Assessing Derawan Island's Coral Reefs over Two Decades: A Machine Learning Classification Perspective.

ABSTRACT: This study aims to understand the dynamic changes in the coral reef habitats of Derawan Island over two decades (2003, 2011, and 2021) using advanced machine learning classification techniques. The motivation stems from the urgent need for accurate, detailed environmental monitoring to inform conservation strategies, particularly in ecologically sensitive areas like coral reefs. We employed non-parametric machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), to assess spatial and temporal changes in coral habitats. Our analysis utilized high-resolution data from Landsat 9, Landsat 7, Sentinel-2, and Multispectral Aerial Photos. The RF algorithm proved to be the most accurate, achieving an accuracy of 71.43% w

SUBMITTER: Manessa MDM 

PROVIDER: S-EPMC10818429 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature