**3. Methods**

Sentinel imagery is a ground-breaking aerospace technology, which was carried out by European Space Agency in 2014 (https://apps. sentinel-hub.com). The missions aim at the goals of agricultural monitoring, emergency management, land cover classification, and water quality. This research applies the satellite images depicted from the Sentinal-2 orbital system. The main reason for this research by utilizing the Sentinel images is that lava flows or products of volcanism can vastly reveal a range of different textures or visual effects of imagery on the satellite photography system compared to the textures of surrounding areas. Yanshan (YS) and Dahei Gou (DHG) are the two vents that generated the youngest morphologies of lava flows covering an area of at least about 70 km2 . Please note that on the geological map, the young lava flows are marked in an area significantly larger than the real extent of the lava flows suggested by our reconnaissance mapping. The images from the Sentinal-2 system reveal a series of flow textures under different observation methods (**Figure 2a**-**d**). Thus, the targeted two vents, that is, YS and DHG, and their eruptive products (mostly lava flows) might be outlined by systematical analyses of remote sensing and GIS methods in relation to the observed lava successions. DEM images downloaded from the ALOS-PALSAR dataset (https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/) that offers digital elevation data with a resolution of 12.5 m. The accessed DEMs were reprojected to the WGS 84/UTM zone 51 N map datum and coordinate system. Using QGIS (version 3.26 – Tisler) and its SAGA and GrassGIS we created slope maps, hill shades, and topography position index maps to check the general geomorphological details of the region. Cross-sections were utilized to understand the general trends of the morphology of the study area (**Figure 3**).

The Q-LAVHA plugin of QGIS software can provide a general model or a simulation with analyses of pre-eruptive and post-eruptive topography to interpret lava flow evolution histories and future hazard assessments [28, 29] (Mossoux et al. 2016). In general, the Q-lavHA program is a QGIS plugin that simulates probabilities of ʻaʻā lavas distributions from one or multiple distributed eruptive vents on a DEM satellite image [29]. The inserted models, such as probabilistic and deterministic models, can

*Eruption Scenario Builder Based on the most Recent Fissure-Feed Lava-Producing Eruptions… DOI: http://dx.doi.org/10.5772/intechopen.109908*

#### **Figure 3.**

*Slope map of the ACVF showing the characteristic texture of the region with scoria cones and extensive lava flows. Cross sections (lines with numbers on the map) revealed a very gentle sloping landscape upon the lava emplaced. Map is on WGS84 projection using NE China local coordinate system.*

provide a range of calculations in relation to probabilities for lava flow propagation and terminal length under spatial aspects [28–32]. The confine of this program is the probabilistic steepest slope on parameters of spatial spreads. The corrective factors are the major algorithm to even the "pits," which may jeopardize the lava modeling processes by obstacles from DEM images. To overcome this issue, the DEM upon the simulation runs need to go through a prescribed preparation outlined in the plug-in manual. We have completed these steps. Moreover, we created a pre-eruptive topography by removing the lava flows on the current DEM. To do this, we created a contour map based on the ALOS-PALSAR 12.5 m resolution DEM and then we manually modified – in a supervised fashion – the contour lines fitting them to the general morphology unaffected by the youngest lavas (**Figure 4a** and **b**).

Based on the new contour lines, we recreated a new pre-eruptive DEM (**Figure 4c**) and visually compared it to the current DEM (**Figure 4d**). To see the validity of the pre-eruptive topography we created slope maps for the pre- and post-eruptivescenario (**Figure 5a** and **b**) as well as aspect maps (**Figure 5c** and **d**) to have visual control over the process. We decided to follow this manual process, in spite of it being more time-consuming as during the supervised process we had continuous connection to the landscape and we were able to self-evaluate the scenes based on our own field experiences. In the end, we also created pre- and post-eruptive topographic position index maps just to see how the lava flow removal affects this parameter (**Figure 6a** and **b**). Finally, we created a geomorphon map for the present-day situation (**Figure 6c**).

For the Q-LAVHA simulation, we tested all the in-built methods and we found the Euclidian simulation performed the best due to the general low slope angle at our

#### **Figure 4.**

*Contour map of the pre-eruptive (a) and post-eruptive (b) surface. Regenerated DEM of the pre-eruptive surface (c) was used to simulate lava flow emplacement. Post-eruptive DEM (d) was used to simulate future lava flow inundation. Maps are on WGS84 projection using geographical coordinate system.*

*Eruption Scenario Builder Based on the most Recent Fissure-Feed Lava-Producing Eruptions… DOI: http://dx.doi.org/10.5772/intechopen.109908*

#### **Figure 5.**

*Pre-eruptive slope map (a) shows the general trend of the landscape, while post-eruptive slope map demonstrates the rugged flow fields (b). Aspect maps of the pre-eruptive morphology (c) indicate a good trend to the general landscape characteristics. Aspect map of the post-eruptive surface (d) shows the rugged nature of the lava flow field. Maps are on WGS84 projection using geographical coordinate system.*

lava flows emplaced (an average of less than 1 degree). Within the simulations, we increased the simulation distances from the measured maximum lava flow runout distances. As other researchers used Q-LAVHA on regions of low slope angles (e.g., flat surface) such as Paricutin in Mexico [33], we followed their recommendations to increase the simulation length well over 10 times of the measured lava flow length. In Paricutin, even 50 times longer simulation length has been applied. In our work, we find the simulation distance about 25 times longer than the average lava flow runout distances provided good results, hence in our work we followed this simulation distance (e.g., 250 km for a 10 km long, or 100 km for a 4 km long lava flow).

