Title: Biomass estimation to support pasture management in Niger
Authors: SCHUCKNECHT ANNEMERONI MICHELEKAYITAKIRE FrancoisREMBOLD FelixBOUREIMA Amadou
Citation: INTERNATIONAL ARCHIVES OF THE PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION SCIENCES vol. XL-7/W3 p. 109-114
Publisher: COPERNICIUS GmbH
Publication Year: 2015
JRC N°: JRC96172
ISSN: 1682-1750
URI: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/109/2015/isprsarchives-XL-7-W3-109-2015.html
http://publications.jrc.ec.europa.eu/repository/handle/JRC96172
DOI: 10.5194/isprsarchives-XL-7-W3-109-2015
Type: Articles in periodicals and books
Abstract: Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998–2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R2 = 0.55, R2cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available.
JRC Directorate:Sustainable Resources

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