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Introduction to Google Earth Engine with R language.

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Dr. Ricardo Dal'Agnol

Ricardo Dalagnol is an Environmental Engineer (2011), Master (2014) and PhD (2020) in Remote Sensing by the National Institute for Space Research (INPE/Brazil). He currently works as a Post-Doctoral Researcher funded by FAPESP in the Division of Earth Observation and Geoinformatics at INPE. He has experience with several applications in the area of Biogeosciences, with a focus on tropical ecology, nature conservation and environmental monitoring in the Amazon rainforest. He specializes in conducting quantitative research using advanced data types such as high-resolution satellite optical and airborne LIDAR data, as well as methods such as deep learning convolutional neural networks. His current project is to develop methods and analyses based on remote sensing to understand tree mortality in the Amazon.

Palestrante
Dr. Ricardo Dal'Agnol

Ricardo Dalagnol is an Environmental Engineer (2011), Master (2014) and PhD (2020) in Remote Sensing by the National Institute for Space Research (INPE/Brazil). He currently works as a Post-Doctoral Researcher funded by FAPESP in the Division of Earth Observation and Geoinformatics at INPE. He has experience with several applications in the area of Biogeosciences, with a focus on tropical ecology, nature conservation and environmental monitoring in the Amazon rainforest. He specializes in conducting quantitative research using advanced data types such as high-resolution satellite optical and airborne LIDAR data, as well as methods such as deep learning convolutional neural networks. His current project is to develop methods and analyses based on remote sensing to understand tree mortality in the Amazon.

Sobre o conteúdo

It will cover basic concepts of using GEE within R, such as installation, image selection, filtering, mosaic and composition, vegetation index calculation, and downloading data to the computer. It can also be run in Python as the GEE syntax is very similar. The idea is for the participant to be able to use the systems and download data to analyze on their own computer. A practical example will be conducted regarding crop mapping using machine learning. Requirements: R and RStudio (newest versions is fine), access to Google Earth Engine (https://code.earthengine.google.com/), access to Google Drive, and basic R language knowledge. Introdução ao Google Earth Engine em linguagem R Serão abordados conceitos básicos do uso do GEE dentro do R como instalação, seleção de imagens, filtragem, mosaico e composição, cálculo de índice de vegetação, e download de dados para o computador. Também pode ser executado em Python pois a sintaxe do GEE é muito parecida. A ideia é o participante ficar apto a utilizar os sistemas e baixar dados para analisar em seu próprio computador. Será realizado um exemplo prático de mapeamento agrícola com machine learning.

Sobre o evento

The IEEE GRSS Young Professionals (YP) & ISPRS Student Consortium (SC) Summer School (SS) are events organized jointly in Brazil and supported by the two main international organizations in the field of remote sensing: IEEE Geoscience and Remote Sensing Society: is a non-profit society, and its area of interest is the theory, concepts and scientific and engineering techniques applied to Remote Sensing of the Earth, ocean, atmosphere, and space. In addition, it is interested in the processing, interpretation, and dissemination of this spatial information. International Society for Photogrammetry and Remote Sensing: is a non-governmental organization that aims to develop international cooperation for the advancement of Photogrammetry and Remote Sensing and its applications.

O acesso ao conteúdo é 100% gratuito. A emissão do certificado custa R$ 12,90.

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