NDVI-based vegetation in response to climate factors based on neighborhood association effect: A case study in Inner Mongolia, China

Abstract

Abstract With the combination of 2014-2018 MODIS NDVI products and climate data (precipitation and temperature) in Inner Mongolia, China, this study aims to explore and verify the effectiveness of incorporating neighborhood association effect in vegetation index modeling. A neighborhood statistical method based on Moore neighborhood was applied to update the original spatial datasets. Geographically weighted regression (GWR) was constructed to compare the model accuracy between original data and updated data. The GWR models were tested under different neighborhood sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9, and 11 × 11 Moore neighborhood sizes). Our work compared the results of different GWR models and the original GWR model that did not consider neighborhood association effect. The results indicated that considering neighborhood association effect could improve GWR model accuracy substantially. In addition, the neighborhood sizes proved to be important factors for measuring neighborhood association effect. We conclude that neighborhood association effect should be integrated to understand vegetation changing trajectory based on climate factors.