Managing plant invasions through the lens of remote sensing: A review of progress and the way forward

Invasibility framework to predict the early colonization of alien Sonneratia in mangrove: Implications for coastal area management

Invasibility, or an ecosystem's susceptibility to invasion, plays a critical role in managing biological invasions but is challenging to quantify due to its dependence on specific ecosystem variables. This limitation restricts the practical application of this concept in the control of alien species. This study aims to simplify invasibility into measurable components and develop an applicable framework to predict early colonization of alien plants within the coastal mangrove ecosystem. We used the unchanneled path length (UPL), a widely applied hydrological connectivity-related indicator, to assess the accessibility of the mangrove. The enhanced vegetation index (EVI), positively correlated with above-ground biomass, was used to evaluate the potential competitive intensity. Firstly, building on existing studies, we developed a four-quadrant concept model integrating the effects of EVI and UPL on the early colonization of the alien species Sonneratia apetala. Our results revealed significant differences in EVI and UPL values between colonized and uncolonized areas, with colonized regions displaying markedly lower values (P < 0.001). Additionally, logistic regression showed a significant negative association between the probability of successful colonization by S. apetala and both indicators (P < 0.001). These results validate the effectiveness of our conceptual model. Furtherly, we identified four key niche opportunities for exotic species in mangrove: mudflats outside the mangrove forest, tidal creeks, canopy gaps, and unmanaged abandoned aquaculture ponds. Overall, this study provides important insight into the ecological processes of alien S. apetala colonization and practical information for management of coastal areas susceptible to invasion. Additionally, it presents a case study on the practical application of the concept of invasibility in the management of alien species.

Field-based measurement tools to distinguish clonal Typha taxa and estimate biomass: a resource for conservation and restoration

Two species of clonal Typha [T. latifolia (native) and T. angustifolia (exotic)] hybridize to form the highly invasive, heterotic (high vigor) T. × glauca in North American wetlands leading to increased primary production, litter accumulation, and biodiversity loss. Conservation of T. latifolia has become critical as invasive Typha has overwhelmed wetlands. In the field, Typha taxa identification is difficult due to subtle differences in morphology, and molecular identification is often unfeasible for managers. Furthermore, improved methods to non-destructively estimate Typha biomass is imperative to enhance ecological impact assessments. To address field-based Typha ID limitations, our study developed a predictive model from 14 Typha characters in 7 northern Michigan wetlands to accurately distinguish Typha taxa (n = 33) via linear discriminant analysis (LDA) of molecularly identified specimens. In addition, our study developed a partial least squares regression (PLS) model to predict Typha biomass from field collected measurements (n = 75). Results indicate that two field measurements [Leaf Counts, Longest Leaf] can accurately differentiate the three Typha taxa and advanced-generation hybrids. The LDA model had a 100% correct prediction rate of T. latifolia. The selected PLS biomass prediction model (sqrt[Typha Dry Mass] ~ log[Ramet Area at 30 cm] + Inflorescence Presence + Total Ramet Height + sqrt[Organic Matter Depth]) improved upon existing simple linear regression (SLR) height-to-biomass predictions. The rapid field-based Typha identification and biomass assessment tools presented in this study advance targeted management for regional conservation of T. latifolia and ecological restoration of wetlands impacted by invasive Typha taxa.

Nitrogen and phosphorus addition promote invasion success of invasive species via increased growth and nutrient accumulation under elevated CO2

In the context of the resource allocation hypothesis regarding the trade-off between growth and defence, compared with native species, invasive species generally allocate more energy to growth and less energy to defence. However, it remains unclear how global change and nutrient enrichment will influence the competition between invasive species and co-occurring native species. Here, we tested whether nitrogen (N) and phosphorus (P) addition under elevated CO2 causes invasive species (Mikania micrantha and Chromolaena odorata) to produce greater biomass, higher growth-related compounds and lower defence-related compounds than native plants (Paederia scandens and Eupatorium chinense). We grew these native and invasive species with similar morphology with the addition of N and P under elevated CO2 in open-top chambers. The addition of N alone increased the relative growth rate (RGR) by 5.4% in invasive species, and its combination with P addition or elevated CO2 significantly increased the RGR of invasive species by 7.5 or 8.1%, respectively, and to a level higher than that of native species (by 14.4%, P < 0.01). Combined N + P addition under elevated CO2 decreased the amount of defence-related compounds in the leaf, including lipids (by 17.7%) and total structural carbohydrates (by 29.0%), whereas it increased the growth-related compounds in the leaf, including proteins (by 75.7%), minerals (by 9.6%) and total non-structural carbohydrates (by 8.5%). The increased concentrations of growth-related compounds were possibly associated with the increase in ribulose 1,5-bisphosphate carboxylase oxygenase content and mineral nutrition (magnesium, iron and calcium), all of which were higher in the invasive species than in the native species. These results suggest that rising atmospheric CO2 concentration and N deposition combined with nutrient enrichment will increase the growth of invasive species more than that of native species. Our result also suggests that invasive species respond more readily to produce growth-related compounds under an increased soil nutrient availability and elevated CO2.

Applicability of Point- and Polygon-Based Vegetation Monitoring Data to Identify Soil, Hydrological and Climatic Driving Forces of Biological Invasions-A Case Study of Ailanthus altissima, Elaeagnus angustifolia and Robinia pseudoacacia

Invasive tree species are a significant threat to native flora. They modify the environment with their allelopathic substances and inhibit the growth of native species by shading, thus reducing diversity. The most effective way to control invasive plants is to prevent their spread which requires identifying the environmental parameters promoting it. Since there are several types of invasive plant databases available, determining which database type is the most relevant for investigating the occurrence of alien plants is of great importance. In this study, we compared the efficiency and reliability of point-based (EUROSTAT Land Use and Coverage Area Frame Survey (LUCAS)) and polygon-based (National Forestry Database (NFD)) databases using geostatistical methods in ArcGIS software. We also investigated the occurrence of three invasive tree species (Ailanthus altissima, Elaeagnus angustifolia, and Robinia pseudoacacia) and their relationships with soil, hydrological, and climatic parameters such as soil organic matter content, pH, calcium carbonate content, rooting depth, water-holding capacity, distance from the nearest surface water, groundwater depth, mean annual temperature, and mean annual precipitation with generalized linear models in R-studio software. Our results show that the invasion levels of the tree species under study are generally over-represented in the LUCAS point-based vegetation maps, and the point-based database requires a dataset with a larger number of samples to be reliable. Regarding the polygon-based database, we found that the occurrence of the invasive species is generally related to the investigated soil and hydrological and climatic factors.

Mapping an alien invasive shrub within conservation corridors using super-resolution satellite imagery

Alien invasive plant species are one of the main drivers of global biodiversity loss. Methods for monitoring the spread of alien invasive plants are needed to improve management and mitigate impact on local biodiversity. Recent advances in deep learning and image fusion holds great potential for mapping and managing alien invasive plants. One such method is super-resolution image reconstruction, where a neural network learns to downscale images from coarse to fine resolution. Within the commercial timber production landscape of KwaZulu-Natal, endangered grassland corridors are threatened by American bramble invasion, impacting plants, birds, arthropods, and soil restoration. Here we aim to improve our understanding of bramble invasion dynamics through using super-resolved satellite mosaics. Bramble was classified with very high accuracies (85%) from the super-resolved satellite mosaic, compared to other conventional satellite imagery with different spectral and spatial resolutions. Using landscape analyses, we identified plantation tree harvesting and prescribed burning to be major drivers increasing bramble cover within the landscape. Bramble cover was highest one year following plantation tree harvesting. Continuous prescribed burning positively influenced bramble. Bramble cover was also high close to streams, and under future invasion projections, bramble will severely impact Ensifera species alongside low priority grasshopper species habitat. Results also indicate that bramble has a significant negative impact on intermediate priority grasshoppers and plant species richness. For controlling bramble invasion within commercial timber production landscapes, we recommend the adoption rotational harvesting, as harvesting entire plantation blocks throughout the landscape will dramatically increase invasion potential of bramble. Current bramble removal programmes should prioritize riparian areas. Special attention is needed to control bramble one year after timber harvesting, as this is when bramble cover is highest. We show the benefits of using super-resolved mosaics to gain new insights into alien invasive species dynamics, while further development of this technique will aid in managing invasive alien plant species at local scales.

