Forest Biometrics and Geomatics

Forest Biometrics and Geomatics

Forest Biometrics and Geomatics

Researchers address problems in forest management and policy by using quantitative methods to reveal the science needed to manage forests and other natural resources.

Faculty Research Programs

This research is interested in active sustainable management of forest, by using various remote sensing techniques to acquire relevant information for the decision making process. Once raw data is collected, we develop, improve or test existing algorithms to supply the needed data for developing management plans or forecast forest dynamics. Our focus is in modeling forest understood in a broad sense using modern techniques, such as computer vision, fractals, or abstract algebra. The main instruments used for data acquisition are unnamed aerial systems equipped with lidar, RGB, and multispectral sensors.

This research focuses on three major areas and seeks to develop or extend: imputation methods that support dynamic forest inventory, silvicultural planning, and habitat analysis; sampling and statistical methods to characterize and quantify status and change of selected attributes including biomass and carbon and applications of lidar to forest measurements and assessments. 

The Aerial Information Systems laboratory is investigating a wide range of lightweight sensors for UAV application on both fixed wing and helicopters to support forest management, forest engineering, forest protection, wildlife habitat and search and rescue operations.

Forest Measurements and Biometrics

This research focuses on three major areas and seeks to develop or extend: imputation methods that support dynamic forest inventory, silvicultural planning, and habitat analysis; sampling and statistical methods to characterize and quantify status and change of selected attributes including biomass and carbon and applications of lidar to forest measurements and assessments. 

Management, Algorithms, and Remote Sensing

This research is interested in active sustainable management of forest, by using various remote sensing techniques to acquire relevant information for the decision making process. Once raw data is collected, we develop, improve or test existing algorithms to supply the needed data for developing management plans or forecast forest dynamics. Our focus is in modeling forest understood in a broad sense using modern techniques, such as computer vision, fractals, or abstract algebra.