Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

• Published in: Remote Sensing of Environment
• Main Authors: Fuchs, Hans, Magdon, Paul, Kleinn, Christoph, Flessa, Heinz
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2009
• Subjects: article; Text; ASTER; carbon estimation; featur selection; forest inventory; forest tundra; global change; k.NN regionalization; multiple linear regression; quickbird; Siberia; permafrost; Tundra
• Online Access: https://doi.org/10.1016/j.rse.2008.07.017; https://www.openagrar.de/receive/timport_mods_00033308; https://www.openagrar.de/servlets/MCRFileNodeServlet/timport_derivate_00033308/dk041625.pdf

This study was part of an interdisciplinary research project on soil carbon and phytomass dynamics of boreal and arctic permafrost landscapes. The 45 ha study area was a catchment located in the forest tundra in northern Siberia, approximately 100 km north of the Arctic Circle. The objective of this study was to estimate aboveground carbon (AGC) and assess and model its spatial variability. We combined multi-spectral high resolution remote sensing imagery and sample based field inventory data by means of the k-nearest neighbor (k-NN) technique and linear regression. Field data was collected by stratified systematic sampling in August 2006 with a total sample size of n=31 circular nested sample plots of 154 m2 for trees and shrubs and 1 m2 for ground vegetation. Destructive biomass samples were taken on a sub-sample for fresh weight and moisture content. Species-specific allometric biomass models were constructed to predict dry biomass from diameter at breast height (dbh) for trees and from elliptic projection areas for shrubs. Quickbird data (standard imagery product), acquired shortly before the field campaign and archived ASTER data (Level-1B product) of 2001 were geo-referenced, converted to calibrated radiances at sensor and used as carrier data. Spectral information of the pixels which were located in the inventory plots were extracted and analyzed as reference set. Stepwise multiple linear regression was applied to identify suitable predictors from the set of variables of the original satellite bands, vegetation indices and texture metrics. To produce thematic carbon maps, carbon values were predicted for all pixels of the investigated satellite scenes. For this prediction, we compared the kNN distance-weighted classifier and multiple linear regression with respect to their predictions. The estimated mean value of aboveground carbon from stratified sampling in the field is 15.3 t/ha (standard error SE=1.50 t/ha, SE%=9.8%). Zonal prediction from the k-NN method for the Quickbird image as carrier is 14.7 ...