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

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-...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 5; p. 1189
Main Authors Svoboda, Jan, Štych, Přemysl, Laštovička, Josef, Paluba, Daniel, Kobliuk, Natalia
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2022
Subjects
Accuracy
Algorithms
Carbon footprint
Classification
Climate change
Cloud computing
Czechia
Emission inventories
Emissions
Environmental policy
Forestry
Google Earth Engine
Greenhouse gases
International agreements
Land use
LULUCF
Machine learning
Methods
Mosaics
Parameters
Random Forest
Remote sensing
Satellites
Sentinel-2
Spatial discrimination
Spatial resolution
Czech Republic
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14051189