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Abstract Purpose of ReviewTerrestrial ecosystems in the Arctic-Boreal region play a crucial role in the global carbon cycle as a carbon sink. However, rapid warming in this region induces uncertainties regarding the future net carbon exchange between land and the atmosphere, highlighting the need for better monitoring of the carbon fluxes. Solar-Induced chlorophyll Fluorescence (SIF), a good proxy for vegetation CO$$^{2}$$ ${}^2$uptake, has been broadly utilized to assess vegetation dynamics and carbon uptake at the global scale. However, the full potential and limitations of SIF in the Arctic-Boreal region have not been explored. Therefore, this review aims to provide a comprehensive summary of the latest insights into Arctic-Boreal carbon uptake through SIF analyses, underscoring the advances and challenges of SIF in solving emergent unknowns in this region. Additionally, this review proposes applications of SIF across scales in support of other observational and modeling platforms for better understanding Arctic-Boreal vegetation dynamics and carbon fluxes. Recent FindingsCross-scale SIF measurements complement each other, offering valuable perspectives on Arctic-Boreal ecosystems, such as vegetation phenology, carbon uptake, carbon-water coupling, and ecosystem responses to disturbances. By incorporating SIF into land surface modeling, the understanding of Arctic-Boreal changes and their climate drivers can be mechanistically enhanced, providing critical insights into the changes of Arctic-Boreal ecosystems under global warming. SummaryWhile SIF measurements are more abundant and with finer spatiotemporal resolutions, it is important to note that the coverage of these measurements is still limited and uneven in the Arctic-Boreal region. To address this limitation and further advance our understanding of the Arctic-Boreal carbon cycle, this review advocates for fostering a SIF network providing long-term and continuous measurements across spatial scales. Simultaneously measuring SIF and other environmental variables in the context of a multi-modal sensing system can help us comprehensively characterize Arctic-Boreal ecosystems with spatial details in land surface models, ultimately contributing to more robust climate projections. 

Summary Leaf‐out in temperate forests is a critical transition point each spring and advancing with global change. The mechanism linking phenological variation to external cues is poorly understood. Nonstructural carbohydrate (NSC) availability may be key.Here, we use branch cuttings from northern red oak (Quercus rubra) and measure NSCs throughout bud development in branch tissue. Given genes and environment influence phenology, we placed branches in an arrayed factorial experiment (three temperatures × two photoperiods, eight genotypes) to examine their impact on variation in leaf‐out timing and corresponding NSCs.Despite significant differences in leaf‐out timing between treatments, NSC patterns were much more consistent, with all treatments and genotypes displaying similar NSC concentrations across phenophases. Notably, the moderate and hot temperature treatments reached the same NSC concentrations and phenophases at the same growing degree days (GDD), but 20 calendar days apart, while the cold treatment achieved only half the GDD of the other two.Our results suggest that NSCs are coordinated with leaf‐out and could act as a molecular clock, signaling to cells the passage of time and triggering leaf development to begin. This link between NSCs and budburst is critical for improving predictions of phenological timing. 

We study a game where agents interacting over a network engage in two coupled activities and have to strategically decide their production for each of these activities. Agent interactions involve local and global network effects, as well as a coupling between activities. We consider the general case where the network effects are heterogeneous across activities, i.e., the underlying graph structures of the two activities differ and/or the parameters of the network effects are not equal. In particular, we apply this game in the context of palm oil tree cultivation and timber harvesting, where network structures are defined by spatial boundaries of concessions. We first derive a sufficient condition for the existence and uniqueness of a Nash equilibrium. This condition can be derived using the potential game property of our game or by employing variational inequality framework. We show that the equilibrium can be expressed as a linear combination of two Bonacich centrality vectors. 

Aerial images provide important situational aware- ness for responding to natural disasters such as hurricanes. They are well-suited for providing information for damage estimation and localization (DEL); i.e., characterizing the type and spatial extent of damage following a disaster. Despite recent advances in sensing and unmanned aerial systems technology, much of post-disaster aerial imagery is still taken by handheld DSLR cameras from small, manned, fixed-wing aircraft. However, these handheld cameras lack IMU information, and images are taken opportunistically post-event by operators. As such, DEL from such imagery is still a highly manual and time-consuming process. We propose an approach to both detect damage in aerial images and localize it in world coordinates, with specific focus on detecting and localizing flooding. The approach is based on using structure from motion to relate image coordinates to world coordinates via a projective transformation, using class activation mapping to detect the extent of damage in an image, and applying the projective transformation to localize damage in world coordinates. We evaluate the performance of our approach on post-event data from the 2016 Louisiana floods, and find that our approach achieves a precision of 88%. Given this high precision using limited data, we argue that this approach is currently viable for fast and effective DEL from handheld aerial imagery for disaster response.