Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, 50145 Firenze, Italy.
Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, 86100 Campobasso, Italy.
Sensors (Basel). 2021 Feb 8;21(4):1182. doi: 10.3390/s21041182.
Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups.
如果使用传统的多光谱卫星传感器进行观测，不同树种组成的不同森林类型可能具有相似的光谱特征。而高光谱图像则可以更连续地表现其光谱特性，因此可以用于分类。意大利航天局开发的新型高光谱 Precursore IperSpettrale della Missione Applicativa（PRISMA）传感器能够在 400 到 2500nm 之间的 240 个连续光谱波段中捕获图像，光谱分辨率小于 12nm。该新型传感器可用于多种遥感应用，包括森林类型识别。本研究通过在意大利的两个研究区进行的两两可分离性分析，比较了新型 PRISMA 传感器与知名的 Sentinel-2 多光谱仪器（MSI）在识别不同森林类型方面的能力，使用了两种不同的命名系统和四种可分离性度量标准。与 Sentinel-2 MSI 相比，PRISMA 高光谱传感器能够更好地识别所有森林类型，并且在命名系统复杂性增加时，性能也有所提高。PRISMA 在两种森林类型（针叶林与阔叶林）之间的识别中平均提高了 40%，在基于主要树种组的五种森林类型之间的识别中提高了 102%。
