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Estimation of net primary productivity of different forest types based on improved CASA model in Jing-Jin-Ji region, China
- Zhang, Ying, Zhang, Xiaoli
- Journal of sustainable forestry 2017 v.36 no.6 pp. 568-582
- carbon sequestration, climate, deciduous forests, forest resources, mixed forests, models, moderate resolution imaging spectroradiometer, primary productivity, seasonal variation, China
- Vegetation net primary productivity and its variations are important biophysical variables for reflecting climate–vegetation relationship. The Jing–Jin–Ji region in Northern China has important and strategic characteristics for forest resources and ecological environment. This study explores the NPP estimation and its spatial-temporal distribution at the regional scale of Jing–Jin–Ji, based on improved Carnegie-Ames-Stanford Approach model using MODIS. The quantitative evidence presented here suggests that NPP distribution was most strongly correlated with forest structures and climate and showed obvious seasonal changes. The simulated average annual NPP and the total NPP were 658.8 and 14884.18 , respectively. The results showed that the contribution of NPP of different forest type to total NPP in the study area was different, and the deciduous broadleaf forest had the maximum NPP in per unit area, followed by mixed forest, evergreen needleleaf forest and deciduous needleleaf forest. The simulated total NPP presented a decreasing trend from northwest to southeast. The analysis on NPP spatial–temporal changes concluded that NPP distribution had a close relationship with the climate seasonal changes. This paper provides not only an estimation NPP of different forest types in north China but also a useful methodology for estimating forest carbon storage at regional levels in China.