Inversion of the Earth’s Bond albedo from space geodesy

The Earth’s Bond albedo is the fraction of total reflected radiative flux emerging from the Earth’s Top of the Atmosphere (ToA) to the incident solar radiation. As such, it is a crucial component in modeling the Earth’s climate. This thesis presents a novel method for estimating the Earth’s Bond albedo, utilising the dynamical effects of Earth radiation pressure on satellite orbits that are directly related to the Bond albedo. Where current methods for estimating the outgoing reflected radiation are based on point measurements of the radiance reflected by the Earth taken in the proximity of the planet, the new method presented in this thesis makes use of the fact that Global Positioning Satellites (GPS) together view the entirety of the ToA surface. The theoretical groundwork is laid for this new method starting from the basic principles of light scattering, satellite dynamics, and Bayesian inference. The feasibility of the method is studied numerically using synthetic data generated from real measurements of GPS satellite orbital elements and the imaging data from the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) spacecraft. The numerical methods section introduces the methods used for forward modeling the ToA outgoing radiation, the Runge-Kutta method for integrating the satellite orbits and the virtual-observation Markov-chain Monte Carlo methods used for solving the inverse problem. The section also describes a simple clustering method used for classifying the ToA from EPIC images. The inverse problem was studied with very simple models for the ToA, the satellites, and the satellite dynamics. These initial results were promising as the inverse problem algorithm was able to accurately estimate the Bond albedo. Further study of the method is required to determine how the inverse problem algorithm works when more realism is added to the models

Inversion of true leaf reflectance from very high spatial resolution hyperspectral images

The spectral reflectance of vegetation obtained from optical sensors provides information on their biophysical and biochemical properties. However, in remote sensing, reflectance is typically computed with respect to the top-of canopy (TOC) surface, resulting in an apparent reflectance due to the differences between the illumination conditions between the observed vegetation elements and the TOC surface. While the TOC reflectance is useful for data with coarse spatial resolution, it leads to erroneous estimates of the vegetation properties when applied to very high spatial resolution (VHR) data where individual leaves are visible. An illumination correction is required to retrieve the true leaf reflectance from the TOC reflectance. The present work investigates an illumination correction method for retrieving the true leaf reflectance from VHR hyperspectral TOC reflectance images based on the spectral invariant theory and a simple mathematical model for the leaf reflectance. The method is tested on simulated and measured data. The results show that the leaf reflectance can be accurately estimated from both data (average RMSD between 0.02 and < 0.12)

Assessing spatial variability and estimating mean crown diameter in boreal forests using variograms and amplitude spectra of very-high-resolution remote sensing data

Funding Information: This work was supported by the Academy of Finland under Grant [317387]. We would like to acknowledge assistance from the University of Helsinki and Ilkka Korpela for providing us with the field measured tree data from Hyyti?l?. Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.The retrieval of forest variables from optical remote sensing data using physically-based models is an ill-posed problem and does not make full use of the high spatial resolution imagery that is becoming available globally. A possible solution to this is to use prior information about the retrieved variables, which constrains the possible solutions and reduces uncertainty in forest variable estimation. Therefore, we tried to quantify physically-based parameters that could be retrieved using the second-order statistics of measured and simulated very-high-resolution (pixel size less than 1 m) images of Finnish boreal forests. These forests have a well-defined structure and are usually not closed, i.e. the reflected signal has a considerable contribution from a green forest floor. We retrieved the second-order statistics using variograms and Fourier amplitude spectra. We found, in line with previous studies, that the range of variograms correlates well (r = 0.83) with the mean crown diameter for spatially homogeneous forest patches, and it can be used to estimate crown diameters with reasonable accuracy (RMSE = 0.42 m). We present a novel approach, which uses the Fourier amplitude spectrum to study the spatial structure of a forest. The approach provided encouraging results with the measured data: despite the lower accuracy (RMSE = 0.67 m) compared with variograms, we found that it could also be used to estimate mean crown diameters for heterogeneous forest areas. The Fourier amplitude spectrum approach did not work with the simulated images. Our results highlight the possibility to obtain further information from very-high-resolution images of forests to solve the ill-posed problem of forest variable estimation from optical remote sensing data using physically-based models.Peer reviewe

Spectral Reflectance Processing via Local Wavelength-Direction Correlations

The spectral bidirectional reflectance distribution function (BRDF) maps incident radiation of a surface to its outgoing counterpart at different wavelengths. This function plays a fundamental role in characterizing the various types of earth surfaces. Due to its high dimensionality, the measurements, analysis, and simulation of spectral BRDF are challenging. In this letter, we introduce a new method for processing spectral reflectance using the so-called data-adjacency, i.e., the correlation between adjacent wavelengths and viewing directions. The results show that the benefits of efficient representation, noise reduction, and analysis capability can all be integrated to the data.Peer reviewe

Forest management and public perceptions -visual versus verbal information.

Forest and landscape management measures have impacts on the amenity value of forests. People may have certain attitudes towards management, in particular near urban areas. The aim of this study was to evaluate the impacts on scenic beauty and recreational value of five different management practices: small clear cutting, thinning, removal of undergrowth, natural state, and traditionally managed cultural landscape. In order to compare visual perceptions with preconceptions, two evaluation methods, visual presentation (pictures produced by image-capture technology) and verbal questions were used. Scenic beauty and recreational value were assessed from slides in which management measures were presented by the pairwise comparison technique. The results indicate that scenic beauty and recreational preferences differ considerably from each other. In the study areas, small clear cuttings had the most positive effect on scenic beauty and natural state had most positive effect on recreational value. Furthermore, preconceptions concerning different silvicultural measures did not consistently correspond to perceptions based on the assessment of visual images. This fact supports the use of visual presentation methods in future preference studies as well as in participatory forest planning projects.</p

Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images

Background Transthyretin amyloidosis (ATTR) is a progressive disease which can be diagnosed non-invasively using bone avid [Tc-99m]-labeled radiotracers. Thus, ATTR is also an occasional incidental finding on bone scintigraphy. In this study, we trained convolutional neural networks (CNN) to automatically detect and classify ATTR from scintigraphy images. The study population consisted of 1334 patients who underwent [Tc-99m]-labeled hydroxymethylene diphosphonate (HMDP) scintigraphy and were visually graded using Perugini grades (grades 0-3). A total of 47 patients had visual grade >= 2 which was considered positive for ATTR. Two custom-made CNN architectures were trained to discriminate between the four Perugini grades of cardiac uptake. The classification performance was compared to four state-of-the-art CNN models. Results Our CNN models performed better than, or equally well as, the state-of-the-art models in detection and classification of cardiac uptake. Both models achieved area under the curve (AUC) >= 0.85 in the four-class Perugini grade classification. Accuracy was good in detection of negative vs. positive ATTR patients (grade = 2, AUC > 0.88) and high-grade cardiac uptake vs. other patients (grade < 3 vs. grade 3, AUC = 0.94). Maximum activation maps demonstrated that the automated deep learning models were focused on detecting the myocardium and not extracardiac features. Conclusion Automated convolutional neural networks can accurately detect and classify different grades of cardiac uptake on bone scintigraphy. The CNN models are focused on clinically relevant image features. Automated screening of bone scintigraphy images using CNN could improve the early diagnosis of ATTR.Peer reviewe