Spatio-temporal mapping of mangrove forest from 1989-2021 using Landsat imagery in Sambas Regency, West Kalimantan Province

Mangrove forests in the Sambas Regency area have changed over the last few decades, both naturally and through anthropogenic activities. The existence of mangrove forests has an important ecological role and contributes to the economic sustainability of communities in the coastal area of Sambas Rege...

Full description

Saved in:
Bibliographic Details
Main Authors: Rafdinal, Linda, Riza, Raynaldo, Adityo, Subrata, Eko
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Classification
Coastal zone
Data collection
Decision trees
Digital Elevation Models
Digital imaging
Government programs
Ground truth
Landsat satellites
Remote sensing
Satellite imagery
Spatial data
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Mangrove forests in the Sambas Regency area have changed over the last few decades, both naturally and through anthropogenic activities. The existence of mangrove forests has an important ecological role and contributes to the economic sustainability of communities in the coastal area of Sambas Regency. This study aims to monitor the condition of mangrove forests in Sambas Regency and provide spatial data related to the distribution and changes that have occurred in the mangrove forests of Sambas Regency in the last few decades. The method used in this study is a combination of remote sensing methods with Landsat satellite imagery data from 1989 to 2021, digital elevation model data from SRTM, aerial map data for virtual ground checks, and ground truth data collection in the field. The classification of mangrove forests is carried out in a supervised classification with the random forest algorithm on the Google Earth Engine platform. The results show that the existing mangrove forest area is 8,571.95 ha, distributed over 10 districts in Sambas Regency. The random forest algorithm classification with 100 decision trees resulted in a model accuracy of 0.970 and overall accuracy of 79.61%. Changes were detected from 1989 in.the form of a reduction in the area of 2,399.72 ha and an increase in the mangrove area of 2,549.95 ha. Several factors that cause changes in mangrove areas are degradation, land conversion into ponds and agricultural areas as well as mangrove expansion both naturally and from mangrove planting by the surrounding community and from government programs. The use of remote sensing data can assist in monitoring changes in mangrove areas in Sambas Regency and can be used as recommendations for conservation, climate change mitigation, and sustainable management of mangrove forests.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0211396