2.6: Google Earth Introduction - Geosciences

2.6: Google Earth Introduction - Geosciences

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Google Earth is a great tool to visualize and explore many of the geologic features that we will discuss in this class. This program is free and easy to use. This first Google Earth assignment will focus on familiarizing you with the program and some of the tools that we will use in later labs. Note that the optimal way to view geology is to go outside. Since that is not an option for an online class, the next best thing is using Google Earth. This is a practical and useful program that has many applications.


Google Earth updates versions periodically. If this occurs the instructions in the labs for this chapter may refer to the older version of the program. If you think this has occurred please let your instructor know.

If Google Earth is not already installed on the computer you are using, then please do the following:

  1. Go to
  2. Click on the Download Google Earth tab at the top of the page, review the Privacy Policy, and click Agree and Download to download the latest version.
  3. Save the file to your desktop, open it and follow the instructions to install.
  4. Open Google Earth.

Before we begin the assignment, let’s first familiarize ourselves with Google Earth. Read each step and spend a few minutes trying things out, which will make things easier later. Also, note that the Mac and PC versions of Google Earth are a little different.

You will use this program extensively throughout the course. Take the time to learn how to navigate it now.

Step 1 – Navigation

Watch each of the tutorial videos at beginner.html#navigation. It is important that you dedicate time to review these virtual resources to help you better understand Google Earth and its capabilities. These will be key to mastering the tool. Navigating in Google Earth can be done in two ways:

  • First, you can use the Search panel in the upper left-hand side of the screen. Just type in a location, address, or coordinate and it will zoom into the position (give it a try now).
  • The second approach is that you can also navigate manually:
  • To move position you can left-click with the mouse and drag the map or click on the hand icon in the upper right corner.
  • You can zoom in and out using the mouse wheel, by right-clicking and dragging the mouse up or down, or by sliding the lowest bar in the upper right corner.
  • Click and hold the mouse wheel in order to rotate the map (left and right) or tilt the scenery (up and down). This can also be done using the arrows surrounding the eye icon in the upper right corner.

Along the bottom of the image it gives several important pieces of information:

  1. Latitude and Longitude
  2. Elevation in reference to sea level
  3. Eye altitude, which indicates how zoomed in or out you are.

For example, let’s check out Niagara Falls. In the Search panel in the upper left, type in Niagara Falls, NY. To better tell where the Falls are, you want to zoom out a little bit. Notice your eye altitude along the bottom right. Use the minus button for the zoom until you are at ~10,000’ eye altitude. Check your latitude (~43º 04’39”N – read as 43 degrees, 4 minutes, and 57 seconds North) and longitude (~79º 04’28”W). It will move as you move your cursor across the screen, as will your elevation. If you want to see a picture of the Falls, just click on one of the many photo icons to see one. Note that the river is headed in a general northerly route – you can zoom in close to see the actual Falls.

Also important to understand with navigation is the concept of bearing. A bearing is the compass direction as measured between two points. It can be expressed as an azimuth bearing in degrees between 0 and 360, as along a circle. 0 and 360 degrees would be north, 90 degrees would be east, 180 degrees would be south, and 270 degrees would be west (Figure 2.9).

Step 2 – Measuring

In order to examine features, we will need to be able to measure them, which is easily managed in Google Earth. Measuring is done using the Ruler Tool, which can be accessed either by clicking the ruler icon in the toolbar above the image or by selecting from the menu across the top Tools, then Ruler.

There are two options with the ruler tool, line, and path. The line option (which is the default option) gives the distance and direction between two points; notice the pull-down menu that gives 11 different options for units of measurements. To make a measurement, after you have selected the ruler tool, you simply click on two different points. The path option gives the distance for a set of two or more points giving the ability to measure a distance that isn’t a straight line.


When measuring features you want to use the Map Length – using the ground length can lead to an incorrect answer.

Gradient will often be measured for this and future labs. Gradient is similar to the slope which indicates how steep or flat an area is. It is calculated as the difference in elevation divided by the horizontal distance. When calculating the gradient, maintain the same units in the numerator and denominator.

