Towards refined estimation of vegetation carbon stock in Auckland, New Zealand using WorldView-2 and LiDAR data: the impact of scaling

Abstract

ABSTRACT It is necessary to estimate carbon (C) stored in urban vegetation for the purpose of carbon accounting and trading. This study aims to develop a refined method for reliably estimating above-ground carbon (AGC) stock of urban vegetation from integrated WorldView-2 imagery and Light Detection And Ranging (LiDAR) data in Auckland, New Zealand. Also assessed in this study is the impact of image resolution on regional AGC estimates by vegetation type. The integration of WorldView-2 imagery with a 2-m digital surface model produced from LiDAR data enables urban vegetation to be mapped into trees (101.5 km2), shrubs (64.9 km2), and grasses (172.2 km2) at a producer’s accuracy over 95.9%. The AGC stock of trees, shrubs and grasses is estimated at 1,134,287, 207,606, and 127,427 Mg C, respectively, from the vegetation map. Overall, the total AGC of all types of vegetation does not vary significantly with image spatial resolution over the range of 5 to 30 m if estimated using the same model. This is because high AGC densities are generalised at a coarser resolution, but the larger pixel size compensates for the decrease. Although the spatial resolution does not affect the most significant spectral predicators of plot-level AGC noticeably, it has an obvious effect on both model accuracy and complexity. Thus, the impact of image resolution on AGC would be pronounced if it were estimated using different models that were the best at a given resolution. Of the three vegetation types, the AGC of shrubs is the most variable with spatial resolution, followed by trees. Thus, the AGC of relatively small but more spatially fragmented vegetation parcels is more susceptible to change in image spatial resolution. The estimation model based on spectral features of vegetation has the lowest room-mean-square-error at 15 m. More research is needed to confirm whether it is true in other natural environments in future studies.