Making the most of machine learning and freely available datasets: a deforestation case study

Abstract

There are many reasons why we study deforestation including predicting at risk areas, predicting deforestation rate and informing the development of conservation policies and programs. Each study will have its own set of objectives to meet (such as setting a deforestation baseline or advising on forest protection policies) and constraints to work within (such as time and data constraints and access to experts). This thesis develops a framework for helping to decide which of several statistical and machine learning methodologies; generalised linear models (GLMs), generalised linear mixed models (GLMMs), artificial neural networks (ANNs), Bayesian networks (BNs) and Gaussian processes (GPs) might be suitable for a given deforestation study.One common constraint on deforestation studies is data availability, as it is often not possible to acquire all the datasets that would ideally be included. High resolution demographic or socioeconomic information can be costly, and obtaining the value of dynamic variables such as road location for the correct point in time may be difficult. By using either freely available or low cost datasets to generate the variables for this thesis, it was possible to evaluate the usefulness of these data in predicting deforestation and identifying its predisposing factors. Their proven utility demonstrates that they could provide effective substitutes in those cases where the ideal datasets are not available.The main datasets used were the Conservation International land use change data for southern Mexico and north-eastern Madagascar, which are raster datasets at 30 m resolution showing forest loss for either two (Mexico) or three (Madagascar) time steps. Predictor variables were also generated from the World Database on Protected Areas, the NASA Landsat digital elevation model and several Natural Earth datasets on city and river location. Random sample points were generated across the forested areas of the study zones and models were then trained to predict whether there would be any deforestation within a 500 m x 500 m zone around each point.Models were implemented using either R or Matlab and their performance was evaluated using sensitivity, specificity, true skill statistic and the area under the receiver operating curve. The results of the best performing model designs for each methodology were mapped to examine whether the predicted high risk areas were close to where actual deforestation occurred. Separate maps were produced for the predicted results at a 50 % probability cut off, as well as across all probabilities to produce a map of predicted high risk areas. Additional maps were created showing the predictions after correcting for the rate of expected deforestation.ii When applied to complex problems such as deforestation analysis, machine learning (ML) techniques have several theoretical and practical advantages over classical statistics, primarily the ability to take into account non-linear relationships. The ML models were therefore expected to outperform the simpler...

Analysis of deforestation patterns and drivers in Swaziland using efficient Bayesian multivariate classifiers

“…These include including statistical approaches, machine learning (ML) and spatial modelling (Mayfield 2015). Traditional parametric approaches, usually in the form of logistic regression, generalized linear or generalized additive models, are widely used compared to machine learning techniques such as Maxent (e.g., Aguilar-Amuchastegui et al 2014).…”