Browsing by Author "Ureta, Fernando"

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    Mapping coastal wetlands using satellite imagery and machine learning in a highly urbanized landscape
    (2022) Munizaga, Juan; García, Mariano; Ureta, Fernando; Novoa, Vanessa; Rojas, Octavio; Rojas Quezada, Carolina Alejandra; CEDEUS (Chile)
    Coastal wetlands areas are heterogeneous, highly dynamic areas with complex interactions between terrestrial and marine ecosystems, making them essential for the biosphere and the development of human activities. Remote sensing offers a robust and cost-efficient mean to monitor coastal landscapes. In this paper, we evaluate the potential of using high resolution satellite imagery to classify land cover in a coastal area in Concepción, Chile, using a machine learning (ML) approach. Two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), were evaluated using four different scenarios: (I) using original spectral bands; (II) incorporating spectral indices; (III) adding texture metrics derived from the grey-level covariance co-occurrence matrix (GLCM); and (IV) including topographic variables derived from a digital terrain model. Both methods stand out for their excellent results, reaching an average overall accuracy of 88% for support vector machine and 90% for random forest. However, it is statistically shown that random forest performs better on this type of landscape. Furthermore, incorporating Digital Terrain Model (DTM)-derived metrics and texture measures was critical for the substantial improvement of SVM and RF. Although DTM did not increase the accuracy in SVM, this study makes a methodological contribution to the monitoring and mapping of water bodies’ landscapes in coastal cities with weak governance and data scarcity in coastal management.
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    Spatiotemporal vegetation dynamics in a highly urbanized Chilean coastal wetland: Insights on long-term natural and anthropogenic influences
    (2024) Munizaga, Juan; Rojas, Octavio; Lagos, Bernardo; Rojas Quezada, Carolina Alejandra; Yepez, Santiago; Hernández, Esteban; Ureta, Fernando; de la Barrera, Francisco; Jato-Espino, Daniel
    This study analyzes the spatiotemporal dynamics of the vegetation of a highly urbanized coastal wetland in the 2000–2020 period, considering natural disturbances and anthropogenic stressors. The wetland system was stratified into four domains: Coastal, Intertidal, Freshwater, and Urban, differentiated by their geomorphological, topographical, and water salinity characteristics, which were validated by ground vegetation sampling. In these domains, spectral indicators of vegetation were used on 884 Landsat images in the Google Earth Engine to determine vegetation types, trends, and phenology. The start of the growing season coincides with the beginning of the Austral winter, exhibiting seasonal behavior, which was interrupted by abrupt natural disturbances such as floods and tsunamis. In addition, a progressive trend associated with the replacement of native species by exotic species was reported in areas with significant anthropogenic stressors (e.g., highways, city edges, and grazing areas), with 45 % presenting an increase in the normalized difference vegetation index. Areas far from anthropogenic stressors maintained their behavior, which is explained by natural factors such as precipitation, temperature, and evapotranspiration. The proposed method strengthens our understanding of the interrelationship between factors that modify the behavior of vegetation in coastal wetlands pressured by anthropogenic stressors and contributes to their management and protection.