APPLICATIONS OF MODERATE-RESOLUTION REMOTE SENSING TECHNOLOGIES FOR SURFACE AIR POLLUTION MONITORING IN SOUTHEAST ASIA

Retrievals from Earth observation satellites are widely used for many applications, including analyzing dynamic lands and measuring atmospheric components. This research aims to evaluate appropriateness of using satellite retrievals to facilitate understanding characteristics of Southeast Asian (SEA) surface air pollution, attributed to regional biomass burnings and urban activities. The studies in this dissertation focused on using satellite retrievals for 1) mapping potential SEA air pollution sources; which are forests, rice paddies, and urban areas, 2) understanding dynamic optical characteristics of SEA biomass-burning aerosols, and 3) inferring surface ozone level. Data used in this study were from three NASA\u27s Earth Observing System (EOS) satellites, which are Terra, Aqua, and Aura. These retrievals have spatial resolution ranging from hundred meters to ten kilometers. Algorithms used for the SEA land cover classification were developed using time-series analyses of surface reflectance in multiple wavelength bands from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. Comparing the results to national statistical databases, good agreement was obtained for spatial estimation of forest areas after correction with plantation areas. For estimation of rice paddies areas, the agreement depended on the rice ecosystems. It was good for rainfed rice and poor for deepwater rice. Models for irrigated and upland rice areas showed overall high coefficients of determination, suggesting that they effectively simulated the spatial distribution of those rice paddies; but were prone to underestimate and overestimate, respectively. Estimated SEA regional rice area was 42×106 ha, which agrees with previous published values. Analysis of the satellite retrieval could identify large urban areas. However, the satellite-derived urban areas also incorrectly included large sandy beaches. Optical properties of SEA background aerosols were investigated through the multivariate analyses of long-term ground-based aerosol measurements acquired from Aerosol Robotic Network (AERONET). The results in this study showed that from mid-September to December, the aerosol had both fine size and high light scattering efficiency. It was assumed to be largely urban/industrial aerosols, possibly coming from eastern China. From January to April, the aerosol had fine size and had single scattering albedo (SSA at 440 nm) of approximately 0.9. It was assumed to be smoke from local biomass burning. From October to January, when seasonal winds are strongest, more SEA urban aerosol was observed. This aerosol had coarser size and had SSA of ~0.9 or less. The appropriateness of using Ozone Monitoring Instrument (OMI) aerosol retrieval to facilitate understanding SEA biomass-burning aerosol properties was evaluated through three lines of evidence. These are 1) comparisons between the results obtained from multivariate analyses of the OMI aerosol retrieval and those obtained from the ground-measured AERONET data, 2) from Atmospheric Infrared Sounder (AIRS) total column CO product, and 3) from MODIS active fire detections. The results showed that the OMI retrieval used for large-scale SEA biomass-burning aerosol characterization was consistent with these alternative measures only when 1 \u3c OMI aerosol optical depth (442 nm) \u3c 3. The OMI aerosol retrieval was then used for the study on dynamic characteristics of biomass burning aerosol. This study considered the aerosols from two forest-fire episodes, 2007 SEA continent and 2008 Indonesian fires. Dependence of the aerosol optical properties on four variables was investigated. These variables were 1) wind speed/direction, 2) relative humidity (RH), 3) land use/cover as a surrogate of fuel type estimated from time-series analysis of MODIS surface reflectance, and 4) age of aerosol estimated from spatial-temporal analysis of MODIS active fire and the wind characteristics. Results from Pearson Chi-square test for independence showed that the dependence between aerosol group memberships with different optical properties and the limiting variables was significant for most cases, except for Indonesian aerosol age factor. These results agree with prior knowledge on regional burning conditions (types of fuel and relative humidity) and aerosol chemical/physical properties (chemical composition related to aerosol optical properties and hygroscopicity). Using EOS-Aura tropospheric column ozone (TCO) to infer surface ozone level was evaluated through analyses of linear relationships between TCO estimated from OMI and Microwave Limb Sounder (MLS) retrievals and coincident TCO from balloon-based ozonesonde measurements. This evaluation was for different tropospheric ozone profile shapes and for different geographical regions (for low, mid, and high latitudes and for Pacific and Atlantic regions). Results indicate that inference on ozone level derived from the satellite-based TCO requires corresponding information about tropospheric ozone profile shape. The use of satellite-based TCO was more appropriate for polluted low-latitude locations where upper troposphere ozone is rare and surface enhanced ozone is high

Spatial Search Strategies for Open Government Data: A Systematic Comparison

The increasing availability of open government datasets on the Web calls for ways to enable their efficient access and searching. There is however an overall lack of understanding regarding spatial search strategies which would perform best in this context. To address this gap, this work has assessed the impact of different spatial search strategies on performance and user relevance judgment. We harvested machine-readable spatial datasets and their metadata from three English-based open government data portals, performed metadata enhancement, developed a prototype and performed both a theoretical and user-based evaluation. The results highlight that (i) switching between area of overlap and Hausdorff distance for spatial similarity computation does not have any substantial impact on performance; and (ii) the use of Hausdorff distance induces slightly better user relevance ratings than the use of area of overlap. The data collected and the insights gleaned may serve as a baseline against which future work can compare.Comment: Paper accepted to GIR'19: 13th Workshop on Geographic Information Retrieval (Lyon, France

Semi-Supervised Learning For Identifying Opinions In Web Content

Thesis (Ph.D.) - Indiana University, Information Science, 2011Opinions published on the World Wide Web (Web) offer opportunities for detecting personal attitudes regarding topics, products, and services. The opinion detection literature indicates that both a large body of opinions and a wide variety of opinion features are essential for capturing subtle opinion information. Although a large amount of opinion-labeled data is preferable for opinion detection systems, opinion-labeled data is often limited, especially at sub-document levels, and manual annotation is tedious, expensive and error-prone. This shortage of opinion-labeled data is less challenging in some domains (e.g., movie reviews) than in others (e.g., blog posts). While a simple method for improving accuracy in challenging domains is to borrow opinion-labeled data from a non-target data domain, this approach often fails because of the domain transfer problem: Opinion detection strategies designed for one data domain generally do not perform well in another domain. However, while it is difficult to obtain opinion-labeled data, unlabeled user-generated opinion data are readily available. Semi-supervised learning (SSL) requires only limited labeled data to automatically label unlabeled data and has achieved promising results in various natural language processing (NLP) tasks, including traditional topic classification; but SSL has been applied in only a few opinion detection studies. This study investigates application of four different SSL algorithms in three types of Web content: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. SSL algorithms are also evaluated for their effectiveness in sparse data situations and domain adaptation. Research findings suggest that, when there is limited labeled data, SSL is a promising approach for opinion detection in Web content. Although the contributions of SSL varied across data domains, significant improvement was demonstrated for the most challenging data domain--the blogosphere--when a domain transfer-based SSL strategy was implemented