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Estimating sagebrush cover in a port...

University of Wyoming., Geography & Recreation.

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Estimating sagebrush cover in a portion of the Pinedale Anticline Project Area using Landsat imagery and ancillary data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Estimating sagebrush cover in a portion of the Pinedale Anticline Project Area using Landsat imagery and ancillary data./
作者:	Storey, Thomas O.
面頁冊數:	100 p.
附註:	Advisers: Steven Prager; Ramesh Sivanpillai.
Contained By:	Masters Abstracts International46-02.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1446900
ISBN:	9780549216575

Estimating sagebrush cover in a portion of the Pinedale Anticline Project Area using Landsat imagery and ancillary data.
Storey, Thomas O.

Estimating sagebrush cover in a portion of the Pinedale Anticline Project Area using Landsat imagery and ancillary data. - 100 p.

Advisers: Steven Prager; Ramesh Sivanpillai.

Thesis (M.A.)--University of Wyoming, 2007.

Sagebrush ecosystems offer many benefits to the state of Wyoming, such as habitat for wildlife. Recently, sagebrush ecosystems in Wyoming have been subjected to many anthropogenic disturbances. The consequences that these disturbances have upon sagebrush ecosystems can be very important to wildlife and sagebrush ecosystems. Remote sensing and GIS are tools that may help in the processing and gathering of sagebrush data; yet, there is limited research supporting best practices for remote sensing of sagebrush in disturbed environments. This thesis addresses the issue of using remote sensing to evaluate sagebrush cover in disturbed areas. To accomplish this goal three objectives were met. These included (1) The investigation of best approaches for the collection and aggregation of field data, (2) The development of approaches for scaling field data using high resolution imagery to provide a basis for determining the amount of sagebrush that occurs in each pixel of commonly available coarse resolution data, and, (3) The evaluation of whether texture, slope, aspect, and elevation can improve the ability of coarser spatial resolution imagery to predict the amount of sagebrush cover in a given location. In the completion of these objectives it was found that the prediction of sagebrush cover can be reasonably accomplished by using binary logistic regression, ocular estimation, and various spectral and ancillary data layers.

ISBN: 9780549216575Subjects--Topical Terms:

524010
Geography.

Estimating sagebrush cover in a portion of the Pinedale Anticline Project Area using Landsat imagery and ancillary data.
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500 $a Source: Masters Abstracts International, Volume: 46-02, page: 0705.
502 $a Thesis (M.A.)--University of Wyoming, 2007.
520 $a Sagebrush ecosystems offer many benefits to the state of Wyoming, such as habitat for wildlife. Recently, sagebrush ecosystems in Wyoming have been subjected to many anthropogenic disturbances. The consequences that these disturbances have upon sagebrush ecosystems can be very important to wildlife and sagebrush ecosystems. Remote sensing and GIS are tools that may help in the processing and gathering of sagebrush data; yet, there is limited research supporting best practices for remote sensing of sagebrush in disturbed environments. This thesis addresses the issue of using remote sensing to evaluate sagebrush cover in disturbed areas. To accomplish this goal three objectives were met. These included (1) The investigation of best approaches for the collection and aggregation of field data, (2) The development of approaches for scaling field data using high resolution imagery to provide a basis for determining the amount of sagebrush that occurs in each pixel of commonly available coarse resolution data, and, (3) The evaluation of whether texture, slope, aspect, and elevation can improve the ability of coarser spatial resolution imagery to predict the amount of sagebrush cover in a given location. In the completion of these objectives it was found that the prediction of sagebrush cover can be reasonably accomplished by using binary logistic regression, ocular estimation, and various spectral and ancillary data layers.
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