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Displaying 1 - 10 of 45An Introduction to Synthetic Aperture Radar (SAR) and Its Applications
Synthetic Aperture Radar (SAR) has the capability to observe the Earth's surface both day and night and through most weather conditions, making it an ideal sensor to support a wide range of science and applications.
Earth Observations for Humanitarian Applications
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Earth Observations for Humanitarian Applications. Refugees, internally displaced people (IDPs), and other displaced populations are made more vulnerable to climate change impacts due to their socio-political marginalization. This three-part, intermediate training presents concrete strategies for mapping localized climate conditions with risks faced by refugee and IDP communities around the world.
The training will focus on flood risk assessments and specific challenges for assessing flood risk in refugee and IDP camps; gauging long-term heat stress in refugee camps and the challenges with decision making surrounding heat risk; and monitoring drought effects on agricultural landscapes in refugee settings using Earth observations (EO) to explore the correlations between anomalies in crop productivity and weather-based factors
Introduction to Lightning Observations and Applications
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Introduction to Lightning Observations and Applications. This three-part, introductory training focuses on global and regional lightning data products that can be applied to disaster risk preparedness.
As the intensity and frequency of extreme weather events are likely to increase due to climate change impacts, lightning activity will likely increase as well, causing more power outages, increased risks of wildfire ignition, and increased numbers of injuries and fatalities. Therefore, information about lightning activity is critical for better preparedness against these disasters.
Large Scale Applications of Machine Learning using Remote Sensing for Building Agriculture Solutions
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Large Scale Applications of Machine Learning using Remote Sensing for Building Agriculture Solutions. Remote sensing data is becoming crucial to solve some of the most important environmental problems, especially pertaining to agricultural applications and food security. Participants will become familiar with data format and quality considerations, tools, and techniques to process remote sensing imagery at large scale from publicly available satellite sources, using cloud tools such as AWS S3, Databricks, and Parquet. Additionally, participants will learn how to analyze and train machine learning models for classification using this large source of data to solve environmental problems with a focus on agriculture.
Transforming Earth Observation (EO) Data into Building Infrastructure Data Sets for Disaster Risk Modeling
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online, intermediate webinar series: Transforming Earth Observation (EO) Data into Building Infrastructure Data Sets for Disaster Risk Modeling. This three-part training, presented in English and Spanish, is open to the public and will cover the basics of natural hazard risk modeling and exposure development with a focus on fusing data from multiple datasets expressly for the purposes of risk assessment. We will also present examples applying the techniques to applications related to flood risk assessment, climate adaptation, and earthquake modeling.
Monitoring Water Quality of Inland Lakes using Remote Sensing
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Monitoring Water Quality of Inland Lakes using Remote Sensing. This advanced-level training will focus on using remote sensing observations from Landsat 8 and 9, Sentinel-2, and Sentinel-3 for assessing water quality parameters, including chlorophyll-a concentration, turbidity, and Total Suspended Solids (TSS) in inland lakes. This training will also highlight the importance of in situ measurements of these parameters, coincident with satellite observations, in developing methodologies for operational water quality monitoring.
Assessing the Impacts of Fires on Watershed Health
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Assessing the Impacts of Fires on Watershed Health. This advanced-level, three-part training will focus on using remote sensing observations for monitoring post-fire impacts on watershed health. Specifically, this training will highlight uses of NASA Earth observations (EO) for pre-fire land cover mapping, watershed delineation and stream mapping, post-fire burn severity mapping, and pre- and post-fire riverine and freshwater water quality.
Application of NASA SPoRT-Land Information System (SPoRT-LIS) Soil Moisture Data for Drought
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Application of NASA SPoRT-Land Information System (SPoRT-LIS) Soil Moisture Data for Drought. This 3-part webinar series is focused on the introduction of the NASA Land Information System (LIS) output of soil moisture at various depths for drought analysis and monitoring. Examples from operational applications as well as practice exercises (for using LIS data for drought monitoring) will be included in this course. Access to the SPoRT-LIS products via online viewer, in GIS formats, and GIS-based display tools, will also be included. Moreover, self-paced microlessons will be available to help users confirm their understanding and improve their skill via homework lessons between live sessions.
Fundamentals of Machine Learning for Earth Science
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new online introductory webinar series: Fundamentals of Machine Learning for Earth Science. This three-part training, presented in English and Spanish, is open to the public and will provide attendees an overview of machine learning in regards to Earth Science, and how to apply these algorithms and techniques to remote sensing data in a meaningful way. Attendees will also be provided with end-to-end case study examples for generating a simple random forest model for land cover classification from optical remote sensing. We will also present additional case studies to apply the presented workflows using additional NASA data.
Crop Mapping using Synthetic Aperture Radar (SAR) and Optical Remote Sensing
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new online advanced webinar series: Crop Mapping using Synthetic Aperture Radar (SAR) and Optical Remote Sensing. This three-part training, presented in English and Spanish, is open to the public and builds on previous ARSET agricultural trainings. Here we present more advanced radar remote sensing techniques using polarimetry and a canopy structure dynamic model to monitor crop growth. The training will also cover methods that use machine learning methods to classify crop type using a time series of Sentinel-1 & Sentinel-2 imagery. This series will include practical exercises using the Sentinel Application Platform (SNAP) and Python code written in Python Jupyter Notebooks, a web-based interactive development environment for scientific computing and machine learning.