Gaussian Bayesian network modeling to improve spatial growth estimates of heterogeneous forests

Y. T. Mustafa*, A. Stein, V. A. Tolpekin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

An approach is presented for improving the spatial estimation of leaf area index (LAI) of a heterogeneous forest by integrating the Physiological Principles Predicting Growth (3-PG) model output with the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. A novel inference strategy within the Gaussian Bayesian networks (GBNs) has been developed in this work to take care of the different structures of the inaccuracies in the two data sources. The Linear Mixture Model (LMM) was used to decompose MODIS pixels using class fraction derived from an aerial and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. In this way spatially heterogeneous output was produced. Results showed that the spatial output obtained with the GBN was more accurate than both the spatial 3-PG model output and the satellite estimate, as the root mean square error reduced from 1.21 to 0.57, and the relative error from 20.25% to 7.73%. In this work, we conclude that the GBNs can improve the spatial estimation of the LAI values of a heterogeneous forest by integrating a spatial 3-PG forest growth model with satellite data.

Original languageEnglish
Title of host publicationAmerican Society for Photogrammetry and Remote Sensing Annual Conference 2012, ASPRS 2012
PublisherASPRS
Pages390-399
Number of pages10
ISBN (Print)9781622764068
Publication statusPublished - 1 Dec 2012
EventAmerican Society for Photogrammetry and Remote Sensing Annual Conference 2012, ASPRS 2012 - Sacramento, United States
Duration: 19 Mar 201223 Mar 2012

Conference

ConferenceAmerican Society for Photogrammetry and Remote Sensing Annual Conference 2012, ASPRS 2012
Abbreviated titleASPRS 2012
CountryUnited States
CitySacramento
Period19/03/1223/03/12

Keywords

  • Gaussian Bayesian networks (GBNs)
  • Leaf area index (LAI)
  • Linear Mixture Model (LMM)
  • Mixed pixels
  • Moderate Resolution Imaging Spectroradiometer (MODIS) sensor
  • Spatial version of Physiological Principals Predicting Growth (3-PG) model

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