James David Shepherd

Quantifying live, above-ground biomass in temperate forests is essential for understanding carbon dynamics and guiding land management, yet it remains challenging at large spatial scales and where forest heterogeneity is high. Here we develop a LiDAR-based framework to map above-ground biomass across indigenous forests in New Zealand. We derive a function in which above-ground biomass per unit area, AGB (tDM ha-1), scales with the square of canopy height, moderated by a spatially varying coefficient that captures spatial variation in forest structure and composition. When the function is applied to large pixel cells (20 m or greater), the coefficient of variation of canopy height within the cell is required to account for deep canopy gaps. This formulation enables airborne LiDAR canopy height models to generate spatially explicit biomass estimates at regional to national scales. Model uncertainty is ±25% at the pixel level but declines to ±5% when aggregated over larger spatial units, supporting robust regional carbon assessments.,We demonstrate the approach through a regional case study in Hawke’s Bay, where indigenous forests represent a substantial carbon reservoir despite extensive historical land clearance. Biomass patterns reflect strong influences of land-use history and topography, with the highest carbon densities in mature upland forests. Collectively, Hawke’s Bay’s indigenous forests store 67 Mt of above-ground carbon, underscoring their significance for regional carbon accounting and environmental sustainability. Our results confirm the utility of regional LiDAR data for above-ground biomass and carbon mapping in temperate forests. Continued improvements in field plot geolocation, calibration datasets, and integration with complementary remote-sensing technologies will further enhance model performance and applicability.

Quantifying live, above-ground biomass in temperate forests is essential for understanding carbon dynamics and guiding land management, yet it remains challenging at large spatial scales and where forest heterogeneity is high. Here we develop a LiDAR-based framework to map above-ground biomass across indigenous forests in New Zealand. We derive a function in which above-ground biomass per unit area, AGB (tDM ha-1), scales with the square of canopy height, moderated by a spatially varying coefficient that captures spatial variation in forest structure and composition. When the function is applied to large pixel cells (20 m or greater), the coefficient of variation of canopy height within the cell is required to account for deep canopy gaps. This formulation enables airborne LiDAR canopy height models to generate spatially explicit biomass estimates at regional to national scales. Model uncertainty is ±25% at the pixel level but declines to ±5% when aggregated over larger spatial units, supporting robust regional carbon assessments.,We demonstrate the approach through a regional case study in Hawke’s Bay, where indigenous forests represent a substantial carbon reservoir despite extensive historical land clearance. Biomass patterns reflect strong influences of land-use history and topography, with the highest carbon densities in mature upland forests. Collectively, Hawke’s Bay’s indigenous forests store 67 Mt of above-ground carbon, underscoring their significance for regional carbon accounting and environmental sustainability. Our results confirm the utility of regional LiDAR data for above-ground biomass and carbon mapping in temperate forests. Continued improvements in field plot geolocation, calibration datasets, and integration with complementary remote-sensing technologies will further enhance model performance and applicability.

For understanding the role of natural forests in the global carbon cycle it is essential to accurately quantify their carbon dynamics. New Zealand’s National Natural Forest Inventory established 954 permanent plots, which were measured every five years over the last two decades. We used these observations to assess changes in live above-ground biomass (AGB). Tree diameters were converted to biomass using species-specific allometric equations, and net change was partitioned into productivity and mortality. Mean net biomass change across all plots was small (-0.2 tDM ha⁻¹ yr⁻¹ in the North Island and -0.5 tDM ha⁻¹ yr⁻¹ in the South Island). However, that small net change resulted from most plots having positive biomass gains, offset by a small number of plots experiencing large mortality losses. This indicates that even New Zealand’s mature natural forests are predominantly continuing to grow, but this is counterbalanced by disturbance-driven mortality. On the small number of plots subject to mortality, losses can be very large and balance overall the ongoing productivity on many growing plots. Mean productivity was 2.7 tDM ha⁻¹ yr⁻¹ in the North Island and 2.5 tDM ha⁻¹ yr⁻¹ in the South Island, with corresponding mean mortality losses of 2.9 and 3.0 tDM ha⁻¹ yr⁻¹. A national map of natural forest productivity was derived using process-based modelling, revealing strong spatial dependence on climatic and edaphic factors. Overall, while New Zealand’s natural forests are dynamic systems, with total biomass remaining stable, management interventions could shift the national carbon balance towards a net sink.

