title: Statistical methodology for estimating health and above-ground biomass of forests
Creators: name: Turton, Amber Elizabeth
Publisher: name: The University of Edinburgh
Year: 2023
Subjects: subject: above-ground biomass
subject: remote sensing
subject: statistics
subject: modelling
subject: spatial modelling
subject: machine learning
subject: uncertainty propagation
subject: validation
subject: change detection
subject: carbon cycle
subject: forest degradation

description: Global forests are highly relevant to the 13th and 15th goals of the United Nations (UN) Sustainable Development Goals (SGDs). These are focused on ’Climate Action’ and ’Life on Land’ respectively. Forests are large carbon pools which can actively take in and release large amounts of carbon dioxide, making a considerable impact on the amount of carbon dioxide in the atmosphere. Above-ground biomass (AGB) is defined as all living biomass above the soil and is a particularly useful metric since around 50% of biomass is carbon. Regular and reliable global maps of AGB, and AGB change, is essential to help policy makers to achieve these SDGs. Various remote sensing techniques, such as Unmanned Aerial Vehicles (UAVs) and satellites, provide global data sources which are sensitive to AGB values. Statistical models or algorithms can then be applied to these data sources to estimate the value of AGB within each pixel. Global AGB maps are created from this data. These maps can be used by policy makers to understand the amount of carbon held in forests and the dispersion of this carbon. Currently global and continental AGB maps created show significant differences in their estimations, this reduces their reliability when used in policy decision making. There are also very few AGB changes maps available, often with high uncertainty estimations. Due to this, there is an immediate need for statistical and machine learning methods to be applied as effectively as possible to make full use of the wide availability of forest data. If effective and reliable ecological maps are created this can improve understanding and encourage forest data to have an increasing influence. This thesis looks to explore the areas for which statistical methods can make substantial improvements on ecological mapping, with particular focus on AGB mapping. Within this thesis, spatial modelling and validation techniques are also investigated. This thesis is separated into two studies. Firstly, the potential areas to improve global AGB maps through statistical methods were reviewed. This review found various major areas, for example, spatial forms of modelling and validation would create more reliable maps. Meanwhile using hierarchical models, in either a frequentist or Bayesian form, can allow more effective data combination and uncertainty propagation. This review brought together over 100 sources from the fields of AGB estimation, statistics and machine learning. This work will facilitate future collaboration between the two research communities. Secondly, an investigation into effective spatial modelling and validation techniques was undertaken. A spatial data set was used to estimate defoliation within forests. It compared the predictive performance of Generalised Additive Models (GAMs) and Random forest (RF) in a spatio-temporal setting. Various cross-validation techniques were used including, spatial, temporal and spatio-temporal forms of cross-validation. This study confirmed previous research that spatial forms of validation are needed in order to compare models effectively. Additionally, this study highlighted GAM as an effective model to be used in a spatial setting as an alternative to machine learning algorithms.