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Scott J. Goetz
Researcher at Northern Arizona University
Publications - 269
Citations - 34468
Scott J. Goetz is an academic researcher from Northern Arizona University. The author has contributed to research in topics: Climate change & Tundra. The author has an hindex of 76, co-authored 246 publications receiving 28080 citations. Previous affiliations of Scott J. Goetz include University of Idaho & University of Maryland, College Park.
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Landscape pattern and successional dynamics in the boreal forest
TL;DR: In this article, the landscape-scale community dynamics of a boreal forest ecosystem were investigated using the Landsat MSS data record form 1973 to 1983 to generate a stochastic description of the key life cycle states of the community landscape elements.
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A narrow window of summer temperatures associated with shrub growth in Arctic Alaska
Laia Andreu-Hayles,Benjamin V. Gaglioti,Benjamin V. Gaglioti,Logan T. Berner,Mathieu Lévesque,Mathieu Lévesque,Kevin J. Anchukaitis,Kevin J. Anchukaitis,Scott J. Goetz,Rosanne D'Arrigo +9 more
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Definition and measurement of tree cover: A comparative analysis of field-, lidar- and landsat-based tree cover estimations in the Sierra national forests, USA
Hao Tang,Xiao-Peng Song,Feng Zhao,Alan H. Strahler,Crystal L. Schaaf,Crystal L. Schaaf,Scott J. Goetz,Chengquan Huang,Matthew C. Hansen,Ralph Dubayah +9 more
TL;DR: In this paper, the authors conducted a comparative analysis of different tree cover data sets, derived from field sample, terrestrial and airborne lidar scans, and Landsat imagery, to investigate factors affecting tree cover estimation in a western mountainous conifer forest in the United States.
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Assessment and extension of the MODIS FPAR products in temperate forests of the eastern United States
Daniel Steinberg,Scott J. Goetz +1 more
TL;DR: In this article, the authors examined the variability in the quality of the collection 4 and 5 MODIS FPAR products over a 1.1 million km2 region dominated by temperate forests, and explored the utility of estimating FPAR from the more spatially extensive MODIS NDVI product using a simple, regionally based linear regression.