Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition
Type: text

, then it should also consider the opposite . Remote Sens . * * 2021 * * , 13 , 2511 7 of 19 The model should be strongly biased towards everyone mentioning a sign of marriage . Instead , we use negative weights for this operation . ④ Finally , we want to perform learning and inference using the specified model . This will build a model based on the data in the database , learn the weights , infer the expected or marginal probabilities of the variables in the model and then load it back into the database . In this way , we can see the probability of the has \ _spouse variable inferred by DeepDive .

Error analysis and debugging. To accurately analyze the experimental results, we first declare a score or a user-defined query sentence and define the part of the labeled data used for training. DeepDive uses this score to estimate the accuracy of the experiment. We declare these definitions in deepdive.conf and define deepdive.calibration.holdout\_fraction as 0.25. The test set is 75% of the labeled data, and the test set is used to verify the correctness of the experimental results. Approximately 1400 labeled data and approximately 1000 data were used for testing. The graph on the left in Figure 4 is the correct rate graph. Under ideal conditions, the red curve should be close to the blue calibration line. However, this is not the case. It may be caused by the sparseness and noise of the training data of the test data. The middle graph in Figure 4 is the predicted number graph of the test set. The forecasted quantity map usually presents a "U" shape. The graph on the right in Figure 4 is the predicted probability quantity graph of the entire data set. Among them, the prediction data falling in the 0.5–0.6 probability interval indicates that there are still some hidden types of instances, and the features of DeepDive are insufficient for these instances. The predicted data whose probability does not fall at (0, 0.1) or (0.9, 1.0) are the data to be extracted. An important indicator to improve the quality of the system is to re-speculate the above data and attribute it to the probability interval (0, 0.1) or (0.9, 1.0).


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition
Type: figure
Figure

Image /page/6/Figure/3 description: The image contains three graphs arranged horizontally. The first graph on the left is titled "Accuracy(Testing Set)". It is a line graph plotting Accuracy against Probability. The x-axis, labeled "Probability", ranges from 0 to 1. The y-axis, labeled "Accuracy", also ranges from 0 to 1. There is a straight blue line from (0,0) to (1,1) and a red line with circular markers that generally follows the blue line but with some deviations. The second graph in the middle is a histogram titled "Predictions(Testing Set)". The x-axis is "Probability" from 0 to 1, and the y-axis is "Predictions" from 0 to 900. It shows a large blue bar on the far left, indicating a high number of predictions (around 900) with low probability, and a very small bar on the far right. The third graph on the right is a histogram titled "Predictions(Whole Set)". The x-axis is "Probability" from 0 to 1, and the y-axis is "Predictions" up to over 3000. This graph also has a large blue bar on the far left (around 3000 predictions), followed by several smaller bars, and a noticeable bar on the far right.

3. Construction of Knowledge Graphs
3.1. Data Acquisition and Storage
3.1.1. Data Acquisition


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition
Type: figure
Figure: Figure 4. The test set correct rate calibration plot (Left), test set prediction plot (Middle), prediction plot for all data sets (Right).

Figure 4. The test set correct rate calibration plot (Left), test set prediction plot (Middle), prediction plot for all data sets (Right).


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage
Type: text

The data of the knowledge graph is usually expressed in the form of triples, representing the relationship between entities or between entities and attribute values. In this article, the relational database MySQL and graph database Neo4j are used. The purpose of using MySQL is to realize the mapping between data and ontology and then to use existing tools to realize data query and reasoning. The purpose of using Neo4j is to update and search data. Neo4j can directly display the query results in the form of graphs to realize the function of knowledge visualization. Neo4j has local storage and data processing functions that are different from general databases, which can ensure high readability and integrity of data.

The extracted data are processed and stored in three tables, called "knowledge", "knowledge\_to\_genre", and "genre", which are imported into a MySQL database. The E-R(Entity Relationship) diagram is shown below (Figure 5).

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3. Construction of Knowledge Graphs
3.1. Data Acquisition and Storage
3.1.2. Data Storage


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage
Type: figure
Figure

Image /page/7/Figure/1 description: An entity-relationship diagram showing the structure of a database with three tables: 'knowledge', 'knowledge\_to\_genre', and 'genre'. The 'knowledge' table has columns 'knowledge\_id' (primary key), 'knowledge\_name', and 'knowledge\_property'. The 'genre' table has columns 'genre\_id' (primary key) and 'genre\_name'. The 'knowledge\_to\_genre' table is a junction table with two foreign key columns, 'knowledge\_id' and 'genre\_id', linking the other two tables. This setup creates a many-to-many relationship, where a record in the 'knowledge' table can be associated with multiple genres, and a genre can be associated with multiple knowledge records.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage
Type: figure
Figure: Figure 5. E-R diagram of data stored in the database.

Figure 5. E-R diagram of data stored in the database.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage
Type: text

In the entity table called "knowledge", each knowledge point is numbered, the attribute "knowledge\_id" is created, and the attribute is used as the primary key. In the entity table called "genre", each category is numbered, the attribute "genre\_id" is created, and the attribute is used as the primary key. In the relation table called "knowledge\_to\_genre", the properties "genre\_id" and "knowledge\_id" were set as the foreign keys of the entity tables "genre" and "knowledge" to create the relationship between genre and knowledge. For example, the knowledge "collinear equation" belongs to the field of "surveying"; "genre" is a "class". In the "genre" class, there are two entities: "surveying" and "remote sensing"; "genre" means the genre to which the knowledge node belongs. A knowledge node belongs to either the "surveying" genre or the "remote-sensing" genre.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction
Type: text

There are two ways to construct ontologies: top-down and bottom-up. The ontology construction of the open domain knowledge graph typically uses a bottom-up method to automatically extract concepts, types of concept, and relationships between concepts from the knowledge graph. The open world is too complex to be considered in a top-down manner. As the world changes, the corresponding concepts are still growing. Most domain knowledge graph ontology construction uses a top-down approach. On the one hand, the concept and scope of the domain knowledge graph are fixed or controllable compared to the open domain knowledge graph; on the other hand, the domain knowledge graph must yield high-accuracy results. Currently, domain knowledge graphs are widely used in voice assistants [18]. These domain knowledge graphs can meet most user needs while ensuring accuracy.

This article uses a top-down approach to construct ontologies, and the creation tool uses Protégé (https://protege.stanford.edu, accessed on 6 May 2021), an ontology editing tool [19]. The specific creation process is described as follows. First, we create the ontology class and two classes of knowledge and genre. Note that all classes are subclasses of "Thing", and all classes must be mutually exclusive; an instance can only be one of the two classes. Second, the relationship between the classes (i.e., the object properties) is created. This article created the object attribute "belong\_to" (We classify the knowledge extracted from the "Surveying" textbook into the "Surveying" class. We define the relationship between this knowledge and the class "surveying" as "belong\_to". Similarly, we classify the knowledge extracted from the "Remote Sensing" textbook as "Remote Sensing" class. We also define the relationship between this knowledge and the class "Remote Sensing" as "belong\_to") to indicate that a certain knowledge point is in a certain field (type). Therefore, its attribute "domain" is defined as the class "knowledge", and its attribute "range" is the class "genre". "Domain" indicates which class the attribute belongs to. "Range" represents the value range of the attribute, which defines the inverse of this attribute as "belong\_to". Thus, ontology describes reasoning rules for knowledge reasoning. The class, object properties, and data properties are shown below (Figure 6). For example, in resource description framework (RDF) data, the knowledge point "electromagnetic waves" belongs to the class of "remote sensing". When inquiring, the knowledge point "electromagnetic wave" can also be found in the class "remote sensing". Finally, class properties (data

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properties) are created and are similar to object properties. Concurrently, Protégé also has a visual display function to show the structure of the ontology. The ontology structure is shown below (Figure 7).

3. Construction of Knowledge Graphs
3.2. Ontology Construction and Storage
3.2.1. Ontology Construction


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction
Type: figure
Figure

Image /page/8/Figure/2 description: A composite image showing three side-by-side screenshots of an ontology editor's user interface, each displaying a different aspect of the 'Entities' tab. Red boxes highlight the selected tabs in each view. The left panel shows the 'Classes' hierarchy, with 'owl:Thing' at the top. Under 'owl:Thing' are 'genre' and 'knowledge'. 'genre' has a subclass 'Geography', which has subclasses 'Remote\_Sensing' and 'Surveying'. The middle panel shows the 'Object properties' hierarchy, with 'owl:topObjectProperty' at the top and a sub-property 'belong\_to'. The right panel shows the 'Data properties' hierarchy, with 'owl:topDataProperty' at the top, and sub-properties 'genre\_id', 'knowledge\_property', 'knowledge\_name', 'knowledge\_id', and 'genre\_name'.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction
Type: figure
Figure: Figure 6. Construction of ontology class (left), object properties (middle), and data properties (right).

Figure 6. Construction of ontology class (left), object properties (middle), and data properties (right).


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction
Type: figure
Figure

Image /page/8/Picture/4 description: A diagram illustrating a simple ontology with three nodes. At the top, a node labeled "owl:Thing" is enclosed in a light purple rectangle with a green border. Below it, two nodes are positioned side-by-side: "knowledge" on the left and "genre" on the right, both in light purple rectangles with black borders. Each node contains a yellow circle to the left of its text. There are three connections: two solid blue lines with hollow triangular arrowheads point from "knowledge" and "genre" up to "owl:Thing", and an orange dashed line with a hollow triangular arrowhead points from "knowledge" to "genre".


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction
Type: figure
Figure: Figure 7. Visual display of ontology structure.

