The Alaska vegetation classification.

Viereck, Leslie A.; Dyrness, C. T.; Batten, Alan R.; Wenzlick, K.J.

doi:10.2737/pnw-gtr-286

Cited by 276 publications

(235 citation statements)

References 97 publications

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“…Point-count survey crews classified habitat within a 150-m radius of the sample point to the Viereck classification system (Viereck et al 1992). When >1 habitat type existed within the circle, the percent of the circle occupied by each habitat type was recorded.…”

Section: Survey Methodologymentioning

confidence: 99%

“…Based on the overall distribution of habitat types encountered, we combined similar vegetation cover types into 5 broad categories derived from the Viereck classification system (Viereck et al 1992). Our 5 cover types were essentially groupings at Viereck level I and II: tall shrub (≥ 1.5 m tall), low shrub (1.5 m-20 cm tall), dwarf shrub (< 20 cm tall), bare ground (rocks, ash), and herbaceous (graminoids, herbs, mosses, lichens; Appendix A).…”

Section: Associations Between Birds Habitats and Elevationmentioning

confidence: 99%

“…Appendix A. Vegetation classification (after Viereck et al 1992) used during the inventory ofAppendix A. Vegetation classification (after Viereck et al 1992) Italicized categories were added to the classification to accommodate specific situations encountered during the inventory. …”

mentioning

confidence: 99%

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A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts

Amundson

Royle

Handel

2014

The Auk

109

217

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Section: Survey Methodologymentioning

confidence: 99%

Section: Associations Between Birds Habitats and Elevationmentioning

confidence: 99%

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A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts

Amundson

Royle

Handel

2014

The Auk

109

217

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“…Alaska can be divided into seven broad physiographic units: (1) northwest Alaska, with moist and wet tundra types (Eriophorum spp. ), ericaceous shrub polygons and saline meadows; (2) arctic Alaska, which includes extensive wet tundra and wet sedge meadows (Eriophorum angustifolium and Carex aquatilis); (3) south-central Alaska, which ranges from the peaks of the Alaska Range to coastal marshes and includes forest cover with extensive areas of black spruce muskeg; (4) southwest Alaska, which includes wet sedge meadows, halophytic wet meadows and wet shrub tundra; (5) southeast Alaska, where forest cover includes extensive regions of black spruce muskeg with halophytic and freshwater sedge and wet meadows dominated by C. lyngbyei on coastal deltas; (6) interior Alaska, with extensive black spruce muskeg forest cover, subarctic lowland sedge and sedge-moss bog meadows; and (7) the Aleutian Islands, where the most widespread community is Empetrum heath [30].…”

Section: Study Areamentioning

confidence: 99%

Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska

et al. 2015

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As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by using Japanese Earth Resources Satellite (JERS-1) data, we applied the "random forests" classification algorithm to variables from L-band ALOS PALSAR data for 2007, topographic data (e.g., slope, elevation) and locational information (latitude, longitude) to derive a map of vegetated wetlands in Alaska, with a spatial resolution of 50 m. We used the National Wetlands Inventory and National Land Cover Database (for upland areas) to select training and validation data and further validated classification results with an independent dataset that we created. A number of improvements were made to the method of Whitcomb et al. (2009): (1) more consistent training data in upland areas; (2) better distribution of training data across Remote Sens. 2015, 7 7273 all classes by taking a stratified random sample of all available training pixels; and (3) a more efficient implementation, which allowed classification of the entire state as a single entity (rather than in separate tiles), which eliminated discontinuities at tile boundaries. The overall accuracy for discriminating wetland from upland was 95%, and the accuracy at the level of wetland classes was 85%. The total area of wetlands mapped was 0.59 million km 2 , or 36% of the total land area of the state of Alaska. The map will be made available to download from NASA's wetland monitoring website.

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“…The growing season integrated Normalized Difference Vegetation Index (NDVI) derived from satellite observations is used as a proxy for vegetation dynamics and ecosystem performance [3][4][5]. Ecosystem performance is usually affected by site condition (e.g., drainage, elevation, slope and aspect, soils, and surface geology) [6][7][8][9], climate changes (e.g., precipitation and surface temperature) [10][11][12][13][14], natural disturbances (e.g., wildfires and floods) [12], and anthropogenic effects (e.g., heavy grazing) [15][16]. Interpreting ecosystem performance variation or ecological disturbance is complex because of the influences of weather, site potential, natural disasters and anthropogenic effects [17].…”

Section: Introductionmentioning

confidence: 99%

Detecting Ecosystem Performance Anomalies for Land Management in the Upper Colorado River Basin Using Satellite Observations, Climate Data, and Ecosystem Models

Wylie

2010

Remote Sensing

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This study identifies areas with ecosystem performance anomalies (EPA) within the Upper Colorado River Basin (UCRB) during 2005-2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions) at the 90% level of confidence. The EPA maps were validated using "percentage of bare soil" ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming) tended to have a higher (lower) percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005)(2006)(2007) persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM) identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions.

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The Alaska vegetation classification.

Cited by 276 publications

References 97 publications

A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts

A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts

Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska

Detecting Ecosystem Performance Anomalies for Land Management in the Upper Colorado River Basin Using Satellite Observations, Climate Data, and Ecosystem Models

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