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Article

A Novel Standardized Collaborative Online Model for Processing and Analyzing Remotely Sensed Images in Geographic Problems

by
Xueshen Zhang
1,†,
Qiulan Wu
1,†,
Feng Zhang
1,*,
Xiang Sun
2,3,4,*,
Huarui Wu
2,3,4,
Shumin Wu
1 and
Xuefei Chen
1
1
School of Information Science & Engineering, Shandong Agricultural University, Tai’an 271018, China
2
Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
3
Key Laboratory of Digital Rural Technology of the Ministry of Agriculture and Rural Affairs, Beijing 100097, China
4
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2023, 12(21), 4394; https://doi.org/10.3390/electronics12214394
Submission received: 3 September 2023 / Revised: 8 October 2023 / Accepted: 23 October 2023 / Published: 24 October 2023
(This article belongs to the Special Issue New Technology of Image & Video Processing)
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Abstract

:
In recent years, remote sensing image processing technology has developed rapidly, and the variety of remote sensing images has increased. Solving a geographic problem often requires multiple remote sensing images to be used together. For an image processing analyst, it is difficult to become proficient in the image processing of multiple types of remote sensing images. Therefore, it is necessary to have multiple image processing analysts collaborate to solve geographic problems. However, as a result of the naturally large volumes of data and the computer resources they consume for analysis, remote sensing images present a barrier in the collaboration of multidisciplinary remote sensing undertakings and analysts. As a result, during the development of the collaborative analysis process, it is necessary to achieve the online processing and analysis of remote sensing images, as well as to standardize the online remote sensing image collaborative analysis process. To address the above issues, a hierarchical collaborative online processing and analysis framework was developed in this paper. This framework defined a clear collaborative analysis structure, and it identifies what kinds of online image processing and analysis activities participants can engage in to successfully conduct collaborative processes. In addition, a collaborative process construction model and an online remote sensing image processing analysis model were developed to assist participants in creating a standard collaborative online image processing and analysis process. In order to demonstrate the feasibility and effectiveness of the framework and model, this paper developed a collaborative online post-disaster assessment process that utilizes radar images and optical remote sensing images for a real forest fire event. This process was based on the BPMN2.0 and OGC dual standards. Based on the results, the proposed framework provides a hierarchical collaborative remote sensing image processing and analysis process with well-defined stages and activities to guide the participants’ mutual collaboration. Additionally, the proposed model can help participants to develop a standardized collaborative online image processing process in terms of process structure and information interactions.

