A Learnable Model with Calibrated Uncertainty Quantification for Estimating Canopy Height from Spaceborne Sequential Imagery

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/.Global-scale canopy height mapping is an important tool for ecosystem monitoring and sustainable forest management. Various studies have demonstrated the ability to estimate canopy height from a single spaceborne multispectral image using end-to-end learning techniques. In addition to texture information of a single-shot image, our study exploits multi temporal information of image sequences to improve estimation accuracy. We adopt a convolutional variant of a long short-term memory (LSTM) model for canopy height estimation from multitemporal instances of Sentinel-2 products. Furthermore, we utilize the deep ensembles technique for meaningful uncertainty estimation on the predictions and postprocessing isotonic regression model for calibrating them. Our lightweight model (∼320k trainable parameters) achieves the mean absolute error (MAE) of 1.29 m in a European test area of 79 km2. It outperforms the state-of-the-art methods based on single-shot spaceborne images as well as costly airborne images while providing additional confidence maps that are shown to be well calibrated. Moreover, the trained model is shown to be transferable in a different country of Europe using a fine-tuning area of as low as ∼2 km2 with MAE = 1.94 m.publishedVersio

The Last Trees Standing: Climate modulates tree survival factors during a prolonged bark beetle outbreak in Europe

Plant traits are an expression of strategic tradeoffs in plant performance that determine variation in allocation of finite resources to alternate physiological functions. Climate factors interact with plant traits to mediate tree survival. This study investigated survival dynamics in Norway spruce (Picea abies) in relation to tree-level morphological traits during a prolonged multi-year outbreak of the bark beetle, Ips typographus, in Central Europe. We acquired datasets describing the trait attributes of individual spruce using remote sensing and field surveys. We used nonlinear regression in a hypothesis-driven framework to quantify survival probability as a function of tree size, crown morphology, intraspecific competition and a growing season water balance. Extant spruce trees that persisted through the outbreak were spatially clustered, suggesting that survival was a nonrandom process. Larger diameter trees were more susceptible to bark beetles, reflecting either life history tradeoffs or a dynamic interaction between defense capacity and insect aggregation behavior. Competition had a strong negative effect on survival, presumably through resource limitation. Trees with more extensive crowns were buffered against bark beetles, ostensibly by a more robust photosynthetic capability and greater carbon reserves. The outbreak spanned a warming trend and conditions of anomalous aridity. Sustained water limitation during this period amplified the consequences of other factors, rendering even smaller trees vulnerable to colonization by insects. Our results are in agreement with prior research indicating that climate change has the potential to intensify bark beetle activity. However, forest outcomes will depend on complex cross-scale interactions between global climate trends and tree-level trait factors, as well as feedback effects associated with landscape patterns of stand structural diversity

Brain Heterogeneity in Schizophrenia and Its Association With Polygenic Risk

Between-individual variability in brain structure is determined by gene-environment interactions, possibly reflecting differential sensitivity to environmental and genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner cortices and smaller subcortical volumes in patients with schizophrenia. However, group-level comparisons may mask considerable within-group heterogeneity, which has largely remained unnoticed in the literature

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Background Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. Methods We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18–94 years of age) and applied the models to the test sets including 648 patients with SZ (18–66 years of age), 185 patients with BD (18–64 years of age), and 990 HC subjects (17–68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen’s d = −0.29) and patients with BD (Cohen’s d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy–based models showed larger group differences than the models based on other DTI-derived metrics. Conclusions Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners

Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study

BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics. CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners