NDLI: Bayesian Network Modeling for Improving Forest Growth Estimates

Content Provider	IEEE Xplore Digital Library
Author	Mustafa, Y.T. Van Laake, P.E. Stein, A.
Copyright Year	1980
Abstract	Estimating the contribution of the forests to carbon sequestration is commonly done by applying forest growth models. Such models inherently use field observations such as leaf area index (LAI), whereas a relevant information is also available from remotely sensed images. This paper aims to improve the LAI estimated from the forest growth model [physiological principals predicting growth (3-PG)] by combining these values with the LAI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. A Bayesian networks (BNs) approach addresses the bias in the 3-PG model and the noise of the MODIS images. A novel inference strategy within the BN has been developed in this paper to take care of the different structures of the inaccuracies in the two data sources. The BN is applied to the Speulderbos forest in The Netherlands, where the detailed data were available. This paper shows that the outputs obtained with the BN were more accurate than either the 3-PG or the MODIS estimate. It was also found that the BN is more sensitive to the variation of the LAI derived from MODIS than to the variation of the LAI 3-PG values. In this paper, we conclude that the BNs can improve the estimation of the LAI values by combining a forest growth model with satellite imagery.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	639
Ending Page	649
Page Count	11
File Size	863987
File Format	PDF
ISSN	01962892
Volume Number	49
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MODIS Biological system modeling Predictive models Atmospheric modeling Satellites Bayesian methods Uncertainty physiological principals predicting growth (3-PG) model Bayesian networks (BNs) leaf area index (LAI) Moderate Resolution Imaging Spectroradiometer (MODIS)
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Improving space-time forest canopy LAI simulation by fusing forest growth model (3-PG) with remote sensing data

Improvement of Spatio-temporal Growth Estimates in Heterogeneous Forests Using Gaussian Bayesian Networks

Application of the Expectation Maximization Algorithm to Estimate Missing Values in Gaussian Bayesian Network Modeling for Forest Growth

Spatial and temporal mapping of leaf area index in Alpine pastures and meadows with satellite MODIS imagery

The importance of measurement errors for deriving accurate reference leaf area index maps for validation Moderate Resolution satellite LAI products (2006)

Myneni, “Validation of Moderate Resolution Imaging Spectroradiometer leaf area index product in croplands of Alpilles

Analysis and optimization of the MODIS Leaf Area Index algorithm retrievals over broadleaf forests (2005)

Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data

Abstract Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland (2004)

Bayesian Network Modeling for Improving Forest Growth Estimates

Similar Documents

Improving space-time forest canopy LAI simulation by fusing forest growth model (3-PG) with remote sensing data

Improvement of Spatio-temporal Growth Estimates in Heterogeneous Forests Using Gaussian Bayesian Networks

Application of the Expectation Maximization Algorithm to Estimate Missing Values in Gaussian Bayesian Network Modeling for Forest Growth

Spatial and temporal mapping of leaf area index in Alpine pastures and meadows with satellite MODIS imagery

The importance of measurement errors for deriving accurate reference leaf area index maps for validation Moderate Resolution satellite LAI products (2006)

Myneni, “Validation of Moderate Resolution Imaging Spectroradiometer leaf area index product in croplands of Alpilles

Analysis and optimization of the MODIS Leaf Area Index algorithm retrievals over broadleaf forests (2005)

Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data

Abstract Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland (2004)

Bayesian Network Modeling for Improving Forest Growth Estimates