Sea surface temperature (SST) is one of the most important parameters in the global ocean-atmospheric system, changes of which can have profound effects on the global climate and may lead to extreme ...weather events such as droughts and floods. Therefore, predicting the dynamics of future SSTs is of vital importance which can help identify these extreme events and alleviate the losses they cause. In this paper, a machine learning method combining the long short-term memory (LSTM) deep recurrent neural network model and the AdaBoost ensemble learning model (LSTM-AdaBoost) is proposed to predict the short and mid-term daily SST considering that LSTM is good at modelling long-term dependencies but suffers from overfitting, while AdaBoost has strong prediction capability and is not easily overfitted. By combining these two strong and heterogeneous models, the prediction errors related to variance may cancel out each other and the final results can be improved. In this method, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SSTs are very small compared to their absolute magnitudes. The seasonality of the SSTA time series is first modelled using polynomial regression and then removed. Then, the deseasonalized time series are used to train the developed LSTM model and AdaBoost model independently. Daily SSTA predictions are made using these two models, and eventually, their predictions are combined as final predictions using the averaging strategy. A case study in the East China Sea that predicts the daily SSTA 10 days ahead shows that the proposed LSTM-AdaBoost combination model outperforms the LSTM and AdaBoost separately, as well as the optimized support vector regression (SVR) model, the optimized feedforward backpropagation neural network model (BPNN), and the stacking LSTM-AdaBoost model (S_LSTM-AdaBoost), when judged using multiple error statistics and from different perspectives. The results suggest that the LSTM-AdaBoost combination model using the averaging strategy is highly promising for short and mid-term daily SST predictions.
•A LSTM-AdaBoost combination method is proposed to predict short and mid-term SST.•36-year satellite-derived time series daily SST data are used.•A case study has been demonstrated in the East China Sea.•The proposed method outperforms LSTM, AdaBoost, SVR, BPNN and S_LSTM-AdaBoost.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Synthetic Aperture Radar Interferometry (InSAR) provides an effective tool to study slow-moving landslides. However, InSAR observations are often contaminated by tropospheric artefacts due to spatial ...and temporal variations of atmospheric refractivity. Particularly, the topography-dependent stratified delays may introduce seasonal oscillation biases into InSAR-measured deformation time series under steep terrains, which cannot be removed by conventional spatial and temporal filtering. In this study we proposed two complementary approaches to correct the stratified tropospheric delays for time series InSAR analysis when studying single landslides. One is the Iterative Linear Model (ILM) as an improved version of the traditional Linear Model (LM). The other is to fuse tropospheric delays predicted by several global weather models (FDWM) with different temporal intervals and spatial resolutions. Both methods are integrated into the standard Small BAseline Subset (SBAS) time series analysis procedure. We evaluated the proposed methods in three landslide-prone areas in southwest China using Sentinel-1 datasets. The experimental results demonstrated that the ILM method removed the seasonal stratified delays mixed in deformation time series, unaffected by the deforming points. The FDWM method achieved an optimal combination of tropospheric delay predictions by four weather models, i.e. ERA-Interim, ERA5, HRES ECMWF, and MERRA-2. Validations using in-situ GPS measurements suggested that the original Root Mean Squared (RMS) values of interferometric phases declined by more than 35% after both ILM and FDWM corrections. The ILM had better performances than the FDWM to correct stratified delay for single landslides, whereas the FDWM can be an effective alternative when the ILM is inapplicable in case of limited coherent points.
