Développement d’une méthode de télédétection pour l’identification d’espèces exotiques envahissantes dans l’agglomération de Québec

Les espèces exotiques envahissantes végétales (EEEv) sont actuellement considérées comme étant à l’origine de plusieurs types d’impacts négatifs dont la perte de la biodiversité et l’altération du fonctionnement des écosystèmes. Dans l’agglomération de Québec, la présence de plusieurs EEEv et les informations partielles sur leur distribution territoriale limitent la mise en place de stratégies efficaces de contrôle et d’éradication. Ces données sur la distribution territoriale peuvent être acquises à partir des inventaires in situ. Cependant, ces derniers nécessitent beaucoup de temps surtout dans les milieux envahis par plusieurs EEEv en même temps tels que les milieux urbains. Ces inventaires ne sont également pas adaptés financièrement et techniquement, lorsqu’il s’agit de grandes étendues ou lorsque les conditions topographiques ne sont pas favorables. La télédétection pourrait être utilisée pour contrer ces limites afin de cartographier les EEEv, suivre leur prolifération et intervenir rapidement. Le but de cette étude consistait donc à élaborer une méthode de cartographie multi-espèces par télédétection de cinq EEEv terrestres présentes dans l’agglomération de Québec, à savoir la renouée du Japon (Fallopia japonica), le phragmite (Phragmites australis), la berce du Caucase (Heracleum mantegazzianum), le nerprun bourdaine (Frangula alnus) et le nerprun cathartique (Rhamnus cathartica). L’approche méthodologique consistait à réaliser une cartographie mono-date et multi-date à l’aide d’images satellitaires WorldView-3 acquises en été, SPOT-7 et GeoEye-1 acquises en automne. Une classification orientée-objet combinée à des méthodes d’apprentissage automatique non paramétriques, à savoir Support Vector Machine (SVM), Random Forest (RF) et Extreme Gradient Boosting (XGBoost) a été utilisée afin de produire des probabilités de présence de ces EEEv. La cartographie des nerpruns a été réalisée à part car leur faible présence sur la zone d’étude et leur distribution sous-couvert à faible densité a nécessité un ajout de l’image GeoEye-1 et un paramétrage des méthodes différent de celui utilisé pour les trois premières EEEv. La combinaison des images WorldView-3 et SPOT-7 a permis d’atteindre d’excellentes performances pour les trois premières EEEv, avec un coefficient Kappa de 0,85 et une précision globale de 91 % en utilisant RF. Les performances individuelles des classes basées sur l’indicateur F1-score ont montré que la renouée du Japon est mieux détectée (F1-score maximal = 0,95), que la berce du Caucase (F1-score maximal = 0,91) et le phragmite (F1-score maximal = 0,87). La classification multi-date des nerpruns est, par contre, moins performante par rapport à celle des autres espèces avec un coefficient Kappa égal à 0,72, une précision globale de 83 % et F1-score maximal égal 0,62. Cette étude montre la possibilité de cartographie et suivi des principales EEEv selon une approche multi-date. Les limites de cette étude, à savoir la faible quantité de données de référence d’EEEv, les coûts élevés d’acquisition et la faible disponibilité des images satellitaires à très haute résolution spatiale ainsi que la distribution des nerpruns en sous-couvert (dans notre zone d’étude) pourraient être réduites en utilisant des images plus accessibles en combinaison avec les techniques de super-résolution. Les données LiDAR à haute densité pourraient également être intégrées à l’imagerie optique afin d’améliorer les performances de cartographie des nerpruns

