Automated and robust geometric and spectral fusion of multi-sensor, multi-spectral satellite images

Die in den letzten Jahrzehnten aufgenommenen Satellitenbilder zur Erdbeobachtung bieten eine ideale Grundlage für eine genaue Langzeitüberwachung und Kartierung der Erdoberfläche und Atmosphäre. Unterschiedliche Sensoreigenschaften verhindern jedoch oft eine synergetische Nutzung. Daher besteht ein dringender Bedarf heterogene Multisensordaten zu kombinieren und als geometrisch und spektral harmonisierte Zeitreihen nutzbar zu machen. Diese Dissertation liefert einen vorwiegend methodischen Beitrag und stellt zwei neu entwickelte Open-Source-Algorithmen zur Sensorfusion vor, die gründlich evaluiert, getestet und validiert werden. AROSICS, ein neuer Algorithmus zur Co-Registrierung und geometrischen Harmonisierung von Multisensor-Daten, ermöglicht eine robuste und automatische Erkennung und Korrektur von Lageverschiebungen und richtet die Daten an einem gemeinsamen Koordinatengitter aus. Der zweite Algorithmus, SpecHomo, wurde entwickelt, um unterschiedliche spektrale Sensorcharakteristika zu vereinheitlichen. Auf Basis von materialspezifischen Regressoren für verschiedene Landbedeckungsklassen ermöglicht er nicht nur höhere Transformationsgenauigkeiten, sondern auch die Abschätzung einseitig fehlender Spektralbänder. Darauf aufbauend wurde in einer dritten Studie untersucht, inwieweit sich die Abschätzung von Brandschäden aus Landsat mittels synthetischer Red-Edge-Bänder und der Verwendung dichter Zeitreihen, ermöglicht durch Sensorfusion, verbessern lässt. Die Ergebnisse zeigen die Effektivität der entwickelten Algorithmen zur Verringerung von Inkonsistenzen bei Multisensor- und Multitemporaldaten sowie den Mehrwert einer geometrischen und spektralen Harmonisierung für nachfolgende Produkte. Synthetische Red-Edge-Bänder erwiesen sich als wertvoll bei der Abschätzung vegetationsbezogener Parameter wie z. B. Brandschweregraden. Zudem zeigt die Arbeit das große Potenzial zur genaueren Überwachung und Kartierung von sich schnell entwickelnden Umweltprozessen, das sich aus einer Sensorfusion ergibt.Earth observation satellite data acquired in recent years and decades provide an ideal data basis for accurate long-term monitoring and mapping of the Earth's surface and atmosphere. However, the vast diversity of different sensor characteristics often prevents synergetic use. Hence, there is an urgent need to combine heterogeneous multi-sensor data to generate geometrically and spectrally harmonized time series of analysis-ready satellite data. This dissertation provides a mainly methodical contribution by presenting two newly developed, open-source algorithms for sensor fusion, which are both thoroughly evaluated as well as tested and validated in practical applications. AROSICS, a novel algorithm for multi-sensor image co-registration and geometric harmonization, provides a robust and automated detection and correction of positional shifts and aligns the data to a common coordinate grid. The second algorithm, SpecHomo, was developed to unify differing spectral sensor characteristics. It relies on separate material-specific regressors for different land cover classes enabling higher transformation accuracies and the estimation of unilaterally missing spectral bands. Based on these algorithms, a third study investigated the added value of synthesized red edge bands and the use of dense time series, enabled by sensor fusion, for the estimation of burn severity and mapping of fire damage from Landsat. The results illustrate the effectiveness of the developed algorithms to reduce multi-sensor, multi-temporal data inconsistencies and demonstrate the added value of geometric and spectral harmonization for subsequent products. Synthesized red edge information has proven valuable when retrieving vegetation-related parameters such as burn severity. Moreover, using sensor fusion for combining multi-sensor time series was shown to offer great potential for more accurate monitoring and mapping of quickly evolving environmental processes

