Estimation of Forest Biomass and Faraday Rotation using Ultra High Frequency Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) data in the Ultra High Frequency (UHF; 300 MHz – 3 GHz)) band have been shown to be strongly dependent of forest biomass, which is a poorly estimated variable in the global carbon cycle. In this thesis UHF-band SAR data from the fairly flat hemiboreal test site Remningstorp in southern Sweden were analysed. The data were collected on several occasions with different moisture conditions during the spring of 2007. Regression models for biomass estimation on stand level (0.5-9 ha) were developed for each date on which SAR data were acquired. For L-band (centre frequency 1.3 GHz) the best estimation model was based on HV-polarized backscatter, giving a root mean squared error (rmse) between 31% and 46% of the mean biomass. For P-band (centre frequency 340 MHz), regression models including HH, HV or HH and HV backscatter gave an rmse between 18% and 27%. Little or no saturation effects were observed up to 290 t/ha for P-band. A model based on physical-optics has been developed and was used to predict HH-polarized SAR data with frequencies from 20 MHz to 500 MHz from a set of vertical trunks standing on an undulating ground surface. The model shows that ground topography is a critical issue in SAR imaging for these frequencies. A regression model for biomass estimation which includes a correction for ground slope was developed using multi-polarized P-band SAR data from Remningstorp as well as from the boreal test site Krycklan in northern Sweden. The latter test site has pronounced topographic variability. It was shown that the model was able to partly compensate for moisture variability, and that the model gave an rmse of 22-33% when trained using data from Krycklan and evaluated using data from Remningstorp. Regression modelling based on P-band backscatter was also used to estimate biomass change using data acquired in Remningstorp during the spring 2007 and during the fall 2010. The results show that biomass change can be measured with an rmse of about 15% or 20 tons/ha. This suggests that not only deforestation, but also forest growth and degradation (e.g. thinning) can be measured using P-band SAR data. The thesis also includes result on Faraday rotation, which is an ionospheric effect which can have a significant impact on spaceborne UHF-band SAR images. Faraday rotation angles are estimated in spaceborne L-band SAR data. Estimates based on distributed targets and calibration targets with high signal to clutter ratios are found to be in very good agreement. Moreover, a strong correlation with independent measurements of Total Electron Content is found, further validating the estimates

Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All this information is gathered in the so called backscattering coefficient (σ0). The subject of this research have been olive groves canopies, where which types of canopy biophysical variables can be derived by a specific optical sensor and then integrated into microwave scattering models has been investigated. This has been undertaken by means of hemispherical photographs and gap fraction procedures. Then, variables such as effective and true Leaf Area Indices have been estimated. Then, in order to characterize this kind of vegetation canopy, two models based on Radiative Transfer theory have been applied and analyzed. First, a generalized two layer geometry model made up of homogeneous layers of soil and vegetation has been considered. Then, a modified version of the Xu and Steven Water Cloud Model has been assessed integrating the canopy biophysical variables derived by the suggested optical procedure. The backscattering coefficients at various polarized channels have been acquired from RADARSAT 2 (C-band), with 38.5° incidence angle at the scene center. For the soil simulation, the best results have been reached using a Dubois scattering model and the VV polarized channel (r2 = 0.88). In turn, when effective LAI (LAIeff) has been taken into account, the parameters of the scattering canopy model are better estimated (r2 = 0.89). Additionally, an inversion procedure of the vegetation microwave model with the adjusted parameters has been undertaken, where the biophysical values of the canopy retrieved by this methodology fit properly with field measured values

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1.0 includes the dense media radiative transfer theory (DMRT), the improved Born approximation (IBA), and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between the IBA and DMRT theories has been shown here. Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e.g., sea ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models.</p

Simulation of the Microwave Emission of Multi-layered Snowpacks Using the Dense Media Radiative Transfer Theory: the DMRT-ML Model

DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1-200 GHz similar to those acquired routinely by spacebased microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large icesheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python

Simulation and Visualization of Nectarine Branching and Fruiting Responses to Pruning Using PrungingSim Software

In this paper, we present a software tool named PruningSim dedicated to dynamic simulation and visualization of plants. The kernel framework provides various classes for event manager to schedule the handling of different types of events. The simulation in PruningSim platform is based on Markov model corresponding to branching processes in plants. Topological and geometrical parameters can be assigned automatically or modified manually in the simple graphical user interface. Visualization and image setting option is also available. Furthermore, we present one case study for simulation nectarine branches' responses to pruning intensities using PruningSim

