Prediction models for estimating above ground biomass and carbon sequestration potential in mangrove forests in Malaysia using remote sensing / Nurul Nadiah Mohd Firdaus Hum, Prof. Dr. Mohd Nazip Suratman and Prof. Madya Sr. Dr. Jasmee Jaafar

Mangrove forests are considered as unique and important ecosystems that occupied the intertidal zone of protected shorelines, 75% of tropical and sub-tropical countries has coastal muddy flat fringed with mangroves. However the wrath of Mother Nature coupled with human activities has become a primary cause of mangrove loss. Their degradation could affect the ecological stability of coastal zones. In contrast to tropical rainforests, mangroves lack high level of research and conservation efforts, which result in limited understanding of these ecosystems. The need for a more efficient method of mangrove inventory prompts this research into supplementing ground-based survey with information from satellite remote sensing. Remote sensing offers a cost-effective ways to monitor natural resources. The objectives of this study are (1) to establish relationships between spectral radiance recorded by the Landsat TM imagery and mangrove stand parameters, (2) to develop predictive models for estimating above ground biomass (AGB) and carbon sequestration of mangrove forests. This study will be conducted in the selected mangrove forests throughout Kedah. The estimates of above-ground biomass (AGB) of mangrove species will be calculated using published allometric functions for mangrove species in 20 m x 20 m quadrats randomly set up in the study areas. A total of 80 mangrove stands will be sampled. The field data will be collected include height, diameter at breast height (OBH), density, crown width and crown closure. The relationships between these attributes and Landsat TM data will be analysed by calculating Pearson's correlation coefficients (r) prior to modelling. Parameter variables will be related to AGB and carbon sequestration includes TM bands and several vegetation indices. An ordinary least squares (OLS) estimator will used to develop multiple linear regression models. "Good" model candidates will be selected based on an analysis of the usefulness of the variables in prediction. A site suitability study will be introduced for further rehabilitation process of mangrove. Results from this study are expected to be useful in strategizing conservation measures to ensure the sustainability of mangrove ecosystems.