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

MODIS: Moderate-resolution imaging spectrometer. Earth observing system, volume 2B

The Moderate-Resolution Imaging Spectrometer (MODIS), as presently conceived, is a system of two imaging spectroradiometer components designed for the widest possible applicability to research tasks that require long-term (5 to 10 years), low-resolution (52 channels between 0.4 and 12.0 micrometers) data sets. The system described is preliminary and subject to scientific and technological review and modification, and it is anticipated that both will occur prior to selection of a final system configuration; however, the basic concept outlined is likely to remain unchanged

A New Dataset of Leaf Optical Traits to Include Biophysical Parameters in Addition to Spectral and Biochemical Assessment

The dataset associated with this paper is available on FRDR at https://www.doi.org/10.20383/103.0606Plant Phenotyping and Imaging Research Centre funded through the Canada First Research Excellence Fund (CFREF) Natural Sciences and Engineering Research Council of Canada (NSERC)To enable future improvement on current leaf optical property models, more data incorporating a larger range of measured properties is needed. To this end, a dataset was collected to associate spectral measurements (ultraviolet, visible, and near infrared) with biochemical and biophysical properties of leaves. The leaves represented in this dataset were selected to provide representation of agricultural species and of leaves with a wide variety of color (pigment) expression, surface characteristics, and age. Data collected for 290 leaf samples studied in this project included multiple spectral measurement geometries and ranges, biochemical assessment of chlorophyll a and b, carotenoids, and anthocyanins, and biophysical assessment of leaf thickness and surface characteristics that has not previously been a focus in other leaf datasets. The methods and results associated with this dataset are described in this work

Estimation of Soil Moisture Using Active Microwave Remote Sensing

The method for developing a soil moisture inversion algorithm using Radar data can be approached in two ways: the multiple-incident angle approach and the change detection method. This thesis discusses how these two methods can be used to predict surface soil moisture. In the multiple incident angle approach, surface roughness can be mapped, if multiple incident angle viewing is possible and if the surface roughness is assumed constant during data acquisitions. A backpropagation neural network (NN) is trained with the data set generated by the Integral Equation Method (IEM) model. The training data set includes possible combinations of backscatter obtained as a result of variation in dielectric constant within the period of data acquisitions. The inputs to the network are backscatter acquired at different incident angles. The outputs are correlation length and root mean square height (rms). Once the roughness is mapped using these outputs, dielectric constant can be determined. Three different data sets, (backscatter acquired from multiplerequencies, multiple-polarizations, and multiple-incident angles) are used to train the NN. The performance of the NN trained by the different data sets is compared. The next approach is the application of the change detection concept. In this approach, the relative change in dielectric constant over two different periods is determined from Radarsat data using a simplified algorithm. The vegetation backscatter contribution can be removed with the aid of multi-spectral data provided by Landsat. A method is proposed that minimizes the effect of incident angle on Radar backscatter by normalizing the acquired SAR images to a reference angle. A quantitative comparison of some of the existing soil moisture estimation algorithms is also mad

Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect

The launch of ADEOS in August 1996 with POLDER, TOMS, and OCTS instruments on board and the future launch of EOS-AM 1 in mid-1998 with MODIS and MISR instruments on board start a new era in remote sensing of aerosol as part of a new remote sensing of the whole Earth system (see a list of the acronyms in the Notation section of the paper). These platforms will be followed by other international platforms with unique aerosol sensing capability, some still in this century (e.g., ENVISAT in 1999). These international spaceborne multispectral, multiangular, and polarization measurements, combined for the first time with international automatic, routine monitoring of aerosol from the ground, are expected to form a quantum leap in our ability to observe the highly variable global aerosol. This new capability is contrasted with present single-channel techniques for AVHRR, Meteosat, and GOES that although poorly calibrated and poorly characterized already generated important aerosol global maps and regional transport assessments. The new data will improve significantly atmospheric corrections for the aerosol effect on remote sensing of the oceans and be used to generate first real-time atmospheric corrections over the land. This special issue summarizes the science behind this change in remote sensing, and the sensitivity studies and applications of the new algorithms to data from present satellite and aircraft instruments. Background information and a summary of a critical discussion that took place in a workshop devoted to this topic is given in this introductory paper. In the discussion it was concluded that the anticipated remote sensing of aerosol simultaneously from several space platforms with different observation strategies, together with continuous validations around the world, is expected to be of significant importance to test remote sensing approaches to characterize the complex and highly variable aerosol field. So far, we have only partial understanding of the information content and accuracy of the radiative transfer inversion of aerosol information from the satellite data, due to lack of sufficient theoretical analysis and applications to proper field data. This limitation will make the anticipated new data even more interesting and challenging. A main concern is the present inadequate ability to sense aerosol absorption, from space or from the ground. Absorption is a critical parameter for climate studies and atmospheric corrections. Over oceans, main concerns are the effects of white caps and dust on the correction scheme. Future improvement in aerosol retrieval and atmospheric corrections will require better climatology of the aerosol properties and understanding of the effects of mixed composition and shape of the particles. The main ingredient missing in the planned remote sensing of aerosol are spaceborne and ground-based lidar observations of the aerosol profiles

The Photometric Effect of Macroscopic Surface Roughness on Sediment Surfaces

The focus of this work was on explaining the effect of macroscopic surface roughness on the reflected light from a soil surface. These questions extend from deciding how to best describe roughness mathematically, to figuring out how to quantify its effect on the spectral reflectance from a soil’s surface. In this document, I provide a background of the fundamental literature in the fields of remote sensing and computer vision that have been instrumental in my research. I then outline the software and hardware tools that I have developed to quantify roughness. This includes a detailed outline of a custom LiDAR operating mode for the GRIT-T goniometer system that was developed and characterized over the course of this research, as well as proposed methods for using convergent images acquired by our goniometer system’s camera to derive useful structure from motion point clouds. These tools and concepts are then used in two experiments that aim to explain the relationship between soil surface roughness and spectral BRF phenomena. In the first experiment, clay sediment samples were gradually pulverized into a smooth powderized state and in steps of reduced surface roughness. Results show that variance in the continuum spectra as a function of viewing angle increased with the roughness of the sediment surface. This result suggests that inter-facet multiple scattering caused a variance in absorption band centering and depth due to an increased path length traveled through the medium. In the second experiment, we examine the performance of the Hapke photometric roughness correction for sand sediment surfaces of controlled sample density. We find that the correction factor potentially underpredicts the effect of shadowing in the forward scattering direction. The percentage difference between forward-modeled BRF measurements and empirically measured BRF measurements is constant across wavelength, suggesting that a factor can be empirically derived. Future results should also investigate the scale at which the photometric correction factor should be applied. Finally, I also outline a structure from motion processing chain aimed at deriving meaningful metrics of vegetation structure. Results show that correlations between these metrics and observed directional reflectance phenomena of chordgrass are strong for peak growing state plants. We observe good agreement between destructive LAI metrics and contact-based LAI metrics

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements

Earth resources: A continuing bibliography with indexes (issue 62)

This bibliography lists 544 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives