Improving estimates and change detection of forest above-ground biomass using statistical methods

Forests store approximately as much carbon as is in the atmosphere, with potential to take in or release carbon rapidly based on growth, climate change and human disturbance. Above-ground biomass (AGB) is the largest carbon pool in most forest systems, and the quickest to change following disturbance. Quantifying AGB on a global scale and being able to reliably map how it is changing, is therefore required for tackling climate change by targeting and monitoring policies. AGB can be mapped using remote sensing and machine learning methods, but such maps have high uncertainties, and simply subtracting one from another does not give a reliable indication of changes. To improve the quantification of AGB changes it is necessary to add advanced statistical methodology to existing machine learning and remote sensing methods. This review discusses the areas in which techniques used in statistical research could positively impact AGB quantification. Nine global or continental AGB maps, and a further eight local AGB maps, were investigated in detail to understand the limitations of techniques currently used. It was found that both modelling and validation of maps lacked spatial consideration. Spatial cross validation or other sampling methods, which specifically account for the spatial nature of this data, are important to introduce into AGB map validation. Modelling techniques which capture the spatial nature should also be used. For example, spatial random effects can be included in various forms of hierarchical statistical models. These can be estimated using frequentist or Bayesian inference. Strategies including hierarchical modelling, Bayesian inference, and simulation methods can also be applied to improve uncertainty estimation. Additionally, if these uncertainties are visualised using pixelation or contour maps this could improve interpretation. Improved uncertainty, which is commonly between 30% and 40%, is in addition needed to produce accurate change maps which will benefit policy decisions, policy implementation, and our understanding of the carbon cycle

Spatial and spatio-temporal models with applications in vegetation dynamics and wildlife population estimation

This thesis applies spatial and spatio-temporal modelling to two broad areas of environmental statistics: wildlife abundance estimation and vegetation dynamics. The first methodology considered is spatial modelling for estimating global characteristics through predicting the value of a response variable at new locations. The approach is based on generalized additive models and illustrated using spatio-temporal fisheries survey data. The method incorporates historical data to overcome shortcomings in the survey design. The GAM-based method substantially improves the precision of estimates over a traditional estimation method and is also useful in explaining complex space-time trends using environmental variables. The second methodology addressed is spatial modelling for the description of the underlying process. Its objectives lie in exploring local properties, such as autocorrelation. Auto-models (Markov Random Fields) are used for modelling discrete data. Autocorrelation is estimated directly from the response, as a fixed effect, through the specification of a conditional probability of each observation, given its neighbouring values. The auto-Poisson model for counts has traditionally been restricted to the modelling of negative autocorrelation. This restriction is overcome by right truncating the Poisson distribution. Further modifications of this model are also investigated. Parameter estimation methods for this truncated auto-Poisson model are then compared via a simulation study. The method with accompanying model selection and validation techniques is illustrated for the auto-Poisson and auto-negative binomial model using seed and mite counts. An example of modelling the presence and absence of deer illustrating the auto-logistic model for binary data is also presented. Finally, methodology for spatio-temporal modelling of the underlying process is considered. The use of transition models for modelling change of semi-natural vegetation in Scotland is investigated. The transition model is extended to incorporate spatial effects and it is shown that estimates of transition probabilities for Markov models can be improved

Laboratory experiments and numerical modeling of wave attenuation through artificial vegetation

It is commonly known that coastal vegetation dissipates energy and aids in shoreline protection by damping incoming waves and depositing sediment in vegetated regions. However, this critical role of vegetation to dampen wave forces is not fully understood at present. A series of laboratory experiments were conducted in the Haynes Coastal Laboratory and 2-D flume at Texas A&M University to examine different vegetative scenarios and analyze the wave damping effects of incident wave height, stem density, wave period, plant type, and water depth with respect to stem length. In wetland regions vegetation is one of the main factors influencing hydraulic roughness. Traditional open-channel flow equations, including the Manning and Darcy- Weisbach friction factor approach, have been successfully applied to determine bottom friction coefficients for flows in the presence of vegetation. There have been numerous relationships derived relating the friction factor to different flow regime boundary layers to try and derive a wave friction factor for estimating energy dissipation due to bottom bed roughness. The boundary layer problem is fairly complex, and studies relating the wave friction factor to vegetation roughness elements are sparse. In this thesis the friction factor is being applied to estimate the energy dissipation under waves due to artificial vegetation. The friction factor is tuned to the laboratory experiments through the use of the numerical model COULWAVE so that the pipe flow formulation can be reasonably applied to wave problems. A numerical friction factor is found for each case through an iterative process and empirical relationships are derived relating the friction factor for submerged and emergent plant conditions to the Ursell number. These relationships can be used to reasonably estimate a wave friction factor for practical engineering purposes. This thesis quantitatively analyzes wave damping due to the effects of wave period, incident wave height, horizontal stem density, water depth relative to stem length, and plant type for a 6 m plant bed length. A friction factor is then determined numerically for each of the laboratory experiments, and a set of equations is derived for predicting a roughness coefficient for vegetation densities ranging between 97 stems/m2 and 162 stems/m2