Spatial Statistical Data Fusion on Java-enabled Machines in Ubiquitous Sensor Networks

Wireless Sensor Networks (WSN) consist of small, cheap devices that have a combination of sensing, computing and communication capabilities. They must be able to communicate and process data efficiently using minimum amount of energy and cover an area of interest with the minimum number of sensors. This thesis proposes the use of techniques that were designed for Geostatistics and applies them to WSN field. Kriging and Cokriging interpolation that can be considered as Information Fusion algorithms were tested to prove the feasibility of the methods to increase coverage. To reduce energy consumption, a compression method that models correlations based on variograms was developed. A second challenge is to establish the communication to the external networks and to react to unexpected events. A demonstrator that uses commercial Java-enabled devices was implemented. It is able to perform remote monitoring, send SMS alarms and deploy remote updates

Multisource and Multitemporal Data Fusion in Remote Sensing

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references

Remote sensing studies and morphotectonic investigations in an arid rift setting, Baja California, Mexico

The Gulf of California and its surrounding land areas provide a classic example of recently rifted continental lithosphere. The recent tectonic history of eastern Baja California has been dominated by oblique rifting that began at ~12 Ma. Thus, extensional tectonics, bedrock lithology, long-term climatic changes, and evolving surface processes have controlled the tectono-geomorphological evolution of the eastern part of the peninsula since that time. In this study, digital elevation data from the Shuttle Radar Topography Mission (SRTM) from Baja California were corrected and enhanced by replacing artifacts with real values that were derived using a series of geostatistical techniques. The next step was to generate accurate thematic geologic maps with high resolution (15-m) for the entire eastern coast of Baja California. The main approach that we used to clearly represent all the lithological units in the investigated area was objectoriented classification based on fuzzy logic theory. The area of study was divided into twenty-two blocks; each was classified independently on the basis of its own defined membership function. Overall accuracies were 89.6 %, indicating that this approach was highly recommended over the most conventional classification techniques. The third step of this study was to assess the factors that affected the geomorphologic development along the eastern side of Baja California, where thirty-four drainage basins were extracted from a 15-m-resolution absolute digital elevation model (DEM). Thirty morphometric parameters were extracted; these parameters were then reduced using principal component analysis (PCA). Cluster analysis classification defined four major groups of basins. We extracted stream length-gradient indices, which highlight the differential rock uplift that has occurred along fault escarpments bounding the basins. Also, steepness and concavity indices were extracted for bedrock channels within the thirty-four drainage basins. The results were highly correlated with stream length-gradient indices for each basin. Nine basins, exhibiting steepness index values greater than 0.07, indicated a strong tectonic signature and possible higher uplift rates in these basins. Further, our results indicated that drainage basins in the eastern rift province of Baja California could be classified according to the dominant geomorphologic controlling factors (i.e., faultcontrolled, lithology-controlled, or hybrid basins)

Proximal soil sensors and geostatistical tools in precision agriculture applications

Recognition of spatial variability is very important in precision agriculture applications. The use of proximal soil sensors and geostatistical techniques is highly recommended worldwide to detect spatial variation not only in fields but also within-field (micro-scale). This study involves, as a first step, the use of visible and near infrared (vis-NIR) spectroscopy to estimate soil key properties (6) and obtain high resolution maps that allow us to model the spatial variability in the soil. Different calibration models were developed using partial least square regression (PLSR) for different soil properties. These calibration models were evaluated by both cross-validation and independent validation. Results show good to excellent calibration models for most of soil properties under study in both cross-validation and independent validation. The on-line maps created using the collected on-line spectra and the calibration models previously estimated for each soil property were compared with three different maps (measured, predicted, error). The second step uses multivariate geostatistical analysis to develop three different geostatistical models (soil, spectral, fusion). The soil model includes 8 soil properties, spectral model includes 4 soil properties and the fusion model includes 12 soil properties. The three models were evaluated by cross-validation and the results show that the goodness of fitting can be considered as satisfactory for the soil model, whereas the performance of the spectral model was quite poor. Regarding the fusion model, it performed quite well, though the model generally underestimated the high values and overestimated the low values. An independent validation data set was used to evaluate the performance of the three models calculating three statistics: mean error (ME), as an indicator of bias; mean standardized squared error (MSSE), as an indicator of accuracy, and root mean squared error (RMSE), as an indicator of precision of estimation. Synthetically, the two, soil and fusion, models performed quite similarly, whereas the performance of the spectral model was much poorer. With regard to delineation of management zones (MZs), the factor cokriging analysis was applied using the three different models. The first factor (F1) for the soil and fusion models was related to soil properties that affect soil fertility, whereas for the spectral model was related to P (-0.88) and pH (-0.42). Based on the first factor of the soil and fusion models, three management zones were delineated and classified as low, medium and high fertility zones using isofrequency classes. Spatial similarity between the yield map and delineated MZs maps based on F1 for the soil and fusion models was calculated. The overall accordance between the two maps was 40.0 % for the soil model and 38.6 % for the fusion model. The two models performed quite similarly. These results can be interpreted as more than 50% of the yield variation was ascribable to more dynamic factors than soil parameters not included in this study, such as agro-meteorological conditions, plant diseases, nutrition stresses, etc. However, the results are quite promising for the application of the proposed approach in site-specific management.</br

