Estimation and Validation of Land Surface Broadband Albedos and Leaf Area Index From EO-1 ALI Data

The Advanced Land Imager (ALI) is a multispectral sensor onboard the National Aeronautics and Space Administration Earth Observing 1 (EO-1) satellite. It has similar spatial resolution to Landsat-7 Enhanced Thematic Mapper Plus (ETM+), with three additional spectral bands. We developed new algorithms for estimating both land surface broadband albedo and leaf area index (LAI) from ALI data. A recently developed atmospheric correction algorithm for ETM+ imagery was extended to retrieve surface spectral reflectance from ALI top-of-atmosphere observations. A feature common to these algorithms is the use of new multispectral information from ALI. The additional blue band of ALI is very useful in our atmospheric correction algorithm, and two additional ALI near-infrared bands are valuable for estimating both broadband albedo and LAI. Ground measurements at Beltsville, MD, and Coleambally, Australia, were used to validate the products generated by these algorithms.This work was supported in part by the National Aeronautics and Space Administration under Grant NCC5462 and by funding provided by the Australian Federal Government to the Commonwealth Scientific and Industrial Research Organization and the Cooperative Research Centre for Sustainable Rice Production, Project 1105

Remote sensing of irrigated crop types and its application to regional water balance estimation

The strengths of moderate to coarse resolution satellite remote sensing in both identifying crop types and estimating crop area has resulted in the widespread use of this technology for agricultural monitoring. Although the spectral information and cost of these remote sensing data are attractive, their spatial resolutions are often perceived as being inadequate for agricultural management at both the individual holding and the paddock level in the rice areas of New South Wales (NSW). Conversely, fine resolution remote sensing (e.g., aerial photography) very often contain spatial detail that will allow management decisions to be made at the paddock level, but these data can be expensive to acquire and subsequent manual digitisation of crop areas is labour intensive when performed each year. This raises at least two associated research questions for the rice industry in southern NSW: (1) ‘how is the rice area best mapped when considering cost, accuracy, timing, and complexity while reconciling the above issues? ‘; and (2) ‘how can spatial accuracy (concerning both areas and positions) be measured and related to relevant management practices in order to influence decisions?’. Additionally, many operational users of remote sensing data perceive it as being an overwhelming data source as it often requires time consuming training and expensive computer software. This results in a further series of issues: (3) ‘can remote sensing be used operationally within the NSW rice industry so that simple methods can be applied using inexpensive software with minimal training in order to achieve similar or increased accuracies?’. Furthermore, use of spatially accurate GIS paddock boundaries has been shown to increase crop classification accuracy. However, this raises further questions: (4) ‘what is the influence of spatial error on management decisions?’; (5) ‘how can the accuracy of GIS data be measured?’; and (6) ‘how are these issues altered when considering the other major summer crops in the region?’. As satellite hyperspectral data (e.g., >100 spectral bands per image) are now available this again raises some questions, such as: (7) ‘does this extra spectral information content translate into additional or more accurate agricultural metrics’; and (8) ‘what is the current capacity in the rice industry of NSW to process this sort of information quickly as to impact management decisions?’. These and other related issues have made up the vast majority of the research from project 1105. Recommendations have been made wherever possible regarding the improvement of spatial analysis or mapping efficiencies. Importantly, the research from project 1105 has been adopted by the local industry – this is proof of ‘impact’ as opposed to only producing ‘outcomes’. The work reported here has concentrated on practical issues with an emphasis on transferring the knowledge gained to industry partners. Prior to addressing these issues, a comprehensive literature review concerning the utility of remote sensing in rice base irrigation systems was performed to ensure that past, present and current opportunities (and constraints) concerning the use of time series remote sensing in the local, national and international context were known and understood. Due to wanting to optimise research results by acquiring as many images as possible with our operating budget for image acquisition all new research (as opposed to the literature review) was conducted on the smallest irrigation areas in southern NSW: Coleambally Irrigation Area (CIA). Before methods can be transferred to the other irrigation areas (i.e., Murrumbidgee and Murray Valley Irrigation areas) some assessment of the similarities of the irrigation systems in terms of non-rice crops and their phenology needs to be performed

Remote sensing of irrigated crop types and its application to regional water balance estimation

The strengths of moderate to coarse resolution satellite remote sensing in both identifying crop types and estimating crop area has resulted in the widespread use of this technology for agricultural monitoring. Although the spectral information and cost of these remote sensing data are attractive, their spatial resolutions are often perceived as being inadequate for agricultural management at both the individual holding and the paddock level in the rice areas of New South Wales (NSW). Conversely, fine resolution remote sensing (e.g., aerial photography) very often contain spatial detail that will allow management decisions to be made at the paddock level, but these data can be expensive to acquire and subsequent manual digitisation of crop areas is labour intensive when performed each year. This raises at least two associated research questions for the rice industry in southern NSW: (1) ‘how is the rice area best mapped when considering cost, accuracy, timing, and complexity while reconciling the above issues? ‘; and (2) ‘how can spatial accuracy (concerning both areas and positions) be measured and related to relevant management practices in order to influence decisions?’. Additionally, many operational users of remote sensing data perceive it as being an overwhelming data source as it often requires time consuming training and expensive computer software. This results in a further series of issues: (3) ‘can remote sensing be used operationally within the NSW rice industry so that simple methods can be applied using inexpensive software with minimal training in order to achieve similar or increased accuracies?’. Furthermore, use of spatially accurate GIS paddock boundaries has been shown to increase crop classification accuracy. However, this raises further questions: (4) ‘what is the influence of spatial error on management decisions?’; (5) ‘how can the accuracy of GIS data be measured?’; and (6) ‘how are these issues altered when considering the other major summer crops in the region?’. As satellite hyperspectral data (e.g., >100 spectral bands per image) are now available this again raises some questions, such as: (7) ‘does this extra spectral information content translate into additional or more accurate agricultural metrics’; and (8) ‘what is the current capacity in the rice industry of NSW to process this sort of information quickly as to impact management decisions?’. These and other related issues have made up the vast majority of the research from project 1105. Recommendations have been made wherever possible regarding the improvement of spatial analysis or mapping efficiencies. Importantly, the research from project 1105 has been adopted by the local industry – this is proof of ‘impact’ as opposed to only producing ‘outcomes’. The work reported here has concentrated on practical issues with an emphasis on transferring the knowledge gained to industry partners. Prior to addressing these issues, a comprehensive literature review concerning the utility of remote sensing in rice base irrigation systems was performed to ensure that past, present and current opportunities (and constraints) concerning the use of time series remote sensing in the local, national and international context were known and understood. Due to wanting to optimise research results by acquiring as many images as possible with our operating budget for image acquisition all new research (as opposed to the literature review) was conducted on the smallest irrigation areas in southern NSW: Coleambally Irrigation Area (CIA). Before methods can be transferred to the other irrigation areas (i.e., Murrumbidgee and Murray Valley Irrigation areas) some assessment of the similarities of the irrigation systems in terms of non-rice crops and their phenology needs to be performed

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation

Remote sensing of mangrove composition and structure in the Galapagos Islands

Mangroves are unique inter-tidal ecosystems that provide valuable ecosystem goods and services. This dissertation investigates new methods of characterizing mangrove forests using remote sensing with implications for mapping and modeling ecosystem goods and services. Specifically, species composition, leaf area, and canopy height are investigated for mangroves in the Galapagos Islands. The Galapagos Islands serve as an interesting case study where environmental conditions are highly variable over short distances producing a wide range of mangrove composition and structure to examine. This dissertation reviews previous mangrove remote sensing studies and seeks to address missing gaps. Specifically, this research seeks to examine pixel and object-based methods for mapping mangrove species, investigate the usefulness of spectral and spatial metrics to estimate leaf area, and compare existing global digital surface models with a digital surface model extracted from new very high resolution imagery. The major findings of this research include the following: 1) greater spectral separability between true mangrove and mangrove associate species using object-based image analysis compared to pixel-based analysis, but a lack of separability between individual mangrove species, 2) the demonstrated necessity for novel machine-learning classification techniques rather than traditional clustering classification algorithms, 3) significant but weak relationships between spectral vegetation indices and leaf area, 4) moderate to strong relationships between grey-level co-occurrence matrix image texture and leaf area at the individual species level, 5) similar accuracy between a very high resolution stereo optical digital surface model a coarse resolution InSAR product to estimate canopy height with improved accuracy using a hybrid model of these two products. The results demonstrate advancements in remote sensing technology and technique, but further challenges remain before these methods can be applied to monitoring and modeling applications. Based on these results, future research should focus on emerging technologies such as hyperspectral, very high resolution InSAR, and LiDAR to characterize mangrove forest composition and structure

