The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

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.

The objective of this study was to investigate techniques for integrating aerial LiDAR data into the forest yield information systems of a forest management company. Nearest neighbour (k-NN) estimation was identified as a useful approach and a 4 000 ha case study was undertaken to provide insight into the performance of the technique in a commercial environment. A field dataset was provided by 213 ground plots installed across the study area. Small-footprint discrete aerial LiDAR data were acquired concurrently and processed to derive descriptive metrics across the study area. A simulated annealing algorithm was produced and used to select important variables that were then used during model development. Sampling error for the k-NN predictions was estimated using an approach that accounted for spatial correlation in the reference dataset. An independent validation dataset was acquired from the forest manager's conventional high-intensity stand assessments within the study area projected to LiDAR acquisition date. Model validation showed excellent correspondence to the independent dataset with the responses total recoverable volume (relative mean deviation 4.3 per cent, RMSE 44.96 m³ ha⁻¹), mean top height (relative mean deviation 1.3 per cent, RMSE 1.34 m), basal area (relative mean deviation 4.5 per cent, RMSE 2.06 m² ha⁻¹) and stand density (relative mean deviation −1.8 per cent, RMSE 47.39 sph). Log-product volumes were estimated using LiDAR metrics and k-NN estimation for all stands with encouraging accuracy and precision. Sampling error was estimated for all stands in the study area, including those with no reference plots. Sampling error estimates were small enough for stand estimates to be useful for the forest manager in all stands. For stands in the validation dataset the sampling errors for stand volume were smaller for the k-NN estimates (median confidence interval (CI) 29.13 m³ ha⁻¹) than for the conventional inventory estimates (median CI 37.9 m³ ha⁻¹). Imputation model error was examined and found to be insignificant.

The objective of this study was to compare the utility of combinations of data from airborne laser scanning (ALS), RapidEye satellite imagery and auxiliary environmental data to predict stand structure in a plantation forest. Both parametric and non-parametric modelling techniques that could simultaneously predict a multivariate response were employed and found to produce predictions with similar levels of accuracy. Response variables were derived from 463 field measurement plots that were used during model development; a further 60 randomly selected plots were set aside for validation of model performance. Candidate predictor variables were extracted from the ALS data, satellite data and auxiliary environmental data, and the variables with the greatest explanatory power were used to create six separate models based on combinations of the data sources. Model validation showed that models using RapidEye data only were the least precise and that adding auxiliary environmental data only led to a moderate improvement in model precision. The model precision observed was similar to those reported previously from studies using satellite data to predict stand structure. Models developed using data from ALS were by far the most precise and adding information from satellite data or auxiliary environmental data led to negligible improvement in the prediction of stand structure. Although the outputs of both model types were similar, the practical efficiencies of using the non-parametric approach make it appealing to meet the demands of managers of industrial plantation forest managers.