Monitoring the Invasion of Spartina alterniflora

Spartina alterniflora was introduced to Beihai, Guangxi (China), for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread of Spartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR) imagery from the unmanned aerial vehicle (UAV). The invasion area of Spartina alterniflora was 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection of Spartina alterniflora invasion. OBIA, object based image analysis for remote sensing (RS) detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population

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

The identification and remote detection of alien invasive plants in commercial forests: An Overview

Invasive alien plants are responsible for extensive economic and ecological damage in forest plantations. They have the ability to aggressively manipulate essential ecosystem structural and functional processes. Alterations in these processes can have detrimental effects on the growth and productivity of forest species and ultimately impact on the quality and quantity of forest wood material. Using direct sampling field-based methods or visual estimations have generally expressed moderate success owing to the logistical and timely impracticalities. Alternatively, remote sensing techniques offer a synoptic rapid approach for detecting and mapping weeds affecting plantation forest environments. This paper reviews remote sensing techniques that have been used in detecting the occurrence of weeds and the implications for detecting S. mauritianum (bugweed); one of the most notorious alien plant invaders to affect southern Africa. Gaining early control of these alien plant invasions would reduce the impacts that may permanently alter our forested ecosystems, contributing to its successful eradication and promoting sustainable forest management practices. Furthermore, the review highlights the difficulties and opportunities that are associated with weed identification using remote sensing and future directions of research are also proposed

Utility of Satellite and Aerial Images for Quantification of Canopy Cover and Infilling Rates of the Invasive Woody Species Honey Mesquite (Prosopis Glandulosa) on Rangeland

Woody plant encroachment into grasslands and rangelands is a world-wide phenomenon but detailed descriptions of changes in geographical distribution and infilling rates have not been well documented at large land scales. Remote sensing with either aerial or satellite images may provide a rapid means for accomplishing this task. Our objective was to compare the accuracy and utility of two types of images with contrasting spatial resolutions (1-m aerial and 30-m satellite) for classifying woody and herbaceous canopy cover and determining woody infilling rates in a large area of rangeland (800 km<sup>2</sup>) in north Texas that has been invaded by honey mesquite (<em>Prosopis glandulosa</em>). Accuracy assessment revealed that the overall accuracies for the classification of four land cover types (mesquite, grass, bare ground and other) were 94 and 87% with kappa coefficients of 0.89 and 0.77 for the 1-m and 30-m images, respectively. Over the entire area, the 30-m image over-estimated mesquite canopy cover by 9 percentage units (10 <em>vs.</em> 19%) and underestimated grass canopy cover by the same amount when compared to the 1-m image. The 30-m resolution image typically overestimated mesquite canopy cover within 225 4-ha sub-cells that contained a range of mesquite covers (1–70%) when compared to the 1-m image classification and was not suitable for quantifying infilling rates of this native invasive species. Documenting woody and non-woody canopy cover on large land areas is important for developing integrated, regional-scale management strategies for rangeland and grassland regions that have been invaded by woody plants

Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae) in Wales (UK)

<p>Japanese Knotweed <i>s.l. </i>taxa are amongst the most aggressive vascular plant Invasive Alien Species (IAS) in the world. These taxa form dense, suppressive monocultures and are persistent, pervasive invaders throughout the more economically developed countries (MEDCs) of the world. The current paper utilises the Object-Based Image Analysis (OBIA) approach of Definiens Imaging Developer software, in combination with very high spatial resolution (VHSR) colour infra-red (CIR) and visible-band (RGB) aerial photography in order to detect Japanese Knotweed <i>s.l. </i>taxa in Wales (UK). An algorithm was created using Definiens in order to detect these taxa, using variables found to effectively distinguish them from landscape and vegetation features. The results of the detection algorithm were accurate, as confirmed by field validation and desk-based studies. Further, these results may be incorporated into Geographical Information Systems (GIS) research as they are readily transferable as vector polygons (shapefiles). The successful detection results developed within the Definiens software should enable greater management and control efficacy. Further to this, the basic principles of the detection process could enable detection of these taxa worldwide, given the (relatively) limited technical requirements necessary to conduct further analyses. </p

Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae) in Wales (UK)

Japanese Knotweed s.l. taxa are amongst the most aggressive vascular plant Invasive Alien Species (IAS) in the world. These taxa form dense, suppressive monocultures and are persistent, pervasive invaders throughout the more economically developed countries (MEDCs) of the world. The current paper utilises the Object-Based Image Analysis (OBIA) approach of Definiens Imaging Developer software, in combination with very high spatial resolution (VHSR) colour infra-red (CIR) and visible‑band (RGB) aerial photography in order to detect Japanese Knotweed s.l. taxa in Wales (UK). An algorithm was created using Definiens in order to detect these taxa, using variables found to effectively distinguish them from landscape and vegetation features. The results of the detection algorithm were accurate, as confirmed by field validation and desk‑based studies. Further, these results may be incorporated into Geographical Information Systems (GIS) research as they are readily transferable as vector polygons (shapefiles). The successful detection results developed within the Definiens software should enable greater management and control efficacy. Further to this, the basic principles of the detection process could enable detection of these taxa worldwide, given the (relatively) limited technical requirements necessary to conduct further analyses

Object oriented classification for tree species identification and detection of japanese knotweed

In graduation thesis the use of object-oriented classification and Kolmogorov-Smirnov test for tree species and Japanese knotweed classification is discussed in the study area of Ljubljana based on aerial and high resolution WorldView-2 satellite imagery. Kolmogorov-Smirnov test is a non-parametric statistical test that evaluates the similarity between two sample sets by comparing their empirical cumulative distribution function. When using empirical cumulative distribution functions all pixels inside the segment are being accounted for instead of just one or two representative values which are likely to be affected by extreme values. A segment represents the homogenous region of pixels with similar functional characteristics. \ud For tree species classification digital canopy model was used along with the high resolution imagery. \ud Trees in urban areas and invasive alien species should be understood as exceptionally heterogeneous and complex spatial phenomena. Vulnerability analysis, benefit estimation and systematic management strategies can therefore only base on qualitative and task-oriented data about spatial distribution and extent, species and condition of the object being considered. \ud Methodology described in this graduation thesis is a promising tool for more effective tree species classification and detection of Japanese knotweed. \u