Climate change increasingly affects tree growth and carbon allocation in subalpine forests across the Western United States, necessitating new approaches to better monitor its impacts. Although the use of thermal data in monitoring tree growth and carbon allocation remains in its infancy, recent research has identified leaf temperature (TL) as a strong predictor of stem radial growth (SRG). The goal of this study was to evaluate the potential of satellite-based estimates of canopy temperature (TC) and soil moisture (SM) to provide insights into daily and annual tree growth dynamics. We utilized generalized additive mixed models and linear mixed models with spaceborne estimates of TC (ECOSTRESS) and SM (SMAP) as the primary predictors of daily resolved SRG (SRGD) across five growing seasons (2020 - 2024) in a subalpine mixed-conifer forest of Central Idaho. Models predicted micrometer-level SRGD with moderate precision and accuracy (R2 = 0.56; RMSE = 21.45 mu m), while the binomial representation of daily growth/no growth (SRGD-B) was predicted with 87 % accuracy. When multiplying the annual mean predicted SRGD by the annual mean difference in days between satellite data acquisitions (5.58 +/- 5.53 days), we were able to predict annual growth performance (%; i.e., how much a tree grew relative to its average growth across years) with high precision (R2 = 0.76) and accuracy (RMSE = 7.63 %). These findings highlight the efficacy of thermal satellite remote sensing data for tracking intra- and inter-annual tree growth dynamics, and the potential for scaling satellite-based tree growth estimates across broad regions.

Understanding stream-groundwater connectivity in response to climate variability is critical for water resource management and ecosystem resilience. Groundwater influx moderates stream temperature and is important for cold-water adapted species such as salmonids. As climate change alters precipitation, temperature, and snowpack conditions, groundwater could help sustain thermal refugia in streams. However, monitoring groundwater connectivity remains challenging, particularly in ungauged, snow dominated, headwater streams, limiting our understanding of stream-groundwater connectivity responses to climatic variability of which the increase of meteorological drought is of particular concern across many regions. In this study we explore the use of diel air-water temperature amplitude ratios to assess groundwater connectivity in central Idaho headwater streams from 2017 to 2024, with the objective to evaluate the influence of the 2021 extreme meteorological drought. We analyzed stream and air temperature patterns via amplitude ratios, and applied a Wilcoxon signed rank test to compare years across 12 stream sites. We applied a linear mixed effects model to estimate the effects of snowmelt timing, rate, runoff, and spring SPEI on amplitude ratios. Results indicate that groundwater connectivity increased in some streams during the 2021 drought, while others remained stable or declined. Additionally, some streams exhibited stronger groundwater connectivity and reduced atmospheric coupling during low snow years, while others maintained consistent thermal dynamics. Streams with greater groundwater connectivity also had lower maximum summer temperatures. These patterns suggest that streams with greater groundwater connectivity may buffer thermal extremes, like the 2021 drought, that are becoming increasingly more common. Further research is needed to explore how hillslope hydrologic dynamics, such as how transpiration and topography mediate snowmelt partitioning, shape stream thermal regimes and long-term habitat suitability.

The magnitude of the terrestrial carbon sink remains a key uncertainty in future climate projections, in part due to poorly understood links between carbon uptake and its allocation to woody biomass in vegetation. Here, in this study, we show that photosynthesis and aboveground growth occur asynchronously across diel to seasonal scales in eight North American oak species. Across 137 tree ring sites, current-year annual growth was insensitive to climate variability after midsummer despite 26 to 36% of annual gross primary productivity (GPP) occurring during this period. Hourly GPP flux and growth measurements at four sites spanning seven site years further demonstrate that wood formation ceases earlier than photosynthesis and is restricted to periods of low atmospheric aridity and temperature. This photosynthesis-growth decoupling intensifies with interannual variability in vapor pressure deficit (r = 0.86, P < 0.05), suggesting that by assuming tight coupling between photosynthesis and woody biomass, current earth system models may overestimate long-term carbon sequestration in forests.