Aim: Woody ecosystems provide critical ecosystem functions and services but are increasingly threatened as invasive pathogens spread globally. Myrtle rust, caused by Austropuccinia psidii, arrived in New Zealand in 2017 and infects at least 12 of 18 species in the susceptible Myrtaceae plant family. Among these are species of structural, successional and cultural importance. We aim to assess whether the functional consequences of Myrtaceae loss could be mitigated if co‐occurring species with shared functional attributes are able to replace them.Location: New Zealand (but with concepts and methodologies that apply globally).Methods: Using a nationwide forest and shrubland plot data set, we assessed community vulnerability to the loss of Myrtaceae species by analysing proportional changes in average trait values when they are absent and produced spatial predictions indicating where species loss might have the greatest impact on community functionality. We then assessed whether compensatory infilling by co‐occurring species would mediate community vulnerability.Results: Forests and shrublands containing Kunzea ericoides and Leptospermum scoparium are highly vulnerable to their loss. Areas most vulnerable overall are the central and south‐eastern North Island, north‐eastern South Island and Stewart Island. For all species, compensatory infilling moderated the impact of their loss. However, if co‐occurring Myrtaceae were unable to respond, possibly if they were also infected, community vulnerability almost always increased because infilling species had different functional attributes, compounding the functional impact.Main Conclusions: Early successional woody plant communities and Myrtaceae‐dominated old‐growth forests are at most risk. Our spatial assessment of species‐level functional impacts from myrtle rust will facilitate better‐informed landscape‐level responses. Management actions and monitoring can now be targeted to areas and communities at greatest risk of losing ecosystem‐level processes.

Remote sensing was used to map the invasion of yellow-flowered legumes on the Central Plateau of New Zealand to inform weed management strategy. The distributions of Cytisus scoparius (broom), Ulex europaeus (gorse) and Lupinus arboreus (tree lupin) were captured with high-resolution RGB photographs of the plants while flowering. The outcomes of herbicide operations to control C. scoparius and U. europaeus over time were also assessed through repeat photography and change mapping. A grid-square sampling tool previously developed by Manaaki Whenua—Landcare Research was used to help transfer data rapidly from photography to maps using manual classification. Artificial intelligence was trialled and ruled out because the number of false positives could not be tolerated. Future actions to protect the natural values and vistas of the Central Plateau from legume invasion were identified. While previous control operations have mostly targeted large, highly visible legume patches, the importance of removing outlying plants to prevent the establishment of new seed banks and slow spread has been underestimated. Outliers not only establish new, large, long-lived seed banks in previously seed-free areas, but they also contribute more to range expansion than larger patches. Our C. scoparius and U. europaeus change mapping confirms and helps to visualise the establishment and expansion of uncontrolled outliers. The power of visualizing weed control strategies through remote sensing has supported recommendations to improve outlier control to achieve long-term, sustainable landscape-scale suppression of invasive legumes.

The southern beech (genus Fuscospora and Lophozonia) forest in New Zealand periodically has “mast” years, during which very large volumes of seeds are produced. This excessive seed production results in a population explosion of rodents and mustelids, which then puts pressure on native birds. To protect the birds, extra pest controls, costing in the order of NZD 20 million, are required in masting areas. To plan pest control and keep it cost-effective, it would be helpful to have a map of the masting areas. In this study, we developed a remote sensing method for the creation of a national beech flowering map. It used a temporal sequence of Sentinel-2 satellite imagery to determine areas in which a yellow index, which was based on red and green reflectance (red-green)/(red + green), was higher than normal in spring. The method was used to produce national maps of heavy beech flowering for the years 2017 to 2021. In 2018, which was a major beech masting year, of the 4.1 million ha of beech forest in New Zealand, 27.6% was observed to flower heavily. The overall classification accuracy of the map was 90.8%. The method is fully automated and could be used to help to identify areas of potentially excessive seed fall across the whole of New Zealand, several months in advance of when pest control would be required.

