Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

Gaia Vaglio Laurin; Johannes Balling; Piermaria Corona; Walter Mattioli; Dario Papale; Nicola Puletti; Maria Rizzo; John Truckenbrodt; Marcel Urban

doi:10.1117/1.JRS.12.016008

5 January 2018 Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

Gaia Vaglio Laurin, Johannes Balling, Piermaria Corona, Walter Mattioli, Dario Papale, Nicola Puletti, Maria Rizzo, John Truckenbrodt, Marcel Urban

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Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016008 (January 2018). https://doi.org/10.1117/1.JRS.12.016008

Abstract

The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction ( R2=0.7) using integrated sensors when an upper bound of 400 Mg ha−1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Gaia Vaglio Laurin, Johannes Balling, Piermaria Corona, Walter Mattioli, Dario Papale, Nicola Puletti, Maria Rizzo, John Truckenbrodt, and Marcel Urban "Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data," Journal of Applied Remote Sensing 12(1), 016008 (5 January 2018). https://doi.org/10.1117/1.JRS.12.016008

Received: 3 August 2017; Accepted: 13 December 2017; Published: 5 January 2018

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