Variance-preserving mosaicing of multiple satellite images for forest parameter estimation: Radiometric normalization Anna Eivazi a,⇑ , Alexander Kolesnikov b , Virpi Junttila a , Tuomo Kauranne a a Department of Mathematics and Physics, Lappeenranta University of Technology, Lappeenranta, Finland b Arbonaut Ltd., Joensuu, Finland article info Article history: Received 19 March 2014 Received in revised form 14 March 2015 Accepted 16 March 2015 Keywords: Relative normalization Image mosaics Pseudo-invariant features Multispectral imagery Landsat time series REDD + MRV 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. Ó 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. 1. Introduction In model-based estimation of geographical quantities based on satellite images, regression models are built on normalized band values of image pixels. There are many applications where not only the fit of estimates to ground truthing is important, but also the fit- ting of variance. One such application is the Measuring, Reporting and Verification (MRV) of the United Nations programme on Reducing Emissions from Deforestation and forest Degradation (REDD+). REDD+ aims to create a financial value (carbon credits) for the carbon stored in forests, especially those of developing countries, in order to reduce greenhouse gas emissions. One of the important steps within REDD+ is to develop a cost- effective and accurate methodology for carbon monitoring over large areas. Such methodology requires an approach that combines together ground measurements and remote sensing technologies (Angelsen, 2008). Possible remote sensing technologies that can be employed are satellite imagery, LiDAR, aerial images and radar data. For REDD + MRV, satellite imagery has several advantages over other remote sensing technologies. Firstly, satellites provide ‘‘wall- to-wall’’ observations of the target area. Secondly, the price of satellite images is considerably cheaper, and even more, some of the satellite imageries, such as Landsat, can be acquired completely free of charge. And lastly, satellites offer reliable historical data. For instance Landsat delivers global images for the last four decades (Gibbs et al., 2007). Despite many benefits satellite imagery provides, there are also challenges that should be addressed. One of them is radio- metric differences between adjacent multitemporal scenes. Due to variation in acquisition conditions (e.g. solar illumination, atmospheric scattering and atmospheric absorption) the same ground object on two overlapping images can result in different spectral values (Yuan and Elvidge, 1996). Because of this, radio- metrically uniform mosaics using multitemporal scenes should be created before employing satellite imagery into carbon assess- ment. Another challenge is the variance suppression that likely occurs during mosaicing of multiple images, whenever it is based on averaging pixel values of overlapping parts of images. REDD + MRV’s credits are based on measuring the change in car- bon captured in forests. If regression estimates for carbon capture are built using satellite images, suppressing the true variance of band values gets translated into suppression of change in carbon captured, and hence into a reduction of the corresponding carbon credit. http://dx.doi.org/10.1016/j.isprsjprs.2015.03.007 0924-2716/Ó 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. ⇑ Corresponding author. E-mail address: anna.eivazi@lut.fi (A. Eivazi). ISPRS Journal of Photogrammetry and Remote Sensing 105 (2015) 120–127 Contents lists available at ScienceDirect ISPRS Journal of Photogrammetry and Remote Sensing journal homepage: www.elsevier.com/locate/isprsjprs