Ecological Land Classification and Mapping

Evaluating the flux and transformation of organic carbon (OC) along the eroding Beaufort Sea coast  (2004—2005)

Client: National Science Foundation.

Although crude estimates of the flux of organic carbon across the eroding coastline of northern Alaska have been developed, little is known about the transformation of terrestrial OC as it crosses the land/ocean interface.  To investigate these issues, this project has four main components designed to: (1) characterize the nature and abundance of soil OC and ground ice in relation to geomorphic environments, (2) estimate the total OC flux along the entire coast and develop empirical models to assess the vulnerability of the coast to increased erosion resulting from decreasing summer sea-ice, (3) determine the biogeochemical transformation and bioavailability of OC associated with various dissolved and particulate forms across the land/sea interface through field study and laboratory experimentation; and (4) integrate our results to the pan-arctic scale through international collaboration. The study involves extensive sampling at 50 sites along the entire Alaska Beaufort Sea coast to develop precise estimates of erosion and OC flux. Intensive sampling at three primary sites along dominant coastline types will evaluate the transformation of the eroded OC. In addition, three secondary sites will be added to broaden the monitoring to other coastline types and to involve local communities in assessing coastal changes. ABR is collaborating with a number of world experts to address this exciting research.  For more information contact tjorgenson@abrinc.com

Repeat photography of Landscapes in historical photographs to assess landscape change in Southwest Alaska  (2003—present)

Client: National Park Service

The goal of the project is to provide a historical and future photographic record of biophysical changes that are occurring in National Parks within the Southwest Alaskan Network (SWAN). Specific objectives are to compile historical photographs of landscape features in the SWAN parks and management information within a database system; screen historical photographs to identify locations for repeated photography and design a monitoring network; and retake photographs at the photo-monitoring network sites.  For more information contact tjorgenson@abrinc.com 

The Nature Conservancy (TNC) Ecosystems Poster for the Alaska-Yukon Arctic Ecoregions (2004)

Client: The Nature Conservancy

ABR GIS specialists produced a comprehensive map of the Alaska-Yukon Arctic ecoregions representing environmental patterns at a broad geographic scale. This map of 37 terrestrial ecosystem classes covers a landscape approximately 117,000 square miles in Alaska from the Brooks Range to the North Slope and incorporates physical, biological, and geographical information. The map is based on a predictive terrestrial ecosystems model developed by The Nature Conservancy to support conservation planning at the ecoregional level.For more information, contact Torre Jorgenson, tjorgenson@abrinc.com

Application of Remote Sensing to Estimate Vegetative Biomass on a Caribou Calving Ground in Northern Alaska (2001–2003)

Client: ConocoPhillips Alaska, Inc.

As an analytic component of the Meltwater Project caribou monitoring study in the southwestern Kuparuk Oilfield, ABR examined patterns of change in vegetative biomass in relation to snow melt and caribou densities during and immediately after calving on the western periphery of an area of concentrated calving. We used both Advanced Very-High Resolution Radiometer (AVHRR; in 2001) and Moderate Resolution Imaging Spectroradiometer (MODIS; in 2002 and 2003) satellite imagery to calculate NDVI, the Normalized Difference Vegetation Index, from the red and near-infrared bands of satellite imagery. NDVI is correlated with vegetative biomass and thus provides a useful tool to examine spatial and temporal patterns of plant growth over large areas of arctic tundra. AVHRR sensors provide a 20-year record of seasonal and annual vegetation changes but have a coarser resolution (1-km pixels) than the newer MODIS sensor (250-m pixels). The finer resolution of MODIS provides a more detailed view of the landscape and reduces the proportion of pixels contaminated by clouds, water, and snow. By using MODIS data, we were able to increase spatial resolution substantially and to reduce the influence of waterbodies in calculating NDVI. NDVI data provide important information about forage quantity and quality, but results must be interpreted in the context of snowmelt patterns, vegetation types, and the amount of area covered by waterbodies. Our ongoing research is focusing on applying advanced MODIS algorithms for accurate cloud detection and fractional snow cover mapping and on evaluating the performance of the Enhanced Vegetation Index in reducing the biases caused by water and other non-vegetative signals. For more information, contact Brian Lawhead, lawhead@abrinc.com