1. Introduction

Remote sensing analysis technology is rapidly developing, and this has resulted in an expanding range of applications. At the same time, the types of remote sensing images are also expanding. By analyzing remote sensing images, humans are able to gain a more comprehensive understanding of the Earth’s environment, both in time and space [1,2]. It is important to note that, when applied to complex or large remote sensing image processing applications, a single type of remote sensing image may not be sufficient in providing adequate source information and image features [3,4,5]. Consequently, multiple experts must collaborate across disciplines in order to obtain a better processing and analysis of remote sensing images’ results, or even to collaborate with experts outside the field of remote sensing image processing in order to solve complex geographic problems, as well as to improve the extraction or segmentation accuracy of remote sensing images. For example, in the processing and analysis of many types of remote sensing images, such as multispectral, hyperspectral, SAR, etc., it is often necessary to use algorithms, such as clustering, segmentation, and imaging in the field of computer vision, to improve the accuracy and spectral responsivity of the remote sensing images to achieve a more accurate and comprehensive data analysis and application [6,7,8,9,10,11,12]. However, multidisciplinary online collaboration analysis usually poses two challenges for processors and analysts. Firstly, due to limited energy, expertise, and resources, it is difficult for an expert to complete the whole problem and to analyze it independently [13]. Therefore, multifaceted collaboration is essential in solving complex problems [14]. The second problem is the data problem. Currently, the majority of data that is used in geographic analysis are remote sensing data. However, remote sensing image data have the following characteristics: large data volumes, spatial and spectral correlations that are more complicated than natural images, and unstable data quality [15,16]. These characteristics may result in certain problems, such as difficulties in the processing of remote sensing data, limitations in data processing via computer hardware and software, and difficulties in the exchange of information. With the development of online remote sensing processing and analysis technology, the method of solving data problems is no longer limited to replacing more advanced computer equipment but is based on the method of online network analysis, utilizing servers, cloud platforms, and other methods to solve data-related problems [17]. Therefore, the combination of collaborative technology with online remote sensing analysis technology is crucial for resolving complex interdisciplinary remote sensing image processing problems.
In the research field of collaborative analysis, scholars have explored interdisciplinary approaches that are aimed at collaboratively solving complex remote sensing processing and analysis problems in the fields of humans and nature, earth sciences, and decision analysis; in addition, they have also developed collaborative prototyping systems to validate these approaches [18,19]. Alternatively, certain scholars have focused on customizing collaborative analysis processes and visualizing collaborative analysis to enhance collaboration among participants in complex problems [20,21,22]. Although all of the above studies have adopted workflow technology as their main engine of collaborative analysis and have achieved certain results, there are still two problems: (1) the majority of collaborative analysis workflows employ non-standardized methods, which have poor shareability and reusability issues, and (2) the studies that use standardized workflows only examine collaborative modeling or visualization, and no studies specifically address collaborative online remote sensing processing images and analysis. To solve the above problems, the BPMN2.0 standard provides a standardized workflow framework with graphical symbols, rules, and sub-processes. This effectively addresses the problem of poor data sharing and reuse during cross-disciplinary analysis [23,24], while reducing the errors encountered when constructing collaborative processes [25]. However, to achieve collaborative and effective online remote sensing image processing and analysis, more effective collaboration models are needed, in addition to standardized workflow engines, in order to enable multifaceted collaboration for image processing and analysis in complex problems.
The term “online remote sensing analysis” refers to a method of processing and analyzing remotely sensed images when using a web-based platform, and this is achieved by manipulating the input data and producing the desired output results [26,27]. However, due to the complexity of spectral and spatial remote sensing data information as opposed to general natural images, certain problems, such as poor sharing and difficult interaction, often occur when interacting with remote sensing information on the Web [28,29,30,31,32]. There is a need for an online remote sensing data interaction standardization method in order to solve this problem. Currently, most solutions use the Open Geospatial Consortium (OGC) service standard to standardize the processing and analysis of remotely sensed data. This standard aims to standardize the processing and analysis of remote sensing images when using syntactically interoperable services. Thus far, scholars have conducted a substantial amount of research and application practice on the OGC standard. Several scholars have recognized the importance of the OGC standard in the field of geographic information [33,34,35], and certain scholars have designed prototype systems based on the OGC standard [36,37,38,39]. However, scholars with limited development abilities must learn how to develop before they can process and analyze remote sensing images, which makes collaborative remote sensing analysis more difficult. To solve this problem, integrating OGC standards into existing geospatial applications on the market can effectively reduce the difficulty of code development for collaborative online remote sensing image processing and analysis systems, while improving the ability of information sharing and generalization [40,41]. Currently, there are mature open-source geospatial applications in the field of geographic information, such as GeoServer, which uses the OGC standard to edit and create maps [42]. Even though certain scholars have proposed the use of geospatial applications for online remote sensing data processing and analysis, they have not combined the use of online remote sensing data processing and analysis with collaborative analysis. Therefore, a standardized model for online remote sensing image analysis and processing that incorporates geospatial components is needed at this stage to achieve a collaborative processing and analysis of online remote sensing images.
In order to help participants establish a complete and clear online remote sensing collaborative processing and analysis procedure, this paper has been based on proposing a conceptual model framework, as well as both a standardized online remote sensing image collaborative processing analysis process construction model and a standardized online remote sensing image processing analysis model. Using the process construction model, a collaborative standardized online remote sensing processing and analysis procedure can be defined to help participants solve the challenges of collaborative multifaceted remote sensing image processing and analysis. The online remote sensing processing analysis model is intended to provide a methodology for collaborative analysis of standardized online remote sensing image processing and analysis that helps participants to define the interaction of remotely sensed information between collaborative processes and between analysis activities.
In this paper, Section 1 analyzes current research progress in collaborative online remote sensing image processing and analysis, and outlines existing problems. In Section 2, we describe the composition of the conceptual model framework for collaborative online remote sensing image processing and analysis. Following this, Section 3 introduces the implementation framework of the collaborative remote sensing image processing analysis process construction model, based on BPMN2.0, and the collaborative online remote sensing image processing analysis model, based on the OGC standard (hereinafter collectively referred to as the process construction model and the online remote sensing image processing analysis model, respectively). A case study of the post-disaster assessment of a forest fire caused by drought and strong winds is analyzed in Section 4 in order to demonstrate the effectiveness of a synergistic online remote sensing image processing analysis framework and model that is based on the OGC and BPMN2.0 dual standards. In Section 5, the paper comes to a conclusion.

