1. Q: Can you briefly explain what the main reason behind this project is?
A: To identify and monitor gullies and provide solutions to land degradation problems in Namibia, using remote sensing based methods. Generally speaking, it is using open source and the cheap solutions as much as possible in an automatic manner.
2. Q: There are standardised methods (multi-technology approach and Volunteering Geographic Information) developed to detect and monitor the evolution of gully-affected areas in Namibia. How are these methods used to detect and monitor the evolution of gully-affected areas?
A: The methods are developed in three different levels. First, one is to detect the gullies looking at their omorphology i.e. the shape they have and some pre-knowledge based on the topography. At this stage, we detect the gullies at pixel levels, and at each pixel, we decide if there is a gully or not. Second stage, we include other datasets from open source, but at this case the idea is to work with time series and we will explore the potential of these datasets to monitor of how the erosion or gully affected areas are evolving in terms of spectral signatures and change in height relief.
3. Q: If the evolution is detected by the aforementioned methods, what that is done to prevent land degradation or stop further damages if already occurred?
A: This is a project to identify and monitor land degradation. This project can generate data sources that can be used by experts from other fields e.g. from geomorphologist to study deeply the evolution of the gullies and agronomists to plan remediation strategies. As professionals in Geoinformatics, the way we can help to stop land degradation is providing databases with kind of priorities areas or maps to experts that can use it to find in place solutions.
4. Q: What are the expected results from these two methods?
A: The first stage, which is based on TanDEM-X datasets analysis, we obtain maps of gully-affected areas. Each pixel is classified as gully or non-gully. From this, we can still derive geomorphological parameters of the gully e.g. perimeter and volume. In the second stage, still under developed, we work on two different approaches. First one, to see how the evolution of NDVI both for inter-annual and intra-annual analysis affects gully surroundings. On the other hand, we make use of RADAR techniques to estimate the change in the topography and its correlation to heavy rains within the gully network.
5. Q: Foundation of this project, which is scheduled for this year, states that several pilot areas where there are gullies of different types and where there are local inhabitants interested in collaborating will be identified.
(i) Q: How long did it take for these areas to be identified and how many areas are identified as to date?
A: We are working with around eight different gullies. Four are in Krumhuk Farm and four are in Kunene just nearby to Opuwo. Time-consuming part is to capture validation areas in the field, but with the developed methods, you can obtain results in few hours.
(ii) What was done to ensure that local inhabitants are willing to collaborate?
A: Of course, this was the challenge of the study. We know in some areas they are willing to collaborate because we have been already collaborating with them in the past. At Krumhuk Farm, there is a big collaboration with the farmers with higher level of involvement. For communal lands in Kunene, we have already been in contact with them and some key contacts are identified in the field to facilitate field work. The real challenge is to let them understand what we are trying to do.
6. Q: What is the duration of this project?
A: Basically this is my PhD project and the expected time is three years. However, duration can be extended if there is a need, maybe if I decide to continue with this fieldwork after my PhD.
7. Q: What other important information you can give in relation to this on-going project?
A: Output knowledge produced by these methods can actually be used by other professionals. The information provided by the gully dynamics data can be used by geomorphologists to better understand the factors like climatic and environmental factors. It can also be used by other land specialists to control and avoid land degradation