2,945 research outputs found

    Measurement methods and variability assessment of the Norway spruce total leaf area: implications for remote sensing

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    Estimation of total leaf area (LAT) is important to express biochemical properties in plant ecology and remote sensing studies. A measurement of LAT is easy in broadleaf species, but it remains challenging in coniferous canopies. We proposed a new geometrical model to estimate Norway spruce LAT and compared its accuracy with other five published methods. Further, we assessed variability of the total to projected leaf area conversion factor (CF) within a crown and examined its implications for remotely sensed estimates of leaf chlorophyll content (Cab). We measured morphological and biochemical properties of three most recent needle age classes in three vertical canopy layers of a 30 and 100-year-old spruce stands. Newly introduced geometrical model and the parallelepiped model predicted spruce LAT with an error \u3e5 % of the average needle LAT, whereas two models based on an elliptic approximation of a needle shape underestimated LAT by up to 60 %. The total to projected leaf area conversion factor varied from 2. 5 for shaded to 3. 9 for sun exposed needles and remained invariant with needle age class and forest stand age. Erroneous estimation of an average crown CF by 0. 2 introduced an error of 2-3 ÎŒg cm-2 into the crown averaged Cab content. In our study, this error represents 10-15 % of observed crown averaged Cab range (33-53 ÎŒg cm-2). Our results demonstrate the importance of accurate LAT estimates for validation of remotely sensed estimates of Cab content in Norway spruce canopies

    Discerning Oriental from European beech by leaf spectroscopy: Operational and physiological implications

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    European beech (Fagus sylvatica L.) forests have recently experienced severe diebacks that are expected to increase in future. Oriental beech (Fagus sylvatica spp. orientalis (Lipsky) Greut. & Burd) is a potential candidate for assisted migration (AM) in European forests due to its greater genetic diversity and potentially higher drought resistance. Yet AM entails not only benefits, but also risks, and it is therefore important to monitor the progression of introduced (sub)species. Here, we demonstrate the potential of leaf spectroscopy to replace resourceintensive genetic analysis and field phenotyping for the discrimination and characterization of these two beech subspecies. We studied two European beech forests, one in France and one in Switzerland, where Oriental beech from the Greater Caucasus was introduced over 100 years ago. During two summers (2021, 2022), we measured leaf spectral reflectance, leaf morphological and biochemical traits from genotyped adult trees. Subspecies prediction models were developed separately for top-of-canopy leaves (amenable to remote sensing) and bottom-of-canopy leaves (easier to harvest) using partial least squares discriminant analysis (PLS-DA) and different sets of spectral predictors. Morphological, biochemical and spectra-derived leaf traits indicated that Oriental beech trees at the sites studied were characterized by higher lignin and nitrogen per unit leaf area than European beech, suggesting more protein-rich leaves on a per-area basis. The model based on top-of-canopy leaf reflectance spectra in the short-wave-infrared region (SWIR I: 1450–1750 nm) most accurately distinguished Oriental from European beech (BA = 0.86 ± 0.08, k = 0.72 ± 0.15), closely followed by models based on SWIR II, and on spectra-derived traits (BA ≄ 0.84, k ≄ 0.67). This study provides a proof-of-principle for the development of spectroscopy-based approaches when monitoring introduced species, subspecies or provenances. Our findings hold promise for upscaling to large forest areas using airborne remote sensing

    Examining the influence of seasonality, condition, and species composition on mangrove leaf pigment contents and laboratory based spectroscopy data

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    The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy) between reflectance (400-1000 nm) and leaf pigment contents (chlorophyll-a (chl-a), chlorophyll-b (chl-b), total carotenoids (tcar), chlorophyll a/b ratio) in three mangrove species (Avicennia germinans (A. germinans), Laguncularia racemosa (L. racemosa), and Rhizophora mangle (R. mangle)) according to their condition (stressed vs. healthy). Based on a sample of 360 leaves taken from a semi-arid forest of the Mexican Pacific, it was determined that during the dry season, the stressed A. germinans and R. mangle show the highest maximum correlations at the green (550 nm) and red-edge (710 nm) wavelengths (r = 0.8 and 0.9, respectively) for both chl-a and chl-b and that much lower values (r = 0.7 and 0.8, respectively) were recorded during the rainy season. Moreover, it was found that the tcar correlation pattern across the electromagnetic spectrum was quite different from that of the chl-a, the chl-b, and chl a/b ratio but that their maximum correlations were also located at the same two wavelength ranges for both seasons. The stressed L. racemosa was the only sample to exhibit minimal correlation with chl-a and chl-b for either season. In addition, the healthy A. germinans and R. mangle depicted similar patterns of chl-a and chl-b, but the tcar varied depending on the species. The healthy L. racemosa recorded higher correlations with chl-b and tcar at the green and red-edge wavelengths during the dry season, and higher correlation with chl-a during the rainy season. Finally, the vegetation index Red Edge Inflection Point Index (REIP) was found to be the optimal index for chl-a estimation for both stressed and healthy classes. For chl-b, both the REIP and the Vogelmann Red Edge Index (Vog1) index were found to be best at prediction. Based on the results of this investigation, it is suggested that caution be taken as mangrove leaf pigment contents from spectroscopy data have been shown to be sensitive to seasonality, species, and condition. The authors suggest potential reasons for the observed variability in the reflectance and pigment contents relationships

