TY - JOUR A1 - Brunn, Ansgar A1 - Uhl, Anna A1 - Melnyk, Oleksandr A1 - Melnyk, Yuliia A1 - Manko, Pavlo A1 - Fesyuk, Vasyl T1 - Remote sensing monitoring of changes in forest cover in the Volyn region BT - A cross section for the first two decades of the 21st century JF - Visnyk of V. N. Karazin Kharkiv National University, series "Geology. Geography. Ecology N2 - 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 Y1 - 2024 U6 - https://doi.org/10.26565/2410-7360-2024-60-19 IS - 60 SP - 272 EP - 283 PB - V. N. Karazin Kharkiv National University ER - TY - CHAP A1 - Melnyk, Oleksandr A1 - Brunn, Ansgar T1 - Seasonal and Long-Term Water Regime Trends of Cheremsky Wetland: Analysis Based on Sentinel-2 Spectral Indices and Composite Indicator Development T2 - Remote Sensing 2025 N2 - Wetlands are critically important ecosystems, but their dynamics, especially in complex regions such as the Ukrainian Polissya, remain poorly understood. This study focuses on the Cheremsky Nature Reserve, an internationally important wetland, to assess long-term (2017–2024) seasonal (spring-summer) trends in water surface conditions. Using Sentinel-2 data and the Google Earth Engine platform, 14 spectral water indices were calculated. Their temporal trends were analyzed using Sen’s method, mutual correlations, and principal component analysis (PCA) to identify the main patterns of variability. Based on the normalized trends and weights obtained from the first two principal components, an integral composite index (CI) for spring and summer seasons was developed. The results revealed seasonal differences in the behavior of the indices and their contribution to the principal components, as well as spatial differentiation of water regime trends within the reserve. The proposed CI allows for an integrated assessment of the long-term dynamics of the wetlands, which is important for the development of conservation and management strategies. KW - remote sensing KW - Sentinel-2 KW - spectral indices KW - composite index KW - Ukrainian Polissya KW - wetland monitoring KW - water surface conditions Y1 - 2025 U6 - https://doi.org/10.3390/rs17142363 VL - 17 IS - 14 ER - TY - JOUR A1 - Manko, Pavlo A1 - Brunn, Ansgar A1 - Melnyk, Oleksandr T1 - Remote sensing methods for estimating tree species of forests in the Volyn region, Ukraine JF - Frontiers in Forests and Global Change N2 - Forest classification is needed to solve a wide range of environmental issues related to of forest classes and succession processes, the extent of afforestation and deforestation and global environmental change. These applications require a very accurate mapping and monitoring of forest types. This article investigates the combination of modern open geographic information systems and remote sensing data in forest management tasks for a specific part of the Ukrainian state area. Based on the existing afforestation plans, the results of the unsupervised classification of Sentinel-2 images and the selection of forest species fragments with closed crowns as training data for supervised classification, classifiers of forest species of the study object were developed with and without taking into account age groups. A supervised classification of research objects is realized and the accuracy of the obtained results is evaluated. It is established that the accuracy of determining forest species on the basis of the proposed method is 90.3 and 91.4%, taking into account age groups and without taking them into account, respectively. Thus, it is found that the modeling of the age groups does not improve the classification result for the test area. KW - remote sensing KW - tree species KW - supervised classification Y1 - 2023 UR - https://doi.org/10.3389/ffgc.2023.1041882 SN - 2624-893X VL - 6 ER -