[Objective] This study aimed to investigate the dynamic changes of vegetation cover and its prediction method. [Method] The NDVl was used as data source to perform the spatial overlay analysis on the vegetation covera...[Objective] This study aimed to investigate the dynamic changes of vegetation cover and its prediction method. [Method] The NDVl was used as data source to perform the spatial overlay analysis on the vegetation coverage changes of the study area in different time period under the GIS platform, with the aim to reveal the spatial distribution rules of the vegetation cover in Eastern Jilin Province during the recent 10 years. The Markov Model and Grey System G （1, 1） theory model were used to predict the vegetation cover change trend in Eastern Jilin Province. [Result] The vegetation cover increased a little, but staying stable in general. The regions with great changes were mainly around the lake and river. The prediction results of Markov Model and Grey System G （1, 1） theory model were consistent with the observed measurement. [Conclusion] This study provided referential basis for the effective protection of the vegetation coverage in mountainous forest, and important reference value for the scientific decision-making on the forest construction planning in Jilin Province as well as in China and sustainable development of social economy.展开更多
One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key ro...One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key role in QOS routing. We propose a random mobility model based on discretetime Markov chain, called ODM. ODM provides a mathematical framework for calculating some parameters to show the future status of mobility nodes, for instance, the state transition probability matrix of nodes, the probability that an edge is valid, the average number of valid-edges and the probability of a request packet found a valid route. Furthermore, ODM can account for obstacle environment. The state transition probability matrix of nodes can quantify the impact of obstacles. Several theorems are given and proved by using the ODM. Simulation results show that the calculated value can forecast the future status of mobility nodes.展开更多
基金Supported by the Project of China Geological Survey(1212010911084)~~
文摘[Objective] This study aimed to investigate the dynamic changes of vegetation cover and its prediction method. [Method] The NDVl was used as data source to perform the spatial overlay analysis on the vegetation coverage changes of the study area in different time period under the GIS platform, with the aim to reveal the spatial distribution rules of the vegetation cover in Eastern Jilin Province during the recent 10 years. The Markov Model and Grey System G （1, 1） theory model were used to predict the vegetation cover change trend in Eastern Jilin Province. [Result] The vegetation cover increased a little, but staying stable in general. The regions with great changes were mainly around the lake and river. The prediction results of Markov Model and Grey System G （1, 1） theory model were consistent with the observed measurement. [Conclusion] This study provided referential basis for the effective protection of the vegetation coverage in mountainous forest, and important reference value for the scientific decision-making on the forest construction planning in Jilin Province as well as in China and sustainable development of social economy.
基金Acknowledgements This work is supported by the Postdoctoral Science Foundation of China under Grant No.20080431142.
文摘One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key role in QOS routing. We propose a random mobility model based on discretetime Markov chain, called ODM. ODM provides a mathematical framework for calculating some parameters to show the future status of mobility nodes, for instance, the state transition probability matrix of nodes, the probability that an edge is valid, the average number of valid-edges and the probability of a request packet found a valid route. Furthermore, ODM can account for obstacle environment. The state transition probability matrix of nodes can quantify the impact of obstacles. Several theorems are given and proved by using the ODM. Simulation results show that the calculated value can forecast the future status of mobility nodes.
