Bioclimatic classification in the Caroni River basin using the maximum likelihood method

Authors

DOI:

https://doi.org/10.21138/GF.901

Abstract

Bioclimatic classification is essential for understanding and managing the territory in a more sustainable way, as well as in biodiversity conservation management and climate change assessment. The supervised Maximum Likelihood (ML) classification method is a powerful tool for bioclimatic classification. Therefore, the ML classifier was applied in the Caroni River basin, using data from global sources, from which averages of climatic variables were extracted. Geographic Weighted Regression (GWR) and the 90 m SRTM DEM were used to improve the scale. The Holdridge classification was

 

applied, this presented an overall accuracy of 93 % the classes of premontane rainforest, very humid tropical forest and premontane rainforest denoted the worst performance, as there are transitive structures involved that the classification model could not capture. Key words: Maximum Likelihood Classification, bioclimatic classification, Geographic Weighted Regression.

Author Biography

Jesús Enrique Andrades Grassi, Dep. de Ordenación de Cuencas. Fac. de Ciencias Forestales y Ambientales. Univ. de los Andes. Venezuela

Profesor Universitario Dedicación Exclusiva Categoría de Agregado, Msc. en Manejo de Cuencas y Estudiante del Doctorado en Ciencias Forestales y Ambientales

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Published

2025-12-31

How to Cite

Guerrero-Evaristo, R. S., Andrades Grassi, J. E., & Rojas-Polanco, M. I. (2025). Bioclimatic classification in the Caroni River basin using the maximum likelihood method. GeoFocus. International Review of Geographical Information Science and Technology, (36), 63–85. https://doi.org/10.21138/GF.901

Issue

No. 36 (2025)

Section

Artículos

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