[HTML][HTML] Sugarcane health monitoring with satellite spectroscopy and machine learning: A review
EK Waters, CCM Chen, MR Azghadi - Computers and Electronics in …, 2025 - Elsevier
Research into large-scale crop monitoring has flourished due to increased accessibility to
satellite imagery. This review delves into previously unexplored and under-explored areas …
satellite imagery. This review delves into previously unexplored and under-explored areas …
Uncertainties in deforestation emission baseline methodologies and implications for carbon markets
Carbon credits generated through jurisdictional-scale avoided deforestation projects require
accurate estimates of deforestation emission baselines, but there are serious challenges to …
accurate estimates of deforestation emission baselines, but there are serious challenges to …
When does artificial intelligence replace process-based models in ecological modelling?
GA Alexandrov - Ecological Modelling, 2025 - Elsevier
Sixteen years ago, Sven Jørgensen, a founder of Ecological Modelling, wrote that artificial
neural networks could be very useful in most cases but cannot replace biogeochemical …
neural networks could be very useful in most cases but cannot replace biogeochemical …
[HTML][HTML] A geostatistical approach to enhancing national forest biomass assessments with Earth Observation to aid climate policy needs
Earth Observation (EO) data can provide added value to nations' assessments of vegetation
aboveground biomass density (AGBD) with minimal additional costs. Yet, neither open …
aboveground biomass density (AGBD) with minimal additional costs. Yet, neither open …
Intergovernmental Panel on Climate Change (IPCC) Tier 1 forest biomass estimates from Earth Observation
Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be
presented with the consistency standards mandated by United Nations Framework …
presented with the consistency standards mandated by United Nations Framework …
Harnessing Biomass Energy: Advancements through Machine Learning and AI Applications for Sustainability and Efficiency
This in-depth examination explores the pioneering potential revealed by the convergence of
Machine Learning (ML) and Artificial Intelligence (AI) in the biomass energy sector. Biomass …
Machine Learning (ML) and Artificial Intelligence (AI) in the biomass energy sector. Biomass …
[HTML][HTML] Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data
X Sun, G Li, Q Wu, J Ruan, D Li, D Lu - Remote Sensing, 2024 - mdpi.com
Forest carbon stock is an important indicator reflecting a forest ecosystem's structures and
functions. Its spatial distribution is valuable for managing natural resources, protecting …
functions. Its spatial distribution is valuable for managing natural resources, protecting …
Improved country-wide estimation of above-ground tropical forest biomass using locally calibrated GEDI spaceborne LiDAR data
Abstract NASA's Global Ecosystem Dynamics Investigation (GEDI) presents an
unprecedented opportunity for cost-effective estimations of above-ground biomass density …
unprecedented opportunity for cost-effective estimations of above-ground biomass density …
Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting
National forest inventory (NFI) programs provide vital information on forest parameters'
status, trend, and change. Most NFI designs and estimation methods are tailored to estimate …
status, trend, and change. Most NFI designs and estimation methods are tailored to estimate …
Field-independent carbon mapping and quantification in forest plantation through remote sensing
Quantifying the carbon-stocking contribution of forest plantations is a crucial but challenging
and expensive process, usually performed through field analysis. For this reason …
and expensive process, usually performed through field analysis. For this reason …