Spatial Data Fusion Research Articles

A boundary finding-based spatiotemporal fusion model for vegetation index

ABSTRACT The spatiotemporal image fusion technique is regarded as an effective and efficient remedy to blend two or more geometrically registered remote sensing images of the same scene acquired from different sensors into a single image with both high temporal and high spatial resolutions. A number of existing fusion methods developed for generating Landsat-like images from the Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully solved limitations on vegetation dynamics monitoring. However, these methods either treat the weighting parameters as constants or adopt insufficient linear regression coefficients (considering only spatial, temporal or spectral weights), which may contribute to blocky artefacts and dissimilar pixels in the predicted image. In this study, a boundary finding-based vegetation index spatiotemporal fusion model (BESFM) is newly proposed to generate high spatial and high temporal resolutions Enhanced Vegetation Index (EVI) images in homogeneous and heterogeneous regions with rapid vegetation changes. The cloud-free Landsat-8 Operational Land Imager (OLI) and the MODIS (MOD09GA) data acquired from two study sites, located in Queensland and New South Wales and Australia, were selected to evaluate the performance of the proposed method. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0.011 and R2 maximum to 0.956). Since the BESFM method not only uses adjustable linear regression assumptions for heterogeneous and homogeneous regions differently but also implements boundary detection-based spatial weight with temporal and spectral weights together to enhance the predicted spatial details, it has the potential to increase the availability of fine spatial resolution data.

Multivariate spatial prediction of air pollutant concentrations with INLA

Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO2, SO2, O3, PM10, and PM2.5 species EC, OC, NO3, NH4, SO4) during the period 2005 to 2014. Out-of-sample prediction showed good performance, particularly for daily PM2.5 species EC (R2 = 0.64), OC (R2 = 0.75), NH4 (R2 = 0.84), NO3 (R2 = 0.73), and SO4 (R2 = 0.80). By employing the integrated nested Laplace approximation (INLA) for Bayesian inference, our approach also provides model-based prediction error estimates. The daily data product at 12 km spatial resolution will be publicly available immediately upon publication. To our knowledge this is the first publicly available data product for major PM2.5 species and several gases at this spatial and temporal resolution.

Urban Fine Management of Multisource Spatial Data Fusion Based on Smart City Construction

With the increase of the types of urban management objects, the intelligent management of the whole city has become a matter of concern in various countries, and it is also one of the indispensable links in urban development. In the construction of cities all over the world, the intelligent and scientific management system has been used innovatively. We provide excellent facilities for transportation development, information exchange, and resource progress. The research on urban fine management based on multisource spatial data fusion is proposed. Aiming at the traffic problems in urban fine management, this paper proposes a deep network architecture based on multisource data fusion. Multisource spatial data fusion technology is used to analyze urban traffic data. Deep network architecture is used to improve the precision management status of a smart city and the accuracy of traffic condition prediction. Then, the convolution neural network technology is explored in the data fusion technology strategy. The research results show that the framework has the ability to deal with heterogeneous data and urban big data and can effectively improve the traffic management state in the construction of a smart city and effectively solve the complexity of urban fine management and processing efficiency in the construction of a smart city.

The process of modelling the elevation surface of a coastal area using the fusion of spatial data from different sensors

Information regarding the depth distribution in a specific aquatic area is not also crucial for the safety of navigation, but also for modelling environmental processes, such as the quick establishment of marine-land boundaries or assessments of flood risk areas. Using elevation data from different available sources can be very convenient for individuals who wish to conduct quick analysis or need to obtain data covering a large area without the need for data collection and surveys. This study proposes a method of combining spatial data from different sources during surface modelling of a coastal area. The spatial data used for elevation surface modelling included hydrographic and topographic data, which are often collected separately for various purposes. Data are saved in different formats with various resolutions and accuracies; thus, a uniform surface model that will allow for easy and accurate analysis is currently lacking. The main aim of this study was to create a model of the surface of a coastal area using input data from various sources with the highest possible accuracy. This paper presents the available spatial data sources for coastal areas, along with the data pre-processing process. Furthermore, spatial data fusion is introduced, along with the results. The entire process of creating the uniform surface model consisted of several steps that are described in detail and visualised. The obtained model was visualised using a three-dimensional map.

