Examining Forest Structure in the Mozambican Dry Deciduous Lowland Forest Utilizing In-Situ and Remotely Sensed Measurements and Observations

Heil, Ethan, Environmental Sciences - Graduate School of Arts and Sciences, University of Virginia
Swap, Robert, Department of Environmental Sciences, University of Virginia
Shugart, Herman, Department of Environmental Sciences, University of Virginia
Moody, Jennie, Department of Environmental Sciences, University of Virginia
Palace, Michael, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire

Land use change in the form of deforestation and degradation is contributing to the release of greenhouse gases on the order of 1.2 ±0.7 Gt CO2e per year. Emissions of this magnitude suggest that the conversion and disturbance of forests play a significant role in forcing global climate change. Although there is growing recognition of this contribution among scientific and political communities, there is an expressed need for more efficient and accurate methods for assessing the role of forests in shifting climate patterns. Recent efforts to measure and monitor forests on large scales have used remotely sensed data from airborne and spaceborne sensors. This study leverages high spatial resolution remotely sensed data with in-situ field measurements to measure forest structure in the Dry Deciduous Lowland Forests of Mozambique. The approach to analysis was motivated by the importance of three-dimensional forest structure in driving forest dynamics. A novel tree crown analysis methodology was employed in order to calculate select structural properties of forests from remotely sensed data. The results of this analysis were not deemed accurate enough to be considered robust geophysical measurements. However, the study provides a step forward in the application of this methodology to the classification of terrestrial surfaces and to the detection of forest degradation due to anthropogenic lumber harvesting disturbance.

MS (Master of Science)
forest structure, Mozambique, remote sensing, forests, forestry
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