Above-ground carbon stock estimation using pleiades satellite imagery of the secondary forest ecosystem in Ibadan, Nigeria

Abstract

Secondary forest ecosystem contributes to global climate change mitigation through carbon sequestration. Above-Ground Biomass (AGB) is the major component for monitoring and estimating Carbon Stocks (CS) and fluxes in tropical forests. However, information on Above-Ground Carbon Stock (AGCS) for the International Institute of Tropical Agriculture (IITA), which hosts relics of the undisturbed secondary forest ecosystem in south-western Nigeria, has not been documented. Therefore, AGCS of the secondary forest ecosystem was estimated using remote sensing techniques. Pleiades satellite data were used for this study. One hundred and forty plots of 50m x 50m were laid in IITA secondary forest using systematic sampling technique at 10% sampling intensity. Pleiades satellite imagery was acquired using Remote Sensing (RS) technique and spectral data for each sample plot extracted. The spectral indices used for AGB estimation were: Normalised Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Infrared Percentage Vegetation Index (IPVI), Optimised Soil Adjusted Vegetation Index (OSAVI) and Re-normalised Difference Vegetation Index (RDVI). Regression equation was used for the prediction of AGB from where the total CS estimate was obtained. Data were analysed using descriptive statistics and linear regression analysis. The AGB and CS ranged from 101.06 to 881,834.92 kg/ha and 50.53 to 440,917.46 kg/ha, respectively. The DVI had the highest AGB value which ranged from 187 to 15,577 kg/ha, followed by IPVI, RDVI and OSAVI which ranged from 7,561 to 12,324 kg/ha, 64.0591 to 133.178 kg/ha, 0.0134 to 0.5621 kg/ha, respectively, while NDVI had the least values which ranged from -0.01 to 0.48 kg/ha. The best AGB estimation model was AGB = exp. (3,496.61 + 0.99 x (RDVI) 1/2); Coefficient of Determination (R2) = 0.93, Bayesian Information Criterion (BIC) = 82.34). The total carbon stock ranged from 11,035 to 18,774 kg/ha. Model with re-normalized difference vegetation index was most suitable among other indices for estimating above-ground carbon stock. Therefore, effective integration of different sensor data will be an important research topic for improving above-ground biomass estimation performance.