Relative radiometric correction methods have been widely used to correct ground illumination difference in multi-temporal satellite data. ALOS (Advanced Land Observing Satellite) data starts to play an important role in forest and carbon assessment, such as the REDD (Reducing Emissions from Deforestation and forest Degradation) program. The objective of the study was to compare three relative radiometric correction methods for five multi-temporal ALOS AVNIR-2 (Advanced Visible and Near Infrared Radiometer) images, and to examine the influence of each correction method on the estimation accuracy of forest attributes with auxiliary field inventory plot data. Both spectral features and textural features were extracted before and after radiometric correction and used in estimation procedure. All the radiometric correction methods used improved the estimation accuracy of forest stem volume at plot level, and they were MAD (multivariate alteration detection) transformation-based normalization, PCA (principle component analysis)-based correction and local radiometric correction, among which MAD transformation-based normalization exceeded others by reducing the relative RMSE by 5.75% with the ordinary least square fitting and 6.8% with the K-MSN (K-Most Similar Neighbour) method both after leave-one-out cross-validation. RMSE for only the corrected area is also calculated, in view of the small proportion of plots in that area. The result can be used to improve the visual effect of mosaics of multi-temporal ALOS scenes, and to retrieve more accurate forest estimates for national forest resources and biomass mapping.
|Original language||English (US)|
|Number of pages||10|
|Journal||ISPRS Journal of Photogrammetry and Remote Sensing|
|State||Published - Mar 2012|
Copyright 2012 Elsevier B.V., All rights reserved.
- Bi-temporal principle component analysis
- Estimation accuracy
- Local radiometric correction
- Multi-temporal images
- Multivariate alteration detection (MAD) transformation
- Pseudo-invariant features