Automated proximal sensing for estimation of the bidirectional reflectance distribution function in a Mediterranean tree-grass ecosystem

Premio Extraordinario de Doctorado de la UAH en el año académico 2015-2016Los sistemas automáticos de proximal sensing permiten adquirir información espectral de las cubiertas terrestres elevada frecuencia temporal, que puede relacionarse con observaciones remotas o de otros tipos de sensores como los sistemas de eddy covariance. Si bien inicialmente los sistemas automáticos empleaban sensores multi-banda, en los últimos años se ha incrementado el uso de sensores hiperespectrales. Si bien estos sensores ofrecen información redundante y de alta resolución espectral, las mediciones están sujetas a múltiples fuentes de incertidumbre; tanto instrumentales (dependencias de la temperatura o el nivel de señal) como direccionales (dependencia de la geometría de observación e iluminación). Las dependencias instrumentales pueden ser minimizadas, por ejemplo, controlando la temperatura del instrumento o el nivel de señal registrado. En otros casos, es necesario parametrizar y emplear modelos para corregir los datos. En la presente tesis doctoral los capítulos 1 al 3 presentan la caracterización completa de un espectrómetro de campo instalado en un sistema automático. Los capítulos 1 y 2 analizan las fuentes de no linealidad en este instrumento, una de las cuales no había sido anteriormente descrita en este tipo de instrumentos. El tercer capítulo muestra el conjunto completo de modelos de corrección de los efectos instrumentales y la cadena de procesado correspondiente. Por otro lado, los sistemas automáticos se enfrentan a efectos direccionales ya que adquieren mediciones continuamente durante el ciclo solar diario y bajo cualquier condición de iluminación. Esto maximiza los rangos de los ángulos de iluminación y también de la fracción difusa de la irradiancia. Esta variabilidad de condiciones de iluminación, combinada con una variación de los ángulos de observación permite obtener la información necesaria para caracterizar las respuestas direccionales de la cubierta observada. Algunos sistemas automáticos multi-angulares ya han sido empleados para realizar esta caracterización mediante la estimación de la Función de Distribución de Reflectividad Bidireccional (BRDF) en ecosistemas homogéneos. Sin embargo, esto no se ha conseguido aún en áreas heterogéneas, como es el caso de los ecosistemas tree-grass o de sabana. Así mismo, los trabajos previos no han considerado los efectos de la radiación difusa en el estudio del BRDF. En el capítulo 4 proponemos una metodología que permite desmezclar y caracterizar simultáneamente la función de distribución de reflectividad hemisférica-direccional de las dos cubiertas de vegetación presentes en el ecosistema, pasto y arbolado. También se analizan los efectos de las diferentes características del método. Finalmente, los resultados se escalan y se comparan con productos globales de satélite como el producto BRDF de MODIS. La conclusión obtenida es que se requieren más esfuerzos en el desarrollo y caracterización de sensores hiperespectrales instalados en sistemas automáticos de campo. Estos sistemas deberían adoptar configuraciones multi-angulares de modo que puedan caracterizarse las respuestas direccionales. Para ello, será necesario considerar los efectos de la radiación difusa; y en algunos casos también la heterogeneidad de la escena

Automated proximal sensing for estimation of the bidirectional reflectance distribution function in a Mediterranean tree-grass ecosystem

