scholarly journals Remote sensing investigation of sand mining in Wenzhou based on domestic satellite data

2020 ◽  
Vol 194 ◽  
pp. 05008
Author(s):  
Wang Jie ◽  
Yin Yaqiu ◽  
Wang Yuhao ◽  
Li Li ◽  
Chen Dong
Keyword(s):  
Remote Sensing ◽  
Information Source ◽  
Satellite Image ◽  
Image Data ◽  
Mineral Resources ◽  
Scientific Basis ◽  
Sand Mining ◽  
Development And Utilization ◽  
Satellite Image Data ◽  
The Government

Taking the domestic high-resolution satellite image data obtained in 2018 as the information source, the remote sensing image was processed, and part of the map spots were verified in the field. Combined with the natural environment, topography and geomorphology of Wenzhou City, the sand mining of Oujiang, Feiyun and Aojiang in Wenzhou City was investigated and monitored by remote sensing. Through the application research, the current situation of sand mining in Wenzhou City is found out, which can provide scientific basis for the government to make the next decision of sand mining restoration and management and the reasonable development and utilization planning of mineral resources.

scholarly journals THE TYPHOON DISASTER ANALYSIS EMERGENCY RESPONSE SYSTEM BASED ON UAV REMOTE SENSING TECHNOLOGY

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 959-965
Author(s):  
Q. J. Chen ◽  
Y. R. He ◽  
T. T. He ◽  
W. J. Fu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Analysis ◽  
Satellite Image ◽  
Image Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Disaster Information ◽  
Satellite Image Data ◽  
Typhoon Disaster

Abstract. The satellite image data has some shortcomings such as poor timeless, incomplete disaster information and so on in the typhoon disaster analysis. Compared with the satellite image data, unmanned aerial vehicle (UAV) remote sensing technology has the characteristics of flexibility, convenience, high resolution and so on. It plays a great role in the aspect of obtaining the images and systematically analyze the disaster data. This research based on UAV technology to obtain the high resolution image data and complied the disaster thematic maps after interpretation, as well as determining the data model. Subsequently, determining the system used Html, Javascript and CSS to build the system framework. Combining with Postgre SQL database, Leaflet map module and Echarts diagram and other technologies to perform the feasibility analysis and the detailed design of the integrated system. Finally, it could accurately and comprehensively obtain the system’s disaster monitoring, the typhoon track display, the diagram statistics and visual analysis of the data processing, as well it could deeply analysis and management for the disaster information and assessment. The application shows that this system could provide the information support for future emergency rescue, which is of great significance for the monitoring and preventing the occurrence natural disasters in the future.

scholarly journals Yield variability prediction by remote sensing sensors with different spatial resolution

2017 ◽  
Vol 31 (2) ◽  
pp. 195-202 ◽  
Author(s):  
Jitka Kumhálová ◽  
Štěpánka Matějková
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Data Availability ◽  
Satellite Image Data ◽  
Landsat Satellite ◽  
Very High

Abstract Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

scholarly journals Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec® 3FR and UAV with the DJI STS-VIS

2019 ◽  
pp. 29-39 ◽  
Author(s):  
Stanislav Dugin ◽  
Oksana Sybirtseva ◽  
Stanislav Golubov ◽  
Yelizaveta Dorofey
Keyword(s):  
Pearson Correlation ◽  
Plant Cover ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Image Data ◽  
Multispectral Data ◽  
Maximum Value ◽  
Substantial Correlation ◽  
Satellite Image Data ◽  
Sentinel 2A

The study of plant cover have been performed by the hyperspectral remote sensing method using ASD FieldSpec® 3FR and DJI STS-VIS measurements. The orthophotoplans are compiled for the test plots of interest at the spatial resolution of 2.5 cm. The substantial correlation for the results of terrestrial verification for the satellite image data in the range of Sentinel-2A bands are confirmed. 15 vegetation indices for the Sentinel-2А wavelength bands were drawn at the Pearson correlation coefficient r > 0.97, with a maximum value of the correlation error of 0.07.

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