A national map of pasture productivity, in terms of mass of dry matter yield per unit area and time, enables evaluation of regional and local land-use suitability. Difficulty in measuring this quantity at scale directed this research, which utilises four years of Sentinel-2 satellite imagery and collected pasture yield measurements to develop a model of pasture productivity. The model uses a Normalised Difference Vegetation Index (NDVI), with spatio-temporal segmentation and averaging, to estimate mean annual pasture productivity across all of New Zealand’s grasslands with a standard error of prediction of 2.2 t/ha/y. Regional aggregates of pasture yield demonstrate expected spatial variations. The pasture productivity map may be used to classify grasslands objectively into stratified levels of production on a national scale. Due to its ability to highlight areas of land use intensification suitability, the national map of pasture productivity is of value to landowners, land users, and environmental scientists.

Repeat frequencies of optical remote sensing satellites have been increasing over the last 40 years, but there is still dependence on clear skies to acquire usable imagery. To increase the quality of data, composited mosaics of satellite imagery can be used. In this paper, we develop an automated method for clearing clouds and producing different types of composited mosaics suitable for use in cloud-affected countries, such as New Zealand. We improve the Tmask algorithm for cloud detection by using a parallax method to produce an initial cloud layer and by using an object-based cloud and shadow approach to remove false cloud detections. We develop several parametric scoring approaches for choosing best-pixel composites with minimal remaining cloud. The automated mosaicking approach produced Sentinel-2 mosaics of New Zealand for five successive summers, 2015/16 through 2019/20, with remaining cloud being less than 0.1%. Contributing satellite overpasses were typically of the order of 100. In comparison, manual methods for cloud clearing produced mosaics with 5% remaining cloud and from satellite overpasses typically of the order of 20. The improvements to cloud clearing enable the use of all possible Sentinel-2 imagery to produce automatic mosaics capable of regular land monitoring, at a reasonable cost.

New Zealand kauri trees are threatened by the kauri dieback disease (Phytophthora agathidicida (PA)). In this study, we investigate the use of pan-sharpened WorldView-2 (WV2) satellite and Light Detection and Ranging (LiDAR) data for detecting stress symptoms in the canopy of kauri trees. A total of 1089 reference crowns were located in the Waitakere Ranges west of Auckland and assessed by fieldwork and the interpretation of aerial images. Canopy stress symptoms were graded based on five basic stress levels and further refined for the first symptom stages. The crown polygons were manually edited on a LiDAR crown height model. Crowns with a mean diameter smaller than 4 m caused most outliers with the 1.8 m pixel size of the WV2 multispectral bands, especially at the more advanced stress levels of dying and dead trees. The exclusion of crowns with a diameter smaller than 4 m increased the correlation in an object-based random forest regression from 0.85 to 0.89 with only WV2 attributes (root mean squared error (RMSE) of 0.48, mean absolute error (MAE) of 0.34). Additional LiDAR attributes increased the correlation to 0.92 (RMSE of 0.43, MAE of 0.31). A red/near-infrared (NIR) normalised difference vegetation index (NDVI) and a ratio of the red and green bands were the most important indices for an assessment of the full range of stress symptoms. For detection of the first stress symptoms, an NDVI on the red-edge and green bands increased the performance. This study is the first to analyse the use of spaceborne images for monitoring canopy stress symptoms in native New Zealand kauri forest. The method presented shows promising results for a cost-efficient stress monitoring of kauri crowns over large areas. It will be tested in a full processing chain with automatic kauri identification and crown segmentation.