Background: Global canopy height models are becoming prolific yet require evaluation across New Zealand's diverse vegetation types to assess their accuracy and applicability. Accurate measurement of canopy height is crucial for estimating above-ground woody biomass, which is essential for modelling carbon emissions and sequestration in the context of climate change. These models generally rely on remote sensing data and machine learning techniques, with Light Detection and Ranging (LiDAR) technology commonly employed for precise measurement. Methods: This study validated the three latest global canopy height models, each provided at a different resolution: 30-metre, 10-metre, and 1-metre. We assessed the accuracy of the selected models by comparing them against canopy height estimates derived from local Airborne Laser Scanning (ALS) datasets, which served as our reference data. Eleven regions across New Zealand were selected based on ALS data availability, encompassing five vegetation and land cover types. Our methodology involved utilising and automating the processing of large New Zealand ALS datasets. To align resolutions for comparison, the reference canopy height was calculated by aggregating average or maximum heights at 10 and 30 m spatial resolution. Model performances were assessed using statistical metrics, including root-mean-square error (RMSE), bias, and R2. Results: Overall, all models exhibited relatively low R2 values, indicating limited capture of canopy height variability. The Potapov 30-metre model performed best with average aggregation in shorter vegetation. In contrast, the Lang 10-metre model showed improved accuracy with maximum aggregation, particularly in taller vegetation, but visual boundaries between different vegetation types were not as distinct. The Tolan 1-metre model provided a balanced approach, minimising biases in lower heights but underestimating taller canopies. Results highlight model-specific strengths for varying vegetation structures and the sensitivity of performances to aggregation methods applied to high-resolution reference ALS data. Conclusions: All three global canopy height models exhibit varied performance across New Zealand's vegetation types. The findings highlight the importance of vegetation-specific applications to optimise each global model's accuracy. Currently, these models are suitable for carbon accounting efforts as supplementary tools rather than replacements for existing methodologies.

Silvicultural operations such as planting, pruning, and thinning are vital for the forest value chain, requiring efficient monitoring to prevent value loss. While effective, traditional field plots are time-consuming, costly, spatially limited, and rely on assumptions that they adequately represent a wider area. Alternatively, unmanned aerial vehicles (UAVs) can cover large areas while keeping operators safe from hazards including steep terrain. Despite their utility, optimal flight parameters to ensure flight efficiency and data quality remain under-researched. This study evaluated the impact of forward and side overlap and flight altitude on the quality of two- and three-dimensional spatial data products from UAV photogrammetry (UAV-SfM) for assessing stand density in a recently thinned Pinus radiata D. Don plantation. A contemporaneously acquired UAV laser scanner (ULS) point cloud provided reference data. The results indicate that the optimal UAV-SfM flight parameters are 90% forward and 85% side overlap at a 120 m altitude. Flights at an 80 m altitude offered marginal resolution improvement (2.2 cm compared to 3.2 cm ground sample distance/GSD) but took longer and were more error-prone. Individual tree detection (ITD) for stand density assessment was then applied to both UAV-SfM and ULS canopy height models (CHMs). Manual cleaning of the detected ULS tree peaks provided ground truth for both methods. UAV-SfM had a lower recall (0.85 vs. 0.94) but a higher precision (0.97 vs. 0.95) compared to ULS. Overall, the F-score indicated no significant difference between a prosumer-grade photogrammetric UAV and an industrial-grade ULS for stand density assessments, demonstrating the efficacy of affordable, off-the-shelf UAV technology for forest managers. Furthermore, in addressing the knowledge gap regarding optimal UAV flight parameters for conducting operational forestry assessments, this study provides valuable insights into the importance of side overlap for orthomosaic quality in forest environments.

An exceptional July 2022 winter storm brought 550 mm of precipitation to the Southern Alps of New Zealand. A series of alpine mass movements occurred during the storm, including a widespread snow avalanche cycle, debris flows, and erosion from rain runoff. We detail the sequence of events in the Kitchener avalanche path. Here, two large snow avalanches were followed by a debris flow. Substantial erosion of deposition and the underlying alluvial fan were induced by runoff from over 300 mm of rain falling after the first avalanche. The Kitchener path saw the largest avalanche since 1986, testing the utility of a diversion berm constructed for a 1:100‐year event. Results from a unmanned aerial vehicle lidar survey and numerical modeling characterize the rain‐on‐snow hazard sequence. In particular, the rain‐on‐snow event occurred on a deep mid‐winter snowpack, offering insights into future hazards posed by increasingly frequent extreme alpine precipitation.