Premio Extraordinario de Doctorado de la UAH en el año académico 2015-2016Los sistemas automáticos de proximal sensing permiten adquirir información espectral de las cubiertas terrestres elevada frecuencia temporal, que puede relacionarse con observaciones remotas o de otros tipos de sensores como los sistemas de eddy covariance. Si bien inicialmente los sistemas automáticos empleaban sensores multi-banda, en los últimos años se ha incrementado el uso de sensores hiperespectrales. Si bien estos sensores ofrecen información redundante y de alta resolución espectral, las mediciones están sujetas a múltiples fuentes de incertidumbre; tanto instrumentales (dependencias de la temperatura o el nivel de señal) como direccionales (dependencia de la geometría de observación e iluminación). Las dependencias instrumentales pueden ser minimizadas, por ejemplo, controlando la temperatura del instrumento o el nivel de señal registrado. En otros casos, es necesario parametrizar y emplear modelos para corregir los datos. En la presente tesis doctoral los capítulos 1 al 3 presentan la caracterización completa de un espectrómetro de campo instalado en un sistema automático. Los capítulos 1 y 2 analizan las fuentes de no linealidad en este instrumento, una de las cuales no había sido anteriormente descrita en este tipo de instrumentos. El tercer capítulo muestra el conjunto completo de modelos de corrección de los efectos instrumentales y la cadena de procesado correspondiente. Por otro lado, los sistemas automáticos se enfrentan a efectos direccionales ya que adquieren mediciones continuamente durante el ciclo solar diario y bajo cualquier condición de iluminación. Esto maximiza los rangos de los ángulos de iluminación y también de la fracción difusa de la irradiancia. Esta variabilidad de condiciones de iluminación, combinada con una variación de los ángulos de observación permite obtener la información necesaria para caracterizar las respuestas direccionales de la cubierta observada. Algunos sistemas automáticos multi-angulares ya han sido empleados para realizar esta caracterización mediante la estimación de la Función de Distribución de Reflectividad Bidireccional (BRDF) en ecosistemas homogéneos. Sin embargo, esto no se ha conseguido aún en áreas heterogéneas, como es el caso de los ecosistemas tree-grass o de sabana. Así mismo, los trabajos previos no han considerado los efectos de la radiación difusa en el estudio del BRDF. En el capítulo 4 proponemos una metodología que permite desmezclar y caracterizar simultáneamente la función de distribución de reflectividad hemisférica-direccional de las dos cubiertas de vegetación presentes en el ecosistema, pasto y arbolado. También se analizan los efectos de las diferentes características del método. Finalmente, los resultados se escalan y se comparan con productos globales de satélite como el producto BRDF de MODIS. La conclusión obtenida es que se requieren más esfuerzos en el desarrollo y caracterización de sensores hiperespectrales instalados en sistemas automáticos de campo. Estos sistemas deberían adoptar configuraciones multi-angulares de modo que puedan caracterizarse las respuestas direccionales. Para ello, será necesario considerar los efectos de la radiación difusa; y en algunos casos también la heterogeneidad de la escena

Etude des interactions feuille/lumière et de leurs implications pour le phénotypage haut débit au champ

NcCe travail de thèse s’inscrit dans le cadre des problématiques liées au phénotypage haut débit des plantes au champ. L’objectif est de réaliser des mesures non destructives à distance, rapides et pertinentes pour caractériser le peuplement végétal ainsi que les organes (feuilles, tiges, épis) qui le constituent. Ce travail de thèse s’articule en deux axes : Un axe plutôt opérationnel où les méthodes existantes, généralement issues de la communauté de télédétection peuvent être adaptées à cette nouvelle problématique. A l’échelle de l’organe (la feuille) l’inversion du modèle de transfert radiatif PROSPECT est testée pour discriminer les variétés de blé. Nous montrons que cette méthode permet d’obtenir une meilleure discrimination par rapport à celle obtenue à partir des mesures destructives classiques. A l’échelle du peuplement végétal, un système semi-automatique permettant de mesurer un grand nombre de micro-parcelles expérimentales est présenté. Les mesures acquises décrivent la dynamique de la fraction de vert, du FIPAR ainsi que d’indices sensible au LAI et à la chlorophylle au cours du cycle du blé. Cette première partie s’achève sur une discussion des améliorations nécessaires pour optimiser la chaîne d’acquisition et d’interprétation des mesures pour le phénotypage haut débit au champ. Le second axe, plus académique, se concentre sur l’amélioration de la connaissance des propriétés directionnelles des feuilles de blé. Un instrument, le conosocope, est utilisé pour mesurer le facteur de réflectance directionnelle sur un grand nombre de feuilles et de directions. On montre que le système vasculaire parallèle des feuilles de blé crée une anisotropie azimutale peu décrite auparavant dans la littérature. Ce phénomène est modélisé en adaptant un modèle physique de micro-facettes caractérisée par une distribution azimutale anisotrope. La rugosité perpendiculaire au système vasculaire apparaît être deux fois supérieure à celle observée parallèlement. Finalement ce modèle est couplé à un modèle de lancer de rayon pour évaluer l’effet de l’anisotrope des propriétés de la feuille sur celles observées au niveau du couvert. Cette avancée dans la compréhension des processus du transfert radiatif au niveau de la feuille ouvre la voie à une interprétation plus fine des mesures radiométriques réalisées à différentes échelles et en particulier pour le phénotypage haut-débit