•We found seasonal tropospheric delay signals in InSAR results over steep terrains.•The iterative linear fitting method is robust to the adverse impacts of moving points.•The fusion method optimally combines delays predicted by multiple weather models.•The modified SBAS approach removes seasonal fluctuations in deformation time series.•The GPS measurements validate the reliability and accuracy of the proposed methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method ...with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution (HR) remote sensing data and emerging of new satellite sensors, ...HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
A new stochastic search strategy inspired by the clonal selection theory in an artificial immune system is proposed for dimensionality reduction of hyperspectral remote-sensing imagery. The clonal ...selection theory is employed to describe the basic features of an immune response to an antigenic stimulus in order to meet the requirement of diversity in the antibody population. In our proposed strategy, dimensionality reduction is formulated as an optimization problem that searches an optimum with less number of features in a feature space. In line with this novel strategy, a feature subset search algorithm, clonal selection Feature-Selection (CSFS) algorithm, and a feature-weighting algorithm, Clonal-Selection Feature-Weighting (CSFW) algorithm, have been developed. In the CSFS, each solution is evolved in binary space, and the value of each bit is either 0 or 1, which indicates that the corresponding feature is either removed or selected, respectively. In CSFW, each antibody is directly represented by a string consisting of integer numbers and their corresponding weights. These algorithms are compared with the following four well-known algorithms: sequential forward selection, sequential forward floating selection, genetic-algorithm-based feature selection, and decision-boundary feature extraction using the hyperspectral remote-sensing imagery acquired by the Pushbroom Hyperspectral Imager and the Airborne Visible/Infrared Imaging Spectrometer, respectively. Experimental results demonstrate that CSFS and CSFW outperform other algorithms and hence provide effective new options for dimensionality reduction of hyperspectral remote-sensing imagery.
In the last half-century, geoscience research has advanced due to multidisciplinary technologies, among which Information and Communication Technology (ICT) has played a vital role. However, ...scientifically organizing these ICTs toward improving geoscience measurements, data processing, and information services has encountered tremendous challenges. This paper reviews a profound revolution in geoscience that has resulted from the Geospatial Sensor Web (GSW), serving as a new cyber-physical spatio-temporal information infrastructure for geoscience on the World Wide Web (WWW). In contrast to previous experiment-based and sensor-based paradigms, the GSW-based paradigm is able to accomplish the following: (1) achieve integrated and sharable management of diverse sensing resources, (2) obtain real-time or near real-time and spatiotemporal continuous data, (3) conduct interoperable and online geoscience data processing and analysis, and (4) provide focusing services with web-based geoscience information and knowledge. As a benefit of the GSW, increasingly more geoscience disciplines are enjoying the value of real-time data, multi-source monitoring, online processing, and intelligence services. This paper reviews the evolution of geoscience research paradigm to demonstrate the scientific background of GSW. Then, we elaborates on four key methods provided by GSW, namely, integrated management, collaborative observation, scalable processing and fusion, and focusing service web capacity. Furthermore, current GSW prototypes and applications for environmental, hydrological, and natural disaster analysis are also reviewed. Moreover, four challenges to the future GSW in geoscience research are identified and analyzed, including integration with the Model Web initiative for sophisticated geo-processing, integration with humans for pervasive sensing, integration with Internet of Things (IoT) to achieve high-quality performance and data mining, and integration with Artificial Intelligence (AI) to provide smart geoservices. We have concluded that GSW has become an indispensable cyber-physical infrastructure, and will play a greater role in geoscience research and application.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Nowadays, biking is flourishing in many Western cities. While many roads are used for both cars and bicycles, buffered bike lanes are marked for the safety of cyclists. In many cities, segregated ...paths are built up to have physical separation from motor vehicles. These types of biking ways are regarded as attributes in geographic information system (GIS) data. This information is required and important in the service of route planning, as cyclists may prefer certain types of bikeways. This paper presents a framework for generating networks of bikeways with attribute information from the data collected on the collaborative street view data platform Mapillary. The framework consists of two layers: The first layer focuses on constructing a bikeway road network using Global Positioning System (GPS) information of Mapillary images. Mapillary sequences are classified into walking, cycling, driving (ordinary road), and driving (motorway) trajectories based on the transportation mode with a trained XGBoost classifier. The bikeway road network is then extracted from cycling and driving (ordinary road) trajectories using a raster-based method. The second layer focuses on extracting attribute information from Mapillary images. Cycling-specific information (i.e., bicycle signs/markings) is extracted using a two-stage detection and classification model. A series of quantitative evaluations based on a case study demonstrated the ability and potential of the framework for extracting bikeway road information to enrich the existing OSM cycling road data.
