基于多植被指数模型的草地地上生物量协同估算

以青海省金银滩草原为研究区,采用Sentinel-2卫星影像结合地面实测数据进行草地地上生物量估算研究。分析了18种典型植被指数与生物量的拟合关系,通过精度评价和敏感性分析确定了不同植被指数模型的适用范围,并提出基于多植被指数模型的协同估算方案来提高草地生物量的制图精度,尝试克服传统单变量植被指数模型适用范围受限的问题。结果表明:18种植被指数与生物量的最优拟合模型呈现幂函数和指数函数两种类型,其中幂函数模型中CIgreen(Green chlorophyll index)所对应的估算精度最高,且当生物量高于0.65 kg·m^(-2)时适用性最强;指数函数模型中NDII(Normalized difference infrared index)所对应的估算精度最高,且当生物量低于0.65 kg·m^(-2)时适用性最强,且NDII与CIgreen模型的适用范围具有互补性。提出的多植被指数协同估算模型对应的R^(2)cv达到了0.61,RMSEcv为0.226 kg·m^(-2),相对于单植被指数模型精度明显提高,R^(2)cv增加7.0%以上,RMSEcv减小超过3.8%。综上,提出的多指数模型协同估算方案充分考虑了不同指数模型的适用范围,提高了牧草生物量的估算精度。

主被动遥感数据协同估算干旱区草原植被生物量

结合主动微波遥感和被动光学遥感反映地表植被的各自优势,发展了一种主被动遥感协同估算干旱区草原植被生物量的模型。该模型将植被覆盖度作为水云模型的附加参数,将总体散射分为植被覆盖区散射和裸土区散射两部分,将水云模型应用到了植被覆盖稀疏区域。利用改进的水云模型和双极化ASAR数据,通过建立方程组估算植被生物量。将该方法用于乌图美仁草原植被生物量的估算,验证了该方法的有效性。结果表明:该主被动遥感协同估算模型能够成功地估算干旱区草原植被生物量,并且取得了较好的估算精度(R2=0.8562,RMSE=0.1813kg/m2)。最后,分析了该方法估算植被生物量的误差来源。

A Stochastic Approach for Cooperative Position Estimation of Multiple Mobile Robots

This paper proposes the cooperative position estimation of a group of mobile robots, which pertbrms disaster relief tasks in a wide area. When searching the wide area, it becomes important to know a robot＇s position correctly. However, for each mobile robot, it is impossible to know its own position correctly. Therefore, each mobile robot estimates its position from the data of sensor equipped on it. Generally, the sensor data is incorrect since there is sensor noise, etc. This research considers two types of the sensor data errors from omnidirectional camera. One is the error of white noise of the image captured by omnidirectional camera and so on. Another is the error of position and posture between two omnidirectional cameras. To solve the error of latter case, we proposed a self-position estimation algorithm for multiple mobile robots using two omnidirectional cameras and an accelerometer. On the other hand, to solve the error of the former case, this paper proposed an algorithm of cooperative position estimation for multiple mobile robots. In this algorithm, each mobile robot uses two omnidirectional cameras to observe the surrounding mobile robot and get the relative position between mobile robots. Each mobile robot estimates its position with only measurement data of each other mobile robots. The algorithm is based on a Bayesian filtering. Simulations of the proposed cooperative position estimation algorithm for multiple mobile robots are performed. The results show that position estimation is possible by only using measurement value from each other robot.