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Accurate mapping of forest canopy height is essential for monitoring forest structure, assessing biodiversity, and informing sustainable management practices. However, obtaining high-resolution canopy height data across large tropical landscapes remains challenging and prohibitively expensive. While machine learning approaches like Random Forest have become standard for predicting forest attributes from remote sensing data, deep learning methods remain underexplored for canopy height mapping despite their potential advantages. To address this limitation, we developed a rapid, automatic, scalable, and cost-efficient deep learning framework that predicts tree canopy height at fine-grained resolution (30 × 30 m) across Indonesian Borneo’s tropical forests. Our approach integrates diverse remote sensing data, including Landsat-8, Sentinel-1, land cover classifications, digital elevation models, and NASA Carbon Monitoring System airborne LiDAR, along with derived vegetation indices, texture metrics, and climatic variables. This comprehensive data pipeline produced over 300 features from approximately 2 million observations across Bornean forests. Using LiDAR-derived canopy height measurements from ~100,000 ha as training data, we systematically compared multiple machine learning approaches and found that our neural network model achieved canopy height predictions with R2 of 0.82 and RMSE of 4.98 m, substantially outperforming traditional machine learning approaches such as Random Forest (R2 of 0.57–0.59). The model performed particularly well for forests with canopy heights between 10–40 m, though systematic biases were observed at the extremes of the height distribution. This framework demonstrates how freely available satellite data can be leveraged to extend the utility of limited LiDAR coverage, enabling cost-effective forest structure monitoring across vast tropical landscapes. The approach can be adapted to other forest regions worldwide, supporting applications in ecological research, conservation planning, and forest loss mitigation.

Environmental degradation contributes substantially to the global burden of disease and concurrent global environmental changes are increasingly recognised as public health threats, worldwide.1 The 196 parties to the UN Convention on Biological Diversity (CBD) have called for increased engagement on biodiversity and health since 2014,2 while calls from stakeholders for integrated decision making are similarly long standing.3 Yet few civil society health organisations have historically engaged with the CBD and its intergovernmental negotiating process.4 This situation is, however, changing. Elements of other COP 15 outcomes5 are also relevant to the health community and to steering health civil society organisation priorities, including separate decisions5 on climate change, biocultural diversity, food systems and soil biodiversity, and synthetic biology. [...]health experts can bolster the health dimension of environmental impact assessments, national ecosystem assessments, and strategic environmental assessments, and ensure their inclusion in decision making.25 The full environmental footprint, including planetary pressures from material use and waste, biodiversity loss, and carbon dioxide emissions, must be estimated for the health sector and addressed.26,27 As emphasised in the GBF, health professionals also need to strengthen their roles in efforts to expand interdisciplinary biodiversity education, and integrate holistic biodiversity–health approaches into national biodiversity and health plans.28 CBD COP 16 will take place in Türkiye in 2024.

Tropical forest loss currently exceeds forest gain, leading to a net greenhouse gas emission that exacerbates global climate change. This has sparked scientific debate on how to achieve natural climate solutions. Central to this debate is whether sustainably managing forests and protected areas will deliver global climate mitigation benefits, while ensuring local peoples’ health and well-being. Here, we evaluate the 10-y impact of a human-centered solution to achieve natural climate mitigation through reductions in illegal logging in rural Borneo: an intervention aimed at expanding health care access and use for communities living near a national park, with clinic discounts offsetting costs historically met through illegal logging. Conservation, education, and alternative livelihood programs were also offered. We hypothesized that this would lead to improved health and well-being, while also alleviating illegal logging activity within the protected forest. We estimated that 27.4 km² of deforestation was averted in the national park over a decade (∼70% reduction in deforestation compared to a synthetic control, permuted P = 0.038). Concurrently, the intervention provided health care access to more than 28,400 unique patients,with clinic usage and patient visitation frequency highest in communities participating in the intervention. Finally, we observed a dose–response in forest change rate to intervention engagement (person-contacts with intervention activities) across communities bordering the park: The greatest logging reductions were adjacent to the most highly engaged villages. Results suggest that this community-derived solution simultaneously improved health care access for local and indigenous communities and sustainably conserved carbon stocks in a protected tropical forest.

A cross-sectional study of children in West Kalimantan, Indonesia, was conducted to examine the relationship between malnutrition history, child IQ, school attendance, socioeconomic status, parental education and parental IQ. In unadjusted analyses, severely stunted children had significantly lower IQ scores than mild-moderately stunted children. This effect was significant when stunting, school attendance and parental education were included in multivariable models but was attenuated when parental IQ was included. Our research underscores the importance of accounting for parental IQ as a critical covariate when modeling the association between childhood stunting and IQ.