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

Remote Sensing of Riparian Areas and Invasive Species

Riparian areas are critical landscape features situated between terrestrial and aquatic environments, which provide a host of ecosystem functions and services. Although important to the environmental health of an ecosystem, riparian areas have been degraded by anthropogenic disturbances. These routine disturbances have decreased the resiliency of riparian areas and increased their vulnerability to invasive plant species. Invasive plant species are non-native species which cause harm to the ecosystem and thrive in riparian areas due to the access to optimal growing conditions.Remote sensing provides an opportunity to manage riparian habitats at a regional and local level with imagery collected by satellites and unmanned aerial systems (UAS). The aim of this study was two-fold: firstly, to investigate riparian delineation methods using moderate resolution satellite imagery; and secondly, the feasibility of UAS to detect the invasive plant Fallopia japonica (Japanese Knotweed) within the defined areas. I gathered imagery from the Landsat 8 OLI and Sentinel-2 satellites to complete the regional level study and collected UAS imagery at a study site in northern New Hampshire for the local level portion. I obtained a modest overall accuracy from the regional riparian classification of 59% using the Sentinel-2 imagery. The local invasive species classification yielded thematic maps with overall accuracies of up to 70%, which is comparable to other studies with the same focus species. Remote sensing is a valuable tool in the management of riparian habitat and invasive plant species

Utilizzo delle texture nella classificazione di vegetazione in immagini ad altissima risoluzione acquisite da UAS

Nell'ambito di un progetto di ricerca di Dottorato in Geomatica, relativo alla messa a punto di un sistema di rilievo multisensore a pilotaggio remoto, sono state analizzate immagini multispettrali e multitemporali acquisite tramite rilievi da UAS. Come area test, è stata scelta una porzione di un vivaio di piante: l'altissima risoluzione delle immagini rende visibili dettagli che possono consentire il riconoscimento delle diverse specie vegetali. Per automatizzare il processo, accanto alle più tradizionali classificazioni pixel-based (basate cioè sulle informazioni radiometriche multispettrali direttamente contenute nelle immagini), si può far ricorso all'utilizzo di variabili derivate di tipo geometrico, che tengano conto delle relazioni spaziali delle variazioni radiometriche. L'uso delle variabili di texture a queste risoluzioni, ben differenti da quelle di immagini satellitari o aeree, è ancora da esplorare. In questo lavoro sono perciò presentati alcuni esperimenti di ottimizzazione di procedure di classificazione, mediante l'uso combinato di variabili radiometriche tradizionali (come RGB e NDVI) e di variabili di texture opportunamente selezionate. In particolare, tali variabili sono state generate con finestre di dimensione crescente; la dimensione ottimale è stata poi individuata tramite analisi dei semivariogrammi. L'aggiunta delle variabili di texture a bande e indici tradizionali ha portato ad un incremento del 17% dell'accuratezza totale di classificazioni con algoritmo supervisionato (Maximum Likelihood)

Riconoscimento di specie arboree mediante classificazione di immagini multispettrali e multitemporali ad altissima risoluzione

