Technical Report

Revue de littérature sur l’utilisation de la télédétection dans le domaine des plantes exotiques envahissantes

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Submerged aquatic vegetation (SAV) is one of the most important biological groups in shallow lakes ecosystems, and it plays a vital role in stabilizing the structure and function of water ecosystems. The study area of this research is Baiyangdian, which is a typical macrophytic lake with complex land cover types. This research aims to solve the low accuracy problem of the remote sensing extraction of SAV, which is mainly caused by water level fluctuations, differences in life-history characteristics, and mixed-pixel phenomena. Here, we developed a phenology–pixel method to determine the spatial distribution of SAV and the start and end dates of its growing season by using all Sentinel-2 images collected over a year on the Google Earth Engine platform. The experimental results show the following: (1) The phenology–pixel algorithm can effectively identify the maximum spatial distribution and growth period of submerged aquatic vegetation in Baiyangdian Lake throughout the year. The unique normalized difference vegetation index (NDVI) peak characteristics of Potamogeton crispus from March to May were used to effectively distinguish it from the low Phragmites australis population. Textural features based on the modified normalized difference water index (MNDWI) index effectively removed the mixed-pixel phenomenon of macrophytic lakes (such as dikes and sparse reeds). (2) A complete five-day interval NDVI time-series dataset was obtained, which removes potential noise on the temporal scale and fills in noisy observations by the harmonic analysis of time series (HANTS) method. We determined the two phenological periods of typical SAV by analyzing the intrayear variation characteristics of NDVI and MNDWI. (3) Using field-survey data for accuracy verification, the overall accuracy of our method was determined to be 94.8%, and the user’s accuracy and producer’s accuracy were 93.3% and 87.3%, respectively. Determining the temporal and spatial distribution of different SAV populations provides important technical support for actively promoting the maintenance and reconstruction of lake and reservoir ecosystems.
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Detecting newly established invasive plants is key to prevent further spread. Traditional field surveys are challenging and often insufficient to identify the presence and extent of invasions. This is particularly true for wetland ecosystems because of difficult access, and because floating and submergent plants may go undetected in the understory of emergent plants. Unpiloted aerial systems (UAS) have the potential to revolutionize how we monitor invasive vegetation in wetlands, but key components of the data collection and analysis workflow have not been defined. In this study, we conducted a rigorous comparison of different methodologies for mapping invasive Emergent (Typha × glauca (cattail)), Floating (Hydrocharis morsus-ranae (European frogbit)), and Submergent species (Chara spp. and Elodea canadensis) using the machine learning classifier, random forest, in a Great Lakes wetland. We compared accuracies using (a) different spatial resolutions (11 cm pixels vs. 3 cm pixels), (b) two classification approaches (pixel- vs. object-based), and (c) including structural measurements (e.g., surface/canopy height models and rugosity as textural metrics). Surprisingly, the coarser resolution (11 cm) data yielded the highest overall accuracy (OA) of 81.4%, 2.5% higher than the best performing model of the finer (3 cm) resolution data. Similarly, the Mean Area Under the Receiving Operations Characteristics Curve (AUROC) and F1 Score from the 11 cm data yielded 15.2%, and 6.5% higher scores, respectively, than those in the 3 cm data. At each spatial resolution, the top performing models were from pixel-based approaches and included surface model data over those with canopy height or multispectral data alone. Overall, high-resolution maps generated from UAS classifications will enable early detection and control of invasive plants. Our workflow is likely applicable to other wetland ecosystems threatened by invasive plants throughout the globe.
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Tidal wetlands are critically important ecosystems that provide ecosystem services including carbon sequestration, storm surge mitigation, water filtration, and wildlife habitat provision while supporting high levels of biodiversity. Despite their importance, monitoring these systems over large scales remains challenging due to difficulties in obtaining extensive up-to-date ground surveys and the need for high spatial and temporal resolution satellite imagery for effective space-borne monitoring. In this study, we developed methodologies to advance the monitoring of tidal marshes and adjacent deepwaters in the Mid-Atlantic and Gulf Coast United States. We combined Sentinel-1 SAR and Landsat 8 optical imagery to classify marshes and open water in both regions, with user’s and producer’s accuracies exceeding 89%. This methodology enables the assessment of marsh loss through conversion to open water at an annual resolution. We used time-series Sentinel-1 imagery to classify persistent and non-persistent marsh vegetation with greater than 93% accuracy. Non-persistent marsh vegetation serves as an indicator of salinity regimes in tidal wetlands. Additionally, we mapped two invasive species: wetlands invasive Phragmites australis (common reed) with greater than 80% accuracy and deepwater invasive Trapa natans (water chestnut) with greater than 96% accuracy. These results have important implications for improved monitoring and management of coastal wetlands ecosystems.
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Phragmites australis (Cav.) Trin. ex Steudel subspecies australis is an aggressive plant invader in North American wetlands. Remote sensing provides cost-effective methods to track its spread given its widespread distribution. We classified Phragmites in three Lake Erie wetlands (two in Long Point Wetland Complex (LP) and one in Rondeau Bay Marsh (RBM)), using commercial, high-resolution (WorldView2/3: WV2 for RBM, WV3 for LP) and free, moderate-resolution (Sentinel 2; S2) satellite images. For image classification, we used mixture-tuned match filtering (MTMF) and then either maximum likelihood (ML) or support vector machines (SVM) classification methods. Using WV2/3 images with ML classification, we obtained higher overall accuracy for both LP sites (93.1%) compared with the RBM site (86.4%); both Phragmites users’ and producers’ accuracies were also higher for LP (89.3% and 92.7%, respectively) compared with RBM (84.3% and 88.4%, respectively). S2 images with SVM classification provided similar overall accuracies for LP (74.7%) and for the RBM (74.3%); Phragmites users’ and producers’ accuracies for LP were 85.3% and 76.3%, and for the RBM, 69.1% and 79.2%, respectively. Using WV2/3, we could quantify small patches (percentage cover ≥ 20%; shoots ≥ 1 m tall; stem counts > 25) with accuracy > 80%, whereas parallel effort with S2 images only accurately quantified high density (> 60% cover), mature shoots (> 1 m tall; Stem counts > 100). By simultaneously mapping young or sparsely distributed Phragmites shoots and dense mature stands accurately, we show our approach can be used for routine mapping and regular updating purposes, especially for post-treatment effectiveness monitoring.
