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Xinyue Chang
Department of Statistics, Iowa State University, Ames, IA 50011, USA

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Journal article
Published: 30 April 2021 in Remote Sensing
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Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and temporal frequency of these surface water maps by using locally trained classifiers and gap-filling missing values via imputation in all available satellite images. We developed the Surface Water IMputation (SWIM) classification framework using R and the Google Earth Engine computing platform to improve water classification compared to the JRC study. The novel contributions of the SWIM classification framework include (1) a cluster-based algorithm to improve classification sensitivity to a variety of surface water conditions and produce approximately unbiased estimation of surface water area, (2) a method to gap-fill every available Landsat image for a region of interest to generate submonthly classifications at the highest possible temporal frequency, (3) an outlier detection method for identifying images that contain classification errors due to failures in cloud masking. Validation and several case studies demonstrate the SWIM classification framework outperforms the JRC dataset in spatiotemporal analyses of small waterbody dynamics with previously unattainable sensitivity and temporal frequency. Most importantly, this study shows that reliable surface water classifications can be obtained for all pixels in every available Landsat image, even those containing cloud cover, after performing gap-fill imputation. By using this technique, the SWIM framework supports monitoring water extent on a submonthly basis, which is especially applicable to assessing the impact of short-term flood and drought events. Additionally, our results contribute to addressing the challenges of training machine learning classifiers with biased ground truth data and identifying images that contain regions of anomalous classification errors.

ACS Style

Charles Labuzzetta; Zhengyuan Zhu; Xinyue Chang; Yuyu Zhou. A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery. Remote Sensing 2021, 13, 1742 .

AMA Style

Charles Labuzzetta, Zhengyuan Zhu, Xinyue Chang, Yuyu Zhou. A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery. Remote Sensing. 2021; 13 (9):1742.

Chicago/Turabian Style

Charles Labuzzetta; Zhengyuan Zhu; Xinyue Chang; Yuyu Zhou. 2021. "A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery." Remote Sensing 13, no. 9: 1742.

Journal article
Published: 04 August 2018 in Sustainability
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This study investigates the relationship between board composition and corporate social responsibility (CSR) performance. Specifically, we examine the impact of board composition (aspects like political experience, academic experience, overseas background, and gender diversity) on CSR performance. We test our hypotheses using data collected from 839 Chinese public firms during the period from 2008 to 2016. Applying generalized least squares regression, the study shows that the political experience, academic experience, and overseas background of the board members are positively related to the firm’s CSR performance. Moreover, we discuss the distinctive relationship between gender diversity and CSR performance in the context of Chinese culture. We extend the CSR literature by examining unique aspects of board composition in the Chinese context and offer fruitful implications for both scholars and practitioners.

ACS Style

Yiming Zhuang; Xinyue Chang; Younggeun Lee. Board Composition and Corporate Social Responsibility Performance: Evidence from Chinese Public Firms. Sustainability 2018, 10, 2752 .

AMA Style

Yiming Zhuang, Xinyue Chang, Younggeun Lee. Board Composition and Corporate Social Responsibility Performance: Evidence from Chinese Public Firms. Sustainability. 2018; 10 (8):2752.

Chicago/Turabian Style

Yiming Zhuang; Xinyue Chang; Younggeun Lee. 2018. "Board Composition and Corporate Social Responsibility Performance: Evidence from Chinese Public Firms." Sustainability 10, no. 8: 2752.