Model-associated forest parameter retrieval using VHF SAR data at the individual tree level

Anatoliy Alekseevich Kononov, Min-Ho Ka

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

A simple statistical extension at the individual tree level for an earlier-developed very high frequency forest backscatter model is proposed. This extended model treats trunk volumes as random quantities. A concept of random forest reflection coefficient is also introduced to characterize radar returns from individual trees. Based on the extended model, a set of algorithms for estimating the mean trunk (stem) volume from synthetic aperture radar data at the individual tree level is developed assuming that the areal tree density is known. The algorithms are specified for different scenarios related to a priori information on parameters of statistical distributions for the trunk volume and fluctuations of the forest reflection coefficient. An approximate lower bound on the standard deviation in the unbiased estimation of the mean trunk (stem) volume is proposed. This bound can be readily obtained by means of computer simulation for any specified statistical distribution for the trunk volume and fluctuations of the forest reflection coefficient. Performance analysis for the proposed algorithms is numerically performed by means of Monte Carlo simulation for a variety of scenarios. This analysis has shown that the algorithms provide nearly unbiased and efficient estimates, and the proposed lower bound is a very accurate approximation. The results of the study have demonstrated that the approach and methods developed in this paper suggest promising solutions in accurate forest parameter retrieval.

Original languageEnglish
Pages (from-to)69-84
Number of pages16
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume46
Issue number1
DOIs
Publication statusPublished - 2008 Jan 1

Fingerprint

retrieval
synthetic aperture radar
statistical distribution
statistical distributions
reflectance
stems
stem
Synthetic aperture radar
very high frequencies
radar data
Radar
computer simulation
backscatter
radar
standard deviation
estimating
computerized simulation
parameter
Computer simulation
estimates

All Science Journal Classification (ASJC) codes

  • Geochemistry and Petrology
  • Geophysics
  • Computers in Earth Sciences
  • Electrical and Electronic Engineering

Cite this

@article{0b21969bf272474294a4ae712007c0ef,
title = "Model-associated forest parameter retrieval using VHF SAR data at the individual tree level",
abstract = "A simple statistical extension at the individual tree level for an earlier-developed very high frequency forest backscatter model is proposed. This extended model treats trunk volumes as random quantities. A concept of random forest reflection coefficient is also introduced to characterize radar returns from individual trees. Based on the extended model, a set of algorithms for estimating the mean trunk (stem) volume from synthetic aperture radar data at the individual tree level is developed assuming that the areal tree density is known. The algorithms are specified for different scenarios related to a priori information on parameters of statistical distributions for the trunk volume and fluctuations of the forest reflection coefficient. An approximate lower bound on the standard deviation in the unbiased estimation of the mean trunk (stem) volume is proposed. This bound can be readily obtained by means of computer simulation for any specified statistical distribution for the trunk volume and fluctuations of the forest reflection coefficient. Performance analysis for the proposed algorithms is numerically performed by means of Monte Carlo simulation for a variety of scenarios. This analysis has shown that the algorithms provide nearly unbiased and efficient estimates, and the proposed lower bound is a very accurate approximation. The results of the study have demonstrated that the approach and methods developed in this paper suggest promising solutions in accurate forest parameter retrieval.",
author = "Kononov, {Anatoliy Alekseevich} and Min-Ho Ka",
year = "2008",
month = "1",
day = "1",
doi = "10.1109/TGRS.2007.907107",
language = "English",
volume = "46",
pages = "69--84",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

Model-associated forest parameter retrieval using VHF SAR data at the individual tree level. / Kononov, Anatoliy Alekseevich; Ka, Min-Ho.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 1, 01.01.2008, p. 69-84.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Model-associated forest parameter retrieval using VHF SAR data at the individual tree level

AU - Kononov, Anatoliy Alekseevich

AU - Ka, Min-Ho

PY - 2008/1/1

Y1 - 2008/1/1

N2 - A simple statistical extension at the individual tree level for an earlier-developed very high frequency forest backscatter model is proposed. This extended model treats trunk volumes as random quantities. A concept of random forest reflection coefficient is also introduced to characterize radar returns from individual trees. Based on the extended model, a set of algorithms for estimating the mean trunk (stem) volume from synthetic aperture radar data at the individual tree level is developed assuming that the areal tree density is known. The algorithms are specified for different scenarios related to a priori information on parameters of statistical distributions for the trunk volume and fluctuations of the forest reflection coefficient. An approximate lower bound on the standard deviation in the unbiased estimation of the mean trunk (stem) volume is proposed. This bound can be readily obtained by means of computer simulation for any specified statistical distribution for the trunk volume and fluctuations of the forest reflection coefficient. Performance analysis for the proposed algorithms is numerically performed by means of Monte Carlo simulation for a variety of scenarios. This analysis has shown that the algorithms provide nearly unbiased and efficient estimates, and the proposed lower bound is a very accurate approximation. The results of the study have demonstrated that the approach and methods developed in this paper suggest promising solutions in accurate forest parameter retrieval.

AB - A simple statistical extension at the individual tree level for an earlier-developed very high frequency forest backscatter model is proposed. This extended model treats trunk volumes as random quantities. A concept of random forest reflection coefficient is also introduced to characterize radar returns from individual trees. Based on the extended model, a set of algorithms for estimating the mean trunk (stem) volume from synthetic aperture radar data at the individual tree level is developed assuming that the areal tree density is known. The algorithms are specified for different scenarios related to a priori information on parameters of statistical distributions for the trunk volume and fluctuations of the forest reflection coefficient. An approximate lower bound on the standard deviation in the unbiased estimation of the mean trunk (stem) volume is proposed. This bound can be readily obtained by means of computer simulation for any specified statistical distribution for the trunk volume and fluctuations of the forest reflection coefficient. Performance analysis for the proposed algorithms is numerically performed by means of Monte Carlo simulation for a variety of scenarios. This analysis has shown that the algorithms provide nearly unbiased and efficient estimates, and the proposed lower bound is a very accurate approximation. The results of the study have demonstrated that the approach and methods developed in this paper suggest promising solutions in accurate forest parameter retrieval.

UR - http://www.scopus.com/inward/record.url?scp=37249040540&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=37249040540&partnerID=8YFLogxK

U2 - 10.1109/TGRS.2007.907107

DO - 10.1109/TGRS.2007.907107

M3 - Article

VL - 46

SP - 69

EP - 84

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 1

ER -