scholarly journals Remote Sensing Big Data Analysis of the Lower Yellow River Ecological Environment Based on Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuntong Liu ◽  
Kuan He ◽  
Fen Qin
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Yellow River ◽  
Environmental Data ◽  
Ecological Environment ◽  
Ecological Civilization ◽  
Data Resource ◽  
The Lower Yellow River ◽  
Management Platform ◽  
Data Management Platform

This paper collects data on the ecological environment of the lower Yellow River through an IoT approach and provides an in-depth analysis of the ecological remote sensing big data. An impervious fusion of multisource remote sensing data cooperation and multimachine learning algorithm cooperation is proposed. The water surface extraction method has improved the extraction accuracy of the construction land and rural settlements in the Yellow River Delta. The data system, big data management platform, and application scenarios of the environmental data resource center are designed specifically, respectively. Based on the spherical mesh information structure to sort out environmental data, an environmental data system containing data characteristics such as information source, timeliness, and presentation is formed. According to the characteristics of various types of environmental data, the corresponding data access, storage, and analysis support system is designed to form the big data management platform. Strengthen the construction of ecological interception projects for farmland receding water. Speed up the construction of sewage treatment facilities. Carry out waste and sewage pipeline network investigation, speed up the construction of urban sewage collection pipeline network, and improve the waste and sewage collection rate and treatment rate. The management platform adopts the Hadoop framework, which is conducive to the storage of massive data and the utilization of unstructured data. Combined with the relevant national policy requirements and the current environmental protection work status, the application scenarios of environmental big data in environmental decision-making, supervision, and public services are sorted out to form a complete data resource center framework. Gray correlation analysis is used to identify the key influencing factors of different types of cities to elaborate the contents of the construction of water ecological civilization in different types of cities and to build a framework of ideas for the construction of urban water ecological civilization to improve the health of urban water ecological civilization. To realize the sustainable development of the lower reaches of the Yellow River, blind logging and reclamation should be avoided in the process of land development, and more efforts should be made to protect tamarisk scrub and reed scrub, which are vegetation communities with positive effects on the regional ecological environment. In urban planning, the proportion of green area and water area within the city should be reasonably increased, so that the city can develop towards a livable city that is more conducive to human-land harmony and sustainability.

Research on the Application of Big Data in the Environmental Decision-Making of the Government

2014 ◽  
Vol 685 ◽  
pp. 524-527
Author(s):  
Yan Ju Zhu
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Video Compression ◽  
Computer Processing ◽  
Environmental Data ◽  
Environmental Decision Making ◽  
Management Platform ◽  
Data Platform ◽  
The Government ◽  
Environmental Decision

The article mainly researches on the application of big data in the environment decision-making of the government. Through the integration of the technology of Internet, video compression, computer processing, we pose the model of the government environmental data platform. The platform includes the environmental data acquisition platform, the environmental decision-making platform and the environmental management platform.

scholarly journals Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ke Sun ◽  
Junping Zhang ◽  
Yingying Zhang
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Tensor Voting ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
The Road ◽  
Road Intersections

Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

scholarly journals Ecological Change Analysis of Lanzhou City Based on Remote Sensing Ecological Index

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Yuhan Huang ◽  
Haowen Yan
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Yellow River ◽  
Gansu Province ◽  
Ecological Environment ◽  
Ecological Change ◽  
Change Analysis ◽  
Ecological Index ◽  
Lanzhou City ◽  
The City

<p><strong>Abstract.</strong> Lanzhou City is the capital of Gansu Province and located in the semi-arid of northwest China. The Yellow River passing through the inner city from west to east, which has formed a special ecological environment. In recent years, the economic level of Lanzhou City has continued to develop, and the degree of urbanization has been continuously improved, which has had a certain impact on the ecological environment of the city. This paper used the Remote Sensing Ecology Index (RSEI) model (Xu, H.Q.,2013) to assess the ecological changes of the four major urban areas Chengguan District, Qilihe District, Anning District and Xigu District of Lanzhou from 2013 to 2017, and evaluate the current ecological environment of the city to provide a basis for the sustainable development of the city.</p>

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