Optimized and automated estimation of vegetation properties: Opportunities for Sentinel-2

La Biosfera es uno de los principales sistemas que conforman la Tierra. Su estudio permite comprender la relación entre la vegetación y el ciclo del carbono y cómo éste puede ser afectado por los cambios en los niveles de CO2 y los usos de suelo. Para el estudio de estas dinámicas a escala global y local, han sido desarrollados diversos modelos que son representaciones de la realidad en una escala y complejidad más simple. Parte de las variables de entrada de estos modelos son obtenidas mediante medidas de teledetección gracias al Global Climate Observing System (GCOS), que ha determinado un conjunto de 50 variables climáticas esenciales que contribuyen a los estudios de cambio climático que lidera la Convención Marco de las Naciones Unidas y el Panel Intergubernamental del Cambio Climático. En esta lista está incluido el índice de área foliar (LAI).El contenido de clorofila en hoja (LCC) es otro parámetro biofísico clave para los estudios de biosfera. El estudio de las propiedades de la vegetación desde el espacio requiere: (1) Métodos óptimos para el procesamiento y la estimación de la información y, (2) Disponibilidad de datos espaciales. Los métodos de procesado y estimación de parámetros biofísicos son necesarios ya que el sensor solo mide los flujos de energía reflejados por las cubiertas vegetales distribuidos espacialmente. Por ello, han sido desarrollados diversos modelos, que van desde complejos modelos con base física hasta modelos estadísticos o la combinación de los anteriores. En el desarrollo de esta tesis se ha reunido una amplia variedad de ellos. la Agencia Espacial Europea (ESA) ha desarrollado la misión Sentinel-2 que está especialmente diseñada para el monitoreo de las propiedades de la vegetación, con las capacidades operativas que cumplen los requerimientos espectrales, espaciales y temporales. Los datos que proporcionará la misión Sentinel-2 permitirán garantizar la continuidad de las misiones Spot y Landsat, aportando un tiempo de revisita menor, mejora de la amplitud de barrido, mayor resolución espectral y una mejor calibración y calidad de imagen. Para el procesamiento y la extracción de información de parámetros biofísicos han sido desarrollados diferentes paquetes computacionales por diversos grupos de investigación. Esta tesis pretende suministrar un conjunto de herramientas computacionales, dinámicas y flexibles que permitan automatizar y evaluar el potencial de los diferentes métodos que en la actualidad han sido publicados y están disponibles para su libre uso. Presenta los resultados científicos de la evaluación del impacto de diferentes parámetros de ajuste en los principales métodos de estimación de parámetros biofísicos, centrándonos en datos simulados del satélite Sentinel-2, previsto para ser lanzado en 2015. Para dicho trabajo se han reunido los principales métodos de estimación que van desde las simples relaciones espectrales hasta los complejos modelos de transferencia radiativa (RTM). Para esto, hemos implementado un conjunto de herramientas informáticas que permiten el diseño y evaluación de diversas estrategias de regularización como son la normalización de los datos, la sinergia entre datos simulados por RTM y datos de campañas de campo o de laboratorio, adición de modelos de ruido a los datos simulados y un amplio conjunto de métodos de regresión tanto paramétricos como no paramétricos. Este trabajo constituye la continuación de mi trabajo Final del Máster de Teledetección, donde he desarrolló una herramienta informática llamado ARTMO (por sus siglas en inglés Automated Radiative Transfer Models Operator) que reunió los RTM de la familia Prospect, SAIL y FLIGTH. Se implementó el método de estimación por tablas de búsqueda (LUT). Esta tesis presenta la evolución de ARTMO que pasa de ser una herramienta informática rígida que no permitía de manera sencilla la ampliación de sus funciones, a un flexible marco de desarrollo (framework software), donde ARTMO se convierte en una plataforma de soporte de diversos módulos implementados de manera independiente. Esta nueva versión de ARTMO permite a cualquier grupo de investigación desarrollar y compartir nuevas funciones, algoritmos y métodos de estimación de parámetros biofísicos. Además, hemos establecido las bases para la creación de una red tanto de usuarios como de desarrolladores en torno al estudio de las propiedades de la vegetación, sirviendo de apoyo para el estudio de nuevos algoritmos de estimación, diseño de nuevos sensores ópticos o para su uso en el campo de la educación.The biosphere is one of the main components of the Earth’s system since it regulates exchanges of energy and mass fluxes at the soil, vegetation and atmosphere level. To know the links between vegetation and the terrestrial energy, water and carbon cycles, and how these might change due to eco-physiological responses to elevated CO2 and changes in land use is of vital importance for the study of the biosphere. To study these exchanges, several kinds of models (scale and target) have been developed. In view of these models, the Global Climate Observing System (GCOS) aims to provide comprehensive information on the total climate system, involving a multidisciplinary range of physical, chemical and biological properties, and atmospheric, oceanic, hydrological, cryospheric and terrestrial processes. Fifty GCOS Essential Climate Variables (ECVs) are required to support the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change. In support of these terrestrial models, but also in support of monitoring local-to-global vegetation dynamics, this Thesis focuses on improved estimation of vegetation properties from optical RS data, and more specifically leaf area index (LAI) and leaf chlorophyll content (LCC). Although LCC is currently not considered as an ECV due to the lack of a globally applicable retrieval algorithm, it is a key variable in vegetation studies. Monitoring the distribution and changes of LAI and LCC is important for assessing growth and vigour of vegetation on the planet. The quantification of these essential vegetation properties are fundamentally important in land-atmosphere processes and parametrization in climate models. LAI variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes such as photosynthesis, respiration, transpiration and rain interception. LCC provides important information about the physiological status of plants and photosynthetic activity, therefore is related to the nitrogen content, water stress and yield forecasting The European Space Agency (ESA)’s forthcoming Sentinel-2 mission is particularly tailored to the monitoring vegetation properties mapping, with operational monitoring capabilities that goes beyond any existing operational mission. A pair of Sentinel-2 polar-orbiting satellites will provide systematic global acquisitions of high-resolution multispectral imagery (10-60 m) with a high revisit frequency on a free and open data policy basis. With the pair of satellites in operation it has a revisit time of five days at the equator (under cloud-free conditions) and 2–3 days at mid-latitudes. Sentinel-2 images will be used to derive the highly prioritized time series of ECVs such as LAI. Sentinel-2 images will also be used provide various experimental variables, e.g. biochemical variables such as LCC. This Thesis is dedicated to tackle the stated recommendation and turn it into consolidated guidelines. The undertaken road map was to work on both generating scientific outputs, as well on developing software to automate the retrieval routines. All essential tools to deliver a prototype retrieval approach that could be embedded into an operational Sentinel-2 processing scheme have been prepared into a scientific software package called ARTMO (Automated Radiative Transfer Models Operator). Physically-based approaches but also latest statisticallybased methods have been implemented into the software package and systematically evaluated. The retrieval methods have been applied to the estimation of LAI and LCC from simulated Sentinel-2 data, but the majority of investigated methods can essentially be applied to derive any detectable vegetation biochemical or biophysical variable. The fundamentals of ARTMO has been laid during J.P. Rivera’s MSc thesis project and has been further developed during the course of my PhD Thesis. The toolbox is built on a suite of radiative transfer models and image processing modules in a modular graphical user interface (GUI) environment. ARTMO has been mainly developed and tested for processing (simulated) Sentinel-2 data in a semiautomatic way, but in principle data from any optical sensor can be processed

