Stefanie M. Herrmann

A Continental-Scale Classification of Rainfall Seasonality Regimes in Africa Based on Gridded Precipitation and Land Surface Temperature Products

AbstractA classification of rainfall seasonality regimes in Africa was derived from gridded rainfall and land surface temperature products. By adapting a method that goes back to Walter and Lieth’s approach of presenting climatic diagrams, relationships between estimated rainfall and temperature were used to determine the presence and pattern of humid, arid, and dry months. The temporal sequence of humid, arid, and dry months defined nonseasonal as well as single-, dual-, and multiple-wet-season regimes with one or more rainfall peaks per wet season. The use of gridded products resulted in a detailed, spatially continuous classification for the entire African continent at two different spatial resolutions, which compared well to local-scale studies based on station data. With its focus on rainfall patterns at fine spatial scales, this classification is complementary to coarser and more genetic classifications based on atmospheric driving forces. An analysis of the stability of the resulting seasonality re...

Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing

A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (ϵn) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of ϵn suggested in this study can be used in grassland regions where no carbon flux measurements are accessible.

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, the actual extent of farmed parkland and spatial patterns of tree cover are largely unknown. We used the rule-based predictive modeling tool Cubist® to estimate field tree cover in the west-central agricultural region of Senegal. A collection of rules and associated multiple linear regression models was constructed from (1) a reference dataset of percent tree cover derived from very high spatial resolution data (2 m Orbview) as the dependent variable, and (2) ten years of 10-day 250 m Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) composites and derived phenological metrics as independent variables. Correlation coefficients between modeled and reference percent tree cover of 0.88 and 0.77 were achieved for training and validation data respectively, with absolute mean errors of 1.07 and 1.03 percent tree cover. The resulting map shows a west-east gradient from high tree cover in the peri-urban areas of horticulture and arboriculture to low tree cover in the more sparsely populated eastern part of the study area. A comparison of current (2000s) tree cover along this gradient with historic cover as seen on Corona images reveals dynamics of change but also areas of remarkable stability of field tree cover since 1968. The proposed modeling approach can help to identify locations of high and low tree cover in dryland environments and guide ground studies and management interventions aimed at promoting the integration of field trees in agricultural systems.

The Map Is not the Territory: How Satellite Remote Sensing and Ground Evidence Have Re-shaped the Image of Sahelian Desertification

Satellite remote sensing, in particular the analysis of coarse resolution time series of vegetation indices, has played an important role in challenging earlier assumptions of widespread desertification in the Sahel. Findings of such analyses show a greening trend in much of the region since the early 1980s, which seems to suggest a positive development. On the other hand, a growing number of field studies of vegetation dynamics across the Sahel offer a more fine-scaled and nuanced picture of changes. Of particular interest with respect to degradation and rehabilitation is the woody component of the vegetation cover, which is less affected by short-term fluctuations in precipitation than the herbaceous component. We synthesized findings from published field studies on changes in the abundance and diversity of woody vegetation across the Sahel and spatially compared them with the remotely sensed greenness trends. Many field sites reported a decline in the abundance of woody vegetation since before the great droughts, in particular of large trees. In addition, the woody vegetation shifted from a diverse species composition towards fewer and more drought tolerant species in the majority of sites. However, some success stories of agroforestry management stood out as well, where formerly degraded farmlands were rehabilitated and in some cases have reached even higher tree densities than in the 1960s. The discrepancy between satellite-observed greening trends and changes in woody vegetation on the ground—in both directions—emphasizes the need of integrating multiple perspectives and scales in the interpretation of greening trends with respect to desertification.

Reduction of tree cover in West African woodlands and promotion in semi-arid farmlands

Woody vegetation in farmland acts as a carbon sink and provides ecosystem services for local people, but no macroscale assessments of the impact of management and climate on woody cover exist for drylands. Here we make use of very high spatial resolution satellite imagery to derive wall-to-wall woody cover patterns in tropical West African drylands. Our study reveals that mean woody cover in farmlands along all semi-arid and sub-humid rainfall zones is 16%, on average only 6% lower than in savannahs. In semi-arid Sahel, farmland management promotes woody cover around villages (11%), while neighbouring savannahs had on average less woody cover. However, farmlands in sub-humid zones have a greatly reduced woody cover (21%) as compared with savannahs (33%). In the region as a whole, rainfall, terrain and soil are the most important (80%) determinants of woody cover, while management factors play a smaller (20%) role. We conclude that agricultural expansion causes a considerable reduction of trees in woodlands, but observations in Sahel indicate that villagers safeguard trees on nearby farmlands which contradicts simplistic ideas of a high negative correlation between population density and woody cover.Farmland management promotes tree cover around villages in the semi-arid Sahel of West Africa, according to analyses of satellite imagery. This implies that a higher population density does not always lead to reduced tree cover.

Beyond Bioproductivity: Engaging Local Perspectives in Land Degradation Monitoring and Assessment

Land degradation monitoring and assessment in the Sahel zone takes advantage of and relies substantially on temporal trends of remote sensing-based vegetation indices, which are proxies for the bioproductivity of the land. However, prior studies have shown that negative or positive trends in bioproductivity are not necessarily associated with degradation or improvement of land condition. We argue that remote sensing-based indices, while having contributed much to dismantling an outdated desertification narrative, are themselves falling short of capturing the whole picture and need to be accompanied by field observations that are relevant to local land users. We used the participatory photo elicitation method in three sites in order to elicit local pastoralists’ perspectives on land degradation and identify the indicators that they use to characterize pasture quality, while empowering them to lead the discussion. The discussion revealed indicators far beyond bioproductivity, including livestock performance as well as composition and quality of the herbaceous and woody vegetative cover, invasive species, soil quality and water availability. We found that the pastoralists’ interest, knowledge and field observations could potentially be harnessed using a crowd-sourcing approach in order to produce a geospatially explicit dataset of land degradation, which would be complementary to the remote sensing-based maps of trends in bioproductivity and could serve as a reference for the development of more targeted remote sensing-based indicators of land degradation.