Remote sensing monitoring of changes in forest cover in the Volyn region: a cross section for the first two decades of the 21st century
Abstract
The aim of the article. This article highlights the significance of forest cover as an important indicator of the state of the environment. It discusses the findings of the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA) 2020 report, which states that the world's forest area has decreased by 178 million hectares since 1990. The case study of Volyn region shows how cloud processing and vegetation classification can help quantify forest dynamics from 2000 to 2020, allowing local authorities and decision makers to monitor and analyze trends in near real time. Overall, this work provides insights into the importance of monitoring forest dynamics and the potential for remote sensing technology to facilitate this process. Data & Methods. Remote sensing is an effective tool for monitoring forest ecology and management, and Google Earth Engine (GEE) is an online platform that combines data from various agencies to analyze environmental data. The article presents a case study of the Volyn region and how cloud processing and vegetation classification were used to assess forest dynamics from 2000 to 2020. The study used data from Landsat 7 Collection 1 Tier 1 composites and the CART algorithm for binary decision tree building. The study was based on information provided by the Main Department of Statistics in the Volyn region on the area of forests and areas where logging was carried out during the specified period. Research results. It is interesting to note that despite the decrease in logging activities, there is an increase in forest cover loss within forest ranges. This could be due to various reasons, such as illegal logging or natural disturbances like fires or disease outbreaks. The use of machine learning methods like CART classification can help to identify and monitor these changes, which can then be used to inform policy decisions and management practices to reduce forest cover loss. In general, in the Volyn region, there is a gradual decrease in the areas where various kinds of logging are carried out from 524 km2 in 2003 to 239 km2 in 2020. In contrast, forest cover loss within forest ranges increased rapidly from 37.85 km2 in 2015 to 84.01 km2 in 2017 and beyond from 5.53 km2 to 10.80 km2 in 2015 and 2017 respectively. In this study, the accuracy assessment was performed using 30% of the control points obtained initially, based on data on the reliability of the land cover. The manufacturer's accuracy and user accuracy were calculated to evaluate error omissions and possibilities of a pixel being categorized in a certain category. The spatial resolution of Landsat 7 data used in this study was 30 m, with a minimum calculation area of 0.337 hectares. The overall accuracy and the coefficient κ are the most representative measures of accuracy, with an average accuracy of classification of OAav=98.82% and κav=0.9764.
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