Empirical approach to satellite snow detection

Lumipeitteellä on huomattava vaikutus säähän, ilmastoon, luontoon ja yhteiskuntaan. Pelkästään sääasemilla tehtävät lumihavainnot (lumen syvyys ja maanpinnan laatu) eivät anna kattavaa kuvaa lumen peittävyydestä tai muista lumipeitteen ominaisuuksista. Sääasemien tuottamia havaintoja voidaan täydentää satelliiteista tehtävillä havainnoilla. Geostationaariset sääsatelliitit tuottavat havaintoja tihein välein, mutta havaintoresoluutio on heikko monilla alueilla, joilla esiintyy kausittaista lunta. Polaariradoilla sääsatelliittien havaintoresoluutio on napa-alueiden läheisyydessä huomattavasti parempi, mutta silloinkaan satelliitit eivät tuota jatkuvaa havaintopeittoa. Tiheimmän havaintoresoluution tuottavat sääsatelliittiradiometrit, jotka toimivat optisilla aallonpituuksilla (näkyvä valo ja infrapuna). Lumipeitteen kaukokartoitusta satelliiteista vaikeuttavat lumipeitteen oman vaihtelun lisäksi pinnan ominaisuuksien vaihtelu (kasvillisuus, vesistöt, topografia) ja valaistusolojen vaihtelu. Epävarma ja osittain puutteellinen tieto pinnan ja kasvipeitteen ominaisuuksista vaikeuttaa luotettavan automaattisen analyyttisen lumentunnistusmenetelmän kehittämistä ja siksi empiirinen lähestymistapa saattaa olla toimivin vaihtoehto automaattista lumentunnistusmenetelmää kehitettäessä. Tässä työssä esitellään kaksi EUMETSATin osittain rahoittamassa H SAFissa kehitettyä lumituotetta ja niissä käytetyt empiiristä lähestymistapaa soveltaen kehitetyt algoritmit. Geostationaarinen MSG/SEVIRI H31 lumituote on saatavilla vuodesta 2008 alkaen ja polaarituote Metop/AVHRR H32 vuodesta 2015 alkaen. Lisäksi esitellään pintahavaintoihin perustuvat validointitulokset, jotka osoittavat tuotteiden saavuttavan määritellyt tavoitteet.Snow cover plays a significant role in the weather and climate system, ecosystems and many human activities, such as traffic. Weather station snow observations (snow depth and state of the ground) do not provide highresolution continental or global snow coverage data. The satellite observations complement in situ observations from weather stations. Geostationary weather satellites provide observations at high temporal resolution, but the spatial resolution is low, especially in polar regions. Polarorbiting weather satellites provide better spatial resolution in polar regions with limited temporal resolution. The best detection resolution is provided by optical and infra-red radiometers onboard weather satellites. Snow cover in itself is highly variable. Also, the variability of the surface properties (such as vegetation, water bodies, topography) and changing light conditions make satellite snow detection challenging. Much of this variability is in subpixel scales, and this uncertainty creates additional challenges for the development of snow detection methods. Thus, an empirical approach may be the most practical option when developing algorithms for automatic snow detection. In this work, which is a part of the EUMETSAT-funded H SAF project, two new empirically developed snow extent products for the EUMETSAT weather satellites are presented. The geostationary MSG/SEVIRI H32 snow product has been in operational production since 2008. The polar product Metop/AVHRR H32 is available since 2015. In addition, validation results based on weather station snow observations between 2015 and 2019 are presented. The results show that both products achieve the requirements set by the H SAF

Linking Canopy Reflectance and Plant Functioning through Radiative Transfer Models

