Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

LudVision Remote Detection of Exotic Invasive Aquatic Floral Species using Data from a DroneMounted Multispectral Sensor

Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring it’s reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. There have been ever­growing reports of invasive species affecting the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can have negative impacts on the surrounding ecosystem and human activities such as agriculture, fishing, and navigation. Our goal was to develop a method to identify the presence of the species. To achieve this, we used images collected by a drone­mounted multispectral sensor. Due to the lack of publicly available data sets containing Ludwigia peploides, we had to create our own data set. We started by carefully studying all the available options. We first experimented with satellite images, but it was impossible to identify the targeted species due to their low resolution. Thus, we decided to use a drone­mounted multispectral sensor. Unfortunately, due to budget limitations, we could not acquire the highly specialized types of equipment that is more commonly used in remote sensing. However, we were confident that our setup would be enough to extract the species’ spectral signature, and that the higher resolution compared to satellites would allow us to use deep learning models to identify the species. The use of the drone allowed for better operational flexibility and to cover a large area. The multispectral sensor allowed us to leverage the information of two additional bands outside the visible spectrum. After visiting the study site multiple times and capturing data at various times of the day, we created a representative data set with different atmospheric conditions. After the data collection, we proceeded to the pre­processing and annotation steps to have a usable data set. In later stages, we proved that extracting the specie’s spectral signature from our data set is possible. This was a significant conclusion, as it proved that it is indeed possible to differentiate the species’ spectral signature with equipment that is not as advanced and specialized as the ones used in other studies. After having a data set, we focused on the next step, which was to develop and validate a method that would be able to identify Ludwigia p on our data. We decided on using semantic segmentation models to identify the species. Given that we only have two additional bands compared to traditional RGB images, we could not approach the problem as a standard remote sensing spectroscopy problem. By using semantic segmentation models, we can leverage both the capabilities of these models to recognize objects and the multispectral nature of our data. Fundamentally, the model has the same behavior as usual but has access to the information of two additional bands.We started by using an existing state­of­the­art semantic segmentation model adapted to handle our data. After doing some initial tests and establishing a baseline, we proposed and implemented some changes to the existing model. The goal of the modifications was to create a model with lower training times and better performance in detecting Ludwigia p. at high altitudes. The result is a new model better suited to our data and application. Our model is faster when it comes to training time while maintaining similar performance and has a slight performance increase in high­altitude images.O sensoriamento remoto é o processo de detetar e monitorizar as características físicas de uma área, medindo à distância a sua radiação refletida e emitida. É amplamente utilizado para monitorizar ecossistemas, principalmente tendo em vista a sua preservação. Há cada vez mais casos de espécies invasoras que afetam o equilíbrio natural dos ecossistemas. As espécies exóticas invasoras têm um impacto crítico quando introduzidas em novos ecossistemas e podem levar à extinção de espécies nativas. Neste estudo, focamo­nos na Ludwigia peploides, considerada pela União Europeia como uma espécie aquática invasora. A sua presença pode ter impactos negativos no ecossistema circundante e nas atividades humanas, como agricultura, pesca e navegação. O nosso objetivo foi desenvolver um método para identificar a presença da espécie. Para isso, usámos imagens capturadas por um sensor multiespectral montado num drone. Devido à falta de conjuntos de dados disponíveis publicamente contendo Ludwigia peploides, tivemos que criar nosso próprio conjunto de dados. Começámos por cuidadosamente estudar todas as opções disponíveis. Primeiro fizemos experiências com imagens de satélite, mas foi impossível identificar a espécie­alvo devido à baixa resolução das imagens. Assim, decidimos usar um sensor multiespectral montado num drone. Infelizmente, devido a limitações orçamentais, não conseguimos adquirir os tipos de equipamentos altamente especializados que são tipicamente usados em sensoriamento remoto. No entanto, estávamos confiantes de que nossa configuração seria suficiente para extrair a assinatura espectral da espécie, e que a alta resolução das nossas imagens comparadas com de satélite, nos permitiria usar modelos de aprendizagem profunda para identificar as espécies. O uso do drone permitiu uma maior flexibilidade operacional e cobertura de uma grande área. O sensor multiespectral permitiu­nos alavancar as informações de duas bandas adicionais fora do espectro visível. Depois de visitar o local de estudo várias vezes e capturar dados em vários momentos do dia, criámos um conjunto de dados representativo com diferentes condições atmosféricas. Após a captura de dados, procedeu­se às etapas de pré­processamento e anotação para ter um conjunto de dados utilizável. Em etapas posteriores, provámos que é possível extrair dos nossos dados a assinatura espectral da espécie. Esta foi uma conclusão significativa, pois comprovou que de fato é possível diferenciar a assinatura espectral da espécie com equipamentos não tão avançados e especializados quanto os utilizados noutros estudos. Depois de termos um conjunto de dados, focamo­nos no próximo passo, que foi desenvolver e validar um método que fosse capaz de identificar Ludwigia p. nos nossos dados. Decidimos usar modelos de segmentação semântica para identificar as espécies. Dado que temos apenas duas bandas adicionais em comparação com as imagens RGB tradicionais, não poderíamos abordar o problema como um problema de espectroscopia de sensoriamento remoto padrão. Ao usar modelos de segmentação semântica, podemos aproveitar não só os recursos desses modelos para reconhecer objetos, mas também a natureza multiespectral de nossos dados. Fundamentalmente, o modelo tem o mesmo comportamento usual, mas tem acesso às informações de duas bandas adicionais. Começamos por usar um modelo de segmentação semântica estado­da­arte existente, que foi adaptado para lidar com nossos dados. Depois de fazer alguns testes iniciais e estabelecer uma base de comparação, propusemos e implementámos algumas modificações ao modelo existente. O objetivo das modificações foi criar um modelo com menores tempos de treino e melhor desempenho na deteção de Ludwigia p. em altitudes elevadas. O resultado é um novo modelo mais adequado aos nossos dados e aplicação. O nosso modelo é mais rápido no que diz respeito ao tempo de treino, mantendo desempenho semelhante, apresentando mesmo um ligeiro aumento de desempenho em imagens de alta altitude

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Living up to the hype of hyperspectral aquatic remote sensing: science, resources and outlook

Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities

Summaries of the Sixth Annual JPL Airborne Earth Science Workshop

This publication contains the summaries for the Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996. The main workshop is divided into two smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on March 4-6. The summaries for this workshop appear in Volume 1; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on March 6-8. The summaries for this workshop appear in Volume 2

Foliar spectra accurately distinguish the invasive common reed from co-occurring plant species throughout a growing season

Les espèces végétales envahissantes sont l'un des principaux facteurs de changement de la biodiversité dans les écosystèmes terrestres. Une détection précise et précoce des espèces exotiques est donc cruciale pour surveiller les invasions en cours et pour prévenir leur propagation. Présentement, les méthodes de surveillance des invasions biologiques permettent de suivre la propagation des envahisseurs à travers les aires de répartition géographique, mais une attention moindre a été accordée à la surveillance des espèces envahissantes à travers le temps. Les plates-formes de télédétection, capables de fournir des informations détaillées sur les variations des traits foliaires dans le temps et l'espace, sont particulièrement bien placées pour surveiller les plantes envahissantes en temps réel. Les changements temporels des traits fonctionnels sont exprimés dans la signature spectrale des espèces par des caractéristiques d'absorption spécifiques de la lumière associés aux pigments photosynthétiques et aux constituants chimiques tous deux liés à la phénologie. Ainsi, les variations temporelles dans la réponse spectrale des plantes peuvent être utilisées afin de mieux identifier des espèces individuelles. L'un des envahisseurs les plus problématiques au Canada est le roseau commun, Phragmites australis (Cav.) Trin. ex Steudel sous-espèce australis, dont la propagation menace la biodiversité des écosystèmes de zones humides en Amérique du Nord. Déterminer la période de l'année où cet envahisseur se distingue d’avantage, du point de vue spectral et fonctionnel, des autres plantes de la communauté serait centrale dans une meilleure gestion du roseau commun. Pour ce faire, nous avons utilisé des traits fonctionnels et une série temporelle de données spectrales foliaires à haute résolution au cours d'une saison de croissance à Boucherville, Québec, Canada, afin de déterminer la séparabilité spectrale de l'envahisseur par rapport aux espèces co-occurrentes et comment cette dernière varie à travers le temps. Nos résultats ont révélé que la spectroscopie foliaire a permis de distinguer le phragmite des espèces co-occurrentes avec une précision de plus de 95% tout au long de la saison de croissance – un résultat prometteur pour le futur de la télédétection des espèces végétales envahissantes.Invasive plant species are one of the main drivers of biodiversity change in terrestrial ecosystems. Accurate detection of exotic species is critical to monitor on-going invasions and early detection of incipient invasions is necessary to prevent further spread. At present, surveillance methods of biological invasions allow to track the spread of invaders across geographic ranges, but less attention has been given to invasive species monitoring across time. Remote sensing platforms, capable of providing detailed information on foliar trait variations across time and space, are uniquely positioned for monitoring invasive plants in real time. Temporal changes in foliar traits are expressed in a species spectral profile through specific absorption features related to variation in photosynthetic pigments and chemical constituents driven by phenology. Thus, variations in a plant’s spectral response can be used to improve the identification of individual species. One of Canada’s most problematic invaders is the common reed, Phragmites australis (Cav.) Trin. ex Steudel subspecies australis, whose spread threatens biodiversity in wetland ecosystems in North America. Determining the time of year when the invader is spectrally and functionally more distinct from other plants in the community would be central to better management of common reed. To do so, we collected a time-series of foliar traits and high-resolution leaf spectral data over the course of a growing season at Boucherville, Quebec, Canada, to determine the spectral separability of the invader from co-occurring species and how its detection varies over time. Our results revealed that leaf-level spectroscopy distinguished Phragmites and co-occurring species with > 95% accuracy throughout the growing season – a promising result for the future remote detection of invasive plant species

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC0000