Organizar un foro mundial en 2016 para tratar los grandes problemas relacionados con la desigualdad del acceso a la tierra y a los recursos naturales, es el objetivo de la firma de la convocatoria del FMAT 2016. (ver convocatoria).

O Comitê Executivo da Rede Brasileira de Pesquisa em Soberania e Segurança Alimentar e Nutricional tem a satisfação de confirmar a realização do III Encontro Nacional de Pesquisa em SSAN nos dias 08, 09 e 10 de Novembro de 2017, na cidade de Curitiba (PR), em local ainda a ser escolhido entre as opções existentes.

This forum will bring together each of the key themes and players from our previous events on smallholder farmer resilience, deforestation and sustainable agriculture. The Sustainable Landscapes Conference will tackle the biggest issues faced by business in creating a net positive impact and ensuring sustainable and resilient agricultural supply chains.

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 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.