Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of ...payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB) images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1) Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2) the inclusion of texture features improved classification accuracy significantly; (3) classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.
In this work, we investigated transport properties of the electronic states in the gate voltage-modulated skyrmion crystal (SkX). Dynamics of conducting electrons in the SkX can be described by the ...double-exchange model or the so-called
t
-
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model, with the
t
term measuring the hopping energy of the electrons between neighboring sites and
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term measuring the strength of the on-site Hund’s coupling between the spin of conducting electrons and local magnetization. As a result of the Hund’s coupling, the band structure of the conducting electrons in the SkX shares similar topological properties with that of gapped graphene, such as its cone-like shape, nonzero band Chern number, and edge states. By linear fitting the cone-shape energy dispersion of the electronic states in the SkX, one can obtain a gapped Dirac model similar to that of the gapped graphene. We use the Green’s function technique and calculate the transmission probability of the electrons tunneling through an electrostatic barrier in the SkX expressed by the double-exchange model. Numerical results of the transport properties of the SkX by the double-exchange model reproduced analytic results from the Dirac model. We further interpreted the resemblance between the transport properties of the two models by the likeness in their wave functions.
Graphical Abstract
Since Google Earth was first released in 2005, it has attracted hundreds of millions of users worldwide and made a profound impact on both academia and industry. It can be said that Google Earth ...epitomized the first-generation of Digital Earth prototypes. The functionalities and merits that have sustained Google Earth’s lasting influence are worth a retrospective review. In this paper, we take the liberty to conduct a bibliometric study of the applications of Google Earth during 2006–2016. We aim first to quantify the multifaceted impacts, and then to develop a structured understanding of the influence and contribution associated with Google Earth. To accomplish these objectives, we analyzed a total of 2115 Scopus publication records using scientometric methods and then proceed to discussion with a selected set of applications. The findings and conclusions can be summarized as follows: (1) the impact of Google Earth has been profound and persistent over the past decade. Google Earth was mentioned in an average of 229 publications per year since 2009. (2) Broadly, the impact of Google Earth has touched upon most scientific disciplines. Specifically, during 2006–2016, Google Earth has been mentioned in 2115 publications covering all of Scopus’s 26 subject areas; (3) the influence of Google Earth has largely concentrated in GIScience, remote sensing and geosciences. The extended influence of Google Earth has reached a wider range of audiences with a concentration in fields such as human geography, geoscience education and archaeology.
Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing ...applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1) Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2) the inclusion of texture features improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas.
Because of the uncertainties and complexities of the factors involved in causing landslides, it is generally difficult to analyze their influences quantitatively and to predict the probability of ...landslide occurrence. In this work, a hybrid method based on Bayesian network (BN) is proposed to analyze earthquake-induced landslide-causing factors and assess their effects. Our study area is Beichuan, China, where landslides have occurred in recent years, including mass landslides triggered by the 2008 Wenchuan earthquake. To provide a robust assessment of landslide probability, key techniques from landslide susceptibility assessment (LSA) modeling with BN are explored, including data acquisition and processing, BN modeling, and validation. In the study, eight landslide-causing factors were chosen as the independent variables for BN modeling. And this study shows that lithology and Arias intensity are the major factors affecting landslides in the study area. On the basis of the a posteriori probability distribution, the occurrence of a landslide is highly sensitive to relief amplitudes above 116.5m. Using a 10-fold cross-validation and a receiver operating characteristic (ROC) curve, the resulting accuracy of the BN model was determined to be 93%, which demonstrates that the model achieves a high probability of landslide detection and is a good alternative tool for landslide assessment.
Abstract
Oceanic transform faults connect spreading centers and are imprinted with previous tectonic events. However, their tectonic interactions are not well understood due to limited observations. ...The Discovery transform fault system at 4°S, East Pacific Rise (EPR), represents a young transform system, offering a unique opportunity to study the interplay between faulting and other tectonic events at an early phases of an oceanic transform system. Discovery regularly hosts
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5–6 characteristic earthquakes, and the seafloor north of Discovery includes a 35 km‐long rift zone that records a complex history of rifting, faulting and volcanism, suggesting that the transform faults likely interact with regional tectonic activity. We apply a machine‐learning enabled workflow to locate 21,391 earthquakes recorded during a 1‐year ocean bottom seismometer experiment in 2008. Our results indicate that seismicity on the western Discovery fault is separated into seven patches with distinct aseismic and seismic slip modes. Additionally, we observe a patch of off‐fault seismicity near where seafloor abyssal hills intersect the rift zone. This seismicity may have been caused by varying opening rates as spreading rate decreases from north to south in the rift zone. Our findings suggest that the Discovery system is still evolving, and that system equilibrium has not been reached between rifting and faulting. These results reflect the complex yet rarely observed interactions between fault slip, plate rotation, and rifting which are likely ubiquitous at oceanic transform systems.
