Heiquan Reservoir, Xining's seventh source of drinking water, plays a significant role in ensuring water for industry, agriculture, and ecology in Xining and the neighboring areas. Therefore, monitoring and evaluating the water quality of Heiquan Reservoir is required. Chlorophyll content in water bodies is an important indication of eutrophication because it can represent environmental changes as well as the impact of human life and productivity on water bodies. This study mainly uses Landsat-8 OLI images from 2015–2019 by constructing an inversion model of chlorophyll sample point concentrations extracted from MODIS-based inversion models. The investigation concluded that the chlorophyll concentration in Xining City's Heiquan Reservoir falls in winter each year, gradually increases in spring, and reaches its highest in summer. Moreover, the parameters influencing the distribution of chlorophyll concentration from water temperature were investigated using inverted Landsat-8 OLI images of Heiquan Reservoir's water surface temperature. It was found that the chlorophyll concentration in Heiquan Reservoir showed a positive correlation with water surface temperature.
With the development of remote sensing technology, feature extraction methods are gradually diversified, mainly through optical and radar remote sensing. Optical remote sensing images contain a wealth of spectral information, whereas radar remote sensing images are all-day and all-weather. But they have some drawbacks. As a result, different features of Sentinel-1A and Landsat-8 images are combined in this paper to exploit their advantages for feature extraction experiments fully. Data preprocessing for Sentinel-1A and Landsat-8 is performed in this paper, followed by polarization feature extraction using H-α-A decomposition for Sentinel-1A and texture feature extraction using GLCM for Sentinel-1A and Landsat-8, followed by feature combination and classification using SVM classifier. Finally, the accuracy of classification results is evaluated. The results of this paper are as follows: the worst accuracy result is based solely on Landsat-8 spectral features combination, with an overall accuracy of only 80.61% and a Kappa coefficient of 0.6702; the accuracy of features combinations based on Landsat-8 spectral plus texture and Sentinel-1A polarization plus texture is improved. The best accuracy is 90.78%, and the Kappa coefficient is 0.8473. The experimental results show that multi-source remote sensing-based feature extraction with multiple feature combinations is more advantageous.
The extraction of building information is of great significance for environmental change detection and urban development, and is also conducive to the country's macro-control and scientific management. Based on the four Landsat8 OLI image data of Huangdao District, Qingdao City in 2015, 2017, 2019 and 2021, this study proposes a building extraction method combing random forest feature selection with SVM algorithm. In this method, first, 22 feature variables are extracted from the images. Second, the random forest is used to screen out the important features. Finally, the selected feature variables are combined with the SVM classifier. In this study, ten features out of 22 features are screened out by RF, and their feature importance reach 94.37%. Moreover, the building extraction effect after feature selection is significantly better than that before selection. The results have shown that the overall accuracy and Kappa of the image classification results based on SVM in each time phase are higher than those based on other machine learning algorithms, and the overall classification accuracy and Kappa are both 96% and 0.95 or more.
Remote sensing building change detection is a key technology for understanding urban land cover and land use. In order to improve the accuracy of building change detection, this paper firstly summarizes a set of optimized building change detection methods for high resolution remote sensing images, and then introduces the steps of the method and the optimization part in detail. Finally, the feasibility of the optimized change detection method was verified in the experimental area. The results of this method are verified by an empirical experiment on the urban sub-center of Tongzhou District. The results show that this method can complete the remote sensing building change detection task under the condition of setting fewer parameters and large change area.
The Terracotta Warriors and Horses of the First Qin Emperor are a highly prized relic in China and the world. Some people believe that the Terracotta Warriors are a realistic representation of the Qin dynasty, while others feel that the Terracotta Warriors are the result of artistic re-creation. Therefore, in order to verify the "realism" of the terracotta warriors and horses, the degree of resemblance between the terracotta warriors and real people was quantified. The representative points of the outer corners of the eyes on the head and face of the terracotta warriors are selected. The method of extracting feature points based on the cross-sectional line of approximation is proposed using point cloud data. The method is based on the approximation method of cross-sectional line extraction, which is more accurate and easier to calculate by converting 3D into 2D. The final experimental results show that the curvature of the outer corner points of the terracotta warriors and the curvature pattern of the real eyes basically match. The method verifies that the facial features of terracotta warriors are highly correlated with those of real people. At the same time, the "realistic" nature of the terracotta warriors is demonstrated.
In view of the city small and medium-sized bridge potential damage area detection efficiency is not high shortcomings, using the ground three-dimensional Laser Scanner (TLS) point cloud data, determine the potential damage area of small and medium-sized span bridge, and accurately detect the key damage location of small and medium-sized span bridge. In this paper, a set of accurate potential damage detection process is proposed, and the process is applied to the Beisha Bridge in Beijing city for potential damage detection analysis. It is divided into three steps :(1) quantification of bridge deck damage by applying the range-gradient algorithm model; (2) feasibility of detecting potential damage of bridge deck by gaussian curvature of point cloud data is analyzed in detail, and the model is constructed; (3) abnormal areas of bridge deck change are identified by taking the experimental data of beizan bridge as an example. The experimental results show that the proposed Gaussian curvature damage identification method is consistent with the displacements and gradients method, which can be applied to the analysis of potential damage mechanism of Bridges.
It is an important task to evaluate the safety during the life of bridges using the corresponding vibration parameters. With
the advantages of non-contact and high accuracy, the new remote measurement technology of GB-InSAR is suitable to
make dynamic measurement for bridges to acquire the vibration parameters. Three key technologies, including stepped
frequency-continuous wave technique, synthetic aperture radar and interferometric measurement technique, are
introduced in this paper. The GB-InSAR is applied for a high-speed railway bridge to measure of dynamic characteristics
with the train passing which can be used to analyze the safety of the monitored bridge. The test results shown that it is an
reliable non-contact technique for GB-InSAR to acquire the dynamic vibration parameter for the high-speed railway
bridges.
This paper presents an example of using the ground-based synthetic aperture radar (GBSAR) technique for the emergency monitoring of a deep foundation excavation. The process includes a quality evaluation for the interferometric data acquired by the IBIS-L radar from Ingegneria Dei Sistemi S.p.A. Atmospheric effects were eliminated through calibration with ground control points (two triangular reflectors) to improve the measuring accuracy of the emergency monitoring. Accuracy of the data obtained was then compared at six check points between the GBSAR technique and a Leica total station TC2003 with prisms. The results indicate that the GBSAR technique is a suitable method of emergency displacement monitoring within a deep foundation excavation and has several significant advantages. These include a higher level of accuracy, near real-time monitoring intervals, and site-wide displacement maps obtained for each observation sampling interval.
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