APPLICATIONS OF MODERATE-RESOLUTION REMOTE SENSING TECHNOLOGIES FOR SURFACE AIR POLLUTION MONITORING IN SOUTHEAST ASIA

Retrievals from Earth observation satellites are widely used for many applications, including analyzing dynamic lands and measuring atmospheric components. This research aims to evaluate appropriateness of using satellite retrievals to facilitate understanding characteristics of Southeast Asian (SEA) surface air pollution, attributed to regional biomass burnings and urban activities. The studies in this dissertation focused on using satellite retrievals for 1) mapping potential SEA air pollution sources; which are forests, rice paddies, and urban areas, 2) understanding dynamic optical characteristics of SEA biomass-burning aerosols, and 3) inferring surface ozone level. Data used in this study were from three NASA\u27s Earth Observing System (EOS) satellites, which are Terra, Aqua, and Aura. These retrievals have spatial resolution ranging from hundred meters to ten kilometers. Algorithms used for the SEA land cover classification were developed using time-series analyses of surface reflectance in multiple wavelength bands from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. Comparing the results to national statistical databases, good agreement was obtained for spatial estimation of forest areas after correction with plantation areas. For estimation of rice paddies areas, the agreement depended on the rice ecosystems. It was good for rainfed rice and poor for deepwater rice. Models for irrigated and upland rice areas showed overall high coefficients of determination, suggesting that they effectively simulated the spatial distribution of those rice paddies; but were prone to underestimate and overestimate, respectively. Estimated SEA regional rice area was 42×106 ha, which agrees with previous published values. Analysis of the satellite retrieval could identify large urban areas. However, the satellite-derived urban areas also incorrectly included large sandy beaches. Optical properties of SEA background aerosols were investigated through the multivariate analyses of long-term ground-based aerosol measurements acquired from Aerosol Robotic Network (AERONET). The results in this study showed that from mid-September to December, the aerosol had both fine size and high light scattering efficiency. It was assumed to be largely urban/industrial aerosols, possibly coming from eastern China. From January to April, the aerosol had fine size and had single scattering albedo (SSA at 440 nm) of approximately 0.9. It was assumed to be smoke from local biomass burning. From October to January, when seasonal winds are strongest, more SEA urban aerosol was observed. This aerosol had coarser size and had SSA of ~0.9 or less. The appropriateness of using Ozone Monitoring Instrument (OMI) aerosol retrieval to facilitate understanding SEA biomass-burning aerosol properties was evaluated through three lines of evidence. These are 1) comparisons between the results obtained from multivariate analyses of the OMI aerosol retrieval and those obtained from the ground-measured AERONET data, 2) from Atmospheric Infrared Sounder (AIRS) total column CO product, and 3) from MODIS active fire detections. The results showed that the OMI retrieval used for large-scale SEA biomass-burning aerosol characterization was consistent with these alternative measures only when 1 \u3c OMI aerosol optical depth (442 nm) \u3c 3. The OMI aerosol retrieval was then used for the study on dynamic characteristics of biomass burning aerosol. This study considered the aerosols from two forest-fire episodes, 2007 SEA continent and 2008 Indonesian fires. Dependence of the aerosol optical properties on four variables was investigated. These variables were 1) wind speed/direction, 2) relative humidity (RH), 3) land use/cover as a surrogate of fuel type estimated from time-series analysis of MODIS surface reflectance, and 4) age of aerosol estimated from spatial-temporal analysis of MODIS active fire and the wind characteristics. Results from Pearson Chi-square test for independence showed that the dependence between aerosol group memberships with different optical properties and the limiting variables was significant for most cases, except for Indonesian aerosol age factor. These results agree with prior knowledge on regional burning conditions (types of fuel and relative humidity) and aerosol chemical/physical properties (chemical composition related to aerosol optical properties and hygroscopicity). Using EOS-Aura tropospheric column ozone (TCO) to infer surface ozone level was evaluated through analyses of linear relationships between TCO estimated from OMI and Microwave Limb Sounder (MLS) retrievals and coincident TCO from balloon-based ozonesonde measurements. This evaluation was for different tropospheric ozone profile shapes and for different geographical regions (for low, mid, and high latitudes and for Pacific and Atlantic regions). Results indicate that inference on ozone level derived from the satellite-based TCO requires corresponding information about tropospheric ozone profile shape. The use of satellite-based TCO was more appropriate for polluted low-latitude locations where upper troposphere ozone is rare and surface enhanced ozone is high

