Change detection as support for monitoring the dynamic of land around reservoir

The change detection technique was used in this study to provide preliminary information on the dynamics of land cover in the region over the western basin of the Tiete River. This area is characterized by sequence of reservoirs and intense agricultural activity, triggering series negative effects. One of the impacts is contamination and proliferation of aquatic organisms in these aquatics environments, increased by release of nutrients from human activities. This work was possible to observe a large switching classes and secondary vegetation bare land, probably related to agricultural activity.

Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

A Low Consumption Real Time Environmental Monitoring System for Smart Cities based on ZigBee Wireless Sensor Network

Nowadays, there is an increasing interest in wireless sensor networks (WSN) for environmental monitoring systems because it can be used to improve the quality of life and living conditions are becoming a major concern to people. This paper describes the design and development of a real time monitoring system based on ZigBee WSN characterized by a lower energy consumption, low cost, reduced dimensions and fast adaptation to the network tree topology. The developed system encompasses an optimized sensing process about environmental parameters, low rate transmission from sensor nodes to the gateway, packet parsing and data storing in a remote database and real time visualization through a web server.

Novel techniques for the remote sensing of photosynthetic processes

Remote sensing (RS) techniques have evolved into an important instrument to investigate forest function. New methods based on the remote detection of leaf biochemistry and photosynthesis are being developed and applied in pilot studies from airborne and satellite platforms (PRI, solar-induced fluorescence; N and chlorophyll content). Non-destructive monitoring methods, a direct application of RS studies, are also proving increasingly attractive for the determination of stress conditions or nutrient deficiencies not only in research but also in agronomy, horticulture and urban forestry (proximal RS). In this work I will focus on some novel techniques recently developed for the estimation of photochemistry and photosynthetic rates based (i) on the proximal measurement of steady-state chlorophyll fluorescence yield, or (ii) the remote sensing of changes in hyperspectral leaf reflectance, associated to xanthophyll de-epoxydation and energy partitioning, which is closely coupled to leaf photochemistry and photosynthesis. I will also present and describe a mathematical model of leaf steady-state fluorescence and photosynthesis recently developed in our group. Two different species were used in the experiments: Arbutus unedo, a schlerophyllous Mediterranean species, and Populus euroamericana, a broad leaf deciduous tree widely used in plantation forestry. Results show that ambient fluorescence could provide a useful tool for testing photosynthetic processes from a distance. These results confirm also the photosynthetic reflectance index (PRI) as an efficient remote sensing reflectance index estimating short-term changes in photochemical efficiency as well as long-term changes in leaf biochemistry. The study also demonstrated that RS techniques could provide a fast and reliable method to estimate photosynthetic pigment content and total nitrogen, beside assessing the state of photochemical process in our plants’ leaves in the field. This could have important practical applications for the management of plant cultivation systems, for the estimation of the nutrient requirements of our plants for optimal growth.

Generating vegetation-spectra as a basis for global monitoring of annual vegetation cycles using DOAS satellite observations

Vegetation-cycles are of general interest for many applications. Be it for harvest-predictions, global monitoring of climate-change or as input to atmospheric models.rnrnCommon Vegetation Indices use the fact that for vegetation the difference between Red and Near Infrared reflection is higher than in any other material on Earth’s surface. This gives a very high degree of confidence for vegetation-detection.rnrnThe spectrally resolving data from the GOME and SCIAMACHY satellite-instrumentsrnprovide the chance to analyse finer spectral features throughout the Red and Near Infrared spectrum using Differential Optical Absorption Spectroscopy (DOAS). Although originally developed to retrieve information on atmospheric trace gases, we use it to gain information on vegetation. Another advantage is that this method automatically corrects for changes in the atmosphere. This renders the vegetation-information easily comparable over long time-spans.rnThe first results using previously available reference spectra were encouraging, but also indicated substantial limitations of the available reflectance spectra of vegetation. This was the motivation to create new and more suitable vegetation reference spectra within this thesis.rnThe set of reference spectra obtained is unique in its extent and also with respect to its spectral resolution and the quality of the spectral calibration. For the first time, this allowed a comprehensive investigation of the high-frequency spectral structures of vegetation reflectance and of their dependence on the viewing geometry.rnrnThe results indicate that high-frequency reflectance from vegetation is very complex and highly variable. While this is an interesting finding in itself, it also complicates the application of the obtained reference spectra to the spectral analysis of satellite observations.rnrnThe new set of vegetation reference spectra created in this thesis opens new perspectives for research. Besides refined satellite analyses, these spectra might also be used for applications on other platforms such as aircraft. First promising studies have been presented in this thesis, but the full potential for the remote sensing of vegetation from satellite (or aircraft) could bernfurther exploited in future studies.