Early detection, herbicide resistance screening, and integrated management of invasive plant species: a review

Invasive plant species (IPS) are often considered weeds that cause high yield losses in crops, negatively affect the environment, and disrupt certain ecosystem services. The negative impact of IPS on biodiversity is increasing and disturbing native vegetation. The management of plant invasions can be divided in two phases (before and after invasion). Prior to introduction it is crucial to develop the knowledge base (biology, ecology, distribution, impact, management) on IPS, prevention measures and risk assessment. After introduction if eradication fails, the monitoring and the integrated management of IPS are imperative to prevent the naturalization and further dispersal. This review uses two major invasive weed species (Amaranthus palmeri S. Wats. and Solanum elaeagnifolium Cav.) as case studies to propose a framework for early detection, rapid herbicide resistance screening, and integrated management. The holistic framework that is presented exploits recent: (i) novel detection tools, (ii) rapid tests and assays for herbicide resistance, and (iii) biology, ecology, distribution traits, and management tools for the IPS. Farmers, advisors, researchers, and policymakers need briefing on IPS growth dynamics, adaptability rates, and response to conventional and novel treatments to prevent new invasions, eradicate isolated stands, and mitigate the impact of invasive weed species in the long term. © 2022 Society of Chemical Industry.

Effect of freshwater on plant species diversity and interspecific associations in coastal wetlands invaded by Spartina alterniflora

Plant invasions in coastal wetlands lead to the degradation of native vegetation; the introduction of freshwater in coastal wetlands would prevent the spread of invasive plants and facilitate the restoration of native vegetation. In this study, we evaluated the effects of freshwater on plant communities in the coastal wetlands of Yancheng, China, invaded by Spartina alterniflora Loisel. Two field investigations were conducted in 2008 and 2018 before and after the introduction of freshwater (started in 2011). The characteristics of plant communities were subjected to hierarchical cluster analysis and compared using several diversity indices. In addition, differences in habitat community composition and interspecific relationships of dominant species were analyzed. The results showed that S. alterniflora reduced the overall species diversity in the region. Plant species diversity increased after freshwater was introduced into the study site when compared to the areas without freshwater introduction. The introduction of freshwater caused a shift often changes in the interspecific relationships between Suaeda salsa (L.) Pall. and other species. The intensified invasion of S. alterniflora changed the interspecific relationship of native halophytes from negative to positive. Although freshwater effectively inhibited further invasion of S. alterniflora, it also increased the risk of expansion of the glycophytes in the community. The results of this study highlight the need for early intervention for restoration of coastal wetlands, preservation of biodiversity, and management of plant resources.

Tradeoffs between UAS Spatial Resolution and Accuracy for Deep Learning Semantic Segmentation Applied to Wetland Vegetation Species Mapping

Recent advances in image classification of fine spatial resolution imagery from unoccupied aircraft systems (UASs) have allowed for mapping vegetation based on both multispectral reflectance and fine textural details. Convolutional neural network (CNN)-based models can take advantage of the spatial detail present in UAS imagery by implicitly learning shapes and textures associated with classes to produce highly accurate maps. However, the spatial resolution of UAS data is infrequently examined in CNN classification, and there are important tradeoffs between spatial resolution and classification accuracy. To improve the understanding of the relationship between spatial resolution and classification accuracy for a CNN-based model, we captured 7.6 cm imagery with a UAS in a wetland environment containing graminoid (grass-like) plant species and simulated a range of spatial resolutions up to 76.0 cm. We evaluated two methods for the simulation of coarser spatial resolution imagery, averaging before and after orthomosaic stitching, and then trained and applied a U-Net CNN model for each resolution and method. We found untuned overall accuracies exceeding 70% at the finest spatial resolutions, but classification accuracy decreased as spatial resolution coarsened, particularly beyond a 22.8 cm resolution. Coarsening the spatial resolution from 7.6 cm to 22.8 cm could permit a ninefold increase in survey area, with only a moderate reduction in classification accuracy. This study provides insight into the impact of the spatial resolution on deep learning semantic segmentation performance and information that can potentially be useful for optimizing precise UAS-based mapping projects.

Unmanned Aerial Vehicle (UAV)-Based Mapping of Acacia saligna Invasion in the Mediterranean Coast

Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping Acacia saligna invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping A. saligna. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced A. saligna maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014.

Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers

Globally, grassland biomes form one of the largest terrestrial covers and present critical social–ecological benefits. In Kenya, Arid and Semi-arid Lands (ASAL) occupy 80% of the landscape and are critical for the livelihoods of millions of pastoralists. However, they have been invaded by Invasive Plant Species (IPS) thereby compromising their ecosystem functionality. Opuntia stricta, a well-known IPS, has invaded the ASAL in Kenya and poses a threat to pastoralism, leading to livestock mortality and land degradation. Thus, identification and detailed estimation of its cover is essential for drawing an effective management strategy. The study aimed at utilizing the Sentinel-2 multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using ensemble machine learning classifiers. To illustrate the potential of Sentinel-2, the detection of Opuntia stricta was based on only the spectral bands as well as in combination with vegetation and topographic indices using Extreme Gradient Boost (XGBoost) and Random Forest (RF) classifiers to detect the abundance. Study results showed that the overall accuracies of Sentinel 2 spectral bands were 80% and 84.4%, while that of combined spectral bands, vegetation, and topographic indices was 89.2% and 92.4% for XGBoost and RF classifiers, respectively. The inclusion of topographic indices that enhance characterization of biological processes, and vegetation indices that minimize the influence of soil and the effects of atmosphere, contributed by improving the accuracy of the classification. Qualitatively, Opuntia stricta spatially was found along river banks, flood plains, and near settlements but limited in forested areas. Our results demonstrated the potential of Sentinel-2 multispectral sensors to effectively detect and map Opuntia stricta in a complex heterogeneous ASAL, which can support conservation and rangeland management policies that aim to map and list threatened areas, and conserve the biodiversity and productivity of rangeland ecosystems.

Tracking Marine Alien Macroalgae in the Mediterranean Sea: The Contribution of Citizen Science and Remote Sensing

The accelerating rate of the introduction of non-indigenous species (NIS) and the magnitude of shipping traffic make the Mediterranean Sea a hotspot of biological invasions. For the effective management of NIS, early detection and intensive monitoring over time and space are essential. Here, we present an overview of possible applications of citizen science and remote sensing in monitoring alien seaweeds in the Mediterranean Sea. Citizen science activities, involving the public (e.g., tourists, fishermen, divers) in the collection of data, have great potential for monitoring NIS. The innovative methodologies, based on remote sensing techniques coupled with in situ/laboratory advanced sampling/analysis methods for tracking such species, may be useful and effective tools for easily assessing NIS distribution patterns and monitoring the space/time changes in habitats in order to support the sustainable management of the ecosystems. The reported case studies highlight how these cost-effective systems can be useful complementary tools for monitoring NIS, especially in marine protected areas, which, despite their fundamental role in the conservation of marine biodiversity, are not immune to the introduction of NIS. To ensure effective and long-lasting management strategies, collaborations between researchers, policy makers and citizens are essential.

A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

Diverse freshwater biological communities are threatened by invasive aquatic alien plant (IAAP) invasions and consequently, cost countries millions to manage. The effective management of these IAAP invasions necessitates their frequent and reliable monitoring across a broad extent and over a long-term. Here, we introduce and apply a monitoring approach that meet these criteria and is based on a three-stage hierarchical classification to firstly detect water, then aquatic vegetation and finally water hyacinth (Pontederia crassipes, previously Eichhornia crassipes), the most damaging IAAP species within many regions of the world. Our approach circumvents many challenges that restricted previous satellite-based water hyacinth monitoring attempts to smaller study areas. The method is executable on Google Earth Engine (GEE) extemporaneously and utilizes free, medium resolution (10–30 m) multispectral Earth Observation (EO) data from either Landsat-8 or Sentinel-2. The automated workflow employs a novel simple thresholding approach to obtain reliable boundaries for open-water, which are then used to limit the area for aquatic vegetation detection. Subsequently, a random forest modelling approach is used to discriminate water hyacinth from other detected aquatic vegetation using the eight most important variables. This study represents the first national scale EO-derived water hyacinth distribution map. Based on our model, it is estimated that this pervasive IAAP covered 417.74 km2 across South Africa in 2013. Additionally, we show encouraging results for utilizing the automatically derived aquatic vegetation masks to fit and evaluate a convolutional neural network-based semantic segmentation model, removing the need for detection of surface water extents that may not always be available at the required spatio-temporal resolution or accuracy. The water hyacinth species discrimination has a 0.80, or greater, overall accuracy (0.93), F1-score (0.87) and Matthews correlation coefficient (0.80) based on 98 widely distributed field sites across South Africa. The results suggest that the introduced workflow is suitable for monitoring changes in the extent of open water, aquatic vegetation, and water hyacinth for individual waterbodies or across national extents. The GEE code can be accessed here.

