Stratified aboveground forest biomass estimation by remote sensing data

Published Date

June 2015, Vol.38:229–241, doi:10.1016/j.jag.2015.01.016

Title 

Stratified aboveground forest biomass estimation by remote sensing data

• Author 

• Hooman Latifi ^a,,
• Fabian E. Fassnacht ^b,c,
• Florian Hartig ^d,
• Christian Berger ^e,
• Jaime Hernández ^f,
• Patricio Corvalán ^f,
• Barbara Koch ^c,

• aUniversity of Wuerzburg, Department of Remote Sensing in Cooperation with German Aerospace Center, Oswald-Kuelpe-Weg 86, Wuerzburg D-97074 Germany
• bInstitute for Geography and Geoecology, Karlsruhe Institute of Technology, Kaiserstraße 12, D-76131 Karlsruhe, Germany
• cUniversity of Freiburg, Chair of Remote Sensing and Landscape Information Systems, Tennenbacherstrasse 4, Freiburg D-79106 Germany
• dUniversity of Freiburg, Department of Biometry and Environmental System Analysis, Tennenbacherstrasse 4, Freiburg D-79106 Germany
• eUniversity of Jena, Department of Earth Observation, Loebdergraben 32, Jena D-07743, Germany
• fLaboratorio de Geomática y Ecología del Paisaje, Universidad de Chile, Av. Santa Rosa 11315, Santiago deChile, Chile

Received 1 November 2014. Revised 23 January 2015. Accepted 27 January 2015. Available online 5 February 2015.

Highlights

• •
  We focus on stratification in remote sensing-assisted biomass models.
• •
  We used dataset based on hyperspectral and LiDAR predictors.
• •
  Benefits from stratification were assessed in a factorial design with other model choices.
• •
  The stratification of measurement units was marginally advantageous.
• •
  Input data type and statistical prediction showed to be most influential on model performances.

Abstract

Remote sensing-assisted estimates of aboveground forest biomass are essential for modeling carbon budgets. It has been suggested that estimates can be improved by building species- or strata-specific biomass models. However, few studies have attempted a systematic analysis of the benefits of such stratification, especially in combination with other factors such as sensor type, statistical prediction method and sampling design of the reference inventory data. We addressed this topic by analyzing the impact of stratifying forest data into three classes (broadleaved, coniferous and mixed forest). We compare predictive accuracy (a) between the strata (b) to a case without stratification for a set of pre-selected predictors from airborne LiDAR and hyperspectral data obtained in a managed mixed forest site in southwestern Germany. We used 5 commonly applied algorithms for biomass predictions on bootstrapped subsamples of the data to obtain cross validated RMSE and r² diagnostics. Those values were analyzed in a factorial design by an analysis of variance (ANOVA) to rank the relative importance of each factor. Selected models were used for wall-to-wall mapping of biomass estimates and their associated uncertainty. The results revealed marginal advantages for the strata-specific prediction models over the unstratified ones, which were more obvious on the wall-to-wall mapped area-based predictions. Yet further tests are necessary to establish the generality of these results. Input data type and statistical prediction method are concluded to remain the two most crucial factors for the quality of remote sensing-assisted biomass models.

Keywords

• LiDAR and hyperspectral remote sensing
• Aboveground biomass
• Statistical prediction
• Post-stratification
• Model performance
• Factorial design

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• ⁎ 
  Corresponding author. Tel.: +49 931 3189638; fax: +49 931 31896380.

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