Random Forest Classification for Calculating the Area of Mangrove Forest in Benoa Bali Bay
- DOI
- 10.2991/978-94-6463-413-6_22How to use a DOI?
- Keywords
- Benoa Bay; Mangrove; Random Forest
- Abstract
The Benoa Bay area in Bali is an interesting area to study because of its influence on the community’s economy and the presence of mangrove forests in overcoming the issue of climate change, especially in Bali. The extent of mangrove forests in the bay of the continent of Bali needs to always be observed considering their great influence on life. This research aims to provide information on the extent of mangrove plants found in Benoa Bay, Bali. The technique used is to classify satellite images using the random forest method so that the area of mangrove plants in Benoa Bay, Bali, can be measured. The steps taken in this research are: Image acquisition, pre-processing, pixel classification using random forest and measuring the area of mangrove plant objects in Benoa Bay, Bali. The area of mangrove forest obtained was 1,230.4 hectares. There was a decrease in area compared to previous research of around 143.1 hectares..
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - I Gede Arta Wibawa AU - I Ketut Gede Suhartana AU - Anak Agung Gde Raka Dalem PY - 2024 DA - 2024/05/13 TI - Random Forest Classification for Calculating the Area of Mangrove Forest in Benoa Bali Bay BT - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023) PB - Atlantis Press SP - 221 EP - 227 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-413-6_22 DO - 10.2991/978-94-6463-413-6_22 ID - Wibawa2024 ER -