Da tempo impiegati nell'agricoltura di precisione, i rilievi di prossimità con UAS cominciano a esser eseguiti anche per applicazioni forestali e ambientali. Il rilievo da UAV con sensori ottici, infatti, consente di acquisire immagini ad altissima risoluzione (GSD dell'ordine di cm), che possono essere opportunamente usate per la classificazione e la distinzione di specie vegetali. Nell'ambito di un progetto di ricerca di Dottorato in Geomatica, è stato effettuato un esperimento di classificazione mediante algoritmi ampiamente utilizzati in telerilevamento, a partire da immagini aeree di prossimità acquisite su un vivaio di piante. Tale area è stata scelta come "poligono test" per la presenza di molte varietà di alberi, raggruppati per specie. Un rilievo multispettrale è stato effettuato durante il periodo di maggior copertura fogliare con esacottero Mikrokopter del Politecnico di Milano (Dip.di Ing. Civile e Ambientale) e camere Nikon 1 J1 e Tetracam ADCLite. I due blocchi di immagini, a colori e a falso colore, sono stati orientati simultaneamente; dal blocco RGB è stato poi ottenuto il modello di superficie e, su questo, sono state generate le due ortofoto (RGB e NIR-RG). Dai canali NIR e Red di quest'ultima, è stato infine calcolato l'indice di vegetazione NDVI. Il rilievo è stato ripetuto anche in autunno per rilevare nuove informazioni spettrali, molto variabili da una specie all'altra. L'ortofoto RGB autunnale è stata creata usando il medesimo DSM estivo. Sulla base della verità al suolo raccolta in campo, è stata effettuata una classificazione supervisionata con algoritmo Max Likelihood su un layer stack di sette bande. Sebbene la copertura fogliare autunnale fosse ridotta, tale classificazione è caratterizzata da un incremento dell'accuratezza totale del 13% rispetto a quella della classificazione effettuata sulle sole bande estive (RGB e NDVI). Il medesimo algoritmo è stato infine impiegato su differenti combinazioni di canali originali e derivati, tra cui un indice di variazione temporale e la trasformazione allo spazio IHS

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

Assessing the effect of band selection on accuracy of pansharpened imagery: application to young woody vegetation mapping

Expansion of woody vegetation has adverse effects on ecosystem services, and thus it is desirable to contain the problem at the early developmental stages. This can be aided by using high spatial resolution remotely-sensed data. The study investigated the effect of band selection during pansharpening on the ability to discriminate young woody vegetation from coexisting land cover types. Red-green-blue (RGB) spectral bands (30 m) of Landsat 8 imagery was pansharpened using the panchromatic band (15 m) of the same image to improve spatial resolution. Near-infrared (NIR), shortwave-infrared 1 (SWIR1) and shortwave-infrared 2 (SWIR2), bands were used respectively as the fourth spectral band during pansharpening, resulting in three pansharpened images. Unsupervised classification was performed on each pansharpened image as well as non-pansharpened multispectral image. The overall accuracies of classification derived from the pansharpened image was higher (87% − 89%) than that derived from the non-pansharpened multispectral image (83%). The study shows that band selection did not affect the classification accuracy of woody vegetation significantly. In addition, the study shows the potential of pansharpened Landsat data in detecting woody vegetation encroachment at the early growth stage.Keywords: Young woody vegetation, Landsat, pansharpening, unsupervised classificatio

Improving tree species classification using UAS multispectral images and texture measures

This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User's and Producer's Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities

Object-Based Image Classification of Summer Crop with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.This research was partly financed by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds, the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain) and the Kearney Foundation of Soil Science (USA). The research of Peña was co-financed by the Fulbright-MEC postdoctoral program, financed by the Spanish Ministry for Science and Innovation, and by the JAEDoc Program, supported by CSIC and FEDER funds. ASTER data were available to us through a NASA EOS scientific investigator affiliation.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe

Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution allows for identification of particular species. LiDAR (Light Detection and Ranging) data provide information about areas such as vegetation structure. Because the species differ in terms of features during the growing season, it is important to know when their spectral responses are unique in the background of the surrounding vegetation. The aim of the study was to identify two expansive grass species: Molinia caerulea and Calamagrostis epigejos in the Natura 2000 area in Poland depending on the period and dataset used. Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used. HySpex images were corrected geometrically and atmospherically before Minimum Noise Fraction (MNF) transformation and vegetation indices calculation. Based on a LiDAR point cloud generated Canopy Height Model, vegetation structure from discrete and full-waveform data and topographic indexes were generated. Classifications were performed using a Random Forest algorithm. The results show post-classification maps and their accuracies: Kappa value and F1 score being the harmonic mean of producer (PA) and user (UA) accuracy, calculated iteratively. Based on these accuracies and botanical knowledge, it was possible to assess the best identification date and dataset used for analysing both species. For M. caerulea the highest median Kappa was 0.85 (F1 = 0.89) in August and for C. epigejos 0.65 (F1 = 0.73) in September. For both species, adding discrete or full-waveform LiDAR data improved the results. We conclude that hyperspectral (HS) and LiDAR airborne data could be useful to id