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1. Harnessing stemflow as a diagnostic tool for canopy disease detection and monitoring

   https://doi.org/10.1016/j.foreco.2025.122674  

   Gordon, D; Burke, David J; Carino-Kyker, Sarah R; Bashian-Victoroff, Claudia; Mabrouk, Adam I; Van_Stan, John T (June 2025, Forest Ecology and Management)

   Monitoring diseases within tree canopies is challenging due to their inaccessibility and the complexity of canopy ecosystems. Here, we explore the potential of stemflow sampling as a novel, ground-based method for detecting and monitoring canopy-associated pathogens. In a case study focused on Litylenchus crenatae ssp. mccannii (LCM), the nematode associated with Beech Leaf Disease (BLD), we collected stemflow samples from 18 Fagus grandifolia Ehrh. (American beech) trees across 12 storm events. eDNA assays detected LCM presence in 7 of those storms, with quantitative PCR-derived gene concentrations ranging from 80 to 158,000 copies mL−1. Higher detections and concentrations coincided with leaf senescence and bud formation periods, and they correlated conditionally with event rainfall amount and pre-storm changes in relative humidity. Although based on a single site and season, these findings demonstrate the potential for stemflow sampling to capture a pathogen’s eDNA (i.e., canopy distress signals) at ground level. This method could complement traditional monitoring, offering another affordable, non-invasive tool for pathogen detection. Additional validation, particularly regarding live versus dead organisms and across varied site conditions, will be essential to evaluate the breadth of value stemflow eDNA offers for canopy disease management and ecological research. 

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2. Leaf angle measurements for temperate tree species in northeastern USA

   https://doi.org/10.6073/pasta/faa03b13e5eab16a5e46a4ba0c2123e5  

   Hastings, Jack H; Baillargeon, Kaitlyn; Ouimette, Andrew P; Ollinger, Scott V (January 2023, Environmental Data Initiative)

   Leaf angle distribution (LAD) measurements were made during the growing season in 2021 at the Harvard Forest in Petersham, MA, USA, and in 2022 at the Thompson Farm Earth Systems Observatory in Durham, NH, USA. At both sites, a level-calibrated digital angle tool was used to measure LAD in upper canopy foliage of common northeastern temperate tree species accessed using a mobile canopy lift. Additionally, at Thompson Farm, measurements were made at multiple heights to characterize differences of LAD in high, middle, and low canopy positions. Here, we have published those measurements, including a summary table of species average leaf angles and calculated parameters for fitted beta distributions. Processing scripts can be made available upon request to the authors. Additionally, leaf chemical, physical, structure, optical and physiological traits have been measured at these site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. 

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3. Using unoccupied aerial vehicles to map and monitor changes in emergent kelp canopy after an ecological regime shift

   https://doi.org/10.1002/rse2.295  

   Saccomanno, Vienna R.; Bell, Tom; Pawlak, Camille; Stanley, Charlotte K.; Cavanaugh, Katherine C.; Hohman, Rietta; Klausmeyer, Kirk R.; Cavanaugh, Kyle; Nickels, Abby; Hewerdine, Waz; et al (February 2023, Remote Sensing in Ecology and Conservation)

   Abstract Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy‐forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource‐focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management. 

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4. Grapevine leaf size influences canopy temperature

   https://doi.org/10.20870/oeno-one.2024.58.2.7735  

   Migicovsky, Zoë; Swift, Joel F; Helget, Zachary; Klein, Laura L; Ly, Anh; Maimaitiyiming, Matthew; Woodhouse, Karoline; Fennell, Anne; Kwasniewski, Misha; Miller, Allison J; et al (April 2024, OENO One)

   Grapevine leaves have diverse shapes and sizes which are influenced by many factors including genetics, vine phytosanitary status, environment, leaf and vine age, and node position on the shoot. To determine the relationship between grapevine leaf shape or size and leaf canopy temperature, we examined five seedling populations grown in a vineyard in California, USA. The populations had one parent with compound leaves of the Vitis piasezkii type and a different second parent with non-compound leaves. In previous work, we had measured the shape and size of the leaves collected from these populations using 21 homologous landmarks. Here, we paired these morphological data with canopy temperature measurements made using a handheld infrared thermometer. After recording time of sampling and canopy temperature, we used a linear model between time of sampling and canopy temperature to estimate temperature residuals. Based on these residuals, we determined if the canopy temperature of each vine was cooler or warmer than expected, based on the time of sampling. We established a relationship between leaf size and canopy temperature: vines with larger leaves were cooler than expected. By contrast, leaf shape was not strongly correlated with variation in canopy temperature. Ultimately, these findings indicate that vines with larger leaves may contribute to the reduction of overall canopy temperature; however, further work is needed to determine whether this is due to variation in leaf size, differences in the openness of the canopy or other related traits. 

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5. Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest

   https://doi.org/10.3390/rs12020309  

   Hastings, Jack H.; Ollinger, Scott V.; Ouimette, Andrew P.; Sanders-DeMott, Rebecca; Palace, Michael W.; Ducey, Mark J.; Sullivan, Franklin B.; Basler, David; Orwig, David A. (January 2020, Remote Sensing)

   The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factors varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands. 

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