The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination.

Elia Vangi,Gherardo Chirici,Saverio Francini,Bruno Lasserre,Marco Marchetti,Francesca Giannetti,Giovanni D’Amico

doi:10.3390/s21041182

Abstract

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.

Highlights

Hyperspectral sensors observe the earth’s surface by simultaneously sampling hundreds of fine narrow contiguous spectral bands with a resolution of up to 0.01 μm in the visible and infrared spectrum
Based on the spectral signatures extracted within the 161 polygons, the four separability metrics between each pair of classes for each of the 230 bands were calculated for both levels of the nomenclature system, and for both the study areas
Similar trends were observed between the two sensors, but with different results in the two study areas

Summary

Introduction

Hyperspectral sensors observe the earth’s surface by simultaneously sampling hundreds of fine narrow contiguous spectral bands with a resolution of up to 0.01 μm in the visible and infrared spectrum. Hyperspectral sensors are not designed for specific applications, and today we are witnessing the rapid development of hyperspectral image processing technology [3] and spaceborne hyperspectral missions [4] For this reason, hyperspectral data are increasingly used in several remote sensing fields such as ecology, atmosphere, ocean, agriculture and forestry [5]. An unideal electromagnetic wave transmission environment means that some bands contain less discriminatory information than others [11], and some spectral intervals may not reveal important information for some applications [12] For these reasons, the large number of hyperspectral bands may affect image classification due to the size, redundancy, and autocorrelation of the data cube

Objectives

Methods

Results

Discussion

Conclusion