazilian district of Rondônia, using Landsat data.  At the time, we had only 15 years or so of Landsat data, but the deforestation was already obvious and alarming.

So, I was particularly interested to run across the following video by Google’s Earth Engine program, and Carnegie Mellon University, which shows and discusses a time lapse of Landsat images of the Amazon rainforest between 1999 to 2011.  As you watch, keep in mind that this video doesn’t show the whole 40 years of Landsat coverage.  It’s only 12 short years.

As Carnegie Mellon scientist Randy Sargent said, “You can continue to argue about why deforestation has happened, but you no longer will be able to argue whether it happened.”

Will you please share your Landsat memories with us?

Let’s start with going over some commonly used freely available data sources.  I’ll start with ASTER imagery.

ASTER , (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is a multispectral imaging instrument that launched in December 1999. ASTER data is used to create detailed maps of land surface temperature, reflectance, and it has two forward and backward looking bands of data, which can be used to generate digital elevation models, or DEMs. It also has a high spatial resolution, ranging from 15 -90 meters, so it can be used for a variety of image analysis applications, such as land cover analysis, vegetation mapping, change detection, and terrain analysis. You can access the imagery from the USGS GloVis Viewer, which is a quick and easy online search and order tool for selected satellite and aerial data.

Another type of data that you can access from the GloVIS site is MODIS data. MODIS (Moderate Resolution Imaging Spectro-radiometer) has a spatial resolution that ranges from 250 m – 1000m, and MODIS views the entire Earth’s surface every 1 to 2 days, acquiring data in 36 spectral bands.  These data products observe features of the land, oceans, and the atmosphere.  MODIS Level 1 and atmosphere products are available through the LAADS web, Land Products are available through the Land Processes DAAC website at the U. S. Geological Survey EROS Data Center, and Cryosphere data products (snow and sea ice cover) are available from the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

Some other types of data that you may come across include ALI and Hyperion. ALI (Advanced Land Imager) provides image data over ten spectral bands, with spatial resolutions ranging from 30 meters for the multispectral bands and 10 meters for the panchromatic band.  Hyperion collects 220 bands of data, with wavelengths ranging from 0.357 to 2.576 micrometers. It has a spatial resolution of 30 meters for all bands. Because it has this many bands, we refer to this type of data as hyperspectral data.  Hyperspectral imaging has wide ranging applications for material identification in mining, geology, forestry, agriculture, and environmental management. These data products are also available for search and download through Earth Explorer or GloVis.

Landsat is one of the most popular of the freely available data sets. LANDSAT-7 is from the most recent Landsat mission, and is currently operated as a primary satellite with a spatial resolution of 30 meters.  LANDSAT-5, from the previous Landsat mission, was equipped with a multispectral scanner (MSS) and thematic mapper (TM), which is a more advanced version of the observation equipment used in the MSS, and observes the Earth’s surface in seven spectral bands that range from visible to thermal infrared regions. It has a spatial resolution of 30 meters, and all Landsat data is in now freely available in the USGS archive.

Another very popular freely available data type is NAIP data. The NAIP (National Agriculture Imagery Program) mission acquires multispectral aerial imagery during the agricultural growing seasons in the continental U.S.  NAIP imagery products are available for free download through the USDA Geospatial Data Gateway.

If you’re interested in learning more about freely available government data, register for our August 29^th webinar, Using Free Government Data and Remote Sensing to Create a More Powerful GIS! What types of analyses are you doing with your data? Are there other types of data you’d like to see highlighted in the web seminar?

Melting snow and ice in Greenland.  Time difference between the two images is only 4 days.  Image courtesy NASA.

Researchers used a combination of satellite imagery including Indian Space Research Organizations (ISRO) OceanSat-2 and MODIS from NASA’s Terra and Aqua satellites.   Results were confirmed with passive microwave data aboard a USAF meteorological satellite.

