scholarly journals Study on Fast Dynamic Monitoring Methods in Coal Mine Collapse Region Based on Multi-Source Remote Sensing Data

2015 ◽  
Vol 03 (01) ◽  
pp. 6-12 ◽  
Author(s):  
云昌 赵
Keyword(s):  
Remote Sensing ◽  
Coal Mine ◽  
Remote Sensing Data ◽  
Dynamic Monitoring ◽  
Monitoring Methods ◽  
Sensing Data ◽  
Collapse Region ◽  
Mine Collapse ◽  
Fast Dynamic

The Dynamic Monitoring on Mining Collapsing around Xinglongzhuang Coal Mine Based on Remote Sensing Images

2013 ◽  
Vol 726-731 ◽  
pp. 4625-4630 ◽  
Author(s):  
Hai Qing Wang ◽  
Ying Jie Zhou ◽  
Ling Chen ◽  
Qing Qing Jing ◽  
Jie Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Economic Development ◽  
Coal Mine ◽  
Local Economic Development ◽  
Remote Sensing Data ◽  
Field Investigation ◽  
Dynamic Monitoring ◽  
Great Contribution ◽  
Remote Sensing Images ◽  
Sensing Data

A large number of old coal mine, such as Xinglongzhuang coal mine, made a great contribution to local economic development and national construction. But, serious mining collapsing was caused also, and local people's livelihood has been affected seriously. The mining collapsing could be identified on remote sensing images by some characteristics. There were 4 period remote sensing data, which was acquired respectively in June 2009, April 2010, June 2011 and July 2012, and field investigation were applied in this articles to study these mining collapsing. The research suggests that the mining collapsing could be divided into the aged type, the middle aged type and the young type. There is a suggestion that, the monitoring and prevention works should be strengthened.

Delineation and mapping of coal mine fire using remote sensing data – a review

2018 ◽  
Vol 40 (17) ◽  
pp. 6499-6529 ◽  
Author(s):  
Shanti Swarup Biswal ◽  
Simit Raval ◽  
Amit Kumar Gorai
Keyword(s):  
Remote Sensing ◽  
Coal Mine ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Mine Fire ◽  
Coal Mine Fire

scholarly journals STUDY ON REMOTE SENSING MONITORING MODEL OF AGRICULTURAL DROUGHT BASED ON RANDOM FOREST DEVIATION CORRECTION

2021 ◽  
pp. 413-422
Author(s):  
Shao Li ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Monitoring ◽  
Monitoring Methods ◽  
Large Area ◽  
Sensing Data ◽  
Monitoring Model

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

scholarly journals The Impact of Coal Exploitation on Tidal Flat Changes, an Investigation Using Remote Sensing Data in Vietnam

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Thi Lan PHAM ◽  
Si Son TONG ◽  
Thi Thu Ha LE ◽  
Thi Le LE ◽  
Huu Duc HOANG
Keyword(s):  
Remote Sensing ◽  
Coal Mining ◽  
Coal Mine ◽  
Tidal Flat ◽  
Remote Sensing Data ◽  
Tidal Flats ◽  
Mining Activities ◽  
Landsat Images ◽  
Sensing Data ◽  
The Impact

Tidal flat plays a crucial role in socio-economic development and ecological environment.Tidal flats in Ha Long-Cam Pha in Vietnam are impacted by human activities, especially coal miningactivities. Using remote sensing data is able to detect, extract, and monitor the changes of tidal flats andexploited coal mine area with multi-temporal, in various scales, and for a large coverage. This studyaims to investigate the impact of coal mining activities on the changes of tidal flats using remote sensingin Cam Pha, Ha Long, one of the biggest coal basins in Vietnam. Digital Elevation Models (DEMs) oftidal flats constructed by Landsat satellite images acquired in years 1989, 2001, and 2014 are comparedto determine the volume changes. Besides, coal mining activities including coal production, waste rockdump area, and the expansion of open coal mine during the period 1989-2014 are investigated usingcorrespondent Landsat images and the reports from the coal mine companies in the study area. Sedimentsamples in tidal flats are analyzed to determine the origin of the sediments. As the results, organic matterin the tidal flats is dominant with the concentration of 459 g/kg to 607 g/kg, which is evidence for theimpact of coal exploitation on the coastal environment. In addition, the relationship between coal mineactivities and tidal flat variation is well observed in this study.

The Intelligent Classifier’s Building of Typical Vegetation Based on Hyper-Spectral Data

2014 ◽  
Vol 1073-1076 ◽  
pp. 1972-1976
Author(s):  
Jie Zhang ◽  
Hao Yan Zhao ◽  
Min Xia Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Dynamic Monitoring ◽  
Large Area ◽  
Desert Vegetation ◽  
Sensing Data ◽  
Monitoring Model ◽  
Hyper Spectral ◽  
Bp Neural Network Model ◽  
Intelligent Process

With 3S comprehensive analysis on vegetation and the further development of hyper-spectral technology, the dynamic monitor of large area vegetation in long-term has become the trend. Intelligent process, combined the remote sensing data and field data, constructing dynamic monitoring model, plays an important guilding role in ecological security and balance. By using hyper-spectral remote sensing data of desert vegetation, three groups of spectral characteristic parameters were selected as input data of typical desert vegetation in the research, and vegetation types were selected as output data. Typical vegetation classifier was constructed based on the BP neural network model to study the vegetation classification.

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