Earth Observation (EO) data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes. EO, empowered by methodologies from Artificial Intelligence (AI), supports various aspects of the UN’s Sustainable Development Goals (SDGs). This dissertation presents author’s major studies using EO to fill in knowledge gaps and develop methodologies and cloud-based applications in selected SDGs, including SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life below Water) and SDG 15 (Life on Land). For SDG 6, the study focuses on spatiotemporal water recharge patterns and interconnections between variables in the Nile watershed countries, highlighting the EO’s potential to implement transboundary water cooperation (SDG 6.5.2). For SDG 11, the studies focus on the indicators of urbanization (SDG 11.a.1 and 11.1.1) and air quality (SDG 11.6.2). Utilizing EO datasets, the annual geographical and population-weighted PM 2.5 levels in Egypt are analyzed in 18 years. The Random Decision Forest is developed to predict the PM10 levels in Cairo. Additionally, a cloud-based impervious surface classifier based on fully convolution neural networks (FCNN) is trained to monitor the urbanization in five Nile basin cities between 2013 and 2019. For SDG 14, the studies focus on marine pollution (SDG 14.1.1) through monitoring and exploring the atmospheric and meteorological factors regulating algal blooms, as well as its primary productivity in the Red Sea. For SDG 15, the mangrove changes over the Western Gulf region is quantified using multi-indices and machine learning models to protect the local threatened species (SDG 15.5.1). The MENA is investigated concerning changes in land coverage using the harmonic analysis of vegetation index (SDG 15.1.1). Finally, the dissertation also summarizes the SDG researches the author involved, including 1) air pollutant in the MENA region; 2) dust impact on rainfall 3) aerosols’ impact on snowfall; 4) oil spill modeling to help proper mitigation; 5) vegetation in the sea turtle nesting Islands; and 6) EO of Mediterranean seagrass Posidonia Oceanica.
","abstract_html":"<p>Earth Observation (EO) data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes. EO, empowered by methodologies from Artificial Intelligence (AI), supports various aspects of the UN’s Sustainable Development Goals (SDGs). This dissertation presents author’s major studies using EO to fill in knowledge gaps and develop methodologies and cloud-based applications in selected SDGs, including SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life below Water) and SDG 15 (Life on Land). For SDG 6, the study focuses on spatiotemporal water recharge patterns and interconnections between variables in the Nile watershed countries, highlighting the EO’s potential to implement transboundary water cooperation (SDG 6.5.2). For SDG 11, the studies focus on the indicators of urbanization (SDG 11.a.1 and 11.1.1) and air quality (SDG 11.6.2). Utilizing EO datasets, the annual geographical and population-weighted PM 2.5 levels in Egypt are analyzed in 18 years. The Random Decision Forest is developed to predict the PM10 levels in Cairo. Additionally, a cloud-based impervious surface classifier based on fully convolution neural networks (FCNN) is trained to monitor the urbanization in five Nile basin cities between 2013 and 2019. For SDG 14, the studies focus on marine pollution (SDG 14.1.1) through monitoring and exploring the atmospheric and meteorological factors regulating algal blooms, as well as its primary productivity in the Red Sea. For SDG 15, the mangrove changes over the Western Gulf region is quantified using multi-indices and machine learning models to protect the local threatened species (SDG 15.5.1). The MENA is investigated concerning changes in land coverage using the harmonic analysis of vegetation index (SDG 15.1.1). Finally, the dissertation also summarizes the SDG researches the author involved, including 1) air pollutant in the MENA region; 2) dust impact on rainfall 3) aerosols’ impact on snowfall; 4) oil spill modeling to help proper mitigation; 5) vegetation in the sea turtle nesting Islands; and 6) EO of Mediterranean seagrass <em>Posidonia Oceanica</em>.</p>","abstract_has_math":false,"creators":["Li, Wenzhao"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Hesham El-Askary","Thomas Piechota","Daniele Struppa","Olga Kalashnikova"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-12-01T08:00:00Z","date_published":"2019-12-01T08:00:00Z","updated_at":"2026-07-24T01:38:09Z","subjects":["Remote Sensing","SDG","Google Earth Engine","Aerosol","Chlorophyll-a","Hydrology","Artificial Intelligence and Robotics","Biogeochemistry","Environmental Health and Protection","Environmental Indicators and Impact Assessment","Environmental Monitoring","Water Resource Management"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/4","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hesham El-Askary","Thomas Piechota","Daniele Struppa","Olga Kalashnikova"]},{"key":"dc:creator","label":"Author","values":["Li, Wenzhao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Remote Sensing","SDG","Google Earth Engine","Aerosol","Chlorophyll-a","Hydrology","Artificial Intelligence and Robotics","Biogeochemistry","Environmental Health and Protection","Environmental Indicators and Impact Assessment","Environmental Monitoring","Water Resource Management"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/4"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Earth