Analysis and Estimation of Net Primary Productivity of Vegetation in Nanjing Using Multi-Sourced Remote Sensing Data

Abstract

Net primary productivity (NPP) is an important indicator of Earth’s ecosystem. However, NPP estimation of cities by high spatial resolution is limited in China, especially Nanjing, and NPP evolution and quantitative analysis of influencing factors in time series are insufficient. Nanjing, as one of the central cities in China, plays a major role in China’s economic development. In this paper, high spatial resolution data combined with Landsat Thematic Mapper (TM) data are used for NPP estimation and analysis in Nanjing City. The research contents of this paper are as follows: (1) To improve the passive effect of “red border region” on GF-1 WFV forest and farmland classification ability, the multispectral and high-resolution images are fused, and the image fusion effect was also evaluated. (2) The NPP of four seasons in Nanjing in 2017 was estimated using the CASA model. (3) The results of NPP estimation are analyzed based on statistical principles. The advantage of this paper lies in the comprehensive utilization of multi-source data and the quantitative analysis of the response mechanism between meteorological data and the NPP estimation value.

Keywords

• GF-1 WFV
• Landsat_8
• image fusion
• NPP of Nanjing