## 6 Discussion
### Advantages and Limitations of the Presented Lava Flow Mapping Approach
In addition, there is always the possibility that areas of volcanic overprinting were not detected by the _dNDVI_ analysis or NHI detection. Another source of error for the modelling of the lava flows is the creation of a runoff model based on the DEMs (Section 4.1). Here, a high degree of generalization is assumed, which has an influence on the result. The resolution and quality of the DEM is elementary, because only in this way morphological structures can be recognized. In Figure 10, the OBIA methodology for the event Karangetang I was tested using different DEMs. All DEMs achieved satisfactory results, with the TanDEM-X (12 m) drainage model representing the lava flow surface most accurately. The comparison confirms that for modelling runoff, coarser resolution does not necessarily lead to worse results [70]. Despite this, higher resolution DEMs achieve smaller-scale runoff models, which in turn allow for a more detailed modelling of lava flows [70]. The DEMs used for the methodology were all generated before the analyzed eruptive phases. Thus, they may not represent the current prevailing morphology of the volcanoes. In particular, explosive eruptions can result in drastic morphological changes [10,71] that are disregarded in this modelling. For applying this classification approach after an eruption with severe morphological changes, an updated DEM must be available. One possible solution for this is to derive DEMs from (tri-) stereoscopic images of VHR optical satellites. These not only provide an actual image of the relief of a volcano after an eruption, but also have high geometric resolution [43]. However, the analyzed events mainly resulted in the discharge of lava with no major changes in the volcanoes morphology (except the lava deposits), which is why the used DEMs provide sufficient quality for modelling the lava flow path. Another way to optimize drainage network modelling, is to calculate flow accumulation with a multi-flow algorithm. In contrast to single-flow algorithms, multi-flow algorithms achieve better results when modelling diverging drainage networks and parallel flows [72]. The single-flow D8 algorithm used in this study [56] (Section 4.1), models runoff into one of the adjacent cells based on the slope gradient. In contrast, multi-flow algorithms, e.g., FD8 [73], DINF [74], MFD-md [72], assume that runoff can flow into more than one adjacent cell, which gives a more realistic representation of the flows [72]. A strong generalization, and thus potential source of error, arises when defining a threshold for distance analysis of objects to the drainage network. If a drain is identified as a potential lava flow, all objects that lie within a defined distance are assigned to the class \"potential lava flow area\" (Section 4.3, Figure 2). Thereby a spatial generalization takes place, which does not correctly reflect the morphological structures of a complex runoff system. A possible optimization to derive the threshold value in the classification of potential lava areas, can be achieved by using the HAND (height above the nearest drainage) layer [75]. The HAND layer is calculated using a topographic algorithm that identifies potential floodplains based on the relative vertical distance to the drainage network [75]. Using the NHI (Section 3), thermal anomalies during the eruptive phase can be detected and used as an initial area for modelling a lava flow. However, very weak thermal anomalies can often be difficult to detect [50]. If a thermal anomaly is detected by the NHI, the entire pixel is classified as a hotspot. There is no sub-pixel analysis to identify the hot object within a pixel, so the total hotspot area is overestimated [50]. In addition, cloud cover and the temporal resolution of Sentinel-2 and Landsat-8 are limiting factors for hotspot detection [46]. Furthermore, no atmospheric correction is applied to the data used in the NHI tool, which can sometimes lead to noise in the SWIR signal [50].