Quantifying expansion and removal of Spartina alterniflora on Chongming island, China, using time series Landsat images during 1995-2018

The rampant encroachment of Spartina alterniflora into coastal wetlands of China over the past decades has adversely affected both coastal ecosystems and socio-economic systems. However, there are no annual or multi-year epoch maps of Spartina saltmarsh in China, which hinders our understanding and management of Spartina invasion. In this study, we selected Chongming island, China, where Spartina saltmarsh had expanded rapidly since its introduction in the 1990s. We investigated phenology of Spartina, Phragmites and Scirpus saltmarshes, and the time series vegetation indices derived from Landsat images showed that Spartina saltmarsh did not green-up in April-May and stayed green in December-January, which differed from the phenology of Phragmites and Scirpus saltmarshes. We developed a pixel- and phenology-based algorithm that used time series Landsat data to identify and map Spartina saltmarsh, and we applied it to quantify the temporal dynamics (expansion and removal) of Spartina saltmarsh on Chongming island during 1995-2018. The resultant maps showed that Spartina saltmarsh area on Chongming island increased from ~4 ha in 1995 to ~2,067 ha in 2012 but dropped substantially to ~729 ha in 2016 after a large-scale ecological engineering project (US$ 186 million) was started to remove Spartina during 2013-2016. Chongming island still had ~1,315 ha Spartina saltmarsh in 2018, and majority of it was distributed outside the Chongming Dongtan National Nature Reserve, which could serve as the sources for reinvasion in the near future. This study demonstrates the feasibility of using time series Landsat images, pixel- and phenology-based algorithm, and GEE platform to identify and map Spartina saltmarsh over years in the region, which is useful to the management of invasive plants in coastal wetlands.

Using remote sensing to characterize riparian vegetation: A review of available tools and perspectives for managers

Riparian vegetation is a central component of the hydrosystem. As such, it is often subject to management practices that aim to influence its ecological, hydraulic or hydrological functions. Remote sensing has the potential to improve knowledge and management of riparian vegetation by providing cost-effective and spatially continuous data over wide extents. The objectives of this review were twofold: to provide an overview of the use of remote sensing in riparian vegetation studies and to discuss the transferability of remote sensing tools from scientists to managers. We systematically reviewed the scientific literature (428 articles) to identify the objectives and remote sensing data used to characterize riparian vegetation. Overall, results highlight a strong relationship between the tools used, the features of riparian vegetation extracted and the mapping extent. Very high-resolution data are rarely used for rivers longer than 100 km, especially when mapping species composition. Multi-temporality is central in remote sensing riparian studies, but authors use only aerial photographs and relatively coarse resolution satellite images for diachronic analyses. Some remote sensing approaches have reached an operational level and are now used for management purposes. Overall, new opportunities will arise with the increased availability of very high-resolution data in understudied or data-scarce regions, for large extents and as time series. To transfer remote sensing approaches to riparian managers, we suggest mutualizing achievements by producting open-access and robust tools. These tools will then have to be adapted to each specific project, in collaboration with managers.

Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States

Invasive plants are causing massive economic and environmental troubles for our societies worldwide. The aim of this study is to employ a set of machine learning classifiers for detecting invasive plant species using remote sensing data. The target species is Kudzu vine, which mostly grows in the south-eastern states of the US and quickly outcompetes other plants, making it a relevant and threatening species to consider. Our study area is Atlanta, Georgia and the surrounding area. Five different algorithms: Boosted Logistic Regression (BLR), Naive Bayes (NB), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM) were tested with the aim of testing their performance and identifying the most optimal one. Furthermore, the influence of temporal, spectral and spatial resolution in detecting Kudzu was also tested and reviewed. Our finding shows that random forest, neural network and support vector machine classifiers outperformed. While the achieved internal accuracies were about 97%, an external validation conducted over an expanded area of interest resulted in 79.5% accuracy. Furthermore, the study indicates that high accuracy classification can be achieved using multispectral Sentinel-2 imagery and can be improved while integrating with airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral data. Finally, this study indicates that dimensionality reduction methods such as principal component analysis (PCA) should be applied cautiously to the hyperspectral AVIRIS data to preserve its utility. The applied approach and the utilized set of methods can be of interest for detecting other kinds of invasive species as part of fulfilling UN sustainable development goals, particularly number 12: responsible consumption and production, 13: climate action, and 15: life on land.

