Abstract Accurate estimates of wildlife densities are important for understanding population dynamics and informing management decisions, but obtaining such estimates can be logistically challenging and costly. Drones and associated sensors provide a novel and potentially efficient pathway to estimate wildlife densities but require critical evaluation of efficacy across large spatial scales. We evaluated the capability of drones to estimate moose ( Alces alces ) density across a large landscape (~12,000 km 2 ) in northern New Hampshire, USA, using infrared imagery collected via local drone surveys. Our objective was to model moose population densities using drones while accounting for imperfect detection (sightability) in forested landscapes. We also compared drone‐based density estimates to existing estimates derived from hunter surveys. Using a stratified random study design, we conducted 62 drone surveys that covered 64.6 km 2 (0.5% of the study area) during January and February 2024. We fit 2 Bayesian binomial mixture models that accounted for sightability to 33 adult moose detections. A simple survey model resulted in a density estimate of 0.64 moose/km 2 across the study area (90% credible interval [CrI] = 0.56–0.77 moose/km 2 ). A second spatial model incorporated covariates to make extrapolated predictions of moose densities at the wildlife management unit (WMU) scale across the study area. Overall, both drone‐based densities were approximately twice the moose density estimated from deer hunter surveys. The precision of drone estimates from the survey model was within the benchmark range for moose management (90% CrI within ±25% of mean), but extrapolated predictions from the spatial model based on environmental covariates were less precise and exhibited somewhat weak correlation (Spearman's correlation = 0.42) with hunter survey estimates at the WMU scale. Our study is among the first to employ a thermal sensor via drone to estimate the density of an unmarked ungulate across a large spatial scale (thousands of square kilometers) using local sampling within a stratified random study design. Our work suggests that drones offer a promising method for estimating density over broad spatial scales without the need for tagged or global positioning system (GPS)‐collared individuals.

Forest canopy near-infrared reflectance and mass-based canopy nitrogen concentration (canopy %N) have been shown to be positively correlated. While the mechanisms underpinning this relationship remain unresolved, the broad range of wavelengths involved points to structural properties that influence scattering and covary with %N. Despite this, efforts that have focused on commonly measured structural properties such as leaf area index (LAI) have failed to identify a causal mechanism. Here, we sought to understand how lidar-derived canopy traits related to additional properties of foliar arrangement and structural complexity modulate the effects of leaf spectra and leaf area index (LAI) on canopy reflectance. We developed a leaf layer spectra model to explore how canopy reflectance would change if complex foliage arrangements were removed, compressing the canopy into optically dense, uniform stacked layers while maintaining the same leaf area index. Model results showed that LAI-weighted leaf reflectance saturates at a leaf area index of approximately two for needleleaf species and four for broadleaf species. When upscaled to estimate plot-level canopy reflectance in the absence of structural complexity (NIRrLAI), results showed a strong positive relationship with canopy %N (r2 = 0.86), despite a negative relationship for individual leaves or “big-leaf” canopies with an LAI of one (NIRrL, r2 = 0.78). This result implies that the relationship between canopy near-infrared reflectance and canopy %N results from the integrated effects of canopy complexity acting on differences in leaf-level optical properties. We introduced an index of relative reflectance (IRr) that shows that the relative contribution of structural complexity to canopy near-infrared reflectance (NIRrC) is related to canopy %N (r2 = 0.55), with a three-fold reduction from potential canopy near-infrared reflectance observed in stands with low %N compared to a two-fold reduction in stands with high %N. These findings support the hypothesis that the correlation between canopy %N and canopy reflectance is the result of interactions between leaf traits and canopy structural complexity.

