Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India

Abstract

Urban forests play a significant role in carbon cycling. Quantification of Aboveground Biomass (AGB) is critical to understand the role of urban forests in carbon sequestration. In the present study, Machine learning (ML) based regression algorithms (SVM, RF, kNN and XGBoost) have been taken into account for spatial mapping of AGB and carbon for the urban forests of Jodhpur city, Rajasthan, India, with the aid of field-based data and their correlations with spectra and textural variables derived from Landsat 8 OLI data. A total of 198 variables were retrieved from the satellite image, including bands, Vegetation Indices (VIs), linearly transformed variables, and Grey Level Co-occurrence textures (GLCM) taken as independent input variables further reduced to 29 variables using Boruta feature selection method. All the models have been compared where with RF algorithm, R2 = 0.83, RMSE = 16.22 t/ha and MAE = 11.86 t/ha. For kNN algorithm R2 = 0.77, RMSE = 28.04 t/ha and MAE = 24.24 t/ha and SVM where R2 = 0.73, RMSE = 89.21 t/ha and MAE = 74.22 t/ha and the best prediction accuracy has been noted with XGBoost algorithm (R2 = 0.89, RMSE = 14.08 t/ha and MAE = 13.66 t/ha) with predicted AGB as 0.51−153.76 t/ha. The study indicates that ML-based regression algorithms have great potential over other linear and multiple regression techniques for spatial mapping of AGB and carbon of urban forests for arid regions.