Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium

Odile Close,Eric Hallot,Beaumont Benjamin,Xavier Fripiat,Sophie Petit

doi:10.3390/land7040154

Abstract

Due to its cost-effectiveness and repeatability of observations, high resolution optical satellite remote sensing has become a major technology for land use and land cover mapping. However, inventory compilers for the Land Use, Land Use Change, and Forestry (LULUCF) sector are still mostly relying on annual census and periodic surveys for such inventories. This study proposes a new approach based on per-pixel supervised classification using Sentinel-2 imagery from 2016 for mapping greenhouse gas emissions and removals associated with the LULUCF sector in Wallonia, Belgium. The Land Use/Cover Area frame statistical Survey (LUCAS) of 2015 was used as training data and reference data to validate the map produced. Then, we investigated the performance of four widely used classifiers (maximum likelihood, random forest, k-nearest neighbor, and minimum distance) on different training sample sizes. We also studied the use of the rich spectral information of Sentinel-2 data as well as single-date and multitemporal classification. Our study illustrates how open source data can be effectively used for land use and land cover classification. This classification, based on Sentinel-2 and LUCAS, offers new opportunities for LULUCF inventory of greenhouse gas on a European scale.

Highlights

IntroductionEach country has to submit an annual inventory of national emissions of greenhouse gas (GHG)
Under the United Nations Framework Convention on Climate Change (UNFCCC) and the KyotoProtocol, each country has to submit an annual inventory of national emissions of greenhouse gas (GHG)
This study focuses on the Land Use, Land Use Change, and Forestry (LULUCF) sector, which differs from the other sectors due to its particularity in presenting sources and sinks of GHG

Summary

Introduction

Each country has to submit an annual inventory of national emissions of greenhouse gas (GHG). This national inventory reports anthropogenic emissions and removals resulting from human activities for a calendar year. Information about land area between emitting and absorbing categories of land is needed to estimate carbon stocks emissions and removals of GHG associated with LULUCF activities [1]. Such inventory reports have to provide information regarding land identification for estimating change in carbon stock. According to [2] definitions, GHG emissions are

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Conclusion