A hybrid solution for offshore wind resource assessment from limited onshore measurements

In wind resource assessments, which are critical to the pre-construction of wind farms, measurements by LiDARs or masts are a source of high-fidelity data, but are expensive and scarce in space and time, particularly for offshore sites. On the other hand, numerical simulations, using for example the Weather Research and Forecasting (WRF) model, generate temporally and spatially continuous data with relatively low-fidelity. A hybrid approach is proposed here to combine the merit of measurements and simulations for the assessment of offshore wind. Firstly a temporal data fusion using deep Multi Fidelity Gaussian Process Regression (MF-GPR) is performed to combine the intermittent measurement and the continuous simulation data at an onshore location. Then a spatial data fusion using a neural network with Non-linear Autoregression (NAR) and Non-linear Autoregression with external input (NARX) are conducted to project the wind from onshore to offshore. The numerical and measured wind speeds along the west coast of Denmark were used to evaluate the method. We show that the proposed data fusion technique using a gappy onshore measurement results in accurate offshore wind resource assessment within a 2% margin error.

Improving the spatiotemporal fusion accuracy of fractional vegetation cover in agricultural regions by combining vegetation growth models

Spatiotemporal fusion has provided a feasible way to generate fractional vegetation cover (FVC) data with high spatial and temporal resolution. However, when the currently available spatiotemporal fusion methods are applied over agricultural regions, they usually underestimate high FVC values at the peak vegetation growth stage with medium FVC values as base data. This mainly results from inconsistencies in the temporal variations between fine- and coarse-resolution data if substantial temporal changes occur in vegetation. Therefore, a Spatial and Temporal Fusion method combining with Vegetation Growth Models (STF-VGM) was proposed to address this problem in this study, which incorporates vegetation growth models into the fusion process. Unlike other spatiotemporal fusion methods that mainly rely on changes in coarse-resolution data for prediction, STF-VGM fully utilizes available coarse- and fine-resolution time series data, including uncontaminated information in cloud/cloud shadow contaminated images. By establishing vegetation growth models with time series data, a conversion relationship between coarse- and fine-resolution FVC that changes along with the nonlinear vegetation change process can be extracted. STF-VGM makes prediction based on this variable relationship. A typical agricultural region located in the North China Plain was selected as the study area. The validation results indicated that the prediction accuracy for high FVC values was significantly improved using STF-VGM compared to the commonly used Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and Flexible Spatiotemporal DAta Fusion (FSDAF) methods (STF-VGM: coefficient of determination (R2) = 0.9491, root mean square error (RMSE) = 0.0650, average difference (AD) = -0.0092; ESTARFM: R2 = 0.9341, RMSE = 0.1127, AD = -0.0631; FSDAF: R2 = 0.9224, RMSE = 0.1110, AD = -0.0599). The satisfactory performance of STF-VGM was also achieved in predicting FVC values at other vegetation growth stages (early growth stage: R2 = 0.8277, RMSE = 0.0440, AD = 0.0027; rapid growth stage: R2 = 0.9183, RMSE = 0.0844, AD = 0.0500). In addition, STF-VGM also has the potential to improve the spatiotemporal fusion accuracy of other vegetation parameters and vegetation indices, which will be evaluated in the future.

Temporal and spatial heterogeneity research of urban anthropogenic heat emissions based on multi-source spatial big data fusion for Xi’an, China

Anthropogenic heat emission (AHE) influences the local energy balance and intensify the urban heat island (UHI) effect. An accurate calculation of the AHE can improve the precision of UHI predictions. However, reliable AHE calculations with high temporal and spatial resolution in domestic research is still lacking. Therefore, this study proposes an approach to estimate the dynamic AHE by integrating multi-source Internet big data and high-precision urban spatial data. First, we quantified the dynamic distribution of residents’ trajectories by tracking multi-stage Internet geographic location data, real-time traffic conditions of Xi’an city, supplemented by on-site drone monitoring. Then the parameters of cooling and the thermal load coefficient of building emissions, personnel cooling loads, and traffic densities were introduced. Finally, the temporal and spatial dynamic rules of the AHE were revealed. Results showed the AHE was subject to a large changing amplitude. The diurnal AHE values of 64% of the blocks ranged from 93 to 498 W/m2, especially in some core commercial areas, the value could reach above 1000 W/m2 during the peak stages. Compared with previous research, this study dynamically evaluates the temporal and spatial heterogeneity of the AHE under different emission scenarios with a short update cycle and high spatial resolution.

Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

Integrating Image and Network-Based Topological Data through Spatial Data Fusion for Indoor Location-Based Services

Nowadays, the importance and utilization of spatial information are recognized. Particularly in urban areas, the demand for indoor spatial information draws attention and most commonly requires high-precision 3D data. However accurate, most methodologies present problems in construction cost and ease of updating. Images are accessible and are useful to express indoor space, but pixel data cannot be applied directly to provide indoor services. A network-based topological data gives information about the spatial relationships of the spaces depicted by the image, as well as enables recognition of these spaces and the objects contained within. In this paper, we present a data fusion methodology between image data and a network-based topological data, without the need for data conversion, use of a reference data, or a separate data model. Using the concept of a Spatial Extended Point (SEP), we implement this methodology to establish a correspondence between omnidirectional images and IndoorGML data to provide an indoor spatial service. The proposed algorithm used position information identified by a user in the image to define a 3D region to be used to distinguish correspondence with the IndoorGML and indoor POI data. We experiment with a corridor-type indoor space and construct an indoor navigation platform.

