2 research outputs found
Joint Study of Above Ground Biomass and Soil Organic Carbon for Total Carbon Estimation using Satellite Imagery in Scotland
Land Carbon verification has long been a challenge in the carbon credit
market. Carbon verification methods currently available are expensive, and may
generate low-quality credit. Scalable and accurate remote sensing techniques
enable new approaches to monitor changes in Above Ground Biomass (AGB) and Soil
Organic Carbon (SOC). The majority of state-of-the-art research employs remote
sensing on AGB and SOC separately, although some studies indicate a positive
correlation between the two. We intend to combine the two domains in our
research to improve state-of-the-art total carbon estimation and to provide
insight into the voluntary carbon trading market. We begin by establishing
baseline model in our study area in Scotland, using state-of-the-art
methodologies in the SOC and AGB domains. The effects of feature engineering
techniques such as variance inflation factor and feature selection on machine
learning models are then investigated. This is extended by combining predictor
variables from the two domains. Finally, we leverage the possible correlation
between AGB and SOC to establish a relationship between the two and propose
novel models in an attempt outperform the state-of-the-art results. We compared
three machine learning techniques, boosted regression tree, random forest, and
xgboost. These techniques have been demonstrated to be the most effective in
both domains