Master of Science

thesisUnderstanding the connection between large-scale meteorology, cloud macrophysical variables, and cloud microphysical variables is needed in order to improve the parameterization of marine boundary layer (MBL) clouds in weather and climate models. For this study, multiple aspects of MBL clouds over the Atmospheric Radiation Measurement Program (ARM) mobile site at Graciosa Island, Azores are examined. Hourly averaged raw variables of cloud fraction, column summed dBZ, liquid water path, first cloud base height, boundary layer static stability, and midtropospheric static stability are clustered together using a K-means clustering algorithm. The cluster output infers seven characteristic cloud regimes that describe the spectrum of warm boundary layer clouds that occurred over Graciosa Island during the deployment. These cloud regimes range from precipitating stratocumulus to nonprecipitating fair weather cumulus to deep clouds associated with broad synoptic scale frontal systems. Using the cluster results and NCEP/NCAR reanalysis, the typical macrophysical and meteorological environments for the MBL cloud regimes are summarized along with their average radar profiles. MBL cloud microphysical properties are then derived using a new retrieval algorithm that assumes the presence of both cloud and precipitation particle modes within a radar resolution volume. Compared to a traditional single mode particle size distribution (PSD), a bimodal PSD is closer to in-situ observations and is expected to provide improved statistics and understanding of the cloud microphysical parameters such as number concentration, precipitation rate, and effective droplet sizes. The bimodal retrieval algorithm can use either ARM ground-based or A-Train satellite-based data as an input. This study finds that ARM and A-Train versions of the bimodal algorithm retrieve plausible microphysics and the reasons for their differences are explored. Case studies are completed using the bimodal retrieval for three shallow cloud regimes with varying precipitation, macrophysical, and synoptic environments. Results show that microphysical quantities do change as the cloud regime varies and validate the connection between the large and small-scale environment of MBL clouds. The specifics of the unique regime-based microphysics are also useful in order to better parameterize these clouds in models