    On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

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    Understanding the causes of variation in plant functional traits is a central issue in ecology, particularly in the context of global change.  Spectroscopy is increasingly used for rapid and non-destructive estimation of foliar traits, but few studies have evaluated its accuracy when assessing phenotypic variation in multiple traits. Working with 24 chemical and physical leaf traits of six European tree species growing on strongly contrasting soil types (i.e. deep alluvium versus nearby shallow chalk), we asked (i) whether variability in leaf traits is greater between tree species or soil type; and (ii) whether field spectroscopy is effective at predicting intraspecific variation in leaf traits as well as interspecific differences.  Analysis of variance showed that inter-specific differences in traits were generally much stronger than intraspecific differences related to soil type, accounting for 25% versus 5% of total trait variation, respectively. Structural traits, phenolic defences and pigments were barely affected by soil type.  In contrast, foliar concentrations of rock-derived nutrients did vary: P and K concentration were lower on chalk than alluvial soils, while Ca, Mg, B, Mn and Zn concentrations were all higher, consistent with the findings of previous ecological studies. Foliar traits were predicted from 400-2500 nm reflectance spectra collected by field spectroscopy using partial least square regression, a method that is commonly employed in chemometrics.  Pigments were best modelled using reflectance data from the visible region (400 - 700 nm), whilst all other traits were best modelled using reflectance data from the shortwave infrared region (1100 - 2500 nm) region. Spectroscopy delivered accurate predictions of species-level variation in traits. However, it was ineffective at detecting intraspecific variation in rock-derived nutrients (with the notable exception of P). The explanation for this failure is that rock-derived elements do not have absorption features in the 400-2500 nm region, and their estimation is indirect, relying on elemental concentrations co-varying with structural traits that do have absorption features in that spectral region (“constellation effects”).  Since the structural traits did not vary with soil type, it was impossible for our regression models to predict intraspecific variation in rock-derived nutrients via constellation effects. This study demonstrates the value of spectroscopy for rapid, non-destructive estimation of foliar traits across species, but highlights problems with predicting intraspecific variation indirectly. We discuss the implications of these findings for mapping functional traits by airborne imaging spectroscopy.David Coomes was supported by a grant from NERC (NE/K016377/1) and Matheus H. Nunes is supported by a PhD scholarship from the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq)

    Measurement methods and variability assessment of the Norway spruce total leaf area: implications for remote sensing

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    Estimation of total leaf area (LAT) is important to express biochemical properties in plant ecology and remote sensing studies. A measurement of LAT is easy in broadleaf species, but it remains challenging in coniferous canopies. We proposed a new geometrical model to estimate Norway spruce LAT and compared its accuracy with other five published methods. Further, we assessed variability of the total to projected leaf area conversion factor (CF) within a crown and examined its implications for remotely sensed estimates of leaf chlorophyll content (C ab). We measured morphological and biochemical properties of three most recent needle age classes in three vertical canopy layers of a 30 and 100-year-old spruce stands. Newly introduced geometrical model and the parallelepiped model predicted spruce LAT with an error <5% of the average needle LAT, whereas two models based on an elliptic approximation of a needle shape underestimated LAT by up to 60%. The total to projected leaf area conversion factor varied from 2.5 for shaded to 3.9 for sun exposed needles and remained invariant with needle age class and forest stand age. Erroneous estimation of an average crown CF by 0.2 introduced an error of 2-3ÎŒgcm−2 into the crown averaged C ab content. In our study, this error represents 10-15% of observed crown averaged C ab range (33-53ÎŒgcm−2). Our results demonstrate the importance of accurate LAT estimates for validation of remotely sensed estimates of C ab content in Norway spruce canopie

    Linking Canopy Reflectance and Plant Functioning through Radiative Transfer Models