Figure 7. Visual display of ontology structure.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.2. Ontology Storage
Type: text

The ontology storage mode in this article is the Relational Database Management System (RDBMS). The principle of RDBMS is to map the ontology to one or more tables, and then divide it into several modes according to the mapping mode, such as horizontal storage, decomposition storage, vertical storage and hybrid storage. As the storage mechanism and data management capabilities of relational databases are relatively mature, relational database storage is widely used in many storage methods.

This article uses the RDF and web ontology language (OWL) to describe ontologies [20]. RDF is used to represent any resource information and describes resources through the mode of attribute-attribute value [21]. OWL is used to express the relationship between classes, the constraints of the set cardinality, the equality relationship, the attribute type, and the characteristics of the attribute [22]. Compared with other ontology description languages, OWL has better description ability and more description vocabulary. The description vocabulary expands the reasoning and query capabilities of the ontology.

The ontology is constructed and stored using the ontology editing tool Protégé. It cannot only construct and operate the ontology but also visually display the generated ontology, including the display of the hierarchical relationship between ontology concepts, as well as the visual display of ontology conceptual entities, entity relationships, and entity attributes. When the ontology is created using the ontology description language OWL and Protégé, semiautomatic construction can be achieved. Protégé can also reason about the ontology based on the hierarchical relationship of the ontology, with the help of Jena's query mode, and can also realize the editing operation of the ontology in the RDF and OWL languages [23].

3. Construction of Knowledge Graphs
3.2. Ontology Construction and Storage
3.2.2. Ontology Storage


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping
Type: text

Two standards to convert the structured data of the relational database into RDF format data have been developed by the RDB2RDF studio of W3C. The process is applied by the D2RQ tool [24].

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The first standard is direct mapping, which is defined by the rule that tables in the database are classes in the associated ontology. For example, there are 3 tables for the data stored in MySQL. After mapping, the ontology has 2 classes instead of the 3 classes previously defined. The columns of tables are attributes, and the rows are instances. The content in each cell of tables is text. If a certain column in the cell is a foreign key, then it will not be able to map the data in the database to the defined ontology. In this case, the shortcomings of direct mapping are marked. In response to this defect, the RDB2RDF studio proposed R2RML and allows users to flexibly edit and set mapping rules. This mapping also provides the ability to view existing relational data in the RDF data mode, which is represented by mapping the structure selected by the customer and the target vocabulary. R2RML mapping is an RDF graph and is recorded in Turtle syntax. R2RML supports different types of mapping implementations. The processor can provide virtual SPARQL protocol and resource description framework query language (SPARQL) endpoints, generate RDF dumps, or provide link data interfaces on the mapped relational data [25]. The specific mapping diagram is shown below (Figure 8).

3. Construction of Knowledge Graphs
3.3. Ontology and Database Mapping


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping
Type: figure
Figure

Image /page/9/Figure/2 description: A diagram illustrating the mapping between an 'Ontology layer' and a 'Data layer', separated by a horizontal line. The Ontology layer at the top shows a hierarchical structure. A central concept 'owl:Thing' is at the apex. Below it are two concepts, 'knowledge' and 'genre', both linked to 'owl:Thing' with arrows indicating they are subclasses. There is also a dashed arrow pointing from 'knowledge' to 'genre'. The Data layer at the bottom displays a database schema with three tables. The 'knowledge' table has columns: 'knowledge\_id', 'knowledge\_name', and 'knowledge\_property'. The 'genre' table has columns: 'genre\_id' and 'genre\_name'. A linking table, 'knowledge\_to\_genre', connects them with columns 'knowledge\_id' and 'genre\_id', representing a many-to-many relationship. Dashed arrows labeled 'mapping' connect the 'knowledge' concept in the ontology layer to the 'knowledge' table in the data layer, and the 'genre' concept to the 'genre' table.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping
Type: figure
Figure: Figure 8. Schematic diagram of ontology to database mapping.

Figure 8. Schematic diagram of ontology to database mapping.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.4. Query and Reasoning
Type: text

This article describes how to use D2RQ to setup a SPARQL service endpoint and use query operations in the browser. A SPARQL endpoint provides a service that is compliant with the SPARQL protocol. Through the default or defined mapping file, RDF data can be queried in the rules of RDF. In other words, to complete the final query, D2RQ converts the SPARQL query into an SQL statement based on the mapping file and then returns the result to the user [26].

The following steps are executed: (1) start the D2R server; (2) enter the browser to start the SPARQL endpoint; and (3) enter the SPARQL statement to execute the query. Concurrently, operations can also be queried by writing Python scripts. The third-party library SPARQL Wrapper of Python can easily interact with endpoints.

Using D2RQ to open the endpoint service has two disadvantages: it does not support publishing RDF data directly to the network through the endpoint and does not support the inference. To solve these problems, certain components of Fuseki, Jena, and the tuple database (TDB) in Apache Jena are investigated experimentally. Fuseki is a SPARQL server provided by Apache Jena, which is primarily run as a web application or as an embedded server. Jena provides resource description framework schema (RDFS). In the case of a single machine, storage layer technology can provide high RDF storage performance [27]. Next, we must define reasoning rules and conduct knowledge reasoning.

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3. Construction of Knowledge Graphs
3.4. Query and Reasoning


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j
Type: text

Based on the literature [2], the knowledge graph has basic query and reasoning functions; however, the visualization function of the knowledge graph has not been reflected. Therefore, the graph database Neo4j is used to visualize the knowledge graph [28]. Neo4j provides good visualizations of knowledge graphs. The specific implementation process and visualization are shown below. The process of generating the knowledge graph is as follows.

First, table data are converted into the format of CSV. These files are then moved to the import directory of Neo4j because Neo4j defaults to open files in the import directory. After starting the database, the Cypher language [29] is used to import data, and the construction mode of the node is created (k: knowledge {name: "absolute orientation"}). This statement creates a node with a knowledge label, and this node has a name attribute, an attribute value of "absolute orientation", and a variable name k. The Cypher sentence for importing structured data entity data into the database is Code 1. The result of Code 1 is shown in Figure 9.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language
Type: text

LOAD CSV WITH HEADERS FROM "file:///knowledge.csv" AS line MERGE (k:knowledge{id:line.knowledge\_id, name:line.knowledge\_name, genre:line.knowledge\_genre})


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language
Type: figure
Figure

Image /page/10/Figure/6 description: A diagram on a white background illustrating a relationship between a large dataset and a specific data point with its neighbors. On the left, a large circle is filled with hundreds of small blue dots. One dot is colored red and enclosed in a red square. An arrow points from this square to the right side of the image. The right side displays a zoomed-in view, showing a central red circle with the text "Remote Sensing". This is surrounded by eleven blue circles, each containing a related term. The terms in the blue circles are: "aspect map", "analytical", "aerial photog...", "adjacent area", "additive colir vie...", another "aerial photog...", "active sensor", "accuracy assess...", "adjoining", "altitude", and another "analytical".


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language
Type: figure
Figure: Figure 9. Part of the visualization shows the result of importing entities into the database.

Figure 9. Part of the visualization shows the result of importing entities into the database.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language
Type: text

The name of the imported file is "knowledge.csv", and the label is "knowledge". Attributes and attribute values correspond to row and column data in the table. The imports of nodes and relationships are similar. The Cypher sentence for importing structured relational data into the database is Code 2. The result of Code 1 is shown in Figure 10.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language
Type: text

LOAD CSV WITH HEADERS FROM "file:///knowledge\_to\_genre.csv" AS line MATCH (from:knowledge{id:line.knowledge\_id}),(to:genre{id:line.genre\_id})

MERGE (from)-[r:belong\_to{Relation:line.knowledge\_to\_genre}]->(to)

The name of the imported file is "knowledge\_to\_genre". The partial result of the imported visualization is shown below (Figure 11). In this picture, the two red nodes, "Surveying" and "Remote Sensing", represent the class of knowledge, and the blue node represents knowledge.

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Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language
Type: figure
Figure

Image /page/11/Figure/1 description: A concept map or mind map visualizing terms related to "Remote Sensing." The central node is a red circle labeled "Remote Sensing." Numerous lines radiate outwards from this central node, each labeled with "belong to," connecting to a large number of surrounding nodes. These outer nodes are blue circles, each containing a specific term or concept. The blue nodes are arranged in several concentric rings around the central red node. Some of the terms in the blue circles include "digital filtering," "infrared imagery," "satellite photo map," "texture analysis," "active sensor," "passive sensor," "image overlay," "stereop.," and "digital elevation model."


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language
Type: text

$\textbf{Figure 10.} \ \ \text{Part of the visualization shows the result of importing relationships into the database}.$


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language
Type: figure
Figure

Image /page/11/Figure/3 description: A network visualization diagram on a white background, showing a central cluster of nodes connected to a large number of peripheral nodes. In the center, there are three red circular nodes labeled 'Remote Sensing' and 'Surveys', with the third label being partially obscured. Adjacent to them is a green node labeled 'Geogra'. These central nodes are connected by thin grey lines to a multitude of light blue circular nodes that form a large, roughly circular or hexagonal shape around the center. Each blue node contains a word or short phrase, such as 'digital image', 'satellite', 'sensor', 'camera', and 'infrared'. The overall structure represents a hub-and-spoke model, illustrating the relationships between central concepts and numerous related terms.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language
Type: figure
Figure: Figure 11. Part of the visualization of the knowledge graph in the field of surveying and remote sensing.

Figure 11. Part of the visualization of the knowledge graph in the field of surveying and remote sensing.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language
Type: text

The database contains 1024 nodes, which belong to the two classes of nodes "knowledge" and "genre". The nodes in the knowledge graph are the primary components of the knowledge graph, primarily the knowledge of surveying and remote sensing. The

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library also contains 1295 relationships, which represent the relationship between nodes in the class "knowledge" and nodes in the class "genre". For example, "belong\_to" is the belonging relationship between knowledge and type. The library also contains attribute information, such as the "genre" and "name" of the "knowledge" node. Certain basic information for creating the database is shown below (Figure 12).


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language
Type: figure
Figure

Image /page/12/Picture/2 description: A screenshot of a user interface panel titled 'Database Information' on a dark grey background with white text. The panel is divided into three sections. The first section, 'Node Labels', contains three oval-shaped labels: a beige one showing '\*(1,024)', a red one with 'genre', and a cyan one with 'knowledge'. The second section, 'Relationship Types', has two grey rectangular labels: one showing '\*(1,295)' and another with 'belong\_to'. The third section, 'Property Keys', displays four grey rectangular labels with the text 'Relation', 'genre', 'id', and 'name'.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language
Type: figure
Figure: Figure 12. Part of the information of the knowledge graph knowledge base in the field of surveying and remote sensing.

**Figure 12.** Part of the information of the knowledge graph knowledge base in the field of surveying and remote sensing.


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph
Type: text

Application analysis of knowledge graph is an important means to verify the value of knowledge graph. This article verifies the application value of the knowledge graph from the two application scenarios of "domain relevance analysis" and "knowledge reasoning in the field of surveying and remote sensing" in the smart campus platform.


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis
Type: text

In many fields, there are various degrees of correlation. For example, in education, there is a common phenomenon in which pieces of knowledge are related between different disciplines of the same major. Zhou and others proposed a method for constructing a scientific knowledge graph based on the degree of interdisciplinary association, which aims to help students quickly understand the relationship between disciplines [30]. The relevance analysis based on the knowledge graph can assist students in choosing courses and improve teaching quality.

In recent years, with the development of science and technology, the "smart campus platform" has risen rapidly. Therefore, in the field of surveying and remote sensing, the knowledge graph can be leveraged in the context of the smart campus platform. The same knowledge is shown to exist in multiple domains, thereby highlighting the association between fields (Figure 13). The association between domains is related to the number of common knowledge between domains. The more the number of common knowledge, the higher the association between fields. In this research, if one knowledge belongs to both "surveying" and "remote sensing", then the knowledge is the common knowledge between the two fields. The more common knowledge between two fields, the higher degree of association between fields.

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Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis
Type: figure
Figure

Image /page/13/Figure/1 description: A data visualization of a network graph on a white background, showing three separate, fan-shaped clusters of nodes. Each cluster has a central red circular node from which numerous thin grey lines radiate out to connect with hundreds of teal circular nodes. The teal nodes are arranged in dense, curved layers around their respective central red node. The cluster on the left is the smallest, the one in the middle is larger, and the one on the right is the largest. There appears to be text within each node, but it is too small to be legible.


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis
Type: figure
Figure: Figure 13. Display of relevance between courses.

Figure 13. Display of relevance between courses.


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis
Type: text

The domain is the class in ontology. In this research, the domains of surveying and remote sensing respectively correspond to the classes of "Surveying" and "Remote Sensing" in the knowledge graph. Among all the knowledge, some knowledge belongs to the class of "Surveying", and some knowledge belongs to the class of "Remote Sensing". Some knowledge belongs to both the class of "Surveying" and the class of "Remote Sensing". These are the common knowledge of the two fields. Then this common knowledge determines the degree of association between the two fields (Figure 13).

The picture (Figure 13) contains nodes of "Surveying" and "Remote Sensing", as well as all the knowledge belonging to the field of remote sensing and surveying, which also contains the relationship "belong\_to" between classes nodes and knowledge. The knowledge graph contains 1022 knowledge points. There are 273 knowledge points shared by the two disciplines. Its repetition rate is 26.7%. According to the degree of relevance, it can be used as a reference when selecting courses. If you were a student in a field unrelated to surveying and remote sensing and sought to have a general and comprehensive understanding of the field, then in order to save time and cost, you could just choose the two subjects of photogrammetry and remote sensing principles and applications. Accordingly, professional students in this field can quickly help such students understand the degree of coverage of knowledge content between subjects, help them quickly understand a nearby subject that has already studied a subject, and improve learning efficiency.


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing
Type: text

Knowledge reasoning is primarily based on known knowledge to infer new knowledge or distinguish incorrect knowledge. The reasoning function of knowledge is a prominent feature of the knowledge graph. Compared to traditional knowledge reasoning,

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knowledge reasoning based on knowledge graphs is more flexible, and its methods are more diverse. There are many methods; however, the method used in this article is primarily based on the reasoning of description logic and thus primarily introduces knowledge reasoning based on this method.

The description logic system is primarily divided into four parts: (1) three basic elements (concepts, relationships and entities); (2) the axiom set, to which the concept belongs; (3) the assertion set of the entity; and (4) the reasoning mechanism. Reasoning tools based on supporting OWL DL (description logic) language include FaCT++, Racer, and pellet [31]. In addition to existing tools, knowledge can be reasoned by writing rules.

In this knowledge graph, the reasoning of knowledge is described by writing rules based on the reasoning method of description logic. This article primarily involves two types of reasoning: entity and relational. Entity reasoning infers unknown entities based on existing entities and relationships (e.g., known entity-relation-unknown entity). Relational reasoning infers the relationship between one or more entities by editing rules under the condition of existing entities (e.g., known entity-unknown relationship-known entity).


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing
Type: text

, Racer , and pellet [ 31 ] . In addition to existing tools , knowledge can be reasoned by writing rules . In this knowledge graph , the reasoning of knowledge is described by writing rules based on the reasoning method of description logic . This article primarily involves two types of reasoning : entity and relational . Entity reasoning infers unknown entities based on existing entities and relationships ( e . g . , known entity - relation - unknown entity ) . Relational reasoning infers the relationship between one or more entities by editing rules under the condition of existing entities ( e . g . , known entity - unknown relationship - known entity ) .

Generally, missing knowledge is inherent to knowledge graphs, that is, the incompleteness of entities or relationships. We can apply the knowledge graph in the field of surveying and remote sensing to complement the knowledge graph in the field of surveying and remote sensing. Knowledge graph completion is an important way to acquire knowledge. The goal of knowledge graph completion is to find these missing items of knowledge and add them to the knowledge graph so that the knowledge graph tends to be complete [32]. A knowledge graph is named G, and its basic components include the entity set  $E = e_1, e_2, \dots e_i$  (*i* is the number of entities), relation set  $R = r_1, r_2 \dots r_j$  (*j* is the number of relations) and corresponding triple set  $T = \{(e_m, r_k, e_n)\}\ (e_m, e_n \in E, r_k \in R)$ . Since the number of entities E and relationships R in the knowledge graph are limited, there may be some entities and relationships that are not in G. According to the content we want to complete, we can divide knowledge completion into three subtasks: ① Given a partial triplet  $T_1 = (?, r_k, e_n)$ , we predict the head entity. ② Given a partial triplet  $T_2 = (e_m, r_k, ?)$ , we predict the tail entity. ③ Given a partial triplet  $T_3 = (e_m, ?, e_n)$ , we predict the relationship between entities. According to whether the entities and relationships belong to the original knowledge graph, we can divide the knowledge graph completion into static knowledge graph completion and dynamic knowledge graph completion. The entities and relationships in the completion of the static knowledge graph are all in the original knowledge graph. The entities and relationships in the completion of the dynamic knowledge graph are not in the original knowledge graph. Through the completion of the knowledge graph, the collection of entities and relationships of the original knowledge graph can be expanded.

Knowledge graph completion is the most widely used field of knowledge reasoning. The original intention of a large number of knowledge graph reasoning algorithms is to be applied to knowledge graph completion, such as the Markov logic network (MLN), translating relation embeddings (TransR), capsule network-based embedding (CapsE), and relational graph neural network with hierarchical attention (RGHAT). All the methods mentioned above can determine whether there is a certain relationship between any entities by reasoning in the vector space, and then realize the completion of the knowledge graph.


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing
Type: text

the collection of entities and relationships of the original knowledge graph can be expanded . Knowledge graph completion is the most widely used field of knowledge reasoning . The original intention of a large number of knowledge graph reasoning algorithms is to be applied to knowledge graph completion , such as the Markov logic network ( MLN ) , translating relation embeddings ( TransR ) , capsule network - based embedding ( CapsE ) , and relational graph neural network with hierarchical attention ( RGHAT ) . All the methods mentioned above can determine whether there is a certain relationship between any entities by reasoning in the vector space , and then realize the completion of the knowledge graph .

Integrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform can help students discover new entities or relationships between entities. For example, relational reasoning is based on the Jena tool, as shown in code 3 (based on the SPARQL language). This code defines a rule named "rule", which means that if there is an entity that belongs to "Remote Sensing", then this entity belongs to "Geography", which is (Remote Sensing-belong\_to-Geography). For example: knowing (Aberration-belong\_to-Remote Sensing), according to this code you can obtain the result: (Aberration-belong\_to-Geography). The visual display of the whole reasoning process is shown in Figure 14.

Remote Sens. **2021**, 13, 2511 16 of 19


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language
Type: text

@prefix:
@prefix owl:
@prefix rdf:
@prefix rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#

@prefix xsd: <XML Schema>.

@prefix rdfs: http://www.w3.org/2000/02/rdf-schema#>.

[rule:(?k:belong\_to ?g)(?g:hasname ?n)(?n:genre\_name 'Remote Sensing')->(?k rdf:type:Geography)]


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language
Type: figure
Figure

Image /page/15/Figure/6 description: A diagram illustrating a process labeled "Inference results". On the left, two elements are shown being added together with a plus sign. The top element is a network graph with a central red node labeled "Remote Sensing" connected to numerous surrounding blue nodes. The bottom element shows a relationship where the red "Remote Sensing" node is linked to a green node labeled "Geogra..." by an arrow labeled "belong\_to". An arrow labeled "Inference results" points from the left side to the right side. On the right, the resulting network graph is displayed, which is similar to the initial graph but with the central node now being the green "Geogra..." node, connected to the same cluster of blue nodes.


Document: 1. Introduction
Section: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language
Type: figure
Figure: Figure 14. Visual display of the inference process.

Figure 14. Visual display of the inference process.


Document: 1. Introduction
Section: 5. Summary and Prospect
Type: text

In the field of surveying and remote sensing, rapid acquisition, efficient processing and effective application of remote-sensing data are the core tasks. However, in the face of the massive amount of data accumulated, as well as the heterogeneous, decentralized, and dynamic update characteristics of the massive amount of data, it is difficult to realize the semantic integration, interoperability and sharing platform construction of massive data application services. Since the knowledge graph is a kind of semantic network, hierarchical interconnection and semantic processing capabilities can be realized in the form of a graphical structure, and the system and relevance of knowledge can be displayed more intuitively. The knowledge graph provides a better organization and management method for isolated information and knowledge. It describes the real-world entities, concepts, and the relationships between entities and concepts in a structured and semantic form and organizes information into a form that is easier for people to understand. The purpose is to express the objective world as a well-structured knowledge expression. It is hailed as a booster for the next generation of artificial intelligence. The contributions of this article are as follows:

- (1) To quickly obtain effective data from massive amounts of heterogeneous, decentralized, and dynamically updated data. This article proposes a method for constructing a subject knowledge graph in the field of surveying and remote sensing.
- (2) To verify the application value of the knowledge graph in the field of surveying and remote sensing. This article verifies its functions from the query, visualization and reasoning of the knowledge graph.
  - The challenges and countermeasures encountered in the research are as follows:
- (1) The connection between the mode layer and the data layer. The knowledge graph in the field of surveying and remote sensing is mainly divided into two parts: mode

Remote Sens. **2021**, 13, 2511 17 of 19

level construction and a data layer. The mode level is the foundation of the data layer. Through the factual expression of the ontology library standard data layer, ontology is the conceptual template of a structured knowledge base. The data layer is composed of a series of knowledge entities or concepts. Knowledge is stored in units of facts. The data layer expresses knowledge in the form of triples (entity 1-relation-entity 2) or (entity-attribute-attribute value). Realizing the association between ontology and data at two levels is a major challenge in constructing a knowledge graph. This article uses the D2RQ tool to realize mapping from ontology to the database. The D2RQ tool converts the structured data of the relational database into data in RDF format. This mapping also provides the ability to view existing relational data that exist in the RDF data model, which is represented by mapping the structure selected by the customer and the target vocabulary. The R2RML mapping itself is an RDF graph and is recorded in Turtle syntax. R2RML supports different types of mapping implementation. The processor can provide virtual SPARQL endpoints on the mapped relational data, or generate RDF dumps, provide a link data interface.

(2) Application and practice of the domain knowledge graph. This article gives an example of the application of integrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform. The knowledge visualization application of the domain knowledge graph on the smart campus platform can assist teachers and students in selecting courses. The domain knowledge graph is applied to knowledge reasoning on the smart campus platform, which can help teachers and students discover and reason about new knowledge, as well as new relationships between knowledge.


Document: 1. Introduction
Section: 5. Summary and Prospect
Type: text

can provide virtual SPARQL endpoints on the mapped relational data , or generate RDF dumps , provide a link data interface . ( 2 ) Application and practice of the domain knowledge graph . This article gives an example of the application of integrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform . The knowledge visualization application of the domain knowledge graph on the smart campus platform can assist teachers and students in selecting courses . The domain knowledge graph is applied to knowledge reasoning on the smart campus platform , which can help teachers and students discover and reason about new knowledge , as well as new relationships between knowledge .

Knowledge in the field of surveying and remote sensing is diverse and complex, and knowledge graphs can be studied in more detail in the following areas. Data in these fields of study are typically images, and the recognition and acquisition of knowledge in image data are understudied; in particular, the knowledge graph of image data lacks a time dimension. Thus, future research should investigate how to add the time dimension to the knowledge graph and how to extend the time dimension to the application of the knowledge graph. If the above problems can be broken through, then the knowledge graph in the field of surveying and remote sensing will have greater potential application value:

- (1) The discovery of new rules of surveying and remote sensing: The continuous increase in surveying and remote-sensing data and the continuous improvement of digital management and utilization technology have provided great convenience for scientific researchers to carry out research work. The surveying and remote-sensing knowledge graph provides support for the insight and discovery of regular knowledge of surveying and remote-sensing resources by associating a large amount of surveying and remote sensing knowledge into a network structure. Researchers can discover various knowledge and rules hidden behind the development process through the analysis of surveying and remote-sensing data to provide relevant scientific research personnel and scientific research policy makers with scientific research directions and a policy-making basis.
- (2) Application of machine learning methods in the analysis of knowledge graphs in the field of surveying and remote sensing: From the development process of the combination of machine learning and knowledge graphs (the knowledge graph as a complex network; traditional machine learning methods to conduct graph mining and analysis on the knowledge graph; further application of deep learning methods and graph neural network methods in knowledge graphs), the value and function of the knowledge graphs have been further embodied. The surveying and remotesensing domain knowledge graph is a special domain knowledge graph, and the analysis method in the general knowledge graph is used to mine and analyse the graph, but it cannot make full use of the structure and characteristics of the surveying and remote-sensing domain knowledge graph. For the specific structural features and entity attributes in the knowledge graph of surveying and remote sensing, it

Remote Sens. **2021**, 13, 2511 18 of 19

is necessary to design specific machine learning methods or deep neural network structures. At the same time, for different application scenarios, different objective functions are usually designed to learn the parameters of the algorithm. Therefore, the study of machine learning and deep learning mining methods for the knowledge graph in the field of surveying and remote sensing is helpful to the further analysis and application of the knowledge graph in the field of surveying and remote sensing.


Document: 1. Introduction
Section: 5. Summary and Prospect
Type: text

knowledge graph . For the specific structural features and entity attributes in the knowledge graph of surveying and remote sensing , it Remote Sens . * * 2021 * * , 13 , 2511 18 of 19 is necessary to design specific machine learning methods or deep neural network structures . At the same time , for different application scenarios , different objective functions are usually designed to learn the parameters of the algorithm . Therefore , the study of machine learning and deep learning mining methods for the knowledge graph in the field of surveying and remote sensing is helpful to the further analysis and application of the knowledge graph in the field of surveying and remote sensing .

(3) Construction of the service platform of the knowledge graph in the field of surveying and remote sensing: In the construction of the knowledge graph in the field of surveying and remote sensing, the work efficiency is affected due to the problem of scattered tools. The next research plan is to build a knowledge graph service platform in the field of surveying and remote sensing to realize the integration of tools and services. At the data source level, it integrates all kinds of open and available data in the field of surveying and remote sensing, as well as data unique to each demand side. Through the provided functions of surveying and remote sensing data acquisition, data storage, ontology construction, graph construction and update, a knowledge graph of the field of surveying and remote sensing that can be updated in time can be constructed. In terms of services, through the provision of knowledge service algorithms and models such as knowledge queries, knowledge visualization, and knowledge reasoning, a service platform provides targeted knowledge services for different roles.

**Author Contributions:** Conceptualization, X.H. and Z.J.; methodology, X.H.; validation, X.L., Q.L. and R.Y.; formal analysis, Z.J.; investigation, L.L.; resources, X.H.; data curation, X.L.; writing—original draft preparation, X.H.; writing—review and editing, L.L., L.Y.; visualization, M.S.; supervision, X.L.; project administration, Z.J.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the National Key Research and Development Project of China (No. 2016YFC0502106), National Science and Technology Major Project of China (No. 2018ZX07111002) and the National Natural Science Foundation of China(No. 41476161).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

**Data Availability Statement:** The storage URL of the structured raw data to construct the knowledge graph is: https://github.com/hao1661282457/Knowledge-graphs.git (accessed on 25 June 2021).

**Acknowledgments:** We thank AJE (https://www.aje.cn/ (accessed on 25 June 2021)), for editing the English text of this manuscript.

Conflicts of Interest: The authors declare no conflict of interest.


Document: s10346-021-01831-1
Type: text

Abstract On June 25, 2020, Jinweng Co in Yiga, Tibet, experienced an outburst flood that resulted in catastrophic damage to farmland and roads. The complex causal factors for glacial lake outburst flooding (GLOF) are not fully understood. This paper provides a systematic analysis of the contributing factors leading to the GLOF disaster in terms of meteorological triggering, glacial activity, lake expansion, landslide, and glacial collapse. The analysis is based on multi-source remote sensing approaches. Pixel offset tracking of Sentinel-1 images shows changes in the glacier flow velocity from 2017 to 2020. Sentinel-2 and Landsat-8 images and inventory data have revealed the expansion of the lake since 1998. The satellite precipitation measurements revealed that the highest daily rainfall in recent years occurred approximately 4 days before the GLOF. Time series synthetic aperture radar (SAR) backscattering images and interferograms suggest that a landslide had occurred from the western lateral moraine into the lake. Additionally, SAR images suggest possible ice collapse from the glacier tongue into the lake. The causal mechanism for the June 2020 GLOF event was likely the dam failure triggered by heavy rainfall and combined with landslides and ice collapses. Our research can provide a reference to identify and mitigate glacial lake outburst disasters in mountainous regions based on satellite optical and radar images.

Keywords Glacial lake outburst flood (GLOF) · Glacier threedimensional flow velocity · Outburst mechanism · Multi-source remote sensing


Document: s10346-021-01831-1
Section: Introduction
Type: text

Glacial lake outburst flood (GLOF) is a sudden and rapid downstream discharge of a significant volume of water. Previous studies (Evans and Clague 1994; Nadim et al. 2006; Yamada 1998; Emmer and Cochachin 2013a, Emmer and Vilímek 2013b, Emmer 2017) identified two groups of factors that induce lake outbursts. The first set consists of dynamic causes, such as slope movement into the lake, earthquakes, heavy rainfall, or snowmelt. The second set includes long-term factors such as the melting of buried ice, hydrostatic pressure changes, and other long-term degradation processes. GLOFs often have catastrophic consequences for communities and infrastructure located downstream. Glacier recede and thinning in high mountains generate the formation and development of glacial lakes (Richardson and Reynolds 2000; Haeberli et al. 2001, 2002). Monitoring-related glacial movement and changes in glacier lakes and the surroundings help us better understand how the ice mass propagates in space, how their movements evolve over time, and how external factors control their

behaviors (Satyabala 2016; Ma et al. 2020). Owing to the inherently challenging landscapes, comprehensive remote sensing methods are needed to study and monitor GLOFs.

The outburst of Jinweng Co ("Co" refers to "lake" in Tibetan) in Yiga village, Tibet, caused intense and sudden flooding on June 25, 2020. This grave disaster inundated and destroyed 382.43 km2 of farmland, washed away over 43.9 km of roads, and flooded 45% of the Yiga Scenic Area project (Wang et al. 2020a). Over 2,000 glacial lakes have been detected across the Himalayas (Fujita et al. 2013), so the potential for further hazards is significant. Moreover, the eastern Himalayas are considered as one of the most severely deformed areas due to intense tectonic activities and earthquakes. The melting, thinning, and receding of temperate glaciers leads to lake expansion and is related to slope movement. Extreme precipitation plays an important role in triggering slope movement, while an increase in temperature can result in increased melting, permafrost degradation, and rockfalls (Hu et al. 2019a; Wang et al. 2020a, b; Lu and Kim 2021).

Many studies have documented that snow/ice accumulation/ avalanches and landslides significantly impact outburst flooding (Allen et al. 2016; Clague and Evans 2000; O'Connor et al. 2001; Worni et al. 2012). The Jinweng Co disaster originated from a parent glacier and included an outburst of the proglacial lake. The realtime monitoring and analysis conducted shortly after the disaster suggested that the intensification of climate warming and cryosphere instability stimulated the occurrence of the GLOF (Wang et al. 2020a). However, the mechanism and triggering factors of the Jinweng Co GLOF process chain should be further investigated. The evolution of the glacier, the proglacial lake, and the surrounding area prior to the GLOF event has not yet been analyzed. A complete assessment of triggering factors (glacier activity, precipitation, air temperature, slope movement, and ice collapse) is still lacking.


Document: s10346-021-01831-1
Section: Introduction
Type: text

Co disaster originated from a parent glacier and included an outburst of the proglacial lake . The realtime monitoring and analysis conducted shortly after the disaster suggested that the intensification of climate warming and cryosphere instability stimulated the occurrence of the GLOF ( Wang et al . 2020a ) . However , the mechanism and triggering factors of the Jinweng Co GLOF process chain should be further investigated . The evolution of the glacier , the proglacial lake , and the surrounding area prior to the GLOF event has not yet been analyzed . A complete assessment of triggering factors ( glacier activity , precipitation , air temperature , slope movement , and ice collapse ) is still lacking .

Thus, this study addresses the following key question: Can we identify precursory characteristics of the 2020 GLOF at Jinweng Co in eastern Tibet? To address this question, we aimed to understand the movement of glaciers and the change in morainedammed lakes, as well as temperature and precipitation records in the study area. We used spaceborne C-band Sentinel-1 data to retrieve the time series two-dimensional (2D) displacement and three-dimensional (3D) glacier flow velocity. The interpretation was then augmented with Sentinel-2 and Landsat-8 optical images and inventory data to construct changes to the area of the lake before and after the event. Meteorological observations were examined to understand the climatic properties of the region. Additionally, we analyzed SAR intensity images and interferograms to infer the glacier tongue changes and identify landslide activity. This led to further clarification regarding the outburst triggering mechanisms.


Document: s10346-021-01831-1
Section: Study area
Type: text

The study region is within the Tanggula Mountains, which are home to some of the highest mountains in the world. It is also the most humid region of the southeastern Qinghai-Tibet Plateau (QTP) (Fig. 1). The topography of the study area is affected by the compression of the Indian Ocean-Eurasian continental plate, uplift of the QTP, strong faulting activity, and fluvial incision. The climate is influenced by both East Asian and Indian monsoons (Ding 2013). Precipitation during the rainy season is mainly caused by airflow from the southwest, west, and northwest (Li et al. 2009). The temperate glaciers in this study area are small and primarily oriented in the north-south direction with large ice caps (You and Yang 2013). Consequently, the area is subject to glacial hazards such as glacier debris flows, GLOFs, and glacier surges, which are further increased by climate change (Allen et al. 2016; Bazai et al. 2021; Chiarle et al. 2007; Wang et al. 2020a, b; Kääb et al. 2021). Generally, a glacial lake outburst results from external forces such as ice or rock fall. snow avalanches, landslides, rainstorms, glacier surges, rapid snow melting, and/or internal processes such as the ablation of buried ice in moraines and the release of lake water inside the ice body (Wang et al. 2020a).

Jinweng Co (30.356 N°, 93.631 ° E) is a proglacial morainedammed lake (Fig. 2) and is also the largest glacial lake within the Nidou Zangbo basin ("Zangbo" refers to rivers in Tibetan) (Zheng et al. 2021). The lake is oriented in a north-south direction and is characterized by steep slopes and lateral moraines. The slopes are approximately 40°, and the average elevation of lateral moraines is more than 100 m above the lake level (Zheng et al. 2021). As shown in Fig. 2d, the length is approximately 1.8 km, and its width near the glacier tongue is 0.24 km, while the width near the dam was 0.33 km before the lake outburst. The parent glacier tongue enters

Fig. 1 Overview of the study area showing Jinweng Co and coverage of multiple datasets outlined in purple (Sentinel-2), orange (Landsat-8), blue (Ascending Sentinel-1), and red (Descending Sentinel-1)

the lake after passing through a steep ice cliff approximately 350 m long with an average slope of approximately 35 ° (Fig. 2). The parent glacier is a temperate glacier with a mean annual temperature of approximately o °C. The snow basin at high altitude serves as the accumulation zone, while the tongue is the ablation zone. This study investigates the triggering factors for the June 2020 GLOF event in Jinweng Co by analyzing the movement of the parent glaciers and changes to the lake and surroundings using multi-satellite remote sensing datasets.


Document: s10346-021-01831-1
Section: Data and methods
Type: text

The multi-track C-band Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 and Landsat-8 optical images were used to investigate the triggering factors of the 2020 GLOF event (Fig. 3). The Copernicus Sentinel-1A/B and Sentinel-2 Level-1C images for this study are available free of cost from the Sentinel Scientific Data Hub of the European Space Agency. Multi-temporal Landsat images were obtained from the Geospatial Data Cloud (http://www.gscloud.cn/). In addition, daily and monthly precipitation measurements from the satellite Global Precipitation Measurement (GPM) (Skofronick 2017) were obtained to analyze the meteorological factors. In addition, inventory data from 1975 to 2018 were obtained from the National Tibetan Plateau Data Center (TPDC) to evaluate temporal variations in Jinweng Co (https://data.tpdc.ac.cn) (Wang 2015).


Document: s10346-021-01831-1
Section: Data and methods > Satellite datasets
Type: text

Ninety-eight interferometric wide (IW) swath mode ascending Sentinel-1 and eighty-two descending Sentinel-1 images from March 2017 through October 2020 were acquired. The pixel spacing in the ground range and azimuth direction was ~ 4 m and ~ 14 m, respectively. Sentinel-2 data with multi-spectral instrument (MSI) images were acquired on May 1 and July 27, 2020, and provided a spatial resolution of 10 m. Landsat-8 MSI images from 2018 to 2019 with a spatial resolution


Document: s10346-021-01831-1
Section: Data and methods > Satellite datasets
Type: figure
Figure

Image /page/1/Figure/11 description: A satellite map of a mountainous, snow-covered region, with longitude ranging from 93°25' to 93°55' and latitude from 30°15' to 30°35'. The map features several colored rectangular overlays indicating the coverage of different satellites: a blue box for 'Ascending Sentinel-1', a red box for 'Descending Sentinel-1', an orange box for 'Landsat-8', and a smaller purple box for 'Sentinel-2'. Two locations, 'Yiga' and 'Jinweng Co', are marked with green dots. A light blue arrow indicates the 'Flood direction' from west to east. A legend in the bottom-left corner defines symbols for Glacier, Lake, and Main city. A scale bar in the bottom-right shows a scale from 0 to 6 km, and a north arrow is in the top-left corner.


Document: s10346-021-01831-1
Section: Data and methods > Satellite datasets
Type: text

Fig. 2 Overview of Jinweng Co and its parent glacier. a An overall view of Jinweng Co and surroundings. **b** The parent glacier before the GLOF event. **c** The crevassed glacier tongue terminated in Jinweng Co. d Morphological parameters of Jinweng Co. e The eastern lateral moraine and steep slopes. f The landslide zone at the western lateral moraine. **g** The parent glacier after the GLOF event. Photos: Q. Quying Source: Guoxiong Zheng (used with permission)


Document: s10346-021-01831-1
Section: Data and methods > Satellite datasets
Type: figure
Figure

Image /page/2/Figure/1 description: A composite image with seven panels, labeled (a) through (g), showing various aerial and landscape views of a glacial environment. Each panel includes a compass rose indicating north. Panel (a) is an overview of a valley with snow-capped mountains, showing the Jinweng Co glacier and a dashed line indicating the 'Flow direction'. Panel (b) is a top-down view of a glacier through clouds. Panel (c) shows a glacier terminating in a milky green glacial lake. Panel (d) displays the dimensions of the lake, labeled as 1.8 km long, 0.33 km wide at one end, and 0.24 km wide at the other. Panel (e) highlights a 'Lateral moraine' with a dashed line along the side of the lake. Panel (f) points out a 'Landslide zone' in red on the mountainside next to the lake. Panel (g) is another high-altitude aerial view of the glacier and surrounding mountains, partially obscured by clouds.


Document: s10346-021-01831-1
Section: Data and methods > Satellite datasets
Type: text

of 30 m were added to derive the temporal changes of Jinweng Co. Information on the Sentinel-1/2 and Landsat data are listed in Table 1. and the data coverages are shown in boxes in Fig. 1.


Document: s10346-021-01831-1
Section: Mapping slope movement with SAR intensity images and interferograms
Type: text

SAR intensity images can be used to detect landscape changes due to their sensitivity to terrain slope, surface roughness, and dielectric constant (e.g., Lu and Meyer 2002; Kim et al. 2017; Zribi and Dechambre 2017). Multi-temporal SAR backscattering intensity images were used in this study to infer changes in glaciers, lakes, and surroundings before the lake outburst event. Interferometric synthetic aperture radar (InSAR) can map surface deformation in the spatial-temporal view, which is a direct manifestation of slope movement (Hu et al. 2019a; Lu and Meyer 2002; Lu et al. 2003; Lu and Kim 2021).


Document: s10346-021-01831-1
Section: Mapping slope movement with SAR intensity images and interferograms > Change detection with optical images
Type: text

The interpretation of optical images is commonly employed to support GLOF mapping and inventory (Strozzi et al. 2010). There are countless glaciers in this study area, which induce enormous glacial lakes as glacier recedes. The Sentinel-2 and Landsat-8 images allow us to recognize geomorphological features related to ice mass movements, such as crevasses, debris flows, and outburst flooding. For instance, a glacial lake may have green patterns with fine texture in one optical image, and after the glacial lake outbursts or shrinks, its edge will form a gray-white submerged zone with obvious breaches and colluvial deposits. Using multi-temporal images, we can interpret the changes in glacial lakes and analyze the possibility of their collapse.


Document: s10346-021-01831-1
Section: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method
Type: text

We carried out the offset tracking procedure implemented by GAMMA software to estimate the two-dimensional displacement (Wegmuller et al. 1998; Werner and Wegmuller 2000; Gomez et al. 2019). Offset tracking includes intensity tracking and speckle tracking based on maximizing the cross-correlation of SAR image patches (e.g., Strozzi et al. 2002; Pritchard et al. 2005) to derive glacier displacement for pairs of images acquired at different times. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) was used to assist the co-registration of the SAR images to minimize geometric artifacts in offset-tracking displacements due to high topography (Kobayashi et al. 2009; Liu et al. 2020). A template of  $64 \times 64$  and  $4 \times 1$  was adopted in this study. Thresholds of amplitude correlation range between 0.2 and 0.4.

Table.1 Basic parameters of the SAR and optical datasets used in this study


Document: s10346-021-01831-1
Section: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method
Type: table
Table

| Data                            | Sentinel-1            |                       | Sentinel-2            | Landsat-8             |
|---------------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
| Pixel Spacing (azimuth × range) | 14 m × 4 m            |                       | 10 m                  | 30 m                  |
| Number of SAR images            | 98(ascending)         | 82(descending)        | 2                     | 2                     |
| Acquisition period              | 2017/03/21-2020/10/31 | 2017/03/16-2020/10/26 | 2020/05/01-2020/07/27 | 2018/06/06-2019/06/25 |


Document: s10346-021-01831-1
Section: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method
Type: text

Fig. 3 Flow chart of remote sensing methods used in this study


Document: s10346-021-01831-1
Section: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method
Type: figure
Figure

Image /page/3/Figure/2 description: A flowchart titled 'Recent Landslides' illustrating a methodology for analyzing landslides using three different types of remote sensing data. The flowchart is organized into three parallel processing streams. The first stream starts with an 'External DEM' (Digital Elevation Model). This data undergoes 'Offset tracking', then 'Stacking', and finally '3-D inversion'. The result is the 'Derivation of 3D annual flow velocity', shown with a corresponding map image. The second stream uses 'Ascending and Descending SAR images'. These images go through 'Co-registration' to create 'Original interferograms', which are then processed by 'Interferograms filtering' to produce 'Deformation interferograms', also illustrated with a sample image. The third stream begins with 'Optical images'. These are used for 'Change detection', leading to an 'Intensity change' analysis. The final output of this stream is 'Optical interpretation', which is depicted with two comparative map images.


Document: s10346-021-01831-1
Section: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method > Pixel offset tracking small baseline subsets (PO-SBAS)
Type: text

Pixel offset (PO) tracking small baseline subsets (SBAS) (PO-SBAS) follows the same motivation as the SBAS-InSAR technique. A sequence of small baseline SAR image pairs that have been previously co-registered with respect to a common reference image was obtained as a starting point (Sansosti et al. 2006; Casu et al. 2011). Subsequently, instead of the phase difference of the selected SAR images, we used their intensities to calculate the pixel offset for both the line-of-sight (LOS) and azimuth directions. Then, the singular value decomposition (SVD) inversion method was applied for the estimated relative LOS and azimuth offsets to generate the corresponding offset-based deformation time series (Sansosti et al. 2006; Casu et al. 2011).


Document: s10346-021-01831-1
Section: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method > Derivation of 3D Glacier velocity
Type: text

To retrieve the 3D glacier flow velocity from ascending and descending SAR images, we first obtained displacement measurements in the azimuth and LOS directions based on small baseline image pairs from both descending and ascending tracks.

The 3D velocity field can be obtained from velocity measurements in four distinct directions (Li et al. 2018; Yang et al. 2020):

$$\left\{ \begin{array}{l} V_{LOS}^{i} = -V_{U}cos\:\theta^{i} + \bullet V_{E}sin(\alpha^{i} - 3\pi/2)sin\:\theta^{i} + V_{N}cos(\alpha^{i} - 3\pi/2)sin\:\theta^{i} \\ V_{AZ}^{i} = -V_{E}cos(\alpha^{i} - 3\pi/2) + V_{N}sin(\alpha^{i} - 3\pi/2) \end{array} \right.$$

where i stands for the orbit direction (A indicates the ascending track, and D indicates the descending track),  $\theta$  is the incidence

angle, and  $\alpha$  is the azimuth angle. E, N, and V refer to the east, north, and vertical directions, respectively. The matrix form is as follows:

$$BX = V (2)$$

 $X = \begin{bmatrix} V^{\mathrm{U}} & V^{\mathrm{E}} & V^{\mathrm{N}} \end{bmatrix}^{\mathrm{T}}; L = \begin{bmatrix} V_{\mathrm{LOS}}^{\mathrm{A}} & V_{\mathrm{AZ}}^{\mathrm{A}} & V_{\mathrm{LOS}}^{\mathrm{D}} & V_{\mathrm{AZ}}^{\mathrm{D}} \end{bmatrix}^{\mathrm{T}}; \text{ and } B \text{ is a}$ design matrix composed of imaging geometry parameters


Document: s10346-021-01831-1
Section: Results > Changes in glacier velocity
Type: text

In this study, the 2D and 3D glacial movements of Jinweng Co from 2017 to 2020 were derived (Fig. 4). The 3D glacier flow velocity during this period is shown in Fig. 4a. The vector arrows indicate the horizontal velocity, whereas the color shows the vertical velocity. In the horizontal direction, the north directed velocity mainly appeared in the glacier trunk of Jinweng Co, with a mean flow velocity of up to 200 m/year. In the vertical direction, the parent glacier moved downward at a velocity of 40 m/year. Owing to sparse acquisitions from descending Sentinel-1, we only obtained the long time series displacement from the ascending track to show the temporal variation (Fig. 4b). As shown in Fig. 4b, P1 was located at the glacier tongue near the lake. The mass transport is evident in the azimuth direction (approximately in the northern direction), as seen in the cumulative displacement of up to 18.4 m. Interestingly, the total displacement during the 2019-2020 cycle (October 2019 to June 2020) at the lower part of the glacier (close to the proglacial lake) is much larger than that observed in the previous years (Fig. 4b).


Document: s10346-021-01831-1
Section: Results > Changes in glacier velocity
Type: figure
Figure

Image /page/4/Figure/0 description: The image contains two plots, labeled (a) and (b).


Document: s10346-021-01831-1
Section: Results > Changes in glacier velocity
Type: text

Plot (a) is a 3D surface plot showing velocity data. The surface depicts a green, hilly terrain with a large blue area. A compass indicates the direction of North. The color of the surface represents the 'Vertical velocity (m/year)', with a color bar at the bottom ranging from -40 (blue) to 40 (red). The blue area corresponds to negative vertical velocities. Black arrows on the surface indicate the 'Horizontal velocity (m/year)', with a scale arrow representing 200 m/year. The plot is labeled with 'Jinweng Co' and a point 'P1'. The axes are labeled with numerical values, such as 93.6 to 93.64 on one axis and 30.325 to 30.365 on the other.

Plot (b) is a scatter plot showing displacement over time. The x-axis represents the 'Date (yyyy/m/d)' from 2017/3/1 to 2020/9/1. The y-axis represents 'Displacement (m)' from 0 to 20. There are two data series: 'Azimuth', plotted with red circles, and 'LOS', plotted with blue triangles. The Azimuth displacement shows a generally increasing trend, starting from approximately 2 m in 2017 and rising to over 18 m by late 2020. An event labeled 'Outburst' is marked on the Azimuth data around early 2020, when the displacement was approximately 17 m. The LOS displacement remains much lower, fluctuating between 0 and about 3 m over the same period.

Fig. 4 a 3D glacier flows velocity during 2017–2020. The color indicates vertical velocities, and the arrow indicates horizontal velocities. b 2D time series displacement for P1 from the ascending track. The location of P1 is shown in a


Document: s10346-021-01831-1
Section: Results > Changes to Jinweng Co
Type: text

To investigate the historical evolution of Jinweng Co, we analyzed its extent of variation and frontal recede using the inventory data from TPDC and by interpreting optical images. Figure 5 shows the temporal variations in Jinweng Co from 1998 to 2020. By superimposing the lake boundaries in the image of 2015 (Fig. 5a), we clearly see that the lake expanded upstream, and the glacier tongue receded. From 1998 to 2020, the proglacial lake expanded dramatically at a mean rate of 21.73 m2/year, and the glacier receded at a mean rate of 0.13 m/year (Fig. 5b).

To better understand the changes to Jinweng Co before and after the catastrophic GLOF in June 2020, we interpreted four summertime optical images, including Sentinel-2 images and Landsat images, and analyzed the variations to the boundary of Jinweng Co, as shown in Fig. 6. The lake area experienced an increasing trend from 2018 to 2020. It showed 0.52 km2 on June 6, 2018; 0.55 km2 on June 25, 2019; and then 0.57 km2 on May 1, 2020. After the GLOF event in June 2020, the lake area decreased to 0.32 km2, as reflected in the July 2020 image. This resulted in a massive  $(\sim 0.25^2 \text{ km})$  downstream water flow from the lake dam (Fig. 6d).


Document: s10346-021-01831-1
Section: Results > Changes to Jinweng Co
Type: figure
Figure

Image /page/4/Figure/5 description: A two-panel figure illustrating the expansion of a glacial lake and the associated glacier retreat over time. Panel (a) is a satellite image map showing the lake's area outlined in different colors for various years between 1998 and 2020. A legend lists the acquisition dates: 1998/10/19 (light blue), 2001/10/21 (green), 2011/11/10 (yellow), 2015/11/15 (red), 2018/10/15 (red), 2019/06/25 (red), and 2020/05/01 (purple). A measurement of 357.3 m is shown on the map, indicating a distance of retreat. The map includes coordinates (around 30°21'N, 93°38'E), a scale bar, and an inset map. Panel (b) is a scatter plot graphing data against time from 1998 to 2020. It shows two datasets: 'Lake area (km²)' plotted with red squares and 'Retreat distance (km)' plotted with green circles. Linear fits are applied to both datasets. The slope for the retreat distance is 0.13 m/year, and the slope for the lake area is 21.73 m²/year. The plot also displays the 95% confidence bands for both linear fits.


Document: s10346-021-01831-1
Section: Results > Changes to Jinweng Co
Type: text

Fig. 5 Temporal evolution of the Jinweng Co during 1998–2020. a Lake boundaries derived from inventory data (1998, 2001, 2011, 2015) and interpretation of Landsat-8/Sentinel-2 images (2018, 2019, 2020); the inset map shows the lake boundary after the collapse. **b** Time series of the lake areas and glacier recede from 1998 to 2020. Red squares are measurements of the lake area in km2; the red line

is a linear regression fit of the area measurements, and aqua shading surrounding the red line refers to the 95% confidence interval of the lake area. Green circles are distance in km of parent glacier frontal recede; the green line is a linear regression fitting within a 95% confidence interval, indicted by pink shading


Document: s10346-021-01831-1
Section: Recent Landslides
Type: figure
Figure

Image /page/5/Figure/1 description: A series of four false-color satellite images, labeled (a) through (d), showing a glacial landscape at different dates. The images are dated as follows: (a) 2018/6/6, (b) 2019/6/25, (c) 2020/5/1, and (d) 2020/7/27. The images depict glaciers in bright cyan, lakes in dark blue, vegetation in green, and bare ground in reddish-brown. Each image includes annotations for the city of Yiga, and elevation markers at 3800m, 4845m, and 6838m. A legend in the bottom left explains the symbols for City, Elevation, Glacier area, Lake area, Ice dam, Natural drainage channel, and Conduit. The legend in panel (d) adds an entry for 'Breach'. The sequence of images shows changes in the glacial lakes over time, with a noticeable drainage of a lake between the images from May 2020 and July 2020. A scale bar in the bottom right of each panel indicates a distance of 2 km.


Document: s10346-021-01831-1
Section: Recent Landslides
Type: text

Fig. 6 A comparison of the Jinweng Co area and the surrounding glaciers before and after the GLOF: a June 6, 2018, Landsat-8 image; b June 25, 2019, Landsat-8 image; c May 1, 2020, Sentinel-2 image; and d July 27, 2020, Sentinel-2 image


Document: s10346-021-01831-1
Section: Discussions > Meteorological conditions before the event
Type: text

The intensity, duration, and frequency of precipitation, as well as temperature rise, can affect the timing and magnitude of glacier movements and GLOF events. To further understand the meteorological conditions before the 2020 event, we processed daily precipitation, monthly precipitation, and air temperature data. Figure 7 shows the meteorological conditions at Jinweng Co. The monthly precipitation fluctuates seasonally, with the rainy season lasting from February to July and very little precipitation during the intervening winter months. The highest monthly rainfall of up to 240 mm occurred during June 2020, and the heaviest daily rainfall reached 45 mm on June 21, 2020. This was approximately 4 days before the GLOF event. Moreover, air temperature changed periodically, with the hottest period of the year from late May to mid-August. Extreme rainfall and temperature conditions are important driving factors that increased discharge into the lake and led to the lake outburst (see discussion later).

It can be seen that the June 25, 2020, outburst occurred at the highest monthly/daily precipitation. The unusually heavy rainfall combined with the warmer summer temperature provides a plausible explanation for the Jinweng Co outburst for two reasons. First, heavy rainfall increased water inflow to Jinweng Co and thus increased discharge. Second, the heavy rainfall on June 21 may have also acted as an indirect trigger of the outburst when this precipitation provoked slope movement into the lake.

The Jinweng Co outburst chain provides a strong example of the role of extreme precipitation and temperature change in a GLOF-related disaster. According to the meteorological reports, the weather has been warming at a rate of 0.5 °C per decade over the last 40 years (You et al. 2016). This suggests that the warming trend constantly persists over Tibet. Based on the results of the glacier movement and lake change, it is certain that climate warming has caused glacier tongue recede, thinning, and lake expansion.


Document: s10346-021-01831-1
Section: Discussions > Slope movement and ice collapse before the GLOF event
Type: text

To understand the factors associated with the June 2020 outburst, we used time series SAR intensity images and two-pass InSAR (e.g., Lu et al. 2003). Figure 8a-e show the time series SAR intensity images from May 28 to July 15 and reveal several changes. The lake expanded from May 28 to June 9, as indicated by the yellow arrows in Fig. 8a, b. In Fig. 8c-e, we found a section at the western lateral moraine moved downslope into the lake on or after June 21, and the deposit from the landslide can be

Fig. 7 Daily and monthly precipitation and air temperature records over the Jinweng Co area. Daily precipitation is shown as blue bars, monthly rainfall in black smoothed curve, and air temperature in purple dots


Document: s10346-021-01831-1
Section: Discussions > Slope movement and ice collapse before the GLOF event
Type: figure
Figure

Image /page/5/Figure/12 description: A combination chart displaying daily precipitation, monthly precipitation, and temperature data from December 2016 to mid-2020. The x-axis represents the date in yyyy/mm/d format. The left y-axis, scaled from 0 to 50, represents daily precipitation in millimeters (mm) and corresponds to the blue vertical bars. The right side has two y-axes: an inner axis for temperature in degrees Celsius (°C), scaled from -20 to 20, corresponding to the magenta scatter plot points; and an outer axis scaled from 0 to 300, which appears to correspond to the black line representing monthly precipitation in mm. All three datasets show a clear seasonal pattern, peaking in the summer months and dipping in the winter months. A red annotation labeled "Outburst" with a dashed vertical line points to a period of high precipitation around May 2020.


Document: s10346-021-01831-1
Section: Discussions > Slope movement and ice collapse before the GLOF event
Type: figure
Figure

Image /page/6/Figure/0 description: A scientific figure displaying ten panels of satellite or radar imagery, arranged in a 2x5 grid and labeled (a) through (j). The top row, panels (a) to (e), shows a time series of grayscale images of a rugged landscape, dated 2020/5/28, 2020/6/9, 2020/6/21, 2020/7/3, and 2020/7/15 respectively. The bottom row, panels (f) to (j), shows a time series of color-coded images of the same area over several years, with date ranges: 2017/5/20-2017/7/31, 2017/6/1-2017/8/24, 2018/6/20-2018/8/7, 2019/7/21-2019/8/26, and 2020/8/8-2020/8/20. Each panel features a main image with a circular magnified inset focusing on a specific area. Panel (a) includes a north arrow and a scale bar indicating 1 Km. A color scale bar in the bottom right corner shows a range from 0 to 2.83 cm, with colors progressing from blue and purple to yellow and green, corresponding to the images in the bottom row.


Document: s10346-021-01831-1
Section: Discussions > Slope movement and ice collapse before the GLOF event
Type: text

Fig. 8 Map of the landslide activity. a-e Sentinel-1 intensity images of the Jinweng Co and its surroundings representing the situation on May 28, June 9, June 21, July 3, and July 15. SAR backscattering images over the landslide are enlarged in the inset to improve visibil-

ity. f-i Deformation interferograms of Jinweng Co and surroundings for several periods during 2017-2020. Each fringe (a complete cycle of color variations) represents a 2.83 cm range change in the radar look direction

seen in Fig. 8d, e. We used InSAR to study possible long-term deformation in the area around the landslide for several periods in 2017-2020 (Fig. 8f-j). Since interferometric coherence for C-band Sentinel-1 images are low in the Jinweng Co surroundings (where the surface is covered with snow in winter), we analyzed interferograms with high coherence acquired in summer (Fig. 8f-j). The baselines between the image pairs were less than 60 m, so the interferograms were insensitive to DEM errors. The deformation maps formed from independent image pairs with

very different atmospheric situations show essentially the same patterns at a section of the lateral moraine. This means that the fringes are a real deformation. Therefore, this section of the lateral moraine was experiencing long-term deformation and likely finally collapsed into the lake due to heavy rainfall on June 21, 2020. Therefore, the rainfall-triggered landslide that occurred on or after June 21, 2020, exerted an important effect on this lake outburst event because the water waves may have overflowed the dam and/or directly caused the rupture of the dam.


Document: s10346-021-01831-1
Section: Discussions > Slope movement and ice collapse before the GLOF event
Type: figure
Figure

Image /page/6/Figure/5 description: A composite scientific figure presented in black and white, comparing two images of a rugged, mountainous terrain. The figure is divided into two main rows. The top row consists of a rectangular image labeled (a) on the left, and three circular magnified images labeled (a1), (a2), and (a3) to its right. The bottom row has a similar layout with a rectangular image labeled (b) and three circular magnified images labeled (b1), (b2), and (b3). Image (a) displays a mountainous landscape with a north arrow in the upper left corner and a scale bar in the lower left indicating a distance of 1 km. Three specific locations are marked with white circles and numbered 1, 2, and 3. Image (b) shows the same terrain as image (a) but without the annotations. The circular images provide close-up views of the numbered locations: (a1) and (b1) correspond to location 1, showing layered rock formations; (a2) and (b2) correspond to location 2, showing a dark, shadowed area with a bright linear feature; and (a3) and (b3) correspond to location 3, showing a high-contrast area of bright and dark patches.


Document: s10346-021-01831-1
Section: Discussions > Slope movement and ice collapse before the GLOF event
Type: text

Fig. 9 (a) Average Sentinel-1 intensity image based on SAR images of May 28 and June 9 2020. (b) Average Sentinel-1 intensity image based on SAR images of June 21, July 3, July 15, and July 27, 2020.

The changes in the glacier tongue, the landslide, and the southern end of the lake are shown by (a1) (b1), (a2) (b2), and (a3) (b3), respectively


Document: s10346-021-01831-1
Section: Recent Landslides
Type: figure
Figure

Image /page/7/Picture/1 description: A diagram illustrating the causes of a landslide in a glacial environment. The diagram shows a body of water labeled "Jinweng Co" contained by a brown "Lateral moraine". On the left, a light blue "Glacier" is shown with a red arrow pointing to the water, labeled "Melting". Above the scene, gray clouds are producing "Extreme rainfall". A yellow arrow points upwards from the water's surface, labeled "Water rising". A green mass of earth is shown falling from the moraine into the lake, labeled "Landslide". On the right, the lake narrows into an "Outlet".


Document: s10346-021-01831-1
Section: Recent Landslides
Type: text

Fig. 10 Schematic diagrams illustrating the triggering factors and final mechanism of dam failure

The time series SAR intensity images also indicate that significant changes occurred before the landslide on the southern end of the lake between June 9 and 21. We compared the average intensity images before and after June 9 (Fig. 9) to investigate this phenomenon. Figure 9 shows a clear difference in the glacier tongue (Fig. 9a1, b1). We suggest that the change in the lake surface on June 21 (Fig. 9, b3) resulted from the collapse of ice on a steep portion of the glacier tongue (Fig. 9(a1), (b1)). Such collapse and runout could also have trapped moraine on the path into the lake. Hence, between June 9 and 21, the ice collapsed under the steep topography and transported the iceberg and moraine into the front of the lake. This caused the obvious variation seen in the June 21 SAR image (Fig. 9). Moreover, the landslide that occurred in the western lateral moraine can be observed through Fig. 9a2, b2. The distinct sliding boundary further demonstrates that the landslide might cause harm to the lake by striking the lake body and may be one of the primary GLOF triggering factors.


Document: s10346-021-01831-1
Section: Recent Landslides > Outburst mechanism of Jinweng Co
Type: text

Based on the displacement features of the parent glacier, the lake changes with SAR intensity, optical images, inventory data, and extreme meteorological conditions, there is evidence that the slope of the western lateral moraine was unstable due to glacier recede. The extremely high precipitation in June 2020 facilitated the erosion of the pre-weakening slope, finally inducing landslide activity. The sketch map in Fig. 10 shows the triggering factors and outburst mechanism of Jinweng Co. First, the hydrologically controlled glacier movement and melting generated masses of destabilizing sediments. Second, due to geomorphic conditions, the steep slopes above the lake produced huge kinetic energy when ice masses slid into the lake. Third, the daily precipitation about 4 days before the GLOF event was the largest in the past 3 years. Anomalous rain events or seasonal changes in the dynamics of the surrounding glaciers might be key triggering factors causing sudden hydro-fracturing and outburst floods in summer. Increased water inflow to the lake caused increased discharge from the morainedammed lake, which may have provoked increased erosion and incision

of the outflow channel into the dam body. More importantly, the landslide activity that occurred from the western lateral moraine on or after June 21 played a key role in the GLOF event because the fast slope movement into the lake is capable of producing water waves. This may have caused direct dam rupture. Overall, the causal chain for triggering the June 2020 outburst is a hydraulic connection established from a possible collapse of the glacial ice, a landslide, heavy precipitation, and warm temperature.

Recent Landslides
Outburst mechanism of Jinweng Co


Document: s10346-021-01831-1
Section: Conclusions
Type: text

Multi-source remote sensing datasets and processing methods combined with meteorological observations were used in this study for GLOF investigation. These were then applied to comprehend the recent movement of parent glaciers and reveal the mechanisms leading to the outburst flood. Offset tracking of multi-track Sentinel-1 images was combined to retrieve the long-term time series displacement and 3D glacier flow velocity field. Optical Sentinel-2 and Landsat-8 images and inventory data were interpreted to reconstruct the long-term changes in the glacial lake. SAR intensity images and interferograms were analyzed to characterize changes in the glacier tongue and lateral moraine collapse.

In this study, to analyze lake outburst factors, we identified two important critical stages for the Jinweng Co outburst. First, ice collapse occurred in the parent glacier from June 9 to 21, and this transported ice and moraine into the front of the lake. Second, a landslide originating from the western lateral moraine occurred forcefully from June 21 to 25 and placed additional deposits into the lake body. The water level increased due to the unusually heavy rainfall on June 21, 2020; when combined with meltwater from glaciers, it provoked anomalous water importation. Due to the availability of pre-weakening lateral moraine combined with heavy rainfall on June 21, landslide activity was a serious factor for triggering dam failure by producing displacement waves. Hence, the main mechanism may be that the landslide mobilized and entered the lake between June 21 and 25, which caused the surface waves, triggered overtopping, and finally caused dam rupture.

The kinetic movement and outburst mechanism factors of Jinweng Co revealed in this study can be used to assess the potential risks of other glacier lakes in the region. Further, with the rise in extreme precipitation and the rapid melting of glaciers that lead to the destabilization of glacial lakes, moraine instabilities under changing climate conditions could become more likely. Therefore, process chains such as Jinweng Co will become increasingly significant in the future.


Document: s10346-021-01831-1
Section: Funding
Type: text

This research was funded by the Scientific Innovation Practice Project of Postgraduates of Chang'an University (Grants No. 300103714010, 300203211263) and the Shuler-Foscue Endowment at Southern Methodist University.


Document: s41467-023-36033-x
Type: text

Glacial lake outburst floods (GLOFs) represent a major hazard and can result in significant loss of life. Globally, since 1990, the number and size of glacial lakes has grown rapidly along with downstream population, while socio-economic vulnerability has decreased. Nevertheless, contemporary exposure and vulnerability to GLOFs at the global scale has never been quantified. Here we show that 15 million people globally are exposed to impacts from potential GLOFs. Populations in High Mountains Asia (HMA) are the most exposed and on average live closest to glacial lakes with ~1 million people living within 10 km of a glacial lake. More than half of the globally exposed population are found in just four countries: India, Pakistan, Peru, and China. While HMA has the highest potential for GLOF impacts, we highlight the Andes as a region of concern, with similar potential for GLOF impacts to HMA but comparatively few published research studies.

Glaciers are particularly sensitive to changes in climate1-3 and are highly visible indicators of climate warming3-5. Over the last three decades there have been substantial decreases in global glacier mass, with ice losses between 2006 and 2016 estimated at  $-332\pm144$  Gt y-16,7. This decline is likely to persist through the 21st century as most glaciers are out of balance with present climate;  $-36\pm8\%$  of current mass loss is a 'lagged response' to past climate forcing8. In many areas, overdeepenings in former glacier beds are uncovered during the course of glacier retreat, which allows melt water to collect as glacial lakes9-11. Glacial lakes can also form via the growth and coalescence of supraglacial ponds on debris-covered glaciers12,13, and in other ice-marginal settings14,15. The formation of glacial lakes can trigger positive feedbacks, whereby lakes promote further ice loss through calving and subaqueous melting, causing additional melt and retreat, and further lake expansion16-18.

Importantly, these lakes can represent a substantial hazard in the form of glacial lake outburst floods (GLOFs). GLOF triggering is complex, with dam breach initiation caused by mass movement-induced impulse waves19,20, lake overfilling due to pluvial, nival and glacial runoff21, and moraine- or ice dam degradation being variably important dependent on setting22,23. Consequently, the probability of a lake

releasing a GLOF is difficult to accurately quantify without detailed and localised studies.