•Generating of bikeways from VGI data i.e. Mapillary street-level images.•Extraction of attribute information of bikeways by using a two-stage detection and classification model.•New method for enriching cycling data with classification information on OpenStreetMap.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Detection of potential slope instabilities across wide areas using SAR interferometry (InSAR) techniques can aid prevention and mitigation of landslide disasters. Nevertheless, the low spatial ...density of coherent radar targets detected by conventional time series InSAR methods in mountainous areas with complex topography and vegetation cover introduces considerable uncertainties into the deformation measurements, and thus results in a high probability of omissions for landslide detection. In this study, a new time series InSAR method named Coherent Scatterers InSAR (CSI) is proposed to solve this problem through the joint exploitation of Persistent Scatterers (PS) and Distributed Scatterers (DS) to increase the quantity of Measurement Points (MPs) in rural environments. We applied this approach to detect potentially unstable slopes at a catchment scale, delineate sliding boundaries, and measure the deformation of major landslides. The archived ALOS PALSAR and ENVISAT ASAR data stacks covering the Danba County in the upper reach of the Dadu River Basin in southwest China, were processed. The PALSAR-measured deformation rate map revealed 17 suspected landslides and their sliding boundaries. By contrast, only six out of these 17 landslides were detected from the ASAR results, as the other 11 landslides were undetected due to the much lower density of coherent points. These detection results were indirectly verified by two different approaches. Two differential interferograms for recently acquired L-band ALOS-2 PALSAR-2 data pairs were visually inspected to check whether these landslides are still in active deformation. The reliability of the InSAR results was further evaluated by comparisons against field survey and in-situ GPS measurements. The spatial and temporal patterns of surface displacements at several slow-moving landslides were analyzed in detail, and general guidelines on the application of the CSI method for landslide investigations were discussed and summarized.
•Both PS and DS targets are exploited to increase the spatial density of measurement points.•Potential landslides over a wide area are detected from the displacement rate map.•L-band InSAR shows great advantages over C-band in measuring landslide displacements.•Spatial/temporal patterns of typical landslide surface displacements are analyzed.•Guidelines on InSAR applications for landslide investigation are discussed and summarized.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
For multi-sensor integrated systems, such as the mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for ...higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Deformation monitoring has been brought to the fore and extensively studied in recent years. Global Navigation Satellite System Reflectometry (GNSS-R) techniques have so far been developed in ...deformation estimation applications, which however, are subject to the influence of mobile satellites. Rather than compensating for the path delay variations caused by mobile satellites, adopting Beidou geostationary Earth orbit (GEO) satellites as transmitters directly eliminates the satellite-motion-induced phase error and thus provides access to stable phase information. This paper presents a novel deformation monitoring concept based on GNSS-R utilizing Beidou GEO satellites. The geometrical properties of the GEO-based bistatic GNSS radar system are explored to build a theoretical connection between deformation quantity and the echo carrier phases. A deformation retrieval algorithm is proposed based on the supporting software receiver, thus allowing echo carrier phases to be extracted and utilized in deformation retrieval. Two field validation experiments are conducted by constructing passive bistatic radars with reflecting plates and ground receiver. Utilizing the proposed algorithm, the experimental results suggested that the GEO-based GNSS reflectometry can achieve deformation estimations with an accuracy of around 1 cm when the extracted phases does not exceed one complete cycle, while better than 3 cm when considering the correct integer number of phase cycles. Consequently, based on the passive bistatic radar system, the potential of achieving continuous, low-cost deformation monitoring makes this novel technique noteworthy.
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