This data is the result of collecting opinions of stakeholders on how they decide to use their land use products for generating benefits (food provision, fodder provision, energy provision, construction material provision and market value provision).

The questionnaire is about current and future direct and indirect drivers of land use/cover change (LUCC), positive or negative impact of drivers of LUCC on food security and water security and trends of land cover and crop types under "business as usual"in the Upper East Region (UER). The questionnaire has been send by e-mail to researchers (experts) in Ghana who are doing or have done research on land use changes in the Upper East Region. The questionnaire is based on interviews with researchers in Ghana on driving forces of land use change in the Upper East Region, conducted in November 2013.

The questionnaire is about current and future direct and indirect drivers of land use/cover change (LUCC) and trends of land cover and crop types under "business as usual" in the Upper East Region (UER). The questionnaire was conducted between Oct. 2014 and Feb. 2015 and is based on interviews with researchers in Ghana on driving forces of land use change in the Upper East Region, conducted in November 2013.

This data is the result of a stakeholder survey to identify their perception on the effects of land use alternatives. Regarding seven different ecosystem services(food provision, fodder provision, energy provision, construction material provision, market value provision, water provision and erosion control), it presents how they can be altered in a positive or a negative way by per cent when stakeholders choose a certain land use scenario.

These LULC maps were created through automatic digital classification of RapidEye imagery acquired in November 2012. Reference (or field) data on which the classification was based were acquired through a field campaign that lasted from June to November 2012. Standard image pre-processing techniques such as geometric and radiometric correction were conducted on the data prior to classification. The Random Forest classification algorithm was used for classification. A single level classification was conducted to reveal six LULC classes. Discrimination between different crop types was not possible due to the use of a mono-temporal image

Land use/cover classification of West Africa based on multi-sensor earth observation data. The regional land cover/use map for West Africa has a spatial resolution of 250 m. It is based on earth observation data from three optical and radar sensors. Annual time series of the optical Moderate Resolution Imaging Spectroradiometer (MODIS) are used for discriminating semi-natural vegetation classes and agricultural classes. Envisat Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS) time series, in combination with MODIS data, are used for delineating permanent and seasonal water bodies. Built-up areas of two density classes are mapped based on radar imagery collected by the satellites TanDEM-X and TerraSAR-X. The accuracy assessment of the multi-sensor land cover map yields an overall accuracy of 80% at legend level 1 (9 classes) and 73% at the more detailed legend level 2 (14 classes). For further details on the mapping approach and a discussion of strengths and limitations on the approach and dataset, we refer to the following publication: Gessner, Ursula; Machwitz, Miriam; Esch, Thomas; Tillack, Adina; Naeimi, Vahid; Kuenzer, Claudia; Dech, Stefan (2015): Multi-sensor mapping of West African land cover using MODIS, ASAR and TanDEM-X/TerraSAR-X data. Remote Sensing of Environment, accepted.

Land use / land cover classification of the districts Bolgatanga and Bongo in Ghana based on very high resolution remote sensing data (5m). Focus of the classification is on the agricultural class, where single crop types are discriminated. Remote sensing base: RapidEye, TerraSAR-X, Landsat.