The estimation of aboveground biomass is commonly recognized for global relevance because of the vegetation role in the carbon cycle. Both active and passive microwave sensors can significantly contribute to this goal because of their high sensitivity to water content and high penetration at lower frequencies (L-/P-bands). In particular, Global Navigation Satellite Systems (GNSSs) are recently receiving increasing interest as source of opportunity to be employed as illuminator for L-band remote sensing, since they could provide low-cost sensors for nondestructive forest biomass estimation over large areas. In this article, we suggest a method to extract forest information using the GNSS direct signals collected in clear sky and below the vegetation canopy at both circular polarizations. An experimental campaign, carried out in the framework of an European Space Agency (ESA) project, was conducted over three poplar forests with different biomass to verify the feasibility of this technique. The relationships between the GNSS measurements and the tree parameters were first assessed and then interpreted and supported by statistical analysis and a theoretical model. The signal collected under the canopy is affected by attenuation and depolarization with respect to the one collected in open air, and this article demonstrated that both direct line-of-sight propagation and volume scattering play a role in the signal magnitude and its fluctuation in time. Although the experimental data set is limited in size and environmental conditions, two inversion algorithms were also tested with the encouraging retrieval results.

Ground-based remote sensing of forests exploiting GNSS signals / Guerriero, L.; Martin, F.; Mollfulleda, A.; Paloscia, S.; Pierdicca, N.; Santi, E.; Floury, N.. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 58:10(2020), pp. 6844-6860. [10.1109/TGRS.2020.2976899]

Ground-based remote sensing of forests exploiting GNSS signals

Pierdicca N.;
2020

Abstract

The estimation of aboveground biomass is commonly recognized for global relevance because of the vegetation role in the carbon cycle. Both active and passive microwave sensors can significantly contribute to this goal because of their high sensitivity to water content and high penetration at lower frequencies (L-/P-bands). In particular, Global Navigation Satellite Systems (GNSSs) are recently receiving increasing interest as source of opportunity to be employed as illuminator for L-band remote sensing, since they could provide low-cost sensors for nondestructive forest biomass estimation over large areas. In this article, we suggest a method to extract forest information using the GNSS direct signals collected in clear sky and below the vegetation canopy at both circular polarizations. An experimental campaign, carried out in the framework of an European Space Agency (ESA) project, was conducted over three poplar forests with different biomass to verify the feasibility of this technique. The relationships between the GNSS measurements and the tree parameters were first assessed and then interpreted and supported by statistical analysis and a theoretical model. The signal collected under the canopy is affected by attenuation and depolarization with respect to the one collected in open air, and this article demonstrated that both direct line-of-sight propagation and volume scattering play a role in the signal magnitude and its fluctuation in time. Although the experimental data set is limited in size and environmental conditions, two inversion algorithms were also tested with the encouraging retrieval results.
2020
biomass; depolarization; Global Navigation Satellite System (GNSS); transmissivity; vegetation
01 Pubblicazione su rivista::01a Articolo in rivista
Ground-based remote sensing of forests exploiting GNSS signals / Guerriero, L.; Martin, F.; Mollfulleda, A.; Paloscia, S.; Pierdicca, N.; Santi, E.; Floury, N.. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 58:10(2020), pp. 6844-6860. [10.1109/TGRS.2020.2976899]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1449539
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