Natural hazard models need accurate digital elevation models (DEMs) to simulate mass movements on real-world terrain. A variety of platforms (terrestrial, drones, aerial, satellite) and sensor technologies (photogrammetry, lidar, interferometric synthetic aperture radar) are used to generate DEMs at a range of spatial resolutions with varying accuracy. As the availability of high-resolution DEMs continues to increase and the cost to produce DEMs continues to fall, hazard modelers must often choose which DEM to use for their modeling. We use satellite photogrammetry and topographic lidar to generate high-resolution DEMs and test the sensitivity of the Rapid Mass Movement Simulation (RAMMS) software to the DEM source and spatial resolution when simulating a large and complex snow avalanche along Milford Road in Aotearoa/New Zealand. Holding the RAMMS parameters constant while adjusting the source and spatial resolution of the DEM reveals how differences in terrain representation between the satellite photogrammetry and topographic lidar DEMs (2 m spatial resolution) affect the reliability of the simulation estimates (e.g., maximum core velocity, powder pressure, runout length, final debris pattern). At the same time, coarser representations of the terrain (5 and 15 m spatial resolution) simulate avalanches that run too far and produce a powder cloud that is too large, though with lower maximum impact pressures, compared to the actual event. The complex nature of the alpine terrain in the avalanche path (steep, rough, rock faces, treeless) makes it a suitable location to specifically test the model sensitivity to digital surface models (DSMs) where both ground and above-ground features on the topography are included in the elevation model. Considering the nature of the snowpack in the path (warm, deep with a steep elevation gradient) lying on a bedrock surface and plunging over a cliff, RAMMS performed well in the challenging conditions when using the high-resolution 2 m lidar DSM, with 99 % of the simulated debris volume located in the documented debris area.

A 16-year series of daily snow-covered area (SCA) for 2000–2016 is derived from MODIS imagery to produce a regional-scale snow cover climatology for New Zealand's largest catchment, the Clutha Catchment. Filling a geographic gap in observations of seasonal snow, this record provides a basis for understanding spatio-temporal variability in seasonal snow cover and, combined with climatic data, provides insight into controls on variability. Seasonal snow cover metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Modes of spatial variability were characterised, whilst also preserving temporal signals by applying raster principal component analysis (rPCA) to maps of annual SCD anomaly. Sensitivity of SCD to temperature and precipitation variability was assessed in a semi-distributed way for mountain ranges across the catchment. The influence of anomalous winter air flow, as characterised by HYSPLIT back-trajectories, on SCD variability was also assessed. On average, SCA peaks in late June, at around 30 % of the catchment area, with 10 % of the catchment area sustaining snow cover for > 120 d yr−1. A persistent mid-winter reduction in SCA, prior to a second peak in August, is attributed to the prevalence of winter blocking highs in the New Zealand region. In contrast to other regions globally, no significant decrease in SCD was observed, but substantial spatial and temporal variability was present. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. This analysis of SCD anomalies revealed strong spatio-temporal variability beyond that associated with topographic controls, which can result in snow cover conditions being out of phase across the catchment. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. While two large-scale climate modes, the SOI and SAM, fail to explain observed variability, specific spatial modes of SCD are favoured by anomalous airflow from the NE, E and SE. These findings illustrate the complexity of atmospheric controls on SCD within the catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow.

