Structural analysis of forests by UAV is currently growing in popularity. Given the reduction in platform costs, and the number of algorithms available to analyze data output, the number of applications has grown rapidly. Forest structures are not only linked to economic value in forestry, but also to biodiversity and vulnerability issues. LiDAR remains the most promising technique for forest structural assessment, but small LiDAR sensors suitable for UAV applications are expensive and are limited to a few manufactures. The estimation of 3D-structures from two-dimensional image sequences called ‘Structure from motion’ (SfM) overcomes this limitation by photogrammetrically reconstructing point clouds similar to those rendered from LiDAR sensors. The result of these techniques in highly structured terrain strongly depends on the methods employed during image acquisition, therefore structural indices might be vulnerable to misspecifications in flight campaigns. In this paper, we outline how image overlap and ground sampling distances affect image reconstruction completeness in 2D and 3D. Higher image overlaps and coarser GSDs have a clearly positive influence on reconstruction quality. Therefore, higher accuracy requirements in the GSD must be compensated by a higher image overlap. The best results are achieved with an image overlap of > 95% and a resolution of > 5 cm. The most important environmental factors have been found to be wind and terrain elevation, which could be an indicator of vegetation density.

Unprecedented tree dieback across Central Europe caused by recent global change‐type drought events highlights the need for a better mechanistic understanding of drought‐induced tree mortality. Although numerous physiological risk factors have been identified, the importance of two principal mechanisms, hydraulic failure and carbon starvation, is still debated. It further remains largely unresolved how the local neighborhood composition affects individual mortality risk. We studied 9435 young trees of 12 temperate species planted in a diversity experiment in 2013 to assess how hydraulic traits, carbon dynamics, pest infestation, tree height and neighborhood competition influence individual mortality risk. Following the most extreme global change‐type drought since record in 2018, one third of these trees died. Across species, hydraulic safety margins (HSMs) were negatively and a shift towards a higher sugar fraction in the non‐structural carbohydrate (NSC) pool positively associated with mortality risk. Moreover, trees infested by bark beetles had a higher mortality risk, and taller trees a lower mortality risk. Most neighborhood interactions were beneficial, although neighborhood effects were highly species‐specific. Species that suffered more from drought, especially Larix spp. and Betula spp., tended to increase the survival probability of their neighbors and vice versa. While severe tissue dehydration marks the final stage of drought‐induced tree mortality, we show that hydraulic failure is interrelated with a series of other, mutually inclusive processes. These include shifts in NSC pools driven by osmotic adjustment and/or starch depletion as well as pest infestation and are modulated by the size and species identity of a tree and its neighbors. A more holistic view that accounts for multiple causes of drought‐induced tree mortality is required to improve predictions of trends in global forest dynamics and to identify mutually beneficial species combinations.

Summary: Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R² (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross‐validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade‐offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long‐table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the ‘rtry’ R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, ‘rtry’ avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. ‘rtry’ is therefore easy to use even for beginners in R. Notably, ‘rtry’ does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While ‘rtry’ primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The ‘rtry’ package is available on the Comprehensive R Archive Network (CRAN; https://cran.r‐project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI‐BGC‐Functional‐Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows.

We stand at the threshold of a transformative era in Earth observation, marked by space‐borne visible‐to‐shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter‐ and intra‐annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub‐seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High‐Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub‐seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open‐source processing methods and cloud‐based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high‐frequency time series data from space‐borne VSWIR imaging spectrometers.