Land Events

One of the main aims of the Land Dialogues series is and has been to highlight Indigenous knowledge and wisdom as a solution to pressing global challenges.  The series does so by creating a virtual space that bridges that gap, where the term “expert” is not limited to academics or researchers, in an effort to both decolonize and democratize knowledge.  In particular, the Land Portal's role is to highlight Indigenous Peoples need for agency and control over the data that is about them, recognizing that data can either amplify equality or exacerbate unequal power structures.  

NASA’s Applied Remote Sensing Training Program (ARSET) opened a new open, online intermediate webinar series: Biodiversity Applications for Airborne Imaging Systems. This four-part webinar series will focus on NASA Earth Observations (EO) that can be used characterize the structure and function of ecosystems and to measure and monitor terrestrial and aquatic biodiversity.

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.

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.