EGU23-4049, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Comparison of coverage obtained by land use classification using landsat and RapidEye. Case study: Tenosique, Tabasco, Mexico.

Jacob Nieto1, Nelly Lucero Ramírez Serrato1, Mariana Patricia Jácome Paz2, and Tania Ximena Ruiz Santos3
Jacob Nieto et al.
  • 1Institute of Geophysics, Deparment of Natural Resource, Remote Sensing Laboratory, University National Autonomous of Mexico, Coyoacán Mexico
  • 2Institute of Geophysics, Deparment of Natural Resource, National Autonomous University of Mexico, Coyoacán Mexico
  • 3Independent Artist and Filmmaker(

Land use classification studies help to quantify the changes in forest cover that may occur at a given site over time. This quantification helps us understand the effect of the natural and anthropogenic processes over the study site. Activities such as agriculture, cattle ranching and illegal logging, which in turn are related to the evolution of the site's public policies, can be evaluated through classification studies. Tenosique area, in the southeast of Mexico, is a clear example of the consequences of these programs, being largely benefited by economic consent for agriculture and more for cattle ranching, and, suffering,  in 1974, a complete  turn in productivity activities because it was given full support in exploration and obtainment of hydrocarbons. This led to a crisis that left the area devastated and later became a protected area in 2008, which resulted in illegal logging, and land use for agriculture within the tropical forest, among others. With remote sensing, the task of quantifying the effect of public policies has become increasingly influential and many studies are being carried out to evaluate the current state of Tenosique. However, the results are known to depend directly on the images and methodologies used for this task.Because of this, this project, proposes, in a practical exercise, to determine how much these results may vary with respect to the images used as input for the supervised classification, and if this variation is significant enough to establish rules of operation on methodologies and determine ranges of the parameters of the images to perform a better land use classification. The aim of this project is to determine the margin of variability in the classification result over a given study area, using images from different satellite platforms, Landsat and RapidEye, together with the analysis of the properties of each image, when acquired by the satellite. In addition, the degree of affectation in the image by meteorological changes such as tropical haze in the source image and its respective corrected image was evaluated. The main results are:  individualization of complications and advantages derived from the resolution of the images, identification of the main steps for the possible corrections that can be needed for the images, advantages that are used for analyzing the metadata before doing some process to the images and finally, presenting a decision tree based on this information. It is important to emphasize that this study allows us to delimit the scope and limitations of the land use classifications made in the study area. Acknowledgments: Tania Ximena for the Planet images and Humberto Abaffy-Castillo, Ulises Gracía-Martínez and Mario Seinos-Jiménez for technical help in the project.

How to cite: Nieto, J., Ramírez Serrato, N. L., Jácome Paz, M. P., and Ruiz Santos, T. X.: Comparison of coverage obtained by land use classification using landsat and RapidEye. Case study: Tenosique, Tabasco, Mexico., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4049,, 2023.

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