Detecting Plant Functional Traits of Grassland Vegetation Using Spectral Reflectance Measurements

Changes in climate and an intensified agricultural use threaten grassland ecosystems in many places. To allow an efficient conservation of grassland vegetation communities, ecologists monitor variations in their plant functional traits (FTs). FTs are morphological, physiological or phenological properties of plants, which are measured at the individual plant level. However, manual measurements of FTs are costly as well as time-consuming and often require destructive sampling techniques. Grassland ecologists and agronomists are thus seeking for novel methods to monitor and map grassland FTs. Remote sensing (RS) may provide a solution to the mentioned problems and allows to collect spatially contiguous and multitemporal information on FTs. To test the performance of RS systems for detecting FTs, the Rengen Grassland Experiment in Germany was selected as study site. Due to more than 70 years of constant fertilization along a gradient from limed only to fully fertilized (treated with lime, nitrogen, phosphorus and potassium), five different plant communities have developed, which differ in their FTs. The spectral reflectance of these plant communities was collected for a period of three years using an ASD Field Spec 3 (FS3) spectroradiometer. Furthermore, 23 different FTs were measured using manual sampling methods. Firstly, it was investigated if and how the five grassland communities can be distinguished using 15 different remotely sensed vegetation indices (VIs). It was found that the performance of single VIs for differentiating the studied plant canopies fluctuates over time. Consequently, it was not possible to distinguish the communities with high accuracy throughout all phases of their phenological development using one VI. To solve this problem, a multi-VI approach using the random forests algorithm is proposed, which automatically selects the ideal sets of VIs for distinguishing grasslands. This technique allows a stable and accurate classification of grassland communities for the entire growing season. Secondly, it was studied how well the FTs of the different grassland communities can be estimated based on FS3 data. Using partial least squares regression (PLSR) it was possible to create one single model for estimating one FT of all studied grassland canopies at all phenological stages based on the spectral reflectance. Among the 23 investigated FTs, nine were modelled with R squared in validation (R2val) larger than 0.6, four with R2val larger than 0.4 and 10 with R2val lower than 0.4. It is concluded that RS allows a cost-efficient, time-saving and non-destructive monitoring of many FTs for a range of plant communities. Thirdly, the potential of different RS systems for detecting FTs was assessed. Based on spectral reflectance data recorded with a full-range FS 3, the bands of two hyperspectral and three multispectral RS sensors were simulated. Using PLSR and hyperspectral RS, 13 FTs were modeled with R2val larger than 0.4 using FS 3, 11 using EnMAP and ten using ASD HandHeld 2 data. Based on multispectral information, R2val larger than 0.4 were reached with Sentinel-2 for nine, Landsat 7 for four and RapidEye for none of the 23 FTs. These results show that hyperspectral RS systems outperform multispectral systems in detecting the FTs of grassland vegetation. It is concluded that hyperspectral RS systems have the potential to collect spatio-temporal information on grassland FTs. Such information may support grassland scientists in adapting the management to changes in climate and land-use intensity and to secure a sustainable agricultural production

