Abstract
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.
Highlights
Conifers are among the most economically important tree species on Earth, forming the basis of major forest industries and contributing substantially to the global annual timber yield [1]
Two sets of remotely sensed data collected from a unmanned aerial vehicles (UAV) and manned aircraft platform were used alongside a manually digitized training data set defined in a Geographical Information System (GIS) to fit supervised classification models using either random forests (RF) or logistic regression (LR)
The field data showed that the P. sylvestris trees within the study area were markedly taller and had larger crowns than the P. ponderosa (Table 3)
Summary
Conifers are among the most economically important tree species on Earth, forming the basis of major forest industries and contributing substantially to the global annual timber yield [1]. Exotic conifer plantations form the cornerstone of the plantation forestry sector in many Southern hemisphere countries These forests provide significant economic [2,3], social [4], and ecological benefits [5,6] and contribute significantly to global carbon sequestration [7,8]. The prevalence of invasions by non-native invasive plants has considerable consequences in many environments and is a significant, and increasingly common, challenge for land managers [10,11] This is true in mountainous areas where increasing levels of anthropogenic disturbance coupled with a changing climate are expected to trigger an increase in the abundance of invasive plants, and upward expansion of exotic invasive species in vulnerable mountainous regions [12]. Many tree species are included in databases of the most widespread and damaging plants [14]
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