No abstract

To cite this version:C. Vega, B. St-Onge. Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models. Remote Sensing of Environment, Elsevier, 2008Elsevier, , 112, p. 1784Elsevier, -p. 1794 entirely automated such that forest height growth curves can be reconstructed and mapped over large areas for which recent lidar data and historical photographs exist.

This paper presents a method for individual tree crown extraction and characterisation from a Canopy Surface Model (CSM). The method is based on a conventional algorithm used for localising LM on a smoothed version of the CSM and subsequently for modelling the tree crowns around each maximum at the plot level. The novelty of the approach lies in the introduction of controls on both the degree of CSM filtering and the shape of elliptic crowns, in addition to a multi-filtering level crown fusion approach to balance omission and commission errors. The algorithm derives the total tree height and the mean crown diameter from the elliptic tree crowns generated. The method was tested and validated on a mountainous forested area mainly covered by mature and even-aged black pine (Pinus nigra ssp. nigra [Arn.]) stands. Mean stem detection per plot, using this method, was 73.97 %.Algorithm performance was affected slightly by both stand density and heterogeneity (i.e. tree diameter classes' distribution). The total tree height and the mean crown diameter were estimated with root mean squared error values of 1.83 m and 1.48 m respectively. Tree heights were slightly underestimated in flat areas and overestimated on slopes. The average crown diameter was underestimated by 17.46 % on average.

Photogrammetric methods using parallaxes can be employed to measure tree heights on aerial photographs. Because it is often impossible to measure ground elevation near trees growing in dense forests, such height measurements remain prone to error. Our objective was to solve this problem by combining a stereomodel and a digital terrain model (DTM) produced by an airborne-scanning system that uses light detection and ranging (lidar). A stereopair of scanned aerial photographs was first registered to a lidar DTM. The elevation of the apex of 202 Thuja occidentalis (L.) individuals was measured by an observer on a digital photogrammetric workstation. The tree base elevations were read from the lidar DTM and subtracted from the corresponding apex elevations to calculate individual tree heights. These were then compared with the heights measured in the field. The average photo-lidar bias was 0.59 m, and the average deviation of 1.01 m decreased to 0.88 m when the bias was removed. It was demonstrated that the photographic clearness of the tree apices influences the height error, while the density of the lidar echoes under the forest canopy does not. Using this method, retrospective studies of changes in tree height become feasible by using archived aerial photographs and recent lidar DTMs.

No abstract

No abstract

No abstract

Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm.