Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data

Recently, urban reforestation programs have emerged as potential carbon sinks and climate mitigates in urban landscapes. Thus, spatially explicit information on net primary productivity (NPP) of reforested trees in urban environments is central to understanding the value of reforestation initiatives in the global carbon budget and climate regulation potential. To date, numerous studies have mainly focused on natural and commercial forests NPP at a regional scale based on coarse spatial resolution remotely sensed data. Generally, local scale NPP studies based on fine spatial resolution data are limited. Therefore, this study sought to estimate aboveground NPP of an urban reforested landscape using biophysical and Sentinel-2 Multispectral Imager data derived variables. Using the MOD17 model, results showed that mean NPP ranged between 6.24 Mg C ha(-1) with high coefficient of determination (R-2 : 0.92) and low RMSE (0.82 Mg ha(-1)) across all reforested trees within the study area. Results also showed a considerable variation in NPP among the reforested trees, with deciduous Acacia and Dalbergia obovate species showing the highest NPP (7.62 Mg C ha(-1) and 7.58 Mg C ha(-1)) respectively), while the evergreen Syzygium cordatum and shrub Artemisia afro had the lowest NPP (4.54 Mg C ha(-1) and 526 Mg C ha(-1)). Furthermore, the multiple linear regression analysis showed that vegetation specific biophysical variables (i.e. leaf area index, Normalized Difference Vegetation Index and Fraction of Photosynthetically Active Radiation) significantly improved the estimation of reforested aboveground NPP at a fine-scale resolution. These findings demonstrate the effectiveness of biophysical and remotely sensed variables in determining NPP (as carbon sequestration surrogate) at fine-scaled reforested urban landscape. Furthermore, the utility of species biometric measurements and MOD17 model offers unprecedented opportunity for improved local scale reforestation assessment and monitoring schedules. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study estimated the aboveground NPP of an urban reforested landscape using biophysical and Sentinel-2 Multispectral Imager data, showing significant variation in NPP among different reforested trees. The multiple linear regression analysis demonstrated the effectiveness of specific biophysical variables in improving the estimation of NPP at a fine-scale resolution.