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Invasive alien species (IAS) are one of the major threats to global and local biodiversity. In forest ecosystems, the threats caused by IAS include hybridization, transmission of diseases and species competition. This review sets out to analyze the impact of alien plant species on forest regeneration, which we consider to be one of the key stages in tree ecology for the survival of forest ecosystems in the future. The focus of the study is directly relevant to practitioners, forest managers and the conservation management of forests. With this systematic review, we aim to provide an overview of 48 research studies reporting on the impact and/or management of IAS in European temperate forests. We followed a multi-step protocol for compiling the publications for the literature review, with nine search queries producing a total of 3,825 hits. After several reduction rounds, we ended up with a grand total of 48 papers. We identified 53 vascular plant species having a negative influence on forest regeneration in Central European forests. In total, 21 tree species are reported to be impacted by IAS in 24 studies. The results of the review synthesis show that five impact mechanisms affect the regeneration success of native tree species: competition for resources, chemical impact on regeneration, physical impact on regeneration, structural impact on regeneration and indirect impact through interaction with other species. We identified in our synthesis management measures that have been recommended for application at different stages of biological invasions. The associated costs and required resources of management measures are under-reported or not accessible by reviewing the scientific literature. We can thus conclude that it is very import to improve the links between science and practical forest management. We expect that this review will provide direction for invasive plant species research and management aimed at protecting biodiversity in European temperate forest ecosystems.
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In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work is to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophytes communities with all sensor combinations. In all combinations, the model-based producer's accuracy (PA) and user's accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.
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Glossy buckthorn (Frangula alnus Mill.) is an alien species in Canada that is invading many forested areas. Glossy buckthorn has impacts on the biodiversity and productivity of invaded forests. Currently, we do not know much about the species' ecology and no thorough study of its distribution in temperate forests has been performed yet. As is often the case with invasive plant species, the phenology of glossy buckthorn differs from that of other indigenous plant species found in invaded communities. In the forests of eastern Canada, the main phenological difference is a delay in the shedding of glossy buckthorn leaves, which occurs later in the fall than for other indigenous tree species found in that region. Therefore, our objective was to use that phenological characteristic to map the spatial distribution of glossy buckthorn over a portion of southern Québec, Canada, using remote sensing-based approaches. We achieved this by applying a linear temporal unmixing model to a time series of the normalized difference vegetation index (NDVI) derived from Landsat 8 Operational Land Imager (OLI) images to create a map of the probability of the occurrence of glossy buckthorn for the study area. The map resulting from the temporal unmixing model shows an agreement of 69% with field estimates of glossy buckthorn occurrence measured in 121 plots distributed over the study area. Glossy buckthorn mapping accuracy was limited by evergreen species and by the spectral and spatial resolution of the Landsat 8 OLI.
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Curly-leaf pondweed (Potamogeton crispus L.) is a perennial, submerged plant native to Eurasia, Africa, and Australia that tolerates fresh or slightly brackish water. The pondweed has been found widely in many lakes in northern China. The Dongping Lake is a typical shallow macrophytic lake in north China, and it is an important junction at the East Route Project of China’s South-to-North Water Diversion Project (ERP-CSNWDP). In mid-summer, the pondweed die-offs result in a critical loss of dissolved oxygen and decaying plants can result in Dongping Lake eutrophication, blocked waterway, and fish kills. In order to maintain the environment security of the ERP-CSNWDP, there is an urgent need to understand the spatial and temporal changes of pondweed’s rapid range expansion in Dongping Lake. We employed moderate-resolution imaging spectroradiometer, normalized difference vegetation index data, and field investigations to extract its phenological characteristic. Landsat images from pondweed blooming and decay phases were acquired for eight representative growth stage years during the period of 1985–2017 to extract pondweed distribution. Through the validation, there was an accuracy of more than 89% for pondweed extraction in 2017. Then, we used a dimidiate pixel model to calculate its fractional green vegetation coverage (FVC). The results illustrated that its range expanded continuously during the 32-year study period. Its habitat changed from the scattered near shore distribution in 1985 to the contiguous coverage of large areas of the lake surface in 2017. The range expansion of pondweed was grouped into three stages. From 1985 to 1996, it had a slow growth stage with a maximum species range of 10.34 km2 in Dongping Lake. During the period of 1996–2001, there was an explosive growth stage during which it become the dominant species in the Lake. From 2001 to 2017, the range continued to increase to 49.07 km2, which comprised 40% of the surface area of the Lake. By spearman rank correlation analysis, we found that there was a significant correlation between pondweed area and the lake eutrophication level (R = 0.79, P < 0.05). The FVC spatial pattern after 2001 exhibited a gradual horizontal decrease from the southeast to the northwest of Dongping Lake. This study illustrates the application of vegetation phenology to assess the spatial and temporal pattern of pondweed. The opening of the ERP-CSNWDP is diverting a fraction of the total flow of the Yangtze River to Northern China via the Dongping Lake and presents an ongoing environmental security risk which will require future pondweed invasive species control and mitigation strategies utilizing ground and satellite-based spectrum observations.