Gradient = change in elevation/horizontal distance

Let’s practice again at Niagara Falls. First move the image slightly higher to fully see the start of the white water, just before the Falls begin (do this by left-clicking the mouse and using the hand to move the image). With the eye altitude still at ~10,000’, let’s measure the distance across the river right at the start of the whitewater before the Falls (where the whitewater stops). First click your ruler icon, then select a point on one side of the river, then move your mouse straight across to the other side. In feet, this should measure ~4,800 feet (don’t stop at the island – measure all the way to the other river bank). Using the pull-down menu, you can change the feet to miles, and the result should be ~0.9 miles. Now let’s practice gradient across the actual Falls. Position your cursor over the actual Falls, and zoom in to an eye altitude of ~1,000 feet. Hold your cursor over the top of the Falls and record the elevation (remember, this is located along the bottom bar). Now move your cursor to the bottom of the Falls and record the elevation. The change in elevation (highest-lowest) will be your numerator. Use the Ruler tool to measure the distance between the two places – this will be your horizontal distance (the denominator). There will be variation in this answer depending on your exact spot along the Falls, but results should be similar to this:

Gradient = (frac{(500’ – 325’)}{75’} = frac{175’}{75’} = 2.3)

Step 3 – Changing the Options

For a few tasks it will be important to change some of the default settings on Google Earth in order to see a feature better or make your work easier. These changes can all be made by going to Tools in the menu bar across the top, then Options in the PC version (for the MAC, go to Google Earth, then Preferences).

  1. Changing the unit for Elevation – From the 3D view tab, in the middle of the box, there is a section entitled “Units of Measurement” that you can change between metric and English units.
  2. Exaggerating Features – Since differences in elevations are much smaller than geographic distances it is sometimes hard to see features. To exaggerate features (that is, make a mountain look taller than it actually is in order to see it better), click on the 3D view tab, in the lower-left side at the “Terrain” section of the box, look for “Elevation Exaggeration (also scales 3D buildings and trees)”. If you want to exaggerate a feature increase this value up to 3. To view the area without any exaggeration, return the value to the default of 1.

2.6: Google Earth Introduction - Geosciences

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2.6: Google Earth Introduction - Geosciences

Investigation of Forest Fires with Google Earth Engine

1.1 What is Google Earth Engine and How it Works:

Google Earth Engine (GEE) allow us to reach satellite image catalogs (catalog of the Sentinel – MODIS – Landsat) and use geospatial or image processing algorithms on those images. There are three different methods (JavaScript – Python – REST) to reach this image catalogs. In this tutorial we use the JavaScript (JS) as it is the native language of GEE.

Javascript has its own Google Earth Engine platform can be reachable from any browser. The browser link is (

When you click to link it will tell you need to register. Process Explained in section 1.5.

When we start the code it sends a request to servers for compute and then gets results. So it will need internet connection.

  • This open source code can be usable by anyone who want to see neutral true data about forest fires and environment also who want to test, develop for better forest fire investigation and researches.
  • This tutorial prepared by Berk Kıvılcım under the mentorship of Berk Anbaroğlu and Nusret Demir.

The code can be able to compute how many forest field burned (in hectare unit as default), Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI) Charts, Burn Severity Rate, time interval of the burning and shows before-after satellite image results by using Sentinel-2 Satellite. Those datas can be downloadable.

IMPORTANT: You need to change something in this code for some spesific results like burn severity calculation. Each time you change something from code please don’t forget reclick to Run button for see the latest results.

1.5 Register to Google Earth Engine:

When you try to access you will face up with register screen. If you registered once you will never face it again.

Click to "here" button then in next page fill the form and send a request to google. Usually your request approve immediately but sometimes it will take a while.

1.6 Google Earth Engine Javascript Browser Layout:

Script Area: The area is where we put our code.

Console Area: The area is where graphical and numerical results displayed.

Map Area: The area is where results of visualization displayed.

2.2 User Workflow Youtube Tutorial Video:

Download and see the source code with the help of notepad softwares or directly open the code from the link below here then copy paste to whole source code into the Google earth engine's script section.

If you wanted to save your code into your Google Earth Engine account create a repository and load the script code into it.

Click NEW, then click Repository:

  • Then click to save it in your repository. You can use the code in this repository anytime you want.

There are two different methods to identify polygons in other name areas.

As default an example area sample identified which is cover to Izmir/Karabaglar region.

First method: If you estimate or know the corner coordinates of the polygon which is you want to work it can be directly input by manually.

