Estimating Gross Primary Production of a Forest Plantation Area Using Eddy Covariance Data and Satellite Imagery

• Published in: Journal of the Indian Society of Remote Sensing 2016-12, Vol.44 (6), p.895-904
• Main Authors: Ahongshangbam, Joyson, Patel, N. R., Kushwaha, S. P. S., Watham, Taibanganba, Dadhwal, V. K.
• Format: Article
• Language: English
• Subjects: Active satellites; Air temperature; Carbon cycle; Covariance; Earth and Environmental Science; Earth Sciences; Fluctuations; Landsat; Landsat satellites; Mathematical models; Meteorological data; Normalized difference vegetative index; Optimization; Parameters; Photosynthetically active radiation; Plantations; Predictions; Primary production; Radiation; Remote sensing; Remote Sensing/Photogrammetry; Research Article; Satellite imagery; Seasonal variations; Surface wetness; Vegetation; Vortices
• ISSN: 0255-660X; 0974-3006
• DOI: 10.1007/s12524-016-0564-7

Gross primary production (GPP) is the basic biophysical parameter of an ecosystem. The quantification of GPP has been a major challenge in understanding the global carbon cycle. Eddy covariance (EC) measurements at flux tower provide valuable direct information on seasonal dynamics of GPP and allow model optimization. In this paper, the GPP of forest plantation was estimated using light use efficiency (LUE-based) model and validated with flux tower GPP observations in Terai Central Forest Division, Nainital, India. The LUE model is mainly based upon the photosynthetically active radiation (PAR), satellite-derived normalized difference vegetation index (NDVI), land surface wetness index (LSWI), and the air temperature. The simulation of the model was carried out using vegetation indices generated from Landsat imagery and the meteorological data from flux tower. The predicted GPP showed distinct significance of spatio-temporal dynamics of GPP. The environmental variables, viz., PAR and NDVI showed distinct effect on the GPP prediction. Comparison between predicted and the measured GPP on flux tower site showed good agreement ( R 2  = 0.626, RMSE = 2.08 and MAPE = 18.46). The study demonstrated the potential of LUE model for estimating GPP and scaling up of GPP over large areas, which is a major parameter in the study of the carbon cycle on regional to global scales.