Expected spatial patterns of alien woody plants in South Africa's protected areas under current scenario of climate change

Although protected areas (PAs) are declared to provide sanctuaries for biodiversity, they are increasingly threatened by the synergistic effects of anthropic factors, invasive alien species and climate change. Consequently, interventions are required to minimize the impacts of these threats on PAs' integrity. To inform these interventions in the South African context and under the current climate change scenario, we tested for geographic patterns of alien woody species across the network of 1,453 PAs using three alien invasion indices - alien species abundance, invaded area ratio and alien species richness. Our analysis shows that, under current climate change scenario, none of the PAs would be effective in shielding against alien plants and PAs that are geographically close tend to share similar invasion patterns. In addition, PAs that are hotspots of alien species are also geographically clustered but these findings are biome-dependent. Our outlier analysis reveals not only an island of disproportionately rich PAs in alien species, but also identifies some alien-poor PAs. We suggest that PAs that are hotspots of alien species as well as outliers of disproportionately rich PAs in alien species should be priority in monitoring and invasion control programmes in the context of the ongoing climate change.

Characterizing the Spatial Distribution of Eragrostis Curvula (Weeping Lovegrass) in New Jersey (United States of America) Using Logistic Regression

The increasing spread of invasive plants has become a critical driver of global environmental change. Once established, invasive species are often impossible to eradicate. Therefore, predicting the spread has become a key element in fighting invasive species. In this study, we examined the efficiency of a logistic regression model as a tool to identify the spatial occurrence of an invasive plant species. We used Eragrostis curvula (Weeping Lovegrass) as the dependent variable. The independent variables included temperature, precipitation, soil types, and the road network. We randomly selected 68 georeferenced points to test the goodness of fit of the logistic regression model to predict the presence of E. curvula. We validated the model by selecting an additional 68 random points. Results showed that the probability to successfully predict the presence of E. Curvula was 82.35%. The overall predictive accuracy of the model for the presence or absence of E. Curvula was 80.88%. Additional tests including the Chi-square test, the Hosmer–Lemeshow (HL) test, and the area under the curve (AUC) values, all indicated that the model was the best fit. Our results showed that E. curvula was associated with the identified variables. This study suggests that the logistic regression model can be a useful tool in the identification of invasive species in New Jersey.

Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data

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.

Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention

Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management.

Custom Scanning Hyperspectral Imaging System for Biomedical Applications: Modeling, Benchmarking, and Specifications

Prototyping hyperspectral imaging devices in current biomedical optics research requires taking into consideration various issues regarding optics, imaging, and instrumentation. In summary, an ideal imaging system should only be limited by exposure time, but there will be technological limitations (e.g., actuator delay and backlash, network delays, or embedded CPU speed) that should be considered, modeled, and optimized. This can be achieved by constructing a multiparametric model for the imaging system in question. The article describes a rotating-mirror scanning hyperspectral imaging device, its multiparametric model, as well as design and calibration protocols used to achieve its optimal performance. The main objective of the manuscript is to describe the device and review this imaging modality, while showcasing technical caveats, models and benchmarks, in an attempt to simplify and standardize specifications, as well as to incentivize prototyping similar future designs.

Strength in Numbers: Combining Multi-Source Remotely Sensed Data to Model Plant Invasions in Coastal Dune Ecosystems

A common feature of most theories of invasion ecology is that the extent and intensity of invasions is driven by a combination of drivers, which can be grouped into three main factors: propagule pressure (P), abiotic drivers (A) and biotic interactions (B). However, teasing apart the relative contribution of P, A and B on Invasive Alien Species (IAS) distributions is typically hampered by a lack of data. We focused on Mediterranean coastal dunes as a model system to test the ability of a combination of multi-source Remote Sensing (RS) data to characterize the distribution of five IAS. Using generalized linear models, we explored and ranked correlates of P, A and B derived from high-resolution optical imagery and three-dimensional (3D) topographic models obtained from LiDAR, along two coastal systems in Central Italy (Lazio and Molise Regions). Predictors from all three factors contributed significantly to explaining the presence of IAS, but their relative importance varied among the two Regions, supporting previous studies suggesting that invasion is a context-dependent process. The use of RS data allowed us to characterize the distribution of IAS across broad, regional scales and to identify coastal sectors that are most likely to be invaded in the future.