Background Quantifying and predicting wildland fire behavior is crucial for fire management, ecological research and mitigating wildfire impacts. Rate of spread (ROS), fireline intensity (FI) and fire radiative power (FRP) are key fire behavior metrics.Aims This study leverages uncrewed aircraft systems (UASs) equipped with thermal infrared (TIR) sensors and machine learning models to quantify and predict fire behavior.Methods Using repeat-pass UAS-based TIR imagery, we derived high-resolution FRP, FI and ROS estimates and trained artificial neural network (ANN) and random forest (RF) models to predict ROS.Key results This approach predicted ROS with low error (mean absolute errors (MAEs) below 0.04 m s-1, root mean squared errors (RMSEs) below 0.06 m s-1 and R2 values above 0.90) in short-term predictions for a single prescribed grassland fire, while maintaining computational efficiency.Conclusions Both ANN and RF models performed well, but RF performed better, with less training data, lower propensity for overfitting and less sensitivity to spatial autocorrelation.Implications Although currently demonstrated as a proof of concept at a single site with a specific fuel type and short-term prediction horizon, our integrated methodology shows research and development potential for supporting data-driven wildfire management strategies aimed at mitigating fire impacts, optimizing resource allocation and improving firefighter safety.

For many animals, visual information is crucial for detecting mates, competitors, and predators, selecting and defending territories, and finding resources. Established field methods for estimating visibility, such as use of profile boards, produce estimates constrained to a limited set of directions and distances that do not account for the continuous and multidirectional nature of visual information. The limitations of profile boards have recently been overcome by estimating visibility from terrestrial lidar data; however, gathering data requires intensive field efforts and data processing that limits application to studies of free-ranging animals. Our goal was to demonstrate an approach for estimating fine-scale, 3-dimensional (3D) visibility in the field based on single scans from a terrestrial laser scanner and to compare results with the profile board method. We gathered data from 25 forest plots from 3 vantage points representing different animal eye heights (25 cm, 75 cm, and 155 cm) using 2 methods: 1) segmented 3D viewsheds estimated using single scans from terrestrial laser scanners (ssTLS) and 2) profile boards. Estimates of visibility increased with eye height but did not differ significantly (P = 0.501) between methods when the viewsheds were segmented to the region of the profile boards. However, the profile boards could not provide the comprehensive and flexible information associated with viewsheds. We demonstrated how single-scan viewsheds can be segmented to characterize ecologically relevant portions of spherical viewsheds and provide a comparison of the 2 methods. The ssTLS approach is an efficient method for collecting fine-scale information on viewsheds in the field that can produce comprehensive estimates of 3D visibility, advancing the assessment of a key but often overlooked property of wildlife habitat measurement.

Wet and cool microenvironments often serve as climate refugia in semiarid regions. However, springs-locations where groundwater reaches the Earth's surface-remain underexplored as climate refugia. This study investigated the potential of spring ecosystems as climate refugia in a semiarid mountainous region of central Idaho, USA. Using high-resolution PlanetScope imagery (2017-2024), we derived seasonal phenophases from a normalized difference vegetation index (NDVI) time series to assess ecological stability at 40 springs and surrounding non-spring areas. We fit a linear mixed-effects model with phenophase as the dependent variable, spring and water year as random effects and climatic water balance (CWB), snow disappearance date (SDD), heat load index (HLI), topographic wetness index (TWI) and their interactions with site type (spring or non-spring) as predictors. We found that springs exhibited significantly lower interannual variability in end of growing season (EOS) timing (24 days less than non-springs). Higher annual CWB, reflecting greater precipitation relative to potential evapotranspiration, corresponded with later EOS timing for both springs and non-springs, but springs were less sensitive to annual CWB as shown by lower effect sizes. Springs phenology showed weak associations with TWI and HLI, underscoring their independence from topographically driven refugia. Our findings highlight springs as climate refugia due to their buffering of water limitations that stabilise late season phenology. Under climate change, water deficits will become more severe, making climate refugia like springs increasingly important. Future research should examine spring recharge processes and incorporate additional snowpack variables to monitor stability across a range of climate conditions.

