Potential of and constraints on the application of remote sensing in Tunisia

Abstract

Remote sensing is becoming a useful operational tool for decision support. Information obtained from space is increasingly available. Since the American Landsat program in 1972, many countries have embarked on Earth observations, and there are now about 100 operational satellites in orbit. The potential and the results offered by remote sensing are remarkable if we consider the relative ease of obtaining information of any kind (especially environmental parameters) in a short period of time and at a certain distance, which is then repeated over time or even, in some cases, almost continuously. It allows a wide spatial coverage, greater objectivity and accuracy, as well as a lower cost overall than conventional sensing methods. For developed countries, remote sensing represents a revolution in the field of environmental monitoring, since these countries are the main suppliers of spatial data and software for processing these data. Developing countries, including Tunisia, have found themselves obligated to align themselves with these changes and transitions, and to actively work for greater participation in the elaboration of these changes in the most effective way. Remote sensing has been used for more than 40 years in Tunisia. It has been applied in various fields of research and development, such as agriculture, mapping, and planning. The main objective of this work was to assess the potential of remote sensing to be applied in each of these domains in Tunisia through a comparative multi-criteria analysis using the analytic hierarchy process (AHP). Four criteria (data used, tools and software, acquired competence, and financial resources) that have a significant impact on the potential for remote sensing application were included in our analysis. Weights were assigned to each parameter through a pairwise comparison matrix based on the AHP method. The results showed that agriculture and land use planning are the areas where remote sensing has the greatest potential, with high weights for all criteria studied. Regarding the criteria studied, it was observed that the criterion of data used is important for all application areas, followed by skills. For agriculture and land use planning, the criterion of data used (c1) is considered the most important, with a high weight of 59.4%. This suggests that the data used are a crucial influence on the application of remote sensing in these application areas. For its application to mapping, the choice of tools used (c2) is crucial, with a high weight of 31.3% for this criterion. For the environment, costs can be an important factor to consider, with a relatively high weight of 12.9% for the financial resources criteria (c4). The results of our study were validated by comparing them with those of a technology needs assessment report for climate adaptation conducted in Tunisia by the Ministry of Environment and Sustainable Development [United Nations Development Programme (2016) Tunisia: technology needs assessment. Report on climate change adaptation. https://tech-action.unepccc.org/wp-content/uploads/sites/2/2016/05/tunisia-tna-report-1feb2016-adaptation.pdf]. According to this report, the highest-priority sectors for the use of new technologies such as remote sensing in Tunisia are agriculture and water resources management as well as coastal, marine, and urban area management. These sectors were selected because of their weight in the country's economy and their vulnerability to climate change among the various sectors analysed in the report (agriculture and water resources management, management of urban, coastal and marine areas, ecosystem management, tourism and health). We used a sensitivity analysis method called the Morris method. This method estimates the importance of each criterion and each interaction between criteria to the variation in the results. The results of this analysis show that criterion c1 (data used) is the most important for all domains, with sensitivity indices ranging from 12.8 to 17.8%. Criterion c2 (tools and software) is also important for the mapping and environmental domains, with sensitivity indices of 11% and 10.2%, respectively. Criterion C3 (acquired competence) is important for the agriculture and environment domains, with sensitivity indices of 13% and 12.3%, respectively. Finally, criterion c4 (financial resources) is also important for the mapping and agriculture domains, with sensitivity indices of 12.1% and 13%, respectively. In conclusion, the sensitivity analysis shows that the overall results are quite robust to variations in the criteria.