K
Kristin K. Arend
Researcher at Ohio Department of Natural Resources
Publications - 8
Citations - 736
Kristin K. Arend is an academic researcher from Ohio Department of Natural Resources. The author has contributed to research in topics: Hypoxia (environmental) & Biology. The author has an hindex of 6, co-authored 6 publications receiving 592 citations. Previous affiliations of Kristin K. Arend include Purdue University & Lake Superior State University.
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Journal ArticleDOI
Assessing and addressing the re-eutrophication of Lake Erie: Central basin hypoxia
Donald Scavia,J. David Allan,Kristin K. Arend,Steven M. Bartell,Dmitry Beletsky,Nate S. Bosch,Stephen B. Brandt,Ruth D. Briland,Irem Daloğlu,Joseph V. DePinto,David M. Dolan,Mary Anne Evans,Troy M. Farmer,Daisuke Goto,Haejin Han,Tomas O. Höök,Roger L. Knight,Stuart A. Ludsin,Doran M. Mason,Anna M. Michalak,R. Peter Richards,James J. Roberts,Daniel K. Rucinski,Edward S. Rutherford,David J. Schwab,Timothy M. Sesterhenn,Hongyan Zhang,Yuntao Zhou,Yuntao Zhou +28 more
TL;DR: In this paper, recent trends in key eutrophication-related properties, assess their likely ecological impacts, and develop load response curves to guide revised hypoxia-based loading targets called for in the 2012 Great Lakes Water Quality Agreement.
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Seasonal and interannual effects of hypoxia on fish habitat quality in central Lake Erie
Kristin K. Arend,Kristin K. Arend,Dmitry Beletsky,Joseph V. DePinto,Stuart A. Ludsin,James J. Roberts,James J. Roberts,Daniel K. Rucinski,Donald Scavia,David J. Schwab,Tomas O. Höök +10 more
TL;DR: The results highlight the importance of differential spatiotemporally interactive effects of DO and temperature on relative fish habitat quality and quantity.
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Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers
Tharindu Abeysinghe,Anita Simic Milas,Kristin K. Arend,Breann Hohman,Patrick Reil,Andrew J. Gregory,Angélica Vázquez-Ortega +6 more
TL;DR: This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN).
Journal ArticleDOI
Different colours of shadows: classification of UAV images
TL;DR: In this study, the Maximum Likelihood (ML) and Support Vector Machine (SVM) classifiers were used to classify a UAV image acquired using a red–green–blue (RGB) camera over the Old Woman Creek National Estuarine Research Reserve in Ohio, USA.
Journal ArticleDOI
Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data
Prabha Amali Rupasinghe,Anita Simic Milas,Kristin K. Arend,Martin Albert Simonson,Christine M. Mayer,Scudder Mackey +5 more
TL;DR: In this article, the authors map land and aquatic vegetation of coastal areas using remote sensing for better management and conservation in many parts of the world, such as India, Australia, and New Zealand.