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

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

Assessing the contribution of understory sun-induced chlorophyll fluorescence through 3-D radiative transfer modelling and field data

A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space as a proxy for the photosynthetic activity of terrestrial vegetation. However, the effect of spatial heterogeneity on the SIF retrievals from canopy radiance derived from images with medium and low spatial resolution remains uncharacterised. In images from forest and agricultural landscapes, the background comprises a mixture of soil and understory and can generate confounding effects that limit the interpretation of the SIF at the canopy level. This paper aims to improve the understanding of SIF from coarse spatial resolutions in heterogeneous canopies by considering the separated contribution of tree crowns, understory and background components, using a modified version of the FluorFLIGHT radiative transfer model (RTM). The new model is compared with others through the RAMI model intercomparison framework and is validated with airborne data. The airborne campaign includes high-resolution data collected over a tree-grass ecosystem with the HyPlant imaging spectrometer within the FLuorescence EXplorer (FLEX) preparatory missions. Field data measurements were collected from plots with a varying fraction of tree and understory vegetation cover. The relationship between airborne SIF calculated from pure tree crowns and aggregated pixels shows the effect of the understory at different resolutions. For a pixel size smaller than the mean crown size, the impact of the background was low (R2 > 0.99; NRMSE 0.2). This study demonstrates that using a 3D RTM model improves the calculation of SIF significantly (R2 = 0.83, RMSE = 0.03 mW m−2 sr−1 nm−1) when the specific contribution of the soil and understory layers are accounted for, in comparison with the SIF calculated from mixed pixels that considers only one layer as background (R2 = 0.4, RMSE = 0.28 mW m−2 sr−1 nm−1). These results demonstrate the need to account for the contribution of SIF emitted by the understory in the quantification of SIF within tree crowns and within the canopy from aggregated pixels in heterogeneous forest canopies

Vliv atmosférické a topografické korekce na přesnost odhadu množství chlorofylu ve smrkových lesních porostech

Odstraňování efektů zemské atmosféry (tzv. atmosférická korekce) je jednou z klíčových součástí předzpracování obrazových dat dálkového průzkumu Země používaných pro kvantitativní nebo semi-kvantitativní analýzu. Přestože v současné době existuje velké množství robustních výpočetních technik kvantitativního odhadu různých parametrů zemského povrchu, vliv atmosférické korekce na výsledky těchto odhadů zpravidla není brán dostatečně v úvahu. Hlavním cílem této práce je zhodnocení vlivu použití různých technik atmosférické korekce na přesnost kvantitativního odhadu množství chlorofylu v lesních porostech smrku ztepilého (Picea abies). Obsah chlorofylu byl určován na podkladě výpočtu vybraných vegetačních indexů, které jsou na obsah chlorofylu citlivé (ANCB650-720, MSR, N718, TCARI/OSAVI a D718/D704). Hodnoty těchto indexů byly simulovány pomocí kombinace modelů radiativního transferu PROSPECT a DART. Výsledné odhady obsahu chlorofylu byly na závěr validovány pomocí výsledků laboratorního stanovení obsahu chlorofylu v odebraných vzorcích smrkových jehlic. Kromě toho byl v rámci práce odvozen nový index pro hodnocení podobnosti dvou srovnávaných spekter nazvaný normalized Area Under Difference Curve (nAUDC). V rámci této práce byla testována potenciální možnost náhrady standardní atmosférické korekce...Removal of atmospheric effects (atmospheric correction) is an essential step in a pre-processing chain of all remotely sensed image data used for any quantitative or semi-quantitative analysis. Although there are many robust computing techniques allowing quantitative estimation of various parameters of the Earth's surface, the influence of atmospheric correction on the accuracy of such estimation is usually not taken into account at all. The main focus of this thesis is to assess the influence of the use of different atmospheric correction techniques on the Norway spruce (Picea abies) canopy chlorophyll content estimation accuracy. Canopy chlorophyll content was estimated using values of chlorophyll sensitive vegetation indices (ANCB650-720, MSR, N718, TCARI/OSAVI and D718/D704) simulated by a coupling of PROSPECT and DART radiative transfer models and validated by a ground-truth dataset. A new spectral similarity index called normalized Area Under Difference Curve (nAUDC) was developed to allow mutual comparison of two spectra originating from hyperspectral datasets corrected by different atmospheric correction methods. Potential substitutability of the standard physically-based ATCOR-4 atmospheric correction by the empirical correction based on the data acquired by the downwelling irradiance...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Terrestrial laser scanning for crop monitoring. Capturing 3D data of plant height for estimating biomass at field scale