INVESTIGATION INTO ITADORI KNOTWEED AS A CONTROL OF BANK EROSION IN NEW HAMPSHIRE RIVERS

As floods increase in frequency and magnitude throughout New England, research on controls of erosion is necessary to help manage riverbank erosion and its implications on river system health and safety of infrastructure situated along the bank. Reynoutria japonica (Itadori knotweed) is an invasive species spreading throughout New Hampshire rivers which is suspected to cause riverbank erosion due to its unique root structure and winter die-back. To examine the impact of knotweed on riverbank erosion, paired knotweed and native species vegetation patches were selected as study sites along the Sugar and Lamprey Rivers, New Hampshire. Study sites were monitored over the course of a year using bank pins, remote sensing (LiDAR and Structure from Motion), and hydraulic modeling. Banks colonized by knotweed experienced 6.8 cm more erosion on average than similar banks with native vegetation. No statistically significant difference was recorded between modeled applied shear stress or estimated critical shear stress values between paired vegetation patches, apart from Sugar Site 6. Overall, the results of the bank erosion monitoring, estimated critical shear stress, and modeled applied shear stress results show that the only significant differences across paired vegetation patches were the presence of Itadori knotweed and an increase in erosion measured at knotweed patches. To minimize the impacts on river ecosystems by knotweed and damage to infrastructure by erosion, river corridor management should consider efforts to remove knotweed from river systems

Monitoring Yellow Floating Heart (Nymphoides peltata) on Lake Carl Blackwell via Remote Sensing

Yellow floating heart (Nymphoides peltata) is an invasive floating-leaf aquatic plant. This species forms dense mats of rhizomatous vegetation that can produce tens of thousands of seeds per square meter. These seeds spread via hydrochory and establish additional colonies. Fragmentation of N. peltata from disturbances further increases its spatial extent, as detached plant material forms new colonies. N. peltata successfully colonized over 40 acres on Lake Carl Blackwell, in Stillwater, Oklahoma. The herbicide glyphosate was applied on 10 dates in 2018 to combat further spread of N. peltata. To evaluate the efficacy of these spraying events, a DJI Phantom 4 Unmanned Aerial Vehicle (UAV) was used to image the spatial extent of N. peltata coverage of infested coves on five dates over the 2018 growing season. These images were used to create orthomosaics and subsequently analyzed to examine temporal changes in plant coverage spatial extent in response to glyphosate treatment. Near infrared (NIR) images from the Sentinel-2 satellite were also used to evaluate plant health based on Normalized Difference Vegetation Index (NDVI). A pilot study evaluated the spatial extent and NDVI changes of one infested cove on the north side of Lake Carl Blackwell. This study includes and is an expansion of that pilot study where five additional coves were analyzed, and comparisons were made between UAV and Sentinel-2 NDVI results at different NDVI thresholds for plant health (0.33, 0.365, and 0.4). A brief project cost comparison between monitoring methods is included. Results show limited success with glyphosate application, as N. peltata maintains a strong presence in five of the six study coves. This study presents an effective methodology for monitoring plant stress temporally using UAV and satellite remote sensing

Phenology-based UAV remote sensing for classifying invasive annual grasses to the species level

