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

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.