Multiscale remote sensing of plant physiology and carbon uptake

This study investigated the use of optical remote sensing for estimating leaf and canopy scale light use efficiency (LUE) and carbon exchange. In addition, a new leaf level model capable of predicting dynamic changes in apparent reflectance due to chlorophyll fluorescence was developed. A leaf level study was conducted to assess the applicability of passive remote sensing as a tool to measure the reduction, and the subsequent recovery, of photosynthetic efficiency during the weeks following transplantation. Spectral data were collected on newly planted saplings for a period of 8 weeks, as well as gas exchange measurements of LUE and PAM fluorescence measurements. A set of spectral indices, including the Photochemical Reflectance Index (PRI), were calculated from the reflectance measurements. A marked depression in photosynthetic rate occurred in the weeks after outplanting followed by a gradual increase, with recovery occurring in the later stages of the experimental period. As with photosynthetic rate, there was a marked trend in PRI values over the study period but no trend was observed in chlorophyll based indices. The study demonstrated that hyperspectral remote sensing has the potential to be a useful tool in the detection and monitoring of the dynamic effects of transplant shock. Relationships between hyperspectral reflectance indices, airborne carbon exchange measurements and satellite observations of ground cover were then explored across a heterogeneous Arctic landscape. Measurements were collected during August 2008, using the University of Edinburgh’s research aircraft, from an Arctic forest tundra zone in northern Finland as part of the Arctic Biosphere Atmosphere Coupling at Multiple Scales (ABACUS) study. Surface fluxes of CO2 were calculated using the eddy covariance method from airborne data that were collected from the same platform as hyperspectral reflectance measurements. Airborne CO2 fluxes were compared to MODIS vegetation indices. In addition, LUE was estimated from airborne flux data and compared to airborne measurements of PRI. There were no significant relationships between MODIS vegetation indices and airborne flux observations. There were weak to moderate (R2 = 0.4 in both cases) correlations between PRI and LUE and between PRI and incident radiation. A new coupled physiological radiative transfer model that predicts changes in the apparent reflectance of a leaf, due to chlorophyll fluorescence, was developed. The model relates a physically observable quantity, chlorophyll fluorescence, to the sub leaf level processes that cause the emission. An understanding of the dynamics of the processes that control fluorescence emission on multiple timescales should aid in the interpretation of this complex signal. A Markov Chain Monte Carlo (MCMC) algorithm was used to optimise biochemical model parameters by fitting model simulations of transient chlorophyll fluorescence to measured reflectance spectra. The model was then validated against an independent data set. The model was developed as a precursor to a full canopy scheme. To scale to the canopy and to use the model on trans-seasonal time scales, the effects of temperature and photoinhibition on the model biochemistry needs to be taken into account, and a full canopy radiative transfer scheme, such as FluorMOD, must be developed

Development of atmospheric correction algorithms for very high spectral and spatial resolution images: application to SEOSAT and the FLEX/Sentinel-3 missions

