Mapping and monitoring forest remnants : a multiscale analysis of spatio-temporal data

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water dynamics with major consequences for ecological relations and decision-making. One basic requirement to quantify and model these processes is the availability of accurate maps of forest cover. Data acquisition and analysis at appropriate scales is the keystone to achieve the mapping accuracy needed for development and reliable use of ecological models.The current and upcoming production of high-resolution data sets plus the ever-increasing time series that have been collected since the seventieth must be effectively explored. Missing values and distortions further complicate the analysis of this data set. Thus, integration and proper analysis is of utmost importance for environmental research. New conceptual models in environmental sciences, like the perception of multiple scales, require the development of effective implementation techniques.This thesis presents new methodologies to map and monitor forests on large, highly fragmented areas with complex land use patterns. The use of temporal information is extensively explored to distinguish natural forests from other land cover types that are spectrally similar. In chapter 4, novel schemes based on multiscale wavelet analysis are introduced, which enabled an effective preprocessing of long time series of Landsat data and improved its applicability on environmental assessment.In chapter 5, the produced time series as well as other information on spectral and spatial characteristics were used to classify forested areas in an experiment relating a number of combinations of attribute features. Feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz . maximum likelihood, univariate and multivariate decision trees, and neural networks. The results showed that maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest in the study area.In chapter 6, a multiscale approach to digital change detection was developed to deal with multisensor and noisy remotely sensed images. Changes were extracted according to size classes minimising the effects of geometric and radiometric misregistration.Finally, in chapter 7, an automated procedure for GIS updating based on feature extraction, segmentation and classification was developed to monitor the remnants of semideciduos Atlantic forest. The procedure showed significant improvements over post classification comparison and direct multidate classification based on artificial neural networks.</p

Radioactive Waste

The safe management of nuclear and radioactive wastes is a subject that has recently received considerable recognition due to the huge volume of accumulative wastes and the increased public awareness of the hazards of these wastes. This book aims to cover the practice and research efforts that are currently conducted to deal with the technical difficulties in different radioactive waste management activities and to introduce to the non-technical factors that can affect the management practice. The collective contribution of esteem international experts has covered the science and technology of different management activities. The authors have introduced to the management system, illustrate how old management practices and radioactive accident can affect the environment and summarize the knowledge gained from current management practice and results of research efforts for using some innovative technologies in both pre-disposal and disposal activities

Water and Wastewater Pipe Nondestructive Evaluation and Health Monitoring: A Review

Civil infrastructures such as bridges, buildings, and pipelines ensure society's economic and industrial prosperity. Specifically, pipe networks assure the transportation of primary commodities such as water, oil, and natural gas. The quantitative and early detection of defects in pipes is critical in order to avoid severe consequences. As a result of high-profile accidents and economic downturn, research and development in the area of pipeline inspection has focused mainly on gas and oil pipelines. Due to the low cost of water, the development of nondestructive inspection (NDI) and structural health monitoring (SHM) technologies for fresh water mains and sewers has received the least attention. Moreover, the technical challenges associated with the practical deployment of monitoring system demand synergistic interaction across several disciplines, which may limit the transition from laboratory to real structures. This paper presents an overview of the most used NDI/SHM technologies for freshwater pipes and sewers. The challenges that said infrastructures pose with respect to oil and natural gas pipeline networks will be discussed. Finally, the methodologies that can be translated into SHM approaches are highlighted

Railway Research

This book focuses on selected research problems of contemporary railways. The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. The last chapter focuses on modeling railway vehicle dynamics using numerical simulation, where the dynamical models are exploited

Deep-Draft Vessel Wake And Wind Wave Hydrodynamics Near A Mixed-Sediment Embankment In Galveston Bay, Texas

Vessel-generated waves are well-documented sources for a substantial amount of the energy impacting shorelines and embankments lining shipping channels. An approximately year-long study was conducted in Galveston Bay, Texas adjacent to the Houston Ship Channel, one of the busiest commercial navigation lanes in the United States. Hydrodynamic data were collected at two research platforms just offshore of a beneficial-use dredge material dike approximately one kilometer eastward of the ship channel. The hydrodynamic data were then analyzed through time-localizing signal analysis techniques known as wavelet transforms. Wavelet transforms facilitate the use of time-frequency domain transformations on nonstationary data (i.e., hydrodynamic bursts containing vessel wake signatures). Time-localizing abilities are the major shortcoming to the standard signal-analysis techniques utilizing Fourier-based transformations. An algorithm was constructed with the wavelet analysis results to identify wake events in the measured data via usage of multiple statistical measures, linear wave theory principles, and hydrodynamic signal behaviors unique to wake events. Data were then investigated for correlations with vessel traffic information, thereby providing direct associations between the observed wake effects and the specific deep-draft vessel inducing the wake event. Results confirmed that wake events, particularly those from inbound traveling deep-draft vessels, were responsible for an outsized portion of the total wave energy impacting the site. Although occurring during less than 5% of the study period, inbound wake events accounted for just over 20% of the total measured wave energy. The median maximum wave energy per minute of inbound and outbound wake events were about 9 and 1.5 times larger than that of wind-only periods, respectively. The strongest correlation between wake events and vessel traffic was by the nondimensional velocity head and length Froude number for inbound vessels with an R2 between 0.44 and 0.52 depending on the tidal elevation. The presented data analysis helps quantify the hydrodynamic energy resulting from vessel traffic available to drive shallow-bay sediment dynamics and shoreline erosion and enables predictions based on expected future vessel traffic volumes and patterns. In addition, this extensive data set is being used to improve our capability to numerically model ship wakes and their impacts in shallow-bay systems

