Scientists Spotlight Top Conservation Themes for Satellite Technology

wcsScientists from the WCS (Wildlife Conservation Society), NASA, and other organizations have partnered to focus global attention on the contribution of satellites to biodiversity conservation in a recently released study entitled “Ten Ways Remote Sensing Can Contribute to Conservation,” featured in the latest edition of the scientific journal Conservation Biology.

Addressing global questions requires global datasets that are enabled by satellite remote sensing; this paper highlights the way in which continuous observations of the Earth’s surface and atmosphere can advance our understanding of how and why the Earth is changing and inform actions that can be taken to halt the degradation of planet’s natural systems.

The findings of the paper will inform discussions on improving protected area management that are underway at the IUCN World Parks Congress, an event held every 10 years by the global conservation community.

Established in many cases to conserve wildlife and the ecosystems they inhabit, protected areas still fall short of protecting species and their ecological needs. In many instances, protected areas such as Nouabalé-Ndoki National Park in The Republic of Congo do not cover the full range of species such as elephants. Remote sensing can be used to gather information needed for managing landscapes beyond protected area networks.

“Remote sensing data from orbiting satellites have been used to measure, understand, and predict environmental changes since the 1970s, but technology that subsequently became available can now be applied much more widely on a whole range of conservation issues,” said WCS Conservation Support scientist Dr. Robert Rose, the lead author of the study. “To that end, we sought out the top thought leaders in conservation and the remote sensing community to identify the best conservation applications of these data.”

“Collaborations such as these that strengthen ties between disparate research communities will create new opportunities to advance conservation,” said co-author Dr Allison Leidner of NASA’s Earth Science Division. “For example, it will help remote sensing scientists tailor their research to meet the needs of field-based researchers and conservation practitioners.”

With funding from NASA to lead the study, Rose and his co-authors brought together 32 thought leaders from both the conservation and remote-sensing communities. The participants interviewed more than 100 experts in both fields and generated 360 questions, which were then whittled down to the Top 10 conservation examples on how remote sensing can be used, including:

    • Species distribution and abundances
    • Species movements and life stages
    • Ecosystem processes
    • Climate change
    • Rapid response
    • Protected areas
    • Ecosystem services
    • Conservation effectiveness
    • Agricultural/aquiculture expansion and changes in land use/cover
    • Degradation and disturbance regimes

With this study, the authors hope to jumpstart a new collaborative initiative that provides guidance to space agencies and other partners on how future Earth observation satellite missions can contribute to advancing wildlife protection and protected area management. Toward that end, the authors initiated the Conservation Remote Sensing Network, which currently has 350 members from around the world, all of whom are interested in applying remote-sensing data to a broad array of conservation challenges.

“A vital part of this new network, which will foster communications and build opportunities between the conservation and remote sensing communities and help develop new remote sensing capabilities, will be to generate interest from both the public and private sector to invest in the use of orbiting Earth observatories to help conserve the planet’s remaining biodiversity,” added Dr. David Wilkie of WCS’s Conservation Support Program.

The authors of the study are: Robert A. Rose of the Wildlife Conservation Society; Dirck Byler of the US Fish and Wildlife Service; J. Ron Eastman of Clark University; Erica Fleishman of the University of California; Gary Geller of NASA Jet Propulsion Laboratory; Scott Goetz of the Woods Hole Research Institute; Liane Guild of NASA Ames Research Center; Healy Hamilton of NatureServe; Matt Hanson of the University of Maryland; Rachel Headley of the Earth Resources Observation and Science Center; Jennifer Hewson of Conservation International; Ned Horning the American Museum of Natural History; Beth A. Kaplin of Antioch University New England; Nadine Laporte of the Woods Hole Research Center; Allison Leidner of the NASA Earth Science Division and Universities Space Research Association; Peter Leimgruber of the Smithsonian Conservation Biology Institute; Jeffrey Morisette of the US Geological Survey; John Musinsky of the National Ecological Observatory Network; Lilian Pintea of the Jane Goodall Institute; Ana Prados of the University of Maryland; Volker C. Radeloff of the University of Wisconsin-Madison; Mary Rowen of the US Agency for International Development; Sassan Saatchi of NASA Jet Propulsion Laboratory; Steve Schill of The Nature Conservancy; Karyn Tabor of Conservation International; Woody Turner of the NASA Earth Science Division; Anthony Vodacek of the Rochester Institute of Technology; James Vogelmann of the US Geological Survey; Martin Wegmann of the University of Wuerzburg; David Wilkie of the Wildlife Conservation Society; and Cara Wilson of the Environmental Research Division, NOAA/NMFS/SWFSC.

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[ Source: Wildlife Conservation Society press release]

OGC Seeks Public Comment on the Earth Observation Metadata Profile of the OGC Observations & Measurements Standard

OGC_newThe membership of the Open Geospatial Consortium (OGC®) seeks public comment on the candidate Earth Observation Metadata Profile of the OGC Observations and Measurements (O&M) Standard.

The Earth Observation (EO) Metadata profile of Observations and Measurements is intended to provide a standard schema for encoding Earth Observation product metadata to support the description and cataloguing of products acquired by sensors aboard EO satellites.

EO products are differentiated by parameters such as the date of acquisition and the image footprint as well as characteristics pertaining to the type of sensor, the type of platform, the applied processing chain, and more. This candidate standard identifies the metadata elements that enable the robust description of general EO products and defines specialisations for specific thematic classes of EO products, such as optical, radar, atmospheric, altimetry, limb-looking and systematic/synthesized EO products. In addition, this document describes the mechanism used to extend these thematic schemas for specific EO missions.

Version 1.0 of the EO Metadata profile of O&M is an OGC Implementation Standard that was adopted in 2012. Since then the standard has been implemented by the EO ground segments of a number of EO missions. During these implementations, a number of improvements and corrections have been identified. The proposed version 1.1 addresses these corrections and improvements.

The documents for the candidate OGC Earth Observation Metadata profile of Observations & Measurements Standard are available for review and comment at (

The OGC is an international consortium of more than 495 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC Standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. OGC Standards empower technology developers to make geospatial information and services accessible and useful with any application that needs to be geospatially enabled. Visit the OGC website at

[Source: OGC press release]

V1 Media Acquires Earth Imaging Journal

image001Earth observation print publication aligns with current geospatial outlets.

V1 Media welcomes the staff of Earth Imaging Journal (EIJ) as we combine forces to address an Earth observation market that is marked by significant growth prospects and exciting opportunities. The bi-monthly print publication is a great fit with V1 Media’s online geospatial outlets and multimedia production capabilities.

“The timing was right for EIJ to be combined with a proficient publishing firm that shares the same values as our expert staff,” said Jeff Specht, founder and current publisher of Earth Imaging Journal, and principal of Earthwide Communications. “I’m excited for EIJ to grow and continue meeting the demands of the dynamic Earth observation market.”

V1 Media is a global integrated media and learning company serving organizations and individuals that measure, model and manage our natural world as well as those that design, develop and engineer today’s built infrastructure. The company is focused on a better understanding of Earth systems and a better-performing built infrastructure.

“We’re excited to expand the online presence of EIJ and to get back into print,” said Matt Ball, founder and editorial director of V1 Media. “There’s a lot of new ground to cover with the successful launch of the next-generation Worldview-3 satellite, the expansive plans of new micro satellite constellation providers, and the emerging importance of unmanned aircraft systems.”

“The timing of this acquisition couldn’t be better in terms of planning for the year ahead as well as the increasing importance of Earth observation,” said Kevin Carmody, group publisher at V1 Media. “The marketplace has embraced the content and polished presentation of EIJ over the years. We’re eager to support that effort while also parlaying that experience into our new endeavors. It offers great synergy between our publications Informed Infrastructure, Sensors & Systems, Asian Surveying & Mapping, and GeoSpatial Stream. This acquisition will also help us better serve our advertisers.”

The transition will take place commencing with the November/December issue of EIJ, with online updates and other offerings ongoing.

[Source: V1 Media press release]

Effects of Pansharpening on Vegetation Indices

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 507-522

By Brian Johnson

“This study evaluated the effects of image pansharpening on Vegetation Indices (VIs), and found that pansharpening was able to downscale single-date and multi-temporal Landsat 8 VI data without introducing significant distortions in VI values. Four fast pansharpening methods—Fast Intensity-Hue-Saturation (FIHS), Brovey Transform (BT), Additive Wavelet Transform (AWT), and Smoothing Filter-based Intensity Modulation (SFIM)—and two VIs—Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR)—were tested. The NDVI and SR formulas were both found to cause some spatial information loss in the pansharpened multispectral (MS) bands, and this spatial information loss from VI transformations was not specific to Landsat 8 imagery (it will occur for any type of imagery).