The use of satellite remote sensing for flood risk management

Over the last decades the impact of natural disasters to the global environment is becoming more and more severe. The number of disasters has dramatically increased, as well as the cost to the global economy and the number of people affected. Among the natural disaster, flood catastrophes are considered to be the most costly, devastating, broad extent and frequent, because of the tremendous fatalities, injuries, property damage, economic and social disruption they cause to the humankind. In the last thirty years, the World has suffered from severe flooding and the huge impact of floods has caused hundreds of thousands of deaths, destruction of infrastructures, disruption of economic activity and the loss of property for worth billions of dollars. In this context, satellite remote sensing, along with Geographic Information Systems (GIS), has become a key tool in flood risk management analysis. Remote sensing for supporting various aspects of flood risk management was investigated in the present thesis. In particular, the research focused on the use of satellite images for flood mapping and monitoring, damage assessment and risk assessment. The contribution of satellite remote sensing for the delineation of flood prone zones, the identification of damaged areas and the development of hazard maps was explored referring to selected cases of study.

PM(10) remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region

The subject of this study is to investigate the capability of spaceborne remote sensing data to predict ground concentrations of PM10 over the European Alpine region using satellite derived Aerosol Optical Depth (AOD) from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the polar-orbiting MODerate resolution Imaging Spectroradiometer (MODIS). The spatial and temporal resolutions of these aerosol products (10 km and 2 measurements per day for MODIS, ∼ 25 km and observation intervals of 15 min for SEVIRI) permit an evaluation of PM estimation from space at different spatial and temporal scales. Different empirical linear relationships between coincident AOD and PM10 observations are evaluated at 13 ground-based PM measurement sites, with the assumption that aerosols are vertically homogeneously distributed below the planetary Boundary Layer Height (BLH). The BLH and Relative Humidity (RH) variability are assessed, as well as their impact on the parameterization. The BLH has a strong influence on the correlation of daily and hourly time series, whilst RH effects are less clear and smaller in magnitude. Despite its lower spatial resolution and AOD accuracy, SEVIRI shows higher correlations than MODIS (rSEV∼ 0.7, rMOD∼ 0.6) with regard to daily averaged PM10. Advantages from MODIS arise only at hourly time scales in mountainous locations but lower correlations were found for both sensors at this time scale (r∼ 0.45). Moreover, the fraction of days in 2008 with at least one satellite observation was 27% for SEVIRI and 17% for MODIS. These results suggest that the frequency of observations plays an important role in PM monitoring, while higher spatial resolution does not generally improve the PM estimation. Ground-based Sun Photometer (SP) measurements are used to validate the satellite-based AOD in the study region and to discuss the impact of aerosols' micro-physical properties in the empirical models. A lower error limit of 30 to 60% in the PM10 assessment from space is estimated in the study area as a result of AOD uncertainties, variability of aerosols properties and the heterogeneity of ground measurement sites. It is concluded that SEVIRI has a similar capacity to map PM as sensors on board polar-orbiting platforms, with the advantage of a higher number of observations. However, the accuracy represents a serious limitation to the applicability of satellites for ground PM mapping, especially in mountainous areas.

