Evaluating ANN, Tabular CNN and Ensemble Random Forest for Aboveground Biomass Estimation: An Integrated Approach Utilizing SAR and Optical Remote Sensing Data

Above-ground biomass estimation in tropical dry deciduous forests using multi-sensor data from ALOS-2 and sentinel-2 with advanced machine learning approaches

Estimation techniques of aboveground biomass in grassland ecosystems based on multi-source data integration

An artificial intelligence driven framework that integrates deep learning and ensemble learning is proposed that can effectively address the shortcomings of traditional methods and existing remote sensing estimations, and significantly improve the estimation performance of AGB in complex scenarios.

Assessing Machine Learning and Deep Learning Approaches for Wood Volume Prediction in Forest Plantations Using Hyperspectral Imagery: Limitations and Challenges

This study evaluates four machine learning algorithms, Random Forest Regressor, Support Vector Regressor (SVR), Multilayer Perceptron, and a 1-D Convolutional Neural Network, for estimating Pinus taeda L. wood volume using EnMAP hyperspectral data. Spectral preprocessing included PCA dimensionality reduction, with perm...

Mapping Mangrove Ecosystems Using Multi-Sensor Spectral-Temporal Metrics: SAR-Optical Integration and Machine Learning Performance in an Arid Coastal Environment

Estimating Annual Wildfire-Related Potential Above-Ground Biomass Loss in Eastern Canadian Boreal Forests Using Multi-Source Remote Sensing and XGBoost

Wildfire impact assessment requires information on both burned areas and the biomass exposed within burned landscapes. We developed a field-calibrated, multi-source remote-sensing framework to estimate above-ground biomass (AGB) and quantify annual wildfire-related potential AGB exposure across the boreal forests of Qu...

Comparative Evaluation of UAV Multispectral and Measured Biophysical Feature Combinations for Winter Wheat Canopy Nitrogen Estimation

Accurate estimation of winter wheat canopy nitrogen concentration (CNC) supports crop diagnosis and precision nitrogen management, yet the relative value and redundancy of multispectral, structural, and chlorophyll-related variables remain unclear in small dat asets. Using 155 multi-temporal observations retained from...