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Wetland biomass is an important indicator of wetland ecosystem health. In this study, four dominant vegetation communities (Carex cinerascens, Phalaris arundinacea, Artemisia selengensis, and Miscanthus sacchariflorus) in the Poyang Lake wetland from 2010 to 2016 were classified from Landsat images using spectral information divergence (SID). We combined aboveground biomass (AGB) field measurements and remote sensing data to establish a suitable model for estimating wetland AGB in Poyang Lake, which is on the Ramsar Convention’s list of Wetlands of International Importance. The results showed that (1) overall, the classification accuracy for vegetation pixels across 5 years ranged from 59.1% to 73.7% and (2) the inter-annual and spatial variations in the AGB of the four vegetation types were clear. C. cinerascens had an average AGB density value of 1.28 kg m−2 in Poyang Lake from 2010 to 2016; M. sacchariflorus had the highest AGB density with an average value of 1.39 kg m−2; A. selengensis had almost the same level at 1.26 kg m−2; and P. arundinacea had the lowest AGB density at 0.64 kg m−2. This study provides useful experience for estimating carbon sequestration of vegetation in freshwater wetlands.
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The ability to differentiate a non-native aquatic plant, Myriophyllum spicatum (Eurasian watermilfoil or EWM), from other submerged aquatic vegetation (SAV) using spectral data collected at multiple scales was investigated as a precursor to mapping of EWM. Spectral data were collected using spectroradiometers for SAV taken out of the water, from the side of a boat directly over areas of SAV and from a lightweight portable radiometer system flown from an unmanned aerial system (UAS). EWM was spectrally different from other SAV when using 651 spectral bands collected in ultraviolet to near-infrared range of 350 to 1000 nm but does not provide a practical system for EWM mapping because this exceeds the capabilities of available airborne hyperspectral imaging systems. Using only six spectral bands corresponding to an available multispectral camera or eight wetlands-centric bands did not reliably differentiate EWM from other SAV and assemblages. However, a modified version of the normalized difference vegetation index (mNDVI), using a ratio of red-edge to red light, was significantly different among dominant vegetation groups. Also, averaging the full range of spectral to 65 10-nm wide bands, similar to available hyperspectral imaging systems, provided the ability to identify EWM separately from other SAV. The UAS-collected spectral data had the lowest remote sensing reflectance versus the out-of-water and boatside data, emphasizing the need to collect optimized data. The spectral data collected for this study support that with relatively clear and calm water, hyperspectral data, and mNDVI, it is likely that UAS-based imaging can help with mapping and monitoring of EWM.
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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.
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Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters.
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Image classification stands as an essential tool for automated mapping, that is demanded by agencies and stakeholders dealing with geospatial information. Decreasing costs or UAV-based surveying and open access to high resolution satellite images such as that provided by European Union’s Copernicus programme are the basis for multi-temporal landscape analysis and monitoring. Besides that, invasive alien species are considered a risk for biodiversity and their inventory is needed for further control and eradication. In this work, a methodology for semi-automatic detection of invasive alien species through UAV surveying and Sentinel 2 satellite monitoring is presented and particularized for Acacia dealbata Link species in the province of Pontevedra, in NW Spain. We selected a scenario with notable invasion of Acaciae and performed a UAS surveying to outline feasible training areas. Such areas were used as bounds for obtaining a spectral response of the cover from Sentinel 2 images with a level of processing 2A, that was used for invasive area detection. Sparse detected areas were treated as a seed for a region growing step to obtain the final map of alien species.
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Abstract: Invasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia saligna, to understand better the key factors influencing their distribution in the coastal plain of Israel. This goal was achieved by integrating airborne-derived hyperspectral imaging and multispectral earth observation for creating species distribution maps. Hyperspectral data, in conjunction with high spatial resolution species distribution maps, were used to train the multispectral images at the species level. We incorporated a series of statistical models to classify the IPS location and to recognize their distribution and density. We took advantage of the phenological flowering stages of Acacia trees, as obtained by the multispectral images, for the support vector machine classification procedure. The classification yielded an overall Kappa coefficient accuracy of 0.89. We studied the effect of various environmental and human factors on IPS density by using a random forest machine learning model, to understand the mechanisms underlying successful invasions, and to assess where IPS have a higher likelihood of occurring. This algorithm revealed that the high density of Acacia most closely related to elevation, temperature pattern, and distances from rivers, settlements, and roads. Our results demonstrate how the integration of remote-sensing data with different data sources can assist in determining IPS proliferation and provide detailed geographic information for conservation and management efforts to prevent their future spread.
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Understanding the spatial dynamics of invasive alien plants is a growing concern for many scientists and land managers hoping to effectively tackle invasions or mitigate their impacts. Consequently, there is an urgent need for the development of efficient tools for large scale mapping of invasive plant populations and the monitoring of colonization fronts. Remote sensing using very high resolution satellite and Unmanned Aerial Vehicle (UAV) imagery is increasingly considered for such purposes. Here, we assessed the potential of several single and multi-date indices derived from satellite and UAV imagery (i.e., UAV-generated Canopy Height Models).
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High-resolution drone aerial surveys combined with object-based image analysis are transforming our capacity to monitor and manage aquatic vegetation in an era of invasive species. To better exploit the potential of these technologies, there is a need to develop more efficient and accessible analysis workflows and focus more efforts on the distinct challenge of mapping submerged vegetation. We present a straightforward workflow developed to monitor emergent and submerged invasive water soldier (Stratiotes aloides) in shallow waters of the Trent-Severn Waterway in Ontario, Canada. The main elements of the workflow are: (1) collection of radiometrically calibrated multispectral imagery including a near-infrared band; (2) multistage segmentation of the imagery involving an initial separation of above-water from submerged features; and (3) automated classification of features with a supervised machine-learning classifier. The approach yielded excellent classification accuracy for emergent features (overall accuracy = 92%; kappa = 88%; water soldier producer's accuracy = 92%; user's accuracy = 91%) and good accuracy for submerged features (overall accuracy = 84%; kappa = 75%; water soldier producer's accuracy = 71%; user's accuracy = 84%). The workflow employs off-the-shelf graphical software tools requiring no programming or coding, and could therefore be used by anyone with basic GIS and image analysis skills for a potentially wide variety of aquatic vegetation monitoring operations.
