NDLI: Estimating net primary productivity of terrestrial vegetation based on remote sensing: a case study in Inner Mongolia, China

Content Provider	IEEE Xplore Digital Library
Author	Wenquan Zhu Yaozhong Pan Haibo Hu Jing Li Gong, P.
Copyright Year	2004
Description	Author affiliation: Coll. of Resources Sci. & Technol., Beijing Normal Univ. (Wenquan Zhu; Yaozhong Pan; Haibo Hu; Jing Li)
Abstract	Some vegetation primary production models have been developed in recent years as research issues related to food security and biotic response to climate warming have become more compelling. An estimation model of net primary productivity (NPP), based on geographic information system (GIS) and remote sensing (RS) technology, is presented. The model, driven with ground meteorological data and remote sensing data, moves beyond simple correlative models to a more mechanistic basis and avoids the need for a full suite of eco-physiological process algorithms that require explicit parameterization. Therefore, it is relatively easier to acquire data. Application and validation of this model in Inner Mongolia, China, was conducted. After the validation with observed data and the comparison with other NPP models, the results showed that the predicted NPP was in good agreement with field measurement, and the remote sensing method can more actually reflect the forest NPP than Chikugo model. These results illustrated the utility of the model for terrestrial primary production over regional scales
Starting Page	528
Ending Page	531
File Size	462263
Page Count	4
File Format	PDF
ISBN	0780387422
DOI	10.1109/IGARSS.2004.1369080
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Productivity Vegetation Remote sensing Production Geographic Information Systems Predictive models Data security Information security Food technology Meteorology
Content Type	Text
Resource Type	Article

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