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

Vrieling, Anton; Meroni, Michele; Mude, Andrew G.; Chantarat, Sommarat; Ummenhofer, Caroline C.; Bie, C.A.J.M. de

doi:10.1016/j.rse.2015.12.003

Cited by 47 publications

(34 citation statements)

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“…The time interval for integration is defined by the start of season (SOS) and the end of season (EOS), estimated for each pixel and season. The model-fit approach of Meroni et al [35], with further modification as in Vrieling et al [36], was used to calculate the phenology parameters (SOS, EOS, maximum value of NDVI). SOS was estimated as the point at which the fitted NDVI model for the season exceeded 20% of the local growing amplitude (i.e., between minimum NDVI before green-up and maximum NDVI of that season), and EOS was estimated as the point at which the model falls below 80% of the decay amplitude (i.e., between maximum NDVI of the season and the following minimum NDVI after decay).…”

Section: Rs Proxy For Biomass Productionmentioning

confidence: 99%

“…Pixels are considered non-vegetated when the variability of the NDVI time series, as measured by the difference between the 95th and 5th percentiles, is below 0.05 NDVI units. Other methodological details can be found in Vrieling et al [36]. The value of cNDVI is thus controlled by the integration limits, the baseline value, and the amplitude and shape of the NDVI seasonal trajectory.…”

Section: Rs Proxy For Biomass Productionmentioning

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: Rs Proxy For Biomass Productionmentioning

confidence: 99%

Section: Rs Proxy For Biomass Productionmentioning

confidence: 99%

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

et al. 2017

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“…Vegetation indices such as the normalized difference vegetation index (NDVI; Tucker, 1978; Vrieling et al, 2016) and the vegetation health index (VHI; Rojas et al, 2011) are routinely assessed to determine drought impacts on food security (Brown, 2016; Enenkel et al, 2015), particularly in regions with simple topography and well‐defined rainfall seasonality (Karnieli et al, 2010). A tipping point measured by NDVI (or related vegetation indices) may be identified through a sharp decrease in greenness before the end of the agricultural season (e.g.…”

Section: Potential Remotely‐sensed Indicators Of Tipping Pointsmentioning

confidence: 99%

Applying Tipping Point Theory to Remote Sensing Science to Improve Early Warning Drought Signals for Food Security

Krishnamurthy¹,

Fisher

Schimel

et al. 2020

Earth's Future

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Famines have long been associated with drought. With the severity of droughts growing in association with climate change, there is increasing pressure to do a better job predicting famines and delivering international aid to avert human suffering and civil instability. We examine recent advances in remote sensing technology, focusing on the latency, historical availability and spatial and temporal scales of the data these satellites provide. Because of their global coverage, seven variables derived from satellite observations emerge as especially pertinent to drought and famine: precipitation (TRMM/GPM), groundwater (GRACE/GRACE‐FO), snow (MODIS), soil moisture (SMOS, SMAP, Sentinel‐1), evapotranspiration (MODIS, ECOSTRESS), vegetation health (Landsat, AVHRR, MODIS, SPOT) and chlorophyll fluorescence (OCO‐2). We discuss tipping point theory as a possible framework for taking advantage of long time series of these satellite data where they exist in order to enhance the effectiveness of existing famine early warning systems.

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“…The ecosystem service supply is not static but depends on the dynamic structures and functions of ecosystems [14]. Weather fluctuations throughout the year affect the biophysical conditions that determine the intra-annual supply of ecosystem services, especially those that depend directly on green biomass production, such as the provision of forage [15] and crop productivity [16,17]. Soil occasionally fall below 0 °C [58].…”

Section: Introductionmentioning

confidence: 99%

Understanding Intra-Annual Dynamics of Ecosystem Services Using Satellite Image Time Series

et al. 2020

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Landscape processes fluctuate over time, influencing the intra-annual dynamics of ecosystem services. However, current ecosystem service assessments generally do not account for such changes. This study argues that information on the dynamics of ecosystem services is essential for understanding and monitoring the impact of land management. We studied two regulating ecosystem services (i. erosion prevention, ii. regulation of water flows) and two provisioning services (iii. provision of forage, iv. biomass for essential oil production) in thicket vegetation and agricultural fields in the Baviaanskloof, South Africa. Using models based on Sentinel-2 data, calibrated with field measurements, we estimated the monthly supply of ecosystem services and assessed their intra-annual variability within vegetation cover types. We illustrated how the dynamic supply of ecosystem services related to temporal variations in their demand. We also found large spatial variability of the ecosystem service supply within a single vegetation cover type. In contrast to thicket vegetation, agricultural land showed larger temporal and spatial variability in the ecosystem service supply due to the effect of more intensive management. Knowledge of intra-annual dynamics is essential to jointly assess the temporal variation of supply and demand throughout the year to evaluate if the provision of ecosystem services occurs when most needed.

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

Cited by 47 publications

References 48 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

Applying Tipping Point Theory to Remote Sensing Science to Improve Early Warning Drought Signals for Food Security

Understanding Intra-Annual Dynamics of Ecosystem Services Using Satellite Image Time Series

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