Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images

This dissertation is an evaluation of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean 2) Coral Reef Change Detection in Remote Pacific Islands using Support Vector Machine Classifiers 3) A Generalized Machine Learning Classifier for Spatiotemporal Analysis of Coral Reefs in the Red Sea. The aim of this dissertation is to propose and evaluate a methodology for developing a robust machine learning classifier that can effectively be deployed to accurately detect coral reefs at scale. The hypothesis is that Landsat data can be used to train a classifier to detect coral reefs in remote sensing imagery and that this classifier can be trained to generalize across multiple sites. Another objective is to identify how well different classifiers perform under the generalized conditions and how unique the spectral signature of coral is as environmental conditions vary across observation sites. A methodology for validating the generalization performance of a classifier to unseen locations is proposed and implemented (Controlled Parameter Cross-Validation,). Analysis is performed using satellite imagery from nine different locations with known coral reefs (six Pacific Ocean sites and three Red Sea sites). Ground truth observations for four of the Pacific Ocean sites and two of the Red Sea sites were used to validate the proposed methodology. Within the Pacific Ocean sites, the consolidated classifier (trained on data from all sites) yielded an accuracy of 75.5% (0.778 AUC). Within the Red Sea sites, the consolidated classifier yielded an accuracy of 71.0% (0.7754 AUC). Finally, long-term change detection analysis is conducted for each of the sites evaluated. In total, over 16,700 km2 was analyzed for benthic cover type and cover change detection analysis. Within the Pacific Ocean sites, decreases in coral cover ranged from 25.3% reduction (Kingman Reef) to 42.7% reduction (Kiritimati Island). Within the Red Sea sites, decrease in coral cover ranged from 3.4% (Umluj) to 13.6% (Al Wajh)

Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean

This study was an evaluation of the spectral signature generalization properties of coral across four remote Pacific Ocean reefs. The sites under consideration have not been the subject of previous studies for coral classification using remote sensing data. Previous research regarding using remote sensing to identify reefs has been limited to in-situ assessment, with some researchers also performing temporal analysis of a selected area of interest. This study expanded the previous in-situ analyses by evaluating the ability of a basic predictor, Linear Discriminant Analysis (LDA), trained on Depth Invariant Indices calculated from the spectral signature of coral in one location to generalize to other locations, both within the same scene and in other scenes. Three Landsat 8 scenes were selected and masked for null, land, and obstructed pixels, and corrections for sun glint and atmospheric interference were applied. Depth Invariant Indices (DII) were then calculated according to the method of Lyzenga and an LDA classifier trained on ground truth data from a single scene. The resulting LDA classifier was then applied to other locations and the coral classification accuracy evaluated. When applied to ground truth data from the Palmyra Atoll location in scene path/row 065/056, the initial model achieved an accuracy of 80.3%. However, when applied to ground truth observations from another location within the scene, namely, Kingman Reef, it achieved an accuracy of 78.6%. The model was then applied to two additional scenes (Howland Island and Baker Island Atoll), which yielded an accuracy of 69.2% and 71.4%, respectively. Finally, the algorithm was retrained using data gathered from all four sites, which produced an overall accuracy of 74.1%

Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

Despite the abundance of research on coral reef change detection, few studies have been conducted to assess the spatial generalization principles of a live coral cover classifier trained using remote sensing data from multiple locations. The aim of this study is to develop a machine learning classifier for coral dominated benthic cover-type class (CDBCTC) based on ground truth observations and Landsat images, evaluate the performance of this classifier when tested against new data, then deploy the classifier to perform CDBCTC change analysis of multiple locations. The proposed framework includes image calibration, support vector machine (SVM) training and tuning, statistical assessment of model accuracy, and temporal pixel-based image dierencing. Validation of the methodology was performed by cross-validation and train/test split using ground truth observations of benthic cover from four dierent reefs. These four locations (Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island) as well as two additional locations (Kiritimati Island and Tabuaeran Island) were then evaluated for CDBCTC change detection. The in-situ training accuracy against ground truth observations for Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island were 87.9%, 85.7%, 69.2%, and 82.1% respectively. The classifier attained generalized accuracy scores of 78.8%, 81.0%, 65.4%, and 67.9% for the respective locations when trained using ground truth observations from neighboring reefs and tested against the local ground truth observations of each reef. The classifier was trained using the consolidated ground truth data of all four sites and attained a cross-validated accuracy of 75.3%. The CDBCTC change detection analysis showed a decrease in CDBCTC of 32% at Palmyra Atoll, 25% at Kingman Reef, 40% at Baker Island Atoll, 25% at Howland Island, 35% at Tabuaeran Island, and 43% at Kiritimati Island. This research establishes a methodology for developing a robust classifier and the associated Controlled Parameter Cross-Validation (CPCV) process for evaluating how well the model will generalize to new data. It is an important step for improving the scientific understanding of temporal change within coral reefs around the globe

A Polyadenylation Factor Subunit Implicated in Regulating Oxidative Signaling in Arabidopsis thaliana

BACKGROUND: Plants respond to many unfavorable environmental conditions via signaling mediated by altered levels of various reactive oxygen species (ROS). To gain additional insight into oxidative signaling responses, Arabidopsis mutants that exhibited tolerance to oxidative stress were isolated. We describe herein the isolation and characterization of one such mutant, oxt6. METHODOLOGY/PRINCIPAL FINDINGS: The oxt6 mutation is due to the disruption of a complex gene (At1g30460) that encodes the Arabidopsis ortholog of the 30-kD subunit of the cleavage and polyadenylation specificity factor (CPSF30) as well as a larger, related 65-kD protein. Expression of mRNAs encoding Arabidopsis CPSF30 alone was able to restore wild-type growth and stress susceptibility to the oxt6 mutant. Transcriptional profiling and single gene expression studies show elevated constitutive expression of a subset of genes that encode proteins containing thioredoxin- and glutaredoxin-related domains in the oxt6 mutant, suggesting that stress can be ameliorated by these gene classes. Bulk poly(A) tail length was not seemingly affected in the oxt6 mutant, but poly(A) site selection was different, indicating a subtle effect on polyadenylation in the mutant. CONCLUSIONS/SIGNIFICANCE: These results implicate the Arabidopsis CPSF30 protein in the posttranscriptional control of the responses of plants to stress, and in particular to the expression of a set of genes that suffices to confer tolerance to oxidative stress

A nanostructural view of the cell wall disassembly process during fruit ripening and postharvest storage by atomic force microscopy

Background: The mechanical properties of parenchyma cell walls and the strength and extension of adhesion areas between adjacent cells, jointly with cell turgor, are main determinants of firmness of fleshy fruits. These traits are modified during ripening leading to fruit softening. Cell wall modifications involve the depolymerisation of matrix glycans and pectins, the solubilisation of pectins and the loss of neutral sugars from pectin side chains. These changes weaken the cell walls and increase cell separation, which in combination with a reduction in cell turgor, bring about textural changes. Atomic force microscopy (AFM) has been used to characterize the nanostructure of cell wall polysaccharides during the ripening and postharvest storage of several fruits. This technique allows the imaging of individual polymers at high magnification with minimal sample preparation. Scope and approach: This paper reviews the main features of the cell wall disassembly process associated to fruit softening from a nanostructural point of view, as has been provided by AFM studies. Key findings and conclusions: AFM studies show that pectin size, ramification and complexity is reduced during fruit ripening and storage, and in most cases these changes correlate with softening. Postharvest treatments that improve fruit quality have been proven to preserve pectin structure, suggesting a clear link between softening and pectin metabolism. Nanostructural characterization of cellulose and hemicellulose during ripening has been poorly explored by AFM and the scarce results available are not conclusive. Globally, AFM could be a powerful tool to gain insights about the bases of textural fruit quality in fresh and stored fruits