Observation (EO) data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes. EO, empowered by methodologies from Artificial Intelligence (AI), supports various aspects of the UN’s Sustainable Development Goals (SDGs). This dissertation presents author’s major studies using EO to fill in knowledge gaps and develop methodologies and cloud-based applications in selected SDGs, including SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life below Water) and SDG 15 (Life on Land). For SDG 6, the study focuses on spatiotemporal water recharge patterns and interconnections between variables in the Nile watershed countries, highlighting the EO’s potential to implement transboundary water cooperation (SDG 6.5.2). For SDG 11, the studies focus on the indicators of urbanization (SDG 11.a.1 and 11.1.1) and air quality (SDG 11.6.2). Utilizing EO datasets, the annual geographical and population-weighted PM 2.5 levels in Egypt are analyzed in 18 years. The Random Decision Forest is developed to predict the PM10 levels in Cairo. Additionally, a cloud-based impervious surface classifier based on fully convolution neural networks (FCNN) is trained to monitor the urbanization in five Nile basin cities between 2013 and 2019. For SDG 14, the studies focus on marine pollution (SDG 14.1.1) through monitoring and exploring the atmospheric and meteorological factors regulating algal blooms, as well as its primary productivity in the Red Sea. For SDG 15, the mangrove changes over the Western Gulf region is quantified using multi-indices and machine learning models to protect the local threatened species (SDG 15.5.1). The MENA is investigated concerning changes in land coverage using the harmonic analysis of vegetation index (SDG 15.1.1). Finally, the dissertation also summarizes the SDG researches the author involved, including 1) air pollutant in the MENA region; 2) dust impact on rainfall 3) aerosols’ impact on snowfall; 4) oil spill modeling to help proper mitigation; 5) vegetation in the sea turtle nesting Islands; and 6) EO of Mediterranean seagrass Posidonia Oceanica.
"]},{"key":"dc:source","label":"Dc Source","values":["W. Li, \"Employing Earth observations and artificial intelligence to address key global environmental challenges in service of the SDGs,\" Ph.D. dissertation, Chapman University, Orange, CA, 2019. https://doi.org/10.36837/chapman.000115"]},{"key":"dc:title","label":"Title","values":["Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs"]}]}],"canonical_facts":{"dc:contributor":["Hesham El-Askary","Thomas Piechota","Daniele Struppa","Olga Kalashnikova"],"dc:creator":["Li, Wenzhao"],"dc:date.available":["2020-01-01T08:00:00Z"],"dc:description.abstract":["Earth Observation (EO) data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes. EO, empowered by methodologies from Artificial Intelligence (AI), supports various aspects of the UN’s Sustainable Development Goals (SDGs). This dissertation presents author’s major studies using EO to fill in knowledge gaps and develop methodologies and cloud-based applications in selected SDGs, including SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life below Water) and SDG 15 (Life on Land). For SDG 6, the study focuses on spatiotemporal water recharge patterns and interconnections between variables in the Nile watershed countries, highlighting the EO’s potential to implement transboundary water cooperation (SDG 6.5.2). For SDG 11, the studies focus on the indicators of urbanization (SDG 11.a.1 and 11.1.1) and air quality (SDG 11.6.2). Utilizing EO datasets, the annual geographical and population-weighted PM 2.5 levels in Egypt are analyzed in 18 years. The Random Decision Forest is developed to predict the PM10 levels in Cairo. Additionally, a cloud-based impervious surface classifier based on fully convolution neural networks (FCNN) is trained to monitor the urbanization in five Nile basin cities between 2013 and 2019. For SDG 14, the studies focus on marine pollution (SDG 14.1.1) through monitoring and exploring the atmospheric and meteorological factors regulating algal blooms, as well as its primary productivity in the Red Sea. For SDG 15, the mangrove changes over the Western Gulf region is quantified using multi-indices and machine learning models to protect the local threatened species (SDG 15.5.1). The MENA is investigated concerning changes in land coverage using the harmonic analysis of vegetation index (SDG 15.1.1). Finally, the dissertation also summarizes the SDG researches the author involved, including 1) air pollutant in the MENA region; 2) dust impact on rainfall 3) aerosols’ impact on snowfall; 4) oil spill modeling to help proper mitigation; 5) vegetation in the sea turtle nesting Islands; and 6) EO of Mediterranean seagrass Posidonia Oceanica.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/4"],"dc:source":["W. Li, \"Employing Earth observations and artificial intelligence to address key global environmental challenges in service of the SDGs,\" Ph.D. dissertation, Chapman University, Orange, CA, 2019. https://doi.org/10.36837/chapman.000115"],"dc:subject":["Remote Sensing","SDG","Google Earth Engine","Aerosol","Chlorophyll-a","Hydrology","Artificial Intelligence and Robotics","Biogeochemistry","Environmental Health and Protection","Environmental Indicators and Impact Assessment","Environmental Monitoring","Water Resource Management"],"dc:title":["Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:09Z"}