2. A Conceptual Modeling Framework for Collaborative Online Remote Sensing Image Processing and Analysis

A well-structured collaborative online remote sensing analysis and image processing procedure, with clear types of collaborative activities, is essential for analysts and processors when carrying out collaborative online geographic problem analysis. For this reason, when constructing a collaborative online remote sensing analysis process, it is imperative to take into account the structure of the collaborative process, as well as the activities undertaken by remote sensing analysts and remote sensing image processors during the analysis stage. For the structure of the collaborative process, certain studies have shown that hierarchical and progressive structures are suitable [22]. Furthermore, it has been shown that collaborative geographic analysis activities can be classified into four categories based on their purpose: awareness-related activities, data-related activities, model-related activities, and application-related activities [43,44]. This paper presented a conceptual framework for collaborative online remote sensing analysis that addresses the above two aspects, thereby aiming to assist participants in building a hierarchical collaborative process that integrates these four types of collaboration. This is illustrated in Figure 1.
As a result of the proposed conceptual model, collaborative online remote sensing analysis involves four phases, including the phase of context definition for collaborative analysis, the phase of resource collection, the phase of construction of the standardized analysis processing flow, and the phase of collaborative implementation. Four types of collaborative analysis activities are carried out by participants during these four phases. As an example, during the context definition phase, participants engage in activities related to awareness, such as discussing the process of geographical problems and the resources and tools required to address them; during the resource collection and standardized analytical treatment process construction phases, participants engage in activities related to the data and models; during the process implementation phase, participants conduct remote sensing analyses and image processing activities in accordance with the constructed analysis process. These phases are important components of the collaborative analysis process and linking these phases with each other is necessary to solve various problems and complete the collaborative process construction for various problems. Figure 1 illustrates the relationships between the stages using arrows. The results obtained in the context definition stage may, for example, guide participants’ behavior in the resource collection stage and also serve as a basis for the standardized analysis and processing process construction stage; the collaborative online remote sensing analysis process is built around the definition of the context and the collection of resources; the collaborative implementation phase involves participants cooperating with each other to resolve practical problems based on standardized processes.
This conceptual model framework describes a collaborative online problem solving process, in which participants collaborate with each other at different stages, share information and knowledge, and engage in a variety of collaborative activities. In the four phases of collaborative online remote sensing image processing and analysis, the following main activities were accomplished by the participants.

2.1. The Context Definition Phase

The context definition phase is the first step in the collaborative online geographic problem solving process. This phase involves participants exchanging ideas and discussing the problem from a macro perspective in order to determine the analysis process and the resources that are required. It is possible to divide the participants into two categories when determining the collaborative online processing and analysis process. The first category of participants discussed the problem from a macro perspective, thereby identifying the main processes of collaborative online analysis. For example, the main process of collaborative disaster analysis begins with the extraction of the disaster area, after which the results obtained from the extraction of the disaster area can be used to further assess the disaster event, i.e., the analysis of the disaster event is divided into a number of sub-problems. A second category of participants discussed the specific implementation of remote sensing analysis sub-problems within the main process. This included, for example, during the disaster region extraction sub-problem, data, remote sensing image preprocessing models, and semantic segmentation models being used.

