Ecological Assessment Of Thailand Based On Remote Sensing And Social Media Data

As an important participating country in our country's "Belt and Road" strategy,the assessment and analysis of the Thailand's ecological environment is of great significance for ecological protection and environmental governance.The application of remote sensing technology in ecological environment assessment has the advantages of large-scale,multi-scale and high time-efficiency.As a new type of data,social media data,its attributes with geographic tags can expand the traditional category of geomatics.Therefore,this paper selected remote sensing data to evaluate the ecological environment of Thailand in recent years,and studies the relationship between social media data and evaluation indicators.The main research contents and results are as follows.Firstly,based on the "pressure-state-response(PSR)" model,the Thai eco-environment assessment index system was established.AHP was used to determine the weight of each index layer.The remote sensing of moderate-resolution imaging spectroradiometer(MODIS)data and lighting data were used.The product of remote sensing and socio-economic data were used as indicator data to carry out the assessment of the ecological environment in Thailand from 2005 to 2015 in terms of pressure,status and response layers and comprehensive conditions.It obtained three phases of 1 km×1 km pixel-level ecological environment assessment results in Thailand in 2005,2010 and 2015.Secondly,the evaluation result shows that Thailand's ecological environment is in the middle level of health,and it shows a degraded trend from 2005 to 2015,with a greater degree of degradation from 2010 to 2015.Among the seven sub-regions in Thailand,the Bangkok area has degraded from the middle level to the poor level,the southern area which had the best ecological status and was at a good level;the central and northeastern areas had poor ecological status.The ecological status of the northern,western and eastern regions was at the middle level.Thirdly,I analyzed the spatial and temporal distribution of Twitter data across Thailand in September 2016.Then I used Twitter data and various indicator data for correlation analysis.The results show that the correlation coefficient between Twitter data and GDP data reaches 0.9037,showing a significant correlation(P<0.01).Twitter data can be used to quickly calculate the economic development level of a region.Finally,according to the linear relationship between Twitter data and GDP data,it is believed that social media data,as a kind of location big data,can be used as an indicator data in the evaluation model in the future environmental evaluation work,and it can improve the results' spatiotemporal resolution.And I got more accurate and higher-resolution evaluation results when I evaluated the ecological environment of the northern region,central region and Bangkok region in 2015,using rasterized Twitter data as indicator data instead of GDP data into the evaluation model.