Linking Coral Reef Remote Sensing and Field Ecology: It’s a Matter of Scale

Announcing recent publication in the Journal of Marine Science and Engineering (JMSE). The full text is available open-access online: Lucas and Goodman, JMSE, 2015, vol. 3(1): 1-20.

Authors: Matthew Q. Lucas and James Goodman

Abstract: Remote sensing shows potential for assessing biodiversity of coral reefs. Important steps in achieving this objective are better understanding the spectral variability of various reef components and correlating these spectral characteristics with field-based ecological assessments. Here we analyze >9400 coral reef field spectra from southwestern Puerto Rico to evaluate how spectral variability and, more specifically, spectral similarity between species influences estimates of biodiversity. Traditional field methods for estimating reef biodiversity using photoquadrats are also included to add ecological context to the spectral analysis. Results show that while many species can be distinguished using in situ field spectra, the addition of the overlying water column significantly reduces the ability to differentiate species, and even groups of species. This indicates that the ability to evaluate biodiversity with remote sensing decreases with increasing water depth. Due to the inherent spectral similarity amongst many species, including taxonomically dissimilar species, remote sensing underestimates biodiversity and represents the lower limit of actual species diversity. The overall implication is that coral reef ecologists using remote sensing need to consider the spatial and spectral context of the imagery, and remote sensing scientists analyzing biodiversity need to define confidence limits as a function of both water depth and the scale of information derived, e.g., species, groups of species, or community level.

Keywords: coral reefs; remote sensing; field spectra; scale; ecology; biodiversity; conservation coral reefs; remote sensing; field spectra; scale; ecology; biodiversity; conservation

Figure 8. Estimates of biodiversity

Figure 8. Estimates of biodiversity calculated using the exponential of Shannon entropy, exp(H′), illustrating influence of increasing spectral similarity amongst reef species as a function of increasing water depth: 0* is biodiversity obtained from photoquadrats, 0** is biodiversity calculated using only those species considered prevalent or sizable enough to significantly influence the remote sensing signal (i.e., species included in the spectral measurements for this study area), and 0–10 is biodiversity calculated with consideration for optical similarities amongst species (i.e., based on hierarchical clustering of reflectance spectra as influenced by the overlying water column).

Coral Reef Reflectance Characteristics – Impacts of increasing water depth on spectral similarity

In a previous post we demonstrated how dendrograms can be used as effective tools for investigating the similarities and differences of reflectance spectra. Here we expand on our earlier discussion, and explore how dendrograms can be used to illustrate the decreasing separability of coral reef spectra with increasing water depth.

Coral reefs are known for their exceptional biodiversity, containing a complex array of mobile and sessile organisms. From a remote sensing perspective, however, coral reefs can be particularly challenging study areas, mostly due to the confounding effects of the overlying water column. Varying water depth and varying water properties can both contribute significant complexity to the interpretation and identification of features on the sea floor.

Given this complexity, it is therefore necessary in remote sensing to simplify coral reef ecosystems into a collection of generalized components, each representing a unique compilation of species and/or substrate types. When grouping species for analysis, and when interpreting image classification output, it is important to understand the spectral similarity – or dissimilarity – of different image features.

As an example, let’s first consider an example where reef habitat composition is represented by four fundamental components: coral, sponge, sand and submerged aquatic vegetation (SAV). As shown in our previous post, the average in situ spectra of these four components exhibit unique reflectance characteristics and can be readily differentiated at 0.1 spectral angle. However, with increasing water depth (approximated here using a semi-analytic model for clear tropical water) the separability of these components decreases. At 3 m water depth it becomes more difficult to differentiate sand from SAV, and coral from sponge; and at 10 m water depth analysis is essentially reduced to a two-component systems: sand versus coral, sponge and SAV.