The spread of invasive plant species severely alters wildfire regimes, degrades critical habitat for native species, and has detrimental impacts on ecosystem function, rangeland productivity, and long-term carbon storage dynamics. Remote sensing technology has greatly improved our understanding of invasive plant ecology and ability to map and monitor plant invasions. Mapping plant invasions to the species level with conventional satellite and airborne data has proven challenging, however, because many invasive species occur at fine spatial scales or are mixed with native species, and satellite passes may occur too infrequently to capture important phenological stages. Imagery derived from readily deployable Unmanned Aerial Vehicles (UAVs) offers high-resolution data over carefully timed acquisition dates during the growing season. However, some challenges remain that are particular to high spatial resolution imagery, where excessive detail from shadows and canopy gaps often result in misclassification, inaccuracy, and a “salt-and-pepper” effect in the final classification. The addition of textural and vegetation height data to a purely spectral pixel-based approach has the potential to mitigate these challenges and improve species-level vegetation classification. Using UAV imagery acquired at specific phenological stages, we investigate which combinations of spectral, textural, vegetation height, and multi-temporal techniques best separate two invasive annual grasses, cheatgrass and medusahead, to the species level.We selected five study sites ranging in area from 8 to 36 hectares (ha) in Paradise Valley, Nevada, which feature a variety of invasive and native species that are typical of the Great Basin region. For three carefully selected dates over the growing season during which cheatgrass and medusahead were most spectrally distinct, we conducted UAV flight campaigns and collected field data on vegetation composition. Imagery was processed in photogrammetric software to produce orthomosaics, digital terrain models, and digital surface models from which vegetation height was derived. Texture analysis was performed over the acquired raster data products. Multi-date spectral, textural, and vegetation height variables were used to predict vegetation class type using Random Forest machine learning methods. The overall goal of this research is to further remote sensing methods for vegetation classification of invaded landscapes to the species level. We investigated which combinations of spectral, textural, vegetation height, and multitemporal techniques best separate two invasive annual grasses - cheatgrass and medusahead. To explore the impact of explanatory variables in our classification, all possible additive combinations of our variables were calculated. We found that multi-temporal texture variables and vegetation height added additional levels of information to our classification and, when combined with multi date spectral information, achieved the highest overall accuracy. Our model resulted in a robust classification across several diverse study sites

Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach

Mapping the occurrence patterns of invasive plant species and understanding their invasion dynamics is a crucial requirement for preventing further spread to so far unaffected regions. An established approach to map invasive species across large areas is based on the combination of satellite or aerial remote sensing data with ground truth data from fieldwork. Unmanned aerial vehicles (UAV, also referred to as unmanned aerial systems (UAS)) may represent an interesting and low-cost alternative to labor-intensive fieldwork. Despite the increasing use of UAVs in the field of remote sensing in the last years, operational methods to combine UAV and satellite data are still sparse. Here, we present a new methodological framework to estimate the fractional coverage (FC%) of the invasive shrub species Ulex europaeus (common gorse) on Chilo´e Island (south-central Chile), based on ultrahigh- resolution UAV images and a medium resolution intra-annual time-series of Sentinel-2. Our framework is based on three steps: 1) Land cover classification of the UAV orthoimages, 2) reduce the spatial shift between UAV-based land cover classification maps and Sentinel-2 imagery and 3) identify optimal satellite acquisition dates for estimating the actual distribution of Ulex europaeus. In Step 2 we translate the challenging co-registration task between two datasets with very different spatial resolutions into an (machine learning) optimization problem where the UAV-based land cover classification maps obtained in Step 1 are systematically shifted against the satellite images. Based on several Random Forest (RF) models, an optimal fit between varying land cover fractions and the spectral information of Sentinel-2 is identified to correct the spatial offset between both datasets. Considering the spatial shifts of the UAV orthoimages and using optimally timed Sentinel-2 acquisitions led to a significant improvement for the estimation of the current distribution of Ulex europaeus. Furthermore, we found that the Sentinel-2 acquisition from November (flowering time of Ulex europaeus) was particularly important in distinguishing Ulex europaeus from other plant species. Our mapping results could support local efforts in controlling Ulex europaeus. Furthermore, the proposed workflow should be transferable to other use cases where individual target species that are visually detectable in UAV imagery are considered. These findings confirm and underline the great potential of UAV-based groundtruth data for detecting invasive species

UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite's output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers

The Use of an Unmanned Aerial Vehicle for Tree Phenotyping Studies

A strip of 20th-century landscape woodland planted alongside a 17th to mid-18th century ancient and semi-natural woodland (ASNW) was investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with a multispectral image camera (MSI). A simple classification approach of normalized difference spectral index (NDSI), derived using principal component analysis (PCA), enabled the identification of the non-native trees within the 20th-century boundary. The tree species within this boundary, classified by NDSI, were further segmented by the machine learning segmentation method of k-means clustering. This combined innovative approach has enabled the identification of multiple tree species in the 20th-century boundary. Phenotyping of trees at canopy level using the UAV with MSI, across 8052 m2, identified black pine (23), Norway maple (19), Scots pine (12), and sycamore (19) as well as native trees (oak and silver birch, 27). This derived data was corroborated by field identification at ground-level, over an area of 6785 m2, that confirmed the presence of black pine (26), Norway maple (30), Scots pine (10), and sycamore (14) as well as other trees (oak and silver birch, 20). The benefits of using a UAV, with an MSI camera, for monitoring tree boundaries next to a new housing development are demonstrated