Mapping urban surface materials using imaging spectroscopy data

Die Kartierung der städtische Oberflächenmaterialien ist aufgrund der komplexen räumlichen Muster eine Herausforderung. Daten von bildgebenden Spektrometern können hierbei durch die feine und kontinuierliche Abtastung des elektromagnetischen Spektrums detaillierte spektrale Merkmale von Oberflächenmaterialien erkennen, was mit multispektralen oder RGB-Bildern nicht mit der gleichen Genauigkeit erreicht werden kann. Bislang wurden in zahlreichen Studien zur Kartierung von städtischen Oberflächenmaterialien Daten von flugzeuggestützten abbildenden Spektrometern mit hoher räumlicher Auflösung verwendet, die ihr Potenzial unter Beweis stellen und gute Ergebnisse liefern. Im Vergleich zu diesen Sensoren haben weltraumgestützte abbildende Spektrometer eine regionale oder globale Abdeckung, eine hohe Wiederholbarkeit und vermeiden teure, zeit- und arbeitsaufwändige Flugkampagnen. Allerdings liegt die räumliche Auflösung der aktuellen weltraumgestützten abbildenden Spektroskopiedaten bei etwa 30 m, was zu einem Mischpixelproblem führt, welches mit herkömmlichen Kartierungsansätzen nur schwer zu bewältigen ist. Das Hauptziel dieser Studie ist die Kartierung städtischer Materialien mit bildgebenden Spektroskopiedaten in verschiedenen Maßstäben und die gleichzeitige Nutzung des Informationsgehalts dieser Daten, um die chemischen und physikalischen Eigenschaften von Oberflächenmaterialien zu erfassen sowie das Mischpixelproblem zu berücksichtigen. Konkret zielt diese Arbeit darauf ab, (1) photovoltaische Solarmodule mit Hilfe von luftgestützten bildgebenden Spektroskopiedaten auf der Grundlage ihrer spektralen Merkmale zu kartieren; (2) die Robustheit der Stichprobe von städtischen Materialgradienten zu untersuchen; (3) die Übertragbarkeit von städtischen Materialgradienten auf andere Gebiete zu analysieren.Mapping urban surface materials is challenging due to the complex spatial patterns. Data from imaging spectrometers can identify detailed spectral features of surface materials through the fine and continuous sampling of the electromagnetic spectrum, which cannot be achieved with the same accuracy using multispectral or RGB images. To date, numerous studies in urban surface material mapping have been using data from airborne imaging spectrometers with high spatial resolution, demonstrating the potential and providing good results. Compared to these sensors, spaceborne imaging spectrometers have regional or global coverage, high repeatability, and avoid expensive, time-consuming, and labor-intensive flight campaigns. However, the spatial resolution of current spaceborne imaging spectroscopy data (also known as hyperspectral data) is about 30 m, resulting in a mixed pixel problem that is challenging to handle with conventional mapping approaches. The main objective of this study is to perform urban surface material mapping with imaging spectroscopy data at different spatial scales, simultaneously explore the information content of these data to detect the chemical and physical properties of surface materials, and take the mixed-pixel problem into account. Specifically, this thesis aims to (1) map solar photovoltaic modules using airborne imaging spectroscopy data based on their spectral features; (2) investigate the sampling robustness of urban material gradients; (3) analyze the area transferability of urban material gradients

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Mapping urban Surface Materials Using Imaging Spectroscopy Data

Urban environment and its processes directly affect human life. Detailed and up-to-date urban surface material maps are of great importance to modelers studying meteorology, climatology and ecology, as well as to authorities seeking to understand the urban growth dynamics and spatial evolution. However, mapping urban surface materials is challenging due to the complex spatial patterns. An established source of up-to-date information is remote sensing, as demonstrated by the widespread usage of SAR, LiDAR and optical data. Data from imaging spectrometers can identify detailed spectral features of surface materials through the fine and continuous sampling of the electromagnetic spectrum, which cannot be achieved with the same accuracy using multispectral or RGB images. To date, numerous studies in urban surface material mapping have been using data from airborne imaging spectrometers with high spatial resolution, demonstrating the potential and providing good results. Compared to these sensors, spaceborne imaging spectrometers have regional or global coverage, high repeatability, and avoid expensive, time-consuming, and labor-intensive flight campaigns. However, the spatial resolution of current spaceborne imaging spectroscopy data (also known as hyperspectral data) is about 30 m, resulting in a mixed pixel problem that is challenging to handle with conventional mapping approaches. The main objective of this study is to perform urban surface material mapping with imaging spectroscopy data at different spatial scales, simultaneously explore the information content of these data to detect the chemical and physical properties of surface materials, and take the mixed-pixel problem into account. Specifically, this thesis aims to (1) map solar photovoltaic modules using airborne imaging spectroscopy data based on their spectral features; (2) investigate the sampling robustness of urban material gradients; (3) analyze the area transferability of urban material gradients. To this end, we detected solar photovoltaics with an overall accuracy of about 80% to 90% by creating and combining spectral indices. This dissertation proved that the developed approach is suitable for accurate photovoltaic detection. We also demonstrated that the concept of urban surface material gradients is robust in sampling and transferable between similar urban areas. With these results, urban material gradients can be a generic technique for urban mapping with spaceborne imaging spectroscopy data. The methods developed invi the three parts of this dissertation improve the usefulness of imaging spectroscopy data for urban material detection from a classical method to the new concept of urban gradients, from airborne to spaceborne data, from pure pixel detection to solving the mixed pixel problem. By introducing and enhancing the gradient concept in urban mapping, the mixed pixel problem can be tackled, which is a promising approach for the analysis of imaging spectroscopy data from ongoing and upcoming spaceborne sensors. Overall, this thesis provides promising urban surface material mapping results by proposing a physical feature based approach as well as confirming and laying the foundation of the generic gradient concept in urban material studies. Further work can build on these results and could open a new field for the application of spaceborne imaging spectroscopy data

