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Vegetation dynamics of the West African Sahel has attracted great scientific interest over the last 40 years because of the dramatic inter-decadal variability observed in the resource base of the region directly impacting on the livelihoods of the West African population. From farmers to pastoralists, agro-pastoralists and forest-users, all depend on the availability of vegetation resources and are affected by fluctuations in the available vegetation resource. Vegetation dynamics are controlled by both natural and human factors, including climate change and variability, increased concentration of CO2 in the atmosphere, grazing pressure, bush fires and agricultural expansion or contraction. The use of satellite data in combination with field data played a major role in the monitoring of vegetation dynamics and land use in the Sahel, since the mega drought of the 1970s and the 1980s. This paper briefly reviews the advance of satellite-based monitoring of vegetation dynamics over these 40 years. We discuss the promises of current and likely future data sources and analysis tools, as well as the need to strengthen in situ data collection to support and validate satellite-based vegetation and land use monitoring and modelling.

Vegetation dynamics of the West African Sahel has attracted great scientific interest over the last 40 years because of the dramatic inter-decadal variability observed in the resource base of the region directly impacting on the livelihoods of the West African population. From farmers to pastoralists, agro-pastoralists and forest-users, all depend on the availability of vegetation resources and are affected by fluctuations in the available vegetation resource. Vegetation dynamics are controlled by both natural and human factors, including climate change and variability, increased concentration of CO2 in the atmosphere, grazing pressure, bush fires and agricultural expansion or contraction. The use of satellite data in combination with field data played a major role in the monitoring of vegetation dynamics and land use in the Sahel, since the mega drought of the 1970s and the 1980s. This paper briefly reviews the advance of satellite-based monitoring of vegetation dynamics over these 40 years. We discuss the promises of current and likely future data sources and analysis tools, as well as the need to strengthen in situ data collection to support and validate satellite-based vegetation and land use monitoring and modelling.

In many higher education curricula, pre-structured step-by-step laboratory exercises in introductory courses in geographical information systems (GIS) are an important part of the training of future geographers. The reasons for this approach to teaching GIS are manifold, such as large numbers of students, off-the-shelf desktop software that is often complex, technical challenges, and scarce faculty resources. Often the reasons are well agreed upon by members of a university faculty and among the students. Research in other fields has shown that the use of a controlled manual for laboratory work often provides low learning potentials. However, not much empirical research has dealt with this issue within a GIS learning environment. Inspired by research on the value of student-generated questions within science education, the authors take a closer look at the type of student-generated questions and their relation to students' self-image of their learning approach in two pre-structured GIS laboratory settings at two Danish universities. They conclude that the vast majority of student-generated questions are of a basic information type and independent of the students' self-image of their learning approach. Further, it is found that wonderment questions, i.e. questions that are reflective in nature and show students the process towards acquiring extended geographical knowledge and software proficiency, are rarely asked.

In many higher education curricula, pre-structured step-by-step laboratory exercises in introductory courses in geographical information systems (GIS) are an important part of the training of future geographers. The reasons for this approach to teaching GIS are manifold, such as large numbers of students, off-the-shelf desktop software that is often complex, technical challenges, and scarce faculty resources. Often the reasons are well agreed upon by members of a university faculty and among the students. Research in other fields has shown that the use of a controlled manual for laboratory work often provides low learning potentials. However, not much empirical research has dealt with this issue within a GIS learning environment. Inspired by research on the value of student-generated questions within science education, the authors take a closer look at the type of student-generated questions and their relation to students' self-image of their learning approach in two pre-structured GIS laboratory settings at two Danish universities. They conclude that the vast majority of student-generated questions are of a basic information type and independent of the students' self-image of their learning approach. Further, it is found that wonderment questions, i.e. questions that are reflective in nature and show students the process towards acquiring extended geographical knowledge and software proficiency, are rarely asked.

