scholarly journals Evaluating the Conservation Efforts of Multi-Projects Using Remote Sensing and Light Use Efficiency Model: A Case of Nyungwe Forest National Park, Rwanda

2018 ◽  
Vol 08 (01) ◽  
pp. 68-86
Author(s):  
Evariste Rutebuka ◽  
Lixiao Zhang ◽  
Ernest Frimpong Asamoah ◽  
Emmanuel Rukundo ◽  
Apollinaire William
2004 ◽  
Author(s):  
Mirco Boschetti ◽  
Emanuela Mauri ◽  
Chiara Gadda ◽  
Lorenzo Busetto ◽  
Roberto Confalonieri ◽  
...  

2019 ◽  
Vol 230 ◽  
pp. 111190 ◽  
Author(s):  
Karl F. Huemmrich ◽  
Petya Campbell ◽  
David Landis ◽  
Elizabeth Middleton

2012 ◽  
Vol 118 ◽  
pp. 60-72 ◽  
Author(s):  
Chaoyang Wu ◽  
Jing M. Chen ◽  
Ankur R. Desai ◽  
David Y. Hollinger ◽  
M. Altaf Arain ◽  
...  

Tellus B ◽  
2002 ◽  
Vol 54 (5) ◽  
pp. 677-687 ◽  
Author(s):  
CAROLINE J. NICHOL ◽  
JON LLOYD ◽  
OLGA SHIBISTOVA ◽  
ALMUT ARNETH ◽  
CAROLA ROSER ◽  
...  

Author(s):  
S. Wang ◽  
Z. Li ◽  
Y. Zhang ◽  
D. Yang ◽  
C. Ni

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.


2016 ◽  
Vol 60 ◽  
pp. 702-709 ◽  
Author(s):  
Wenping Yuan ◽  
Yang Chen ◽  
Jiangzhou Xia ◽  
Wenjie Dong ◽  
Vincenzo Magliulo ◽  
...  

2011 ◽  
Vol 8 (4) ◽  
pp. 999-1021 ◽  
Author(s):  
J. E. Horn ◽  
K. Schulz

Abstract. Non-stationary and non-linear dynamic time series analysis tools are applied to multi-annual eddy covariance and micrometeorological data from 44 FLUXNET sites to derive a light use efficiency model for gross primary production on a daily basis. The extracted typical behaviour of the canopies in response to meteorological forcing leads to a model formulation allowing for a variable influence of the environmental drivers temperature and moisture availability modulating the light use efficiency. Thereby, the model is applicable to a broad range of vegetation types and climatic conditions. The proposed model explains large proportions of the variation of the gross carbon uptake at the study sites while the optimized set of six parameters is well defined. With the parameters showing explainable and meaningful relations to site-specific environmental conditions, the model has the potential to serve as basis for general regionalization strategies for large scale carbon flux predictions.


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