Von den Tropen bis zur Tundra hat sich die Pflanzenwelt durch Anpassungen an lokale Umwelteinflüsse diversifiziert. Diese Anpassungen sind in der Funktionsweise der Pflanzen manifestiert, welche unter anderem Wachstum, Fortpflanzung, Konkurrenzfähigkeit oder Ausdauer beinhalten. Pflanzenfunktionen haben nicht nur direkten Einfluss auf die Artenzusammensetzung, sondern auch auf großräumige Prozesse wie Bio- und Atmossphäreninteraktionen oder Stoffkreisläufe. Folglich wurden viele Forschungsanstrengungen unternommen um Pflanzenfunktionen weiter zu verstehen und zu erfassen, z.B. darauf abzielend generalisierende Modelle von Pflanzenfunktionen zu entwickeln oder individuelle Pflanzenmerkmale als Indikatoren für Pflanzenfunktion zu identifizieren. Trotz der wissenschaftlichen Fortschritte fehlt ein vollständiges Bild der Funktionsvielfalt der Pflanzenwelt, sowohl in geographischer als auch funktioneller Hinsicht. Dies ist im Wesentlichen auf die Komplexität und die logistischen Einschränkungen bei der Messung von Pflanzenfunktionen im Feld zurückzuführen. Um dieses Bild zu vervollständigen wird insbesondere optischen Erdbeobachtungsdaten ein hohes Potenzial zugeschrieben. Optische Erdbeobachtungssensoren erfassen das vom Kronendach reflektierte Sonnenlicht. Letzteres wird durch verschiedene biochemische und strukturelle Pflanzenmerkmale (im Folgenden optische Merkmale) beeinträchtigt (z.B. Blattchlorophyllgehalt oder Blattwinkel). Das Abfangen und Absorbieren von Sonnenlicht ist die Grundlage des pflanzeneigenen Metabolismus und folglich liegt es Nahe, dass diese optischen Merkmale direkt mit Pflanzenfunktionen zusammenhängen. Der Zusammenhang dieser optische Merkmale mit Pflanzenfunktionen wurde jedoch noch nicht systematisch untersucht, und ebenso ist der Zusammenhang zwischen Pflanzenfunktion und Kronendachreflektion noch nicht vollständig untersucht. Die physikalischen Interaktionen von Licht und optischen Pflanzenmerkmalen sind bereits hinreichend verstanden und in Strahlungstransfermodellen (RTM) für Vegetationskronendächer formuliert. RTM können als prozessbasierte Modelle betrachtet werden, die die Reflektion des Kronendachs in Abhängigkeit von optische Merkmalen, dem Bodenhintergrund und der Sonnen-Sensorgeometrie modellieren. Das Ziel und die Innovation dieser Dissertation war die kausalen Zusammenhänge zwischen Kronendachreflektion und Pflanzenfunktion mittels RTM zu verstehen und zu nutzen. Es wurde gezeigt, dass für die Fernerkundung von Pflanzenfunktionen die Kopplung von Kronendachreflektion und Pflanzenfunktionen durch RTM mehrere Potentiale bietet: Erstens, ermöglichen RTM die Kartierung von Pflanzenmerkmalen. Innerhalb einer Fallstudie wurde gezeigt, dass eine Inversion von RTM mit hyperspektralen Daten eine Kartierung von optischen Merkmalen erlaubt, für die keine Felddaten zur Modellkalibrierung benötigt werden. Die kartierten Merkmale zeigten eine hohe Übereinstimmung mit Merkmalsausprägungen aus unabhängigen Datenbanken und spiegelten die im Feld gemessenen ökologischen Gradienten wider. Dies deutet darauf hin, dass RTM-Inversion als äußerst übertragbare Methode betrachtet werden kann, um räumliche Karten von Pflanzenmerkmalen zu erstellen, die als Proxies für Pflanzenfunktionen dienen können. Allerdings erfordert die Implementierung von RTM Inversionen fundierte Kenntnisse über die Prinzipien der Strahlentransfermodellierung und der zu untersuchenden Vegetationscharakteristiken. Zweitens, ermöglichen RTM die Untersuchung von Zusammenhängen zwischen Pflanzenfunktion und der Kronendachreflektion. In der vorliegenden Thesis wurden simulierte Kronendachspektren aus einem RTM verwendet, um den Beitrag der optischen Merkmale zu den spektralen Unterschieden zwischen Pflanzenfunktionstypen zu erfassen. Die Ergebnisse zeigten die dominanten Pflanzenmerkmale und die entsprechenden spektralen Charakteristiken die für eine fernerkundliche Unterscheidung der Pflanzenfunktion von großer Relevanz sind. Darüber hinaus wurde gezeigt, dass RTM-basierte Simulationen Einschränkungen von Fallstudien kompensieren und Kenntnisse über die Zusammenhänge von Pflanzenfunktionen, Pflanzeneigenschaften und Kronendachtreflektion erweitern können. Diese Kenntnisse bilden die Grundlage für die Entwicklung und Verbesserung von Sensoren und Algorithmen zur Fernerkundung von Pflanzenfunktionen. Drittens, erweitern RTM und die darin enthaltenen optischen Merkmale unsere Möglichkeiten Unterschiede in der Pflanzenfunktion zu verstehen und zu quantifizieren. Mit Hilfe von in-situ gemessenen Merkmalsausprägungen konnte gezeigt werden, dass die in RTM enthaltenen optischen Merkmale kausal mit primären Pflanzenfunktionen zusammenhängen. Dies wiederum bedeutet, dass die Reflexion des Kronendachs unmittelbar mit den primären Funktionen der Pflanze zusammenhängt (‘Reflektion folgt Funktion’). Darüber hinaus wurde festgestellt, dass optische Merkmale vergleichbare oder sogar höhere Korrelationen mit den verwendeten pflanzlichen Funktionsgradienten aufweisen als die in der Pflanzenökologie üblich verwendeten Merkmale. Entsprechend bieten RTM sowohl eine alternative Perspektive als auch ein Set von Pflanzenmerkmalen mit denen Unterschiede der Pflanzenfunktion charakterisiert und quantifiziert werden können. Diese Merkmale können somit als wertvolle Ergänzung oder Alternative zu den in der Pflanzenökologie üblichen Merkmalen dienen. Zusammengefasst zeigt diese Thesis, dass RTM unsere Möglichkeiten erweiterten können die funktionelle Vielfalt der globalen Vegetationsbedeckung weiter zu verstehen und zu erfassen und führt zukunftsrelevante Forschungspotentiale auf

