Random Forest Classification of Land Use, Land-Use Change and Forestry (LULUCF) Using Sentinel-2 Data—A Case Study of Czechia

Jan Svoboda,Josef Laštovička,Daniel Paluba,Natalia Kobliuk,Přemysl Štych

doi:10.3390/rs14051189

Abstract

Land use, land-use change and forestry (LULUCF) is a greenhouse gas inventory sector that evaluates greenhouse gas changes in the atmosphere from land use and land-use change. This study focuses on the development of a Sentinel-2 data classification according to the LULUCF requirements on the cloud-based platform Google Earth Engine (GEE). The methods are tested in selected larger territorial regions (two Czech NUTS 2 units) using data collected in 2018. The Random Forest method was used for classification. In terms of classification accuracy, a combination of these parameters was tested: The Number of Trees (NT), the Variables per Split (VPS) and the Bag Fraction (BF). A total of 450 combinations of different parameters were tested. The highest accuracy classification with an overall accuracy = 89.1% and Cohen’s Kappa = 0.84 had the following combination: NT = 150, VPS = 3 and BF = 0.1. For classification purposes, a mosaic was created using the median method. The resulting mosaic consisted of all Sentinel-2 bands in 10 and 20 m spatial resolution. Altitude values derived from SRTM and NDVI variance values were also included in the classification. These added bands were the most significant in terms of Gini importance.

Highlights

The resulting mosaic consisted of all Sentinel-2 bands in 10 and 20 m spatial resolution
This study focuses on the development of an Random Forest (RF)-based classification method that allows the classification of Sentinel-2 data according to LULUCF requirements
EPSG: coordinate system), and the Stratified that allows the classification of Sentinel-2 data in Google Earth Engine (GEE) according to the LULUCF requireSampling on preliminary classification was for the accuracy ments

Summary

Introduction

The resulting mosaic consisted of all Sentinel-2 bands in 10 and 20 m spatial resolution. Altitude values derived from SRTM and NDVI variance values were included in the classification These added bands were the most significant in terms of Gini importance. The land cover/land-use change (LCLUC) program is one of the most important sources of information on the development of global environmental change. States Framework Convention on Climate Change (UNFCCC) have declared the LCLUCs monitoring to be highly relevant, as LCLUCs have a significant impact on climate change and the global carbon cycle. For these purposes, the binding regulation is provided for the inventory and reporting of relevant land use classes, so-called LULUCF—land use, land-use change and forestry (see Decision 529/2013/EU, European Commission 2013). Full LULUCF integration fits well with ongoing international efforts to integrate forests and other aspects into the climate policy framework, e.g., the context of REDD+ (Reduced Emissions from Deforestation and Forest Degradation) [4–6]

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Conclusion