Forest biomass resulting from tree radial growth can remain on the landscape over decadal to centennial timescales and plays a critical role in forest carbon cycling. However, visually green vegetation may not be a good proxy for carbon allocation to growth as the phenology and environmental sensitivity of photosynthesis may be different from radial growth. Here we investigate the decoupling between photosynthesis and tree radial growth across intra to interannual timescales for seven North American oak species (Quercus spp.) at four sites (Lamont Sanctuary, NY; Morton Arboretum, IL; Pace Forest, VA; & Tonzi Vaira, CA, USA). Using point dendrometers and wood anatomy, we find that oak trees generally commenced radial growth (cell division and expansion) one month prior to full canopy development and peak carbon assimilation estimated using eddy covariance, satellite and in-situ remote sensing, and leaf-level chlorophyll fluorescence. Further, radial growth was essentially completed by early summer, two to three months prior to the early autumn end of the photosynthetic activity, and before the annual peak in temperature and vapour pressure deficit (VPD) and lowest soil moisture. This suggests that high summer aridity limits carbon allocation to growth more strongly than assimilation. Tree-ring width chronologies for these species across North America further supports that results that earlywood growth depends on prior season climate and assimilated carbon while latewood growth ends by early-summer and responds primarily to current year climate variability. In summary, temporal decoupling between radial growth and photosynthesis and the stronger constraint of summer aridity on growth than photosynthesis appears to be widespread among multiple North American temperate oak species. As summers continue to warm and dry under climate change, this source-sink (or photosynthesis-growth) decoupling needs to be better resolved to constrain forest carbon cycling, as increasing aridity will likely influence the ability of trees to allocate carbon to long-term storage as woody biomass.

Hyperspectral remote sensing (RS) has demonstrated to be useful for estimating vegetation photosynthetic traits, such as the maximum carboxylation rate of Rubisco (Vcmax) and the electron transport rate (Jmax). However, the spectral ranges used by RS models for predicting photosynthetic traits vary, and their predictive performance across different crop varieties is poor. This study aimed to investigate whether augmenting the modeling dataset's variability through various nitrogen experiments on tea chrysanthemum could improve RS-based photosynthetic trait models' applicability. Leaf-level measurements linked high-throughput spectral reflectance observations with photosynthetic traits obtained via a portable photosynthesis system. Results revealed strong correlations between the green and red edge bands and photosynthetic traits. Among newly developed vegetation indices, the structure insensitive pigment index [SIPI(850,691,476)] and SIPI(850, 699, 579) effectively predicted Vcmax and Jmax, respectively. In contrast, partial least squares regression (PLSR) modeling combined with reflectance from 400 to 1000 nm outperformed in estimating photosynthetic traits of tea chrysanthemum and exhibited excellent performance in a multi-variety validation dataset, indicating applicability across different varieties. Our findings suggest that increasing the modeling dataset's variability enhances the universality of RS estimation models for photosynthetic traits, providing a valuable tool for breeders to efficiently collect photosynthetic trait information.

Efficient measurement of photosynthetic traits,such as the maximum carboxylation rate of Rubisco(Vcmax)and electron transport rate(Jmax),is essential for advancing research and breeding aimed at enhancing crop pro-ductivity.Traditional methods are time-intensive,which limits their scalability.Remote sensing presents an opportunity for estimating these traits;however,it often lacks an affordable platform for effective spatial map-ping,a critical aspect of phenotyping.This study explored the use of unmanned aerial vehicle(UAV)multispectral data to estimate and spatially map photosynthetic traits in tea chrysanthemums during the branching and budding stages under an open canopy.Over six field experiments across varieties conducted in 2022-2023,we captured canopy reflectance using UAV-mounted multispectral sensors,calculated spectral indices,and measured the photosynthetic traits of the upper leaves using a portable photosynthesis system.The results indicated that certain indices,particularly those incorporating green and red-edge bands,effectively estimated photosynthetic traits,with the simplified canopy chlorophyll content index(SCCCI)yielding the most accurate Vcmax estimates(R2=0.52)and the chlorophyll vegetation index(CVI)providing the best estimates for Jmax(R2=0.38).The inte-gration of variable selection with partial least squares regression(PLSR)modeling further enhanced the precision of the model(Vcmax:R2=0.70;Jmax:R2=0.63).Our findings demonstrate that UAV-acquired multispectral data can effectively map photosynthetic traits with high spatial resolution,establishing it as a valuable tool for rapid phenotyping and spatial assessment of photosynthetic capacity in crop fields.

