Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass

Author:

Fassnacht F.E.,Hartig F.ORCID,Latifi H.,Berger C.,Hernández J.ORCID,Corvalán P.,Koch B.

Funder

Deutsches Zentrum für Luft- und Raumfahrt e.V.

BioComsa, Consorcio Tecnológico de Biocombustibles S.A.

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference95 articles.

1. Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest;Anderson;Remote Sensing of Environment,2008

2. Multitemporal repeat pass SAR interferometry of boreal forests;Askne;IEEE Transactions on Geoscience and Remote Sensing,2005

3. Potential of polarimetric SAR interferometry for forest carbon accounting;Balzter,2003

4. The Laser Vegetation Imaging Sensor (LVIS): a medium-altitude, digitation-only, airborne laser altimeter for mapping vegetation and topography;Blair;ISPRS Journal of Photogrammetry and Remote Sensing,1999

5. Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data;Bortolot;ISPRS Journal of Photogrammetry and Remote Sensing,2005

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