Carbon Stock Assessment of Pagatban Communicty Conserved Mangrove Forest in Negros Oriental, Philippines Using Planet NICFI Data Lawas, C. J. C. 1*, Carreon, J. C. M .2, Cortes A. C.3
1CVSC: University Researcher, University of the Philippines Cebu
2DBES: Alumnus, University of the Philippines Cebu
3DBES: Former Faculty, University of the Philippines Cebu
Abstract
Pagatban Mangrove Community Forest in Negros Occidental Philippines is a community conserved forest where no carbon assessment has been conducted to date. Quantifying the carbon stored in this conserved forest is essential to assess its potential to mitigate climate change to ensure its sustainable management and protection. Moreover, at the local government level establishing a rapid and comprehensive method of estimating carbon stocks of their mangrove forests is an important input in their data based policy decision making to reach carbon neutrality. This study implemented a non-destructive method of assessment through ground-based measurements in combination with high resolution and analysis ready remotely sensed data from Planet NICFI. Carbon estimation using allometric equations per sampling plots were calculated using the field-based DBH measurement. Remotely sensed data from Planet NICFI was analyzed to obtain the NDVI vegetation index and spatial extent of the mangrove area. The calculated field-based values were then correlated with the obtained transformed NDVI values. Results show that the NDVI values of the study area range from 0.44 - 0.87. The regression analysis between the NDVI values per sampling plot for the measured carbon stock is expressed as Y= 949.75x + 416.81 with coefficient of determination (R2) 0.99. Total carbon stocks estimated from field-based estimation using allometric equation is 19,430.4280 Ton/Ha and total carbon stocks estimated using the transformed NDVI values is 19,430.4306 Ton/Ha. The correlation between the field calculated values for the carbon stock and the estimated values using the transformed NDVI values showed a strong positive relationship with an r value of 0.938. The results indicated the potential of using field-based measurements in combination with the use of high resolution, analysis ready NICFI Planet data for local carbon assessment.
