Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa $<0.996$ ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants.

Background: Natural forests cover approximately 29% of New Zealand’s landmass and represent a large terrestrial carbon pool. In 2002 New Zealand implemented its first representative plot-based natural forest inventory to assess carbon stocks and stock changes in these mostly undisturbed old-growth forests. Although previous studies have provided estimates of biomass or carbon stocks, these were either not fully representative or lacked data from important pools such as dead wood (coarse woody debris). The current analysis provides the most complete estimates of carbon stocks and stock changes in natural forests in New Zealand.Results: We present estimates of per hectare carbon stocks and stock changes in live and dead organic matter pools excluding soil carbon based on the first two measurement cycles of the New Zealand Natural Forest Inventory carried out from 2002 to 2014. These show that New Zealand’s natural forests are in balance and are neither a carbon source nor a carbon sink. The average total carbon stock was 227.0 ± 14.4 tC·ha^− 1(95% C.I.) and did not change significantly in the 7.7 years between measurements with the net annual change estimated to be 0.03 ± 0.18 tC·ha^− 1·yr^− 1. There was a wide variation in carbon stocks between forest groups. Regenerating forest had an averaged carbon stock of only 53.6 ± 9.4 tC·ha^− 1but had a significant sequestration rate of 0.63 ± 0.25 tC·ha^− 1·yr^− 1, while tall forest had an average carbon stock of 252.4 ± 15.5 tC·ha^− 1, but its sequestration rate did not differ significantly from zero (− 0.06 ± 0.20 tC·ha^− 1·yr^− 1). The forest alliance with the largest average carbon stock in above and below ground live and dead organic matter pools was silver beech-red beech-kamahi forest carrying 360.5 ± 34.6 tC·ha^− 1. Dead wood and litter comprised 27% of the total carbon stock.Conclusions: New Zealand’s Natural Forest Inventory provides estimates of carbon stocks including estimates for difficult to measure pools such as dead wood and roots. It also provides estimates of uncertainties including effects of model prediction error and sampling variation between plots. Importantly it shows that on a national level New Zealand’s natural forests are in balance. Nevertheless, this is a nationally important carbon pool that requires continuous monitoring to identify potential negative or positive changes.

The spread of exotic conifers from commercial plantation forests has significant economic and ecological implications. Accurate methods for invasive conifer detection are required to enable monitoring and guide control. In this research, we combined spectral information from aerial imagery with data from airborne laser scanning (ALS) to develop methods to identify invasive conifers using remotely-sensed data. We examined the effect of ALS pulse density and the height threshold of the training dataset on classification accuracy. The results showed that adding spectral values to the ALS metrics/variables in the training dataset led to significant increases in classification accuracy. The most accurate models (kappa range of 0.773–0.837) had either four or five explanatory variables, including ALS elevation, the near-infrared band and different combinations of ALS intensity and red and green bands. The best models were found to be relatively invariant to changes in pulse density (1–21 pls/m²) or the height threshold (0–2 m) used for the inclusion of data in the training dataset. This research has extended and improved the methods for scattered single tree detection and offered valuable insight into campaign settings for the monitoring of invasive conifers (tree weeds) using remote sensing approaches.

Forestry management worldwide has become increasingly effective at obtaining high timber yields from productive forests. In New Zealand, a focus on improving an increasingly successful and largelyPinus radiataplantation forestry model over the last 150 years has resulted in some of the most productive timber forests in the temperate zone. In contrast to this success, the full range of forested landscapes across New Zealand, including native forests, are impacted by an array of pressures from introduced pests, diseases, and a changing climate, presenting a collective risk of losses in biological, social and economic value. As the national government policies incentivise reforestation and afforestation, the social acceptability of some forms of newly planted forests is also being challenged. Here, we review relevant literature in the area of integrated forest landscape management to optimise forests as nature‐based solutions, presenting ‘transitional forestry’ as a model design and management paradigm appropriate to a range of forest types, where forest purpose is placed at the heart of decision making. We use New Zealand as a case study region, describing how this purpose‐led transitional forestry model can benefit a cross section of forest types, from industrialised forest plantations to dedicated conservation forests and a range of multiple‐purpose forests in between. Transitional forestry is an ongoing multi‐decade process of change from current ‘business‐as‐usual’ forest management to future systems of forest management, embedded across a continuum of forest types. This holistic framework incorporates elements to enhance efficiencies of timber production, improve overall forest landscape resilience, and reduce some potential negative environmental impacts of commercial plantation forestry, while allowing the ecosystem functioning of commercial and non‐commercial forests to be maximised, with increased public and biodiversity conservation value. Implementation of transitional forestry addresses tensions that arise between meeting climate mitigation targets and improving biodiversity criteria through afforestation, alongside increasing demand for forest biomass feedstocks to meet the demands of near‐term bioenergy and bioeconomy goals. As ambitious government international targets are set for reforestation and afforestation using both native and exotic species, there is an increasing opportunity to make such transitionsviaintegrated thinking that optimises forest values across a continuum of forest types, while embracing the diversity of ways in which such targets can be reached.