Integration of coseismic deformation into WebGIS for near real-time disaster evaluation and emergency response

Earthquakes are one of the destructive natural disasters. Immediate emergency response in the first few hours is important for life rescue. The near real-time ground deformation maps generated after earthquakes are crucial for hazard assessments, which normally take a couple of hours or longer to be generated using conventional ways. In this study, we propose a near real-time coseismic ground deformation map generation system with the aim of assisting rapid seismic hazard evaluations and emergency responses. This framework adopts the source parameters published by seismological agencies and uses the empirical equations to generate the near real-time coseismic ground deformation maps. The source parameters of an earthquake, such as the focal mechanism, are programmatically accessed from the United States Geological Survey National Earthquake Information Center (USGS-NEIC) in a nearly real-time manner. The ground deformation estimated using empirical equations is integrated as self-adapting spatial data fusion and visualized on an interactive WebGIS platform. We develop the WebGIS platform, namely QuickDeform at https://www.insar.com.cn , and successfully applied the system to several recent large magnitude earthquakes. We find that the proposed framework functions robustly and proficiently to automatically generate the seismic deformation map within several minutes after the occurrence of an earthquake. The generated deformation map shows good agreement when compared to the data from real earthquakes. QuickDeform can be used as a volunteered geographic information platform for crowdsourcing disaster data for rescue and model validation.

Spatial analysis and visualization of global data on multi-resolution hexagonal grids

In this article, computation for the purpose of spatial visualization is presented in the context of understanding the variability in global environmental processes. Here, we generate synthetic but realistic global data sets and input them into computational algorithms that have a visualization capability; we call this a simulation–visualization system. Visualization is key here, because the algorithms which we are evaluating must respect the spatial structure of the input. We modify, augment, and integrate four existing component technologies: statistical conditional simulation, Discrete Global Grids (DGGs), Array Set Addressing, and a visualization platform for displaying our results on a globe. The internal representation of the data to be visualized is built around the need for efficient storage and computation as well as the need to move up and downresolutions in a mutually consistent way. In effect, we have constructed a Geographic Information System that is based on a DGG and has desirable data storage, computation, and visualization capabilities. We provide an example of how our simulation–visualization system may be used, by evaluating a computational algorithm called Spatial Statistical Data Fusion that was developed for use on big, remote-sensing data sets.

Research on location fusion of spatial geological disaster based on fuzzy SVM

In order to effectively improve geological disaster response capacity, disaster tolerance capacity, reduce human and financial losses and other aspects of spatial data fusion plays a key role. Whether the location information can be effectively fused is able to monitor the occurrence of geological hazards and effectively identify the existing risks. In machine learning, the similarity and complementarity between support vector machine and fuzzy inference is the basis of their fusion. Support vector machine can achieve knowledge acquisition and learning, while fuzzy inference has the ability to infer knowledge rules. Aiming at the different attributes and dimensions of information spatial location data, a fuzzy fusion method based on support vector machine is proposed to describe Support vector machine related theories and models. Comparing the efficiency of support vector machine-fusion algorithm on GPLUS, OKLAHOMA and UNC with the other three algorithms, there is a great advantage in RMSE and time. The algorithm in this paper also has good performance in the three data sets on F1, which shows that the algorithm has a good effect.

VECTOR AND RASTER DATA LAYERED FUSION AND 3D VISUALIZATION

Abstract. Although contemporary geospatial science has made great progress, spatial data fusion of vector and raster data is still a problem in the geoinformation science environment. In order to solve the problem, this paper proposes a method which merges vector and raster data. Firstly, the row and column numbers of the raster data, and the X, Y values of the vector data are represented by Morton code in the C++ environment, respectively. Secondly, we establish the the raster data table and the vector data table in the Oracle database to store the vector data and the raster data. Third, this paper uses the minimum selection bounding box method to extract the top data of the building model. Finally, we divide the vector and raster data into four steps to obtain the fusion data table, and we call the fusion data in the database for 3D visualization. This method compresses the size of data of the original data, and simultaneously divides the data into three levels, which not only solves the problem of data duplication storage and unorganized storage, but also can realize vector data storage and the raster data storage in the same database at the same time. Thus, the fusion original orthophoto data contains the gray values of building roofs and the elevation data, which can improve the availability of vector data and the raster data in the 3D Visualization application.

