Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Abstract

From in situ measured woody cover we develop a phenology driven model to estimate the canopy cover of woody species in the Sahelian drylands at 1 km scale. The model estimates the total canopy cover of all woody phanerophytes and the concept is based on the significant difference in phenophases of dryland trees, shrubs and bushes as compared to that of the herbaceous plants. Whereas annual herbaceous plants are only green during the rainy season and senescence occurs shortly after flowering towards the last rains, most woody plants remain photosynthetically active over large parts of the year. We use Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l'Observation de la Terre (SPOT) -VEGETATION (VGT) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series and test 10 metrics representing the annual FAPAR dynamics for their ability to reproduce in situ woody cover at 43 sites (163 observations between 1993 and 2013) in the Sahel. Both multi-year field data and satellite metrics are averaged to produce a steady map. Multiple regression models using the integral of FAPAR from the onset of the dry season to the onset of the rainy season, the start date of the growing season and the rate of decrease of the FAPAR curve achieve a cross validated r 2 /RMSE (in % woody cover) of 0.73/3.0 (MODIS) and 0.70/3.2 (VGT). The extrapolation to Sahel scale shows agreement between VGT and MODIS at an almost nine times higher woody cover than in the global tree cover product MOD44B which only captures trees of a certain minimum size. The derived woody cover map of the Sahel is made publicly available and represents an improvement of existing products and a contribution for future studies of drylands quantifying carbon stocks, climate change assessment, as well as parametrization of vegetation dynamic models.

Desertification, resilience, and re-greening in the African Sahel – a matter of the observation period?

“…Herrmann and Tappan (2013) found a reduction of woody species richness and a shift to more xeric species since the early 1980s, as well as an increasing dominance of shrubs in central Senegal. Other studies from Senegal and Mali confirmed these results (Maranz, 2009;Miehe et al, 2010;Herrmann and Tappan, 2013;Brandt et al, 2014bBrandt et al, , 2015Brandt et al, , 2016aSpiekermann et al, 2015). Kaptué et al (2015) refer to a post-drought tree population recovery and present evidence of a decline in populations of economically and culturally important trees and shrubs despite the increase in woody cover.…”

“…Since the millennium, additional data like MODIS and SPOT-Vegetation (VGT) NDVI data have been used (e.g. Herrmann and Tappan, 2013;Dardel et al, 2014a;Brandt et al, 2015Brandt et al, , 2016aBrandt et al, , b, 2017aTong et al, 2017). Reviews are provided by Higginbottom and Symeonakis (2014), Knauer et al (2014), and Mbow et al (2014.…”

“…On the basis of these studies, scientists postulated a re-greening trend in the Sahel since the early 1980s (e.g. Anyamba and Tucker, 2005;Herrmann and Hutchinson, 2005;Herrmann et al, 2005;Olsson et al, 2005;Helldén and Tottrup, 2008;Hiernaux et al, 2009b;Nicholson et al, 2012;Anyamba et al, 2014;Fensholt et al, 2013, Brandt et al, 2016a, b, 2017a. Various authors mentioned limitations in assessing vegetation changes (re-greening) when using coarse-resolution NOAA-AVHRR and similar datasets for the following reasons.…”

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

“…A diverse set of analytical methods that exploit phenological information in time-series of multispectral images has been developed for distinguishing and mapping vegetation cover types [20][21][22][23][24][25], estimating and mapping biomass and vegetation fuel loads [26][27][28][29][30] and for investigating how vegetation and related ecosystem processes respond to management, disturbance, weather, drought and climate change [31][32][33][34][35][36][37]. These methods examine the seasonal growth dynamics of the vegetation through quantification and analysis of various metrics of the time signal observed within one or more years.…”

“…Gessner et al [55] applied decision tree regression to a set of satellite-derived phenology metrics for the wet and dry season to estimate fractional cover of woody, herbaceous and bare soil in savanna regions of southern Africa. Brandt et al [25] estimated and mapped woody plant cover in the Sehelian drylands of Africa by using multiple regression modeling of the relationships between various satellite-derived phenological metrics and field data.…”