Kmoch, Alexander, juhendajaFang, WenyiTartu Ülikool. Geograafia osakondTartu Ülikool. Loodus- ja täppisteaduste valdkond2025-09-152025-09-152025https://hdl.handle.net/10062/115908Visualizing environmental data effectively is crucial for understanding ecological patterns and communicating spatial information. This study compares three cartographic visualization techniques for raster data: univariate mapping, bivariate mapping, and value-by-alpha mapping. To achieve this, this research applied Principal Component Analysis (PCA) to five remote sensing indices (NDVI, NDWI, BSI, NDMI, and LST), and visualized the first two principal components (PC1 and PC2) across four diverse landscapes in Estonia. The objective is to assess the strengths and limitations of these methods in representing complex ecological conditions. Univariate maps were created using a GIS-based weighted overlay method, applying PC1 loadings to generate single-variable sustainability maps. Bivariate mapping included standard bivariate maps, corner models emphasizing extreme combinations, and diagonal models highlighting variable interactions. Value-by-alpha maps employed color to encode PC1 and transparency for PC2, enhancing interpretability. At the same time, quantile and equal interval classifications were applied and compared to illustrate their influence on visual contrast and interpretation. Results indicate that univariate maps offer clear, easily interpretable spatial distributions which are suitable for public communication. Bivariate maps effectively display complex ecological states, with the corner model highlighting extremes and the diagonal model emphasizing variable divergence. Value-by-alpha maps selectively highlight critical zones, though their performance depends on transparency settings. Quantile classification enhances contrast but can overemphasize rare values, while equal interval classification maintains a direct representation of data ranges. Ultimately, the choice of classification and visualization method must align with map objectives whether for exploration analysis, public communication, or expert interpretation. The proposed framework advances multivariate environmental visualization by integrating PCA-based dimensionality reduction with tailored cartographic strategies, offering practical guidance for effective ecological communication and decision-making.enAttribution-NonCommercial-NoDerivs 3.0 Estoniahttp://creativecommons.org/licenses/by-nc-nd/3.0/ee/cartographic techniquesmagistritöödThesis