Just replace those coordinates with which coordinates you want to use. You can add or remove more polygon corner. Those coordinates must be in Geodetic Coordinate format (latitude and longitude) which is based on WGS84 Ellipsoid .

  • Second Method: If you want to select your area manually follow the steps below here. Also you can easily find your work area with search bar just typing your adresses.

Step - 1: Click to draw polygon button.

Step - 2: Draw your polygon on map area.

  • Also you can change the back layer of the visualization area for better visual understand. Two different options in here. Map and Satellite. If you select the satellite the program will show the area with high quality satellite images.

  • When you finish the drawing polygon, the properties of this polygon will Show up above the script. Just don’t forget delete the whole initial ‘var geometry’ because we identify new polygon.

2.6 Choosing Dates Before and After Burn:

  • The dates are in ‘yyyy-mm-dd’ format. Enter a date you think was before the fire into the “var Start’s date section” and after the fire into the “var End’s date section”. When inputing the dates try to close them to date during burn. The closer to date during burn is gives better results.

Some Date Selection Tips:

Tip - 1: If your "var End" date is to close to present day you should change the "var END's" 50. This 50 means 50 days later from the "var End's" so sometimes it exceed the present day.

Tip - 2.1: Trees will show different health situation with different seasons and also some exterior parameters like snow can effect the results. For example you think the fire starts at july. Then select your time interval between june and August not like the January to December. In conclusion if Start and End dates close to burn date results will be more correct. If your region not tropical and you know the burn not start during winter season you can apply the filter below here for winter-autumn changes.

Tip - 2.2: This season based erros in charts can be ignorable. Initial date filter shows the all data during the whole year (first day to 365th day) but If you change this initial values in the code's line 60 then you can filter the datas. For example If you write 122 and 275 as start and end dates of the filter then the code only count the dates between 1 May to 1 October so not count the winter days and in the conclusion there would be no winter data in charts. In the example above here 122 represent 122th day of the year which is means 1 May and 275 means 275th day of the year and its the 1 October.

  • Tip - 3: Google Earth Engine is a new technology so If you try to get old fire datas like 1999-2007-2010. There would be no data catalog so the code won't work and If you try to get newest fire datas like today's date the satellite probably not collect the data yet so you should wait a couple days or check the other satellite's datas.

2.7 Training Sample Selection:

The code uses supervised classification for calculting how much area burned. For this process code needs to select some training areas. We can select it with point or polygon methods. I suggest to use polygon method.

Firstly create classes. Default classes are ("soil" (soil or dry land), "tree" (healthy vegetation area), "burned" (burned areas)) so this classes must be created by user but still new classes can be added.

  • Determinede class properties. Type your Name "soil", "tree" or "burned". Those names need to be exactly same from tutorial. Select FeatureCollection. Write LC which is mean landcover (do not type landcover or something different just type LC) to property section then write the values started from 01. Burned areas property value always must be 03. The reason behind that is the code export the 3rd class from classified image as only burned area layer so class value must be 3.

  • If you want to add more classes than default there would be an extra step. Create your class and determine properties then in the code add .merge('your_new_class') to "var feature".
  • Also change classified picture's map.addLayer's max:___ to how many classes you want. In the beginning we have 3 classes so max: would be 3. We should change it.

Now we entered all things the code needed for run. Just click to run button and see the results.

3.1 Visual Image Analysing:

When we click run, the code process some algorithms which they will explained in workflow section of the tutorial. Results are show in console and map panels. For visual analysis use map section and for the numerical analysis check to console section.

After the running code its divide map area into two section. The left part shows RGB – NBR images before burn and classified image after burn. The right part shows RGB – NBR images after burn and only the burned areas which is calculated from classified image.

RGB Image: The image which is formed from red, green, blue layers. This layer shows us to what human eye can see.

NBR Image: NBR image maded from NBR calculation results. This layer help us to understand burning.

Classified Image: The image which is shows dry ground, healthy vegetation areas and burned areas.

Only Burned: The layer which is represent only the burned areas.


Please check the classification is good or not. Its all about your training sample selection. Best way to check it look the "only burned layer (white layer)" and "RGB Later layer or base satellite vision" together. If burned areas not cover the most of real burned areas or cover too much area like dry ground or healthy vegetation then click to Reset button. This button will turn back to inital layout which is shown in section 2.5's screenshot. Don't worry your previous training samples will be protected. Then select more training samples from where the classification looks wrong.

  • After the clicking run button, charts automaticly will show up in console section. There is two different chart. One shows the NBR and the other one shows NDVI. This both charts gave us the information about vegitation health and date interval of the burning.

  • If you wanted to understand burning date interval put your Mouse on to graphic’s line. The breaking points (where the graphic mostly changed) will shows the date of burning. For example: According to below graphic the fire started at ‘16-08-2019‘ and continued until the ’21-08-2019’. This example based on İzmir Karabağlar and also its burning date data published from trthaber (turkish radio and television institution news).
  • Referance web site for Izmır Karabağlar forest fire new is

This code provide some numerical calculations like how many hectare burned or how bad is the burned area. Before the start we need to understand GSD (Ground Sample Distance).

GSD: Ground Sample Distance is the lenght of each pixel’s edges corresponds in the real world distance. In this tutorial we used Sentinel-2 satellite which has the 10 meter GSD lenght for maximum resolution. That means each pixel correspond 100 (10*10) meter square area. Less GSD number means better resolution.


Mining contributes to land cover changes, directly and indirectly affecting the natural landscape and local communities. The need for robust analyses of social and environmental data at the mine site and mine region scale is essential for regulators and mining companies to effectively identify, monitor, sustainably mitigate and manage mining impacts. This study characterized and evaluated land cover changes and their concurrent impacts on socio-environmental land uses in a Philippines mining landscape. Using composites of multispectral Landsat images, vegetation indices and a Digital Elevation Model (DEM), classified land use and land cover time-series maps were created. High-level land covers with a coarse thematic resolution that could be successfully characterized using Landsat historical imagery were first mapped using supervised Random Forest classification in Google Earth Engine (GEE). Subsequently, web-based mapping by local experts was used to characterise key fine thematic resolution land use categories within selected zones of importance which were not possible to characterise using Landsat alone. The time-series provided an accurate estimate of change, and revealed significant temporal trends at the regional scale between 1994 and 2018. Trends included a significant decrease in primary vegetation and an increase in built-up areas, mining and irrigated agriculture. Some notable fine-scale land use changes revealed by the analysis were the increase in social development projects post 2005 and conversions between citrus and paddy agriculture these were initially balanced but leaned prominently towards paddy cultivation post 2010. An assessment of land use and land cover transitions provided key insights to several socio-environmental indicators, including environmental quality, habitat loss, population distribution and livelihood, essential to characterise and support the management of the mine's socio-ecological system. The paper concludes by reflecting on the methods developed, evaluating their limitations and presenting potential ways to improve the workflow to better support social change assessments.

What is a virtual field guide?

The development of the internet and tools available such as mobile technologies for students to access information, has rapidly increased in the previous decade (Kaplan & Haenlein, 2010). Now more than ever is there a notion of collaborative rich data sets created online in communities from amateurs to professionals, for which both educators and students, can access to enhance their learning environments (Litherland & Stott, 2012). Although, there is little argument in literature to deny the benefits of fieldwork, it does come with its challenges.

Virtual Field Guides (VFG), Virtual Field Trips or Virtual Fieldwork are terms used interchangeably throughout literature yet they are contested concepts with varying definitions (Litherland & Stott, 2012). Virtual Field Trips in essence try to capture the real world environment of a specific location or region through a collection of data, photographs, cartography and other technologies such as GIS, without the cost of physically being there (Carmichael & Tscholl, 2011). As argued by Stainfield, Fisher, Ford, and Solem (2000), the best examples of Virtual Field Trips are the ones which incorporates both old and new methods, yet allow participation and exploration of the environment and for students to develop the skills associated with those methods. E-learning, which is defined as “electronically mediated asynchronous and synchronous communication for the purpose of constructing and confirming knowledge” (Garrison, 2011, p. 2) have shown to increase learning through active participation rather than passive (Fletcher, France, Moore, & Robinson, 2007). This model of learning is a key concept behind the educational benefits of using virtual field guides/trips in higher education teachings. It encourages the use of participation and engagement with the virtual environment with peers and tutors.