Here’s a quick rundown of how these satellites can detect the glaciers melting:

MODIS is a keystone instrument for global studies of atmosphere, land, and ocean processes.  It has 36 bands with bands 1-19 and band 26 in the visible and near infrared range, and remainder bands in the thermal range from 3 to 15 mm. It provides daylight reflection and day/night emission spectral imaging of any point on the Earth every 1-2 days.  Bands 1-19 are used in measuring solar reflectance, giving us properties like albedo and helping us assess land cover classes (vegetation, open water, desert, etc.) and land cover changes.  The remainder of the bands in the mid- and thermal-IR portion of the electromagnetic spectrum are useful in determining land cover temperature, sea surface temperature and for measuring and correcting for atmospheric effects.  Click here if you want to know the gory details of the science behind the MODIS algorithms – it’s pretty interesting!

Using specific thermal and IR bands, MODIS can measure Land Surface Temperatures (LST)

Oceansat-2 has a scanning scatterometer.  Looking at the backscatter of the Ku-band operating at 13.515GHz (active microwave) you can observe different properties between dry and wet snow.  When the snow becomes wet (in this case due to melting) the backscatter decreases significantly because water is highly absorbent in radar frequencies.  The figure below illustrates nicely what the difference between wet and dry snow looks like.

Two views of a portion of Greenland ice sheet showing contrast in radar backscatter between wet and frozen conditions.  Image courtesy NASA-JPL

The drastic melting in Greenland was discovered by Son Nghiem of NASA’s Jet Propulsion Laboratory who was analyzing Radar data from Oceansat-2 and noticed almost the entire region had undergone some degree of melting.  After double checking to make sure there was no data error, he contacted his colleague at NASA Goddard, Dorothy Hall, who took a look at some MODIS Land Surface Temperature data which also showed some abnormally high temperatures in the region.  This also coincided with a large heat dome that had parked itself over Greenland for the better part of a week at that time, following several earlier anomalously high heat events earlier in the summer.  The Radar results were verified using another Air Force meteorology satellite for a third data point.

Melting events like this have occurred in Greenland in the past, about every 150 years according to ice cores.  The last such event was back in 1889, so this event is roughly on the correct time frame, and will hopefully be an isolated event.  Continued episodes like this could have enormous impacts on the North Atlantic climate, impacting ocean currents and atmospheric weather patterns.

These sensors provide regional, large scale coverage on the order of kilometer scale pixel sizes.  This type of coverage is excellent for global and continental scale global monitoring.  In fact, it’s necessary for applications of that scale – if we used higher resolution sensors we’d have an unnecessary amount of data to deal with.  When we look at hurricanes through AVHRR, it still takes a lot of coverage to capture the whole thing.  If we did that with meter scale sensors, the data volume would be almost unmanageable for most systems.

Different resolutions of the same area

But, there are definitely applications for the high resolution imagery.  While understanding continental scale climate patterns like in Antarctica or Greenland, it is necessary to understand and measure changes at a much smaller scale.  Commercial imagery like from GeoEye and DigitalGlobe are excellent for this type of application – they can provide us details ready for analysis in a local GIS.    A great example of this is the South Cascade Glacier in northern Washington.  This is a small alpine glacier that has retreated significantly in the last 50 years and is a site of detailed in situ and remotely sensed analysis.

Photographs of the South Cascade Glacier, which has lost nearly half of it’s volume since 1958

GeoEye imagery showing satellite images of the South Cascade Glacier from 2002 (upper right), 2004 (UL), 2007 (LR), and 2011 (LL).

Using Image Analysis and GIS it is easy to map area extent changes in the glacial toe over the 9 year time span.

Using standard remote sensing techniques for measuring albedo and land cover properties, it is very easy to map exactly fast how the glacier his retreating.  This meter scale data is far too high resolution to be useful over the scale of an area like Greenland, but it is perfect for using it on areas like the South Cascade Glacier in which changes are happening on the order of 1, 5, or 10 meters.  So, different data types have different value depending on the nature of the study.

References:

http://www.nasa.gov/topics/earth/features/greenland-melt.html

http://modis.gsfc.nasa.gov/data/atbd/atbd_mod11.pdf

http://www2.bren.ucsb.edu/~dozier/Class/esm236/Reading/Konig.pdf

http://glaciers.research.pdx.edu/south-cascade-glacier

I’m just getting back from the Esri International Users Conference where the 40th anniversary of Landsat (ERTS) was widely celebrated. At the imagery social on Wednesday, I even had a piece of Landsat birthday cake.

The video heralds the launch of ERTS as a new chapter in space history. For me and many of my peers Landsat was a new chapter in our geospatial science education. I’m positive Landsat MSS provided the first satellite image I saw in printed form and Landsat 5 was certainly the first digital data I analyzed in remote sensing class. At that time, Landsat had already been collecting for 20 years, yet the technology seemed so new and cutting edge. Of course, we had tools more advanced than the “Color Additive Viewer” (see the video), but nothing close to what exists today in image processing packages like ENVI.

The 40th anniversary has been a great reminder of the value of this data continuity mission. What impact has Landsat had on you during your education or career?

The US Department of Labor lists Geospatial Technology as a high growth industry.  You may notice this just by being a part of the industry.  In order to mint the graduates with the right skills many universities and colleges are improving the geospatial training and education beyond just GIS and into remote sensing and image analysis.  As more academic programs involve remote sensing and image analysis, and as more organizations, commercial companies and government agencies bring geospatial data on board, the more skilled geospatial analysts will be needed to be masters of the tools used to process imagery.

At the Esri UC this week, we’ll be showcasing how ENVI image analysis software can enhance an ArcGIS site license.  How is your school adopting remote sensing and image analysis?  Come by booth #1413 at the Esri User Conference to learn more about how ENVI can improve the skills of your students.

(image source: http://www10.giscafe.com/blogs/gissanjay/2012/06/29/colorado-forest-fires-photo-interpretation/)

As of July 10, 2012 there are currently just two active fires in the state of Colorado, 11 new large fires have been reported across the country, and 17 states are experiencing active large fires. So far this year 2,723,393 acres have burned compared with the 10-year average of 2,888,097 acres lost annually.

Also in the news, the first half of 2012 was just reported as the hottest year on record since record-keeping began in 1895! The image below compares this year’s temperatures to the two hottest years on record. It’s safe to say we are not off to a good start!

Are the fires and the warming pattern related? It’s perplexing that the second-hottest year on record to date was 2010 – the year in which the least amount of acreage burned in the past 10 years with a loss of only 1,558,974 acres. Several states have set all-time high temperature records this year more than 170 of those records set in the second half of June!

Let’s just hope that information paired with new technology will enable us to have positive effects on environmental shifts in years to come. For more statistics see:

http://www.nifc.gov/fireInfo/nfn.htm

http://www.columbia.edu/~mhs119/Temperature/T_moreFigs/

http://www.columbia.edu/~mhs119/Temperature/T_moreFigs/

Amongst the many coral reef professionals gathered in Cairns, which include a diverse mix of ecologists, biologists, geologists, oceanographers and managers, there are also a collection of the world’s foremost experts in remote sensing of coral reefs. These scientists provide the “eyes from above” that deliver large-scale overviews of entire coral reef systems.

Dr. Vittorio Brando and Dr. Elizabeth Botha from CSIRO present their poster on coral reef remote sensing

In the context of coral reef science, remote sensing encompasses a number of different but related disciplines, including photography, multispectral and hyperspectral imaging, lidar, radar and acoustics. Measurements are acquired from airplanes, satellites, ships, underwater vehicles, and from land. The commonality is that the output from each technology produces two-dimensional, and in some cases three-dimensional, images of the reef and its surrounding environment.

Analysis of these images spans a variety of techniques. At its most straightforward level, remote sensing can be used to simply visualize coral reefs and manually interpret what is present in a given areas, such as identifying the locations of reef, seagrass, sand, water, mangrove, beach and land. More significantly, these images can be quantitatively analyzed to derive vital measurements of reef distribution, properties and health. For example, imagery can be used to determine parameters such as habitat composition, water clarity, water depth, topographic complexity and water temperature. Knowledge of such parameters is critical for understanding how reefs function as well as how they respond to changes in the environment.

The field of coral reef remote sensing has evolved significantly in the past decade, with new technologies and improved analysis methods enabling increasingly complex scientific and management questions to be addressed using image-based tools. As evident during the symposium, remote sensing is now omnipresent throughout the coral reef community. The information derived from remote sensing provides descriptive maps that are basis for scientific investigations and form the foundation of many coral reef management plans.

It has been encouraging to see the breadth and sophistication of applications in the remote sensing presentations at the ICRS. It will be exciting to see how coral reef remote sensing continues to grow in the coming years.

Draft cover: Coral Reef Remote Sensing

The second day of ICRS 2012 saw the exciting launch of an innovative book entitled “Coral Reef Remote Sensing: A guide for mapping, monitoring and management.” This groundbreaking new book explains and demonstrates how satellite and other imaging technologies, referred to collectively as “remote sensing,” are essential for understanding and managing coral reef environments around the world.

The book is produced by an international group of coral reef scientists and managers who collectively demonstrate for the first time how the unique data provided by the world’s satellite and other imaging sensors are used for the full range of science and monitoring activities required to understand and manage coral reefs. These remote sensing resources are now unparalleled in the types of information they produce, the level of detail, the area covered and the length of the time over which data has been collected.  When used in combination with field data and knowledge of coral reef ecology and oceanography, remote sensing is an essential source of information for understanding, assessing and managing coral reefs around the world.

The assembled team of authors are from research institutions, governments and non-government organizations around the world. The lead editor of the book is HySpeed Computing president, James Goodman, in collaboration with co-editors Samuel Purkis from Nova Southeastern University and Stuart Phinn from University of Queensland. The authors produced a book that comprehensively explains each remote sensing data collection technology, and more importantly how each technology is used for coral reef management activities around the world.

The book is scheduled to be available January 2013 from Springer publishing. It is accessible to a general audience as well as remote sensing specialists, resource managers, and anyone else working with coral reef ecosystems.

Color-infrared images of Las Vegas, collected by Landsat 1 in 1972, and by Landsat 7 in 2010.

Aside from being interesting and a bit appalling to watch, the video also illustrates one of the many reasons that it can be so valuable to have compatible remotely sensed data collected repeatedly over a long time period.  Data from the Landsat program is unparalleled in its ability to directly measure and illustrate change on the earth’s surface.  As NASA’s description of the video puts it,

“Landsat data have been instrumental in increasing our understanding of forest health, storm damage, agricultural trends, urban growth, and many other ongoing changes to our land resources. Studies using Landsat data have helped land managers keep track of the pace of urbanization in locations around the world.”

This week I want to share an article that caught my eye in the June edition of Photogrammetric Engineering and Remote Sensing: “An Efficient Remote Sensing Solution to Update the NCWI”, B.R. Stein, B. Zheng, I. Kokkinidis, N. Kayastha, T. Seigler, K. Gokkaya, R. Gopalakrishnan, and W. Hwang. This article features the winning project which answered the 2012 GeoLeague Challenge sponsored by the ASPRS Student Advisory Council.

The goal of the 2012 GeoLeague Challenge was to develop a strategy for updating the National Coastal Wetlands Inventory (NCWI) that was not only time and cost-efficient but also addressed shortcomings in current approaches and increase accuracy. Further, the proposed solution should be scalable to the national level and repeatable for efficient inventory update projects every five to 10 years.

Here are a few key points from the article I found exciting:

1) The authors base their approach around the “additive benefits of a data fusion” and propose the utilization of Landsat, LiDAR, and Radar data in an “unprecedented” data fusion combination to utilize key quality components from various data sources.

Why is this exciting?  The success of this project can be modeled across multiple disciplines ranging from land use and land planning, to forestry and environmental monitoring, and to any other industry looking at data fusion as a new way to utilize imagery to solve problems.

