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Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

European Commission, Joint Research Centre, Directorate of Sustainable Resources, Ispra 21027, Italy
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research—Atmospheric Environmental Research, Garmisch-Partenkirchen 82467, Germany
Ministry of Livestock, General Directorate of Production and Animal Industries, Niamey 23220, Niger
Author to whom correspondence should be addressed.
Academic Editors: Jose Moreno, Lalit Kumar and Prasad S. Thenkabail
Remote Sens. 2017, 9(5), 463;
Received: 24 February 2017 / Revised: 27 April 2017 / Accepted: 2 May 2017 / Published: 10 May 2017
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
Livestock plays an important economic role in Niger, especially in the semi-arid regions, while being highly vulnerable as a result of the large inter-annual variability of precipitation and, hence, rangeland production. This study aims to support effective rangeland management by developing an approach for mapping rangeland biomass production. The observed spatiotemporal variability of biomass production is utilised to build a model based on ground and remote sensing data for the period 2001 to 2015. Once established, the model can also be used to estimate herbaceous biomass for the current year at the end of the season without the need for new ground data. The phenology-based seasonal cumulative Normalised Difference Vegetation Index (cNDVI), computed from 10-day image composites of the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data, was used as proxy for biomass production. A linear regression model was fitted with multi-annual field measurements of herbaceous biomass at the end of the growing season. In addition to a general model utilising all available sites for calibration, different aggregation schemes (i.e., grouping of sites into calibration units) of the study area with a varying number of calibration units and different biophysical meaning were tested. The sampling sites belonging to a specific calibration unit of a selected scheme were aggregated to compute the regression. The different aggregation schemes were evaluated with respect to their predictive power. The results gathered at the different aggregation levels were subjected to cross-validation (cv), applying a jackknife technique (leaving out one year at a time). In general, the model performance increased with increasing model parameterization, indicating the importance of additional unobserved and spatially heterogeneous agro-ecological effects (which might relate to grazing, species composition, optical soil properties, etc.) in modifying the relationship between cNDVI and herbaceous biomass at the end of the season. The biophysical aggregation scheme, the calibration units for which were derived from an unsupervised ISODATA classification utilising 10-day NDVI images taken between January 2001 and December 2015, showed the best performance in respect to the predictive power (R2cv = 0.47) and the cross-validated root-mean-square error (398 kg·ha−1) values, although it was not the model with the highest number of calibration units. The proposed approach can be applied for the timely production of maps of estimated biomass at the end of the growing season before field measurements are made available. These maps can be used for the improved management of rangeland resources, for decisions on fire prevention and aid allocation, and for the planning of more in-depth field missions. View Full-Text
Keywords: food security; Sahel; Niger; rangeland productivity; livestock; MODIS; NDVI food security; Sahel; Niger; rangeland productivity; livestock; MODIS; NDVI
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MDPI and ACS Style

Schucknecht, A.; Meroni, M.; Kayitakire, F.; Boureima, A. Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments. Remote Sens. 2017, 9, 463.

AMA Style

Schucknecht A, Meroni M, Kayitakire F, Boureima A. Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments. Remote Sensing. 2017; 9(5):463.

Chicago/Turabian Style

Schucknecht, Anne, Michele Meroni, Francois Kayitakire, and Amadou Boureima. 2017. "Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments" Remote Sensing 9, no. 5: 463.

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