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Using statistical methods that do not directly represent the causality between variables to attribute climate and plant traits as controlling ecosystem functions may lead to biased perceptions. We revisited this issue using a causal graphical model, the Bayesian network (BN), capable of quantifying causality by conditional probability tables. Based on expert knowledge and climate, vegetation, and ecosystem function data from the FLUXNET flux stations, we constructed a BN representing the causal relationship of climate–plant-trait–ecosystem functions. Based on the sensitivity analysis function of the BN, we attributed the control of climate and plant traits over ecosystem functions and compared the results with those based on random forests and correlation analysis. The main conclusions of this study include the following: BN can be used for the quantification of causal relationships between complex ecosystems in response to climate change and enables the analysis of indirect effects among variables. The causality reflected in the BN is as good as the expert knowledge of the causal links. Compared to BN, the feature importance difference between “mean vapor pressure deficit and cumulative soil water index” and “maximum leaf area index and maximum vegetation height” reported by random forests is higher and can be overestimated. With the causality relation between correlated variables constructed, a BN-based sensitivity analysis can reduce the uncertainty in quantifying the importance of correlated variables. The understanding of the mechanism of indirect effects of climate variables on ecosystem functions through plant traits can be deepened by the chain casuality quantification in BNs.

The main objective of the Belspo-funded LIMAMAL project was to support archaeologists in creating 3D terrain visualizations based on Lidar data and Pléiades stereoscopic imagery, and a combination or “fusion” thereof. A case study was developed to demonstrate the application of Pléiades imagery and light detection and ranging (lidar) technologies for prospection and visualization of the Mesoamerican archaeological landscape. Based on this case study, guidelines in English and Spanish have been developed to explain the technical processing. The project involved a stakeholder: the National Institute of Anthropology and History (INAH), a Mexican federal government institution responsible for research, conservation, protection and spreading of knowledge on Mexican cultural heritage. The case study and guidelines were presented to the stakeholder and other interested parties during several meetings held during a short mission to Yucatan, Mexico in the spring of 2022.

The main objective of the Belspo-funded LIMAMAL project was to support archaeologists in creating 3D terrain visualizations based on Lidar data and Pléiades stereoscopic imagery, and a combination or “fusion” thereof. A case study was developed to demonstrate the application of Pléiades imagery and light detection and ranging (lidar) technologies for prospection and visualization of the Mesoamerican archaeological landscape. Based on this case study, guidelines in English and Spanish have been developed to explain the technical processing. The project involved a stakeholder: the National Institute of Anthropology and History (INAH), a Mexican federal government institution responsible for research, conservation, protection and spreading of knowledge on Mexican cultural heritage. The case study and guidelines were presented to the stakeholder and other interested parties during several meetings held during a short mission to Yucatan, Mexico in the spring of 2022.

With the rapid accumulation of water flux observations from global eddy-covariance flux sites, many studies have used data-driven approaches to model water fluxes, with various predictors and machine learning algorithms used. However, it is unclear how various model features affect prediction accuracy. To fill this gap, we evaluated this issue based on records of 139 developed models collected from 32 such studies. Support vector machines (SVMs; average R-squared = 0.82) and RF (random forest; average R-squared = 0.81) outperformed other evaluated algorithms with sufficient sample size in both cross-study and intra-study (with the same data) comparisons. The average accuracy of the model applied to arid regions is higher than in other climate types. The average accuracy of the model was slightly lower for forest sites (average R-squared = 0.76) than for croplands and grasslands (average R-squared = 0.8 and 0.79) but higher than for shrubland sites (average R-squared = 0.67). Using Rn/Rs, precipitation, Ta, and the fraction of absorbed photosynthetically active radiation (FA-PAR) improved the model accuracy. The combined use of Ta and Rn/Rs is very effective, especially in forests, while in grasslands the combination of Ws and Rn/Rs is also effective. Random cross-validation showed higher model accuracy than spatial cross-validation and temporal cross- validation, but spatial cross-validation is more important in spatial extrapolation. The findings of this study are promising to guide future research on such machine-learning-based modeling.