Coral Components Dendrogram

Let’s now compare and contrast the above results with the same analysis applied to all of the individual spectra used to create these component averages. This includes measurements from 24 coral species, 10 sponge species, 3 SAV species and areas of sand. As shown below, many individual species (and in some cases small groups of species) can be readily differentiated when the overlying water column is not considered. However, when the effects of the water column are included, the ability to distinguish individual species diminishes significantly with increasing water depth.

Coral Species Dendrogram 0mCoral Species Dendrogram 3mCoral Species Dendrogram 10m

While the relationship between water depth and spectral similarity is to be expected, what is particularly informative from these dendrograms is the ability to discern which species group together at different depths. For example, note that coral species do not always group with other coral species, but are observed to also group with both sponges and SAV. Additionally, because spectra do not necessarily group according to type, it becomes apparent that three spectral groups can be reasonably differentiated at 10 m rather than just two groups as suggested from the analysis using just averages for each component.

Such information can be immensely valuable for guiding image analysis, as well as aiding the interpretation of results. So if you’re working on remote sensing of coral reefs, it’s worth exploring the spectral characteristics of the dominant species in your study area, and investigating how spectral similarity changes with water depth.

Related post: Assessing Spectral Similarity – Visualizing hierarchical clustering using a dendrogram

Acknowledgement: Spectral data used in the above examples were collected using a GER-1500 spectrometer by M. Lucas at the University of Puerto Rico at Mayaguez for a NASA EPSCoR sponsored research project on the biodiversity of coastal and terrestrial ecosystems.

Assessing Spectral Similarity – Visualizing hierarchical clustering using a dendrogram

When conducting remote sensing analysis, it can often be very instructive to evaluate the spectral similarity – or dissimilarity – of different image features.

Below we demonstrate the use of a dendrogram to quantitatively analyze and visually assess the similarity and hierarchical clustering of reflectance spectra.

Understanding spectral relationships can impart valuable information on: the potential spectral variability within a given scene, the capacity to differentiate certain unique features in an image, the need for clustering spectrally similar features, or the number of spectral endmembers that could be used to describe a particular area. Spectral analysis can therefore be an important first step in understanding the capabilities and limitations of different analysis methods and application objectives.

Spectra are typically obtained from field or laboratory measurements, from an existing spectral library, or derived from the image itself. Similarity between individual spectra, or between clusters of similar spectra, can be mathematically analyzed using different distance metrics, such as root-mean-square error or spectral angle. The magnitude of similarity amongst these distances can then be used as an indicator of the ability to differentiate and/or identify spectral features using different image processing techniques.

When analyzing a relatively small set of spectra, evaluating results from a similarity assessment can be easily achieved by simply examining the output directly. For example, if we consider a set of coral reef spectra representing coral, sponge, sand and submerged aquatic vegetation (SAV), analysis using spectral angle reveals measurable differences between all four spectra. This is also apparent by plotting the spectra themselves, and seeing that each feature exhibits unique characteristics.

Spectral analysis coral reef

However, when the number of spectra is increased, and the spectral relationships become more complex, visual assessment of both the spectral signatures and similarity output becomes more difficult to interpret. For example, expanding our coral reef analysis, we now investigate spectra from 10 individual sponge species, and find the interpretation of results to be less obvious. There are clearly species exhibiting close similarities, as well as differences, but it is not immediately apparent which species can be easily differentiated and which species need to be clustered.

Spectral angle sponge species

Reflectance spectra sponge species

A useful method that can assist with this analysis is utilizing output from the distance calculations to build a dendrogram. This provides a visual representation of spectral relationships, as well as quantitative information relevant to image analysis. For example, as shown below, of the 10 sponge species in our analysis, 6 of these species can be differentiated at 0.1 spectral angle; however, there are two sets of species, (i) Chondrilla spp. and C. caribensis and (ii) A. lacunosa and I. felix, that are closely similar at 0.1 spectral angle and would need to be clustered at this level of analysis.

Dendrogram sponge species

To further illustrate the utility of using a “spectral dendrogram”, we expand our coral reef analysis to include spectra from 24 coral species, 3 SAV species and sand, in addition to the 10 sponge species. As evident from the dendrogram, spectral relationships in this analysis are significantly more complex. For example, at 0.1 spectra angle there are multiple situations where different spectral types (e.g., coral and sponge, or SAV and sponge) are closely similar and can’t be spectrally differentiated. This has important implications, and potential limitations, for subsequent spectral analysis and image classification results.

Dendrogram coral reef species

These are but a few examples illustrating the types of visualization and levels of information that can be derived from these plots. However, potential applications are as varied as your spectra, so we invite you to explore the use of dendrograms in your own spectral analysis.

Related post: Coral Reef Reflectance Characteristics – Impacts of increasing water depth on spectral similarity

Acknowledgement: Spectral data used in the above examples were collected using a GER-1500 spectrometer by M. Lucas at the University of Puerto Rico at Mayaguez for a NASA EPSCoR sponsored research project on the biodiversity of coastal and terrestrial ecosystems.

Bathymetry and Water Column Correction using 4SM – A HyPhoon user success story

This is part of a series on user success stories that showcase applications and accomplishments using HyPhoon datasets. Send us your story!

The Accomplishment: Dr. Yann Morel derived bathymetry and water column corrected reflectance for Heron Reef using his Self-calibrated Supervised Spectral Shallow-sea Modeler (4SM). Output includes water depth, as shown below, and water column corrected reflectance at the seafloor. Both products are being added to the Heron Reef dataset, and will soon be available for the community to download.

4SM Heron Reef

CASI hyperspectral (left) and 4SM derived bathymetry (right)

Data: The source data used for this success story was the 2002 CASI hyperspectral imagery from the Heron Reef dataset provided courtesy of the Center for Spatial Environmental Research at the University of Queensland. Heron Reef is located at the southern end of the Great Barrier Reef in Australia. The imagery has 1 m pixels and a spectral range from 400-800 nm with 19 spectral bands.

4SM Overview: The 4SM model is based on established physical principles of shallow water optics, operates without need for field data or atmospheric correction, and works with both multispectral and hyperspectral imagery. The model assumes both water and atmospheric conditions are uniform throughout the given scene, requires the presence of both deep water and bare land pixels, and is most applicable to clear shallow water at 0-30 meters depth.

At its core, 4SM utilizes a variant of Lyzenga’s method to calculate the slopes for all bi-dimensional band pairs, which are then used to interpolate diffuse attenuation coefficients for all visible bands. Surface glint is minimized using information from a NIR or SWIR band, bare land pixels are used to derive the slope of the soil line and the water volume reflectance, and deep water pixels are used to approximate deep water radiance. All of this information is then combined to drive an optimization approach for estimating water depth.

Output ultimately includes both water depth and water column corrected reflectance, which can both be used for further habitat and geomorphic analyses.

For more on 4SM: http://www.watercolumncorrection.com/

To access HyPhoon data: http://hyphoon.hyspeedcomputing.com/data-sets/

EnMAP Coral Reef Simulation – The first of its kind

The GFZ German Research Center for Geosciences and HySpeed Computing announce the first ever simulation of a coral reef scene using the EnMAP End-to-End Simulation tool. This synthetic, yet realistic, scene of French Frigate Shoals will be used to help test marine and coral reef related analysis capabilities of the forthcoming EnMAP hyperspectral satellite mission.

EeteS EnMAP Simulation FFS

EeteS simulation of EnMAP scene for French Frigate Shoals, Hawaii

EnMAP (Environmental Mapping and Analysis Program) is a German hyperspectral satellite mission scheduled for launch in 2017. As part of the satellite’s development, the EnMAP End-to-End Simulation tool (EeteS) was created at GFZ to provide accurate simulation of the entire image generation, calibration and processing chain. EeteS is also being used to assist with overall system design, the optimization of fundamental instrument parameters, and the development and evaluation of data pre-processing and scientific-exploitation algorithms.