The aim of the Virtual Field Trip at present, has not been to replace the traditional fieldtrip but to introduce students to the fundamental skills needed to understand their environment before going on the ‘real’ fieldtrip (Gilmour, 1997). Due to the lack of “virtual” such as being immersed in a 3D augmented reality, the term Virtual Field Guide (VGF) will be used from here on, instead of virtual field trip.

VFG’s are often a repository of various data, yet what makes them more than just this, is often an element of educator led discussion situated within a framework of tasks to be completed (Stott, Litherland, Carmichael, & Nuttall, 2014). Some VFG’s try to create an opportunity of travel for the students without ever leaving the confines of the classroom. For example Jacobson, Militello, and Baveye (2009), created a VFG were the course was broken down into days and stops with specific tasks to be completed at each one, much like a real fieldtrip. Older VFG’s are more simplistic by just making available data such as photographs, maps or videos with tutor led commentary for students to get a feel for the environment (Baggott la Velle, 2005).

Spatial scale is of vital importance for geoscience disciplines and must be taken into account when considering VFGs (Jones, McCaffrey, Clegg, Wilson, Holliman, et al., 2009). The scale of virtual field guides often differ depending on their purpose and their aims (Ramasundaram, Grunwald, Mangeot, Comerford, & Bliss, 2005). Spatial scale in VFGs can be small scale providing large overviews of topographical data such as mountain ranges (Stott, Nuttall, & McCloskey, 2009 Eusden, Duvall, & Bryant, 2012) and national parks (McMorrow, 2005). Small spatial scale VFG’s can provide a student with a deeper understanding and situational awareness of the topic or location that they are studying (Jacobson et al., 2009). Often students do not maximise their time on fieldwork due to lacking the bigger conceptual picture (Falk, Martin, & Balling, 1978). Providing a large overview of a field location helps a student to formulate ideas and apply knowledge to how that field site sits within the wider world. Small scale VFGs however lack finer details, for example in the VFG designed by Arrowsmith, Counihan, and McGreevy (2005) students anecdotally mentioned that they misinterpreted the distances between sites and that steepness of gradients were vastly under estimated.

Larger spatial scale virtual field guides provide the opposite in the sense that they are highly detailed and can vary from meters of a walking path, to a smaller section of cliff face (Pringle, Westerman, & Gardiner, 2004). Larger scale VFGs are more practical as they replicate what would be seen if a student were to visit in reality (Jones et al., 2009). Details are more visible at this scale, with individual rocks and trees shown in high detail which allow students to explore and research in depth. At this scale it further facilitates students’ skill development by practicing skills here that they may use on real field work which would be difficult with a smaller spatial scale VFG. However, larger scale VFGs are large in terms of data size due to their high detail and so when creating a VFG there must be a trade-off of scale and detail (Arrowsmith et al., 2005).

One issue with VFGS is their lack of standardisation. While there is no agreed spatial scale for VFGs due to their varying purpose and nature as commented on by Arrowsmith et al. (2005) multilayers of VFG scale that are all linked to each other provide the best learning experience for students. In their study they had a three scale approach that incorporated small to large spatial scales. The first VFG was a small scale overview of an entire park, the second was a larger spatial scale of the area that they would conduct most of their fieldwork and finally a large spatial scale that was a site specific VFG was developed with a geospatial link between all three ‘nested’ models.


Interpretation Results

The landslides in the study area had the following image characteristics. 1) There are abnormal arc shapes developed on the rear margin of the landslide body, including "round chair-shaped" and "dustpan-shaped" landslide back wall steep ridges, curved terrain variation lines, and abnormal color lines, among other feature. 2) Landslides protruding toward the bottom of the valley often have slight topography. Landslides often form dammed lakes in the valleys, which occasionally discharge water. The original "V"-shaped loess valley bottom becomes flat terrain, which has been mostly transformed into cultivated land. 3) Most landslides are distributed in the partial deficit areas of steep slopes, such as valleys and rivers. Landslides cause river water to shift to the side of the river where the landslide has not occurred. 4) The valley slopes on both sides of the steep loess valley have abnormally flat cultivated land.

