排序方式： 共有7条查询结果，搜索用时 127 毫秒
1
1.
The July 1, 2017 Wangjiawan landslide in Ningxiang County,China 总被引：1，自引：0，他引：1
Yuen Zhang Deying Li Kunlong Yin Lixia Chen Yong Xu Tsehaie Woldai Zhipeng Lian 《Landslides》2018,15(8):1657-1662
Many landslides were triggered by heavy rainfall from 29 June to 1 July 2017 in Ningxiang County, Hunan Province, China. A field investigation into one of the landslides, known as the Wangjiawan landslide, was undertaken on July 3, 2017, to understand the landslide mechanisms and the factors that triggered the event. The landslide is a translational and rotational slide that degraded downslope to an earth flow. It occurred on a steeply dipping mud-rich slate intercalation. Field investigation shows that the landslide had a movement rate of more than 25 m/s and had been triggered by up to 338 mm of continuous rainfall over 2 days. The landslide was fast-moving because it occurred on a steep slope and showed a rapid reduction in shear strength. The landslide resulted in nine fatalities and 19 injuries, most during initial rescue efforts. To mitigate a possible secondary landslide disaster, later emergency measures including evacuation of the survivors, setting up warning signs, and covering landslide cracks with plastic sheeting were adopted. 相似文献
2.
GPS interactive time series analysis software 总被引：4，自引：3，他引：1
Time series analysis is an important part of geodetic and geodynamic studies, especially when continuous GPS observations are used to explore areas with a low rate of deformation. In this domain, having precise and robust tools for processing and analyzing position time series is a prerequisite. To meet this requirement, a new software package called GPS Interactive Time Series Analysis was developed using the MATLAB language. Along with calculating basic statistics and quality parameters such as mean and variance, the software is capable of importing and visualizing different time series formats, determining and removing jumps and outliers, interpolating data, and producing numerical and publication quality graphical outputs. Furthermore, bivariate statistical analysis (such as correlation coefficients, curvilinear and nonlinear regression), residual analysis, and spectral analysis (such as auto-spectrum, Lomb–Scargle spectrum, evolutionary power spectrum, and wavelet power spectrum) form the main analysis features of the software. 相似文献
3.
4.
5.
Gayantha R. L. Kodikara Tsehaie Woldai 《International Journal of Digital Earth》2018,11(10):1020-1038
Here, we demonstrate the application of Decision Tree Classification (DTC) method for lithological mapping from multi-spectral satellite imagery. The area of investigation is the Lake Magadi in the East African Rift Valley in Kenya. The work involves the collection of rock and soil samples in the field, their analyses using reflectance and emittance spectroscopy, and the processing and interpretation of Advanced Spaceborne Thermal Emission and Reflection Radiometer data through the DTC method. The latter method is strictly non-parametric, flexible and simple which does not require assumptions regarding the distributions of the input data. It has been successfully used in a wide range of classification problems. The DTC method successfully mapped the chert and trachyte series rocks, including clay minerals and evaporites of the area with higher overall accuracy (86%). Higher classification accuracies of the developed decision tree suggest its ability to adapt to noise and nonlinear relations often observed on the surface materials in space-borne spectral image data without making assumptions on the distribution of input data. Moreover, the present work found the DTC method useful in mapping lithological variations in the vast rugged terrain accurately, which are inherently equipped with different sources of noises even when subjected to considerable radiance and atmospheric correction. 相似文献
6.
Application of Data-Driven Evidential Belief Functions to Prospectivity Mapping for Aquamarine-Bearing Pegmatites,Lundazi District,Zambia 总被引：6，自引：0，他引：6
A case application of data-driven estimation of evidential belief functions (EBFs) is demonstrated to prospectivity mapping in Lundazi district (eastern Zambia). Spatial data used to represent recognition criteria of prospectivity for aquamarine-bearing pegmatites include mapped granites, mapped faults/fractures, mapped shear zones, and radioelement concentration ratios derived from gridded airborne radiometric data. Data-driven estimates EBFs take into account not only (a) spatial association between an evidential map layer and target deposits but also (b) spatial relationships between classes of evidences in an evidential map layer. Data-driven estimates of EBFs can indicate which spatial data provide positive or negative evidence of prospectivity. Data-driven estimates of EBFs of only spatial data providing positive evidence of prospectivity were integrated according to Dempster’s rule of combination. Map of integrated degrees of belief was used to delineate zones of relative degress of prospectivity for aquamarine-bearing pegmatites. The predictive map has at least 85% prediction rate and at least 79% success rate of delineating training and validation deposits, respectively. The results illustrate usefulness of data-driven estimation of EBFs in GIS-based predictive mapping of mineral prospectivity. The results also show usefulness of EBFs in managing uncertainties associated with evidential maps. 相似文献
7.
Tsehaie Woldai 《地球空间信息科学学报》2020,23(1):107-123
ABSTRACTOver the last 20 years, Africa has witnessed a slow but steady advancement in space-based technologies as they are increasingly recognized as an essential tool for decision-making that can leapfrog African development. A critical review on the outcome of a survey questionnaire focused on African private sector industries and universities, services and education/training in EO and Geo-Information Sciences, combined with literature review, and personal contacts reveal optimism for success in four sectors. These include the public sector (Government ministries and departments); Academic institutions (universities/colleges/national or regional centers); and space agencies and private sector companies. These sectors are intertwined and fundamental for creating an enabling environment for solutions to a broad spectrum of pressing priorities: job creation, poverty alleviation, and sustainable resource management. The result shows that there is an uptake in the number of institutions and market segments created. To date, there are more than 90 academic institutions and over 53 national space agencies in 28 countries. Within the 53 national space agencies, 11 African countries have already launched a total of 36 satellites into orbit, and additional five are expected by the first quarter of 2021; another five by 2025; thus, amounting to 46 satellites not foreseen ten years ago. In addition, there are now ten receiving and tracking stations in six African countries and 17 scientific National Associations or Societies with specialized expertise in Geo-Information technologies. The updated survey on the private sector in 2019 ascertained that around 4110 people are working in 130 of the 229 EO and Geo-Information Science companies identified in Africa. Ongoing investigations reiterate that companies dealing with space-based datasets and Geo-Information Sciences together with the private spin-off companies today absorb more than 15,000 people and the assumption is that this number is going to exceed 100,000 by the year 2025. 相似文献
1