EeteS has previously been utilized to simulate various terrestrial scenes, such as agriculture and forest areas, but until now had not previously been used for generating a coral reef scene. Considering the economic and ecologic importance of coral reef ecosystems, the ability to refine existing analysis tools and develop new algorithms prior to launch is a critical step towards efficiently implementing new reef remote sensing capabilities once EnMAP is operational.

The input imagery for the French Frigate Shoals simulation was derived from a mosaic of four AVIRIS flightlines, acquired in April 2000 as part of an airborne hyperspectral survey of the Northwestern Hawaiian Islands by NASA’s Jet Propulsion Laboratory. Selection of this study area was based in part on the availability of this data, and in part due to the size of the atoll, which more than adequately fills the full 30 km width of an EnMAP swath. In addition to flightline mosaicking, image pre-processing included atmospheric and geographic corrections, generating a land/cloud mask, and minimizing the impact of sunglint. The final AVIRIS mosaic was provided as a single integrated scene of at-surface reflectance.

For the EeteS simulation, the first step was to transform this AVIRIS mosaic into raw EnMAP data using a series of forward processing steps that model atmospheric conditions and account for spatial, spectral, and radiometric differences between the two sensors. The software then simulates the full EnMAP image processing chain, including onboard calibration, atmospheric correction and orthorectification modules to ultimately produce geocoded at-surface reflectance.

The resulting scene visually appears to be an exact replica of the original AVIRIS mosaic, but more importantly now emulates the spatial and spectral characteristics of the new EnMAP sensor. The next step is for researchers to explore how different hyperspectral algorithms can be used to derive valuable environmental information from this data.

For more information on EnMAP and EeteS: http://www.enmap.org/

EeteS image processing and above description performed with contributions from Drs. Karl Segl and Christian Rogass (GFZ German Research Center for Geosciences).

Coral Reef Remote Sensing – A new book for coral reef science and management

Coral Reef Remote SensingAnnouncing publication of “Coral Reef Remote Sensing: A Guide for Mapping, Monitoring and Management”, edited by Dr. James Goodman, president of HySpeed Computing, and his colleagues Dr. Sam Purkis from Nova Southeastern University and Dr. Stuart Phinn from University of Queensland.

This ground breaking new book explains and demonstrates how satellite, airborne, ship and ground based imaging technologies, collectively referred to as “remote sensing”, are essential for understanding and managing coral reef environments around the world.

The book includes contributions from an international group of leading coral reef scientists and managers, demonstrating how remote sensing resources are now unparalleled in the types of information they produce, the level of detail provided, the area covered  and the length of the time over which they have been collected.  When used in combination with field data and knowledge of coral reef ecology and oceanography, remote sensing contributes an essential source of information for understanding, assessing and managing coral reefs around the world.

The authors have produced a book that comprehensively explains how each remote sensing data collection technology works, and more importantly how they are each used for coral reef management activities around the world.

In the words of Dr. Sylvia Earle – renowned scientist and celebrated ocean explorer:

This remarkable book, Coral Reef Remote Sensing: A Guide for Mapping, Monitoring and Management, for the first time documents the full range of remote sensing systems, methodologies and measurement capabilities essential to understanding more fully the status and changes over time of coral reefs globally. Such information is essential and provides the foundation for policy development and for implementing management strategies to protect these critically endangered ecosystems.

I wholeheartedly recommend this book to scientists, students, managers, remote sensing specialists, and anyone who would like to be inspired by the ingenious new ways that have been developed and are being applied to solve one of the world’s greatest challenges: how to take care of the ocean that takes care of us.

If it had been available in 1834, Charles Darwin would surely have had a copy on his shelf.

We invite you to explore the book (including a sneak peek inside the chapters) and see how you can put the information to use on your own coral reef projects.

Google Maps Goes Underwater – A fish eye view of coral reefs

Maps have come a long way in recent years, thanks in part to efforts at places like Google and Microsoft as well as the ready availability of high resolution commercial satellite imagery from companies like GeoEye and DigitalGlobe. Combine this with on-the-ground photography and the result is an amazing ability to visualize our planet in ever increasing detail.

