Abstract
Based on Internet-of-Things and multi-sensor technology, an intelligent wireless monitoring system was developed to obtain field ecological parameters and provide forest fire warning in real-time. The GPRS and China’s Beidou satellite communication were selectively used for date transmission in the field with weak cell phone signals. This monitoring system is mainly composed of several field ecological monitoring stations, a cloud server, and online system software. Atmosphere, soil, sunlight and plant parameters of different regions are obtained real-time by sensors stably and reliably. This system has functions such as field ecological data storage, dynamic query, report generation, and data analysis. As an example of typical application, the forest fire weather grade, which was supplemented with the litter layer soil humidity, was calculated to realize the early warning of the local forest fire in this system through continuous experiments at Beijing Jiufeng National Forest Park from March to May 2017 and Inner Mongolia from March to June 2018. The success ratios of data transmission through Beidou satellite were 98.57%, 99.43%, 99.59%, and 98.85%, respectively, in Beijing, and through GPRS were 99.89% and 99.90% in Inner Mongolia. Long-term real-time field ecological monitoring and forest fire warning were successfully realized. This system can be widely used for big data field acquisition and analysis in forest and agriculture regions.
Highlights
IntroductionThe real-time continuous monitoring of field ecological system parameters can generate massive big data information sources to reveal the relationships between various ecological factors and their internal variation rules, support ecological conservation actions, and prevent ecological disasters [1]
The real-time continuous monitoring of field ecological system parameters can generate massive big data information sources to reveal the relationships between various ecological factors and their internal variation rules, support ecological conservation actions, and prevent ecological disasters [1].Currently, the methods for obtaining the parameters for monitoring field ecology, such as atmosphere, soil, light, and plants, fall into four classes
The microenvironment monitoring system for forest ecology can acquire and compute the local meteorological factors required for forecasting the grade of forest fire risk in real time, which plays an important role in improving the fire weather forecast and correct decision making
Summary
The real-time continuous monitoring of field ecological system parameters can generate massive big data information sources to reveal the relationships between various ecological factors and their internal variation rules, support ecological conservation actions, and prevent ecological disasters [1]. Class 2 consists in monitoring the meteorological data or spectroscopic data in a forest region using a meteorological satellite or remote sensing satellite [5,6,7] This method can only achieve macroscopic wide-range monitoring and early warning of the ecological changes and disasters in forests and cannot obtain fine microscopic data. Class 4 consists in building designated ecological monitoring stations forest region saving [14,15,16] of to forests This method is widely used because of its advantages, such in asthe high accuracy, automatically measure the ecological microenvironment data of farmland and forests. Many ground-based ecological is widely used because of its such or asinfrared high accuracy, of labor, and to monitoring stations are built toadvantages, collect the visual images saving and microclimate datacapacity for forest continuously monitor for a long time.
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