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

Remote sensing of montane forest structure and biomass : a canopy relectance model inversion approach

xvi, 156 leaves : ill. (some col.), maps ; 29 cm.The multiple-forward-mode (MFM) inversion procedure is a set of methods for indirect canopy relectance model inversion using look-up tables (LUT). This thesis refines the MFM technique with regard to: 1) model parameterization for the MFM canopy reflectance model executions and 2) methods for limiting or describing multiple solutions. Forest stand structure estimates from the inversion were evaluated using 40 field validation sites in the Canadian Rocky Mountains. Estimates of horizontal and vertical crown radius were within 0.5m and 0.9m RMSE for both conifer and deciduous species. Density estimates were within 590 stems/ha RMSE for conifer and 310 stems/ha RMSE for deciduous. The most effective inversion method used a variable spectral domain with constrained, fine increment LUTs. A biomass estimation method was also developed using empirical relationships with crown area. Biomass density estimates using the MFM method were similar to estimates produced using other multispectral analysis methods (RMSE=50t/ha)

NASA's Upper Atmosphere Research Program UARP and Atmospheric Chemistry Modeling and Analysis Program (ACMAP): Research Summaries 1994 - 1996. Report to Congress and the Environmental Protection Agency

Under the mandate contained in the FY 1976 NASA Authorization Act, the National Aeronautics and Space Administration (NASA) has developed and is implementing a comprehensive program of research, technology, and monitoring of the Earth's upper atmosphere, with emphasis on the stratosphere. This program aims at expanding our understanding to permit both the quantitative analysis of current perturbations as well as the assessment of possible future changes in this important region of our environment. It is carried out jointly by the Upper Atmosphere Research Program (UARP) and the Atmospheric Chemistry Modeling and Analysis Program (ACMAP), both managed within the Science Division in the Office of Mission to Planet Earth at NASA. Significant contributions to this effort are also provided by the Atmospheric Effects of Aviation Project (AEAP) of NASA's Office of Aeronautics. The long-term objectives of the present program are to perform research to: understand the physics, chemistry, and transport processes of the upper atmosphere and their effect on the distribution of chemical species in the stratosphere, such as ozone; understand the relationship of the trace constituent composition of the lower stratosphere and the lower troposphere to the radiative balance and temperature distribution of the Earth's atmosphere; and accurately assess possible perturbations of the upper atmosphere caused by human activities as well as by natural phenomena. In compliance with the Clean Air Act Amendments of 1990, Public Law 101-549, NASA has prepared a report on the state of our knowledge of the Earth's upper atmosphere, particularly the stratosphere, and on the progress of UARP and ACMAP. The report for the year 1996 is composed of two parts. Part 1 summarizes the objectives, status, and accomplishments of the research tasks supported under NASA UARP and ACMAP in a document entitled, Research Summary 1994-1996. Part 2 is entitled Present State of Knowledge of the Upper Atmosphere 1996.- An Assessment Report. It consists primarily of the Executive Summary and Chapter Summaries of the World Meteorological Organization Global Ozone Research and Monitoring Project Report No. 37, Scientific Assessment of Ozone Depletion: 1994, sponsored by NASA, the National Oceanic and Atmospheric Administration (NOAA), the UK Department of the Environment, the United Nations Environment Program, and the World Meteorological Organization. Other sections of Part 11 include summaries of the following: an Atmospheric Ozone Research Plan from NASA's Office of Mission to Planet Earth; summaries from a series of Space Shuttle-based missions and two recent airborne measurement campaigns; the Executive Summary of the 1995 Scientific Assessment of the Atmospheric Effects of Stratospheric Aircraft, and the most recent evaluation of photochemical and chemical kinetics data (Evaluation No. 12 of the NASA Panel for Data Evaluation) used as input parameters for atmospheric models

Télédétection micro-onde de surfaces enneigées en milieu arctique : étude des processus de surface de la calotte glaciaire Barnes, Nunavut, Canada

