Home About Browse Search
Svenska


Remote sensing aided spatial prediction of forest stem volume

Wallerman, Jörgen (2003). Remote sensing aided spatial prediction of forest stem volume. Diss. (sammanfattning/summary) Umeå : Sveriges lantbruksuniv., Acta Universitatis agriculturae Sueciae. Silvestria, 1401-6230 ; 271
ISBN 91-576-6505-2
[Doctoral thesis]

[img]
Preview
PDF
1MB

Abstract

Modern technology such as the Global Positioning System (GPS) and Geographical 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 practical aspects of the methods, is evaluated. In particular, accuracy is compared with Ordinary Least Squares regression (OLS) using remotely sensed data. Spatial prediction was, with a few exceptions, more accurate than OLS regression. The largest improvement, 49% lower root mean square error (RMSE), was obtained for plot-level predictions by ordinary kriging using information of edges detected in remotely sensed images, although the method is dependent on densely sampled field data. Promising results were also obtained by simple kriging with varying local means. This method performed well (26% lower RMSE than OLS regression for stand-level predictions), is rather straight-forward to apply in practice, and not as dependent on densely sampled field data. The Bayesian state-space model did not provide improved predictions compared to OLS regression. However, Bayesian modelling is promising for application of spatial models of higher complexity than possible with the other methods.

Authors/Creators:Wallerman, Jörgen
Title:Remote sensing aided spatial prediction of forest stem volume
Year of publishing :March 2003
Volume:271
Number of Pages:42
Papers/manuscripts:
NumberReferences
ALLWallerman, J., Joyce, S., Vencatasawmy, C.P. & Olsson, H. 2002. Prediction of forest stem volume using kriging adapted to detected edges. Canadian Journal of Forest Research 32, 509-518. Wallerman, J., Vencatasawmy, C.P. & Olsson, H. 2002. Geostatistical prediction of forest stem volume using Landsat TM data. Accepted for publication in Photogrammetric Engineering & Remote Sensing. Wallerman, J., Vencatasawmy, C.P. & Olsson, H. 2002. Prediction of forest stem volume using spatial regression of Landsat TM data. Submitted. Wallerman, J., Vencatasawmy, C.P. & Bondesson, L. 2003. Spatial simulation of forest using Bayesian state-space models and remotely sensed data. Manuscript.
Place of Publication:Umeå
ISBN for printed version:91-576-6505-2
ISSN:1401-6230
Language:English
Publication Type:Doctoral thesis
Full Text Status:Public
Agris subject categories.:K Forestry > K10 Forestry production
U Auxiliary disciplines > U40 Surveying methods
Subjects:Not in use, please see Agris categories
Agrovoc terms:geographical information systems, global positioning systems, remote sensing, simulation models, forest inventories, forest surveys, forecasting, methods
Keywords:spatial dependence, autocorrelation, forest model, Gibbs sampler
URN:NBN:urn:nbn:se:slu:epsilon-21
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-21
ID Code:190
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: Jörgen Wallerman
Deposited On:12 Mar 2003 00:00
Metadata Last Modified:02 Dec 2014 10:02

Repository Staff Only: item control page

Downloads

Downloads per year (since September 2012)

View more statistics

Downloads
Hits