Mangrove land cover classification using unmanned aerial vehicles in Jailolo Bay, West Halmahera, Indonesia

Coastal environment remote sensing and geographic information systems (GIS) technologies are currently developing rapidly. Both of these technologies can be used to determine near real time conditions of coastal ecosystems. One method used for coastal ecosystem mapping is aerial photomapping by usin...

Full description

Saved in:
Bibliographic Details
Published inAquaculture, Aquarium, Conservation & Legislation Vol. 17; no. 1; pp. 133 - 141
Main Authors Abubakar, Salim, Wahidin, Nurhalis, Kepe, Rene C, Djamaluddin, Rignolda, Harahap, Zulhan A
Format Journal Article
LanguageEnglish
Published Cluj-Napoca Bioflux SRL 01.02.2024
Subjects
Accuracy
Aerial photography
Aircraft
Algorithms
Cameras
Classification
Climate change
Coastal ecosystems
Coastal environments
Coastal zones
Coasts
Data collection
Data processing
Drone aircraft
Drone vehicles
Drones
Ecological monitoring
Ecosystems
Geographic information systems
Geographical information systems
Global positioning systems
GPS
Information systems
Land cover
Mangrove swamps
Mangroves
Mapping
Photomapping
Remote sensing
Sensors
Software
Unmanned aerial vehicles
Indonesia
Online AccessGet full text

Cover

More Information
Summary:Coastal environment remote sensing and geographic information systems (GIS) technologies are currently developing rapidly. Both of these technologies can be used to determine near real time conditions of coastal ecosystems. One method used for coastal ecosystem mapping is aerial photomapping by using unmanned aerial vehicles (UAV) or commonly known as drones. Remote sensing techniques are known to be fast and efficient for monitoring mangrove ecosystems when compared to conventional field observations, which are expensive, time consuming, and sometimes unachievable due to poor accessibility of mangrove areas. This study aimed to investigate the ability of drone aerial photography to classify mangrove land cover using an object-based approach in Jailolo Bay, North Maluku Province, and to analyze the accuracy of land cover mapping resulted. Mangrove ecosystem classification carried out using the nearest neighbor (NN) algorithm was able to identify 7 classes of mangrove land covers. The classification accuracy obtained was very high and the mangrove classes could be accurately mapped.
ISSN:1844-8143
1844-9166