In this study, the historical landslides in the 28,000 km 2 area of the southwestern edge of Ordos was interpreted in detail. Figure 2 shows the spatial distribution of the interpreted historical landslides. The landslides are mainly distributed along faults to the north of the Longxian–Qishan–Mazhao Fault, east of Longxian, south of Lingtai, and the uplift area of the fault block south of Qianyang. There are 6,876 landslides in this dense area, with a total area of 643 km 2 . There are relatively few dense landslides on the Loess Plateau, Weihe river terraces, and floodplains on the southwest side of the Longxian–Qishan–Mazhao Fault due to topographical conditions. At the same time, dense landslides were not interpreted in the bedrock area of the Qishan mountains and north of Fengxiang.

FIGURE 2. Interpreted historical landslide distribution map at the Southwest of Ordos. LQMF: Longxian–Qishan–Mazhao Fault QBF: Qianyang𠄻iaojiao Fault GGF: Guguan–Guozhen Fault TGF: Taoyuan-Guichuansi Fault QLNPF: Qinling North Margin Fault TBF: Taibai Mountain Fault WHF: Weihe Fault and BSF: Beishan Piedmont Fault. The purple dashed oval area is the extreme earthquake zone of the AD 600 Qinlong earthquake (Wang, 2018) the blue dashed rectangle represents the surface rupture of the AD 600 Qinlong earthquake along the Longxian–Qishan–Mazhao fault of the Dazhuangke�ngjiacao section (Li et al., 2019) the area denoted by the white dotted line is the dense landslide area and projection lines A𠄺′ and B𠄻′ correspond to Figure 4.

The area density analysis of the interpreted landslides in the study area (Figure 3) shows that, although there are landslides in the study area, the high-density areas occur on the northeastern side of the Longxian–Qishan–Mazhao Fault. The area density can reach as high as 28�% while the area density value at the center of the high-density area is 4- to 5-fold greater than the background density of the Loess Plateau, highlighting this as an abnormal area.

FIGURE 3. Interpreted historical landslide areal density map. LQMF:Longxian–Qishan–Mazhao Fault AD 600 M6:AD 600 Qinlong M6 earthquake AD 1704 M6:AD 1704 Longxian M6 earthquake

The 6,876 landslides in the dense area were projected onto the projection line along the horizontal and vertical strike of the Longxian–Qishan–Mazhao Fault we then counted the frequency and cumulative area of the landslides (at 10 km intervals) (Figure 4).

FIGURE 4. Frequency and cumulative area along strike and vertical strike of the Long xian–Qishan–Mazhao Fault (10 km) (Projection lines A𠄺′ and B𠄻′ are shown in Figure 2).

Along the strike of the Longxian–Qishan–Mazhao Fault, landslides are mainly concentrated within a range of 90 km between Longxian and Qishan (reaching 6,003 events, accounting for 87.3% of the total number of landslides). The cumulative landslide area is 557.4 km 2 , accounting for 86.7% of the total landslide area. The peak appears at approximately 10 km northwest of Qishan County. By projecting the landslide body onto the projection line perpendicular to the strike of the Longxian–Qishan–Mazhao Fault, we observe that the main body of the landslide is distributed on the northeast side of the Longxian–Qishan–Mazhao Fault, where there is a sharp reduction in the number and area of landslides southwest of the fault. This is because the southwest side of the fault is the loess tableland and Weihe River terraces and floodplains, which do not have topographical conditions suitable for large-scale landslides. The landslide-intensive area is distributed unilaterally along the Longxian–Qishan–Mazhao Fault.

Landslide Database and Parameter Statistics

Based on our interpretations, parameter assignments were made for the landslides in dense areas on a case by case basis to establish a coseismic landslide database. The manually assigned attributes of the landslide database included the length, width, elevation of the scarp top and foot edge, and the top and bottom elevations of each located slope. Accorded to these assigned attributes, we calculated several landslide attributes, including the landslide height, H (elevation of the scarp top-elevation of the foot edge), slope difference (the top and bottom elevation difference of each located slope), aspect ratio, and landslide height/slope difference ratio, i.e., H/(R − V).

A statistical analysis of the landslide parameters was conducted based on the coseismic landslide database. The length advantage interval of the historical landslides in the dense area along the southwestern margin of the Ordos Block is 100� m this interval accounts for 82% of the total number of landslides. The width advantage interval is 100� m this interval accounts for 72.6% of the total number of landslides (Figures 5A,B). The aspect ratio of the landslide represents the plane spread of the landslide, which ranged from 0.1 to 5.6 for the historical landslides in the dense areas, mainly concentrated between 0.5 and 2.5. This interval accounts for 91% of the total landslides. An aspect ratio of ≤ 0.5 accounted for 5.6% of the total landslides while an aspect ratio of Ϣ.5 accounted for 3.4% of the total landslides, with an average of 1.25 (Figure 5C).

