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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. 

This dataset is a compilation of leaf trait measurements for tree species in the northeastern United States collected between 2017 and 2022 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains 1351 samples, including 18 chemical, physical and structural traits collected across 25 different tree species. Traits include stable isotopes for carbon (C) and nitrogen (N), percent C and N, C:N ratio, total chlorophyll (chl), chl a, chl b, chl a:b ratio, leaf mass per area, average leaf dry mass, average leaf area, length, and width, leaf water content, average petiole length and petiole dry mass, and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions have been included. Leaf physiological and optical traits have been measured concurrently on many of these samples and published separately. 

LiDAR data were acquired over the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH during leaf-off conditions in November 2022. Data were acquired using a LiVox Avia lidar sensor on a Green Valley International LiAirV70 payload. The LiVox Avia is a triple echo 905 nm lidar sensor with a non-repetitive circular scanning pattern that can retrieve ~700,000 returns per second. The sensor payload was flown on board a DJI M300 at an altitude of ~65 m above ground level in a double grid pattern with ~32 m flight line spacing, yielding a return density across the sampling area >500 points per square meter. Returns were georeferenced to WGS84 UTM Zone 19N coordinates with heights above ellipsoid using Green Valley International’s LiGeoreference software with automatic boresight calibration. Outliers were removed, then flight line point clouds were merged. Returns were classified as ground and non-ground returns using Green Valley International’s Lidar360 software and output as LAS (v 1.4) data sets. LAS files were subsequently tiled for publication. 

LiDAR data were acquired over the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH during the growing season. Data were acquired using a LiVox Avia lidar sensor on a Green Valley International LiAirV70 payload. The LiVox Avia is a triple echo 905 nm lidar sensor with a non-repetitive circular scanning pattern that can retrieve ~700,000 returns per second. The sensor payload was flown on board a DJI M300 at an altitude of ~65 m above ground level in a double grid pattern with ~32 m flight line spacing, yielding a return density across the sampling area >500 points per square meter. Returns were georeferenced to WGS84 UTM Zone 19N coordinates with heights above ellipsoid using Green Valley International’s LiGeoreference software with automatic boresight calibration. Outliers were removed, then flight line point clouds were merged. Returns were classified as ground and non-ground returns using Green Valley International’s Lidar360 software and output as LAS (v 1.4) data sets. LAS files were subsequently tiled for publication. 

Orthorectified flight line hyperspectral cubes retiled for publication. Collectively, the tiled hyperspectral cubes cover the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH. Data were acquired using a Headwall Photonics, Inc. Nano VNIR hyperspectral line scanning imager with 273 bands from 400-1000 nm. The sensor was flown on board a DJI M600 hexacopter at an altitude of ~80 m above the forest canopy, yielding ~6 cm GSD. Flight lines were converted from raw sensor observations to upwelling radiance a using a vendor-supplied radiometric calibration file for the sensor, then converted to reflectance using a calibration tarp with known reflectance. Finally, cubes were orthorectified using a 1m DSM in Headwall’s SpectralView software, mosaicked to individual flight line cubes, then subsequently tiled for publication. 

Leaf temperature measurements were collected during the summer of 2020 within forested areas at the Thompson Farm Earth Systems Observatory in Durham, New Hampshire, USA. Located within the property is a registered Ameriflux site, Thompson Farm Forest (US-TFF), as well as experimental throughfall exclusion plots that are part of DroughtNet (experiment running since 2015). Leaf temperature measurements were made within the footprint of the eddy covariance flux tower as well as within both control and throughfall exclusion treatment plots. Upper canopy foliage was accessed using a bucket lift and in situ measurements made using a handheld thermal IR sensor. All data were paired with concurrent meteorological measurements from US-TFF or data from a co-located NOAA CRN station (NH Durham 2 SSW). Additionally, leaf chemical, physical, structure, and physiological traits have been measured at this site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. Specific to this leaf temperature dataset, leaf-level light, temperature, and vpd photosynthetic response curves were measured. 

To quantify the effects of tree height and canopy position on delta13C and delta18O of wood cellulose, we sampled 399 trees and saplings of eight species at nine forest stands across New Hampshire and Vermont, along with nearby saplings growing in the open. Samples were collected in 2017-18, and we analyzed the combined alpha-cellulose from growth rings formed in 2013-2017 for each tree. Carbon data are published in: Vadeboncoeur, M., K. Jennings, A. Ouimette, and H. Asbjornsen. (2020) Correcting tree-ring d13C time series for tree-size effects in eight temperate tree species. Tree Physiology. https://doi.org/10.1093/treephys/tpz138 

Abstract Stable carbon isotope ratios (δ13C) in tree rings have been widely used to study changes in intrinsic water-use efficiency (iWUE), sometimes with limited consideration of how C-isotope discrimination is affected by tree height and canopy position. Our goals were to quantify the relationships between tree size or tree microenvironment and wood δ13C for eight functionally diverse temperate tree species in northern New England and to better understand the physical and physiological mechanisms underlying these differences. We collected short increment cores in closed-canopy stands and analyzed δ13C in the most recent 5 years of growth. We also sampled saplings in both shaded and sun-exposed environments. In closed-canopy stands, we found strong tree-size effects on δ13C, with 3.7–7.2‰ of difference explained by linear regression vs height (0.11–0.28‰ m−1), which in some cases is substantially stronger than the effect reported in previous studies. However, open-grown saplings were often isotopically more similar to large codominant trees than to shade-grown saplings, indicating that light exposure contributes more to the physiological and isotopic differences between small and large trees than does height. We found that in closed-canopy forests, δ13C correlations with diameter at breast height were nonlinear but also strong, allowing a straightforward procedure to correct tree- or stand-scale δ13C-based iWUE chronologies for changing tree size. We demonstrate how to use such data to correct and interpret multi-decadal composite isotope chronologies in both shade-regenerated and open-grown tree cohorts, and we highlight the importance of understanding site history when interpreting δ13C time series. 

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.