Retrieval of canopy structural parameters from multiangle observations using an artificial neural network

Abdelgadir A. Abuelgasim, Sucharita Gopal, Alan H. Strahler

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper describes a procedure for the retrieval of canopy structural parameters (e.g. height, shape, density) from multiangle reflectance measurements using an artificial neural network (ANN). The objective is to train a neural network to learn the association of canopy structural parameters with its corresponding directional reflectance pattern. The Li-Strahler [1] geometric-optical mutual shadowing model is used to simulate the bidirectional reflectance of a canopy based on the geometry of the trees. The reflectance generated from the model is used as an input to a multilayer feed-forward neural network, with the canopy structural parameters as outputs. ANNs have great potentials to learn the relation (or any continuous function) between input patterns and desired outputs without any prior knowledge of the mapping function. Using the neural network retrieval approach, the R2 between the model predicted canopy parameters and the actual parameters of density is 0.85 and 0.75 for the tree crown diameter and canopy height.

Original languageEnglish
Pages1426-1428
Number of pages3
Publication statusPublished - 1996
Externally publishedYes
EventProceedings of the 1996 International Geoscience and Remote Sensing Symposium. Part 3 (of 4) - Lincoln, NE, USA
Duration: May 28 1996May 31 1996

Conference

ConferenceProceedings of the 1996 International Geoscience and Remote Sensing Symposium. Part 3 (of 4)
CityLincoln, NE, USA
Period5/28/965/31/96

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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