Determination of agricultural crops and spectral Signatures by processing satellite images using The supervised classification method

Authors

DOI:

https://doi.org/10.31381/perfiles_ingenieria.v20i15.3548

Keywords:

remote sensing, spectral signature, supervised classification

Abstract

The present research corresponds to the processing of satellite images, in order to show the classification processes and the multitemporal variability based on the dispersion of coded pixels, on which you can observe the variations in the uses of land destined for agricultural activity in the Chincha valley, showing increases in cultivated areas such as cotton (6.55%) and significant decrease in cultivated areas such as sweet potato (48.09%); also obtaining spectral signatures of crops such as; cotton, artichoke, sweet potato and citrus, which can be used as source data for future intelligent monitoring systems with robots and drones applied to agriculture.

Downloads

Download data is not yet available.

Author Biography

Javier Hipólito Rivas León, Universidad Ricardo Palma, Lima, Perú.

Professor of the subjects Mechatronics applied to agriculture and gas, Electronic Circuits, Building Automation and Geographic Information Systems applied to Engineering at the Professional Schools of Mechatronics and Civil Engineering at Ricardo Palma University. He has a master's degree in Telecommunications, Teaching and University Management; expert in electronics and geomatics.

References

C. Dallos. “Modelo Para Clasificación de Imágenes Multiespectrales a partir de los Complejos de Células Abstractas (CCA)”, tesis de maestría, Universidad Distrital Francisco José de Caldas, 2017 [En línea]. Disponible en: https://repository.udistrital.edu.co/bitstream/handle/11349/5404/ DallosBustosCristianDavid2017.pdf?sequence=1&isAllowed=y [Accedido: 28-dic-2020].

E. Posada, H. Ramírez y N. Espejo, Manual de prácticas de percepción remota con el programa ERDAS IMAGINE 2011, Bogotá, Colombia: ONU-IGAC, 2012.

J. Gao, Digital Analysis of Remotely Sensed Imagery, New York, USA: McGraw-Hill, 2009.

C. Pérez y A. Muñoz, Teledetección: Nociones y Aplicaciones, Salamanca, España: Universidad de Salamanca, 2006.

T. Lillesand, R. Kiefer, y J. Chipman, Remote sensing and image interpretation, New York, USA: John Wiley & Sons, 2008.

tics

Downloads

Published

2020-12-31

How to Cite

Rivas León, . J. H. (2020). Determination of agricultural crops and spectral Signatures by processing satellite images using The supervised classification method. Engineering Profiles, 16(16), 85–92. https://doi.org/10.31381/perfiles_ingenieria.v20i15.3548

Issue

Vol. 16 No. 16 (2020): Perfiles de Ingeniería

Section

Ingeniería Mecatrónica

License

Copyright (c) 2020 Javier Hipólito Rivas León

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

In the event that the manuscript is approved for its next publication, the authors retain the copyright and assign to the journal the right of publication, edition, reproduction, distribution, exhibition and communication in the country of origin, as well as in the abroad, through print and electronic media in different databases. Therefore, it is established that after the publication of the articles, the authors may make other types of independent or additional agreements for the non-exclusive dissemination of the version of the article published in this journal (publication in books or institutional repositories), provided that it is explicitly indicated that the work has been published for the first time in this journal. 

To record this procedure, the author must complete the following forms:

  • Format 1 - Author information.
  • Format 2 - Affidavit of originality and authorization for the publication of articles.
  • Format 3 - Compliance with open science.