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

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.

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. 

Having secure land tenure, ensuring people's confidence in accessing, controlling and utilizing their land, is crucial for promoting sustainable land management practices. It also plays a key role in building resilience against the impacts of climate change, including extreme weather events, rising sea levels, and increased desertification due to higher temperatures.

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.

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.

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.

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.

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.

Recent studies have shown that globally, riverine and coastal floods are increasing in intensity and duration. In addition, the number of people living in flood-prone areas has increased substantially during the last two decades.

Healthy ecosystems provide essential pieces of daily life for humans such as clean water, food, medicines, carbon sequestration, protection against extreme weather, and much more. Natural capital accounting recognizes that these assets must be maintained, managed, and measured for critical decision-making activities.

Structures such as buildings, roads, and other infrastructure absorb and re-emit the sun’s heat more than natural landscapes such as forests and water bodies. Urban areas, where these structures are highly concentrated and greenery is limited, become “islands” of higher temperatures relative to outlying areas.