PROJECT TITLE :

Calibration of Aboveground Forest Carbon Stock Models for Major Tropical Forests in Central Sumatra Using Airborne LiDAR and Field Measurement Data

ABSTRACT:

Despite substantial policy attention, tropical forests in Southeast Asian region are releasing large quantity of carbon to the atmosphere because of accelerating deforestation. Accurately determining forest statistics and characterizing aboveground forest carbon stocks (AFCSs) are forever difficult in the region. In order to develop a lot of accurate estimates of AFCS, this study collected airborne LiDAR and field measurements knowledge and calibrated AFCS models to estimate carbon stock in the tropical forests in central Sumatra. The study region consists of natural forests, together with peat swamp, dry moist, regrowth, and mangrove, and plantation forests, including rubber, acacia, oil palm, and coconut. To cover the different forest varieties, sixty field plots of 1 ha in size were inventoried. Eight transects crossing these field plots were acquired to calibrate the LiDAR to AFCS models. The AFCS values for the field plots ranged from 4 to 161 Mg ha-1. General models were fitted while not considering forest types, whereas a particular model was fitted for each specific forest kind. 5 various general models with different LiDAR metrics were calibrated with model performance expressed as R2 starting from zero.73 to 0.87 and root-meansquare error (RMSE) values ranging from seventeen.four to 25.zero Mg ha-1 . Seven forest-specific AFCS models were calibrated for different forest sorts, with R2 values ranging from 0.seventy two to 0.97 and RMSE values ranging from one.4 to ten.seven Mg ha-one. The performance of each model was cross-validated by iteratively removing one information purpose. Whereas forest-specific models offer higher AFCS estimates, the overall models are still helpful when forest varieties are ambiguous.