Integrating multiple vegetation indices via an artificial neural network model for estimating the leaf chlorophyll content of Spartina alterniflora under interspecies competition.
Environ Monit Assess
; 189(11): 596, 2017 Oct 31.
Article
em En
| MEDLINE
| ID: mdl-29086121
ABSTRACT
The invasive species Spartina alterniflora and native species Phragmites australis display a significant co-occurrence zonation pattern and this co-exist region exerts most competitive situations between these two species, competing for the limited space, directly influencing the co-exist distribution in the future. However, these two species have different growth ratios in this area, which increase the difficulty to detect the distribution situation directly by remote sensing. As chlorophyll content is a key indicator of plant growth and physiological status, the objective of this study was to reduce the effect of interspecies competition when estimating Cab content; we evaluated 79 published representative indices to determine the optimal indices for estimating the chlorophyll a and b (Cab) content. After performing a sensitivity analysis for all 79 spectral indices, five spectral indices were selected and integrated using an artificial neural network (ANN) to estimate the Cab content of different competition ratios the Gitelson ratio green index, the transformed chlorophyll absorption ratio index/optimized soil-adjusted vegetation index, the modified normalized difference vegetation index, the chlorophyll fluorescence index, and the Vogelmann chlorophyll index. The ANN method yielded better results (R 2 = 0.7110 and RMSE = 8.3829 µg cm-2) on average than the best single spectral index (R 2 = 0.6319 and RMSE = 9.3535 µg cm-2), representing an increase of 10.78% in R 2 and a decrease of 10.38% in RMSE. Our results indicated that integrating multiple vegetation indices with an ANN can alleviate the impact of interspecies competition and achieve higher estimation accuracy than the traditional approach using a single index.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Poaceae
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Environ Monit Assess
Assunto da revista:
SAUDE AMBIENTAL
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
China