Remote sensing studies and morphotectonic investigations in an arid rift setting, Baja California, Mexico

The Gulf of California and its surrounding land areas provide a classic example of recently rifted continental lithosphere. The recent tectonic history of eastern Baja California has been dominated by oblique rifting that began at ~12 Ma. Thus, extensional tectonics, bedrock lithology, long-term climatic changes, and evolving surface processes have controlled the tectono-geomorphological evolution of the eastern part of the peninsula since that time. In this study, digital elevation data from the Shuttle Radar Topography Mission (SRTM) from Baja California were corrected and enhanced by replacing artifacts with real values that were derived using a series of geostatistical techniques. The next step was to generate accurate thematic geologic maps with high resolution (15-m) for the entire eastern coast of Baja California. The main approach that we used to clearly represent all the lithological units in the investigated area was objectoriented classification based on fuzzy logic theory. The area of study was divided into twenty-two blocks; each was classified independently on the basis of its own defined membership function. Overall accuracies were 89.6 %, indicating that this approach was highly recommended over the most conventional classification techniques. The third step of this study was to assess the factors that affected the geomorphologic development along the eastern side of Baja California, where thirty-four drainage basins were extracted from a 15-m-resolution absolute digital elevation model (DEM). Thirty morphometric parameters were extracted; these parameters were then reduced using principal component analysis (PCA). Cluster analysis classification defined four major groups of basins. We extracted stream length-gradient indices, which highlight the differential rock uplift that has occurred along fault escarpments bounding the basins. Also, steepness and concavity indices were extracted for bedrock channels within the thirty-four drainage basins. The results were highly correlated with stream length-gradient indices for each basin. Nine basins, exhibiting steepness index values greater than 0.07, indicated a strong tectonic signature and possible higher uplift rates in these basins. Further, our results indicated that drainage basins in the eastern rift province of Baja California could be classified according to the dominant geomorphologic controlling factors (i.e., faultcontrolled, lithology-controlled, or hybrid basins)

Optimizing peak gust and maximum sustained wind speed estimates from mid-latitude wave cyclones

Wind storms cause significant damage and economic loss and are a major recurring threat in many countries. Maximum sustained and peak gust weather station data from multiple historic wind storms occurring over more than three decades across Europe were analyzed to identify storm tracks, intensities, and areas of frequent high wind speeds. Wind surfaces for maximum sustained and peak gust winds were estimated based on an anisotropic (directionally-dependent) kriging interpolation methodology. Overall, wind speed magnitudes and high intensity locations were identified accurately for each storm. Directional trends and wind swaths were also consistently located in appropriate locations based on known storm tracks. Anisotropic kriging proved to be superior to isotropic (non-directional) kriging when modeling continental-scale wind storms because of the identification of strong directional correlations across space. Results suggest that coastal areas and mountainous areas experience the highest wind intensities during wind storms. These same areas also experience high variability over short distances and thus the highest error measurements associated with concurrent interpolated surfaces. For this reason, various covariates were utilized in conjunction with the cokriging interpolation technique and improved the interpolated wind surfaces for five wind storms that impacted both the mountainous and topographically-varied Alps region and the coastal regions of Europe. Land cover alone reduced station-measured standard error most significantly in a majority of the models, while aspect and elevation (singularly and collectively) also reduced station standard error in most models as compared to the original kriging models. Additional comparisons between different areal scales of kriging/cokriging models revealed that some surface wind variability is muted at the continental scale, but identifiable at the local scale. However, major patterns and trends are more difficult to ascertain for local-scale surfaces when compared to continental-scale surfaces. Large station error can be reduced through local kriging/cokriging, but additional research is needed to merge local-scale semivariograms with continental-scale models. Results showed substantial improvements in wind speed surface estimates over previous estimates and have major implications for catastrophe modeling companies, insurance needs, and construction standards. Implications of this research may be transferrable to other geographies and create an impetus for database and covariate improvement