Remote sensing of volcanic plumes using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been used to quantify SO2 emissions from passively degassing volcanoes. This dissertation explores ASTER’s capability to detect SO2 with satellite validation, enhancement techniques and extensive processing of images at a variety of volcanoes. ASTER is compared to the Mini UV Spectrometer (MUSe), a ground based instrument, to determine if reasonable SO2 fluxes can be quantified from a plume emitted from Lascar, Chile. The two sensors were in good agreement with ASTER proving to be a reliable detector of SO2. ASTER illustrated the advantages of imaging a plume in 2D, with better temporal resolution than the MUSe. SO2 plumes in ASTER imagery are not always discernible in the raw TIR data. Principal Component Analysis (PCA) and Decorrelation Stretch (DCS) enhancement techniques were compared to determine how well they highlight a variety of volcanic plumes. DCS produced a consistent output and the composition of the plumes was easy to identify from explosive eruptions. As the plumes became smaller and lower in altitude they became harder to distinguish using DCS. PCA proved to be better at identifying smaller low altitude plumes. ASTER was used to investigate SO2 emissions at Lascar, Chile. Activity at Lascar has been characterized by cyclic behavior and persistent degassing (Matthews et al. 1997). Previous studies at Lascar have primarily focused on changes in thermal infrared anomalies, neglecting gas emissions. Using the SO2 data along with changes in thermal anomalies and visual observations it is evident that Lascar is at the end an eruptive cycle that began in 1993. Declining gas emissions and crater temperatures suggest that the conduit is sealing. ASTER and the Ozone Monitoring Instrument (OMI) were used to determine the annual contribution of SO2 to the troposphere from the Central and South American volcanic arcs between 2000 and 2011. Fluxes of 3.4 Tg/a for Central America and 3.7 Tg/a for South America were calculated. The detection limits of ASTER were explored. The results a proved to be interesting, with plumes from many of the high emitting volcanoes, such as Villarrica, Chile, not being detected by ASTER.

Observations of volcanic emissions using satellite remote sensing

Volcanoes pose a threat to the human population at regional and global scales and so efficient monitoring is essential in order to effectively manage and mitigate the risks that they pose. Volcano monitoring from space has been possible for over thirty years and now, more than ever, a suite of instruments exists with the capability to observe emissions of gas and ash from a unique perspective. The goal of this research is to demonstrate the use of a range of satellite-based sensors in order to detect and quantify volcanic sulphur dioxide, and to assess the relative performances of each sensor against one another. Such comparisons are important in order to standardise retrievals and permit better estimations of the global contribution of sulphur dioxide to the atmosphere from volcanoes for climate modelling. In this work, retrievals of volcanic sulphur dioxide from a number of instruments are compared, and the individual performances at quantifying emissions from large, explosive volcanic eruptions are assessed. Retrievals vary widely from sensor to sensor, and often the use of a number of sensors in synergy can provide the most complete picture, rather than just one instrument alone. Volcanic emissions have the ability to result significant economic loses by grounding aircraft due to the high risk associated with ash encountering aircraft. As sulphur dioxide is often easier to measure than ash, it is often used as a proxy. This work examines whether this is a reasonable assumption, using the Icelandic eruption in early 2010 as a case study. Results indicate that although the two species are for the most part collocated, separation can occur under some conditions, meaning that it is essential to accurately measure both species in order to provide effective hazard mitigation. Finally, the usefulness of satellite remote sensing in quantifying the passive degassing from Turrialba, Costa Rica is demonstrated. The increase in activity from 2005 – 2010 can be observed in satellite data prior to the phreatic phase of early 2010, and can therefore potentially provide a useful indication of changing activity at some volcanoes.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.

GROUND-BASED AND SATELLITE REMOTE SENSING OF PAROXYSMAL ERUPTIONS AT ETNA VOLCANO, 2011-2012.

Mt Etna's activity has increased during the last decade with a tendency towards more explosive eruptions that produce paroxysmal lava fountains. From January 2011 to April 2012, 25 lava fountaining episodes took place at Etna's New South-East Crater (NSEC). Improved understanding of the mechanism driving these explosive basaltic eruptions is needed to reduce volcanic hazards. This type of activity produces high sulfur dioxide (SO2) emissions, associated with lava flows and ash fall-out, but to date the SO2 emissions associated with Etna's lava fountains have been poorly constrained. The Ultraviolet (UV) Ozone Monitoring Instrument (OMI) on NASA's Aura satellite and the Atmospheric Infrared Sounder (AIRS) on Aqua were used to measure the SO2 loadings. Ground-based data from the Observatoire de Physique du Globe de Clermont-Ferrand (OPGC) L-band Doppler radar, VOLDORAD 2B, used in collaboration with the Italian National Institute of Geophysics and Volcanology in Catania (INGV-CT), also detected the associated ash plumes, giving precise timing and duration for the lava fountains. This study resulted in the first detailed analysis of the OMI and AIRS SO2 data for Etna's lava fountains during the 2011-2012 eruptive cycle. The HYSPLIT trajectory model is used to constrain the altitude of the observed SO2 clouds, and results show that the SO2 emission usually coincided with the lava fountain peak intensity as detected by VOLDORAD. The UV OMI and IR AIRS SO2 retrievals permit quantification of the SO2 loss rate in the volcanic SO2 clouds, many of which were tracked for several days after emission. A first attempt to quantitatively validate AIRS SO2 retrievals with OMI data revealed a good correlation for high altitude SO2 clouds. Using estimates of the emitted SO2 at the time each paroxysm, we observe a correlation with the inter-paroxysm repose time. We therefore suggest that our data set supports the collapsing foam (CF) model [1] as driving mechanism for the paroxysmal events at the NSEC. Using VOLDORAD-based estimates of the erupted magma mass, we observe a large excess of SO2 in the eruption clouds. Satellite measurements indicate that SO2 emissions from Etnean lava fountains can reach the lower stratosphere and hence could pose a hazard to aviation. [1] Parfitt E.A (2004). A discussion of the mechanisms of explosive basaltic eruptions. J. Volcanol. Geotherm. Res. 134, 77-107.

