The sagebrush shrub steppe ecosystem is of great concern to researchers, conservationists, and the general public because of the documented declines associated with it. Monitoring in the past has generally been point-based and lacking in long-term data. To overcome these deficiencies, an automated method of monitoring was developed using GIS and remote sensing. Geospatial layers of vegetation, soils, fire history, roads, streams, and springs were acquired and processed to characterize selected monitoring locations. A temporal set of Landsat satellite imagery for the past 30 years was normalized to reduce the effects of sun angle, haze, and sensor change. After normalization, a Tasseled Cap Transformation was adapted with local coefficients to provide a landscape metric which was sensitive to actual ground conditions and meaningful at management level. The Tasseled Cap outputs of brightness and greenness are a relative measure of bare ground and plant productivity, respectively. When measured over time, brightness and greenness provided diagnostic trends and condition of treated big sagebrush communities

京都大学 / 0048 / 新制・論文博士 / 博士(地球環境学) / 乙第12828号 / 論地環博第8号 / 新制||地環||24(附属図書館) / 31315 / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)准教授 西前 出, 教授 渡邉 紹裕, 教授 小方 登 / 学位規則第4条第2項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

To address accuracy concerns of the National Land Cover Dataset (NLCD), this case study compares impervious surface from the NLCD to a Detailed Land Cover Dataset (DLCD) for the Town of Blacksburg, Virginia over two time periods (2005/2006 and 2011) at spatial aggregation scales (fine to coarse) and scopes (site-specific to area-extent). When comparing the total impervious surface area, the NLCD overestimated the DLCD by appreciable amounts (12-27%) for the entire town and across all specified land use zones for both time periods examined. A binary pixel-wise accuracy assessment of impervious surface revealed that the NLCD performed well for all scopes except for the single family land use zone (user accuracy <40%). The spatial aggregation of pixels to 90-m led to improved agreement between the two datasets. Using the DLCD as a reference, an empirical normalization equation was successfully applied to the NLCD to further reduce overestimation and data skewness. / Master of Science

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