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This paper aims to assess the performance of generalized aboveground biomass (AGB) allometric models in the Australian context by investigating the correlation between the AGB estimates and the lidar-based individual tree parameters. A hybrid tree segmentation algorithm is proposed to segment an airborne lidar point cloud into individual trees. Although the diameter at breast height (DBH) of a tree is a crucial parameter for the AGB estimation, a typical airborne lidar data contains only a few points representing partial DBH, hence a localized DBH regression model is proposed. Principal component analysis is applied to examine the multicollinearity in the input variables and ridge regression is applied to remove the less important variables. Four machine learning techniques, namely random forest, support vector regression, multilayer perceptron and radial basis function, are applied to generate AGB regression models. The qualities of the calibrated AGB models are assessed by calculating the adjusted-coefficient-of-determination, leave-one-out cross-validation, the Akaike information criterion, normalized-mean-square-error and the model efficiency index. The test results indicate that the random forest-based AGB model outperforms other machine learning techniques. It is concluded that, if the environmental conditions of tree samples resemble the study region, the generalized AGB allometric model would perform well with the tree samples regardless of their geographical context. [ABSTRACT FROM AUTHOR]

• Data-driven approaches to study physiological response of sentinel oysters to environmental condition. • Predicting heart rate and shell gape of oysters from environmental water quality variables. • Predicting water quality variables from heart rate and shell gape of oysters. • Application of machine learning models to provide a biological perspective of environmental changes. The research presented in this paper was conducted as part of a project that aimed at using biosensors on sentinel oysters to provide a biological perspective of environmental changes. The physiological response patterns (e.g. heart rate variability, shell gape) of sentinel oysters can provide valuable insight into their exposure to environmental stressors. Sensors were attached to measure both heart rate and shell gape (physiological behaviour) of oysters placed at different depths in the water column. Sensors were also deployed to measure water quality that represents the environmental condition the oysters were in. The objective of this study is to utilise the data from different sensors to investigate how environmental conditions modulate physiological response of oysters. We have utilised a set of machine learning models to develop data-driven approaches that can predict heart rate and shell gape (opening and closing action) of oysters from water quality variables, and vice-versa. The level of prediction accuracy indicates how well environmental conditions influence the physiological response of oysters. The effectiveness of the developed approaches is evaluated using data collected from two different deployments of sensors in South East Tasmania, Australia. Experimental results demonstrate that the presented data-driven approaches can provide accurate predictions of physiological and water quality variables, for the data set considered in this study. The prediction error (in terms of MAPE) for HR and water quality is in the range of 3.17%-8.21% and 0.72%–2.48%, respectively, and classification accuracy (F-Score) for shell gape varies between 0.96 and 0.99. [ABSTRACT FROM AUTHOR]