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Remote Sens. 2016, 8(6), 462; doi:10.3390/rs8060462

Landscape-Level Associations of Wintering Waterbird Diversity and Abundance from Remotely Sensed Wetland Characteristics of Poyang Lake

1
Department of Landscape Architecture & Environmental Planning, College of Environmental Design, University of California Berkeley, Berkeley, CA 94720-2000, USA
2
Department of Environmental Science, Policy and Management, Division of Ecosystem Science, College of Natural Resources, University of California, Berkeley, CA 94720-3114, USA
3
Department of Forest and Wildlife Ecology, College of Agriculture and Life Sciences, University of Wisconsin-Madison, Madison, WI 53706-1598, USA
4
The International Crane Foundation, Baraboo, WI 53913, USA
5
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
6
Poyang Lake Ecological Research Station for Environment and Health, Duchang 332600, China
*
Author to whom correspondence should be addressed.
Academic Editors: Javier Bustamante, Alfredo R. Huete, Patricia Kandus, Ricardo Díaz-Delgado, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 28 February 2016 / Revised: 24 May 2016 / Accepted: 25 May 2016 / Published: 31 May 2016
(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)
View Full-Text   |   Download PDF [7638 KB, uploaded 31 May 2016]   |  

Abstract

Poyang Lake, the largest freshwater wetland in China, provides critical habitat for wintering waterbirds from the East Asian Flyway; however, landscape drivers of non-uniform bird diversity and abundance are not yet well understood. Using a winter 2006 waterbird survey, we examined the relationships among metrics of bird community diversity and abundance and landscape characteristics of 51 wetland sub-lakes derived by an object-based classification of Landsat satellite data. Relative importance of predictors and their sets was assessed using information-theoretic model selection and the Akaike Information Criterion. Ordinary least squares regression models were diagnosed and corrected for spatial autocorrelation using spatial autoregressive lag and error models. The strongest and most consistent landscape predictors included Normalized Difference Vegetation Index for mudflat (negative effect) and emergent grassland (positive effect), total sub-lake area (positive effect), and proportion of submerged vegetation (negative effect). Significant spatial autocorrelation in linear regression was associated with local clustering of response and predictor variables, and should be further explored for selection of wetland sampling units and management of protected areas. Overall, results corroborate the utility of remote sensing to elucidate potential indicators of waterbird diversity that complement logistically challenging ground observations and offer new hypotheses on factors underlying community distributions. View Full-Text
Keywords: wetlands; lakes; remote sensing; waterbird; biodiversity; conservation; spatial autocorrelation; object-based image analysis; ecology; habitat wetlands; lakes; remote sensing; waterbird; biodiversity; conservation; spatial autocorrelation; object-based image analysis; ecology; habitat
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Dronova, I.; Beissinger, S.R.; Burnham, J.W.; Gong, P. Landscape-Level Associations of Wintering Waterbird Diversity and Abundance from Remotely Sensed Wetland Characteristics of Poyang Lake. Remote Sens. 2016, 8, 462.

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