Due to increasing global demand for natural rubber products, rubber (Hevea brasiliensis) plantation expansion has occurred in many regions where it was originally considered unsuitable. However, accurate maps of rubber plantations are not available, which substantially constrain our understanding of the environmental and socio-economic impacts of rubber plantation expansion. In this study, the rubber plantations was accurate mapped from Landsat satellite imagery based on object-oriented classification method in Yangjiang State Farm in Hainan Island in 2010. The results show that: (1) The rubber plantation area in Yangjiang State Farm was estimated at 5866 hm 2 in 2010, which was slightly higher than the stand inventory data (5190 hm 2 ) in 2009. (2) The resulting rubber plantation map has a high accuracy according to the confusion matrix by using the ground truth ROIs. The overall accuracy is 90% and the kappa coefficient is 0.9. It showed that objectoriented classification method is suitable for mapping rubber plantation from Landsat satellite imagery.
The information of tropical crop production environment includes the wind velocity and direction data, light intensity, air humidity, air temperatures, soil moisture, concentration of carbon dioxide, rainfall and so on. These data will be of great reference value to environmental control and scientific field management. As the characteristics of tropical crop growth and tropical climate conditions, information acquisition and tracking about the environment of crop growth are quite behind the time. This research is precisely centers on this core question to launch, carrying on analysis on the key technology of intelligent sensor and monitoring about tropical crop production environment. We build a system on a tiny210 chip using android operating system. Sensors that will be linked include air humidity & temperature sensor, illumination sensor, soil moisture sensor, wind-direction sensor and wind power sensor. This research focuses on field information dynamic acquisition and management. This study can offer references for in field environmental control and further research work.
Based on the growth characteristic of tropical greenhouse crop and field intelligent management demands for information of temperature, humidity, gas and light, the intelligent monitoring system on tropical greenhouse crop production environment is studied and designed respectively. The system includes two parts of hardware and software. In the part of hardware, mainly expounding hardware's function and communication protocol with software. It is composed of TINY210 development board and sensors. Software includes JAVA programming language, COM communication port, communication protocol, data collection and management. Finally, the system has been tested in the laboratory and the scheme's feasibility has been validated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.