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I conducted an exhaustive literature review on Japanese knotweeds s.l. (including Reynoutria japonica, R. sachalinensis and R. ×bohemica), especially on the effects of these invasive plants on biodiversity and ecological processes or the chemical and physical characteristics of invaded habitats. A total of 44 studies have been published, the earliest in 2005, in peer-reviewed journals. Most studies were conducted in Europe, the others in the USA. Invasive knotweeds have major negative impacts on native plants, while the abundant litter produced and the deep rhizome system alter soil chemistry to the benefit of the invaders. However, the effects of knotweeds on other groups of species vary, with a combination of losers (soil bacteria, most arthropods and gastropods, some frogs and birds) and winners (most fungi, detritivorous arthropods, aquatic shredders, a few birds). This literature review highlights significant knowledge gaps of the effects of knotweeds on biodiversity (vertebrates) and ecological processes (ecohydrology). To what extent knotweed invasions have an impact on the population dynamics of native plants and animals on a regional to national scale remains to be verified. Although there is some evidence that knotweed invasions have negative effects on the environment, the research to date remains modest and a more extensive effort is needed to better define the environmental impacts of these plant invaders.
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Invasive species spread on natural ecosystems is one of the most important causes of biodiversity loss. To disentangle the invasive plant impact on communities it is essential to combine experimental and observation approaches, which enable the correct interpretations of results and lead to the right decisions for management. 2.We examined the invasion of southern Brazil's grasslands by Eragrostis plana, which is currently the most problematic invasive species in the region. By means of an experiment on invaded communities complemented by observation of non-invaded communities, we assessed E. plana impact on vegetation, evaluated community response to its removal and discussed the effectiveness of removal methods. Fifty permanent 1 x 1 m plots were located on natural grassland that was partially invaded by E. plana. Removal was done annually from 2012 to 2015 and consisted of five treatments (n = 10): (i) clipping above-ground biomass on one occasion; (ii) clipping above-ground biomass periodically; (iii) herbicide and (iv) hand-pulling, plus (v) control treatment with no-removal. Additionally, 10 plots located in an adjacent non-invaded area were monitored. 3.All removal treatments reduced E. plana cover across years, but were not enough to eradicate it. Our results revealed not only differences between invaded and non-invaded communities, but also an effect of E. plana removal on resident species richness and total cover. 4.At the local scale, we demonstrated the impact of E. plana invasion on grassland vegetation, suggesting a reduction of resident species richness and total cover. Invasive species removal changed communities differently from invaded ones, but not resembling non-invaded references, suggesting that community recovery may need more time for reestablishment or that some restoration strategies are required. 5.Synthesis and applications. This study demonstrated the impact on vegetation of the most important invasive species in southern Brazil's natural grasslands. We highlighted the advantages of combining observations of non-invaded communities and experimental studies on invaded communities, with and without invasive removal, to help infer causal relationships in ecological invasion research. This article is protected by copyright. All rights reserved.
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Invasive alien species (IAS) threaten human livelihoods and biodiversity globally. Increasing globalization facilitates IAS arrival, and environmental changes, including climate change, facilitate IAS establishment. Here we provide the first global, spatial analysis of the terrestrial threat from IAS in light of twenty-first century globalization and environmental change, and evaluate national capacities to prevent and manage species invasions. We find that one-sixth of the global land surface is highly vulnerable to invasion, including substantial areas in developing economies and biodiversity hotspots. The dominant invasion vectors differ between high-income countries (imports, particularly of plants and pets) and low-income countries (air travel). Uniting data on the causes of introduction and establishment can improve early-warning and eradication schemes. Most countries have limited capacity to act against invasions. In particular, we reveal a clear need for proactive invasion strategies in areas with high poverty levels, high biodiversity and low historical levels of invasion.
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The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high-resolution image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus peltatus, Callitriche obtusangula, Potamogeton natans L., Sparganium emersum R. and Potamogeton crispus L., were classified from the data using object-based image analysis and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image resulted in 53% overall accuracy. These consistent results not only show promise for species-level mapping in such biodiverse environments but also prompt a discussion on assessment of classification accuracy.
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In situ hyperspectral reflectance data were studied at 50 wavebands (10 nm bandwidth) in the 400 to 900 nm spectral range to determine their potential for discriminating among 6 aquatic weed species: curly-leaf pondweed (Potamogeton cris-pus L.), hydrilla (Hydrilla verticillata [L.F.] Royle), Eurasian watermilfoil (Myriophyllum spicatum L.), northern milfoil (Myriophyllum sibiricum Kom.), hybrid milfoil (Myriophyllum spicatum * Myriophyllum sibiricum), and parrotfeather (Myrio-phyllum aquaticum [J.M. da Conceicao] Vellozo). The species were studied on 3 dates: May 11, May 30, and July 1, 2009. All 6 species were studied on the 2 May dates, while only 4 spe-cies (hydrilla, Eurasian watermilfoil, hybrid milfoil, and par-rotfeather) were studied on the July date. To determine the optimum bands for discriminating among the species, 2 pro-cedures were used: multiple comparison range test and step-wise discriminant analysis. Multiple comparison range test results for both May dates showed that most separations among species occurred at bands in the green-red edge, red, and red–near-infrared (NIR) edge spectral regions. For the July date, the largest number of separations among species occurred at all green and most red bands, as well as some red-NIR edge and NIR bands. Using stepwise discriminant analysis, 9 bands for May 11 and 10 bands for May 30 in the blue to NIR spectral regions had the highest power of dis-crimination among the 6 species. For the July date, 7 bands in the red-NIR edge and NIR regions were useful for discrim-inating among the 4 species.