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.

Incorporation of Remote PM2.5 Concentrations into the Downscaler Model for Spatially Fused Air Quality Surfaces.

The United States Environmental Protection Agency (EPA) has implemented a Bayesian spatial data fusion model called the Downscaler (DS) model to generate daily air quality surfaces for PM2.5 across the contiguous U.S. Previous implementations of DS relied on monitoring data from EPA’s Air Quality System (AQS) network, which is largely concentrated in urban areas. In this work, we introduce to the DS modeling framework an additional PM2.5 input dataset from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network located mainly in remote sites. In the western U.S. where IMPROVE sites are relatively dense (compared to the eastern U.S.), the inclusion of IMPROVE PM2.5 data to the DS model runs reduces predicted annual averages and 98th percentile concentrations by as much as 1.0 and 4 μg m−3, respectively. Some urban areas in the western U.S., such as Denver, Colorado, had moderate increases in the predicted annual average concentrations, which led to a sharpening of the gradient between urban and remote areas. Comparison of observed and DS-predicted concentrations for the grid cells containing IMPROVE and AQS sites revealed consistent improvement at the IMPROVE sites but some degradation at the AQS sites. Cross-validation results of common site-days withheld in both simulations show a slight reduction in the mean bias but a slight increase in the mean square error when the IMPROVE data is included. These results indicate that the output of the DS model (and presumably other Bayesian data fusion models) is sensitive to the addition of geographically distinct input data, and that the application of such models should consider the prediction domain (national or urban focused) when deciding to include new input data.

Research on spatial observation data fusion method based on array photoelectric system

Research on spatial observation data fusion method based on array photoelectric system

Delineation of management zones with spatial data fusion and belief theory

Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.

Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies

The last decade has seen an explosion in data sources available for monitoring and prediction of environmental phenomena. While several inferential methods have been developed to make predictions on the underlying process by combining these data, an optimal sampling design for additional data collection in the presence of multiple heterogeneous sources has not yet been developed. Here, we provide an adaptive spatial design strategy based on a utility function that combines both prediction uncertainty and risk-factor criteria. Prediction uncertainty is obtained through a spatial data fusion approach based on fixed rank kriging that can tackle data with differing spatial supports and signal-to-noise ratios. We focus on the application of low-cost portable sensors, which tend to be relatively noisy, for air pollution monitoring, where data from regulatory stations as well as numeric modeling systems are also available. Although we find that spatial adaptive sampling designs can help to improve predictions and reduce prediction uncertainty, low-cost portable sensors are only likely to be beneficial if they are sufficient in number and quality. Our conclusions are based on a multi-factorial simulation experiment, and on a realistic simulation of pollutants in the Erie and Niagara counties in Western New York.

Spatial filtering and Bayesian data fusion for mapping soil properties: A case study combining legacy and remotely sensed data in Iran

In a spatial mapping context, we address in this paper the issue of combining at best soil data coming from legacy soil surveys with soil information that is indirectly obtained from remote sensing (RS). We show first how spatial scale issues need to beproperly addressed for soil mapping in order to increase the predictive performances of spatial prediction models, where the two prediction techniques that are compared here are kriging and random forests (RF). By relying afterwards on a Bayesian data fusion approach, we then emphasize the benefit of combining the output of these two prediction models in order to get a single prediction result that leads to an improved final map.The advocated methodology is illustrated with the mapping of soil saturation percentage (SP) over a 10,480 km2 area located in Iran, were SP is a key soil parameter for sustainable development and land management. A set of 396 soil profiles were obtained from legacy soil surveys and were used to compute vertically averaged SP values. In parallel, a set of RS covariates were obtained from a 90 meters resolution Landsat image and a digital elevation model. Based on the modeling of the spatialdependence both for soil SP data and for RS covariates, it is shown how the relationship between them is improved by using a filtered kriging technique that allows us to focus on their long range component only. Using these filtered SP value and RS covariates, predictions were obtained separately from kriging and from a RF model at the nodes of a 90 meters resolution grid. Even if the performances of these two models are almost identical with R2 values equal to 0.66–0.67, it is shown afterwards that a Bayesian data fusion procedure that combines both predictions yields improved performance with a R2 value equal to 0.86. These results clearly emphasize the importance of properly addressing scale issues and the benefit of data fusion inorder to improve the quality of the final map. Though the study was conducted here for SP values, we believe this methodology and these findings are relevant when it comes to handle other soil properties in a spatial mapping context that involves several data sources.

多源空间数据融合的城市人居环境监测模型与应用研究

多源空间数据融合的城市人居环境监测模型与应用研究