Imaging spectroscopy- and lidar-derived estimates of canopy composition and structure to improve predictions of forest carbon fluxes and ecosystem dynamics

2014 ◽  
Vol 41 (7) ◽  
pp. 2535-2542 ◽  
Author(s):  
A. S. Antonarakis ◽  
J. W. Munger ◽  
P. R. Moorcroft
Keyword(s):  
Imaging Spectroscopy ◽  
Carbon Fluxes ◽  
Forest Carbon ◽  
Ecosystem Dynamics ◽  
Composition And Structure ◽  
Canopy Composition

scholarly journals Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

2018 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Xianming Dou ◽  
Yongguo Yang ◽  
Jinhui Luo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Eddy Covariance ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Forest Carbon ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Inference System ◽  
Learning Techniques

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

An integrated remote sensing and model approach for assessing forest carbon fluxes in China

2021 ◽  
pp. 152480
Author(s):  
Junfang Zhao ◽  
Dongsheng Liu ◽  
Yun Cao ◽  
Lijuan Zhang ◽  
Huiwen Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Fluxes ◽  
Forest Carbon ◽  
Model Approach

scholarly journals Potential knowledge gain in large-scale simulations of forest carbon fluxes from remotely sensed biomass and height

2011 ◽  
Vol 261 (3) ◽  
pp. 515-530 ◽  
Author(s):  
V. Bellassen ◽  
N. Delbart ◽  
G. Le Maire ◽  
S. Luyssaert ◽  
P. Ciais ◽  
...  
Keyword(s):  
Large Scale ◽  
Carbon Fluxes ◽  
Remotely Sensed ◽  
Forest Carbon ◽  
Knowledge Gain ◽  
Large Scale Simulations

Estimating net forest carbon fluxes by the integration of ground and remote sensing data

2009 ◽  
pp. 97-108
Author(s):  
Fabio Maselli ◽  
Marta Chiesi ◽  
Luca Fibbi ◽  
Marco Moriondo ◽  
Marco Bindi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Carbon Fluxes ◽  
Forest Carbon ◽  
Sensing Data

A comparison of alternative modelling approaches to evaluate the European forest carbon fluxes

2010 ◽  
Vol 260 (3) ◽  
pp. 241-251 ◽  
Author(s):  
Boris Ťupek ◽  
Giuliana Zanchi ◽  
Pieter J. Verkerk ◽  
Galina Churkina ◽  
Nicolas Viovy ◽  
...  
Keyword(s):  
Carbon Fluxes ◽  
Forest Carbon ◽  
European Forest ◽  
Modelling Approaches
Sign in / Sign up

Export Citation Format

Share Document