Mapping urban surface materials with imaging spectroscopy data on different spatial scales

This work focuses on the development of methods for mapping urban surface materials by means of imaging spectroscopy data with different spatial resolution. General findings from this work represent a sensor- and site-independent framework for the automated extraction of spectrally pure pixels using an urban image spectral library while coping with its potential incompleteness. The extraction of spectrally pure pixels serves as a basic prerequisite for the subsequent use of image analysis methods to obtain detailed urban surface material maps. These material maps enabled the determination of gradual material transitions that were finally related to complex spectral mixtures resulting from 30 m spatial resolution imaging spectroscopy data to analyse typical material compositions within certain administrative units. The findings demonstrate the great potential of using upcoming spaceborne imaging spectroscopy data for a regular area-wide mapping of surface materials in urban areas. Im Fokus dieser Arbeit stand die Entwicklung von Methoden zur Kartierung urbaner Oberflächenmaterialien mittels abbildender Spektroskopiedaten unterschiedlicher räumlicher Auflösung. Das vorgestellte Konzept zur automatisierten sensor- und ortsunabhängigen Extraktion spektral reiner Pixel aus flugzeuggetragenen Fernerkundungsdaten berücksichtigt dabei die mögliche Unvollständigkeit einer urbanen Bildspektralbibliothek. Die Extraktion spektral reiner Pixel dient als Grundvoraussetzung für den späteren Einsatz von Bildanalyseverfahren zur Gewinnung detaillierter Kartierungen urbaner Oberflächenmaterialien. Aus diesen sind Materialgradienten ableitbar, die mit den komplexen Spektralmischungen aus Hyperspektraldaten mit 30 m räumlicher Auflösung in Verbindung gebracht wurden. Die Analyse typischer Materialzusammensetzungen innerhalb städtischer Verwaltungseinheiten zeigt das enorme Potential zukünftiger Hyperspektralsatelliten für die Erfassung des Materialvorkommens von Städten

