The main study objective was to develop robust processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating forest biophysical parameters measuring individual trees identifiable on the three-dimensional lidar surface. This study derived the digital terrain model from lidar data using an iterative slope-based algorithm and developed processing methods for directly measuring tree height, crown diameter, and stand density. The lidar system used for this study recorded up to four returns per pulse, with an average footprint of 0.65 m and an average distance between laser shots of 0.7 m. The lidar data set was acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern United States (37° 25' N, 78° 41' W). Lidar processing techniques for identifying and measuring individual trees included data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The window size was based on canopy height and forest type. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top, by fitting a four-degree polynomial on both profiles. The ground-truth plot design followed the U.S. National Forest Inventory and Analysis (FIA) field data layout. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field inventory data, including volume, basal area, and biomass. FIA subplots of 0.017 ha each were pooled together in two categories, deciduous trees and pines. For the pine plots, lidar measurements explained 97% of the variance associated with the mean height of dominant trees. For deciduous plots, regression models explained 79% of the mean height variance for dominant trees. Results for estimating crown diameter were similar for both pines and deciduous trees, with R2 values of 0.62-0.63 for the dominant trees. R2 values for estimating biomass were 0.82 for pines (RMSE 29 Mg/ha) and 0.32 for deciduous (RMSE 44 Mg/ha). Overall, plot level tree height and crown diameter calculated from individual tree lidar measurements were particularly important in contributing to model fit and prediction of forest volume and biomass. / Ph. D.

Recently, products of Unmanned Aerial System (UAS) integrated through SIFT algorithm and dense cloud matching using structure from motion has gained prominence with tree-level inventory maintenance in forestry. Various studies have been carried out by using UAS imagery to quantify and map forest structure of simple coniferous stands. However, most of the previous works employ methodologies that require manual inputs and lack of reproducibility to other forest systmes. Manual detection of trees and calculation of their attributes can be a time-consuming and complicated process which can be overcome with an automated technique applied by forest managers and/or landowners is highly desired to take full advantage of the readily available UAS remote sensing images. This study presents a methodology for automated measurements of tree height, crown area and crown diameter of hardwood species using UAS images. Different UAS platforms were employed to gather digital data of two hardwood plantations at Martell, Indiana. The resulting aerial images were used to generate the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for the forest stand from which the Crown Height Model (CHM) was derived. The canopy height model can be inputted to the web platform deployed through shiny server (https://feilab.shinyapps.io/Crown/) to derive individual tree parameters automatically. The results show that this automated method provides a high accuracy in individual tree identification (F-score> 90%) and tree-level measurements (RMSEht<1.2m and RMSEcrn<1m). Moreover, tree-level parameter estimation for 4,600 trees were calculated in less than 30 minutes based on a post-processed DSM from UAS-SfM derived images with minimal manual inputs. This study demonstrates the feasibility of automated inventory and measure of tree-level attributes in hardwood plantations with UAS images.

Segmentation of an image into individual tree crowns is a key step in the processing of remotely sensed data for forestry practice. The doctoral thesis gives a broad overview of this topic. It comprehends theoretical context from mathematical point of view and defines basic terms from airborne imaging and laser scanning. Mathematical methods of tree detection are focused on a robust adaptation to the actual conditions in a region of interest. A novel approach of crown area delineation is introduced, it combines a seeded region growing technique with an active contour as a crown boundary representation. The parametrisation of all algorithms is analysed in a practical half of the thesis and more application-oriented issues are mentioned. Executable computer programs are attached.