Atmospheric Downscaling using Multi-Objective Genetic Programming

Numerical models are used to simulate and to understand the interplay of physical processes in the atmosphere, and to generate weather predictions and climate projections. However, due to the high computational cost of atmospheric models, discrepancies between required and available spatial resolution of modeled atmospheric data occur frequently. One approach to generate higher-resolution atmospheric data from coarse atmospheric model output is statistical downscaling. The present work introduces multi-objective Genetic Programming (MOGP) as a method for downscaling atmospheric data. MOGP is applied to evolve downscaling rules, i.e., statistical relations mapping coarse-scale atmospheric information to the point scale or to a higher-resolution grid. Unlike classical regression approaches, where the structure of the regression model has to be predefined, Genetic Programming evolves both model structure and model parameters simultaneously. Thus, MOGP can flexibly capture nonlinear and multivariate predictor-predictand relations. Classical linear regression predicts the expected value of the predictand given a realization of predictors minimizing the root mean square error (RMSE) but in general underestimating variance. With the multi-objective approach multiple cost/fitness functions can be considered which are not solely aimed at the minimization of the RMSE, but simultaneously consider variance and probability distribution based measures. Two areas of application of MOGP for atmospheric downscaling are presented: The downscaling of mesoscale near-surface atmospheric fields from 2.8 km to 400 m grid spacing and the downscaling of temperature and precipitation series from a global reanalysis to a set of local stations. (1) With growing computational power, integrated modeling platforms, coupling atmospheric models to land surface and hydrological/subsurface models are increasingly used to account for interactions and feedback processes between the different components of the soil-vegetation-atmosphere system. Due to the small-scale heterogeneity of land surface and subsurface, land surface and subsurface models require a small grid spacing, which is computationally unfeasible for atmospheric models. Hence, in many integrated modeling systems, a scale gap occurs between atmospheric model component and the land surface/subsurface components, which potentially introduces biases in the estimation of the turbulent exchange fluxes at the surface. Under the assumption that the near surface atmospheric boundary layer is significantly influenced by land surface heterogeneity, MOGP is used to evolve downscaling rules that recover high-resolution near-surface fields of various atmospheric variables (temperature, wind speed, etc.) from coarser atmospheric data and high-resolution land surface information. For this application MOGP does not significantly reduce the RMSE compared to a pure interpolation. However, (depending on the state variable under consideration) large parts of the spatial variability can be restored without any or only a small increase in RMSE. (2) Climate change impact studies often require local information while the general circulation models used to create climate projections provide output with a grid spacing in the order of approximately 100~km. MOGP is applied to estimate the local daily maximum, minimum and mean temperature and the daily accumulated precipitation at selected stations in Europe from global reanalysis data. Results are compared to standard regression approaches. While for temperature classical linear regression already achieves very good results and outperforms MOGP, the results of MOGP for precipitation downscaling are promising and outperform a standard generalized linear model. Especially the good representation of precipitation extremes and spatial correlation (with the latter not incorporated in the objectives) are encouraging.Numerische Modelle, welche für Wettervorhersagen und Klimaprojektionen verwendet werden, simulieren das Zusammenspiel physikalischer Prozesse in der Atmosphäre. Bedingt durch den hohen Rechenaufwand atmosphärischer Modelle treten jedoch häufig