Mapping invasive plants using RPAS and remote sensing

The ability to accurately detect invasive plant species is integral in their management, treatment, and removal. This study focused on developing and evaluating RPAS-based methods for detecting invasive plant species using image analysis and machine learning and was conducted in two stages. First, supervised classification to identify the invasive yellow flag iris (Iris pseudacorus) was performed in a wetland environment using high-resolution raw imagery captured with an uncalibrated visible-light camera. Colour-thresholding, template matching, and de-speckling prior to training a random forest classifier are explored in terms of their benefits towards improving the resulting classification of YFI plants within each image. The impacts of feature selection prior to training are also explored. Results from this work demonstrate the importance of performing image processing and it was found that the application of colour thresholding and de-speckling prior to classification by a random forest classifier trained to identify patches of YFI using spectral and textural features provided the best results. Second, orthomosaicks generated from multispectral imagery were used to detect and predict the relative abundance of spotted knapweed (Centaurea maculosa) in a heterogeneous grassland ecosystem. Relative abundance was categorized in qualitative classes and validated through field-based plant species inventories. The method developed for this work, termed metapixel-based image analysis, segments orthomosaicks into a grid of metapixels for which grey-level co-occurrence matrix (GLCM)-based statistics can be computed as descriptive features. Using RPAS-acquired multispectral imagery and plant species inventories performed on 1m2 quadrats, a random forest classifier was trained to predict the qualitative degree of spotted knapweed ground-cover within each metapixel. Analysis of the performance of metapixel-based image analysis in this study suggests that feature optimization and the use of GLCM-based texture features are of critical importance for achieving an accurate classification. Additional work to further test the generalizability of the detection methods developed is recommended prior to deployment across multiple sites.remote sensingremotely piloted aircraft systemsRPASinvasive plant speciesmachine learnin

Drone-Based Identification and Monitoring of Two Invasive Alien Plant Species in Open Sand Grasslands by Six RGB Vegetation Indices

Today, invasive alien species cause serious trouble for biodiversity and ecosystem services, which are essential for human survival. In order to effectively manage invasive species, it is important to know their current distribution and the dynamics of their spread. Unmanned aerial vehicle (UAV) monitoring is one of the best tools for gathering this information from large areas. Vegetation indices for multispectral camera images are often used for this, but RGB colour-based vegetation indices can provide a simpler and less expensive solution. The goal was to examine whether six RGB indices are suitable for identifying invasive plant species in the QGIS environment on UAV images. To examine this, we determined the shoot area and number of common milkweed (Asclepias syriaca) and the inflorescence area and number of blanket flowers (Gaillardia pulchella) as two typical invasive species in open sandy grasslands. According to the results, the cover area of common milkweed was best identified with the TGI and SSI indices. The producers’ accuracy was 76.38% (TGI) and 67.02% (SSI), while the user’s accuracy was 75.42% (TGI) and 75.12% (SSI), respectively. For the cover area of blanket flower, the IF index proved to be the most suitable index. In spite of this, it gave a low producer’s accuracy of 43.74% and user’s accuracy of 51.4%. The used methods were not suitable for the determination of milkweed shoot and the blanket flower inflorescence number, due to significant overestimation. With the methods presented here, the data of large populations of invasive species can be processed in a simple, fast, and cost-effective manner, which can ensure the precise planning of treatments for nature conservation practitioners