Spatial scaling of net primary productivity based on process model in Changbai Mountain Natural Reserve

Being one of the most important parameters to reflect the structural characteristics of the forestland ecosystem, Net Primary Productivity (NPP) becomes a very important area in the research of global change and carbon cycling as its distribution can provide the integral information for a sustainable development in terms of managing the natural resources. This paper presented an algorithm for the spatial scaling of NPP. Based on the Improved Boreal Ecosystem Productivity Simulator (IBEPS), the algorithm produced the NPP results at different resolutions in the area of Changbai Mountain Natural Reserve in Jilin Province, Northeast China, by using the remotely sensed images with high spatial resolution, Enhanced Thematic Mapper plus (ETM+), and moderate spatial resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) respectively. Field experiments were carried out in order to get some ancillary data and also to validate the simulated NPP values. The NPP at the spatial resolution of 30 meters was scaled up to the scale of 1 kilometers spatial resolution and the distributed NPP (NPPd) was obtained. Based on the distributed NPP, the lumped NPP (NPPI, simulated NPP with 1 kilometer resolution parameters) was corrected (as NPPI_CORRECTED) by using the contextual approach of spatial scaling, where the area fractions were used to derive the surface parameters at different resolutions. The corrected result indicates that the precision after scaling gets improved in comparison with the lumped NPP. The correlation coefficient is increased from 0.898 to 0.960, and the standard difference gets decreased from 49.84 gC/m² to 41.02 gC/m². This method can better meet the requirements of remote sensing applications of large scale.