Evaluating the Effectiveness of Planted Mangrove Forests Using Vegetation Indices and Land Surface Temperature: A Remote Sensing Approach in Bagan Nakhoda Omar, Malaysia Mohd Fairuz Bin Fuazi (a*), Wan Shafrina Binti Wan Mohd Jaafar (a)
a) Institute of Climate Change, The National University of Malaysia
*p145711[at]siswa.ukm.edu.my
Abstract
Mangrove restoration has become a critical strategy in mitigating coastal degradation and enhancing ecosystem resilience. This study aims to assess the effectiveness of planted mangrove forests in Bagan Nakhoda Omar, Selangor, using a multi-temporal remote sensing approach. The analysis integrates three vegetation indices-Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Green NDVI (GNDVI)-derived from SPOT 5 and SPOT 6 imagery, alongside Land Surface Temperature (LST) extracted from Landsat 5 and Landsat 8 data. Five temporal datasets spanning from 2006 to 2024 were analyzed to monitor the growth and health of mangrove plantations initiated in 2008 and 2009.
The effectiveness of the restoration efforts was evaluated through Pearson correlation analysis between the vegetation indices and LST. Results indicate a consistent increase in VI values and a corresponding decrease in LST over time, suggesting improved canopy cover and microclimatic regulation. Strong negative correlations (r > -0.70) between VIs and LST were observed in later years, particularly in 2019 and 2024, reflecting the success of long-term planting initiatives. The integration of multiple VIs provided a more robust assessment of vegetation dynamics, while LST served as a complementary indicator of ecosystem recovery.
This study demonstrates the utility of combining spectral and thermal remote sensing metrics for evaluating mangrove restoration outcomes. The findings offer valuable insights for coastal ecosystem monitoring and support evidence-based decision-making in future rehabilitation programs.
