CONIFEROUS FOREST CLASSIFICATION AND INVENTORY USING LANDSAT AND DIGITAL TERRAIN DATA.

Janet Franklin, Thomas L. Logan, Curtis E. Woodcock, Alan H. Strahler

Research output: Contribution to journalArticle

  • 81 Citations

Abstract

Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a forest classification and inventory system (FOCIS) developed at the University of California, Santa Barbara, and the Jet Propulsion Laboratory. Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine-processing techniques to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Results were presented which illustrate the power of FOCIS methods to produce timely accurate large-area inventories with comparable accuracies and reduced costs when compared to conventional timber inventory methods.

LanguageEnglish (US)
Pages139-149
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
VolumeGE-24
Issue number1
StatePublished - 1800
Externally publishedYes

Fingerprint

Timber
coniferous forest
Landsat
timber inventory
Photointerpretation
timber
stratification
photointerpretation
Propulsion
Costs
costs
jet propulsion
vegetation
Processing
cost

ASJC Scopus subject areas

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

Cite this

Franklin, J., Logan, T. L., Woodcock, C. E., & Strahler, A. H. (1800). CONIFEROUS FOREST CLASSIFICATION AND INVENTORY USING LANDSAT AND DIGITAL TERRAIN DATA. IEEE Transactions on Geoscience and Remote Sensing, GE-24(1), 139-149.

CONIFEROUS FOREST CLASSIFICATION AND INVENTORY USING LANDSAT AND DIGITAL TERRAIN DATA. / Franklin, Janet; Logan, Thomas L.; Woodcock, Curtis E.; Strahler, Alan H.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-24, No. 1, 1800, p. 139-149.

Research output: Contribution to journalArticle

Franklin, J, Logan, TL, Woodcock, CE & Strahler, AH 1800, 'CONIFEROUS FOREST CLASSIFICATION AND INVENTORY USING LANDSAT AND DIGITAL TERRAIN DATA.' IEEE Transactions on Geoscience and Remote Sensing, vol. GE-24, no. 1, pp. 139-149.
Franklin, Janet ; Logan, Thomas L. ; Woodcock, Curtis E. ; Strahler, Alan H./ CONIFEROUS FOREST CLASSIFICATION AND INVENTORY USING LANDSAT AND DIGITAL TERRAIN DATA.In: IEEE Transactions on Geoscience and Remote Sensing. 1800 ; Vol. GE-24, No. 1. pp. 139-149
@article{f169a99aefc6476b97e9a1619525e8ba,
title = "CONIFEROUS FOREST CLASSIFICATION AND INVENTORY USING LANDSAT AND DIGITAL TERRAIN DATA.",
abstract = "Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a forest classification and inventory system (FOCIS) developed at the University of California, Santa Barbara, and the Jet Propulsion Laboratory. Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine-processing techniques to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Results were presented which illustrate the power of FOCIS methods to produce timely accurate large-area inventories with comparable accuracies and reduced costs when compared to conventional timber inventory methods.",
author = "Janet Franklin and Logan, {Thomas L.} and Woodcock, {Curtis E.} and Strahler, {Alan H.}",
year = "1800",
language = "English (US)",
volume = "GE-24",
pages = "139--149",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

TY - JOUR

T1 - CONIFEROUS FOREST CLASSIFICATION AND INVENTORY USING LANDSAT AND DIGITAL TERRAIN DATA.

AU - Franklin,Janet

AU - Logan,Thomas L.

AU - Woodcock,Curtis E.

AU - Strahler,Alan H.

PY - 1800

Y1 - 1800

N2 - Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a forest classification and inventory system (FOCIS) developed at the University of California, Santa Barbara, and the Jet Propulsion Laboratory. Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine-processing techniques to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Results were presented which illustrate the power of FOCIS methods to produce timely accurate large-area inventories with comparable accuracies and reduced costs when compared to conventional timber inventory methods.

AB - Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a forest classification and inventory system (FOCIS) developed at the University of California, Santa Barbara, and the Jet Propulsion Laboratory. Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine-processing techniques to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Results were presented which illustrate the power of FOCIS methods to produce timely accurate large-area inventories with comparable accuracies and reduced costs when compared to conventional timber inventory methods.

UR - http://www.scopus.com/inward/record.url?scp=0022435632&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0022435632&partnerID=8YFLogxK

M3 - Article

VL - GE-24

SP - 139

EP - 149

JO - IEEE Transactions on Geoscience and Remote Sensing

T2 - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 1

ER -