Animals at risk of predation select habitat that enhances security from predators. Two properties of cover related to security are concealment (i.e., habitat structure that blocks an individual from detection by others) and visibility (i.e., visual information accessible relative to habitat structure). Although these properties are often negatively correlated, they are not always inverse; animals in habitat with heterogeneous structure may be able to select for both. We investigated habitat use by pygmy rabbits ( Brachylagus idahoensis) at 2 scales (patch and microsite) to evaluate the influence of both structural properties of cover and visual properties (concealment and visibility) on habitat use by prey. We contrasted vegetation structure at paired used and unused patches. At each patch, we measured concealment and viewshed (i.e., visibility) in 3 orientations (i.e., aerial, terrestrial, and overall) and structural density using lidar. We also measured heights of the 3 tallest shrubs. Additionally, within used patches, we assessed the density of fecal pellets as an index of intensity of use and also measured distance to nearest burrow. At the patch scale, rabbits selected for structural properties of cover (dense vegetation and tall shrubs), but not visual properties of cover. Pygmy rabbits more intensively used microsites associated with high terrestrial concealment and in proximity to burrows. Our results suggest that pygmy rabbits may perceive greater threat from terrestrial as opposed to aerial predators at both scales, and they also indicate a nuanced relationship between properties of cover and habitat use.

In the mountainous regions of the Western United States, increasing wildfire activity and climate change are putting forests at risk of regeneration failure and conversion to non-forests. During periods with unfavorable climatic conditions, locations that are suitable for post-fire tree regeneration (regeneration refugia) may be essential for forest recovery. These refugia could provide scattered islands of recovering forest from which broader forest recovery may be facilitated. Spring ecosystems provide cool and wet microsites relative to the surrounding landscape and may act as regeneration refugia, though few studies have investigated their influence on post-fire regeneration. To address this knowledge gap, we quantified coniferous tree regeneration adjacent to and away from springs in mixed-conifer forests in a mountainous region of central Idaho, USA. Our research objectives were to (1) quantify post-fire conifer density near and away from springs, (2) assess the relative importance of distance to a spring compared with other biophysical factors important to post-fire regeneration, and (3) examine the temporal trends of post-fire seedling establishment near and away from springs. In areas burned at high severity from fires in 1988, 2000, and 2006, we sampled transects at 27 springs for the count, age, and height of extant conifer seedlings, as well as topographic factors and distance to surviving seed source. We modeled the relative effects of distance to a spring, topographic variables (slope, heat load index, elevation), post-fire climate, and distance to surviving seed source for the two dominant species, Douglas fir (Pseudotsuga menziesii) and lodgepole pine (Pinus contorta), using a generalized linear mixed-effects model. Our study revealed that proximity to springs resulted in higher conifer density and earlier establishment after high-severity wildfire when conditions for available seeds and topography were also met. Our results demonstrate that springs are important and previously undescribed regeneration refugia with landscape-scale implications for post-fire forest recovery in increasingly water-limited environments. Springs are relatively abundant features of montane landscapes and may offer continued regeneration refugia for post-fire recovery into the future, but additional springs mapping and hydroclimatic considerations are needed.

This collaboration between the Nez Perce Tribe and the University of Idaho aimed to address the unique needs and perspectives required for Tribal Natural Resources Management (TNRM). TNRM involves the governance and caretaking of the land and waters, emphasizing the recognition of cultural significance, sovereignty, self-determination, and traditional knowledge systems. A workforce development program was created, focusing on Fisheries, Forestry, and Fire Management, while being grounded in Indigenous knowledge, Indigenous STEM identities, and culturally sustaining pedagogy. The philosophical foundations of the program emphasized the importance of integrating Indigenous knowledge and learning approaches alongside technical skills. By broadening the conceptions of "what counts" in science, students were encouraged to recognize the value of Indigenous knowledge and develop a sense of responsibility toward caring for the Land and waters. Data collected from the program revealed its success in helping students connect Indigenous ways of knowing to their understanding of STEM. Students found meaning in Indigenous knowledge as a means to perpetuate Nimiipuu lifeways, while also recognizing the utility of Western STEM. The involvement of Elders and Native professionals as teachers in the STEM curriculum highlighted the importance of intergenerational knowledge transmission. By combining Indigenous ways of knowing with technical skills, the program successfully laid the groundwork for students to become future leaders in Tribal Natural Resources Management, equipped with the necessary cultural, environmental, and scientific expertise to caretake Lands and waters effectively.