This paper presents a novel approach to the preliminary, low-cost, national-scale mapping of wind energy, solar energy and certain categories of bio-energy resources in developing countries, using Mali as an example. The methods applied make extensive use of satellite remote sensing and meteorological mesoscale modeling. The paper presents first results from applying the methodology in Mali and discusses the appropriateness of the results obtained. It is shown that northern Mali has considerable wind energy potential, while average wind speeds in the southern part are too low to make wind power a competitive option. Solar energy resources are shown to be abundant in all of Mali, though the highest values are found in the south. The temporal variation is relatively limited. Bio-energy resources are also concentrated in the south, but there are small pockets of high vegetation productivity in the irrigated areas of the Niger inland delta that might be interesting from a renewable energy resource perspective. Finally, the paper discusses the role that renewable energy resources might play in the energy systems of Mali, given the spatio-temporal distribution of renewable energy resources. It is argued that at the current price of about 70 US$/barrel for fossil fuels, renewable energy resources are becoming economically as well as environmentally attractive options.

This paper presents a novel approach to the preliminary, low-cost, national-scale mapping of wind energy, solar energy and certain categories of bio-energy resources in developing countries, using Mali as an example. The methods applied make extensive use of satellite remote sensing and meteorological mesoscale modeling. The paper presents first results from applying the methodology in Mali and discusses the appropriateness of the results obtained. It is shown that northern Mali has considerable wind energy potential, while average wind speeds in the southern part are too low to make wind power a competitive option. Solar energy resources are shown to be abundant in all of Mali, though the highest values are found in the south. The temporal variation is relatively limited. Bio-energy resources are also concentrated in the south, but there are small pockets of high vegetation productivity in the irrigated areas of the Niger inland delta that might be interesting from a renewable energy resource perspective. Finally, the paper discusses the role that renewable energy resources might play in the energy systems of Mali, given the spatio-temporal distribution of renewable energy resources. It is argued that at the current price of about 70 US$/barrel for fossil fuels, renewable energy resources are becoming economically as well as environmentally attractive options.

This paper presents a novel approach to the preliminary, low-cost, national-scale mapping of wind energy, solar energy and certain categories of bio-energy resources in developing countries, using Mali as an example. The methods applied make extensive use of satellite remote sensing and meteorological mesoscale modeling. The paper presents first results from applying the methodology in Mali and discusses the appropriateness of the results obtained. It is shown that northern Mali has considerable wind energy potential, while average wind speeds in the southern part are too low to make wind power a competitive option. Solar energy resources are shown to be abundant in all of Mali, though the highest values are found in the south. The temporal variation is relatively limited. Bio-energy resources are also concentrated in the south, but there are small pockets of high vegetation productivity in the irrigated areas of the Niger inland delta that might be interesting from a renewable energy resource perspective. Finally, the paper discusses the role that renewable energy resources might play in the energy systems of Mali, given the spatio-temporal distribution of renewable energy resources. It is argued that at the current price of about 70 US$/barrel for fossil fuels, renewable energy resources are becoming economically as well as environmentally attractive options.

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10-day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT-4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT-4 vegetation (VGT) 10-day composite (S10) NDVI data. Three years of AVHRR PAL (1998-2000) and seven years of GIMMS (1998-2004) have been compared to 8 km resampled SPOT-4 VGT (1998-2004) data. The dynamic range of SPOT-4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT-4 VGT NDVI. Ortho-regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT-4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT-4 VGT compared to the PAL/ SPOT-4 VGT. Analyses on decade data likewise show that a linear relation exists between Spot-4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot-4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot-4 VGT NDVI compared to AVHRR PAL versus Spot-4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composit

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10-day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT-4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT-4 vegetation (VGT) 10-day composite (S10) NDVI data. Three years of AVHRR PAL (1998-2000) and seven years of GIMMS (1998-2004) have been compared to 8 km resampled SPOT-4 VGT (1998-2004) data. The dynamic range of SPOT-4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT-4 VGT NDVI. Ortho-regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT-4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT-4 VGT compared to the PAL/ SPOT-4 VGT. Analyses on decade data likewise show that a linear relation exists between Spot-4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot-4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot-4 VGT NDVI compared to AVHRR PAL versus Spot-4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composit

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10-day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT-4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT-4 vegetation (VGT) 10-day composite (S10) NDVI data. Three years of AVHRR PAL (1998-2000) and seven years of GIMMS (1998-2004) have been compared to 8 km resampled SPOT-4 VGT (1998-2004) data. The dynamic range of SPOT-4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT-4 VGT NDVI. Ortho-regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT-4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT-4 VGT compared to the PAL/ SPOT-4 VGT. Analyses on decade data likewise show that a linear relation exists between Spot-4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot-4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot-4 VGT NDVI compared to AVHRR PAL versus Spot-4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composit