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

Mthembeni Mngadi; John Odindi; Onisimo Mutanga; Mbulisi Sibanda

doi:10.1016/j.scitotenv.2021.149958

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

Sci Total Environ. 2022 Jan 1:802:149958. doi: 10.1016/j.scitotenv.2021.149958. Epub 2021 Aug 27.

Authors

Mthembeni Mngadi¹, John Odindi², Onisimo Mutanga³, Mbulisi Sibanda⁴

Affiliations

¹ School of Agricultural, Earth and Environmental Sciences, Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg, South Africa. Electronic address: 212518076@stu.ukzn.ac.za.
² School of Agricultural, Earth and Environmental Sciences, Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg, South Africa. Electronic address: odindi@ukzn.ac.za.
³ School of Agricultural, Earth and Environmental Sciences, Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg, South Africa. Electronic address: MutangoO@ukzn.ac.za.
⁴ School of Agricultural, Earth and Environmental Sciences, Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg, South Africa. Electronic address: SibandaM3@ukzn.ac.za.

PMID: 34525750
DOI: 10.1016/j.scitotenv.2021.149958

Abstract

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 (R²: 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 afra had the lowest NPP (4.54 Mg C ha^-1 and 5.26 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.

Keywords: Carbon flux; MOD17; Photosynthetic active radiation; Species.

MeSH terms

Carbon
Carbon Cycle
Ecosystem
Forests*
Plant Leaves
Trees*

Substances

Carbon