Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 452533
Title Fusion of pan-tropical biomass maps using weighted averaging and regional calibration data
Author(s) Ge, Y.; Avitabile, V.; Heuvelink, G.B.M.; Wang, J.; Herold, M.
Source International Journal of applied Earth Observation and Geoinformation 31 (2014). - ISSN 0303-2434 - p. 13 - 24.
DOI https://doi.org/10.1016/j.jag.2014.02.011
Department(s) Laboratory of Geo-information Science and Remote Sensing
Soil Geography and Landscape
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2014
Keyword(s) remotely-sensed imagery - land-cover datasets - multisensor data - forest biomass - carbon-dioxide - sensing data - soil maps - classification - deforestation - validation
Abstract Biomass is a key environmental variable that influences many biosphere–atmosphere interactions. Recently, a number of biomass maps at national, regional and global scales have been produced using different approaches with a variety of input data, such as from field observations, remotely sensed imagery and other spatial datasets. However, the accuracy of these maps varies regionally and is largely unknown. This research proposes a fusion method to increase the accuracy of regional biomass estimates by using higher-quality calibration data. In this fusion method, the biases in the source maps were first adjusted to correct for over- and underestimation by comparison with the calibration data. Next, the biomass maps were combined linearly using weights derived from the variance–covariance matrix associated with the accuracies of the source maps. Because each map may have different biases and accuracies for different land use types, the biases and fusion weights were computed for each of the main land cover types separately. The conceptual arguments are substantiated by a case study conducted in East Africa. Evaluation analysis shows that fusing multiple source biomass maps may produce a more accurate map than when only one biomass map or unweighted averaging is used.
Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.