Investigation of novel approaches for aerodynamic data fusion

Modern industrial applications require reliable and accurate aerodynamic data for design and optimization. This data is generally produced using CFD simulations and wind tunnel testing. Although these approaches offer significant individual benefits, they also have certain limitations. CFD fails to yield accurate solutions towards the edge of the envelope, whereas the wind tunnel experimental data offers data only at specific sensor locations. Data fusion techniques combine the individual strengths of these data sources to deliver accurate and reliable data. POD-based data fusion techniques like Gappy POD and regularized Gappy POD are well-established and widely used in various studies. These techniques compute the data fusion result via a least-square fit in the POD subspace. Recently shallow artificial neural networks have also been used in data fusion techniques to reconstruct the flow solution. This thesis proposes an alternative data fusion approach called Gappy ANN and compares it with Gappy POD. The idea of Gappy ANN is to replace the POD subspace with a solution space generated via a shallow artificial neural network. The advantage of this approach is that knowledge of the sensor positions can be directly considered when creating the solution space. This thesis demonstrates the performance and robustness of Gappy POD and Gappy ANN on an aerodynamic test case fusing highquality experimental and numerical data. Gappy POD performs better in reconstructing the flow solution than Gappy ANN, showing only minimal errors. To improve their prediction accuracy, DEIM-based sensor placement strategies are applied to the POD reduced space and ANN solution space to obtain the new optimal sensor locations. Gappy POD and Gappy ANN solutions are recomputed using these new locations. The results for both experimental and DEIM-based indices are analyzed to demonstrate the significance of the DEIM-based algorithm for the Gappy approaches in improving prediction accuracy

Multisource and multitemporal data fusion in remote sensing:A comprehensive review of the state of the art

The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several

EXPLORING SPATIAL AND TEMPORAL VARIABILITY OF SOIL AND CROP PROCESSES FOR IRRIGATION MANAGEMENT

Irrigation needs to be applied to soils in relatively humid regions such as western Kentucky to supply water for crop uptake to optimize and stabilize yields. Characterization of soil and crop variability at the field scale is needed to apply site specific management and to optimize water application. The objective of this work is to propose a characterization and modeling of soil and crop processes to improve irrigation management. Through an analysis of spatial and temporal behavior of soil and crop variables the variability in the field was identified. Integrative analysis of soil, crop, proximal and remote sensing data was utilized. A set of direct and indirect measurements that included soil texture, electrical conductivity (EC), soil chemical properties (pH, organic matter, N, P, K, Ca, Mg and Zn), NDVI, topographic variables, were measured in a silty loam soil near Princeton, Kentucky. Maps of measured properties were developed using kriging, and cokriging. Different approaches and two cluster methods (FANNY and CLARA) with selected variables were applied to identify management zones. Optimal scenarios were achieved with dividing the entire field into 2 or 3 areas. Spatial variability in the field is strongly influenced by topography and clay content. Using Root Zone Water Quality Model 2.0 (RZWQM), soil water tension was modeled and predicted at different zones based on the previous delineated zones. Soil water tension was measured at three depths (20, 40 and 60 cm) during different seasons (20016 and 2017) under wheat and corn. Temporal variations in soil water were driven mainly by precipitation but the behavior is different among management zones. The zone with higher clay content tends to dry out faster between rainfall events and reveals higher fluctuations in water tension even at greater depth. The other zones are more stable at the lower depth and share more similarities in their cyclic patterns. The model predictions were satisfactory in the surface layer but the accuracy decreased in deeper layers. A study of clay mineralogy was performed to explore field spatial differences based on the map classification. kaolinite, vermiculite, HIV and smectite are among the identified minerals. The clayey area presents higher quantity of some of the clay minerals. All these results show the ability to identify and characterize the field spatial variability, combining easily obtainable data under realistic farm conditions. This information can be utilized to manage resources more effectively through site specific application