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Landsat TM and ETM+ satellite images from 2001 to 2011 were used to map the extent and change of the invasive shrubs common and glossy buckthorn (Frangula alnus and Rhamnus cathartica) at Irwin Prairie State Nature Preserve (IPSNP), and throughout Oak Openings, a 1,500 km2 region, located in NW Ohio, USA and SE Michigan near Lake Erie. In the Oak Openings, buckthorn directly threatens native biodiversity and habitat health of this globally rare ecosystem. Buckthorn that forms as dense shrub thicket in the understory is often obscured from satellite view by other canopy and is not spectrally dissimilar enough to be characterized using multispectral images. To address this, time series tasseled cap greenness images of land surface areas dominated by buckthorn thicket (which exhibit early leaf-out, late senescence) was used to identify areas covered by thicket with a heterogeneous background. A time series of vegetation index values was calculated from 49 Landsat images and combined with in-situ observations to define the land surface phenology of buckthorn thicket and other vegetation types. The phenological differences among land surfaces dominated by distinct vegetation types in the Oak Openings Region were used to map the extent of buckthorn thicket using a supervised classification method. Buckthorn thicket was identified in 0.43 % of the classified pixels (940 ha) in the 2007–2011 imagery and in 0.31 % (690 ha) of the 2001–2006 images. A Kappa test of the 2007–2011 classification yielded a value of 0.73 with 88 % overall accuracy of presence or absence of thicket based on 60 samples throughout the Oak Openings. The areal extent of buckthorn thicket increased by 39 % (255 ha) in the study area over the time period from 2001 to 2011.
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Under the current high anthropic pressure and climate change scenarios, a trend towards increasing changes in the trophic status of shallow lakes, and the development of opportunistic floating species is to be expected. This raises the need for monitoring and management actions to prevent widespread environmentally negative effects (e.g., anoxia). An efficient approach to monitoring water quality and primary producers in inland waters is to integrate in situ with remote sensing data. In this work, satellite multispectral data acquired by Sentinel-2 A are used to assess the intra-annual spatial and temporal dynamics of phytoplankton abundance, in terms of chlorophyll-a (Chl-a) concentration and macrophyte Leaf Area Index (LAI) in a shallow eutrophic fluvial lake system (Mantua Lakes, Italy). Chl-a concentrations and LAI were derived from Sentinel-2 A data by applying a semi-empirical band ratio algorithm combined with a bio-optical model (BOMBER) for the former (Chl-a), and a semi-empirical model for the latter (LAI). These products were validated against in situ data (rRMSE = 20% for both products; R2 = 0.93 for Chl-a; R2 = 0.83 for LAI). Phytoplankton maps showed a marked intra-annual spatial and temporal variability, generally revealing a Chl-a concentration gradient from lotic to lentic waters. Air temperature was the main driver of Chl-a concentration, followed by water discharge and precipitation. The macrophyte LAI followed aquatic plant growth seasonality, and was independent of the hydro-meteorological data. Allochthonous and invasive macrophyte species (such as Nelumbo nucifera and Ludwigia hexapetala) had higher LAI compared than the Mantua Lakes’ autochthonous floating-leaved species (e.g., Trapa natans and Nuphar lutea). Maps of the abundance of primary producers can be used to follow the temporal and spatial evolution of different communities and support management actions, e.g., by identifying potential algal bloom hotspots, or the optimal timing for measures to control invasive species overgrowth.
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Spatial information of the dominant species of submerged aquatic vegetation (SAV) is essential for restoration projects in eutrophic lakes, especially eutrophic Taihu Lake, China. Mapping the distribution of SAV species is very challenging and difficult using only multispectral satellite remote sensing. In this study, we proposed an approach to map the distribution of seven dominant species of SAV in Taihu Lake. Our approach involved information on the life histories of the seven SAV species and eight distribution maps of SAV from February to October. The life history information of the dominant SAV species was summarized from the literature and field surveys. Eight distribution maps of the SAV were extracted from eight 30 m HJ-CCD images from February to October in 2013 based on the classification tree models, and the overall classification accuracies for the SAV were greater than 80%. Finally, the spatial distribution of the SAV species in Taihu in 2013 was mapped using multilayer erasing approach. Based on validation, the overall classification accuracy for the seven species was 68.4%, and kappa was 0.6306, which suggests that larger differences in life histories between species can produce higher identification accuracies. The classification results show that Potamogeton malaianus was the most widely distributed species in Taihu Lake, followed by Myriophyllum spicatum, Potamogeton maackianus, Potamogeton crispus, Elodea nuttallii, Ceratophyllum demersum and Vallisneria spiralis. The information is useful for planning shallow-water habitat restoration projects.
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Small unmanned aircraft systems (UAS) combined with automated image analysis may provide an efficient alternative or complement to labour-intensive boat-based monitoring of invasive aquatic vegetation. A small mapping drone was assessed for collecting high-resolution (≤5 cm/pixel) true-colour and near-infrared imagery revealing the distribution of invasive water soldier (Stratiotes aloides) in the Trent–Severn Waterway, Ontario (Canada). We further evaluated the capacity of an object-based image analysis approach based on the Random Forests classification algorithm to map features in the imagery, chiefly emergent and submerged water soldier colonies. The imagery contained flaws and inconsistencies resulting from data collection in suboptimal weather conditions that likely negatively impacted classification performance. Nevertheless, our best-performing classification had a producer’s and user’s accuracy for water soldier of 81% and 74%, respectively, an overall accuracy of 78%, and a kappa value of 61%, indicating “substantial” accuracy. This trial provides an instructive case study on results achieved in a “real-world” application of a UAS for environmental monitoring, notably characterized by time constraints for data collection and analysis. Beyond avoiding data collection in unfavourable weather conditions, adaptations of the image segmentation process and use of a true discrete-band multispectral camera may help to improve classification accuracy, particularly of submerged vegetation.