Diskrepanzen zwischen benötigter und verfügbarer Auflösung atmosphärischer Daten auf. Ein möglicher Ansatz, höher aufgelöste atmosphärische Daten aus vergleichsweise grobem Modelloutput zu generieren, ist statistisches Downscaling. Die vorliegende Arbeit stellt multi-objektives Genetic Programming (MOGP) als Methode für das Downscaling atmosphärischer Daten vor. MOGP wird verwendet, um Downscaling Regeln (statistische Beziehungen) zu generieren, welche grobskalige atmosphärische Daten auf die Punktskala oder ein höher aufgelöstes Gitter abbilden. Im Gegensatz zu klassischen Regressionsansätzen, in welchen die Struktur des Regressionsmodells vorgegeben wird, entwickelt MOGP Modellstruktur und Modellparameter simultan. Dieses erlaubt es, auch nicht lineare und multivariate Beziehungen zwischen Prädiktoren und Prädiktand zu berücksichtigen. Ein klassisches lineares Regressionsmodel schätzt den Erwartungswert des Prädiktanden, eine Realisierung von Prädiktoren gegeben, und minimiert somit den mittleren quadratischen Fehler (root mean square error, RMSE), aber unterschätzt im Allgemeinen die Varianz. Mit einem multi-objektiven Ansatz können multiple Kostenfunktionen berücksichtigt werden, welche nicht ausschließlich auf die Minimierung des RMSE ausgelegt sind, sondern simultan auch Varianz und Wahrscheinlichkeitsverteilung berücksichtigen. In dieser Arbeit werden zwei verschiedene Anwendungen von MOGP für atmosphärisches Downscaling präsentiert: Das Downscaling mesoskaliger oberflächennaher atmosphärischer Felder von einem 2.8km auf ein 400 m Gitter und das Downscaling von Temperatur- und Niederschlagszeitreihen von globalen Reanalysedaten auf lokale Stationen. (1) Mit wachsender Rechenleistung werden integrierte Modellplattformen, welche Atmosphären-modelle mit Landoberflächenmodellen und hydrologischen Bodenmodellen koppeln, immer häufiger verwendet, um auch die Interaktionen und Feedbacks zwischen den Komponenten des Boden-Vegetations-Atmosphären Systems zu berücksichtigen. Aufgrund kleinskaliger Heterogenitäten in Landoberfläche und Boden benötigen die Landoberflächen- und Bodenmodelle eine hohe Gitterauflösung. Für atmosphärische Modelle hingegen ist eine solch hohe Auflösung rechnerisch nicht praktikabel. Daher findet sich typischerweise ein Skalenunterschied zwischen atmosphärischer und Landoberflächen-/hydrologischer Modellkomponente. Solch ein Skalensprung kann jedoch zu Problemen bei der Schätzung der turbulenten Flüsse zwischen Atmosphäre und Boden führen, da die turbulenten Flüsse in nichtlinearer Weise vom Zustand des Bodens und der bodennahen Atmosphäre abhängen. Die mit MOGP entwickelten Downscaling Regeln verwenden grob aufgelöste atmosphärische Daten und hoch aufgelöste Landoberflächen-Informationen, um hoch aufgelöste Felder verschiedener bodennaher atmosphärischer Variablen (Temperatur, Windgeschwindigkeit etc.) generieren. Die Regeln basieren somit auf der Annahme, dass die bodennahe atmosphärische Grenzschicht signifikant von der Heterogenität der Landoberfläche beeinflusst wird. Zwar erreicht MOGP für diese Anwendung nur selten eine signifikante Reduktion des RMSE gegenüber einer reinen Interpolation, jedoch kann, abhängig von der betrachteten atmosphärischen Variablen, ein großer Teil der räumlichen Variabilität wiederhergestellt werden ohne oder mit nur sehr geringem Anstieg des RMSE. (2) Studien zur Auswirkung des Klimawandels benötigen oft hochaufgelöste oder lokale atmosphärische Daten. Der Output globaler Klimamodelle, mit Hilfe derer Klimaprojektionen erstellt werden, ist gemeinhin zu grob. MOGP wird verwendet, um Tagesmaximum, -minimum und -mittel der Temperatur sowie den täglich akkumulierten Niederschlag an lokalen Stationen in Europa zu schätzen. Die Resultate werden mit linearen Regressionsmethoden verglichen. Für das Downscaling von Temperatur liefert eine klassische lineare Regression bereits sehr gute Resultate, welche MOGP im Allgemeinen an Qualität übertreffen. Für Niederschlag hingegen sind die MOGP Resultate vielversprechend, auch im Vergleich zu generalisierten linearen Modellen. Insbesondere die Repräsentation von Niederschlagsextremen und räumlicher Korrelation (letzteres ist nicht Bestandteil der Kostenfunktionen) sind vielversprechend

