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Big Data Monitoring System Design and Implementation of Invasive Alien Plants Based on WSNs and WebGIS

机译：基于WSN和WebGIS的侵入性外星植物的大数据监测系统设计与实现

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Invasive alien plants (IAPs) are an important reason for biodiversity crisis and changes of local ecosystem and landscape. The key content of IAPs investigating research is about how to timely and efficiently monitor the growth and occurrence of IAPs. Timely gathering IAPs' occurrence is helpful for governments to prevent and control them in time. Moreover, in the Big data era, the increasing data of observation data has been a problem for researchers to understand the growth law and controlling efficiencies about IAPs. In this paper, an IAPs monitoring system based on WSNs and WebGIS is presented. This system can collect not only long-term and real-time environment information, but images of monitoring stations by WSNs. Google Maps and ArcGIS are used to display the WSNs and IAPs occurrence information. Especially, the Hadoop-based image processing interface is taken advantage of to process the big data acquired by this system. This system solves the problems about surveying IAPs (e.g., a single type of data and slow update rate). Since it has been already running since February 2012, continuous monitoring and evaluation of the long-term effect of the prevention and control methods about IAPs will be achieved as well.
机译：侵入式外星植物（IAPS）是生物多样性危机和当地生态系统和景观变化的重要原因。 IAPS调查研究的关键内容是如何及时，有效地监测IAP的生长和发生。及时收集IAPS的事件有助于各国政府预防和控制它们。此外，在大数据时代，观察数据的日益增加的数据对于研究人员来说是理解IAP的增长法和控制效率的问题。本文介绍了基于WSN和WebGI的IAPS监控系统。该系统不仅可以收集长期和实时环境信息，而是由WSN的监控站的图像。 Google地图和ArcGIS用于显示WSN和IAPS发生信息。特别地，基于Hadoop的图像处理界面利用了处理该系统获取的大数据。该系统解决了对测量IAPS（例如，单一类型的数据和更新速率）的问题。由于它自2012年2月起已经在运行，因此也将实现持续监测和评估预防和控制方法关于IAPS的长期效果。

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