Variance-preserving mosaicing of multiple satellite images for forest parameter estimation: Radiometric normalization

Abstract Measuring, Reporting and Verification (MRV) systems of the United Nations programme on Reducing Emissions from Deforestation and forest Degradation (REDD+) aim to provide robust and reliable data on carbon credits over large areas. Multitemporal satellite mosaics are often the only cost-effective remote sensing data that allow such coverage. Although a number of methods for producing mosaics has been proposed, most of them are dependent on the order in which tiles to normalized are presented to the algorithm and suffer from loss of input scenes’ variance which can substantially reduce the carbon credits. In this study we propose a variance-preserving mosaic (VPM) algorithm that considers all images at the same time, minimizes overall error of the normalization and aims to preserve average variance of input images. We have compared the presented method with a popular relative normalization algorithm commonly used nowadays. The proposed algorithm allows to avoid iterative pair-wise normalization, results in visually uniform mosaics while maintaining also the original image variance.