Esri Joins the Consortium for Ocean Leadership

Esri logoChief Scientist Dawn Wright Brings Spatial Thinking to Ocean Policy Leaders

The Consortium for Ocean Leadership (Ocean Leadership) has elected Esri, the world leader in GIS, as a member. Chief scientist Dawn Wright will represent Esri by bringing her extensive knowledge of ocean geospatial technology to the consortium. Esri will be participating in an elite group of over 100 world premier oceanographic research and educational institutions, aquariums, and industry partners.

“The Consortium for Ocean Leadership brings together research, education, and industry entities to advance a shared ocean agenda,” said Robert B. Gagosian, president and CEO of Ocean Leadership. “We are very pleased to have Esri join our membership, as its commitment to our oceans and dedication to the development of the frameworks for scientific collaboration will aid us in shaping the future of ocean science through discovery, understanding, and action.”

Dr. Dawn Wright, Esri's chief scientist.

Dr. Dawn Wright, Esri’s chief scientist.

Ocean Leadership shapes the future of ocean science, technology, and education by managing and coordinating programs that involve marine life inventories, ocean observatories, and natural resources management. Centered in Washington, DC, Ocean Leadership is the program office for the National Ocean Sciences Bowl, National Oceanographic Partnership Program, the Interagency Working Group on Ocean Observations, and the Ocean Research and Resources Advisory Panel.

“Ocean Leadership’s invitation to Esri to join its oceans efforts is extremely important,” said Wright. “Esri was founded on the belief that comprehending the relationships of the earth’s systems is integral to the health of the planet. We share the values of Ocean Leadership and look forward to participating in its efforts to advance ocean science and to shape policy for sustainable ocean management.”

Consortium members represent some of the nation’s most prestigious ocean organizations. The consortium provides community-based scientific advice and recommendations and is widely respected on Capitol Hill as well as in the entire US ocean research and education community. Wright attended the meeting in which Esri was ratified as an Ocean Leadership member on October 25, 2012, in Washington, DC. She will soon be called on to serve on Ocean Leadership committees.

“Successful decision making and effective policy building is based on spatial thinking, spatial data, and spatial methods,” said Wright. “Esri’s affiliation with Ocean Leadership adds a spatial component that will help drive our nation’s initiatives to improve the management of the oceans.

“Membership in Ocean Leadership will advance Esri’s Ocean GIS Initiative, which is the company’s commitment to developing products and strategic plans for ocean science, resources management, and conservation.”

[Source: Esri press release]

Spatial and Temporal Analysis of Deforestation and Forest Degradation in Selangor: Implication to Carbon Stock Above Ground

4th Conference on Data Mining and Optimization (DMO)4th Conference on Data Mining and Optimization (DMO), 02-04 September 2012

Syed Abdullah and Sharifah Mastura

“This paper aims to develop an operational methodology for monitoring spatial and temporal changes due to deforestation in Selangor over a 22 year period. The driving forces determining the changes were also analysed. Overall, the results show that the causes of deforestation were the economic factors, namely agriculture intensification, and population dynamics, related to the process of urbanization. However, deforestation statistics shows only a total of 10 percent decrease; it is the degradation of the remaining forest that is the major concern. Knowledge on deforestation and its driving forces in Selangor is very important as it provides the basis for the calculation of the total amount of carbon stock above ground. It also gives insight into the appropriate intervention measures that can be taken to increase carbon stock, thus reducing the release of carbon dioxide emission to the atmosphere.”

The Influence of DEM Quality on Mapping Accuracy of Coniferous- and Deciduous-Dominated Forest Using TerraSAR‑X Images

Remote Sensing, 2012, 4(3), 661-681

Sonia M. Ortiz, Johannes Breidenbach, Ralf Knuth and Gerald Kändler

“Climate change is a factor that largely contributes to the increase of forest areas affected by natural damages. Therefore, the development of methodologies for forest monitoring and rapid assessment of affected areas is required. Space-borne synthetic aperture radar (SAR) imagery with high resolution is now available for large-scale forest mapping and forest monitoring applications. However, a correct interpretation of SAR images requires an adequate preprocessing of the data consisting of orthorectification and radiometric calibration. The resolution and quality of the digital elevation model (DEM) used as reference is crucial for this purpose. Therefore, the primary aim of this study was to analyze the influence of the DEM quality used in the preprocessing of the SAR data on the mapping accuracy of forest types.

Application of the classification model in Biberach.

Application of the classification model in Biberach. (a) Map of deciduous- and
coniferous-dominated forest based on combined leaf-on and leaf-off TerraSAR-X images (acquired in 2008 and 2009) preprocessed with the ALS DTM; (b) official forest stand map (established 2007); (c) ortho-photographs (acquired on 2007).

“In order to examine TerraSAR-X images to map forest dominated by deciduous and coniferous trees, High Resolution SpotLight images were acquired for two study sites in southern Germany. The SAR images were preprocessed with a Shuttle Radar Topography Mission (SRTM) DEM (resolution approximately 90 m), an airborne laser scanning (ALS) digital terrain model (DTM) (5 m resolution), and an ALS digital surface model (DSM) (5 m resolution). The orthorectification of the SAR images using high resolution ALS DEMs was found to be important for the reduction of errors in pixel location and to increase the classification accuracy of forest types. SAR images preprocessed with ALS DTMs resulted in the highest classification accuracies, with kappa coefficients of 0.49 and 0.41, respectively. SAR images preprocessed with ALS DTMs resulted in greater accuracy than those preprocessed with ALS DSMs in most cases. The classification accuracy of forest types using SAR images preprocessed with the SRTM DEM was fair, with kappa coefficients of 0.23 and 0.32, respectively.Analysis of the radar backscatter indicated that sample plots dominated by coniferous trees tended to have lower scattering coefficients than plots dominated by deciduous trees. Leaf-off images were only slightly better suited for the classification than leaf-on images. The combination of leaf-off and leaf-on improved the classification accuracy considerably since the backscatter changed between seasons, especially in deciduous-dominated forest.”

Read the paper [PDF]