IEEE GRSS UK&Ireland SEMINAR “Tree species classification from remotely sensed data: From hyperspectral to RGB”, Dr. Matheus Pinheiro Ferreira

#grss #lecture #remote #sensing #geoscience #tree #crown #classification #vegetation #tropical #CNN

Dr Matheus Pinheiro Ferreira at the Universidade de São Paulo will present “Tree species classification from remotely sensed data: From hyperspectral to RGB” at 1pm (UK time) on 11th April 2025. 

This seminar is hosted by the IEEE GRSS UK&Ireland Chapter and co-hosted by the IEEE GRSS Chapters in Italy, Romania and Germany.

Registration

IEEE and GRSS members as well as non-IEEE Members are invited to Register and participate. IEEE members should include their IEEE Membership Number when registering.

Access to online Meeting

Registered Participants will be provided with the link prior to the event.

Abstract

Tree species classification is crucial for managing and conserving highly diverse tropical forests. However, spatially explicit information on tree species distribution is typically limited to small-scale field inventories, making large-scale monitoring challenging. Remote sensing (RS) offers a powerful alternative, particularly very-high-resolution imagery (GSD ≤ 1 m), which enables species classification at the level of individual tree crown (ITC) level. Hyperspectral imaging have demonstrated the potential of fine spectral resolution for distinguishing tree species based on subtle biochemical and structural differences. More recently, deep learning, especially convolutional neural networks (CNNs), has emerged as a promising approach to automate tree species classification using ultra-high-resolution drone-acquired RGB images. Additionally, unsupervised deep learning methods are being explored to augment ITC datasets, addressing the challenge of scarce labeled data in tropical forests. In this talk, I will explore how different RS data sources—from hyperspectral to high-resolution RGB—can be leveraged to improve tree species classification in tropical environments. I will discuss recent advances in CNN-based classification and the role of unsupervised learning in enhancing ITC datasets.

Bio

Dr  Matheus Pinheiro Ferreira holds a degree in Forest Engineering from the Universidade Federal do Paraná (UFPR, 2010), with additional training at the University of Freiburg, Germany. He earned his M.Sc. (2012) and Ph.D. (2017) in Remote Sensing from the National Institute for Space Research (INPE). His research focuses on forest resource monitoring and quantification through remote sensing and forest inventories. In recent years, he has dedicated his work to developing artificial intelligence methods for tree species mapping, floristic diversity assessment, and carbon stock and sequestration monitoring in tropical forests using data from active and passive remote sensors. He has expertise in hyperspectral remote sensing and radiative transfer modeling. From 2018 to 2024, he was an Assistant Professor in the Cartographic Engineering Section at the Military Institute of Engineering (IME). He is an Assistant Professor at the Universidade de São Paulo (USP) in the Department of Forest Sciences at the Luiz de Queiroz College of Agriculture (ESALQ).