Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification

Paolo De Fioravante,Michele Munafò,Luca Congedo,Gherardo Chirici,Pasquale Dichicco,Marco Marchetti,Alice Cavalli,Chiara Giuliani,Tania Luti

doi:10.3390/land10060611

Abstract

The study of land cover and land use dynamics are fundamental to understanding the radical changes that human activity is causing locally and globally and to analyse the continuous metamorphosis of landscape. In Europe, the Copernicus Program offers numerous territorial monitoring tools to users and decision makers, such as Sentinel data. This research aims at developing and implementing a land cover mapping and change detection methodology through the classification of Copernicus Sentinel-1 and Sentinel-2 satellite data. The goal is to create a versatile and economically sustainable algorithm capable of rapidly processing large amounts of data, allowing the creation of national-scale products with high spatial resolution and update frequency for operational purposes. Great attention was paid to compatibility with the main activities planned in the near future at the national and European level. In this sense, a land cover classification system consistent with the European specifications of the EAGLE group has been adopted. The methodology involves the definition of distinct sets of decision rules for each of the land cover macro-classes and for the land cover change classes. The classification refers to pixels’ spectral and backscatter characteristics, exploiting the main multi-temporal indices while proposing two new ones: the NDCI to distinguish between broad-leaved and needle-leaved trees, and the Burned Index (BI) to identify burned areas. This activity allowed for the production of a land cover map for 2018 and the change detection related to forest disturbances and land consumption for 2017–2018, reaching an overall accuracy of 83%.

Highlights

Land cover is defined as the biophysical cover of Earth’s surface, as artificial surfaces, agricultural areas, forests and semi-natural areas, water and wetlands by the Directive2007/02 of the European Commission [1,2]
Land cover information is used for evaluating progress towards UN Sustainable Development Goals (SDGs) targets [9], such as target 15.3, which is related to the reach of the Land Degradation Neutrality (LDN)
The map has 83% overall accuracy (Table 7). This value offers a general estimation of the accuracy, but it is influenced by the distribution of the error between the classes, especially those of land cover change

Summary

Introduction

Land cover is defined as the biophysical cover of Earth’s surface, as artificial surfaces, agricultural areas, forests and semi-natural areas, water and wetlands by the Directive2007/02 of the European Commission [1,2]. The comprehension of the processes that lead to the transformation of the territory are crucial for sustainable resource management, environmental protection, modelling of climate change scenarios, and for the assessment of the impacts on the earth system directly linked to human activities [5]. Is important to the study of Earth phenomena, which can be monitored and assessed at different spatial and temporal resolutions [6,7]. Several user communities such as decision-makers, non-governmental organizations, European communities, scientists and researchers require different types of land cover information for their activities and policies [8]. Land cover information is used for evaluating progress towards UN Sustainable Development Goals (SDGs) targets [9], such as target 15.3, which is related to the reach of the Land Degradation Neutrality (LDN)

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Conclusion