2) The proposed workflow involves utilizing various data combinations with various analytic methods on a small scale, then applying the best combination(s) to larger scale study areas and eventually applying them to national study areas.

Why is this exciting?  Scientific and time-tested algorithms like maximum likelihood classification are coupled with newer approaches to geospatial data analysis such as object-based imagery classification. Accuracy we have not been able to achieve in the past may be reached by combining multiple analytic approaches with new data combinations – thanks to increasingly rich data and new technology.

3) All facets of this proposal are so well constructed it is truly impressive. The scientific information, business case, timeline, implementation plan, and funding considerations are comprehensive and concise.

Why is this exciting?  I won’t be a bit surprised when I see a follow-up article outlining this group’s achievements and notification that the NCWI updates are successfully underway – thanks to this “Efficient Remote Sensing Solution…”

As Cherie discussed in her last piece on assessing fire damage with imagery, an additional contributor to fire fuel load for the fires that have been gripping Colorado are dead lodge pole pines.  The lodge poles in Colorado are being killed by the endemic Mountain Pine Beetle.  At VISualize, Matt Hallas (an intern here at Exelis VIS) presented on the mapping of pine beetle tree destruction in Colorado using Landsat imagery to map the migration of the beetles as they move across the Rocky Mountain region.  Matt used NDVI to successfully locate distressed tree stands and map the progression of Pine Beetles northward across the state over the past decade.

Matt’s presentation about the impact of Pine Beetles on the health of forests and Cherie’s blog on post fire assessment are both great examples of how imagery is being used across the spectrum of landscape classification and change detection.   As Matt and Cherie demonstrated, image analysis is a powerful tool and I believe NGO’s will soon be leveraging remote sensing to a much greater degree in their work to conserve important habitats.

The Disaster Monitoring Constellation (DMC) consists of several similar satellites orbiting in a configuration that enables a daily revisit for most points on the Earth’s surface.  If a historical record going back farther than 2002 is important, DMC has it covered: the DMC spatial resolution and placement of its three spectral bands were designed to match up well with Landsat TM.  The satellites were built by Surrey Satellite Technology Ltd (SSTL), and are currently operated for various international governments by DMC International Imaging.   The constellation currently includes AISAT-1 (Algeria), BilSAT (Turkey), NigeriaSAT-1 (Nigeria), UK-DMC (United Kingdom), Beijing-1 (China), UK-DMC2 (United Kingdom), Deimos-1 (Spanish commercial), NigeriaSAT-2 (Nigeria), and NigeriaSAT-X (Nigeria).

This NigeriaSat-1 image of New Orleans, USA in 2005 shows an area affected by Hurricane Katrina. New Orleans is visible in the centre. Dark areas in the city indicate flooding, and at full detail (not shown here) it is possible to see which streets are submerged.

Data from DMC sensors have been used to monitor the effects of the Indian Ocean Tsunami in December 2004 and Hurricane Katrina August 2005, in addition to many other disasters.

RapidEye

The RapidEye constellation consists of five satellites which, like the DMC constellation, were designed and implemented by SSTL (this time subcontracted to MacDonald Dettwiler and Associates ).  Each satellite carries an identical sensor, designed and implemented by Jena Optronik, which measures five visible and near infrared bands at 5 m spatial resolution.  The sensors include a unique red edge band that make RapidEye data appropriate for monitoring changes in chlorophyll content.  Consequently, RapidEye data can be used to monitor vegetation health, distinguish different species of vegetation, and monitor protein and nitrogen content in vegetation.

Natural-color RapidEye imagery of Kolontár, Hungary, before and after an alumina plant accident in which a dam holding back a reservoir of caustic sludge failed catastrophically, resulting in flooding of nearby towns and villages. Source: RapidEye.

Scientists recently gathered at the World Wildlife Fund building in Washington, DC to share ideas, discuss their latest research, and identify pathways to improve our ability to utilize remote sensing imagery as the basis for effecting positive change in the health our planet. Conversations heard around the conference were lively, with exciting ideas for collaborations and new research directions emerging after every presentation.

