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Estimation of boreal forest attributes from very high resolution pleiades data

Persson, Henrik (2016). Estimation of boreal forest attributes from very high resolution pleiades data. Remote sensing. 8 :9 , 1-19
[Research article]

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Official URL: http://dx.doi.org/10.3390/rs8090736


In this study, the potential of using very high resolution Pleiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing alternatives. Height metrics were extracted from image matching of the images acquired from different incidence angles. Spectral derivatives were derived by performing principal component analysis of the spectral bands and lastly, second order textural metrics were extracted from a gray-level co-occurrence matrix, computed with an 11 x 11 pixels moving window. The analysis took place at two Swedish test sites, Krycklan and Remningstorp, containing boreal and hemi-boreal forest. The lowest RMSE was estimated with 1.4 m (7.7%) for Lorey's mean height, 1.7 m (10%) for airborne laser scanning height percentile 90, 5.1 m(2)ha(-1) (22%) for basal area, 66 m(3)ha(-1) (27%) for stem volume, and 26 tonsha(-1) (26%) for above-ground biomass, respectively. It was found that the image-matched height metrics were most important in all models, and that the spectral and textural metrics contained similar information. Nevertheless, the best estimations were obtained when all three explanatory sources were used. To conclude, image-matched height metrics should be prioritised over spectral metrics when estimation of forest attributes is concerned.

Authors/Creators:Persson, Henrik
Title:Estimation of boreal forest attributes from very high resolution pleiades data
Series Name/Journal:Remote sensing
Year of publishing :2016
Page range:1-19
Number of Pages:19
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Agris subject categories.:K Forestry > K01 Forestry - General aspects
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:canopy, image matching, forestry, Pleiades, biomass, VHR
Permanent URL:
Additional ID:
Type of IDID
Web of Science (WoS)000385488000046
ID Code:14057
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:20 Feb 2017 09:09
Metadata Last Modified:09 Sep 2020 14:17

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