Next Article in Journal
Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion
Next Article in Special Issue
Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing
Previous Article in Journal
Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2
Previous Article in Special Issue
Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 463; doi:10.3390/rs9050463

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

1
European Commission, Joint Research Centre, Directorate of Sustainable Resources, Ispra 21027, Italy
2
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research—Atmospheric Environmental Research, Garmisch-Partenkirchen 82467, Germany
3
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
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)
View Full-Text   |   Download PDF [7175 KB, uploaded 10 May 2017]   |  

Abstract

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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top