A Landsat-based analysis of tropical forest dynamics in the Central Ecuadorian Amazon : Patterns and causes of deforestation and reforestation

Tropical deforestation constitutes a major threat to the Amazon rainforest. Monitoring forest dynamics is therefore necessary for sustainable management of forest resources in this region. However, cloudiness results in scarce good quality satellite observations, and is therefore a major challenge for monitoring deforestation and for detecting subtle processes such as reforestation. Furthermore, varying human pressure highlights the importance of understanding the underlying forces behind these processes at multiple scales but also from an interand transdisciplinary perspective. Against this background, this study analyzes and recommends different methodologies for accomplishing these goals, exemplifying their use with Landsat timeseries and socioeconomic data. The study cases were located in the Central Ecuadorian Amazon (CEA), an area characterized by different deforestation and reforestation processes and socioeconomic and landscape settings. Three objectives guided this research. First, processing and timeseries analysis algorithms for forest dynamics monitoring in areas with limited Landsat data were evaluated, using an innovative approach based in genetic algorithms. Second, a methodology based in image compositing, multisensor data fusion and postclassification change detection is proposed to address the limitations observed in forest dynamics monitoring with timeseries analysis algorithms. Third, the evaluation of the underlying driving forces of deforestation and reforestation in the CEA are conducted using a novel modelling technique called geographically weight ridge regression for improving processing and analysis of socioeconomic data. The methodology for forest dynamics monitoring demonstrates that despite abundant data gaps in the Landsat archive for the CEA, historical patterns of deforestation and reforestation can still be reported biennially with overall accuracies above 70%. Furthermore, the improved methodology for analyzing underlying driving forces of forest dynamics identified local drivers and specific socioeconomic settings that improved the explanations for the high deforestation and reforestation rates in the CEA. The results indicate that the proposed methodologies are an alternative for monitoring and analyzing forest dynamics, particularly in areas where data scarcity and landscape complexity require approaches that are more specialized.Landsat-basierte Analyse der Dynamik tropischer Wälder im Zentral-Ecuadorianischen Amazonasgebiet: Muster und Ursachen von Abholzung und Wiederaufforstung Die tropische Entwaldung stellt eine große Bedrohung für den AmazonasRegenwald dar. Daher ist die Überwachung von Walddynamiken eine notwendige Maßnahme, um eine nachhaltige Bewirtschaftung der Waldressourcen in dieser Region zu gewährleisten. Jedoch verschlechtert Bewölkung die Qualität der Satellitenaufnahmen und stellt die hauptsächliche Herausforderung für die Überwachung der Entwaldung sowie die Detektierung einhergehender Prozesse, wie der Wiederaufforstung, dar. Darüber hinaus zeigt der unterschiedliche menschliche Nutzungsdruck, wie wichtig es ist, die zugrundeliegenden Kräfte hinter diesen Prozessen auf mehreren Ebenen, aber auch interund transdisziplinär, zu verstehen. Variierender anthropogener Einfluss unterstreicht die Notwendigkeit, unterschwellige Prozesse (oder "Driver") auf multiplen Skalen aus interund transdisziplinärer Sicht zu verstehen. Darauf basierend analysiert und empfiehlt die vorliegende Studie unterschiedliche Methoden, welche unter Verwendung von LandsatZeitreihen und sozioökonomischen Daten zur Erreichung dieser Ziele beitragen. Die Untersuchungsgebiete befinden sich im ZentralEcuadorianischen Amazonasgebiet (CEA). Einem Gebiet, das einerseits durch differenzierte Entwaldungsund Aufforstungsprozesse, andererseits durch seine sozioökonomischen und landschaftlichen Gegebenheiten geprägt ist. Das Forschungsprojekt hat drei Zielvorgaben. Erstens werden auf genetischen Algorithmen basierten Verfahren zur Verarbeitung der Zeitreihenanalyse für die Überwachung der Walddynamik in Gebieten, für die nur begrenzte LandsatDaten vorhanden waren, bewertet. Zweitens soll eine Methode in Anlehnung an Satellitenbildkompositen, Datenfusion von mehreren Satellitenbildern und Veränderungsdetektion gefunden werden, die Einschränkungen der Walddynamik durch Entwaldung mithilfe von ZeitreihenAlgorithmen thematisiert. Drittens werden die Ursachen der Entwaldung/Abholzung im CEA anhand der geographischen gewichteten RidgeRegression, die zur einen verbesserten Analyse der sozioökonomischen Information beiträgt, bewertet. Die Methodik für das WalddynamikMonitoring zeigt, dass trotz umfangreicher Datenlücken im LandsatArchiv für das CEA alle zwei Jahre die historischen Entwaldungsund Wiederaufforstungsmuster mit einer Genauigkeit von über 70% gemeldet werden können. Eine verbesserte Analysemethode trägt außerdem dazu bei, die für die Walddynamik verantwortlichen treibenden Kräfte zu identifizieren, sowie lokale Treiber und spezifische sozioökonomische Rahmenbedingungen auszumachen, die eine bessere Erklärung für die hohen Entwaldungsund Wiederaufforstungsraten im CEA aufzeigen. Die erzielten Ergebnisse machen deutlich, dass die vorgeschlagenen Methoden eine Alternative zum Monitoring und zur Analyse der Walddynamik darstellen; Insbesondere in Gebieten, in denen Datenknappheit und Landschaftskomplexität spezialisierte Ansätze erforderlich machen

Interactions among land cover, disturbance, and productivity across Arctic-Boreal ecosystems of Northwestern North America from remote sensing

Arctic and Boreal ecosystems are experiencing accelerated carbon cycling that coincides with trends in the normalized difference vegetation index (NDVI), a widely used remotely sensed proxy for vegetation productivity. Meanwhile, a variety of processes are extensively altering Arctic-Boreal land cover, complicating the relationship between NDVI and productivity. Because high-quality information on land cover is lacking, understanding of relationships among Arctic-Boreal greenness trends, productivity, and land cover change is lacking. Multidecadal time series of moderate resolution (30 m) reflectance data from Landsat and high resolution (<4 m) imagery were used to map annual cover and quantify changes in land cover over the study domain of NASA’s Arctic-Boreal Vulnerability Experiment. Results identify two primary modes of ecosystem transformation that are consistent with increased high latitude productivity: (1) in the Boreal biome, simultaneous decreases in Evergreen Forest area and increases in Deciduous Forest area caused by fire and harvest; and (2) climate change-induced expansion of Arctic Shrub and Herbaceous vegetation. Land cover change imposes first-order control on the sign and magnitude of NDVI trends. Over a quarter of NDVI trends were associated with land cover change. Relative to locations with stable land cover, areas of land cover change were twice as likely to exhibit statistically significant trends in Landsat-derived NDVI. The highest magnitude trends were concentrated in areas of forest disturbance and regrowth and shrub expansion, while undisturbed land showed subtler, but widespread, greening trends. Based on Orbiting Carbon Observatory-2 data, sun-induced fluorescence, a proxy for productivity, reflected relationships among land cover, disturbance age, and productivity that were not fully captured in NDVI data. In contrast with NDVI, time series of aboveground biomass provide physically-based measures of productivity in forests. Using Landsat-based land cover and reflectance and ICESat lidar data, aboveground biomass was mapped annually across the study domain. Most forests showed increasing biomass, with wildfires imposing substantial interannual variability and harvest imposing steady biomass losses. This dissertation provides new information on how disturbances are driving land cover and productivity change across Arctic-Boreal northwestern North America and reveals insights regarding the interpretation of remote sensing observations in these biomes

Sensor Independent Deep Learning for Detection Tasks with Optical Satellites

The design of optical satellite sensors varies widely, and this variety is mirrored in the data they produce. Deep learning has become a popular method for automating tasks in remote sensing, but currently it is ill-equipped to deal with this diversity of satellite data. In this work, sensor independent deep learning models are proposed, which are able to ingest data from multiple satellites without retraining. This strategy is applied to two tasks in remote sensing: cloud masking and crater detection. For cloud masking, a new dataset---the largest ever to date with respect to the number of scenes---is created for Sentinel-2. Combination of this with other datasets from the Landsat missions results in a state-of-the-art deep learning model, capable of masking clouds on a wide array of satellites, including ones it was not trained on. For small crater detection on Mars, a dataset is also produced, and state-of-the-art deep learning approaches are compared. By combining datasets from sensors with different resolutions, a highly accurate sensor independent model is trained. This is used to produce the largest ever database of crater detections for any solar system body, comprising 5.5 million craters across Isidis Planitia, Mars using CTX imagery. Novel geospatial statistical techniques are used to explore this database of small craters, finding evidence for large populations of distant secondary impacts. Across these problems, sensor independence is shown to offer unique benefits, both regarding model performance and scientific outcomes, and in the future can aid in many problems relating to data fusion, time series analysis, and on-board applications. Further work on a wider range of problems is needed to determine the generalisability of the proposed strategies for sensor independence, and extension from optical sensors to other kinds of remote sensing instruments could expand the possible applications of this new technique