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    Von den Tropen bis zur Tundra hat sich die Pflanzenwelt durch Anpassungen an lokale UmwelteinflĂŒsse diversifiziert. Diese Anpassungen sind in der Funktionsweise der Pflanzen manifestiert, welche unter anderem Wachstum, Fortpflanzung, KonkurrenzfĂ€higkeit oder Ausdauer beinhalten. Pflanzenfunktionen haben nicht nur direkten Einfluss auf die Artenzusammensetzung, sondern auch auf großrĂ€umige Prozesse wie Bio- und AtmossphĂ€reninteraktionen oder StoffkreislĂ€ufe. 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 fĂŒr Pflanzenfunktion zu identifizieren. Trotz der wissenschaftlichen Fortschritte fehlt ein vollstĂ€ndiges Bild der Funktionsvielfalt der Pflanzenwelt, sowohl in geographischer als auch funktioneller Hinsicht. Dies ist im Wesentlichen auf die KomplexitĂ€t und die logistischen EinschrĂ€nkungen bei der Messung von Pflanzenfunktionen im Feld zurĂŒckzufĂŒhren. Um dieses Bild zu vervollstĂ€ndigen 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) beeintrĂ€chtigt (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 zusammenhĂ€ngen. Der Zusammenhang dieser optische Merkmale mit Pflanzenfunktionen wurde jedoch noch nicht systematisch untersucht, und ebenso ist der Zusammenhang zwischen Pflanzenfunktion und Kronendachreflektion noch nicht vollstĂ€ndig untersucht. Die physikalischen Interaktionen von Licht und optischen Pflanzenmerkmalen sind bereits hinreichend verstanden und in Strahlungstransfermodellen (RTM) fĂŒr VegetationskronendĂ€cher formuliert. RTM können als prozessbasierte Modelle betrachtet werden, die die Reflektion des Kronendachs in AbhĂ€ngigkeit von optische Merkmalen, dem Bodenhintergrund und der Sonnen-Sensorgeometrie modellieren. Das Ziel und die Innovation dieser Dissertation war die kausalen ZusammenhĂ€nge zwischen Kronendachreflektion und Pflanzenfunktion mittels RTM zu verstehen und zu nutzen. Es wurde gezeigt, dass fĂŒr 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, fĂŒr die keine Felddaten zur Modellkalibrierung benötigt werden. Die kartierten Merkmale zeigten eine hohe Übereinstimmung mit MerkmalsausprĂ€gungen aus unabhĂ€ngigen Datenbanken und spiegelten die im Feld gemessenen ökologischen Gradienten wider. Dies deutet darauf hin, dass RTM-Inversion als Ă€ußerst ĂŒbertragbare Methode betrachtet werden kann, um rĂ€umliche Karten von Pflanzenmerkmalen zu erstellen, die als Proxies fĂŒr Pflanzenfunktionen dienen können. Allerdings erfordert die Implementierung von RTM Inversionen fundierte Kenntnisse ĂŒber die Prinzipien der Strahlentransfermodellierung und der zu untersuchenden Vegetationscharakteristiken. Zweitens, ermöglichen RTM die Untersuchung von ZusammenhĂ€ngen 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 fĂŒr eine fernerkundliche Unterscheidung der Pflanzenfunktion von großer Relevanz sind. DarĂŒber hinaus wurde gezeigt, dass RTM-basierte Simulationen EinschrĂ€nkungen von Fallstudien kompensieren und Kenntnisse ĂŒber die ZusammenhĂ€nge von Pflanzenfunktionen, Pflanzeneigenschaften und Kronendachtreflektion erweitern können. Diese Kenntnisse bilden die Grundlage fĂŒr 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 MerkmalsausprĂ€gungen konnte gezeigt werden, dass die in RTM enthaltenen optischen Merkmale kausal mit primĂ€ren Pflanzenfunktionen zusammenhĂ€ngen. Dies wiederum bedeutet, dass die Reflexion des Kronendachs unmittelbar mit den primĂ€ren Funktionen der Pflanze zusammenhĂ€ngt (‘Reflektion folgt Funktion’). DarĂŒber hinaus wurde festgestellt, dass optische Merkmale vergleichbare oder sogar höhere Korrelationen mit den verwendeten pflanzlichen Funktionsgradienten aufweisen als die in der Pflanzenökologie ĂŒblich 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 ErgĂ€nzung oder Alternative zu den in der Pflanzenökologie ĂŒblichen 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 fĂŒhrt zukunftsrelevante Forschungspotentiale auf

    Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

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    Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole

    Extrapolating hyperspectral anthocyanin indices to multispectral satellite sensors---applications to fall foliage in New England

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    Anthocyanin, thought to be a universal indicator of plant stress, is a red pigment found in many plant species and can seen in New England autumns. Detecting its presence is useful for ecosystem analysis and monitoring changes during autumn senescence. Currently fall foliage is subjectively measured; creation of a satellite-based anthocyanin index will provide an objective measurement and enhance understanding of the distribution of plant stress and senescence over large areas. Anthocyanin indices were tested hyperspectrally in a laboratory setting, then indices were simulated for Hyperion, MERIS, MODIS, and Landsat TM/ETM+ to see which most accurately represents changes in anthocyanin concentration, and finally indices were applied to actual imagery. Results of this study found that (1/R564)-(1/R697) was the best approximation for anthocyanin; the red:green ratio was the best overall estimator of anthocyanin using simulated satellite bands; and real imagery from MODIS and MERIS satellite sensors can detect a fall foliage signal

    Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination

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    The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high accuracy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non-infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance provides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread
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