Unmanned Aerial Vehicles for Vegetation Mapping: Opportunities and Challenges

Pflanzen sind eng mit einer Reihe von Ökosystemprozessen und -dienstleistungen wie die Bereitstellung von Lebensmitteln und Trinkwasser, die Klimaregulierung sowie die Bodenbildung und Kohlenstoffspeicherung verbunden. Deshalb können Vegetationseigenschaften wie Artenreichtum, Biodiversität und Pflanzenmerkmale zur Bewertung und Überwachung von Ökosystemprozessen genutzt werden. Die genaue Beobachtung von Vegetationsveränderungen ist daher entscheidend für das Verständnis der aktuellen und zukünftigen Ökosystemdynamik. Fernerkundungsdaten haben hohes Potenzial Vegetationseigenschaften und -prozesse räumlich abzubilden. Die zunehmende Verfügbarkeit von sehr hochauflösenden Fernerkundungsdaten ermöglicht auch die Untersuchung von feinskaligen Prozessen. Die für niedriger aufgelöste Fernerkundungsdaten entwickelten Auswertungsverfahren sind häufig nicht auf sehr hochaufgelöste Daten übertragbar. Daher werden neue Verfahren benötigt, um das volle Potenzial auszuschöpfen. Die Vorteile von sehr hochauflösenden Daten liegen unter anderem in der Erkennung von einzelnen Pflanzen und der besseren räumlichen Feinabstimmung mit Felddaten. Diese Vorteile ermöglichen die genaue Kartierung von Pflanzenarten auf der Ebene einzelner Individuen und Vegetationseigenschaften auf der Ebene von Pflanzengesellschaften, wie die Biodiversität, oberirdische Biomasse oder Artenzusammensetzung. Unbemannte Luftfahrzeuge (UAVs) werden als kostengünstige Plattform zur Gewinnung von Daten mit sehr hoher Auflösung, insbesondere für kleine Gebiete, verwendet. Daher ist ihr Einsatz gut zur Entwicklung neuer Methoden geeignet. Das Ziel dieser Arbeit war die Feststellung von Vorteilen und Limitierungen der Nutzung von UAVs zur Vegetationskartierung. Der Fokus der Arbeit lag auf zwei Hauptthemen, die Kartierung von Pflanzenarten und kleinräumigen Ökosystemprozessen. Eine der Fallstudien zeigte, dass die Verwendung von sehr hochauflösenden Daten zur Klassifizierung von Pflanzenarten durch die Überlappung verschiedener Arten erschwert wird. Daher ist Nutzung solcher Daten zur direkten Kartierung von Grünlandarten nur für Habitate mit geringer Vegetationsbedeckung und einfachen Strukturen, wie beispielsweise Dünenhabitate, vielversprechend. Eine zweite Fallstudie ergab, dass der Schattenwurf von Baumkronen den Erfolg von UAV-basierten Klassifikationen der invasiven Baumarten $\textit{Ulex europaeus}$, $\textit{Acacia dealbata}$ und $\textit{Pinus radiata}$ erheblich beeinflusst. Dabei machte es keinen Unterschied ob optische Daten oder Informationen über die Textur oder Kronenstruktur verwendet wurden. Anhand von Simulationen wurde dargestellt, dass jede Art aufgrund ihrer spezifischen Kronenarchitektur unterschiedliche Schatten erzeugt. Die optimalen Zeitfenster zur Klassifikation im Verlaufe eines Tages unterscheiden sich daher zwischen den einzelnen Arten. In einer dritten Fallstudie wurde gezeigt, dass Merkmale der oberirdischen Vegetation als Proxy genutzt werden können um Kartierungen von unterirdischen Kohlenstoffvorräten in Mooren zu verbessern. Ein empirisches Modell wurde genutzt um unter- und oberirdische Merkmale zu verknüpfen. Dafür wurden kontinuierliche Daten mit Informationen über Höhe, Biomasse, sowie den Artenreichtum und die Artenzusammensetzung der Vegetation verwendet. UAV Daten wurden genutzt um die relevanten oberirdischen Merkmale zu kartieren. Der unterirdische Kohlenstoffvorrat wurde dann durch die Parametrisierung des plotbasierten Modells mit den UAV-Extrapolationen kartiert. Dies deutet darauf hin, dass auch Ökosystemeigenschaften mit geringem direkten Einfluss auf die Reflektanz mit Hilfe von Vegetationsmerkmalen als Proxies kartiert werden können. Da bei Kopplung empirischer Modelle in jedem Modellierungsschritt fehlerbehaftete Voraussagen entstehen können, wird ein solcher Ansatz nur empfohlen, wenn starke empirische Verbindungen zwischen den feldbasierten Variablen vorliegen. Diese Arbeit zeigt, dass mit UAVs erhobene Erdbeobachtungsdaten geeignet sind, um die technischen und umweltbedingten Voraussetungen für eine erfolgreiche Kartierung von Pflanzenarten zu erforschen, um neue Methoden zu entwickeln, welche die Genauigkeit von Klassifikationen aus sehr hochaufgelösten Daten erhöhen und um Vegetationseigenschaften mit unterirdischen Gradienten zu verknüpfen. Die Arbeit enthält außerdem Empfehlungen und Vorschläge für die zukünftige Erforschung von feinskaligen Vegetationsprozessen

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