Forests account for nearly 90 % of the world's terrestrial biomass in the form of carbon and they support 80 % of the global biodiversity. To understand the underlying forest dynamics, we need a long-term but also relatively high-frequency, networked monitoring system, as traditionally used in meteorology or hydrology. While there are numerous existing forest monitoring sites, particularly in temperate regions, the resulting data streams are rarely connected and do not provide information promptly, which hampers real-time assessments of forest responses to extreme climate events. The technology to build a better global forest monitoring network now exists. This white paper addresses the key structural components needed to achieve a novel meta-network. We propose to complement - rather than replace or unify - the existing heterogeneous infrastructure with standardized, quality-assured linking methods and interacting data processing centers to create an integrated forest monitoring network. These automated (research topic-dependent) linking methods in atmosphere, biosphere, and pedosphere play a key role in scaling site-specific results and processing them in a timely manner. To ensure broad participation from existing monitoring sites and to establish new sites, these linking methods must be as informative, reliable, affordable, and maintainable as possible, and should be supplemented by near real-time remote sensing data. The proposed novel meta-network will enable the detection of emergent patterns that would not be visible from isolated analyses of individual sites. In addition, the near real-time availability of data will facilitate predictions of current forest conditions (nowcasts), which are urgently needed for research and decision making in the face of rapid climate change. We call for international and interdisciplinary efforts in this direction.

Radial stem growth is a key ecosystem process resulting in long-term carbon sequestration. Despite recognition of its importance to global carbon cycling, high uncertainties remain regarding how radial growth phenology (e.g., the onset, mid, and cessation of radial growth) will be affected by climate change. In this study, we evaluated to what extent high spatially (3 x 3 m) and temporally (up to daily) resolved satellite imagery from PlanetScope can be used to monitor stem growth phenology. For this, we made use of detailed stem growth phenological ob-servations of six common European tree species measured by automated point dendrometers at 14 distinct sites across Switzerland between 2017 and 2021. These growth phenological observations were then linked through multiple regression modeling with metrics extracted from spectral index time series. Our results show that the remote sensing-based models enable monitoring the onset (root mean squared deviation (RMSD) ranges from 5.96 to 27.04 days) and mid-stages of stem growth (RMSD ranges from 10.20 to 36.34 days) with reasonable accuracy as opposed to the cessation of stem growth that showed low accuracy (RMSD ranges from 16.02 to 153.63 days). The accuracy of the remote sensing-based prediction models and their optimal suite of predictors varied across species. The latter has important implications for the remote sensing of stem growth phenology in mixed forests, suggesting that it is important for satellite sensors to resolve individual tree crowns. Overall, our results suggest the need for novel spectral indices that capture the spectral components of mechanistic linkages between stem growth and canopy properties that go beyond the mere detection of leaf phenology. When employing such spectral indices, remote sensing could make it possible to detect not only shifts in leaf phenology caused by climate change but also those in stem growth on a broad spatial scale.

Wildfires have nearly become a guaranteed annual event in most western National Forests. Severe fire effects can be mitigated with a goal of minimizing the hydrologic response and promoting soil and vegetation recovery towards the pre-disturbance condition. Sometimes, post-fire actions include salvage logging to recover timber value and to remove excess fuels. Salvage logging was conducted after three large wildfires on the Lolo National Forest in Montana, USA, between 2017 and 2019. We evaluated detrimental soil disturbance (DSD) on seven units that were burned at low, moderate, and high soil burn severity in 2022, three to five years after the logging occurred. We found a range of exposed soil of 5%–25% and DSD from 3% to 20%, and these values were significantly correlated at r = 0.88. Very-high-resolution WorldView-2 imagery that coincided with the field campaign was used to calculate Normal Differenced Vegetation Index (NDVI) across the salvaged areas; we found that NDVI values were significantly correlated to DSD at r = 0.87. We were able to further examine this relationship and determined NDVI threshold values that corresponded to high-DSD areas, as well as develop a model to estimate the contributions of equipment type, seasonality, topography, and burn severity to DSD. A decision-making tool which combines these factors and NDVI is presented to support land managers in planning, evaluating, and monitoring disturbance from post-fire salvage logging.

