An improved phenology-based CASA model for estimating net primary production of forest in central China based on Landsat images. Issue 21 (2nd November 2018)

• Record Type: Journal Article
• Authors: Pei, Yanyan; Huang, Jinliang; Wang, Lihui; Chi, Hong; Zhao, Yajie
• Is Part Of: International journal of remote sensing. Volume 39:Issue 21(2018)
• Page Start: 7664
• Page End: 7692
• Publication Date: 2018-11-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1478464
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: The optimum temperature ( ) in the current Carnegie–Ames–Stanford Approach (CASA) model was defined as the mean temperature of the month when normalized difference vegetation index (NDVI) reaches its maximum. However, it requires improvements from a comprehensive perspective due to that the stability of the maximum NDVI acquisition is subjected to a variety of factors. The article proposed an improved CASA model by redefining the optimum temperature based on phenology ( ) to model the net primary production (NPP) of forest in Shennongjia, central China, and analysed the relationship between annual mean NPP and topography. Logistic function was used to model the phenological phases of forest and was redefined as the mean temperature during the period of maturity stability. The improved was lower than the for five forest types. Specifically, the average of evergreen broadleaf forest, deciduous broadleaf forest, evergreen needleleaf forest, deciduous needleleaf forest, and mixed forest were 22.72°C, 23.31°C, 24.05°C, 23.41°C, and 23.18°C, respectively, whereas the corresponding average were 24.42°C, 24.90°C, 24.54°C, 24.57°C, and 24.43°C, respectively. The NPP observations transformed from field measured biomass were used to evaluate the accuracy of NPP estimated from the -based CASA model and the -based CASA model. The result indicated that the accuracy of the -based CASA model was higher than that of the -based CASA model, with the coefficients of determination of 0.837 (root mean square error (RMSE) = 75 g C m –2 year –1 ) and 0.632 (RMSE = 122 g C m –2 year –1 ), respectively. The total NPP of forest in Shennongjia modelled by the -based CASA model and the -based CASA model were 1.40 and 1.35 Tg C year –1, respectively. The relationship between the annual mean NPP and altitude showed a quadratic polynomial function at the altitude from 500 to 3000 m, while the relationship between the annual mean NPP and aspect showed a sine function when aspect in the range of 4.5–360.0°. The results demonstrate that the improvement of CASA model ( -based CASA model) is of great significance in phenology and plays as a promising alternative method to model NPP for forest.