Photometric monitoring of non-resolved space debris and databases of optical light curves.

The population of space debris increased drastically during the last years. Collisions involving massive objects may produce large number of fragments leading to significantly growth of the space debris population. An effective remediation measure in order to stabilize the population in LEO, is therefore the removal of large, massive space debris. To remove these objects, not only precise orbits, but also more detailed information about their attitude states will be required. One important property of an object targeted for removal is its spin period and spin axis orientation. If we observe a rotating object, the observer sees different surface areas of the object which leads to changes in the measured intensity. Rotating objects will produce periodic brightness vari ations with frequencies which are related to the spin periods. Photometric monitoring is the real tool for remote diagnostics of the satellite rotation around its center of mass. This information is also useful, for example, in case of contingency. Moreover, it is also important to take into account the orientation of non-spherical body (e.g. space debris) in the numerical integration of its motion when a close approach with the another spacecr aft is predicted. We introduce the two databases of light curves: the AIUB data base, which contains about a thousand light curves of LEO, MEO and high-altitude debris objects (including a few functional objects) obtained over more than seven years, and the data base of the Astronomical Observatory of Odessa University (Ukraine), which contains the results of more than 10 years of photometric monitoring of functioning satellites and large space debris objects in low Earth orbit. AIUB used its 1m ZIMLAT telescope for all light curves. For tracking low-orbit satellites, the Astronomical Observatory of Odessa used the KT-50 telescope, which has an alt-azimuth mount and allows tracking objects moving at a high angular velocity. The diameter of the KT-50 main mirror is 0.5 m, and the focal length is 3 m. The Odessa's Atlas of light curves includes almost 5,5 thousand light curves for ~500 correlated objects from a time period of 2005-2014. The processing of light curves and the determination of the rotation period in the inertial frame is challenging. Extracted frequencies and reconstructed phases for some interesting targets, e.g. GLONASS satellites, for which also SLR data were available for confirmation, will be presented. The rotation of the Envisat satellite after its sudden failure will be analyzed. The deceleration of its rotation rate within 3 years is studied together with the attempt to determine the orientation of the rotation axis.

Beyond deforestation monitoring in conservation hotspots: Analysing landscape mosaic dynamics in north-eastern Madagascar

Due to its extraordinary biodiversity and rapid deforestation, north-eastern Madagascar is a conservation hotspot of global importance. Reducing shifting cultivation is a high priority for policy-makers and conservationists; however, spatially explicit evidence of shifting cultivation is lacking due to the difficulty of mapping it with common remote sensing methods. To overcome this challenge, we adopted a landscape mosaic approach to assess the changes between natural forests, shifting cultivation and permanent cultivation systems at the regional level from 1995 to 2011. Our study confirmed that shifting cultivation is still being used to produce subsistence rice throughout the region, but there is a trend of intensification away from shifting cultivation towards permanent rice production, especially near protected areas. While large continuous forest exists today only in the core zones of protected areas, the agricultural matrix is still dominated by a dense cover of tree crops and smaller forest fragments. We believe that this evidence makes a crucial contribution to the development of interventions to prevent further conversion of forest to agricultural land while improving local land users' well-being.