College of Behavioral & Social Sciences
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations..
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Item Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan(MDPI, 2018-03-21) Khan, Ahmad; Hansen, Matthew C.; Potapov, Peter V.; Adusei, Bernard; Pickens, Amy; Krylov, Alexander; Stehman, Stephen V.While publicly available, cost-free coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems, commercial high spatial resolution satellite data are often preferred alternative for fine scale land tenure agricultural systems such as found in Pakistan. In this article, we integrated commercial 5 m spatial resolution RapidEye and free 30 m Landsat imagery in characterizing winter wheat in Punjab province, Pakistan. Specifically, we used 5 m spatial resolution RapidEye imagery from peak of the winter wheat growing season to derive training data for the characterization of time-series Landsat data. After co-registration, each RapidEye image was classified into wheat/no wheat labels at the 5 m resolution and then aggregated as percent cover to 30 m Landsat grid cells. We produced four maps, two using RapidEye derived continuous training data (of percent wheat cover) as input to a regression tree model, and two using RapidEye derived categorical training data as input to a classification tree model. From the RapidEye-derived 30 m continuous training data, we derived Map 1 as percent wheat per pixel, and Map 2 as binary wheat/no wheat classification derived using a 50% threshold applied to Map 1. To create the categorical wheat/no wheat training data, we first converted the continuous training data to a wheat/no wheat classification, and then used these categorical RapidEye training data to produce a categorical wheat map from the Landsat data. Two methods for categorizing the training data were used. The first method used a 50% wheat/no wheat threshold to produce Map 3, and the second method used only pure wheat (≥75% cover) and no wheat (≤25% cover) training pixels to produce Map 4. The approach of Map 4 is analogous to a standard method in which whole, pure, high-confidence training pixels are delineated. We validated the wheat maps with field data collected using a stratified, two-stage cluster design. Accuracy of the maps produced from the percent cover training data (Map 1 and Map 2) was not substantially better than the accuracy of the maps produced from the categorical training data as all methods yielded similar overall accuracies (±standard error): 88% (±4%) for Map 1, 90% (±4%) for Map 2, 90% (±4%) for Map 3, and 87% (±4%) for Map 4. Because the percent cover training data did not produce significantly higher accuracies, sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other like landscapes, training data for supervised classification may be collected directly from Landsat images without the need for high resolution reference imagery.Item Seeing Love As I Know It: Love Prototypes as a Source of Positive Illusions in Romantic Relationships(2019) Venaglia, Rachel B.; Lemay, Edward P.; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Love is prototypically organized such that some features of love are clearer, better examples of the concept than others, but little work has been done to explain how laypeople’s love prototypes translate into cognition and emotion in actual romantic relationships. To help fill this gap, this dissertation examined the role of love prototypes as a source of positive illusions in perceiving romantic partners, as well as the implications of these perceptions for relationships. More specifically, it was predicted that though people would be somewhat accurate in their perceptions of their partner’s traits, feelings, and behaviors, people would also perceive their partner as possessing the traits, feelings, and behaviors that are consistent with the features most central to their idea of love. In turn, it was expected that when people perceive their romantic partner consistently with their central love prototypes, they would feel more loved and satisfied in their relationship. A three-wave longitudinal study tested these predictions. It was consistently found that people’s individualized love prototypes predicted their perceptions of their partner, suggesting that love prototypes are indeed a source of positive illusions in relationships. Perceptions of partner’s traits, feelings, and behaviors were also predicted by partner’s actual traits, feelings, and behaviors, thus demonstrating that people are both accurate and biased in their perceptions of their partner. Further, the association between perceivers’ love prototype centrality and their perceptions of their partner was especially strong when they had a strong desire to be loved by their partner, and was weaker when perceivers were higher in avoidant attachment, ambivalent attachment, rejection sensitivity, and, counter to predictions, when the feature being perceived was more ambiguous. Mixed support was found for the role of self-esteem and relational-interdependent self-construal as moderators of the relationship between perceivers’ love prototype centrality and their partner perceptions. Importantly, the more people perceived their partner as consistent with their love prototypes, the more loved and satisfied they felt in their relationship, though this greater felt love was limited to a particular context. Overall, these findings demonstrate that illusory perceptions that one’s partner aligns with one’s love prototypes are beneficial for perceivers.