Technology, Science and Culture

From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them

ADVANCED VIBRATION PROCESSING TECHNIQUES FOR CONDITION MONITORING AND QUALITY CONTROL IN I.C. ENGINES AND HARVESTING MACHINES

The topic of this thesis is the development and the implementation of advanced vibration processing techniques for machine condition monitoring and diagnostics with two fields of applications: the quality control of I.C. engines by means of cold tests, and the monitoring and control of harvesting processes. The cold test, i.e. the final test after the assembly line and before shipping the engine to the customer, consists of the final quality control of the engine in a non-combustion state. Techniques for engine condition monitoring based on the analysis of vibration signals are widely used. However, these techniques are often applied to engine tests in firing conditions. This thesis addresses the use of several signal processing tools as a means for the monitoring and the diagnosis of assembly faults through the cold test technology. Firstly, an approach based on the use of Symmetrized Dot Patterns for the visual characterization of vibration signatures is proposed in order to obtain reliable thresholds for the pass/fail decision after the cold test. Secondly, the fault identification is discussed on the basis of the cyclostationary modelling of the signals. The first-order cyclostationarity is exploited through the analysis of the Time Synchronous Average (TSA). Subsequently, secondorder cyclostationarity is analysed by means of the Wigner-Ville Distribution (WVD), Wigner-Ville Spectrum (WVS) and Mean Instantaneous Power (MIP). Moreover, Continuous Wavelet Transform (CWT) is presented and compared with the WVD and WVS. The choice of different wavelet functions and some methods for the CWT map optimization (i.e. purification method and the average across the scale vi method (TDAS)) are also considered. Moreover, the capabilities of the Instantaneous Angular Speed (IAS) in detecting assembly faults have been tested. It is worth noting that the cyclostationary and time-frequency technique capabilities have been verified for both simulated and real signals. The experimental results indicate that the image correlation of Symmetrised Dot Patterns is a good solution that can be used in the cold test technology in order to increase its efficiency and fault detection capability. Moreover, it will be proved that the first order cyclostationary analysis is able to identify the presence of assembly faults but it is not appropriate to localise the faults. The second order analysis overcomes this problem indicating the angular position of the mechanical part affected by the fault. This is achieved by means of a correlation between the results obtained from the cyclostationarity analysis and the angular position of the mechanical events. Concerning the time-frequency analysis, the WVS as well as the CWT, using both Morlet mother wavelet and TDAS method can be considered good tools to characterise the transients due to the fault events in the timefrequency domain. Thanks to this research study it is possible to understand which of the above-mentioned techniques is effective for an easy and fast quality control and for the diagnosis of the considered assembly faults. Moreover, the limits and drawbacks of both monitoring and diagnostic procedures are shown. The originality of the first part of the research mainly concerns the use of vibration measurements for the quality control of engines at the end of the assembly line while the greater part of methods used for cold test applications focuses on pressure and torque measurements. The second part of this thesis concerns the analysis of relationships between the harvesting process parameters relative to a nonconventional harvesting machine and its vibration response. Common and uncommon features extracted from a segmentation analysis have been correlated with the harvesting process efficiency in order to define the optimal monitoring feature subset. Moreover, the Discrete Wavelet Transform method is performed in order to find the vii frequency range mostly characterised by impulsive components. In addition, some outlines obtained through the vibro-acoustic analysis performed in the angular domain are also given. Two different indoor and outdoor test rigs have been built to test the machine under different setting conditions in order to evaluate their influence over the vibration response of the threshing unit. The test results are used to identify how the vibration generation is linked to the crop distribution during the threshing process. Good correlations have been obtained by analysing the concave middle radial signal and by calculating the relationships that exist between some time domain features and the efficiency parameters. These features can be assumed as good indexes in explaining the crop distribution between the rotor and the concave and, consequently, the efficiency of the process. Moreover, it will be shown that the vibroacoustic features selected are well-connected to the different sources of the concave excitation. The main original contribution of this second part concerns the use of the vibration signal as an effective way to monitor the harvesting process. It can also be considered as a proper quality control indicator for the user during field operations