Plain Language Summary
Oceanic transform faults are major plate boundaries connecting mid‐ocean ridges. Despite their important role in plate tectonics, their interactions with adjacent mid‐ocean ridges and surrounding oceanic plates are not well understood. The Discovery transform fault system at 4°S, East Pacific Rise, is a young oceanic transform system formed approximately 1 My ago, offering a unique opportunity to study the interplay between faulting and other tectonic events at an early phase of an OTF. Discovery faults have quasi‐periodical magnitude (
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) 5–6 earthquakes. Using ocean bottom seismometer data recorded over 1 year, we find that seismicity of the western Discovery fault can be grouped into seven patches, indicating division of alternating slip modes that either releases tectonic strain by
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> 5 earthquakes or creep steadily. North of the western Discovery fault, a ∼10 km wide rift zone, abundant seamounts, and abyssal hills form an interactive tectonic complex. We observe a patch of off‐fault seismicity coinciding with seafloor abyssal hills near their intersection with the rift zone. This off‐fault seismicity indicates ongoing deformation within the oceanic plate and possible spatial variations in rifting rates. Our results suggest that the Discovery system is still evolving with rifting and faulting accommodating plate spreading simultaneously.
Key Points
The western Discovery transform fault has seven patches that are likely dominated by alternating seismic and aseismic slip modes
Machine‐learning method helps to identify off‐fault seismicity along abyssal hills, indicating ongoing deformation within the oceanic plate
The Discovery transform system is young and still evolving, forming an interactive system with faulting, rifting, and plate rotation
Sky View Factor (SVF) is a commonly used indicator of urban geometry. The availability of street-level SVF measurements has been fairly limited due to the high costs of field survey. The Google ...Street View (GSV) serves a massive storage of panorama data that can be utilized to obtain SVF measurements. Yet, automatic extraction of SVFs from panoramas is a complicated process that involves multiple sophisticated computation technologies including machine learning, big image data processing, SVF estimation and geographic information systems (GIS), which constitute major hurdles for the end users. In this light, we developed an easy-to-use GIS-integrated tool (GSV2SVF) to streamline the workflow of extracting SVFs from GSV images and therefore making this vast treasure trove of information conveniently available to everyone at a mouse click. As by-products in addition to the SVF, the results obtained from each GSV panorama are accompanied with the tree view factor (TVF) and the building view factor (BVF), which together can provide a more holistic characterization of the outdoor built environment. GSV2SVF is freely available with source code at https://github.com/jian9695/GSV2SVF. A video is available at https://github.com/jian9695/GSV2SVF/blob/master/Video.mp4 and https://youtu.be/k00wCnuzuvE.
•Obtain sky, tree and building view factors from Google Street View at a mouse click.•Map view factors on Google Maps and export in structured formats.•Batch process large numbers of Google Street View panoramas.
This article defines a new operator called the q-Babalola convolution operator by using quantum calculus and the convolution of normalized analytic functions in the open unit disk. We then study a ...new class of analytic and bi-univalent functions defined in the open unit disk associated with the q-Babalola convolution operator. The main results of the investigation include some upper bounds for the initial Taylor–Maclaurin coefficients and Fekete–Szego inequalities for the functions in the new class. Many applications of the finds are highlighted in the corollaries based on the various unique choices of the parameters, improving the existing results in Geometric Function Theory.
Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple ...endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery.
Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide ...continuous SVF estimates over a large urban area, but such data are not always available and are difficult to acquire. Street view panoramas have become widely available in urban areas worldwide: Google Street View (GSV) maintains a global network of panoramas excluding China and several other countries; Baidu Street View (BSV) and Tencent Street View (TSV) focus their panorama acquisition efforts within China, and have covered hundreds of cities therein. In this paper, we approach this issue from a big data perspective by presenting and validating a method for automatic estimation of SVF from massive amounts of street view photographs. Comparisons were made with SVF estimates derived from two independent sources: a LiDAR-based Digital Surface Model (DSM) and an oblique airborne photogrammetry-based 3D city model (OAP3D), resulting in a correlation coefficient of 0.863 and 0.987, respectively. The comparisons demonstrated the capacity of the proposed method to provide reliable SVF estimates. Additionally, we present an application of the proposed method with about 12,000 GSV panoramas to characterize the spatial distribution of SVF over Manhattan Island in New York City. Although this is a proof-of-concept study, it has shown the potential of the proposed approach to assist urban climate and urban planning research. However, further development is needed before this approach can be finally delivered to the urban climate and urban planning communities for practical applications.
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