Landscape Inventory and Harvest Strategies for Individual Western Juniper in Eastern Oregon

Western juniper is a native species in eastern Oregon that became invasive during the last century since its range increased fivefold from 1936 to 1988. Western juniper’s ability to absorb rainfall and groundwater has deleterious effects on stream flow and sensitive sage grouse habitat in eastern Oregon. New methods of western juniper remediation have been proposed, including harvesting and processing western juniper as merchantable timber. An essential part of these methods is the accurate survey of trees and a harvesting plan. The objective of my thesis was to estimate the existing juniper resource in Wheeler county, Oregon, and to develop a harvesting plan for the resource. To estimate the existing juniper resource, I implemented two methods of segmenting individual tree using their ground-projected crown and applied them to orthorectified multispectral aerial images. This work involved estimating the canopy height model from multispectral aerial imagery, which was performed with a generative adversarial network. The generated canopy height model was used with the most accurate tree crown detection method to create a map of juniper locations, with their corresponding height. Based on the knowledge of location and size of each juniper I developed a novel individual tree level harvest strategy. The strategy was evaluated using two heuristic techniques, simulated annealing and record to record, on two areas, each of approximately 1600 ha. The results indicate that landscape restoration by removing junipers depends on the amount and value of the junipers, and in many cases, financial incentives would likely be needed

Use of Advance Driver Assistance System Sensors for Human Detection and Work Machine Odometry

This master thesis covers two major topics, the first is the use of Advance driver assistance system (ADAS) sensors for human detection, and second is the use of ADAS sensors for the odometry estimation of the mobile work machine. Solid-state Lidar and Automotive Radar sensors are used as the ADAS sensors. Real-time Simulink models are created for both the sensors. The data is collected from the sensors by connecting the sensors with the XPC target via CAN communication. Later the data is later sent to Robot operating system (ROS) for visualization. The testing of the Solid-state Lidar and Automotive Radar sensors has been performed in different conditions and scenarios, it isn’t limited to human detection only. Detection of cars, machines, building, fence and other multiple objects have also been tested. Moreover, the two major cases for the testing of the sensors were the static case and the dynamic case. For the static case, both the sensors were mounted on a stationary rack and the moving/stationary objects were detected by the sensors. For the dynamic case, both the sensors were mounted on the GIM mobile machine, and the machine was driven around for the sensors to detect an object in the environment. The results are promising, and it is concluded that the sensors can be used for the human detection and for some other applications as well. Furthermore, this research presents an algorithm used to estimate the complete odometry/ ego-motion of the mobile work machine. For this purpose, we are using an automotive radar sensor. Using this sensor and a gyroscope, we seek a complete odometry of the GIM mobile machine, which includes 2-components of linear speed (forward and side slip) and a single component of angular speed. Kinematic equations are calculated having the constraints of vehicle motion and stationary points in the environment. Radial velocity and the azimuth angle of the objects detected are the major components of the kinematic equations provided by the automotive radar sensor. A stationary environment is a compulsory clause in accurate estimation of radar odometry. Assuming the points detected by the automotive radar sensor are stationary, it is then possible to calculate all the three unknown components of speed. However, it is impossible to calculate all the three components using a single radar sensor, because the latter system of equation becomes singular. Literature suggests use of multiple radar sensors, however, in this research, a vertical gyroscope is used to overcome this singularity. GIM mobile machine having a single automotive radar sensor and a vertical gyroscope is used for the experimentation. The results have been compared with the algorithm presented in [32] as well as the wheel odometry of the GIM mobile machine. Furthermore, the results have also been tested with complete navigation solution (GNSS included) as a reference path