High-Resolution Remote Sensing Images Can Better Estimate Changes in Carbon Assimilation of an Urban Forest

Abstract

Urban forests have the potential to sink atmospheric CO2. With the improvement of coverage of vegetation in urban environments, more attention has been paid to the carbon sequestration potential of the urban forest. However, the high fragmentation of urban forests makes it difficult to evaluate their carbon budget on a regional scale. In this study, the GPP-NIRv relationship model was employed to estimate GPP in Suzhou by MODIS, Landsat-8 and Sentinel-2 remote sensing data, and to further explore what kind of remote images can figure out the spatial-temporal pattern of GPP in urban forests. We found that the total GPP of the terrestrial ecosystem in Suzhou reached 8.43, 8.48, and 9.30 Tg C yr-1 for MODIS, Landsat-8, and Sentinel-2, respectively. Monthly changes of GPP were able to be derived by MODIS and Sentinel-2, with two peaks in April and July. According to Sentinel-2, urban forests accounted for the majority of total GPP, with an average of about 44.63%, which was larger than the results from GPP products with coarser resolutions. Additionally, it is clear from the high-resolution images that the decline of GPP in May was due to human activities such as the rotation of wheat and rice crops and the pruning of urban forests. Our results improve the understanding of the contribution of the urban forest to the carbon budget and highlight the importance of high-resolution remote sensing images for estimating urban carbon assimilation.