Remote sensing aided spatial prediction of forest stem volume

• Main Author: Jörgen Wallerman
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 2003
• Subjects: Northern Sweden
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.96.6490; http://diss-epsilon.slu.se/archive/00000190/01/91-576-6505-2.fulltext.pdf

. Information Systems (GIS) provide new opportunities for forest inventory. These technologies allow representation of forest variables using rasters with cell sizes on the order of 25 m. Such rasters can be estimated from remotely sensed data using models of the relationship between the image’s digital number and the forest variables. This thesis investigates the possibility of using estimation methods incorporating remotely sensed data as well as spatial similarity of neighbouring field measurements, to improve prediction accuracy compared to using only remotely sensed data. Two new spatial prediction methods are presented and evaluated: ordinary kriging using information about edges detected in remotely sensed images, and prediction using Markov Chain Monte Carlo (MCMC) simulation of a new Bayesian state-space model. In addition, ordinary kriging, stratified ordinary kriging, ordinary cokriging, collocated ordinary cokriging, simple kriging with varying local means, and spatial regression using the autoregressive response model, are also evaluated. The methods are applied to predict forest stem volume per hectare in boreal forest in northern Sweden (Lat. 64°14’N, Long. 19°40’E) using Landsat TM data and a large field sampled dataset. Prediction accuracy, as well as