1. Abstract As a temperature-delineated boundary, the Arctic treeline is predicted to shift northward in response to warming. However, the evidence for northward movement is mixed, with some sections of the treeline advancing while others remain stationary or even retreat. To identify the drivers of this variation, we need a landscape-level understanding of the interactions occurring between climate, tree growth, and population regeneration. In this study, we assessed regeneration alongside annual tree growth and climate during the 20th century. We used an ageheight model combined with tree height from aerial lidar to predict the age structure of 38,652 white spruce trees across 250 ha of Arctic treeline in the central Brooks Range, Alaska, USA. We then used age structure analysis to interpret the trends in regeneration and tree-ring analysis to interpret changes in annual tree growth. The climate became significantly warmer and drier circa 1975, coinciding with divergent responses of regeneration and tree growth. After 1975, regeneration of saplings (trees ≤ 2m tall) decreased compared to previous decades whereas annual growth in mature trees (trees >2m tall) increased by 54% (p<0.0001, Wilcoxon test). Tree-ring width was positively correlated with May-August temperature (p<0.01, Pearson coefficient) during the 20th century. However, after circa 1950, the positive correlation between temperature and growth weakened (i.e., temperature divergence) while the positive correlation with July precipitation strengthened (p<0.01, Pearson coefficient), suggesting that continued drying may limit future growth at this section of Arctic treeline. We conclude that while warmer temperatures appear to benefit annual growth in mature trees, the warmer and drier environmental conditions in spring and summer inhibit regeneration and therefore may be inhibiting the northward advance at this Arctic treeline site. Researchers should consider the interactions between temperature, water availability, and tree age when examining the future of treeline and boreal forest in a changing climate.

Tree wood growth is a key physiological process governing the seasonal duration of carbon sequestration, but few methods exist for remotely monitoring the phenology of wood growth. Hence, scalable methods for detecting and monitoring tree growth onset are needed, particularly in climate-sensitive regions like the forest-tundra ecotone (FTE). Because snow disappearance date (SDD) is observable across large spatial scales using satellite remote sensing and may coincide with tree growth onset at high latitudes, we tested the reliability of remotely sensed SDD as a proxy for tree growth onset at the FTE. We hypothesize that: (1) satellite based SDD estimates from the Moderate Resolution Imaging Spectroradiometer (SDDMODIS) and PlanetScope imagery (SDDPlanet) are not statistically different (p > 0.05) from in situ estimates of SDD from soil temperature probes (SDDST), and; (2) estimates of SDDMODIS or SDDPlanet are not significantly different from tree growth onset, thus providing novel monitoring methods for tree wood growth onset at the FTE. We used data across two growing seasons from two field sites - one in Alaska, USA (AK) and one in Northwest Territories, Canada (NWT). Results differed between sites, with remote and in situ SDD estimates in AK occurring simultaneously, while remotely estimated SDD preceded in situ SDD in NWT. All SDD estimates were statistically different from tree growth onset in AK in both years. While comparisons showed possible synchrony between SDD estimates and tree growth onset at NWT, these results were limited and suggested the influence of other biophysical drivers in tree growth onset. These results highlight the phenological heterogeneity of the FTE and the key knowledge gaps remaining in our understanding of processes driving tree growth onset at this ecotone. Thus, there is a clear need for more research into the relationships between tree growth onset and remote sensing information at the FTE.

Hourly-resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate-induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra-annual (daily to weekly) scales. Near-surface (in situ) thermal remote sensing-with its sensitivity to plant water status-may provide an approach for monitoring intra-annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019-2021) to address these questions. Results showed that leaf temperature (T-L) in combination with environmental variables explained up to three-quarters of the SRV-based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one-half of the variability in daily stem radial growth. The time of day when T-L was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales.

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine‐scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant‐based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close‐range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis . Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data‐driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions.

Light availability drives vertical canopy gradients in photosynthetic functioning and carbon (C) balance, yet patterns of variability in these gradients remain unclear. We measured light availability, photosynthetic CO2  and light response curves, foliar C, nitrogen (N) and pigment concentrations, and the photochemical reflectance index (PRI) on upper and lower canopy needles of white spruce trees (Picea glauca) at the species' northern and southern range extremes. We combined our photosynthetic data with previously published respiratory data to compare and contrast canopy C balance between latitudinal extremes. We found steep canopy gradients in irradiance, photosynthesis and leaf traits at the southern range limit, but a lack of variation across canopy positions at the northern range limit. Thus, unlike many tree species from tropical to mid-latitude forests, high latitude trees may not require vertical gradients of metabolic activity to optimize photosynthetic C gain. Consequently, accounting for self-shading is less critical for predicting gross primary productivity at northern relative to southern latitudes. Northern trees also had a significantly smaller net positive leaf C balance than southern trees suggesting that, regardless of canopy position, low photosynthetic rates coupled with high respiratory costs may ultimately constrain the northern range limit of this widely distributed boreal species.