Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy
In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m<sup>3</sup>/ha), which can be considered as a proxy of...
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doaj-301badc5efa446568e03bb446b3e18772021-02-24T00:01:16ZengMDPI AGRemote Sensing2072-42922021-02-011380980910.3390/rs13040809Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central ItalyEmanuele Santi0Marta Chiesi1Giacomo Fontanelli2Alessandro Lapini3Simonetta Paloscia4Simone Pettinato5Giuliano Ramat6Leonardo Santurri7Institute of Applied Physics–National Research Council of Italy (IFAC–CNR), Via Madonna del Piano 10, 50019 Florence, ItalyInstitute of BioEconomy–National Research Council of Italy (IBE–CNR), Via Madonna del Piano 10, 50019 Florence, ItalyInstitute of Applied Physics–National Research Council of Italy (IFAC–CNR), Via Madonna del Piano 10, 50019 Florence, ItalyInstitute of Applied Physics–National Research Council of Italy (IFAC–CNR), Via Madonna del Piano 10, 50019 Florence, ItalyInstitute of Applied Physics–National Research Council of Italy (IFAC–CNR), Via Madonna del Piano 10, 50019 Florence, ItalyInstitute of Applied Physics–National Research Council of Italy (IFAC–CNR), Via Madonna del Piano 10, 50019 Florence, ItalyInstitute of Applied Physics–National Research Council of Italy (IFAC–CNR), Via Madonna del Piano 10, 50019 Florence, ItalyInstitute of Applied Physics–National Research Council of Italy (IFAC–CNR), Via Madonna del Piano 10, 50019 Florence, ItalyIn this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m<sup>3</sup>/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson's correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m<sup>3</sup>/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m<sup>3</sup>/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.https://www.mdpi.com/2072-4292/13/4/809SARforest biomasswoody volumeANNinversion algorithms |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Emanuele Santi Marta Chiesi Giacomo Fontanelli Alessandro Lapini Simonetta Paloscia Simone Pettinato Giuliano Ramat Leonardo Santurri |
spellingShingle |
Emanuele Santi Marta Chiesi Giacomo Fontanelli Alessandro Lapini Simonetta Paloscia Simone Pettinato Giuliano Ramat Leonardo Santurri Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy Remote Sensing SAR forest biomass woody volume ANN inversion algorithms |
author_facet |
Emanuele Santi Marta Chiesi Giacomo Fontanelli Alessandro Lapini Simonetta Paloscia Simone Pettinato Giuliano Ramat Leonardo Santurri |
author_sort |
Emanuele Santi |
title |
Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy |
title_short |
Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy |
title_full |
Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy |
title_fullStr |
Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy |
title_full_unstemmed |
Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy |
title_sort |
mapping woody volume of mediterranean forests by using sar and machine learning: a case study in central italy |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-02-01 |
description |
In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m<sup>3</sup>/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson's correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m<sup>3</sup>/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m<sup>3</sup>/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales. |
topic |
SAR forest biomass woody volume ANN inversion algorithms |
url |
https://www.mdpi.com/2072-4292/13/4/809 |
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