Unpiloted aerial system (UAS) light detection and ranging (lidar) and structure-from-motion (SfM) photogrammetry have emerged as viable methods to map high-resolution snow depths ( similar to 1 m). These technologies enable a better understanding of snowpack spatial distribution and its evolution over time, advancing hydrological and ecological applications. This is particularly critical in mixed vegetation environments, where both forest canopy and open areas influence snow accumulation and melt patterns. In this study, a series of UAS lidar/SfM snow depth maps were collected during the 2020/2021 winter season in Durham, New Hampshire, USA, with three objectives: (1) quantifying UAS lidar/SfM snow depth retrieval performance using in situ magnaprobe measurements, (2) conducting a quantitative comparison of lidar and SfM retrievals of shallow snow depths ( < 35 cm) throughout the winter, and (3) understanding the spatial distribution of snow depth and its relationship with terrain features. Eight UAS surveys were conducted over approximately 0.35 km(2) including both open fields and a mixed forest. In the field, lidar had a slightly lower error than SfM, compared with in situ observations, with a mean absolute difference (MAD) of 3.5 cm for lidar and 4.0 cm for SfM. Snow depth maps from SfM and lidar were fairly consistent in the field, with only marginal differences on most dates. In the forest, SfM greatly overestimated in situ snow depths compared with lidar (lidar MAD = 6.3 cm, SfM MAD = 31.4 cm). There was no clear agreement between SfM and lidar snow depth values for individual 1 m(2) pixels in the forest (MAD = 55.7 cm). Using the concept of temporal stability, we found that the spatial distribution of snow depth captured by lidar was generally consistent throughout the period, indicating a strong influence from static land characteristics. Considering both areas (forest and field), the spatial distribution of snow depth was primarily influenced by vegetation type while also reflecting the effects of soil variables (e.g., soil organic matter). When the field and forest areas were analyzed separately, the spatial distribution was distinctly affected by slope and the shadowing effects of the forest canopy.

Abstract. Uncrewed Aerial Systems (UAS) lidar and structure-from-motion (SfM) photogrammetry have emerged as viable methods to map high-resolution snow depths (~1 m). These technologies enable a better understanding of snowpack spatial structure and its evolution over time, advancing hydrologic and ecological applications. In this study, a series of UAS lidar/SfM snow depth maps were collected during the 2020/21 winter season in Durham, New Hampshire, USA with three objectives: (1) quantifying UAS lidar/SfM snow depth retrieval performance using multiple in-situ measurement techniques (magnaprobe and field cameras), (2) conducting a quantitative comparison of lidar and SfM snow depths (< 35 cm) throughout the winter, and (3) better understanding the spatial structure of snow depth and its relationship with terrain features. The UAS surveys were conducted over approximately 0.35 km2 including both open fields and a mixed forest. In the field, lidar had a lower error than SfM compared to in-situ observations with a Mean Absolute Error (MAE) of 3.0 cm for lidar and 5.0–14.3 cm for SfM. In the forest, SfM greatly overestimated snow depths compared to lidar (lidar MAE = 2.7–7.3 cm, SfM MAE = 32.0–44.7 cm). Even though snow depth differences between the magnaprobe and field cameras were found, they had only a modest impact on the UAS snow depth validation. Using the concept of temporal stability, we found that the spatial structure of snow depth captured by lidar was generally consistent throughout the period indicating a strong influence from static land characteristics. Considering all areas (forest and fields), the spatial structure of snow depth was primarily influenced by vegetation type (e.g., fields, deciduous, and coniferous forests). Within the field, the spatial structure was primarily correlated with slope and forest canopy shadowing effects.

