| Title | Approaches to fractional land cover and continuous field mapping: a comparative assessment |
| |
| Author | Fernandes, R A ;
Fraser, R; Latifovic, R; Cihlar, J; Beaubien, J; Du,
Y |
| Source | Remote Sensing of Environment 2001., https://doi.org/10.1016/j.rse.2002.06.006 |
| Year | 2001 |
| Alt Series | Earth Sciences Sector, Contribution Series 20042963 |
| Publisher | Elsevier BV |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Released | 2004 01 01 |
| Abstract | Subpixel land cover mapping involves the estimation of surface properties using sensors whose spatial sampling is coarse enough to produce mixtures of the properties within each pixel. This study
evaluates five algorithms for mapping subpixel land cover fractions and continuous fields of vegetation properties within the BOREAS study area. The algorithms include a conventional "hard", per-pixel classifier, a neural network, a
clustering/look-up-table approach, multivariate regression, and linear least squares inversion. A land cover map prepared using a Landsat TM mosaic was adopted as the source of fine scale calibration and validation data. Coarse scale mixtures of five
basic land cover classes and continuous vegetation fields, both corresponding to the field of view of SPOT-VEGETATION imagery (1.15-km pixel size), were synthesised from the TM mosaic using a modelled point spread function. Two measures of land cover
distribution were used, fractions of fine scale land cover categories and continuous fields of vegetation structural characteristics. The subpixel algorithms were applied using both proximate (<100 km) and distant (>400 km) separation between
training and validation regions. "Hard" classification performed poorly in estimating proportions or continuous fields. The neural network, look-up-table and multivariate regression algorithms produced good matches of spatial patterns and regional
land cover composition for the proximate treatment. However, all three methods exhibited substantial biases with the distant treatment due to the characteristics of the training data. Linear least squares inversion offers a relatively unbiased but
less precise alternative for subpixel proportion and fraction mapping as it avoids calibration to the a priori distribution of land cover in the training data. In general, a combination of multivariate regression for proximate training data and
linear least squares inversion for distant training data resulted in woody fraction estimates within 20% of the Landsat TM classification-based estimates. |
| GEOSCAN ID | 219765 |
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