Forest leaf area density profiles from the quantitative fusion of radar and hyperspectral data

Robert N. Treuhaft, Gregory P. Asner, Beverly E. Law, Steven Van Tuyl

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

The leaf area density (LAD) of a forest is an important indicator of forest biomass and is therefore pertinent to monitoring carbon sequestration and change. Quantitative physical models were used to estimate forest LAD from radar and hyperspectral airborne remote sensing observations. A parameter-estimation technique based on physical models minimizes the need for in situ observations and thereby facilitates global remote sensing of forest structure. Using data from the NASA Airborne Synthetic Aperture Radar (AIRSAR) and the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) over three forest plots in Central Oregon, parameters were estimated separately from the radar and hyperspectral data and then combined to form LAD. Gaussian relative LAD profiles were estimated from multialtitude interferometric and polarimetric AIRSAR data. Leaf area indices (LAI) were estimated from AVIRIS data and used to normalize the relative density profiles to produce LAD as a function of height. LAD was also determined from field measurements of geometric tree properties and LAI. LADs in the three forest plots were in the 0.02-0.18 m2 m-3 range, with LAIs in the range 0.8-2.4 m2 m-2. The agreement between the remotely sensed and field-measured LAD was typically 0.02 m2m-3 but occasionally as high as 0.06 m2m-3, which was within a 1 -2 standard error range. More complex models for the remotely sensed relative density, along with more diverse radar observation strategies, will improve LAD accuracy in the future. LAD estimation will also improve when radar, hyperspectral, and other relevant remote sensing data sets are combined in a single parameter-estimation process, as opposed to the separate estimations performed in this first LAD demonstration.

Original languageEnglish (US)
Pages (from-to)XXIII-XXIV
JournalJournal of Geophysical Research Atmospheres
Volume107
Issue number21
DOIs
StatePublished - Jan 1 2002
Externally publishedYes

Fingerprint

radar
radar data
leaves
leaf area
Radar
Fusion reactions
fusion
Remote sensing
Infrared imaging
profiles
Synthetic aperture radar
Parameter estimation
NASA
Spectrometers
leaf area index
airborne radar
synthetic aperture radar
remote sensing
Biomass
Demonstrations

Keywords

  • Hyperspectral imaging spectroscopy
  • Interferometric radar
  • Leaf area density
  • Vegetation profiling

ASJC Scopus subject areas

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

Cite this

Forest leaf area density profiles from the quantitative fusion of radar and hyperspectral data. / Treuhaft, Robert N.; Asner, Gregory P.; Law, Beverly E.; Van Tuyl, Steven.

In: Journal of Geophysical Research Atmospheres, Vol. 107, No. 21, 01.01.2002, p. XXIII-XXIV.

Research output: Contribution to journalArticle

Treuhaft, Robert N. ; Asner, Gregory P. ; Law, Beverly E. ; Van Tuyl, Steven. / Forest leaf area density profiles from the quantitative fusion of radar and hyperspectral data. In: Journal of Geophysical Research Atmospheres. 2002 ; Vol. 107, No. 21. pp. XXIII-XXIV.
@article{2a3ec0fa67b24295a83ce8a653d4101c,
title = "Forest leaf area density profiles from the quantitative fusion of radar and hyperspectral data",
abstract = "The leaf area density (LAD) of a forest is an important indicator of forest biomass and is therefore pertinent to monitoring carbon sequestration and change. Quantitative physical models were used to estimate forest LAD from radar and hyperspectral airborne remote sensing observations. A parameter-estimation technique based on physical models minimizes the need for in situ observations and thereby facilitates global remote sensing of forest structure. Using data from the NASA Airborne Synthetic Aperture Radar (AIRSAR) and the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) over three forest plots in Central Oregon, parameters were estimated separately from the radar and hyperspectral data and then combined to form LAD. Gaussian relative LAD profiles were estimated from multialtitude interferometric and polarimetric AIRSAR data. Leaf area indices (LAI) were estimated from AVIRIS data and used to normalize the relative density profiles to produce LAD as a function of height. LAD was also determined from field measurements of geometric tree properties and LAI. LADs in the three forest plots were in the 0.02-0.18 m2 m-3 range, with LAIs in the range 0.8-2.4 m2 m-2. The agreement between the remotely sensed and field-measured LAD was typically 0.02 m2m-3 but occasionally as high as 0.06 m2m-3, which was within a 1 -2 standard error range. More complex models for the remotely sensed relative density, along with more diverse radar observation strategies, will improve LAD accuracy in the future. LAD estimation will also improve when radar, hyperspectral, and other relevant remote sensing data sets are combined in a single parameter-estimation process, as opposed to the separate estimations performed in this first LAD demonstration.",
keywords = "Hyperspectral imaging spectroscopy, Interferometric radar, Leaf area density, Vegetation profiling",
author = "Treuhaft, {Robert N.} and Asner, {Gregory P.} and Law, {Beverly E.} and {Van Tuyl}, Steven",
year = "2002",
month = "1",
day = "1",
doi = "10.1029/2001JD000646",
language = "English (US)",
volume = "107",
pages = "XXIII--XXIV",
journal = "Journal of Geophysical Research Atmospheres",
issn = "0148-0227",
number = "21",