Résumé : La région de l'archipel canadien, située en Arctique, connaît actuellement d'importants changements climatiques, se traduisant notamment par une augmentation des températures, une réduction de l'étendue de la banquise marine et du couvert nival terrestre ou encore une perte de masse significative des calottes glaciaires disséminées sur les îles de l'archipel. Parmi ces calottes glaciaires, la calotte Barnes, située en Terre de Baffin, ne fait pas exception comme le montrent les observations satellitaires qui témoignent d'une importante perte de masse ainsi que d'une régression de ses marges, sur les dernières décennies. Bien que les calottes glaciaires de l'archipel canadien ne représentent que quelques dizaines de centimètres d'élévation potentielle du niveau des mers, leur perte de masse est une composante non négligeable de l'augmentation actuelle du niveau des mers. Les projections climatiques laissent à penser que cette contribution pourrait rester significative dans les décennies à venir. Cependant, afin d'estimer les évolutions futures de ces calottes glaciaires et leur impact sur le climat ou le niveau des mers, il est nécessaire de caractériser les processus physiques tels que les modifications du bilan de masse de surface. Cette connaissance est actuellement très limitée du fait notamment du sous-échantillonnage des régions arctiques en terme de stations météorologiques permanentes. Une autre particularité de certaines calottes de l'archipel canadien, et de la calotte Barnes en particulier, est de présenter un processus d'accumulation de type glace surimposée, ce phénomène étant à prendre en compte dans l'étude des processus de surface. Pour pallier au manque de données, l'approche retenue a été d'utiliser des données de télédétection, qui offrent l'avantage d'une couverture spatiale globale ainsi qu'une bonne répétitivité temporelle. En particulier les données acquises dans le domaine des micro-ondes passives sont d'un grand intérêt pour l'étude de surfaces enneigées. En complément de ces données, la modélisation du manteau neigeux, tant d'un point de vue des processus physiques que de l'émission électromagnétique permet d'avoir accès à une compréhension fine des processus de surface tels que l'accumulation de la neige, la fonte, les transferts d'énergie et de matière à la surface, etc. Ces différents termes sont regroupés sous la notion de bilan de masse de surface. L'ensemble du travail présenté dans ce manuscrit a donc consisté à développer des outils permettant d'améliorer la connaissance des processus de surface des calottes glaciaires du type de celles que l'on rencontre dans l'archipel canadien, l'ensemble du développement méthodologique ayant été réalisé sur la calotte Barnes à l'aide du schéma de surface SURFEX-CROCUS pour la modélisation physique et du modèle DMRT-ML pour la partie électromagnétique. Les résultats ont tout d'abord permis de mettre en évidence une augmentation significative de la durée de fonte de surface sur la calotte Barnes (augmentation de plus de 30% sur la période 1979-2010), mais aussi sur la calotte Penny, elle aussi située en Terre de Baffin et qui présente la même tendance (augmentation de l'ordre de 50% sur la même période). Ensuite, l'application d'une chaîne de modélisation physique contrainte par diverses données de télédétection a permis de modéliser de manière réaliste le bilan de masse de surface de la dernière décennie, qui est de +6,8 cm/an en moyenne sur la zone sommitale de la calotte, qui est une zone d'accumulation. Enfin, des tests de sensibilité climatique sur ce bilan de masse ont permis de mettre en évidence un seuil à partir duquel cette calotte voit disparaître sa zone d'accumulation. Les modélisations effectuées suggèrent que ce seuil a de fortes chances d'être atteint très prochainement, pour une augmentation de température moyenne inférieure à 1°C, ce qui aurait pour conséquence une accélération de la perte de masse de la calotte. // Abstract : Significant climate change is curently monitored in the Arctic, and especially in the region of the canadian arctic archipellago. This climate warming leads to recession of seaice extent and seasonnal snow cover, and also to large mass loss of the archipellago’s ice caps. One of the most southern ice cap, the Barnes Ice Cap, located on the Baffin Island, is no exception to significant mass loss and margins recession as satellite observations exhibited over the last decades. Despite the relative low sea level potential of the small ice caps located in the canadian arctic achipellago in regards to major ice sheets, Antarctica and Greenland, their contribution to the current sea level rise is significant. Climate projections show that this contribution could accelerate significant over the next decades. However, to estimate the future evolution of these ice caps and their impact on climate or sea level rise, a better characterisation of the surface processes such as the evolution of the surface mass balance is needed. This knowledge is currently very limited, mainly due to the sparse covering of automatic weather stations or in-situ measurements over the Arctic. Furthermore, several ice caps, among with the Barnes Ice Cap, present a superimposed ice accumulation area which particularities have to be taken into account in the surface processes studies. Given the lack of in-situ data, the approach choosen in this work is to use remote sensing data, that have the advantage to offer a good spatial and temporal coverage. In particular, passive microwave data are very suitable for snowy surfaces studies. To complement these data, physical and electromagnetic snowpack modeling provide a fine characterisation of surface processes such as snow accumulation. The whole work presented in this manuscript thus consisted in developping specific tools to improve the understanding of surface processes of small arctic ice caps. This methodological development was performed and applied on the Barnes Ice Cap using the surface scheme SURFEX-CROCUS and the electromagnetic model DMRT-ML. First results highlight a significant increase in surface melt duration over the past 3 decades on the Barnes Ice Cap (increase of more than 30% over 1979-2010 period). A similar trend is also monitored over the Penny Ice Cap, located in the south part of the Baffin Island (increase of more than 50% over the same period). Then, the surface mass balance over the last decade was modeled by using a physical based modeling chain constrained by remote sensing data. The results give a mean net accumulation of +6,8 cm y−1 on the summit area of the ice cap. Finaly, sensitivity tests, performed to investigate the climatic sensitivity of the surface mass balance, highlight a threshold effect that may lead to a complete disapearence of the accumulation area of the Barnes Ice Cap. With a temperature increase less than 1°C, modeling results suggest it is likely that the threshold will be reached rapidly leading to an increase in mass loss from the ice cap