A new method of estimating shelterbelt carbon storage on the regional scale: Combined the single tree carbon storage with tree numbers

Abstract

The carbon storage potential of planted shelterbelts was gradually recognized in recent years, but there was a lack of shelterbelt knowledge for carbon inventory analyses because of their narrow linear feature and extensive distribution. Considering its own characteristics of the shelterbelt, this paper proposed a new remote sensing model for estimating the carbon storage of farmland shelterbelt (CSBelt) based on the single tree carbon storage and tree numbers. In this model, the single tree carbon storage model was established by shelterbelt age, which could be identified from time series images. Tree numbers of each shelterbelt, as another important parameter in the CSBelt model, was detected by numbers of rows, columns, and preservation rate, which were monitored by high resolution remote sensing image and geographic information system method. Finally, overall accuracy (OA), mean absolute error (MAE), and mean error (ME) was used to validate the remotely identified data. The results showed that, the OA of the single tree carbon storage was 80.4% within the error range of 0.05 MgC/tree, the MAE was 0.036 MgC/tree, and ME was 0.020 MgC/tree; the OA of tree numbers was 71.7% within the error range of 100 trees/km, the MAE was 94 trees/km, and ME was 73 trees/km; the OA of shelterbelt carbon storage was 82.6% within the error range of 100 MgC/km, the MAE was 49.63 MgC/km, and ME was 33.78 MgC/km. Compared to other carbon storage models, this model requires fewer parameters and is not restricted by the stand conditions, which has better universality and realizability. This research is helpful for the accurate assessment of shelterbelt ecosystem carbon storage, which is of great significance for predicting climate change and formulating strategies. The results of this research will also provide data support for shelterbelt management on a regional scale.