Sentinel-2 Imagery Processing for Tree Logging Observations on the Białowieża Forest World Heritage Site

Abstract

Deforestation is currently among the most critical ecological issues, which need to be addressed urgently. Hence, identification of effective environmental monitoring methods is of top priority, especially in locations where no precise ground-based data are available. Constant development of remote sensing technology provides an increasing number of tools needed for that purpose, based on extraction of information about Earth’s surface. One of the most advanced Earth Observation (EO) programs is Copernicus, established by European Space Agency (ESA). It incorporates a constellation of Sentinel satellites continuously delivering imagery, which can serve as input data for further environmental analyses. They can be performed in the Sentinel Application Platform (SNAP), the software also developed by ESA. The Sentinel-2 (S-2) mission was designed specifically for Earth’s surface observation. It acquires high-resolution data within visible and infrared range of electromagnetic spectrum (EMS), which has found applications in forest cover monitoring. In this paper, S-2 imagery was processed in SNAP software to determine its potential for deforestation observation on the example of 2017 tree logging in Białowieża Forest. For this purpose, images from October 2016 and 2018, covering the area of interest, were downloaded from the Copernicus Open Hub Platform. They then underwent pre-processing, involving atmospheric correction, resampling, and subset operations. As a part of environmental analysis, a set of chosen radiometric and biophysical indices was computed to preliminarily determine their usefulness for deforestation mapping. Index values were extracted from tree logging areas using pinpoints and region of interest (ROI) mask. The most effective indicators were the MERIS Terrestrial Chlorophyll Index (MTCI) and the Brightness Index (BI). The Normalized Difference Vegetation Index (NDVI), as well as the Ratio Vegetation Index (RVI), also displayed promising results. The results were visualized in Quantum GIS (QGIS) software, provided by the Open Source Geospatial Foundation (OSGeo).