Northern peatlands are a globally significant source of methane (CH4), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH4 production in peatlands will be key to predicting annual emissions changes, with stable carbon isotopes (delta C-13-CH4) being a powerful tool for characterizing these drivers. Given that delta C-13-CH4 is used in top-down atmospheric inversion models to partition sources, our ability to model CH4 production pathways and associated delta C-13-CH4 values is critical. We sought to characterize the role of environmental conditions, including hydrologic and vegetation patterns associated with permafrost thaw, on delta C-13-CH4 values from high-latitude peatlands. We measured porewater and emitted CH4 stable isotopes, pH, and vegetation composition from five boreal-Arctic peatlands. Porewater delta C-13-CH4 was strongly associated with peatland type, with delta C-13 enriched values obtained from more minerotrophic fens (-61.2 +/- 9.1 parts per thousand) compared to permafrost-free bogs (-74.1 +/- 9.4 parts per thousand) and raised permafrost bogs (-81.6 +/- 11.5 parts per thousand). Variation in porewater delta C-13-CH4 was best explained by sedge cover, CH4 concentration, and the interactive effect of peatland type and pH (r(2) = 0.50, p < 0.001). Emitted delta C-13-CH4 varied greatly but was positively correlated with porewater delta C-13-CH4. We calculated a mixed atmospheric delta C-13-CH4 value for northern peatlands of -65.3 +/- 7 parts per thousand and show that this value is more sensitive to landscape drying than wetting under permafrost thaw scenarios. Our results suggest northern peatland delta C-13-CH4 values are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models. Plain Language Summary Peatlands are abundant across the boreal-Arctic landscape and are important sources of methane, a powerful greenhouse gas. The amount of methane emitted into the atmosphere depends on multiple factors including the organic material being decomposed and the microbial processes ("pathways") that produce methane. The different pathways of methane production leave distinct fingerprints ("stable carbon isotopes") on methane that provide information on how that methane was formed and help to trace methane in the atmosphere back to its ground source. We looked at how stable carbon isotopes change across five northern peatland locations and wanted to know what controls those changes. We found that stable carbon isotopes differ between bogs and fens and are further controlled by soil pH and the abundance of sedge vegetation present. We used this information to test how stable carbon isotopes from northern peatlands might change as the landscape becomes wetter or drier due to the impacts of climate change. We found that peatland stable carbon isotopes are most sensitive to potential landscape drying opposed to wetting which has important implications for improving methane emission models.

Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s−1, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s−1 and 4 m s−1 and snow-on flight speeds of 6 m s−1 over open fields and between 2 and 4 m s−1 over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover.

The accuracy and precision of snow depth measurements depend on the measuring device and the conditions of the site and snowpack in which it is being used. This study compares collocated snow depth measurements from a magnaprobe snow depth probe and a Federal snow tube in an ephemeral snow environment. We conducted three snow depth sampling campaigns from December 2020 to February 2021 that included 39 open-field and coniferous-, mixed-, and deciduous-forest sampling sites in Durham, New Hampshire, United States. For all sampling campaigns and land cover types, with a total of 936 paired observations, the magnaprobe snow depth measurements were consistently deeper than those of the snow tube. There was a 12 % average difference between the magnaprobe (14.9 cm) and snow tube (13.2 cm) average snow depths with a greater difference in the forest (1.9 cm) than the field (1.3 cm). This study suggests that snow depth measurements using a Federal snow tube can avoid overprobing with an ephemeral snowpack in forested environments.

Abstract. The accuracy and consistency of snow depth measurements depend on the measuring device and the conditions of the site and snowpack in which it is being used. This study compares collocated snow depth measurements from a magnaprobe automatic snow depth probe and a Federal snow tube, then uses these measurements to validate snow depth maps from an unpiloted aerial system (UAS) with an integrated Light Detection and Ranging (lidar) sensor. We conducted three snow depth sampling campaigns from December 2020 to February 2021 that included 39 open field, coniferous, mixed, and deciduous forest sampling sites in Durham, New Hampshire, United States. Average snow depths were between 9 and 15 cm. For all sampling campaigns and land cover types, the magnaprobe snow depth measurements were consistently deeper than the snow tube. There was a 12 % average difference between the magnaprobe (14.9 cm) and snow tube (13.2 cm) average snow depths with a greater difference in the forest than the field. The lidar estimates of snow depth were 3.6 cm and 1.9 cm shallower on average than the magnaprobe and snow tube, respectively. While the magnaprobe had a better correlation with the UAS lidar, the root mean square errors were higher for the magnaprobe than the snow tube, likely due to overprobing by the magnaprobe into leaf litter. Even though the differences between the in-situ sampling methods resulted in modest performance differences when used to validate the UAS lidar snow depths in this study, measuring vegetation height, leaf litter, and soil frost with in-situ snow depths from multiple sampling techniques helped to account for the errors of in-situ snow depth for robust validation of the UAS snow depth maps.

