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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2014
    In:  Journal of Earth Science Vol. 25, No. 3 ( 2014-6), p. 537-543
    In: Journal of Earth Science, Springer Science and Business Media LLC, Vol. 25, No. 3 ( 2014-6), p. 537-543
    Type of Medium: Online Resource
    ISSN: 1674-487X , 1867-111X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
    detail.hit.zdb_id: 2501172-8
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  • 2
    In: Global Change Biology, Wiley, Vol. 29, No. 8 ( 2023-04), p. 2203-2226
    Abstract: Although soil ecological stoichiometry is constrained in natural ecosystems, its responses to anthropogenic perturbations are largely unknown. Inputs of inorganic fertilizer and crop residue are key cropland anthropogenic managements, with potential to alter their soil ecological stoichiometry. We conducted a global synthesis of 682 data pairs to quantify the responses of soil carbon (C), nitrogen (N), and phosphorus (P) and grain yields to combined inputs of crop residue plus inorganic fertilizer compared with only inorganic fertilizer application. Crop residue inputs enhance soil C (10.5%–12%), N (7.63%–9.2%), and P (2.62%–5.13%) contents, with an increase in C:N (2.51%–3.42%) and C:P (7.27%–8.00%) ratios, and grain yields (6.12%–8.64%), indicating that crop residue alleviated soil C limitation caused by inorganic fertilizer inputs alone and was able to sustain balanced stoichiometry. Moreover, the increase in soil C and C:N(P) ratio reached saturation in ~13–16 years after crop residue return, while grain yield increase trend discontinued. Furthermore, we identified that the increased C, N, and P contents and C:N(P) ratios were regulated by the initial pH and C content, and the increase in grain yield was not only related to soil properties, but also negatively related to the amount of inorganic N fertilizer input to a greater extent. Given that crop residual improvement varies with soil properties and N input levels, we propose a predictive model to preliminary evaluate the potential for crop residual improvement. Particularly, we suggest that part of the global budget should be used to subsidize crop residue input management strategies, achieving to a win‐win situation for agricultural production, ecological protection, and climate change mitigation.
    Type of Medium: Online Resource
    ISSN: 1354-1013 , 1365-2486
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2020313-5
    SSG: 12
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  • 3
    In: Ecological Indicators, Elsevier BV, Vol. 149 ( 2023-05), p. 110161-
    Type of Medium: Online Resource
    ISSN: 1470-160X
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2063587-4
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  • 4
    In: Sensors, MDPI AG, Vol. 19, No. 10 ( 2019-05-20), p. 2329-
    Abstract: In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2052857-7
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  • 5
    In: Remote Sensing, MDPI AG, Vol. 9, No. 12 ( 2017-12-12), p. 1299-
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2513863-7
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  • 6
    In: Remote Sensing, MDPI AG, Vol. 11, No. 10 ( 2019-05-21), p. 1202-
    Abstract: The accurate and quick derivation of the distribution of damaged building must be considered essential for the emergency response. With the success of deep learning, there is an increasing interest to apply it for earthquake-induced building damage mapping, and its performance has not been compared with conventional methods in detecting building damage after the earthquake. In the present study, the performance of grey-level co-occurrence matrix texture and convolutional neural network (CNN) features were comparatively evaluated with the random forest classifier. Pre- and post-event very high-resolution (VHR) remote sensing imagery were considered to identify collapsed buildings after the 2010 Haiti earthquake. Overall accuracy (OA), allocation disagreement (AD), quantity disagreement (QD), Kappa, user accuracy (UA), and producer accuracy (PA) were used as the evaluation metrics. The results showed that the CNN feature with random forest method had the best performance, achieving an OA of 87.6% and a total disagreement of 12.4%. CNNs have the potential to extract deep features for identifying collapsed buildings compared to the texture feature with random forest method by increasing Kappa from 61.7% to 69.5% and reducing the total disagreement from 16.6% to 14.1%. The accuracy for identifying buildings was improved by combining CNN features with random forest compared with the CNN approach. OA increased from 85.9% to 87.6%, and the total disagreement reduced from 14.1% to 12.4%. The results indicate that the learnt CNN features can outperform texture features for identifying collapsed buildings using VHR remotely sensed space imagery.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2513863-7
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  • 7
    Online Resource
    Online Resource
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 2 ( 2018-02-04), p. 48-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 2 ( 2018-02-04), p. 48-