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The wetland plant species, Phragmites australis, is present on every continent except Antarctica. Both native and non-native subspecies thrive in the USA with the non-natives quickly displacing native wetland plants. Along the Gulf Coast, Phragmites grows in very dense stands, and at heights of greater than 4.6 m, is usually the tallest grass species in a wetland, estuary, and marsh ecosystems. Phragmites is known to alter the ecology of these wetland systems making them less suitable as habitat for many species of flora and fauna. Furthermore, Phragmites presents a navigation hazard to smaller boats by impairing visibility along shorelines and around bends of canals and rivers. Management efforts targeting non-native Phragmites rely heavily on accurately mapping invaded areas. Historically, mapping has been done through walking the perimeter of a stand with a Global Positioning System (GPS) unit, using satellite imagery, or through the use of aerial photography from manned aircraft. These methods are time consuming, are expensive, can have an inadequate resolution, and in some cases are prone to human error. In this work, an Unmanned Aerial System (UAS) was used to capture visible imagery to create a basin-wide distribution map of a large wetland along the US Pearl River delta in southeastern Louisiana. The imagery was collected in the summer and individual images were mosaicked to create a larger map. We then evaluated the use of texture analysis on the mosaics to automatically map the invasive. Specifically, Gabor filters, grey level co-occurrence matrices, segmentation-based fractal texture analysis, and wavelet-based texture analysis were compared for mapping the Phragmites. Our experimental results, conducted using the imagery we collected over four study areas (approximately 2250 ha) along the US Pearl River delta, indicate the proposed texture-based approach yields an average accuracy of 85%, an average kappa accuracy of 70%. These maps have shown to be very useful for resource managers to hasten the eradication efforts of Phragmites.
Article
Invasive plant species spread presents a challenge, requiring better mapping and monitoring for control. Remote sensing (RS) provides an efficient tool to map invasive plants in diverse ecosystems. Yet applications of RS for invasive plant mapping largely rely on spatial and spectral patterns. The use of invasive plant functional traits can improve RS mapping, using ecological insights on processes and functions associated with invasion. We summarize research utilizing plant functional traits in RS mapping of invasive species from the years 2000 to 2014. Based on this review, we summarize plant traits that can be related to spatial and spectral properties, and used to discriminate invasive alien plants from native vegetation. Phenological and structural plant traits have been relatively well exploited via RS for invasion studies. In comparison, there has been limited utilization of physiological traits (with the exception of properties such as nitrogen content). This is an area that merits further research attention, via the linkage of ecophysiological field research with RS.
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Nous presentons, dans cet article, la liste des taxons de plantes vasculaires exotiques (especes, sous-especes, varietes, hybrides) qui sont naturalises sur le territoire du Quebec. Cette liste a ete constituee a partir de listes publiees precedemment, d’une revue de litterature, de bases de donnees accessibles sur Internet et grâce a des renseignements fournis par des botanistes professionnels. Un taxon a ete inclus dans la liste uniquement s’il existait une preuve valable de la presence de la plante sur le territoire quebecois, que ce soit sous la forme d’un specimen d’herbier ou par le biais d’une mention attestee par un botaniste, photographie a l’appui. Plusieurs informations ont ete colligees pour chaque taxon de la liste, soit le continent d’origine, le motif d’introduction et l’annee de la plus vieille preuve de naturalisation. Un total de 899 taxons de plantes vasculaires (880 especes, auxquelles s’ajoutent 18 hybrides), groupes au sein de 95 familles, ont ete introduits au Quebec depuis le debut du xviie siecle et se sont par la suite naturalises. La plupart des taxons (63 %) sont des plantes vivaces et la vaste majorite (82 %) proviennent d’Eurasie. Environ 39 % des taxons ont ete introduits a des fins ornementales et 18 % a des fins utilitaires ; il n’a pas ete possible de decouvrir un motif d’introduction pour 403 taxons (45 % du total). La flore du Quebec serait constituee d’environ 26 a 28 % de plantes exotiques, un pourcentage similaire a celui estime pour l’Ontario ou pour plusieurs autres Etats voisins de la province. Cette mise a jour de la liste des plantes vasculaires exotiques naturalisees du Quebec n’est probablement pas complete, mais sa publication vise a inciter les botanistes quebecois a l’enrichir au cours des prochaines annees.
Article
Exotic vascular plants are increasingly numerous, and decision support systems identifying the most problematic species are needed to help environmental managers to develop control strategies. The fundamental tool in this respect is a list of weeds, or a weed risk assessment. We propose here a list for the province of Quebec constructed using an innovative approach based on 1) well-defined criteria, 2) consideration of all potential problems associated with exotic plants, 3) use of credible scientific data, 4) assessment by a panel composed of experts with diverse expertise and who are also potential users of the list, 5) use of a decision support approach, and 6) a debate among experts in order to reach a verdict concerning the status (weed, no weed) of each candidate plant. The list contains 87 of the 908 taxa of exotic vascular plants that are naturalized in Quebec, i.e., 9.6% of the total. About two thirds of the weeds are problematic for agricultural or ornamental plant production or for forestry; the others are, in decreasing numerical order, problematic for biodiversity or natural ecosystem functions, health, landscaping or home gardening, and recreational activities. Evaluating the threat posed to biodiversity by individual species was a challenge, because few relevant studies have been published. The use of well-defined criteria greatly reduced (by a factor of 3) the number of weed species from an initial list based on individual uncensored expert opinions. The resulting list is not definitive, and should be used with caution. However, we estimate that our approach is more rigorous than the other qualitative approaches developed to date, with performance that matches the semi-quantitative or quantitative tools frequently used for assessing invasive plants, such as the Australian Weed Risk Assessment.
Article
Submerged aquatic vegetation (SAV) is widely recognized as an important habitat and indicator of water quality in large rivers and estuaries. Despite the perceived importance, system-wide assessments of cover, susceptibility to change, and ecological functioning are rare because of the geographical scope and multi-disciplinary expertise required. A collaboration between scientists, estuarine managers, and environmental educators was initiated to map the SAV and the Eurasian water chestnut (Trapa natans) in the Hudson River estuary from Hastings-on-Hudson north to Troy, New York. These groups provided diverse scientific and estuarine management expertise to enable the first broad delimitation of SAV in the Hudson and sampling of beds to describe abundance, biomass, and species composition and to address management and education needs and opportunities. The areal extent of SAV based on a combination of 1995 and 1997 photographs in the study area was 1,802 hectares (4,453 acres), ∼6% of the river area and ∼18% of the shallows (defined as less than 3 meters deep at low tide). Trapa natans covered 575 hectares (1,421 acres), 2% of the river area and 6% of the shallows. In the most heavily vegetated portion of the Hudson (approximately 150–200 kilometers of river), the coverage by plants (both SAV and T. natans) approached 25% of the river bottom area. Results of this work have been integrated into the federal and state regulatory processes, local resource users, and local science education programs. Finally, we have initiated a volunteer SAV monitoring program.
