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Mediterranean Forest Monitoring Using Optical and Microwave Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7584

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Dr. Emanuele Santi

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Guest Editor

IFAC-CNR, Via Madonna del Piano 10, 50019 Firenze, Italy
Interests: microwave remote sensing; soil moisture; vegetation biomass; snow water equivalent; SAR and microwave radiometers; GNSS-R, retrieval algorithms development; machine learning
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Dr. Simonetta Paloscia

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Guest Editor

Consiglio Nazionale delle Ricerche, Institute of Applied Physics, Florence, Italy
Interests: microwave remote sensing; soil moisture; vegetation biomass; snow water equivalent; SAR; microwave radiometry
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

Forests and woodlands are the most widely distributed vegetation ecosystems on the planet, covering approximately 4000 million ha. The importance of forest monitoring is universally recognized, due to the role played by forests in provisioning a large number of different services and in acting as the main terrestrial carbon sink. In this respect, the possibility to estimate forest parameters from active and passive remote sensing observations is undoubtedly appealing.

Passive visible/infrared sensors have been widely used for this scope, although these sensors can only detect the upper layer of the canopy, and they can only operate in clear-sky conditions.

Active optical sensors (LiDAR, light detection, and ranging) have been employed for the mapping of forest variables, including the tree heights. However, the only satellite operating with onboard a LiDAR system was the laser altimeter ICESat, which has been recently replaced by ICESat-2, launched in 2018.

Active and passive microwave instruments are suitable tools for forest investigations: in particular, the SAR capabilities in mapping the forest biomass have been largely demonstrated. The P band SAR of the European Space Agency BIOMASS mission, which will be launched in 2021, is expected to provide accurate estimates of the forest biomass, by overcoming the signal saturation for high biomass values that has been observed at L band and higher frequencies.

Both SMAP and SMOS L-band radiometers provide, as an output product, vegetation optical depth (VOD). Although it is not a direct measure of AGB, VOD is related to the forest biomass, and it is the only parameter available on a global scale with daily revisiting.

A very promising sensitivity to forest biomass was exhibited by GNSS reflectometry, a new technique that makes use of the so called “signals of opportunity” available through the global navigation satellite systems.

This Special Issue aims at providing an overview of microwave sensor capabilities, with the support of optical/infrared sensors, in estimating the main forest parameters and especially the above-ground biomass (AGB). Mediterranean forests will be focused on especially, which represent a very complex environment for addressing the problem, as they are characterized by high spatial fragmentation, heterogeneity, and discontinuity in canopies that significantly affect retrieval.

Dr. Emanuele Santi
Dr. Simonetta Paloscia
Guest Editors

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Keywords

SAR
Microwave radiometers
GNSS reflectometry
LiDAR
Hydrological cycle
Above-ground biomass
Mediterranean forests.

Published Papers (2 papers)

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Research

18 pages, 9708 KiB

Open AccessArticle

Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy

by Emanuele Santi, Marta Chiesi, Giacomo Fontanelli, Alessandro Lapini, Simonetta Paloscia, Simone Pettinato, Giuliano Ramat and Leonardo Santurri

Remote Sens. 2021, 13(4), 809; https://doi.org/10.3390/rs13040809 - 23 Feb 2021

Cited by 5 | Viewed by 2209

Abstract

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 m³/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 m³/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m³/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. Full article

(This article belongs to the Special Issue Mediterranean Forest Monitoring Using Optical and Microwave Remote Sensing)

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22 pages, 5641 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas

by Alessandro Lapini, Simone Pettinato, Emanuele Santi, Simonetta Paloscia, Giacomo Fontanelli and Andrea Garzelli

Remote Sens. 2020, 12(3), 369; https://doi.org/10.3390/rs12030369 - 22 Jan 2020

Cited by 40 | Viewed by 4627

Abstract

In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo-SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in-situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L-, C- and X-bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non-forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient (

\bar{σ}

°) was computed for each sensor-polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L- and X-bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors. Full article

(This article belongs to the Special Issue Mediterranean Forest Monitoring Using Optical and Microwave Remote Sensing)

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