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Satellite-Born Monitoring of Gross Primary Productivity

David Marcelis

Published by LAP Lambert Academic Publishing
ISBN 10: 3847305425 / ISBN 13: 9783847305422
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Title: Satellite-Born Monitoring of Gross Primary ...

Publisher: LAP Lambert Academic Publishing

Binding: Paperback

Book Condition: New

Book Type: Paperback

Description:

Paperback. 72 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.In the face of climate change, knowledge of the global carbon balance is critical. A free global GPP product is available from the MODIS satellite sensor. Its algorithm is based on a biome specific maximum Radiation Use Efficiency (RUE) value. On a small spatial scale, local growing conditions create a high spatial variability in RUE which the biome specific RUE value in the MODIS GPP algorithm does account for. As a result, MODIS GPP has often been found unreliable at local and regional scales. An alternative method is to remotely estimate RUE by means of the Photochemical Reflectance Index (PRI). Various studies have already found strong correlations between PRI and RUE in Mediterranean and boreal forests. The objective of this study was to investigate the usage of the PRI as an estimator for RUE for the purpose of monitoring GPP with MODIS data in temperate forests. A GPP dataset was established for the Veluwe forests in The Netherlands for 2002-2006 by using MODIS data and the PRI as estimator of RUE. The results of this study support the thesis that on a small spatial scale MODIS GPP is unreliable and that accuracy can be improved using the PRI approach. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783847305422

About this title:

Synopsis: In the face of climate change, knowledge of the global carbon balance is critical. A free global GPP product is available from the MODIS satellite sensor. Its algorithm is based on a biome specific maximum Radiation Use Efficiency (RUE) value. On a small spatial scale, local growing conditions create a high spatial variability in RUE which the biome specific RUE value in the MODIS GPP algorithm does account for. As a result, MODIS GPP has often been found unreliable at local and regional scales. An alternative method is to remotely estimate RUE by means of the Photochemical Reflectance Index (PRI). Various studies have already found strong correlations between PRI and RUE in Mediterranean and boreal forests. The objective of this study was to investigate the usage of the PRI as an estimator for RUE for the purpose of monitoring GPP with MODIS data in temperate forests. A GPP dataset was established for the Veluwe forests in The Netherlands for 2002-2006 by using MODIS data and the PRI as estimator of RUE. The results of this study support the thesis that on a small spatial scale MODIS GPP is unreliable and that accuracy can be improved using the PRI approach.

About the Author: David Marcelis (1986) graduated from Wageningen University in 2011 with a MSc in Geo-Information Science and a BSc in Forest and Nature Conservation (2009). He is employed as a remote sensing specialist at WaterWatch BV and studies the monitoring of cropland characteristics using microwave data for his PhD at Delft University of Technology.

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