FIGURE 5. Landslide parameter statistics. (A), statistics on the relationship between landslide length and frequency (B), statistics on the relationship between landslide width and frequency (C), statistics on the relationship between landslide aspect ratio and frequency (D), statistics on the relationship between landslide area and Frequency (E), statistics on the relationship between landslide H/(R − V) and frequency.

The term H/(R − V) refers to the ratio of the height, H, of a landslide to the slope difference (R–V: Ridge–Valley), which represents the ratio of the longitudinal length of the landslide to the slope length where the landslide is located, ranging from 0 to 1. The greater the value of H/(R − V), the greater the proportion of landslides in the slope in the longitudinal direction. Among the landslides in the dense areas, 85.4% of landslides have H/(R − V) ratios Ϡ.6 while 57.7% are greater than 0.8 (Figure 5E). This shows that the scarp tops of these landslides basically reach the Loess Plateau, with notable landform deficits While the foot edge accumulation basically reaches the bottom of the valley, which can lead to the damming of loess valleys at different scales, forming abrupt landform sedimentary features these features are consistent with the observation results collected during the field survey (Figures 6A𠄽𠄽).

FIGURE 6. Google images of typical historical landslides and barrier lakes (A𠄼), UAV. photos of historical landslides(d), and photos of rainfall landslides (E).

In terms of the area, the number of small-area landslides is relatively small landslides with an area greater than 10,000 m 2 account for 93.2% of the total number of landslides. The area advantage of historical landslides interpreted in the study area is 10,000�,000 m 2 the number of landslides in this section accounts for 82.3% of the total number of landslides in the dense area of this study (Figure 5D).

Geochronology and geochemistry of tuff beds from the Shicaohe Formation of Shennongjia Group and tectonic evolution in the northern Yangtze Block, South China

Meso- to Neoproterozoic magmatic events are widespread in the Yangtze Block. The geochronology and tectonic significance of the Shennongjia Group in the Yangtze Block are still highly controversial. An integrated geochronology and geochemistry approach provides new insights into the geochronological framework, tectonic setting, magmatic events, and basin evolution of the northern Yangtze Block. Our new precise sensitive high-resolution ion microprobe U–Pb data indicate a deposition age of 1180 ± 15 Ma for the Shicaohe Formation subalkaline basaltic tuff that is geochemically similar to modern intracontinental rift volcanic rocks. The integration of available geochemical data together with our new U–Pb ages indicates the Shicaohe Formation subalkaline basaltic tuff formed ca. 1180 in a continental rift-related setting on a passive continental margin. The Shennongjia Group is topped by the Zhengjiaya Formation volcanic sequence, indicating arc-related igneous events at 1103 Ma. The transition of the late Mesoproterozoic tectonic regime from intracontinental extension to convergence occurred between ca. 1180 and 1103 Ma in the northern Yangtze Block. Tectonic evolution in the Neoproterozoic led to accretion along the northern margin of the Yangtze Block. These results provide geochronological evidence, which is of utmost importance for reconfiguration of the chronostratigraphic framework and for promoting research on Mesoproterozoic strata in China, thereby increasing understanding of magmatic events and basin evolutionary history in the northern Yangtze Block.

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University rankings have become important in higher education worldwide (Hägg & Wedlin, 2013 Rauhvargers, 2013), as evidenced by their increasing number and the increasing number of papers published annually about them. Before 2010, there were five international university ranking systems today, there are 17 1 . In 2009, researchers published fewer than 20 journal articles on the topic in 2019, they published over 100 according to the Scopus database. Universities participate in rankings and pursue higher ranks to obtain greater visibility, attract higher quality students and faculty, and get more resources from stakeholders (Hazelkorn, 2015 Hazelkorn & Gibson, 2017 Hou & Jacob, 2017).

