Research And Implementation Of Grassland Grass Monitoring System In Inner Mongolia Based On Big Data Technology

Inner Mongolian grasslands are an important part of the Eurasian grasslands and an important ecological security barrier in the north of China,with strategic importance in maintaining ecosystem security and stability,biodiversity integrity,carbon sequestration,and residents’ income,and regional harmony and stability.However,grassland degradation has become one of the most serious environmental problems in Inner Mongolia grasslands,and its already exposed and potential risks pose a serious threat to the sustainable development of grasslands.However,the monitoring of grassland conditions in Inner Mongolia is mostly conducted by ground-based manual surveys,which are time-consuming and labor-intensive,with limited coverage and poor representativeness,and cannot quickly reflect the spatial and temporal differences in the growth of vast grasslands,affecting the timeliness of grassland management measures.Remote sensing has the characteristics of all-weather,no dead-end continuous observation.Internet of Things(Io T)as the support of intelligent grassland can realize realtime data communication and information exchange.The combination of remote sensing and Io T technology can achieve rapid and dynamic monitoring of grassland ecological indicators.However,for the large amount of earth observation data generated by remote sensing observation and the detailed and specific real-time data generated by Io T monitoring stations,the traditional data processing can no longer meet the demand for real-time processing of these data,and there is an urgent need to use big data technology to analyze these data to obtain important information in the field of grassland resources and ecology in the whole area.This thesis researches and implements a grassland grass monitoring system based on big data technology for the above problems,using Hadoop and Spring Boot framework,using Spark as the core analysis engine of the platform,combining HDFS,HBase,and My SQL as the storage system,using front-end visualization technology to design the monitoring big screen,combining Io T and remote sensing technology to The second is the use of Geo Trellis,a high-performance geographic data processing engine,to parallelize the NDVI percentile grass monitoring algorithm,which has important research significance and application value for promoting the benign development of natural grassland resources and sustainable development of grazing livestock industry in Inner Mongolia.