}

TY - JOUR

T1 - Forest leaf area density profiles from the quantitative fusion of radar and hyperspectral data

AU - Treuhaft, Robert N.

AU - Asner, Gregory P.

AU - Law, Beverly E.

AU - Van Tuyl, Steven

PY - 2002/1/1

Y1 - 2002/1/1

N2 - The leaf area density (LAD) of a forest is an important indicator of forest biomass and is therefore pertinent to monitoring carbon sequestration and change. Quantitative physical models were used to estimate forest LAD from radar and hyperspectral airborne remote sensing observations. A parameter-estimation technique based on physical models minimizes the need for in situ observations and thereby facilitates global remote sensing of forest structure. Using data from the NASA Airborne Synthetic Aperture Radar (AIRSAR) and the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) over three forest plots in Central Oregon, parameters were estimated separately from the radar and hyperspectral data and then combined to form LAD. Gaussian relative LAD profiles were estimated from multialtitude interferometric and polarimetric AIRSAR data. Leaf area indices (LAI) were estimated from AVIRIS data and used to normalize the relative density profiles to produce LAD as a function of height. LAD was also determined from field measurements of geometric tree properties and LAI. LADs in the three forest plots were in the 0.02-0.18 m2 m-3 range, with LAIs in the range 0.8-2.4 m2 m-2. The agreement between the remotely sensed and field-measured LAD was typically 0.02 m2m-3 but occasionally as high as 0.06 m2m-3, which was within a 1 -2 standard error range. More complex models for the remotely sensed relative density, along with more diverse radar observation strategies, will improve LAD accuracy in the future. LAD estimation will also improve when radar, hyperspectral, and other relevant remote sensing data sets are combined in a single parameter-estimation process, as opposed to the separate estimations performed in this first LAD demonstration.

AB - The leaf area density (LAD) of a forest is an important indicator of forest biomass and is therefore pertinent to monitoring carbon sequestration and change. Quantitative physical models were used to estimate forest LAD from radar and hyperspectral airborne remote sensing observations. A parameter-estimation technique based on physical models minimizes the need for in situ observations and thereby facilitates global remote sensing of forest structure. Using data from the NASA Airborne Synthetic Aperture Radar (AIRSAR) and the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) over three forest plots in Central Oregon, parameters were estimated separately from the radar and hyperspectral data and then combined to form LAD. Gaussian relative LAD profiles were estimated from multialtitude interferometric and polarimetric AIRSAR data. Leaf area indices (LAI) were estimated from AVIRIS data and used to normalize the relative density profiles to produce LAD as a function of height. LAD was also determined from field measurements of geometric tree properties and LAI. LADs in the three forest plots were in the 0.02-0.18 m2 m-3 range, with LAIs in the range 0.8-2.4 m2 m-2. The agreement between the remotely sensed and field-measured LAD was typically 0.02 m2m-3 but occasionally as high as 0.06 m2m-3, which was within a 1 -2 standard error range. More complex models for the remotely sensed relative density, along with more diverse radar observation strategies, will improve LAD accuracy in the future. LAD estimation will also improve when radar, hyperspectral, and other relevant remote sensing data sets are combined in a single parameter-estimation process, as opposed to the separate estimations performed in this first LAD demonstration.

KW - Hyperspectral imaging spectroscopy

KW - Interferometric radar

KW - Leaf area density

KW - Vegetation profiling

UR - http://www.scopus.com/inward/record.url?scp=36448973118&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=36448973118&partnerID=8YFLogxK

U2 - 10.1029/2001JD000646

DO - 10.1029/2001JD000646

M3 - Article

AN - SCOPUS:36448973118

VL - 107

SP - XXIII-XXIV

JO - Journal of Geophysical Research Atmospheres

JF - Journal of Geophysical Research Atmospheres

SN - 0148-0227

IS - 21

ER -