University rankings claim to provide valid and useful information for determining academic and research excellence (Moed, 2017). Administrators rely on them as indicators of improvement over time, as methods to determine institutional priorities, and as benchmarking tools against peer institutions. Faculty, staff, and students and their parents use university rankings as tools to help them decide which institutions to apply to for employment or higher education. Rankings also boost faculty professional reputation. Governments and funding agencies use university rankings for information about the performance of their higher education institutions or the ones in which they have invested resources. Media outlets utilize them to create commercial opportunities (Hägg & Wedlin, 2013 Hazelkorn, 2015). Universities constantly strive to become world class and aim to improve their rankings. These rankings are thus perceived by many at higher education institutions as ultimate tools for assessing academic and research performance. According to Hazelkorn (2015), Moed (2017), and Rauhvargers (2013), university ranking systems have made enormous progress in quality during the past decade. Their systems are currently much more informative and user friendly than they were some 10 years ago. Yet, more work is needed to improve them. There is a large body of literature on the role and nature of university rankings. Notable reviews of this literature can be found in Hazelkorn (2015), Johnes (2018), Moed (2017), Olcay and Bulu (2017), and Soh (2017).

Developers of ranking systems use a variety of metrics for assessing and comparing the academic and research performance of universities, including expert opinion, publication and citation metrics, intellectual property metrics (e.g., patents), research and development income and expenditures, student-faculty ratios, and international outlook (e.g., percentage of foreign faculty and students Vernon, Balas, & Momani, 2018). Highly prestigious honors, awards, prizes, and medals, which play major roles at universities (Ma & Uzzi, 2018), are rarely considered in university rankings. Of the 12 international university rankings examined by Vernon et al. (2018), only two included prizes in their criteria: the Academic Ranking of World Universities (the Shanghai Ranking) 2 and the Center for World University Rankings 3 . The recently developed University Three Missions Moscow International University Rankings (MosIUR) 4 , which was first published in 2017, became the third university ranking system to use awards as one of its criteria. In this study, we use the terms awards, prizes, honors, and medals interchangeably.

It is unclear why so few university ranking systems include awards in their analyses or among their performance indicators. A contributing factor, however, could be the lack of a standard list of, or method to use for, prestigious awards. The Shanghai Ranking, for example, uses only the Nobel Prize and the Fields Medal as measures of the quality of faculty and education (with 30% of the total ranking score). This decision, however, raises doubts about the reliability of the rankings, because few individuals and institutions worldwide win these two prestigious awards (Dobrota & Dobrota, 2016 Hou & Jacob, 2017). The Center for World University Rankings (CWUR) bases 35% of its total ranking score on awards. CWUR uses 30 awards as a measure of universities’ education and faculty quality without explaining how and why they selected these awards over others 5 . MosIUR assigns 6% of its total university score on prizes, using the IREG List of 99 International Academic Awards, which is based on the the study by Zheng and Liu (2015) 6 . The IREG list, however, misses 36 of the highly prestigious international awards identified in this study, includes 20 awards that none of the sources or methods used in this study has classified as highly prestigious, and includes 15 awards given from 2005 to 2019 exclusively to individuals affiliated with institutions located in a single country—a fact that in our opinion disqualifies these awards as international.

Awards identify and confirm distinctive research, advance scientific discoveries, and confer credibility to persons, ideas, and disciplines (Ma & Uzzi, 2018). Awards are also among the highest forms of recognition researchers accord one another (Frey & Neckermann, 2009). Moreover, receiving a major award provides much greater visibility within the scientific community and beyond, and measures research quality and contribution to society in general better than citations can (Seglen, 1992). In short, awards serve as important, easy signaling functions about academic and research excellence (Gallus & Frey, 2017).

The increasing number of awards worldwide and their merit in research assessment and funding decisions necessitates a standard list of the most prominent international academic awards (Jiang & Liu, 2018 Ma & Uzzi, 2018). Such a list would be instrumental in identifying, characterizing, and differentiating the academic and research excellence of authors, centers, institutes, schools, universities, and countries. This study describes how we created such a list. We then use the list to answer the following research question: To what extent does the use of highly prestigious international academic awards affect university rankings?

Answering this question may encourage the producers of rankings to consider awards as an indicator to generate more accurate assessments and comparisons of universities’ academic and research performance. Answering this question may also lead to giving more weight to awards within the academic community, increasing the number and range of highly prestigious awards, and encouraging more high-quality academic and research work worldwide.