📚 Volume 31, Issue 11
📋 ID: iNF0odY
Authors
Dr. Maria Gomez, Dr. Hiroshi Tanaka, Dr. Amina El-Sayed
, Marc Williams
Department of Environmental Science, University of São Paulo, São Paulo, Brazil; Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan; Department of Botany, Cairo University, Cairo, Egypt
Keywords
Biodiversity
Remote Sensing
Machine Learning
Tropical Rainforests
Conservation
Satellite Imagery
Abstract
Tropical rainforests are critical to global biodiversity, yet assessing their rich biodiversity is challenging due to dense canopy cover and remote locations. This study presents an integrated approach using remote sensing data and advanced machine learning algorithms to assess biodiversity in these regions. By leveraging satellite imagery and environmental data, we train a neural network model to predict biodiversity indices across the Amazon Basin. Our results demonstrate the potential of combining technological advancements with ecological data to enhance biodiversity monitoring and conservation efforts. This approach not only improves the accuracy of biodiversity assessments but also offers a scalable solution for other forested regions worldwide.
🔐
Login to Download PDF
Please login with your Paper ID and password to access the full PDF.
🔑 Login to Download
📝 How to Cite
Dr. Maria Gomez, Dr. Hiroshi Tanaka, Dr. Amina El-Sayed , Marc Williams (2024).
"Utilizing Remote Sensing and Machine Learning for Biodiversity Assessment in Tropical Rainforests".
Wulfenia, 31(11).