Color infrared Landsat 8 images acquired on 29 May 2013 (a) and 5 November 2013 (b). Red, Green, and Blue colors correspond to Band 5, Band 4, and Band 3. The yellow rectangle shows the location of the inset maps in Section 5.3.

Color infrared Landsat 8 images acquired on 29 May 2013 (a) and 5 November 2013 (b). Red, Green, and Blue colors correspond to Band 5, Band 4, and Band 3. The yellow rectangle shows the location of the inset maps in Section 5.3.

“BT, SFIM, and other similar pansharpening methods that inject spatial information from the panchromatic (Pan) band by multiplication, lose all of the injected spatial information after the VI calculations. FIHS, AWT, and other similar pansharpening methods that inject spatial information by addition, lose some spatial information from the Pan band after VI calculations as well. Nevertheless, for all of the single- and multi-date VI images, the FIHS and AWT pansharpened images were more similar to the higher resolution reference data than the unsharpened VI images were, indicating that pansharpening was effective in downscaling the VI data. FIHS best enhanced the spectral and spatial information of the single-date and multi-date VI images, followed by AWT, and neither significantly over- or under-estimated VI values. ”

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

rs-remotesensing-logoRemote Sensing, Volume 6, Issue 7, Published Online 07 July 2014

Akpona Okujeni, Sebastian van der Linden, Benjamin Jakimow, Andreas Rabe, Jochem Verrelst, and Patrick Hostert

“Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN) or limited (RFR and PLSR) performances when comprehensively mapping urban land cover.

Generation of binary and ternary synthetic mixtures.

Generation of binary and ternary synthetic mixtures.

“Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.”

Group on Earth Observations (GEO) Sessions and Special Exhibit at the 2014 Esri User Conference

Esri logoGroup on Earth Observations (GEO) Special Exhibit

Come learn about the Group on Earth Observations (GEO). Comprised of 90 member nations, the European Commission and 77 Participating Organizations who are working together to unleash the power of open Earth observation data.

Designed to improve the quality of life of people everywhere, GEO focuses putting sound science to work across nine essential areas: agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water and weather.

GEO’s mandate is to drive the interoperability of many thousands of space-based, airborne, and in situ Earth observations around the globe.

Esri in cooperation with Institute of Atmospheric Pollution Research of the National Research Council of Italy (CNR-IIA) have team together to make it easy for the Esri user community to benefit from and to contribute to GEO. Esri has also supported the work of the Open Geospatial Consortium (OGC) in their contributions to the work of GEO.

Come see the GEO Data Access Broker of CNR-IIA Florentine Division and Esri Community Portal for GEO to see how you can participate; and to learn about the good work of GEO.

GEO Sessions

“Introduction to GEO”
Tuesday: 3:15 p.m. – 4:30 p.m., Room 29 A/B (SDCC)

  • Hear from the Director of GEO-Dr. Barb Ryan
  • Panel of GEO activist
  • See Demonstration of Esri Community Portal for GEO

“Esri Community Portal for GEO”
Thursday: 1:30 p.m. – 2:45 p.m., Room 3 (SDCC)

  • Tech Workshop
  • Learn about GEO Appathon

Machine Learning Approaches to Coastal Water Quality Monitoring using GOCI Satellite Data

GISRSGIScience & Remote Sensing, Volume 51, Issue 2, 2014 — Special Issue: Coastal Remote Sensing

By Yong Hoon Kim, Jungho Im, Ho Kyung Ha, Jong-Kuk Choi, and Sunghyun Ha

“Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and support vector regression (SVR) were evaluated for coastal water quality estimation. In situ measurements (63 samples) collected during four days in 2011 and 2012 were used as reference data. Due to the limited number of samples, leave-one-out cross validation (CV) was used to assess the performance of the water quality estimation models. Results show that SVR outperformed the other two machine learning approaches, yielding calibration R2 of 0.91 and CV root-mean-squared-error (RMSE) of 1.74 mg/m3 (40.7%) for chl-a, and calibration R2 of 0.98 and CV RMSE of 11.42 g/m3 (63.1%) for SPM when using GOCI-derived radiance data. Relative importance of the predictor variables was examined. When GOCI-derived radiance data were used, the ratio of band 2 to band 4 and bands 6 and 5 were the most influential input variables in predicting chl-a and SPM concentrations, respectively. Hourly available GOCI images were useful to discuss spatiotemporal distributions of the water quality parameters with tidal phases in the west coast of Korea.”