The VISualize conference was generously hosted by the World Wildlife Fund at their offices in Washington, DC.

Attendees of the 2-day VISualize event, sponsored by Exelis Visual Information Solutions, included representatives from non-governmental organizations such as the World Wildlife Fund, the Nature Conservancy, and Wildlife Conservation Society, government agencies such as NASA and the USDA, universities such as Johns Hopkins and University of Maryland, and commercial companies such as Tech-X, Esri and HySpeed Computing.

The leadoff keynote address by Gerry Kinn from Esri challenged attendees to be active participants in addressing the many environmental issues facing our planet – where as scientists “we have a responsibility to identify, characterize and communicate scientific issues.” Using a variety of illustrative examples, Mr. Kinn demonstrated that remote sensing imagery is very rich in content, and is becoming increasingly used throughout society, not just for environmental decisions but also in areas of civil infrastructure, transportation and security, to name a few. This vast network of geographic knowledge continues to grow on a daily basis, contributing to our collective ability to better understand our surroundings and make more informed decisions. Remote sensing is the foundation for much of this knowledge, providing the ability to visualize our planet and more importantly a quantitative tool to measure our planet.

Additional speakers covered diverse topics such as assessing conservation measures in the Gobi Desert, tracking the decline of artic sea ice, predicting future deforestation trends in Cameroon, examining the spatial and temporal distribution on Mountain Pine Beetle infestations in Colorado, and using hyperspectral remote sensing to quantify mangrove restoration along degraded coastlines in Malaysia. There were also talks on how to improve the accuracy of existing image products, such as from the MODIS and GOES instruments, which are used extensively in climate and environmental research, thereby increasing the effectiveness of the subsequent scientific analysis and policy decisions made using this imagery.

A central topic of discussion in the presentation by HySpeed Computing, and a common theme heard around the conference, is the need for increased collaboration, improved access to analysis tools, and greater data accessibility. For example, Dr. Robert Rose from the Wildlife Conservation Society stressed that collaboration is particularly imperative for non-governmental organizations and developing countries where funding resources are often limited. He proposed the revitalization of a “conservation remote sensing working group” to encourage communication amongst academia, government, non-government and commercial organizations. With such initiatives in mind, and echoing the thoughts of many attendees, there was a clear message for scientists to get involved and make a difference.

In the event of large disasters, such as this destructive fire, a rapid assessment of the damage is necessary to grasp the severity and extent of hard-hit areas. Responders and analysts need to easily understand and identify the scope of damaged regions and quickly create visualizations that contain useful geospatial information, reflecting conditions on the ground, in order to direct rescue team efforts. Given limited time and resources, assessing the immediate risk of damage to people and property needs to be quickly and accurately. GIS and geospatial imagery are excellent resources for effectively managing fire damage assessment activities. To assess the damage over large geographic areas, recent satellite imagery or aerial photography can be used to gain an overall understanding of the landscape. Additionally, a general idea of the magnitude of damage can be identified from geospatial data.

The overall fire area can be readily identified from satellite imagery and points on the ground, then the general boundaries can be overlaid onto a map. The power of image analysis tools can be used, even on PDF maps, to help derive new geospatial information for fire personnel, capturing the extent of fire damage or the amount of burn scarring, for example. This type of information can be extracted from imagery and used analytically in conjunction with existing datasets in a GIS framework.

A PDF map image, like the one shown above, can be viewed and processed, allowing for the isolation the fire burn regions, location of hotspots, and quantification of burn extents, or as a new data layer for further analysis. Geospatial analysis results often provide critical, time-sensitive information that can be used to update a geodatabase or to create a map for dissemination to emergency workers on the ground.

After the fire has been extinguished there is still a lot of work to be done, reconstruction and damage mitigation – tearing down and rebuilding structures, removing debris, controlling for post-fire erosion conditions, etc. – will all be time intensive tasks and that require a direct and efficient application of limited resources, which can also be support with GIS applications and geospatial imagery.