Remote sensing of leaf area index : enhanced retrieval from close-range and remotely sensed optical observations

A wide range of models used in agriculture, ecology, carbon cycling, climate and other related studies require information on the amount of leaf material present in a given environment to correctly represent radiation, heat, momentum, water, and various gas exchanges with the overlying atmosphere or the underlying soil. Leaf area index (LAI) thus often features as a critical land surface variable in parameterisations of global and regional climate models, e.g., radiation uptake, precipitation interception, energy conversion, gas exchange and momentum, as all areas are substantially determined by the vegetation surface. Optical wavelengths of remote sensing are the common electromagnetic regions used for LAI estimations and generally for vegetation studies. The main purpose of this dissertation was to enhance the determination of LAI using close-range remote sensing (hemispherical photography), airborne remote sensing (high resolution colour and colour infrared imagery), and satellite remote sensing (high resolution SPOT 5 HRG imagery) optical observations. The commonly used light extinction models are applied at all levels of optical observations. For the sake of comparative analysis, LAI was further determined using statistical relationships between spectral vegetation index (SVI) and ground based LAI. The study areas of this dissertation focus on two regions, one located in Taita Hills, South-East Kenya characterised by tropical cloud forest and exotic plantations, and the other in Gatineau Park, Southern Quebec, Canada dominated by temperate hardwood forest. The sampling procedure of sky map of gap fraction and size from hemispherical photographs was proven to be one of the most crucial steps in the accurate determination of LAI. LAI and clumping index estimates were significantly affected by the variation of the size of sky segments for given zenith angle ranges. On sloping ground, gap fraction and size distributions present strong upslope/downslope asymmetry of foliage elements, and thus the correction and the sensitivity analysis for both LAI and clumping index computations were demonstrated. Several SVIs can be used for LAI mapping using empirical regression analysis provided that the sensitivities of SVIs at varying ranges of LAI are large enough. Large scale LAI inversion algorithms were demonstrated and were proven to be a considerably efficient alternative approach for LAI mapping. LAI can be estimated nonparametrically from the information contained solely in the remotely sensed dataset given that the upper-end (saturated SVI) value is accurately determined. However, further study is still required to devise a methodology as well as instrumentation to retrieve on-ground green leaf area index . Subsequently, the large scale LAI inversion algorithms presented in this work can be precisely validated. Finally, based on literature review and this dissertation, potential future research prospects and directions were recommended.Ei saatavill

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)