Terrestrial laser scanning (TLS) is a young remote sensing method, but the trustworthiness of such measurements offers great potential for accurate surveying. TLS allows non-experts to rapidly acquire 3D data of high density. Generally, this acquisition of accurate geoinformation is increasingly desired in various fields, however this study focuses on the application of TLS for crop monitoring. The increasing cost and efficiency pressure on agriculture induced the emergence of site specific crop management, which requires a comprehensive knowledge about the plant development. An important parameter to evaluate this development or rather the actual plant status is the amount of plant biomass, which is however directly only determinable with destructive sampling. With the aim of avoiding destructive measurements, interest is increasingly directed towards non-contact remote sensing surveys. Nowadays, different approaches address biomass estimations based on other parameters, such as vegetation indices (VIs) from spectral data or plant height. Since the plants are not taken it is feasible to perform several measurements across a field and across the growing season. Hence, the change of spatial and temporal patterns can be monitored. This study applies TLS for objectively measuring and monitoring plant height as estimator for biomass at field scale. Overall 35 TLS campaigns were carried out at three sites over four growing seasons. In each campaign a 3D point cloud, covering the surface of the field, was obtained and interpolated to a crop surface model (CSM). A CSM represents the crop canopy in a very high spatial resolution on a specific date. By subtracting a digital terrain model (DTM) of the bare ground from each CSM, plant heights were calculated pixel-wise. Manual measurements aligned well with the TLS data and demonstrated the main benefit of CSMs: the highly detailed acquisition of the entire crop surface. The plant height data were used to estimate biomass with empirically developed biomass regression models (BRMs). Validation analyses against destructive measurements were carried out to confirm the results. The spatial and temporal transferability of crop-specific BRMs was shown. In one case study, the estimations from plant height and six VIs were compared and the benefit of fusing both parameters was investigated. The analyses were based on the TLS-derived CSMs and spectral data measured with a field spectrometer. The important role of plant height as a robust estimator was shown in contrast to a varying performance of BRMs based on the VIs. A major benefit through the fusion of both parameters in multivariate BRMs could not be concluded in this study. Nevertheless, further research should address this fusion, with regard to the capability of VIs to assess information about the vegetation cover or biochemical and biophysical parameters

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

Scale challenges in inventory of forests aided by remote sensing

The impact of changing the scale of observation on information derived from forest inventories is the basis of scale-related research in forest inventory and analysis (FIA). Interactions between the scale of observation and observed heterogeneity in studied variables highlight a dependence on scale that affects measurements, estimates, and relationships between inventory data from terrestrial and remote sensing surveys. This doctoral research defines "scale" as the divisions of continuous space over which measurements are made, or hierarchies of discrete units of study/analysis in space. Therefore, the "scale of observation" (also known as support) refers to that integral of space over which statistics are computed and forest inventory variables regionalized. Given the ubiquitous nature of scale issues, a case study approach was undertaken in this research (Articles I-IV) with the goal to provide fundamental understanding of responses to the scale of observation for specific FIA variables. The studied forest inventory variables are; forest stand structural heterogeneity, forest cover proportion and tree species identities. Forest cover proportion (or simply forest area) and tree species are traditional and fundamental forest inventory variables commonly assessed over large areas using both terrestrial samples and remote sensing data whereas, forest stand structural heterogeneity is a contemporary FIA variable that is increasingly demanded in multi-resource inventories to inform management and conservation efforts as it is linked to biodiversity, productivity, ecosystem functioning and productivity, and used as auxiliary data in forest inventory. This research has two overall aims: 1. To improve the understanding of the association between the scale of observation and observed heterogeneity in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. 2. To contribute knowledge to the estimation of scale-dependence in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. Different scales of observation were considered across the four case studies encompassing individual leaf, crown-part or branch, single-tree crown, forest stand, landscape and global levels of analysis. Terrestrial and remote sensing data sets from a variety of temperate forests in Germany and France were utilized across case studies. In cases where no inventory data were available, synthetic data was simulated at different scales of observation. Heterogeneity in FIA variable estimates was monitored across scales of observation using estimators of variance and associated precision. As too much heterogeneity is hardly interpreted due to a low signal to noise ratio, object-based image analysis (OBIA) methods were used to manage heterogeneity in high resolution remote sensing data before evaluating scale dependence or scaling across observed scales. Similarly, ensemble classification techniques were applied to address methodological heterogeneity across classifiers in a case study on classification of two physically and spectrally similar Pinus species. Across case studies, a dependence on the scale of observation was determined by linking estimates of heterogeneity to their respective scales of observation using linear regression and a combination of geo-statistics and Monte-Carlo approaches. In order to address scale-dependence, thresholds to scale domains were identified so as to enable efficient observation of studied FIA variables and scaling approaches proposed to bridge observations across scales. For scaling, this research evaluated the potential of different regression techniques to map forest stand structural heterogeneity and tree species wall-to-wall from remote sensing data. In addition, radiative transfer modelling was evaluated in the transfer between leaf and crown hyperspectra, and a global sampling grid framework proposed to efficiently link different stages of survey sampling. This research shows that the scale of observation affected all studied FIA variables albeit to varying degrees, conditioned on the spatial structure and aggregation properties of the assessed FIA variable (i.e. whether the variable is extensive, intensive or scale-specific) and the method used in aggregation on support (e.g. mean, variance, quantile etc.). The scale of observation affected measurements or estimates of the studied FIA variables as well as relationships between spatially structured FIA variables. The scale of observation determined observed heterogeneity in FIA variables, affected parameter retrieval from radiative transfer models, and affected variable selection and performance of models linking terrestrial and remote sensing data. On the other hand, this research shows that it is possible to determine domains of scale dependence within which to efficiently observe the studied FIA variables and to bridge between scales of observation using various scaling methods. The findings of this doctoral research are relevant for the general understanding of scale issues in FIA. Research in Article I, for example, informs optimization of plot sizes for efficient inventory and mapping of forest structural heterogeneity, as well as for the design of natural resource inventories. Similarly, research in Article II is applicable in large area forest (or general land) cover monitoring from sampling by both visual interpretation of high resolution remote sensing imagery and terrestrial surveys. This research is also useful to determine observation design for efficient inventory of land cover. Research in Article III contributes in many contexts of remote sensing assisted inventory of forests especially in management and conservation planning, pest and diseases control and in the estimation of biomass. Lastly, research in Article IV highlights scale-related effects in passive optical remote sensing of forests currently understudied and can ultimately contribute to sensor calibration and modelling approaches