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 ( 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.
Highlights
Forest monitoring is commonly recognized as a vital task due to the role played by these ecosystems in carbon cycle evolution, which act as the main terrestrial carbon sinks [1]
A very suitable sensor for forest investigations is the synthetic aperture radar (SAR), which was carried onboard historical satellites, such as ALOS-1, RADARSAT-1 and ENVISAT, and subsequently launched ones, e.g., Cosmo-SkyMed, TerraSAR-X, ALOS-2, RADARSAT-2 and Sentinel-1
The current study addressed this issue by performing a multi-step data analysis of forest parameters in a forest area of Central Italy (San Rossore) representative of Mediterranean conditions
Summary
Forest monitoring is commonly recognized as a vital task due to the role played by these ecosystems in carbon cycle evolution, which act as the main terrestrial carbon sinks [1]. Optical sensors have been widely used for a long time, these sensors have the limitation of operating in clear-sky conditions and are sensitive to the upper layer of the canopy only (e.g., [3,4]). This makes the investigation of equatorial, boreal and mountain areas rather difficult, due to the frequent and consistent cloud cover. Microwave frequencies have the advantage to be independent of cloud cover and solar radiation and can significantly penetrate vegetation canopy Both emission and backscatter are considerably influenced by moisture content and by the geometrical features of plant constituents, according to frequency and polarization. A very suitable sensor for forest investigations is the synthetic aperture radar (SAR), which was carried onboard historical satellites, such as ALOS-1, RADARSAT-1 and ENVISAT, and subsequently launched ones, e.g., Cosmo-SkyMed, TerraSAR-X, ALOS-2, RADARSAT-2 and Sentinel-1
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