Modelling the amount of carbon stock using remote sensing in urban forest and its relationship with land use change

Abstract

The estimation of biomass has been highly regarded for assessing carbon sources. In this paper, ALOS PALSAR, Sentinel-1, Sentinel-2 and ground data are used for estimating of above ground biomass (AGB) with SVM-genetic model Moreover Landsat satellite data was used to estimate land use change detection. The wide range of vegetation, textural and principal component analysis (PCA) indices (using optical images) and backscatter, decomposition and textural features (from radar images) are derived together with in situ collected AGB data into model to predict AGB. The results indicated that the coefficient of determination (R2) for ALOS PALSAR, Sentinel-1, Sentinel-2 were 0.51, 0.50 and 0.60 respectively. The best accuracy for combining all data was 0.83. Afterwards, the carbon stock map was calculated. Landsat series data were acquired to document the spatiotemporal dynamics of green spaces in the study area. By using a supervised classification algorithm, multi-temporal land use/cover data were extracted from a set of satellite images and the carbon stock time series simulated by using carbon stock maps and green space (urban forest) maps.