Being dynamic in time and space, seasonal snow represents a difficult target for ongoing in situ measurement and characterisation. Improved understanding and modelling of the seasonal snowpack requires mapping snow depth at fine spatial resolution. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial variability of snow depth is evaluated within an alpine catchment of the Pisa Range, New Zealand. Digital surface models (DSMs) at 0.15 m spatial resolution in autumn (snow-free reference) winter (2 August 2016) and spring (10 September 2016) allowed mapping of snow depth via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by the propagation of check point residuals from the aero-triangulation of constituent DSMs and via comparison of snow-free regions of the spring and autumn DSMs. The accuracy of RPAS-derived snow depth was validated with in situ snow probe measurements. Results for snow-free areas between DSMs acquired in autumn and spring demonstrate repeatability yet also reveal that elevation errors follow a distribution that substantially departs from a normal distribution, symptomatic of the influence of DSM co-registration and terrain characteristics on vertical uncertainty. Error propagation saw snow depth mapped with an accuracy of ±0.08 m (90 % c.l.). This is lower than the characterization of uncertainties on snow-free areas (±0.14 m). Comparisons between RPAS and in situ snow depth measurements confirm this level of performance of RPAS photogrammetry while also highlighting the influence of vegetation on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine-scale spatial variability. Despite limitations accompanying RPAS photogrammetry, which are relevant to similar applications of surface and volume change analysis, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological catchment ( ∼ 0.4 km2) at very high resolution. Resolving snowpack features associated with redistribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data will enhance understanding of physical processes controlling spatial distributions of seasonal snow and their relative importance on varying spatial and temporal scales.

Abstract. Dynamic in time and space, seasonal snow represents a difficult target for ongoing in situ measurement and characterisation. Improved understanding and modelling of the seasonal snowpack requires mapping snow depth at fine spatial resolution. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial variability of snow depth is evaluated within an alpine catchment of the Pisa Range, New Zealand. Digital surface models (DSM) at 0.15 m spatial resolution in autumn (snow-free reference) winter (02/08/2016) and spring (10/09/2016) allowed mapping of snow depth via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by the propagation of check point residuals from the aero-triangulation of constituent DSMs, and via comparison of snow-free regions of the spring and autumn DSMs. The accuracy of RPAS-derived snow depth was validated with in situ snow probe measurements. Results for snow free areas between DSMs acquired in autumn and spring demonstrate repeatability, yet also reveal that elevation errors follow a distribution substantially departing from a normal distribution, symptomatic of the influence of DSM co-registration and terrain characteristics on vertical uncertainty. Error propagation saw snow depth mapped with an accuracy of ±0.08 m (90 % c.l.). This is lower than the characterization of uncertainties on snow-free areas (±0.13 m). Comparisons between RPAS and in situ snow depth measurements confirm this level of performance of RPAS photogrammetry, while also highlighting the influence of vegetation on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, which are relevant to similar applications of surface and volume change analysis, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin (~ 0.4 km2), at very high resolution. Resolving snowpack features associated with re-distribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data will enhance understanding of physical processes controlling spatial distribution of seasonal snow, and their relative importance at varying spatial and temporal scales.

Remotely piloted aircraft systems (RPAS) offer the opportunity to map small areas photogrammetrically at very high resolution in an on-demand fashion. RPAS datasets may subsequently provide very high resolution three dimensional terrain and surface models. While the use of RPAS for both qualitative and quantitative mapping is increasing rapidly, examples related to snow are promising, but limited (De Michele et al. 2015; Durand 2015; Bühler et al. 2016; Harder et al. 2016; Marti et al. 2016).

Image matching via correlation of remotely sensed imagery is an increasingly popular technique for measuring glacier flow because it can provide spatially distributed measurements while mitigating many of the challenges accompanying in situ measurements of glacier velocity. Despite the increasing use of such techniques, and the requirement for well understood uncertainties where analysis of temporal variability is sought, studies that incorporate an assessment of accuracy based on in situ data remain rare. This study presents a rigorous methodology to quantify the uncertainties that apply to glacier flow velocities derived from optical satellite imagery. This method is applied on three successive ASTER images of Tasman Glacier, New Zealand. Evaluation of the quality of co-registration between image pair constituents allows unique uncertainties to be calculated for individual velocity measurements. These uncertainties have magnitudes that correspond to sub-pixel co-registration errors and account for anisotropic co-registration variance. Compared to estimated velocities, uncertainties on the order of 2–30% are achieved. Flow-fields for the 2009–2010 and 2010–2011 periods were derived using various spectral bands and validated with in situ data obtained by GPS survey over a nearly coincident period. This revealed the importance of considering the spectral characteristics of the target surface when selecting bands for image correlation, while validation demonstrated the success of the implemented methodology. Uncertainties derived for the flow-fields permitted statistical significance of velocity change between measurement periods to be assessed. Significant velocity changes, observed across the glacier surface, suggest that the Tasman Glacier behaves in a more dynamic way than previously recognised, highlighting the advantages of the methodology described here.