Segmentation of vegetation patches was tested using canopy height models (CHMs) representing the height difference between digital surface models (DSMs), generated by matching digital aerial images from the Z/I Digital Mapping Camera, and a digital elevation model (DEM) based on airborne laser scanner data. Three different combinations of aerial images were used in the production of the CHMs to test the effect of flight altitude and stereo overlap on segmentation accuracy. Segmentation results were evaluated using the standard deviation of photo-interpreted tree height within segments, as well as by visual comparison to existing maps. In addition, height percentiles extracted from the CHMs were used to estimate tree heights. Tree height estimation at the segment level yielded root mean square error (RMSE) values of 2.0 m, or 15.1%, and an adjusted coefficient of determination (adjusted R 2 ) of 0.94 when using a CHM from images acquired at an altitude of 1200 m above ground level (agl) and with an along-track stereo overlap of 80%. When a CHM based on images acquired at 4800 m agl and an overlap of 60% was used, the corresponding results were an RMSE of 2.2 m, or 16.0%, and an adjusted R 2 of 0.92. Tree height estimation at the plot level was most accurate for densely forested plots dominated by coniferous tree species (RMSE of 2.1 m, or 9.8%, and adjusted R 2 of 0.88). It is shown that CHMs based on aerial images acquired at 4800 m agl and with 60% along-track stereo overlap are useful for the segmentation of vegetation and are at least as good as those based on aerial images collected at a lower flight altitude or with greater overlap.

This study had the aim of investigating the utility of image-based point cloud data for estimation of vertical canopy cover (VCC). An accurate measure of VCC based on photogrammetric matching of aerial images would aid in vegetation mapping, especially in areas where aerial imagery is acquired regularly. The test area is located in southern Sweden and was divided into four vegetation types with sparse to dense tree cover: unmanaged coniferous forest; pasture areas with deciduous tree cover; wetland; and managed coniferous forest. Aerial imagery with a ground sample distance of 0.24 m was photogrammetrically matched to produce dense image-based point cloud data. Two different image matching software solutions were used and compared: MATCH-T DSM by Trimble and SURE by nFrames. The image-based point clouds were normalized using a digital terrain model derived from airborne laser scanner (ALS) data. The canopy cover metric vegetation ratio was derived from the image-based point clouds, as well as from raster-based canopy height models (CHMs) derived from the point clouds. Regression analysis was applied with vegetation ratio derived from near nadir ALS data as the dependent variable and metrics derived from image-based point cloud data as the independent variables. Among the different vegetation types, vegetation ratio derived from the image-based point cloud data generated by using MATCH-T resulted in relative root mean square errors (rRMSE) of VCC ranging from 6.1% to 29.3%. Vegetation ratio based on point clouds from SURE resulted in rRMSEs ranging from 7.3% to 37.9%. Use of the vegetation ratio based on CHMs generated from the image-based point clouds resulted in similar, yet slightly higher values of rRMSE. ARTICLE HISTORY

The aim of this study was to investigate to which degree the accuracy of vegetation classification could be improved by combining optical satellite data and airborne laser scanner (ALS) data, compared with using satellite data only. A Satellite Pour l'Observation de la Terre (SPOT) 5 scene and Leica ALS 50-II data from 2009, covering a test area in the mid-Sweden (latitude 60° 43' N, longitude 16° 43' E), were used in maximum likelihood and decision tree classifications. Training and validation data were obtained from the interpretation of digital aerial photo stereo models. Combination of SPOT and ALS data gave classification accuracies up to 72%, compared with 56% using only SPOT data. This indicates that integrating features from large area laser scanning may lead to significant improvements in satellite data-based vegetation classifications.