Google has recently taken the popular ‘Street View’ functionality into the underwater realm, offering 360° panoramas of reef locations in Australia, Hawaii and the Philippines. See examples of this imagery at maps.google.com/ocean. With a viewpoint normally reserved for sea creatures and those fortunate enough to scuba dive in such locations, this imagery now provides a window into the natural wonders of the underwater realm for anyone with access to Google Maps.Google Street View - Coral Reef

The underwater imagery for this project is being acquired in a partnership between Google and the Catlin Seaview Survey, who are using an innovative underwater panoramic camera to capture these unique images. The Catlin Seaview Survey is acquiring photographic records of reef and other marine locations around the world, providing a permanent snapshot of environmental and habitat conditions at the time the photos were recorded. Thus, the imagery you see isn’t just remarkable to look at, but it also serves a valuable scientific purpose.

Underwater locations haven’t been the only stops along the way for Google’s Street View technology. As part of the Google World Wonders Project, other locations include world heritage sites around the globe, such as Stonehenge, Yosemite National Park and the Hiroshima Peace Memorial, to name a few. The Street View technology offers an interactive 360° panorama that allows users take a virtual stroll through each location as if they were there.

As imagery such as this becomes more commonplace, and accessing satellite views of our neighborhood streets grows routine, don’t let the ease of these applications fool you. There is an amazing amount of technology behind acquiring this imagery and creating these maps. There are the satellites and cameras used to acquire the data, the algorithms used to assemble the images into seamless mosaics, the web software used to deliver the imagery to the user, and the people and companies who put it all together. It’s a complex process with many years of research needed to make it a reality. It will be exciting to see what comes next.

GPU Accelerated Processing – An example application using coral reef remote sensing

HySpeed Computing recently concluded a two-year grant, funded by the National Science Foundation, to utilize GPU computing to accelerate a remote sensing tool for the analysis of submerged marine environments. The grant was performed in collaboration with researchers in the Center for Subsurface Sensing and Imaging Systems at Northeastern University, integrating expertise from across multiple disciplines.

Remote sensing of submerged ecosystems, such as coral reefs, is a particularly challenging problem. In order to effectively extract information about habitats located on the seafloor, analysis must compensate for confounding influences from the atmosphere, water surface and water column. The interactions involved are complex, and the algorithms used to perform this process are not trivial. The most promising methods used for this task are those that utilize hyperspectral remote sensing as their foundation. An example of one such algorithm, developed by HySpeed Computing founder James Goodman, and selected as the basis for the GPU acceleration project, is summarized below.

Hyperspectral algorithm overview

Overview of coral reef remote sensing algorithm

The hyperspectral algorithm is comprised of two main processing stages, an inversion model and an unmixing model. The inversion model is based on a non-linear numerical optimization routine used to derive environmental information on water properties, water depth and bottom albedo from the hyperspectral imagery. The unmixing model is then used to derive habitat characteristics of the benthic environment. The overall algorithm is effective; however, the inversion model is considered computationally time consuming. It was determined that algorithm efficiency could be improved using GPU computing.

Analysis indicated that the numerical optimization routine was the primary computational bottleneck and thus the logical area to focus acceleration efforts. The first approach, using a steepest descent optimization routine programmed using CUDA, provided a moderate 3x speedup. But results indicated that greater acceleration could be achieved using a different optimization method. After careful consideration, a quasi-newton optimization scheme was ultimately selected and implemented in OpenCL such that a portion of the processing is retained on the CPU while just the computationally intensive function evaluations are implemented on the GPU. This arrangement more equally distributes the processing load across GPU and CPU resources, and thus represents a more efficient solution. In the end, analysis showed that the GPU accelerated version of the model (OpenCL: BFGS-B) is 45x faster than the original model (IDL:LM), and thus approaches the capacity for real-time processing of this complex algorithm.

Hyperspectral algorithm processing times

Comparison of relative processing times for hyperspectral inversion model

At a broader level, this project demonstrated the advantages of incorporating GPU computing into remote sensing image analysis. This is particularly relevant given the growing need for high-performance computing in the remote sensing community, which continues to expand as a result of the increasing number of satellite and airborne sensors, greater data accessibility, and expanded utilization of data intensive technologies. As this trend continues, so too will the opportunities for GPU computing.

This work was made possible by contributions from Prof. David Kaeli and students Matt Sellitto and Dana Schaa at Northeastern University.

A Glimmer of Hope – It’s not all doom and gloom for coral reefs

International Coral Reef Symposium 2012 – Cairns, Australia – Final Thoughts

Jeremy Jackson

Dr. Jeremy Jackson presents his plenary address after receiving the Darwin Medal

On the final day of ICRS 2012 Dr. Jeremy Jackson was awarded the Darwin Medal for outstanding achievement in coral reef science. In his plenary speech following the award, Dr. Jackson asserted that despite the “doom and gloom” predictions there is indeed hope for the world’s coral reefs.

Hope in coral reef conservation is indeed an important message. Recent years have seen an overwhelming media focus on the many threats to coral reefs, the reports of significant decrease in reef health, and dire predictions of continued reef decline. Although important to understand and address, these discussions included little room for the optimistic side of coral reef science.

The message of hope has been a common theme at this year’s ICRS conference. Dr. Jackson presented evidence of hope for reef resilience, Dr. Ove Hoegh-Guldberg cited reasons for optimism in scientific achievements, Dr. Jane Lubchenco presented examples of successful conservation efforts, and International Coral Reef Society president Dr. Robert Richmond said “don’t worry… be happy”, stating that there is tremendous cause for optimism in the coral reef community. The overall message is that coral reef scientists and managers are making a difference in the fate of coral reefs.

This does not imply that coral reef experts can relax. Significant effort is still needed to continue improving our understanding of coral reefs, to get the message out and engage the greater community, and to take action with effective management plans. Success in these areas is dependent on developing new partnerships and collaborations and moving forward with a global voice for reef conservation.

These are fitting thoughts to conclude a week of success stories at ICRS 2012, with a message of optimism and a need for a more connected community.

Reef Management in the Cloud – Application of innovative new technologies

International Coral Reef Symposium 2012 – Cairns, Australia – Thoughts from Day 5

The “cloud” and “cloud computing” are becoming increasingly prevalent in consumer applications. Our email is stored in the cloud, much of our personal data is stored in the cloud, and our mobile devices commonly access information stored in the cloud. The same technology that makes these applications possible is now being harnessed for environmental management.

Julie Scopelitis

Dr. Julie Scopelitis demonstrating the Qehnelo software

Preserving natural ecosystems typically incorporates a complex balance of scientific, political, societal, and economic facts, needs and viewpoints. However, the data needed to perform the associated decision making process is often stored in physically separate locations. As a result, despite growing global connectivity, accessing and integrating this data can be a challenge, particularly in remote locations.

The cloud, or more specifically the vast network of remote servers and its associated software, is the foundation allowing access to diverse sets of data. Rather than copy and transmit copies of large volumes of data and/or software to different users, cloud computing allows users to remotely access distributed storage locations. In many cases this approach is not only more efficient, but also more democratic, allowing greater distribution of limited computing resources to a larger number of users.

An interesting example of cloud technology is Qehnelo, a web-based software product created by the New Caledonian company Bluecham. Qehnelo, whose name derives from a native phrase for “open door”, integrates remote data access with high-level decision support models. Dr. Julie Scopelitis is working on using this innovative software for coral reef monitoring and management. Through this software, Julie is able to better leverage her own expertise and ultimately put the power of advanced technology into the hands of managers, conservationists and scientists.

It is exciting to see such innovative new technology being used for coral reef management. We are sure to see this trend continue as computing resources become more affordable and more accessible.