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

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

Biomass estimation to support pasture management in Niger

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 (R2 = 0.55, R2cv = 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.JRC.H.4-Monitoring Agricultural Resource

Remote Sensing Based Yield Estimation in a Stochastic Framework – Case Study of Durum Wheat in Tunisia

Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by the information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument.JRC.H.4-Monitoring Agricultural Resource

Thigh Pain Occurrence Rate in a Short, Tapered, Porous, Proximally-Coated Cementless Femoral Stem - Clinical and Radiological Results at 2-Year Follow-Up

Abstract Introduction: Short stems have been designed with the purpose of preserving bone tissue, decreasing the incidence of thigh pain and facilitating surgical techniques. The aim of our study was to assess whether a shortened tapered conventional stem was able to reduce the incidence of thigh pain.Methods:Between March 2010 and December 2012, 200 patients were enrolled in the study. Visual analogue scale (VAS) that included mapping of the pain, Harris Hip Score (HHS), Short Form-12 (SF-12) and radiographic outcomes were evaluated prior to surgery as well as at 6, 12 and 24 months post-operatively.Results: After 6 months, 6 patients (3%) had thigh pain. After 12 months, 3 patients (1.5%) complained about thigh pain. After 2 years, 2 patients (1%) had thigh pain. There was no correlation between pain and clinical, radiological, or demographic variables.Conclusion:The shortened tapered conventional stem resulted in a lower incidence of thigh pain for up to 2-years following surgery, compared with conventional or other short stems

Ultrasound-assisted synthesis of WOx-decorated ZnO photocatalysts for NOx abatement

Heterojunctions based on ZnO have numerous applications, such as water splitting, sensing and energy storage [1]. Recently, ZnO/WO3 composites have shown promising results in the sonocatalytic and photocatalytic degradation of aqueous and gas pollutants [2]. Several synthetic approaches have been reported, including chemical vapor deposition, magnetron sputtering, hydrothermal methods and high temperature annealing. Ultrasound-assisted synthesis can provide a scalable and cost-effective strategy to tailor the catalyst structural and morphological properties [3]. In the present work, pristine ZnO and ZnO/WOx composites were synthesized via a sonochemical method, studying the role of the ultrasound amplitude and mode (continuous/pulsed), metal precursor, WOx content and post-synthetic annealing. The resulting materials were extensively characterized, investigating their structural, morphological, optical, and surface properties. Samples were tested towards the photocatalytic removal of NOx under both UV and visible light irradiation in a batch reactor. A good degree of crystallinity is appreciable even before calcination and better morphological features are observed with respect to reference samples prepared without ultrasounds. The morphological properties can be further tuned by changing the metal precursor and adding a post-synthetic annealing step. Photocatalytic activity is promoted with respect to both benchmark samples (Figure 1)

Investigating the relationship between the inter-annual variability of satellite-derived vegetation phenology and a proxy of biomass production in the Sahel

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 regional scale. This study describes a 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 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.JRC.H.4-Monitoring Agricultural Resource

Development of a national and sub-national crop calendars data set compatible with remote sensing derived land surface phenology

Crop calendars are a fundamental component of agricultural production monitoring systems since they help analysts to focus on the seasons when different crop types are actually growing in the field. The Earth Observation based early warning system ASAP (Anomaly hot Spots of Agricultural Production) uses land surface phenology (LSP) metrics as proxy for crop calendars and applies parameters, such as the start and end of the season (SOS and EOS respectively) to define the period of active agricultural vegetation growth at pixel level. However, such information is not crop specific and it remains therefore relevant to use crop calendars from independent sources providing crop specific key phenological timings, such as sowing, growing and harvesting. Several institutions, including FAO and USDA make available crop calendars at the national level, which are widely used for agricultural monitoring. The LSP derived SOS and EOS metrics can be associated with sowing and harvesting from such crop calendars. This report describes a method for the attribution of each growing season derived from LSP to a crop type listed in existing crop calendars. Based on a set of rules, we compare the growing seasons derived from LSP with the timings of the crop calendars, and select those crops where a match between LSP and crop calendar information is found. Agricultural statistics, including harvested area and production, are used in order to verify the correct identification and relevance of crop types by country. The method also allows to downscale the existing national level crop calendars to the sub-national level. It therefore makes available sub-national level crop calendars, which are highly valuable for crop monitoring at that scale. The resulting crop calendars are available in the ASAP download section: https://mars.jrc.ec.europa.eu/asap/download.phpJRC.D.5-Food Securit

Annual green water resources and vegetation resilience indicators: Definitions, mutual relationships, and future climate projections

Satellites offer a privileged view on terrestrial ecosystems and a unique possibility to evaluate their status, their resilience and the reliability of the services they provide. In this study, we introduce two indicators for estimating the resilience of terrestrial ecosystems from the local to the global levels. We use the Normalized Differential Vegetation Index (NDVI) time series to estimate annual vegetation primary production resilience. We use annual precipitation time series to estimate annual green water resource resilience. Resilience estimation is achieved through the annual production resilience indicator, originally developed in agricultural science, which is formally derived from the original ecological definition of resilience i.e., the largest stress that the system can absorb without losing its function. Interestingly, we find coherent relationships between annual green water resource resilience and vegetation primary production resilience over a wide range of world biomes, suggesting that green water resource resilience contributes to determining vegetation primary production resilience. Finally, we estimate the changes of green water resource resilience due to climate change using results from the sixth phase of the Coupled Model Inter-comparison Project (CMIP6) and discuss the potential consequences of global warming for ecosystem service reliability.Fil: Zampieri, Matteo. Joint Research Centre; ItaliaFil: Grizzetti, Bruna. Joint Research Centre; ItaliaFil: Meroni, Michele. Joint Research Centre; ItaliaFil: Scoccimarro, Enrico. No especifíca;Fil: Vrieling, Anton. No especifíca;Fil: Naumann, Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Toreti, Andrea. Joint Research Centre; Itali

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

Author Posting.© The Author(s), 2015. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing of Environment 174 (2016): 44-55, doi:10.1016/j.rse.2015.12.003.Pastoralist households across East Africa face major livestock losses during drought periods that can cause persistent poverty. For Kenya and southern Ethiopia, an existing index insurance scheme aims to reduce the adverse effects of such losses. The scheme insures individual households through an area-aggregated seasonal forage scarcity index derived from remotely-sensed normalized difference vegetation index (NDVI) time series. Until recently, insurance contracts covered animal losses and indemnity payouts were consequently made late in the season, based on a forage scarcity index incorporating both wet and dry season NDVI data. Season timing and duration were fixed for the whole area (March-September for long rains, October-February for short rains). Due to demand for asset protection insurance (pre-loss intervention) our aim was to identify earlier payout options by shortening the temporal integration period of the index. We used 250m-resolution 10-day NDVI composites for 2001-2014 from the Moderate Resolution Imaging Spectroradiometer (MODIS). To better describe the period during which forage develops, we first retrieved per-pixel average season start- and end-dates using a phenological model. These dates were averaged per insurance unit to obtain unit-specific growing period definitions. With these definitions a new forage scarcity index was calculated. We then examined if shortening the temporal period further could effectively predict most (>90%) of the interannual variability of the new index, and assessed the effects of shortening the period on indemnity payouts. Our analysis shows that insurance payouts could be made one to three months earlier as compared to the current index definition, depending on the insurance unit. This would allow pastoralists to use indemnity payments to protect their livestock through purchase of forage, water, or medicines.AV was funded under a contract from the International Livestock Research Institute. CCU was supported by the U.S. National Science Foundation under grant OCE-1203892.2016-12-1