Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach

We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/

Hierarchical Bayes models for daily rainfall time series at multiple locations from heterogenous data sources

We estimate a Hierarchical Bayesian models for daily rainfall that incorporates two novelties for estimating spatial and temporal correlations. We estimate the within site time series correlations for a particular rainfall site using multiple data sources at a given location, and we estimate the across site covariance in rainfall based on location distance. Previous rainfall models have captured cross site correlations as a functions of site specific distances, but not within site correlations across multiple data sources, and not both aspects simultaneously. Further, we incorporate information on the technology used (satellite versus rain gauge) in our estimations, which is also a novel addition. This methodology has far reaching applications in providing more accurate and complex weather insurance contracts based combining information from multiple data sources from a single site, a crucial improvement in the face of climate change. Secondly, the modeling extends to many other data contexts where multiple datasources exist for a given event or variable where both within and between series covariances can be estimated over time

Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging. © 2015 Author(s)

Weld Joints Inspection Using Multisource Data and Image Fusion

The problem of inspecting weld joints is very complex, especially in critical parts of machines and vehicles. The welded joint is typically inspected visually, chemically or using radiography imaging. The flaw detection is a task for specialized personnel who analyze all the data on each stage of the inspection process separately. The inspection is prone to human error, and is labor intensive. In the stages of weld joint visual control geometrical measurements are performed, joint alignment, straightness, deformation, as well as the weld\u27s uniformity. Coloration my show the heat impact zone, and melted parts of the base material. Also during this stage the unwanted cracks, pores and other surface defects can be spotted. On the other side during the X-ray inspection other flaws can be discovered. Pores, cracks, lack of penetration and slag inclusions can be observed. The author’s goal was to develop a multisource data system of easier flaw detection, and possibly inspection process automation. The methods consisted of three image sources: X-ray, laser profilometer, and imaging camera. The proposed approach consists combining spatial information in the acquired data from all sources. A novel approach of data mixing is proposed to benefit from all the information. The signal form the profilometer enables geometrical information extraction. Deformation and alignment error assessment. The radiogram provides information about the hidden flaws. The color image gives information about texture and color of the surface as well as helps in combining multiple sources

Infrastructures and services for remote sensing data production management across multiple satellite data centers

With the number of satellite sensors and date centers being increased continuously, it is becoming a trend to manage and process massive remote sensing data from multiple distributed sources. However, the combination of multiple satellite data centers for massive remote sensing (RS) data collaborative processing still faces many challenges. In order to reduce the huge amounts of data migration and improve the efficiency of multi-datacenter collaborative process, this paper presents the infrastructures and services of the data management as well as workflow management for massive remote sensing data production. A dynamic data scheduling strategy was employed to reduce the duplication of data request and data processing. And by combining the remote sensing spatial metadata repositories and Gfarm grid file system, the unified management of the raw data, intermediate products and final products were achieved in the co-processing. In addition, multi-level task order repositories and workflow templates were used to construct the production workflow automatically. With the help of specific heuristic scheduling rules, the production tasks were executed quickly. Ultimately, the Multi-datacenter Collaborative Process System (MDCPS) were implemented for large-scale remote sensing data production based on the effective management of data and workflow. As a consequence, the performance of MDCPS in experiments environment showed that those strategies could significantly enhance the efficiency of co-processing across multiple data centers

Assessment of Crop Yield Prediction Capabilities of CNN Using Multisource Data

The growing abundance of digitally available spatial, geological, and climatological data opens up new opportunities for agricultural data-based input–output modeling. In our study, we took a convolutional neural network model previously developed on Unmanned Aerial Vehicle (UAV) image data only and set out to see whether additional inputs from multiple sources would improve the performance of the model. Using the model developed in a preceding study, we fed field-specific data from the following sources: near-infrared data from UAV overflights, Sentinel-2 multispectral data, weather data from locally installed Vantage Pro weather stations, topographical maps from National Land Survey of Finland, soil samplings, and soil conductivity data gathered with a Veris MSP3 soil conductivity probe. Either directly added or encoded as additional layers to the input data, we concluded that additional data helps the spatial point-in-time model learn better features, producing better fit models in the task of yield prediction. With data of four fields, the most significant performance improvements came from using all input data sources. We point out, however, that combining data of various spatial or temporal resolution (i.e., weather data, soil data, and weekly acquired images, for example) might cause data leakage between the training and testing data sets when training the CNNs and, therefore, the improvement rate of adding additional data layers should be interpreted with caution.acceptedVersionPeer reviewe

Master of Science

thesisThe 2012 Great Utah Shakeout highlighted the necessity for increased coordination in the collection and sharing of spatial data related to disaster response during an event. Multiple agencies must quickly relay scientific and damage observations between teams in the field and command centers. Spatial Data Infrastructure (SDI) is a framework that directly supports information discovery and access and use of the data in decision making processes. An SDI contains five core components: policies, access networks, data handling facilities, standards, and human resources needed for the effective collection, management, access, delivery, and utilization of spatial data for a specific area. Implementation of an SDI will increase communication between agencies, field-based reconnaissance teams, first responders, and individuals in the event of a disaster. The increasing popularity of location-based mobile social networks has led to spatial data from these sources being used in the context of managing disaster response and recovery. Spatial data acquired from social networks, or Volunteer Geographic Information (VGI), could potentially contribute thousands of low-cost observations to aid in damage assessment and recovery efforts that may otherwise be unreported. The objective of this research is to design and develop an SDI to allow the incorporation of VGI, professional Geographic Information System (GIS) layers, a mobile application, and scientific reports to aid in the disaster management process. A secondary goal is to assess the utility of the resulting SDI. The end result of combining the three systems (e.g., SDE, a mobile application, and VGI), along with the network of relevant users, is an SDI that improves the volume, quality, currency, accuracy, and access to vital spatial and scientific information following a hazard event

Multi-frequency Black Hole Imaging for the Next-Generation Event Horizon Telescope

The Event Horizon Telescope (EHT) has produced images of the plasma flow around the supermassive black holes in Sgr A* and M87* with a resolution comparable to the projected size of their event horizons. Observations with the next-generation Event Horizon Telescope (ngEHT) will have significantly improved Fourier plane coverage and will be conducted at multiple frequency bands (86, 230, and 345 GHz), each with a wide bandwidth. At these frequencies, both Sgr A* and M87* transition from optically thin to optically thick. Resolved spectral index maps in the near-horizon and jet-launching regions of these supermassive black hole sources can constrain properties of the emitting plasma that are degenerate in single-frequency images. In addition, combining information from data obtained at multiple frequencies is a powerful tool for interferometric image reconstruction, since gaps in spatial scales in single-frequency observations can be filled in with information from other frequencies. Here we present a new method of simultaneously reconstructing interferometric images at multiple frequencies along with their spectral index maps. The method is based on existing Regularized Maximum Likelihood (RML) methods commonly used for EHT imaging and is implemented in the eht-imaging Python software library. We show results of this method on simulated ngEHT data sets as well as on real data from the VLBA and ALMA. These examples demonstrate that simultaneous RML multi-frequency image reconstruction produces higher-quality and more scientifically useful results than is possible from combining independent image reconstructions at each frequency.Comment: 25 pages, 15 figures. Accepted to Ap