1998
DOI: 10.1191/030913398675385488
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Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters

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Cited by 67 publications
(69 citation statements)
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“…Space-borne optical remote sensing is a reliable source of information for assessment of forest characteristics over wide areas [7]. The synoptic view and the regular acquisition cycle of image data, combined with the burgeoning selection of techniques available for attribute estimation, make remotely sensed data an appropriate and valuable source of data for assessment of forest condition and detection of change-offering information to augment costly and time consuming field campaigns for inventory update and re-measurement [8].…”
Section: Introductionmentioning
confidence: 99%
“…Space-borne optical remote sensing is a reliable source of information for assessment of forest characteristics over wide areas [7]. The synoptic view and the regular acquisition cycle of image data, combined with the burgeoning selection of techniques available for attribute estimation, make remotely sensed data an appropriate and valuable source of data for assessment of forest condition and detection of change-offering information to augment costly and time consuming field campaigns for inventory update and re-measurement [8].…”
Section: Introductionmentioning
confidence: 99%
“…Biophysical variables can be derived from remote sensing data using statistical, physical and hybrid retrieval methods [7][8][9]. Statistical methods rely on models to relate spectral data with the biophysical variable of interest, usually through some form of regression.…”
Section: Introductionmentioning
confidence: 99%
“…Remote sensing is an increasingly established source of data to support mapping and monitoring of forested areas, including aspects related to condition, structure, and dynamics [18,19]. The capacity of remote sensing to characterize forests has been furthered by the advent of airborne scanning LiDAR (Light Detection And Ranging) technology and related information extraction techniques [20].…”
Section: Introductionmentioning
confidence: 99%