Ground-and satellite-based evidence of the biophysical mechanisms behind the greening Sahel

Brandt M., Mbow C., Diouf A.A., Verger A., Samimi C., Fensholt R. (2015) Ground-and satellite-based evidence of the biophysical mechanisms behind the greening Sahel. Global Change Biology. 21: 1610-1620.
Link
Doi: 10.1111/gcb.12807

Abstract:

After a dry period with prolonged droughts in the 1970s and 1980s, recent scientific outcome suggests that the decades of abnormally dry conditions in the Sahel have been reversed by positive anomalies in rainfall. Various remote sensing studies observed a positive trend in vegetation greenness over the last decades which is known as the re-greening of the Sahel. However, little investment has been made in including long-term ground-based data collections to evaluate and better understand the biophysical mechanisms behind these findings. Thus, deductions on a possible increment in biomass remain speculative. Our aim is to bridge these gaps and give specifics on the biophysical background factors of the re-greening Sahel. Therefore, a trend analysis was applied on long time series (1987-2013) of satellite-based vegetation and rainfall data, as well as on ground-observations of leaf biomass of woody species, herb biomass, and woody species abundance in different ecosystems located in the Sahel zone of Senegal. We found that the positive trend observed in satellite vegetation time series (+36%) is caused by an increment of in situ measured biomass (+34%), which is highly controlled by precipitation (+40%). Whereas herb biomass shows large inter-annual fluctuations rather than a clear trend, leaf biomass of woody species has doubled within 27 years (+103%). This increase in woody biomass did not reflect on biodiversity with 11 of 16 woody species declining in abundance over the period. We conclude that the observed greening in the Senegalese Sahel is primarily related to an increasing tree cover that caused satellite-driven vegetation indices to increase with rainfall reversal. Copyright © 2015 John Wiley & Sons Ltd214 April 2015 10.1111/gcb.12807 Primary Research Article Primary Research Articles © 2014 John Wiley & Sons Ltd.

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Fodder biomass monitoring in Sahelian rangelands using phenological metrics from FAPAR time series

Diouf A.A., Brandt M., Verger A., El Jarroudi M., Djaby B., Fensholt R., Ndione J.A., Tychon B. (2015) Fodder biomass monitoring in Sahelian rangelands using phenological metrics from FAPAR time series. Remote Sensing. 7: 9122-9148.
Link
Doi: 10.3390/rs70709122

Abstract:

Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI) and in situ biomass data. This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and in situ biomass. A model with three variables-large seasonal integral (LINTG), length of growing season, and end of season decreasing rate-performed best (MAE = 605 kg·DM/ha; R2 = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999-2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg·DM/ha; R2 = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area in ecoregions increased overall accuracy (MAE = 489.21 kg·DM/ha; R2 = 0.77), indicating that a relation between metrics and ecosystem properties exists. LINTG was the main explanatory variable for woody rangelands with high leaf biomass, whereas for areas dominated by herbaceous vegetation, it was the PEAK metric. The proposed approach outperformed the established biomass NDVI-based product (MAE = 818 kg·DM/ha and R2 = 0.51) and should improve the operational monitoring of forage resources in Sahelian rangelands. © 2015 by the authors.

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GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999-2010

Verger A., Baret F., Weiss M., Filella I., Penuelas J. (2015) GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999-2010. Remote Sensing of Environment. 166: 126-137.
Link
Doi: 10.1016/j.rse.2015.05.027

Abstract:

Land-surface modelling would benefit significantly from improved characterisation of the seasonal variability of vegetation at a global scale. GEOCLIM, a global climatology of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR)-both essential climate variables-and fraction of vegetation cover (FCOVER), is here derived from observations from the SPOT VEGETATION programme. Interannual average values from the GEOV1 Copernicus Global Land time series of biophysical products at 1-km resolution and 10-day frequency are computed for 1999 to 2010. GEOCLIM provides the baseline characteristics of the seasonal cycle of the annual vegetation phenology for each 1-km. pixel on the globe. The associated standard deviation characterises the interannual variability. Temporal consistency and continuity is achieved by the accumulation of multi-year observations and the application of techniques for temporal smoothing and gap filling. Specific corrections are applied over cloudy tropical regions and high latitudes in the Northern Hemisphere where the low number of available observations compromises the reliability of estimates. Artefacts over evergreen broadleaf forests and areas of bare soil are corrected based on the expected limited seasonality. The GEOCLIM data set is demonstrated to be consistent, both spatially and temporally. GEOCLIM shows absolute differences lower than 0.5 compared with MODIS (GIMMS3g) climatology of LAI for more than 80% (90%) of land pixels, with higher discrepancies in tropical and boreal latitudes. ECOCLIMAP systematically produces higher LAI values. The phenological metric for the date of maximum foliar development derived from GEOCLIM is spatially consistent (correlation higher than 0.9) with those of MODIS, GIMMS3g, ECOCLIMAP and MCD12Q2 with average differences within 14. days at the global scale. © 2015 Elsevier Inc.

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