    Abstract: Soil spectroscopy is a promising technique for soil analysis, and has been successfully utilized in the laboratory. When it comes to space, the presence of vegetation significantly affects the performance of imaging spectroscopy or hyperspectral imaging on the retrieval of topsoil properties. The Forced Invariance Approach has been proven able to effectively suppress the vegetation contribution to the mixed image pixel. It takes advantage of scene statistics and requires no specific a priori knowledge of the referenced spectra. However, the approach is still mainly limited to lithological mapping. In this case study, the objective was to test the performance of the Forced Invariance Approach to improve the estimation accuracy of soil salinity for an agricultural area located in the semi-arid region of Northwest China using airborne hyperspectral data. The ground truth data was obtained from an eco-hydrological wireless sensing network. The relationship between Normalized Difference Vegetation Index (NDVI) and soil salinity is discussed. The results demonstrate that the Forced Invariance Approach is able to improve the retrieval accuracy of soil salinity at a depth of 10 cm, as indicated by a higher value for the coefficient of determination (R2). Consequently, the vegetation suppression method has the potential to improve quantitative estimation of soil properties with multivariate statistical methods.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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  • 8
    Online Resource
    Online Resource
    Walter de Gruyter GmbH ; 2023
    In:  Open Geosciences Vol. 15, No. 1 ( 2023-07-14)
    In: Open Geosciences, Walter de Gruyter GmbH, Vol. 15, No. 1 ( 2023-07-14)
    Abstract: The reconstruction and analysis of building models are crucial for the construction of smart cities. A refined building model can provide a reliable data support for data analysis and intelligent management of smart cities. The colors, textures, and geometric forms of building elements, such as building outlines, doors, windows, roof skylights, roof ridges, and advertisements, are diverse; therefore, it is challenging to accurately identify the various details of buildings. This article proposes the Multi-Task Learning AINet method that considers features such as color, texture, direction, and roll angle for building element recognition. The AINet is used as the basis function; the semantic projection map of color and texture, and direction and roll angle is used for multi-task learning, and the complex building facade is divided into similar semantic patches. Thereafter, the multi-semantic features are combined using hierarchical clustering with a region adjacency graph and the nearest neighbor graph to achieve an accurate recognition of building elements. The experimental results show that the proposed method has a higher accuracy for building detailed edges and can accurately extract detailed elements.
    Type of Medium: Online Resource
    ISSN: 2391-5447
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2023
    detail.hit.zdb_id: 2799881-2
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  • 9
    Online Resource
    Online Resource
    Walter de Gruyter GmbH ; 2022
    In:  Open Geosciences Vol. 14, No. 1 ( 2022-02-09), p. 98-110
    In: Open Geosciences, Walter de Gruyter GmbH, Vol. 14, No. 1 ( 2022-02-09), p. 98-110
    Abstract: To solve engineering geological problems, including water conservancy, transportation, and mining, it is necessary to obtain information on rock mass structures, such as slopes, foundation pits, and tunnels, in time. The traditional method for obtaining structural information requires manual measurement, which is time consuming and labor intensive. Because geological information is complicated and diverse, it is not practical for general deep learning methods to obtain full-scale structural surface trace images to prepare training samples. Transfer learning can abstract high-level features from low-level features with a small number of training samples, which can automatically express the inherent characteristics of objects. This article proposed a rock mass structural surface trace extraction method based on the transfer learning technique that considers the attention mechanism and shape constraints. For the general test set, the accuracy of rock mass structural surface trace recognition with the proposed method can reach 87.2%. Experimental results showed that the proposed method has advantages in extracting complicated geological structure information and is valuable for providing technical support for the extraction of geological information in the construction of water conservancy, transportation, mining, and related projects.
    Type of Medium: Online Resource
    ISSN: 2391-5447
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2799881-2
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  • 10
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 2 ( 2020-01-14), p. 602-
    In: Applied Sciences, MDPI AG, Vol. 10, No. 2 ( 2020-01-14), p. 602-
    Abstract: The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main threats. It is essential to retrieve the detailed information of affected buildings after earthquakes. Very high-resolution satellite imagery plays a key role in retrieving building damage information since it captures imagery quickly and effectively after the disaster. In this paper, the pretrained Visual Geometry Group (VGG)Net model was applied for identifying collapsed buildings induced by the 2010 Haiti earthquake using pre- and post-event remotely sensed space imagery, and the fine-tuned pretrained VGGNet model was compared with the VGGNet model trained from scratch. The effects of dataset augmentation and freezing different intermediate layers were also explored. The experimental results demonstrated that the fine-tuned VGGNet model outperformed the VGGNet model trained from scratch with increasing overall accuracy (OA) from 83.38% to 85.19% and Kappa from 60.69% to 67.14%. By taking advantage of dataset augmentation, OA and Kappa went up to 88.83% and 75.33% respectively, and the collapsed buildings were better recognized with a larger producer accuracy of 86.31%. The present study showed the potential of using the pretrained Convolutional Neural Network (CNN) model to identify collapsed buildings caused by earthquakes using very high-resolution satellite imagery.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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