Examining forest net primary productivity dynamics and driving forces in northeastern China during 1982–2010

Forest net primary productivity (NPP) is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegie-Ames-Stanford Approach (CASA) model with normalized difference vegetation index (NDVI) sequences derived from Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products. To address the problem of data inconsistency between AVHRR and MODIS data, a per-pixel unary linear regression model based on least squares method was developed to derive the monthly NDVI sequences. Results suggest that estimated forest NPP has mean relative error of 18.97% compared to observed NPP from forest inventory. Forest NPP in the northeastern China increased significantly during the twenty-nine years. The results of seasonal dynamic show that more clear increasing trend of forest NPP occurred in spring and autumn. This study also examined the relationship between forest NPP and its driving forces including the climatic and anthropogenic factors. In spring and winter, temperature played the most pivotal role in forest NPP. In autumn, precipitation acted as the most important factor affecting forest NPP, while solar radiation played the most important role in the summer. Evaportranspiration had a close correlation with NPP for coniferous forest, mixed coniferous broadleaved forest, and broadleaved deciduous forest. Spatially, forest NPP in the Da Hinggan Mountains was more sensitive to climatic changes than in the other ecological functional regions. In addition to climatic change, the degradation and improvement of forests had important effects on forest NPP. Results in this study are helpful for understanding the regional carbon sequestration and can enrich the cases for the monitoring of vegetation during long time series.