Article
Wetlands provide many environmental and societal benefits. Unfortunately, the importance of wetlands has only recently been acknowledged after centuries of drainage and conversion to other land uses. An emerging threat to North American wetlands is the introduction of invasive plant species such as Phragmites australis, a reed introduced from Europe. Previous high spatial resolution satellite imagery used for mapping Phragmites was limited spectrally to four bands (blue, green, red, and near-infrared). A recently launched satellite, Worldview-2, has four additional spectral bands that may allow for more accurate mapping of Phragmites. In this study, a single-date Worldview-2 image was used to map wetland vegetation at Walpole Island, Canada. Object-based and per-pixel maximum likelihood classifications were performed on a four-band subset simulating traditional multispectral imagery and the full eight-band set of Worldview-2. The overall classification accuracy of 94.0% achieved for the eight-band object-based method was the highest of the four classifications methods used. The accuracy achieved by the eight-band object-based classification shows that single-date Worldview-2 image is promising for distinguishing Phragmites from native wetland plant species late in the growing season in coastal Great Lakes wetlands.
Article
European frog-bit (EFB) is an invasive species of concern in Ontario, Quebec, and northern New York (USA). The ability to manage and control EFB in these jurisdictions is limited. Improved means to rapidly detect new or emerging EFB colonies could significantly enhance management success and help minimize ecological damage from this invader. This study investigated the feasibility of using high-resolution multispectral Quickbird imagery to detect EFB for a 6 km section of the South Nation River in Ontario, Canada. The objective of this study was to determine if the spectral signature of EFB stands are separable from other wetland vegetation in situ, for a typically colonized wetland. A three-stage supervised fuzzy classification methodology based on fuzzy segmentation, feature analysis, and defuzzification was used to conduct a land cover classification involving a species-level class for EFB. Validation of classifier performance was assessed using ground truth of the percentage canopy cover per species collected through a field survey and high-resolution aerial photography. Classification results indicated this methodology produced a good approximation of EFB's spatial distribution. The land cover classification had a Kappa coefficient of 84.3%, with 81.0% and 77.9% user's and producer's accuracies, respectively, for the EFB class. The results of this study demonstrated the feasibility of using high-resolution multispectral satellite imagery for EFB detection.
Article
Remote sensing has rarely been used as a tool to map and monitor submerged aquatic vegetation (SAV) in rivers, due to a combination of insufficient spatial resolution of available image data and strong attenuation of light in water through absorption and scattering. The latter process reduces the possibility to use spectral reflectance information to accurately classify submerged species. However, increasing availability of very high resolution (VHR) image data may enable the use of shape and texture features to help discriminate between species by taking an object based image analysis (OBIA) approach, and overcome some of the present limitations.
Article
Plant invasions represent a threat not only to biodiversity and ecosystem functioning but also to the character of traditional landscapes. Despite the worldwide efforts to control and eradicate invasive species, their menace grows. New techniques enabling fast and precise monitoring and providing information on spatial structure of invasions are needed for efficient management strategies to be implemented. We present remote sensing assessment of a noxious invasive species Heracleum mantegazzianum (giant hogweed) that integrates different data sources, spatial and spectral resolutions, and image processing techniques. Panchromatic, multispectral and color very high spatial resolution (VHR) aerial photography (1947–2006, resolution 0.5 m), and medium spatial resolution satellite data (Rapid Eye 2010, resolution 5 m) were analyzed to assess their potential for hogweed monitoring by using pixel- (both supervised and unsupervised) and object-based image analysis (OBIA, automated hierarchical, iterative, and rule-based). Both point and grid based accuracy assessment was carried out. Described methods of object-based image analysis of VHR data enabled monitoring of hogweed at high classification accuracies measured by various means, regardless of the spectral resolution of the data provided that the data came from the species flowering period. Although the proposed automated processing of VHR data is relatively time-effective and standardized, application over large areas would be rather demanding due to the size of datasets, and multispectral satellite data of medium spatial resolution (lower than the size of individuals) was therefore tested. On such imagery, only larger stands could be identified but still the pixel-based supervised classification achieved moderate accuracy. Depending on the size of the area of interest and the detail needed the very high or medium spatial resolution data (acquired at the species flowering period) are to be used. High accuracies achieved for VHR data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparable precise, fast and efficient.
Article
Japanese knotweed (Fallopia japonica) is listed among 100 of the World's worst invasive alien species and poses an increasing threat to ecosystems and agriculture in Northern America, Europe, and Oceania. This study proposes a remote sensing method to detect local occurrences of F. japonica from low-cost digital orthophotos taken in early spring and summer by concurrently exploring its temporal, spectral, and spatial characteristics. Temporal characteristics of the species are quantified by a band ratio calculated from the green and red spectral channels of both images. The normalized difference vegetation index was used to capture the high near-infrared (NIR) reflectance of F. japonica in summer while the characteristic texture of F. japonica is quantified by calculating gray level co-occurrence matrix (GLCM) measures. After establishing the optimum kernel size to quantify texture, the different input features (spectral, spatial, and texture) were stacked and used as input to the random forest (RF) classifier. The proposed method was tested for a built-up and semi-natural area in Slovenia.
Article
The National Estuarine Research Reserve (NERR) program is a nationally coordinated research and monitoring program that identifies and tracks changes in ecological resources of representative estuarine ecosystems and coastal watersheds. In recent years, attention has focused on using high spatial and spectral resolution satellite imagery to map and monitor wetland plant communities in the NERRs, particularly invasive plant species. The utility of this technology for that purpose has yet to be assessed in detail. To that end, a specific high spatial resolution satellite imagery, QuickBird, was used to map plant communities and monitor invasive plants within the Hudson River NERR (HRNERR). The HRNERR contains four diverse tidal wetlands (Stockport Flats, Tivoli Bays, Iona Island, and Piermont), each with unique water chemistry (i.e., brackish, oligotrophic and fresh) and, consequently, unique assemblages of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). A maximum-likelihood classification was used to produce 20-class land cover maps for each of the four marshes within the HRNERR. Conventional contingency tables and a fuzzy set analysis served as a basis for an accuracy assessment of these maps. The overall accuracies, as assessed by the contingency tables, were 73.6%, 68.4%, 67.9%, and 64.9% for Tivoli Bays, Stockport Flats, Piermont, and Iona Island, respectively. Fuzzy assessment tables lead to higher estimates of map accuracies of 83%, 75%, 76%, and 76%, respectively. In general, the open water/tidal channel class was the most accurately mapped class and Scirpus sp. was the least accurately mapped. These encouraging accuracies suggest that high-resolution satellite imagery offers significant potential for the mapping of invasive plant species in estuarine environments.
Article
Multiseason reflectance data from radiometrically and geometrically corrected multispectral SPOT-5 images of 10-m resolution were combined with thorough field campaigns and land cover digitizing using a binary classification tree algorithm to estimate the area of marshes covered with common reeds (Phragmites australis) and submerged macrophytes (Potamogeton pectinatus, P. pusillus, Myriophyllum spicatum, Ruppia maritima, Chara sp.) over an area of 145,000 ha. Accuracy of these models was estimated by cross-validation and by calculating the percentage of correctly classified pixels on the resulting maps. Robustness of this approach was assessed by applying these models to an independent set of images using independent field data for validation. Biophysical parameters of both habitat types were used to interpret the misclassifications. The resulting trees provided a cross-validation accuracy of 98.7% for common reed and 97.4% for submerged macrophytes. Variables discriminating reed marshes from other land covers were the difference in the near-infrared band between March and June, the Optimized Soil Adjusted Vegetation Index of December, and the Normalized Difference Water Index (NDWI) of September. Submerged macrophyte beds were discriminated with the shortwave-infrared band of December, the NDWI of September, the red band of September and the Simple Ratio index of March. Mapping validations provided accuracies of 98.6% (2005) and 98.1% (2006) for common reed, and 86.7% (2005) and 85.9% (2006) for submerged macrophytes. The combination of multispectral and multiseasonal satellite data thus discriminated these wetland vegetation types efficiently. Misclassifications were partly explained by digitizing inaccuracies, and were not related to biophysical parameters for reedbeds. The classification accuracy of submerged macrophytes was influenced by the proportion of plants showing on the water surface, percent cover of submerged species, water turbidity, and salinity. Classification trees applied to time series of SPOT-5 images appear as a powerful and reliable tool for monitoring wetland vegetation experiencing different hydrological regimes even with a small training sample (N = 25) when initially combined with thorough field measurements.
Article
Early leafing and extended leaf longevity can be important mechanisms for the invasion of the forest understory. We compared the leaf phenology and photosynthetic characteristics of Berberis thunbergii, an early leafing invasive shrub, and two co-occurring native species, evergreen Kalmia latifolia and late leafing Vaccinium corymbosum, throughout the 2004 growing season. Berberis thunbergii leafed out 1 month earlier than V. corymbosum and approximately 2 weeks prior to the overstory trees. The photosynthetic capacity [characterized by the maximum carboxylation rate of Rubisco (V (cmax)) and the RuBP regeneration capacity mediated by the maximum electron transport rate (J (max))] of B. thunbergii was highest in the spring open canopy, and declined with canopy closure. The 2003 overwintering leaves of K. latifolia displayed high V (cmax) and J (max) in spring 2004. In new leaves of K. latifolia produced in 2004, the photosynthetic capacity gradually increased to a peak in mid-September, and reduced in late November. V. corymbosum, by contrast, maintained low V (cmax) and J (max) throughout the growing season. In B. thunbergii, light acclimation was mediated by adjustment in both leaf mass per unit area and leaf N on a mass basis, but this adjustment was weaker or absent in K. latifolia and V. corymbosum. These results indicated that B. thunbergii utilized high irradiance in the spring while K. latifolia took advantage of high irradiance in the fall and the following spring. By contrast, V. corymbosum generally did not experience a high irradiance environment and was adapted to the low irradiance understory. The apparent success of B. thunbergii therefore, appeared related to a high spring C subsidy and subsequent acclimation to varying irradiance through active N reallocation and leaf morphological modifications.
Article
Turkey is a country rich in lakes and wetlands--monitoring of all these will require advances in technology such as remote sensing. In this study, the aquatic plants of the large and shallow Lake Mogan, located in Central Anatolia were identified and mapped using high spatial resolution Quickbird imagery. As Lake Mogan is an important bird area the assessment of submerged plant species is of great value for ecosystem conservation and management. Quickbird multispectral image acquired on August 6, 2005 was geometrically corrected and a water mask was used based on strong absorption of Near Infrared (NIR) wavelengths by calm, clear and deep water. The water mask was applied using band reflectance values for a specific pixel satisfying the conditions of band decreasing property (Green>Red>NIR) and NIR<NIR(threshold). Unsupervised classification was applied to the wetland-only image to identify submerged plant vegetation classes. Spectral similarity among the isodata classes was used to decrease the number of the classes to the available species in the lake. Classification of Quickbird satellite data with an unsupervised classification technique provided high accuracy for identification and mapping of submerged plant coverage and of different submerged plant species and water classes (83.02% and 71.69%). Quickbird sensor data were found to be very useful for classifying submerged plants in a large and shallow lake. However, the closeness of the dates of field data collection to that of the sensor overpass and mixed pixel problem are the main limitations in the near-ideal conditions for submerged vegetation classification with satellite data having high spatial resolution.