Bioclimatic Characterization of Jalisco (Mexico) Based on a High-Resolution Climate Database and Its Relationship with Potential Vegetation.
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| Title: | Bioclimatic Characterization of Jalisco (Mexico) Based on a High-Resolution Climate Database and Its Relationship with Potential Vegetation. |
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| Authors: | Ochoa-Ramos, Norma-Yolanda1 (AUTHOR), Macías-Rodríguez, Miguel Ángel2 (AUTHOR), Giménez de Azcárate, Joaquín3 (AUTHOR), Álvarez-Esteban, Ramón4 (AUTHOR), Penas, Ángel1,5 (AUTHOR), del Río, Sara1,5 (AUTHOR) sriog@unileon.es |
| Source: | Remote Sensing. Apr2025, Vol. 17 Issue 7, p1232. 28p. |
| Subjects: | Vegetation dynamics, Ecological models, Databases, Remote sensing, Geographic information systems |
| Geographic Terms: | Mexico |
| Abstract: | Bioclimatic classifications provide critical insights into the relationships between climatic variables and the geographic distribution of organisms. Advances in high-resolution climate data, geobotanical integration, and spatial analysis techniques have improved the delineation of bioclimatic units, enabling more precise characterization of terrestrial ecosystems. This study characterizes the bioclimatic conditions of Jalisco, Mexico, through the identification of bioclimatic units and variants using bioclimatic indices and parameters. High-resolution climate data (1980–2018) from the CHELSA database and GIS-based spatial analysis were employed to delineate bioclimatic patterns and their correlation with climatophyllous potential vegetation. The results identified one macrobioclimate and two bioclimates—Tropical pluviseasonal (56.62%) and Tropical xeric (43.38%)—as well as two bioclimatic variants, six thermotypes, and seven ombrotypes. Notably, 49.84% of the territory exhibits bioclimatic variants, and a total of 42 isobioclimates were associated with 14 types of climatophyllous potential vegetation. These findings provide a foundation for understanding vegetation dynamics and support territorial planning and land management. The integration of remote sensing and bioclimatic analysis enhances the identification of spatial heterogeneity in climate–vegetation relationships, facilitating applications in ecological modeling, drought assessment, and conservation planning. This study contributes to ongoing research on terrestrial ecosystem functioning, aligning with current advancements in remote sensing-based environmental analysis. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Bioclimatic classifications provide critical insights into the relationships between climatic variables and the geographic distribution of organisms. Advances in high-resolution climate data, geobotanical integration, and spatial analysis techniques have improved the delineation of bioclimatic units, enabling more precise characterization of terrestrial ecosystems. This study characterizes the bioclimatic conditions of Jalisco, Mexico, through the identification of bioclimatic units and variants using bioclimatic indices and parameters. High-resolution climate data (1980–2018) from the CHELSA database and GIS-based spatial analysis were employed to delineate bioclimatic patterns and their correlation with climatophyllous potential vegetation. The results identified one macrobioclimate and two bioclimates—Tropical pluviseasonal (56.62%) and Tropical xeric (43.38%)—as well as two bioclimatic variants, six thermotypes, and seven ombrotypes. Notably, 49.84% of the territory exhibits bioclimatic variants, and a total of 42 isobioclimates were associated with 14 types of climatophyllous potential vegetation. These findings provide a foundation for understanding vegetation dynamics and support territorial planning and land management. The integration of remote sensing and bioclimatic analysis enhances the identification of spatial heterogeneity in climate–vegetation relationships, facilitating applications in ecological modeling, drought assessment, and conservation planning. This study contributes to ongoing research on terrestrial ecosystem functioning, aligning with current advancements in remote sensing-based environmental analysis. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17071232 |
