Satellite-Based Herbaceous Biomass Estimates in the Pastoral Zone of Niger

Wylie, Bruce K.; Denda, I.; Pieper, Rex D.; Harrington, John A.; Reed, Barbara M.; Southward, G. Morris

doi:10.2307/4002804

Cited by 32 publications

(34 citation statements)

References 9 publications

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“…The sampling of herbaceous biomass was performed along a transect, applying a double-sampling approach combining weighed biomass and visual estimates that finally yields estimates of dry matter production. The sampling method is described in more detail in Maidagi et al [5] and Wylie et al [8].…”

Section: Biomass Datamentioning

confidence: 99%

“…The first studies in the late 1980s [5][6][7][8] applied linear regression between maximum standing biomass and normalised difference vegetation index (NDVI)-based indices (time-integrated and maximum NDVI) derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery. The Ministry of Livestock in Niger currently uses a similar method based on NDVI data from the Satellite Pour l'Observation de la Terre-Vegetation (SPOT-VGT) and, since 2014, data from the Meteorological Operational satellite programme (MetOp) AVHRR.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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 (R 2 cv = 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.

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Section: Biomass Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…Herbaceous biomass values were obtained by applying a double-sampling approach combining visual estimates and weighted biomass. For a more detailed description of the sampling approach see e.g., Maidagi et al (1987) and Wylie et al (1995). After discarding sampling sites located in agricultural areas and those having less than four measurements in the target period, the Bm dataset was statistically analysed and screened for outliers.…”

Section: Study Areamentioning

confidence: 99%

“…The majority of studies utilized lowand medium resolution optical or radar data and an empirical relationship between field biomass measurements and a RS indicator (Eisfelder et al, 2012). The first studies of herbaceous biomass estimation in Niger date back to the late 1980's and utilized linear regressions between maximum standing biomass and NDVI-based metrics (maximum and time-integrated NDVI) derived from NOAA AVHRR imagery (Maidagi et al, 1987;Wylie et al, 1988Wylie et al, , 1991Wylie et al, , 1995. A similar method is currently applied by the Ministry of Livestock in Niger (MoL) using NDVI data from SPOT-VEGETATION (VGT), and since 2014 from METOP-AVHRR.…”

Section: Introductionmentioning

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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“…Winter annuals respond opportunistically to precipitation events, and constitute a variable fraction of ground cover against a matrix of spatially varying soil backgrounds. The use of multi-temporal satellite data for measuring inter-annual changes in vegetation patterns in semi-arid rangeland environments is common, e.g., [10][11][12], however the selection of appropriate dates for image acquisition is problematic given the ephemeral nature of winter annuals in the southwest.…”

Section: Introductionmentioning

confidence: 99%

Estimating Winter Annual Biomass in the Sonoran and Mojave Deserts with Satellite- and Ground-Based Observations

2013

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Winter annual plants in southwestern North America influence fire regimes, provide forage, and help prevent erosion. Exotic annuals may also threaten native species. Monitoring winter annuals is difficult because of their ephemeral nature, making the development of a satellite monitoring tool valuable. We mapped winter annual aboveground biomass in the Desert Southwest from satellite observations, evaluating 18 algorithms using time-series vegetation indices (VI). Field-based biomass estimates were used to calibrate and evaluate each algorithm. Winter annual biomass was best estimated by calculating a base VI across the period of record and subtracting it from the peak VI for each winter season (R 2 = 0.92). The normalized difference vegetation index (NDVI) derived from 8-day reflectance data provided the best estimate of winter annual biomass. It is important to account for the timing of peak vegetation when relating field-based estimates to satellite VI data, since post-peak field estimates may indicate senescent biomass which is inaccurately represented by VI-based estimates. Images generated from the best-performing algorithm show both spatial and temporal variation in winter annual biomass. Efforts to manage this variable resource would be enhanced by a tool that allows the monitoring of changes in winter annual resources over time.

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Satellite-Based Herbaceous Biomass Estimates in the Pastoral Zone of Niger

Cited by 32 publications

References 9 publications

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

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

Biomass estimation to support pasture management in Niger

Estimating Winter Annual Biomass in the Sonoran and Mojave Deserts with Satellite- and Ground-Based Observations

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