Remote Sensing in Applications of Geoinformation

Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis

Doctor of Philosophy

dissertationA database of cirrus particle size distributions (PSDs), with concomitant meteorological variables, has been constructed using data collected with the Twodimensional Stereo (2D-S) probe. Parametric functions are fit to each measured PSD. Full statistical descriptions are given for unimodal fit parameters. Three statistical tests were developed in order to determine the utility of bimodal fits and the efficacy of unimodal fits, and an investigation into the relationship between the parameterized PSDs and several meteorological variables was made. Next, a parameterization of a "universal" cirrus PSD is given. This parameterization constitutes an improvement on earlier works due both to the size of the dataset and to updated instrumentation. Despite earlier works that predicted a gammadistribution tail to the universal ice PSD, it is shown here that the tail is best described by an inverse gamma distribution. A method for predicting any PSD given the universal shape and two independent remote sensing measurements is demonstrated. The constructed PSD database is then used to address a straightforward question: how similar are the statistics of PSD datasets collected using the recently developed 2D-S probe to cirrus PSD datasets collected using older Particle Measuring Systems (PMS) 2D Cloud (2DC) and 2D Precipitation (2DP) probes? It is seen, given the same cloud field and given the same assumptions concerning ice crystal cross-sectional area, density, and radar cross section, that the parameterized 2D-S and the parameterized 2DC predict similar distributions of inferred shortwave extinction coefficient, ice water content, and 94 GHz radar reflectivity. However, the parameterized 2DC predicts a statistically significant higher number of total ice crystals and a larger ratio of small ice crystals to large ice crystals. Finally, the beginnings of two works in their early stages are presented. First, the probability structure of the parameterized PSDs is considered in light of application to the Bayesian inference of cirrus microphysical properties from remote sensing measurements. Then, the collection of measured PSDs, along with a forward model for radar reflectivity, is used to investigate uncertainty in computations of radar reflectivity from modeled moments of cirrus cloud PSDs

Spectral LADAR: Active Range-Resolved Imaging Spectroscopy

Imaging spectroscopy using ambient or thermally generated optical sources is a well developed technique for capturing two dimensional images with high per-pixel spectral resolution. The per-pixel spectral data is often a sufficient sampling of a material's backscatter spectrum to infer chemical properties of the constituent material to aid in substance identification. Separately, conventional LADAR sensors use quasi-monochromatic laser radiation to create three dimensional images of objects at high angular resolution, compared to RADAR. Advances in dispersion engineered photonic crystal fibers in recent years have made high spectral radiance optical supercontinuum sources practical, enabling this study of Spectral LADAR, a continuous polychromatic spectrum augmentation of conventional LADAR. This imaging concept, which combines multi-spectral and 3D sensing at a physical level, is demonstrated with 25 independent and parallel LADAR channels and generates point cloud images with three spatial dimensions and one spectral dimension. The independence of spectral bands is a key characteristic of Spectral LADAR. Each spectral band maintains a separate time waveform record, from which target parameters are estimated. Accordingly, the spectrum computed for each backscatter reflection is independently and unambiguously range unmixed from multiple target reflections that may arise from transmission of a single panchromatic pulse. This dissertation presents the theoretical background of Spectral LADAR, a shortwave infrared laboratory demonstrator system constructed as a proof-of-concept prototype, and the experimental results obtained by the prototype when imaging scenes at stand off ranges of 45 meters. The resultant point cloud voxels are spectrally classified into a number of material categories which enhances object and feature recognition. Experimental results demonstrate the physical level combination of active backscatter spectroscopy and range resolved sensing to produce images with a level of complexity, detail, and accuracy that is not obtainable with data-level registration and fusion of conventional imaging spectroscopy and LADAR. The capabilities of Spectral LADAR are expected to be useful in a range of applications, such as biomedical imaging and agriculture, but particularly when applied as a sensor in unmanned ground vehicle navigation. Applications to autonomous mobile robotics are the principal motivators of this study, and are specifically addressed