The terminal length is considered as FLOWGO Model [31] through the definitions of fixed length values, a function of statistical length probabilities, and thermalrheological properties of open-channel lava flow [7]. While Q-LAVHA offers the FLOWGO simulation, it requires numerous physico-chemical parameters [19]. As we do not have most of those data, we have not explored the FLOWGO to the full extent, instead run some tests with likely parameters drafted from variable literature [34–36]. As field evidence indicates that the eruption, at least in the Yanshan vent complex reached high intensity in times as evidenced by the presence of abundant clastogenic lava flows, we applied high magma mass flux rates, mafic composition, high-temperature conditions, and wide flow channel parameters.

The lava flow simulation method was performed by Q-LAVHA within QGIS. Three different modeling concepts have been carried out: points, a line, and a polygon [29]. A single point or multiple points are the envisions of vents that probably erupted flows around Yanshan areas. Lines are the simulations of fissure vents. Polygons

#### **Figure 6.**

*Pre-eruptive (a) and post-eruptive geomorphon maps show clearly the topographical differences the landscape received through the lava inundation. Codes refer to the following parameters: 1) flat; 2) summit; 3) ridge; 4) shoulder; 5) spur; 6) slope; 7) hollow; 8) footslope; 9) valley; 10) depression. Topographic position index map (c) of the post-eruptive (present-day) landforms show the variety of landforms the region has. Codes are: 1) canyons, deeply incised streams; 2) midslope drainages, shallow valleys; 3) upland drainages, headwaters; 4) U-shaped valleys; 5) plains; 6) open slopes; 7) upper slopes, mesas; 8) local ridges, hills in valleys; 9) midslope ridges, small hills in plains; 10) mountain tops, high ridges. Maps are on WGS84 projection using geographical coordinate system.*

## *Eruption Scenario Builder Based on the most Recent Fissure-Feed Lava-Producing Eruptions… DOI: http://dx.doi.org/10.5772/intechopen.109908*

imply vent swarms. For the simulation processes, the preparation of DEM images is necessary. The resolution of a DEM image used is approximately 12.5 m in pixel sizes. Then, the contour map was treated in a supervised fashion, essentially adjusting the contours where the lava flow clearly acts as an addition to the morphology. Utilizing the GrassGIS plugin of QGIS is the next step to rebuilding a DEM image with a new corrected pre-eruptive contour line. It is also important that all the maps must be projected properly to UTM coordinate with the NE China sections.

In the end, we applied 5 m average lava flow thickness and run the flow simulation on the current topography to see potential lava inundation in case a similar eruption take place in the same vent locations as those created in the 2000 AD lava flow fields. As mentioned above, several methods of Sentinel Imagery have been applied to the areas of YS and DHG.

False-color imagery (**Figure 2a**) aims at observations of at least one non-visible wavelength to image Earth, which is generally composed of red and green bands in a very popular recognition of images. False-color imagery is most used to assess plant density and healthy conditions since plants reflect near-infrared and green light while they absorb red. Cities and exposed grounds are gray or brown, and water appears blue or black. In this image, the youngest lava flows appear as green color regions with specific flow-banded patterns. Ash is shown up in pink smooth pattern that fills topography lows. From ash, loose alluvial deposits can be difficult to distinguish but their surface pattern is slightly different.

Short wave infrared composite (SWIR) measurements (**Figure 2b**) can estimate how much water is present in plants and soil as water absorbs SWIR wavelengths in the optical spectrum. Shortwave-infrared bands are also helpful in distinguishing between cloud types (e.g., water clouds vs. ice clouds), snow and ice, all of which appear to be white in visible light. In this classification, vegetation appears in green gradients, soils and infrastructures are in brown gradients, and water appears to be black. Newly burned lands are strongly reflected in SWIR bands, making them visible for mapping fire events. Each rock type differently reveals shortwave infrared light, making it possible to map geology by comparing different colors of reflected SWIR light. Barren lava flow surfaces appear in pink tones reflecting the quick depletion of moisture from porous, dark lava surfaces.

Geology 8, 11, and 12 composite uses both shortwave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (**Figure 2c**). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near-infrared (NIR) band 8 highlights vegetation, contributing to the differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation and land, especially geologic features that can be useful for mining and mineral exploration. In the Arxan region, young lava flows appear bright blue and differ significantly from the background brownish-reddish (mostly dry vegetated) regions (**Figure 2c**).

Geology 12, 8, and 2 composite use shortwave infrared (SWIR) band 12 to differentiate among different rock types. Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near-infrared (NIR) band 8 highlights vegetation, and band 2 detects moisture, both contributing to the differentiation of ground materials. The composite is utilized for finding geological formations and features (e.g., faults and fractures), lithology (e.g., granite and basalt), and mining applications. At Arxan, the image shows clearly the young lava surfaces against the vegetated background (**Figure 2d**).
