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Estimating field-plot data of forest stands using airborne laser scanning and SPOT HRG data
Authors:J Wallerman  J Holmgren  
Affiliation:

aSwedish University of Agricultural Sciences, Faculty of Forest Sciences, Remote Sensing Laboratory, SE 901 83 UMEÅ, Sweden

Abstract:Airborne laser scanner systems provide detailed forest information that can be used for important improvements in forest management decisions. Planning systems under development use plot-survey data to represent forest stands in large forest holdings which enables new flexible methods to model the forest and optimize selection of silvicultural treatments. In Sweden today, only averages of forest stand variables are used, and the survey methods used do not provide plot-survey data for all stands in large forest holdings. This is a task possibly solved using airborne laser scanner data. Various measures can be derived from laser data, each describing different forest variables, such as tree height distribution, vegetation density and vertical tree crown structure. Here, imputation of field plot (10 m radius) data using measures derived from airborne laser scanner data (TopEye) and optical image data (SPOT 5 HRG satellite sensor) were evaluated as a method to provide data for new long-term management planning systems. In addition to commonly applied measures, the semivariogram of laser measurements was evaluated as a new measure to extract spatial characteristics of the forest. The study used data from 870, 10 m radius field plots (0 to 812 m3 ha− 1) surveyed for a 1200 ha large forest estate in the south of Sweden. At the best, combining measures derived from laser scanner data and SPOT 5 data, stand mean volume was estimated with a root mean square error (RMSE) of 20% of the sample mean and stem density with 22% RMSE. Bias of stem density estimates was 5%, and stand stem volume 4%. Although these accuracies are sufficient for operational application, estimates of tree species proportions and within-stand variation were clearly not.
Keywords:Laser scanning  Forest management  Imputation  Semivariogram
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