15The ability to understand gully erosion development is closely related to our ability to quantify 16 the morphology of gullies. At present, various technologies are at hand to collect data at 17 increasing levels of detail. However, many of the developed technologies are time-consuming, 18 difficult to apply or expensive. As an alternative, image-based modelling offers a cost-efficient, 19 flexible and rapid method to quantify gully morphology from photographs taken in the field. In 20 this study, the use of image-based modelling was tested and fine-tuned to quantify the 21 CITE AS Frankl, A., Stal, C., Abraha, A., Nyssen, J., Rieke-Zapp, D., De Wulf, A., Poesen, J., 2015. Detailed recording of gully morphology in 3D through image-based modelling. Catena 127, 92-101. 2 morphology of four gully heads in contrasting biophysical environments prone to gully erosion: 22 two bank gullies in Central Belgium and two permanent gullies in Northern Ethiopia. Ground 23 photographs (n = 88-235) were taken with a reflex Canon EOS 450D camera having a 20 mm 24 wide-angle lens with a fixed focal length. The data collection occurred during days of 30-100% 25 cloud cover and after removing excessive vegetation in the gullies. Processing of the 26 photographs occurred in PhotoScan 1.0.2. software using the semi-automated Structure from 27Motion-Multi View Stereo (SfM-MVS) workflow, and allowed to produce 3D Digital Elevation 28Models with accuracies that range from millimetres to centimetres. In addition, for the same 29 surface, 2.5D models were created in ArcGIS. Gully morphological properties were derived and 30 include cross-sections, total volume and volume of undercut walls and soil pipe inlets. For the 31 volume calculation, OPTOCAT software was used. Cross-sections were also quantified by tape 32 meter measurements. When compared to 3D models, cross-sections quantified from tape meter 33 measurements and from 2.5D models underestimate the cross-sectional area by <1-14% and 0-34 2.5% respectively. Considering gully volume, 2.5D and 3D approximations show only small 35 differences, related to the volume of soil pipe inlets and undercutting areas. These differences, 36 however, highlight the erosive activity of the gullies, and are important to understand gully 37 dynamics in detail. Geomorphologically spoken, areas where undercutting or soil piping occurs 38 are among the most dynamic and reveal where important morphologic changes are about to occur. 39The accuracies reported in this study are similar to those obtained in other studies that consider 40 surfaces of similar scales. In sum, image based modelling is a promising tool to study in detail 41 gully morphology in 3D, which is the closest approximation of the surface morphology. 42 Frankl, A., Stal, C., Abraha, A., Nyssen, J., Rieke-Zapp, D., De Wulf, A., Poesen, J., 2015. Detailed recording of gully morphology in 3D through image-based modelling. Catena 127, 92-101. 3 43 CITE AS

Quantitative research on land use and land cover (LUC) in Africa usually addresses the second half of the twentieth century, by using remote sensing data. Terrestrial photographs, which are available since 1868 in Ethiopia, are seldom used in a quantitative way. This paper presents a methodology that allows to produce land use and land cover (LUC) maps on the basis of old terrestrial photographs. Therefore, land use and land cover was investigated on historical and present-day photographs, and these interpretations were warped to the horizontal plane of the map. The resulting maps allow to gain better insights into LUC changes over a period of 140 years. The results show that woody vegetation increased strongly, together with an increase in built-up area. This occurred especially at the expense of bushland. The study validates pervious findings and shows that improved land management strategies in one of the world's most degraded areas can lead to environmental rehabilitation

. The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t 1 and t 2 , are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t 2 -t 1 . Based on the change model, the surface and volume of the building changes can be quantified.

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