TEA PLANTATION MAPPING USING UAV MULTISPECTRAL IMAGERY

Authors

  • Dedi Septyadi Department of Geography, Faculty of Mathematics and Natural Science Universitas Indonesia
  • Enrico Gracia Department of Geography, Faculty of Mathematics and Natural Science Universitas Indonesia
  • Riza Putera Syamsuddin Department of Geography, Faculty of Mathematics and Natural Science Universitas Indonesia

DOI:

https://doi.org/10.30536/j.ijreses.2023.v20.a3817

Keywords:

tea plantation mapping, OBIA, spatial analysis, vegetation index, UAV

Abstract

Tea is one of Indonesia’s most famous commodities, which is dominantly planted on the Java Island of Indonesia. Tea is one of the leading sources of exports, and the Indonesian government is very concerned about the stability of their export commodity sustainability. Therefore, monitoring and evaluating its sustainability and availability become necessary. One of the solutions to the tea plantation monitoring and management program is mapping through remote sensing and GIS. In this study, high-resolution multispectral imageries are captured from a UAV and used to map the tea plantation with three vegetation indexes (VIs). An Object-Based Image Analysis (OBIA) is used to classify the tea field’s condition based on spectral characteristics. The results of this study are: (i) high-resolution multispectral imageries can be used to map the tea plantation with different VIs, and (ii) SAVI is the best VI to map the tea plantation since it has the lowest RMSE value with observed data. Hopefully, this study can support the government program on their export commodity with valuable baseline information on the tea plantation.

Downloads

Published

2025-11-25

Issue

Vol. 20 No. 1 (2023)

Section

Articles

License

Creative Commons License

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

Copyright Notice for the International Journal of Remote Sensing and Earth Sciences (IJReSES)

Copyright Holder: Author(s).

By submitting an article to IJReSES, author(s) agree to the following terms:

1. Grant of Publishing Rights: Authors grant IJReSES the license to publish the article and to identify itself as the original publisher. This includes the right to make the article available in all forms and media.

2. Commercial Rights: Authors grant IJReSES the rights to produce and sell hardcopy volumes of the journal. These volumes may be purchased by libraries, individuals, or other entities.

3. Third-Party Use: Authors agree to allow any third party to freely use the article, provided that the original authors are credited and the article is cited appropriately. This facilitates the dissemination and impact of the work.

4. Creative Commons License: The article is distributed under the Creative Commons Attribution non Commercial Share Alike 4.0 License (CC BY-NC-SA 4.0). This license allows others to distribute, remix, adapt, and build upon the work, even commercially, as long as the original author is credited for the original creation.

5. Associated Published Material: Unless otherwise stated, any associated published material (such as supplementary data, graphics, and multimedia) is distributed under the same CC BY-NC-SA 4.0 License.

By adhering to these terms, authors ensure the wide dissemination and accessibility of their work, contributing to the advancement of knowledge in the fields of remote sensing and earth sciences.