A phenology-based method to derive biomass production anomalies for food security monitoring in the Horn of Africa

Meroni, Michele; Verstraete, M. M.; Rembold, Felix; Urbano, Ferdinando; Francois, Kayitakire

doi:10.1080/01431161.2014.883090

Cited by 58 publications

(50 citation statements)

References 27 publications

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“…Phenological variables were retrieved from the FAPAR time series using the model-fit approach described in Meroni et al [9]. Briefly, the seasonality (being uni-or bi-modal) is detected analyzing the autocorrelation of the FAPAR time series.…”

Section: Methodsmentioning

confidence: 99%

“…For instance, an earlier (or later) than usual development will in any case generate a positive (or negative) NDVI anomaly in the initial part of the season, even if the overall production is not actually affected. Approaches accounting for vegetation phenology in the analysis of overall growing season productivity have recently been proposed to overcome this possible limitation [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

et al. 2014

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Abstract:In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the OPEN ACCESSRemote Sens. 2014, 6 5869 maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

et al. 2014

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“…In this study, we chose the approach recently published by Meroni et al (2014), which is well capable of dealing with the bimodal seasonality common to East Africa. The was at least 0.10.…”

Section: Phenological Analysis From Ndvi Seriesmentioning

confidence: 99%

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Vrieling

Meroni

Mude

et al. 2016

Remote Sensing of Environment

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“…Mean FAPAR images of every dekad (i.e., 10 days) in the year (n=36) calculated for the period 1999-2012 were used as input for the classification. All non-vegetated areas, defined as pixels with an overall variability of the entire FAPAR time series (as measured by the 95th-5th percentile difference) less than the FAPAR uncertainty (assumed to be 0.1 as in Meroni et al, 2014b), were masked out. The resulting ISODATA classification yielded eight classes from which six were covered by sample sites (class 1 to 6).…”

Section: Rs Proxy For Biomass Productionmentioning

confidence: 99%

“…The time interval for integration is dynamically adjusted for every site and every year based on the start and end of the season (SOS, EOS). The required phenology parameters (SOS, EOS; maximum value of FAPAR, maxv) were calculated using the model-fit approach of Meroni et al (2014b). In general, the value of CFAPAR depends on the shape of the FAPAR seasonal trajectory, the integration limits and the baseline value.…”

Section: Rs Proxy For Biomass Productionmentioning

confidence: 99%

Biomass estimation to support pasture management in Niger

Schucknecht

Meroni

Francois

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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 (R 2 = 0.55, R 2 cv = 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.

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A phenology-based method to derive biomass production anomalies for food security monitoring in the Horn of Africa

Cited by 58 publications

References 27 publications

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Biomass estimation to support pasture management in Niger

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