Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models

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.

Implications of differing input data sources and approaches upon forest carbon stock estimation

“…Thus, site productivity is not static (Skovsgaard and Vanclay 2007) and in a site with stand replacing disturbance regimes (fire, harvesting), the original site index estimate may no longer reflect true site productivity (Stearns-Smith 2001). Third, photogrammetric methods for estimating tree height are prone to error under certain stand conditions, as it is often impossible to measure ground elevation near trees growing in dense forest (St-Onge et al 2004;Véga and St-Onge 2008). Any error in the original photogrammetric estimate of height is propagated in the original estimation of site index, thereby impacting height projections when the inventory is grown forward.…”

“…Tree heights can be computed from calculating the difference between the ground (last pulse returns) and top of canopy (first pulse returns) when the position and three-dimensional angle of the instrument is known (either from satellite Global Positioning Systems (GPS) and/or Inertial Navigation Systems (INS) measurements; Véga and St-Onge 2008). The error associated with LIDAR measurements of tree height are typically between 0.5 and 1.0 m (Persson et al 2002;Naesset 1997Naesset , 2002Magnussen and Boudewyn 1998;Magnussen et al 1999;Naesset and Økland 2002), and LIDAR is considered more accurate for height measurement than common field-based measurements (Naesset and Økland 2002).…”

Deciphering the Precision of Stereo IKONOS Canopy Height Models for US Forests with G-LiHT Airborne LiDAR

“…We evaluated two existing stereo forest canopy height model (CHM) approaches, one without ground control points (GCPs) and one with GCPs as others have shown substantial improvement in IKONOS mapping accuracy with GCPs [7,8]. We used IKONOS Geo stereo imagery and build upon prior methodologies developed in Quebec [9,10] and compared results against an airborne instrument, Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) [11]. We analyzed the precision of IKONOS Geo stereo data with G-LiHT acting as truth, and estimated costs to complete surveys per hectare.…”

“…Using multi-angle aerial photography, Gong et al [22] reported overall accuracies of 94% and 90% for tree height and crown radius measurements, respectively. However, only a few have used stereo HRSI to measure forest canopy height [5,9,10,[23][24][25]. Baltsavias et al [26] has provided limitations to achieving good results through image matching for vegetation including: (1) limited texture; (2) distinct object discontinuities; (3) repetitive objects; (4) occlusions; and (5) multilayered objects.…”

“…Often best results have been found relating field measurements to small footprint LiDAR, and HSRI has been found to be the second best option [6]. From prior successful outputs of these studies in urban environments and a handful of forest canopy studies [5,10,32], we performed an assessment of the capabilities of existing remote sensing data to map canopy heights in three different biogeographic forested regions of the CONUS including Appalachian, Atlantic Coastal Plain, and North American Pacific Maritime. From prior studies [8,23] we believe comparing DSMs derived from IKONOS will enable the assessment of the best approach to extract CHMs.…”