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Terrestrial and airborne laser scanning and structure from motion techniques have emerged as viable methods to map snow depths. While these systems have advanced snow hydrology, these techniques have noted limitations in either horizontal or vertical resolution. Lidar on an unpiloted aerial vehicle (UAV) is another potential method to observe field- and slope-scale variations at the vertical resolutions needed to resolve local variations in snowpack depth and to quantify snow depth when snowpacks are shallow. This paper provides some of the earliest snow depth mapping results on the landscape scale that were measured using lidar on a UAV. The system, which uses modest-cost, commercially available components, was assessed in a mixed deciduous and coniferous forest and open field for a thin snowpack (< 20 cm). The lidar-classified point clouds had an average of 90 and 364 points/m2 ground returns in the forest and field, respectively. In the field, in situ and lidar mean snow depths, at 0.4 m horizontal resolution, had a mean absolute difference of 0.96 cm and a root mean square error of 1.22 cm. At 1 m horizontal resolution, the field snow depth confidence intervals were consistently less than 1 cm. The forest areas had reduced performance with a mean absolute difference of 9.6 cm, a root mean square error of 10.5 cm, and an average one-sided confidence interval of 3.5 cm. Although the mean lidar snow depths were only 10.3 cm in the field and 6.0 cm in the forest, a pairwise Steel–Dwass test showed that snow depths were significantly different between the coniferous forest, the deciduous forest, and the field land covers (p < 0.0001). Snow depths were shallower, and snow depth confidence intervals were higher in areas with steep slopes. Results of this study suggest that performance depends on both the point cloud density, which can be increased or decreased by modifying the flight plan over different vegetation types, and the grid cell variability that depends on site surface conditions.

Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as wind speed and direction, sol...

Abstract. Shallow snowpack conditions, which occur throughout the year in many regions as well as during accumulation and ablation periods in all regions, are important in water resources, agriculture, ecosystems, and winter recreation. Terrestrial and airborne (manned and unmanned) laser scanning and structure from motion (SfM) techniques have emerged as viable methods to map snow depths. Lidar on an unmanned aerial vehicle is also a potential method to observe field and slope scale variations of shallow snowpacks. This paper describes an unmanned aerial lidar system, which uses commercially available components, for snow depth mapping on the landscape scale. The system was assessed in a mixed deciduous and coniferous forest and open field for a shallow snowpack (

The concept of "big" data is nothing new to archaeologists; we have long made a profession of collecting, organizing, and analyzing a surfeit of data describing everything from minute artifact attributes to landscape-wide environmental characteristics. Regardless of this abundance, we have and continue to confront the self-same problem inherent in "big" data, namely what analyses will actually help us use these data to advance understandings of past human behaviors. With burgeoning remote sensing technologies archaeology faces a new wave of "big" data, but how do these techniques improve our ability to make the inferential leaps to bridge the present to the past and bring new insights forward? We argue that, to date, remote sensing techniques (satellite, aerial, and unpersonned aerial imagery) have been applied somewhat narrowly to mostly high-resolution site-based research in archaeology. To truly unleash the capabilities of these techniques, and expand our capacity for wrangling "big" data to more fully investigate past patterns, we need to conduct iterative analyses incorporating remotely sensed data on bounded archaeological sites and regions and unbounded landscapes. A case study from the Late Precontact (ca. A.D. 1200-1600) period in the northern Great Lakes of North America detailing how such an iterative approach can be initiated is explored here.

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.