Valerie Graw

Mapping Marginality Hotspots – Geographical Targeting for Poverty Reduction

This mapping approach aims to make the marginalized and poor visible by identifying areas with difficult biophysical and socio-economic conditions. Mapping using different data sources and data types gives deeper insight into possible causal interlinkages and offers the opportunity for comprehensive analysis. The maps highlight areas where different dimensions of marginality overlap – the marginality hotspots – based on proxies for marginality dimensions representing different spheres of life. Furthermore, overlaying the marginality hotspots with the number of poor shows where most of the poor could be reached to help them to escape the spiral of poverty. Marginality hotspots can be found in particular in India and Nepal as well as in several countries in Central and Eastern Africa, such as Eritrea, Mozambique, Central African Republic, the Democratic Republic of Congo, Northern Sudan and large parts of Niger. Maps showing the overlap between marginality and poverty highlight that the largest number of marginalized poor are located in India and Bangladesh, as well as in Ethiopia, Southeastern Africa and some parts of Western Africa.

Mapping Marginality Hotspots

In this chapter the authors applied innovative Geographical Information Systems mapping techniques to illustrate spatial dimensions of marginality at continental and regional levels. They sought to make the marginalized and poor more visible by identifying areas where many poor people live under difficult biophysical and socio-economic conditions. A broad set of variables covering ecological, social, and economic dimensions were described using existing datasets to identify ‘marginality hotspots’ which were then overlaid with poverty distribution data. Areas where a high percentage of poor people coincided with marginality hotspots were found in Central and South East Africa, especially the northern parts of Niger and in Chad, the Central African Republic, the Democratic Republic of the Congo, Mozambique, Malawi, and Burundi.

Vegetation monitoring with satellite time series: an integrated approach for user-oriented knowledge extraction

Climate change, food insecurity and limited land and water resources strengthen the need for operational and spatially explicit information on vegetation condition and dynamics. The detection of vegetation condition as well as multiannual and seasonal changes using satellite remote sensing, however, depends on the choice of data including length and frequency of time series. Thus, this contribution focuses on the derivation of the optimal remotely sensed data for vegetation monitoring and extraction of relevant metrics. Time series of satellite data from Landsat-8, Sentinel-1/2, and MODIS were used to identify characteristics of vegetation at different spatiotemporal scales. We derived parameters, such as: maximum and amplitude based on vegetation index time series, as well as Land Surface Temperature (LST). Along with optical data, we used backscattering intensity over consecutive vegetation growing seasons. The analysis was carried out using Google Earth Engine, a cloud computing platform which allows to access various data archives and conduct data-intensive analysis. Taking advantage of this platform, we developed a web-based application named GreenLeaf. The application is computing metrics and plotting time series, based on parameters defined by the user. The derived vegetation condition parameters provide sufficient information to detect vegetation change. In addition, the images acquired from near-coincident dates provide similar information over continuous surfaces. The developed application contributes to the use of satellite data and the simplification of data access for users with limited remote sensing experience and/or restricted processing power. Aiming at providing this knowledge to stakeholders can further support decision making on multiple scales.

In Memoriam: Gunter Menz

Remote Sensing Research Group, Department of Geography, University of Bonn, Meckenheimer Allee 166,53115 Bonn, Germany; andreas.rienow@ruhr-uni-bochum.de (A.R.); agatha.okhai@gmail.com (A.A.);hendrik.fluegel@googlemail.com (H.F.); s.heinemann@uni-bonn.de (S.H.); s6tohenn@uni-bonn.de (T.H.);k.hentze@geographie.uni-bonn.de (K.H.); f.kirimi@geographie.uni-bonn.de (F.K.);s6sokoch@uni-bonn.de (S.K.); bkoner@uni-bonn.de (B.K.-K.); s3anortw@uni-bonn.de (A.O.);schultz@giub.uni-bonn.de (J.S.); kstaar@uni-bonn.de (K.S.); kuria@geographie.uni-bonn.de (K.B.T.);anna.vetter@uni-bonn.de (A.V.)

Global Estimates of the Impacts of Grassland Degradation on Livestock Productivity from 2001 to 2011

In response to the needs for estimating the cost of grassland degradation to determine the cost of inaction and for identifying cost-effective strategies to address the consequent loss of livestock productivity, we developed a modeling framework where global statistics databases and remote sensing data/analyses coupled with empirical/statistical modeling are designed to quantify the global cost of grassland degradation. By using this framework, we identified grassland degradation hotspots over the period of 2001 to 2011 and estimated changes in livestock productivity associated with changes in grassland productivity within the hotspots. Ignoring environmental benefits and losses in live weight of livestock not slaughtered or sold, the cost of livestock productivity was estimated about 2007 US

The Economics of Desertification, Land Degradation, and Drought; Toward an Integrated Global Assessment

6.8 billion. Although on-farm cost is small in Sub-Saharan Africa due to the low livestock productivity, the impact on human welfare would be much more severe in the region where majority of the population is below the poverty line. This implies that addressing grassland degradation is even more urgent in the region, given the increasing demand for livestock products and the potential contribution to poverty reduction. Taking action toward grassland degradation could simultaneously reduce poverty and promote carbon sequestration while conserving socio-economic, cultural, and ecological benefits that livestock provide.