Advanced high spectral and spatial resolution imager spectrometers on board new generation of Earth Observation missions bring new exciting opportunities to the remote sensing scientific community. However, this progress goes hand in hand with new challenges. The exploitation of data acquired from these family of advanced instruments requires new processing algorithms able to deal with these particularities. As part of this evolution, atmospheric correction algorithms - a mandatory processing step applied prior to the Earth surface reflectance data exploitation - must be adapted or reformulated, thereby paying special attention to how atmospheric effects disturb the acquired signal in the spectral and spatial domains. For these reasons, this Thesis aims to develop new atmospheric correction strategies to be applied over very high spectral and spatial resolution data. Following this goal, this Thesis was conducted in the framework of two missions during their development phase: (1) the FLEX/Sentinel–3 tandem space mission (for high spectral resolution data) and, (2) the Ingenio/SEOsat space mission (for high spatial resolution data). In the context of these missions, an additional challenge is introduced when acquiring proximal remote sensing data for their validation. This is especially relevant for the FLEX mission, which is dedicated to monitor the weak Solar Induced Chlorophyll Fluorescence (SIF) signal. Following this motivation, the main objectives of this Thesis are threefold: The first objective involved to analyse atmospheric effects on the Ingenio/SEOsat high spatial and low spectral resolution satellite mission and to propose a new atmospheric correction strategy. This strategy was called Hybrid and combines: (1) a per–pixel atmospheric radiative transfer model inversion technique making use of auxiliary data to characterize the atmospheric state, followed by (2) an image deconvolution technique modelling the atmospheric MTF to correct for atmospheric spatial effects. The Hybrid method was applied to Sentinel–2 data, particularly over bands acquired at 10 m resolution due to its similarities with the Ingenio/SEOsat mission. The second objective involved to define a novel atmospheric correction strategy for the FLEX/Sentinel-3 tandem mission. The proposed strategy is a two-steps method where information from Sentinel-3 instruments, OLCI and SLSTR, is first used in synergy to characterize the aerosol and water vapour presence. The high spectral resolution of FLEX data is subsequently exploited to refine the previously aerosol characterization. As part of this objective, the suitability of all the approximations assumed in the formulation proposed for the atmospheric inversion of FLEX data was validated against the FLEX mission requirements. The third objective involved to develop a strategy that deals with the atmospheric correction of very high spectral and spatial resolution data acquired at lower atmospheric scales such as Unmanned Aerial Vehicles or systems mounted on towers. In this Thesis, it was demonstrated that even when acquiring the signal at proximal remote sensing scale, i.e., few meters from the target oxygen absorption must be compensated to properly estimate SIF within these spectral regions. For this reason, a strategy to compensate for the oxygen absorption while properly dealing with the instrumental spectral response function convolution was presented and tested using simulated data. Altogether, this work identified challenges associated to atmospheric correction when applying to high spatial and especially to very high spectral resolution data. In this Thesis, adequate formulations have been developed to resolve these difficulties, and successful methodologies have been designed for the particular cases of SEOsat (high spatial resolution) and FLEX (high spectral resolution); two future remote sensing space missions that will be launched in the forthcoming years

Optické vlastnosti listu ve vztahu k anatomickým vlastnostem listu

K předpovědi reakcí ekosystémů na faktory prostředí se běžně používají funkční znaky rostlin na úrovni listu, popisující projevy globálních změn klimatu na úrovni ekosystémů. Mezi funkční znaky rostlin řadíme jak biofyzikální vlastnosti listu (např. obsah fotosyntetických pigmentů a obsahu vody) tak jeho strukturní vlastnosti (např. tloušťka listu a poměr fotosyntetických a nefotosyntetických pletiv listu). Biofyzikální a strukturní vlastnosti listu je možné zjišťovat buď destruktivně v laboratoři, nebo nedestruktivně s využitím optických vlastností listu. Ačkoli je odhadování obsahu chlorofylu na základě optických vlastností listů dobře zavedenou metodou, vliv struktury a vnitřní anatomie listů na jejich optické vlastnosti je důkladně studován teprve v posledních dvou dekádách. Publikace zahrnuté v mé práci a většina práce je věnována evropským opadavým dřevinám, typickým pro temperátní a hemiboreální lesy s listy vykazujícími podobnou dorziventrální strukturu, (tj. mezofyl je diferencován na palisádový a houbovitý parenchym). Dále má disertační práce zahrnuje studii vlivu strukturních znaků povrchu listů dvou skupin bylin na jejich optické vlastnosti. V této studii byly použity dvě skupiny fylogeneticky blízkých bylin se srovnatelnou vnitřní strukturou listů (mutanty Arabidopsis thaliana L. a...Plant functional traits at the leaf level are commonly used to predict ecosystem responses to environmental factors and describe global climate change processes at the ecosystem level. Plant functional traits include both leaf biophysical traits (e.g., photosynthetic pigment content and water content) and structural traits (e.g., leaf thickness and proportion of photosynthetic and non-photosynthetic tissues). Leaf biophysical and structural traits can be detected either destructively in the laboratory or non-destructively using leaf optical properties. Although estimating chlorophyll content from leaf optical properties is a well-established methodology, the influence of leaf structure and internal anatomy on leaf optical properties has only been thoroughly studied in the last two decades. The papers included in my thesis and my thesis itself are mostly focused on the study of typical European deciduous trees of temperate and hemiboreal forests with leaves having a dorsiventral structure (i.e., the mesophyll is differentiated into palisade and spongy parenchyma). Furthermore, my thesis includes a study on the effect of leaf surface structural traits on optical properties. In this study, two groups of phylogenetically close herbs with comparable internal leaf structure were used (mutants of...Department of Experimental Plant BiologyKatedra experimentální biologie rostlinFaculty of SciencePřírodovědecká fakult

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