File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/IGARSS.2004.1369080
- Scopus: eid_2-s2.0-15944401381
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Estimating net primary productivity of terrestrial vegetation based on remote sensing: A case study in inner Mongolia, China
Title | Estimating net primary productivity of terrestrial vegetation based on remote sensing: A case study in inner Mongolia, China |
---|---|
Authors | |
Keywords | Primary production Inner Mongolia Remote sensing Geographic information system |
Issue Date | 2004 |
Citation | International Geoscience and Remote Sensing Symposium (IGARSS), 2004, v. 1, p. 528-531 How to Cite? |
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 ecophysiological 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. |
Persistent Identifier | http://hdl.handle.net/10722/296564 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhu, Wenquan | - |
dc.contributor.author | Pan, Yaozhong | - |
dc.contributor.author | Hu, Haibo | - |
dc.contributor.author | Li, Jing | - |
dc.contributor.author | Gong, Peng | - |
dc.date.accessioned | 2021-02-25T15:16:10Z | - |
dc.date.available | 2021-02-25T15:16:10Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | International Geoscience and Remote Sensing Symposium (IGARSS), 2004, v. 1, p. 528-531 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296564 | - |
dc.description.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 ecophysiological 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. | - |
dc.language | eng | - |
dc.relation.ispartof | International Geoscience and Remote Sensing Symposium (IGARSS) | - |
dc.subject | Primary production | - |
dc.subject | Inner Mongolia | - |
dc.subject | Remote sensing | - |
dc.subject | Geographic information system | - |
dc.title | Estimating net primary productivity of terrestrial vegetation based on remote sensing: A case study in inner Mongolia, China | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/IGARSS.2004.1369080 | - |
dc.identifier.scopus | eid_2-s2.0-15944401381 | - |
dc.identifier.volume | 1 | - |
dc.identifier.spage | 528 | - |
dc.identifier.epage | 531 | - |