2.2. The Resource Collection Phase

In the resource collection phase, the participants focus on activity preparations, such as data collection and sharing for collaborative online analytics, model construction and sharing for analytical processing, and sub-process model construction and sharing. The data collected and shared during the data collection and sharing activities include both online and offline data. Offline data can be directly used for activities or can be shared using the OGC standards. Then, the data are called, processed, and analyzed by other participants in the collaborative process. Online data can be accessed through the OGC services, and the way in which these remote sensing data are shared and accessed using the OGC standards contributes to an improvement in data utilization [45,46]. A key objective of the online remote sensing analysis model’s construction and sharing activity is to enable the online analysis and sharing of models, as well as to provide a method for sharing and executing remote sensing analysis and processing models. As an example, the OGC service is used to encapsulate the disaster area semantic segmentation model, the disaster loss assessment model, and the remote sensing image preprocessing model into a WPS service process. This is then utilized to set the corresponding model’s input and output parameters, as well as to realize the online analysis of the disaster model. In the collection and sharing of the sub-process models’ activity, participants store, share, and transfer sub-processes that are created in other domains with the extensible markup language (XML). It is important to note that the “sub-process model” refers to processes that are constructed, while other issues related to remote sensing analysis are being addressed.

2.3. The Construction Phase

In the construction phase of the standardized analysis and processing process, participants must construct the image processing and analysis processes by adopting the relevant standards of workflow in a problem- and resource-oriented manner. In other words, the aim is to develop a standardized collaborative online analysis and processing process based on the processing and analysis processes determined in the first phase, with the resources collected in the second phase. This standardized remote sensing analysis process helps to improve the resource reuse rate of collaborative online analysis, as well as greatly helps to improve the collaborative nature of the participants in the collaborative analysis process. In addition, participants are able to gain a better understanding of the problem solving process in another domain, thereby reducing the possibility of the errors attributable to individual understandings during a collaborative process. It is evident in the results of the first stage of process customization that the main process consists of a number of sub-problems. Each of these sub-problems are composed of multiple solution steps, i.e., multiple analytical sub-processes. In order to improve the manageability of collaborative online analysis processes, it is necessary to separate remote sensing analysis workspaces, sub-processes, and realization activities. Consequently, standard processes should be constructed in accordance with hierarchical structures.

2.4. The Collaborative Implementation Phase

In the collaborative implementation phase, participants complete the corresponding analysis and processing sub-processes in accordance with the constructed analysis and processing process, as well as cooperate with one another to complete the implementation of the entire analysis and processing process. In other words, each step of the sub-problem is performed by a different participant, and the participant only needs to complete the corresponding analysis and processing steps according to the sub-processes, and this is achieved without having to understand or master remote sensing analysis methods or the processes of other procedures.

3. Implementation Models

A standardized remote sensing image processing and analysis procedure helps participants to collaborate, solve the problem of poor sharing and the reuse of collaborative online problem analysis, and enhance the efficiency of collaboration in the process of collaborative image processing and analysis. The construction of a standardized online collaborative analysis and processing procedure not only requires a standardized process construction model, but also a standardized information interaction model that can be used for online remote sensing image processing and analysis. Consequently, this paper proposed a collaborative remote sensing image processing and analysis process construction model based on the BPMN2.0 standard, as well as an online remote sensing image processing and analysis model based on the OGC standard.

3.1. Modeling of the Collaborative Remote Sensing Image Processing and Analysis Process Based on BPMN2.0

A clear and effective standardized collaborative online process for handling analytics not only improves the efficiency of collaboration, but also helps to reduce the number of errors that occur during process construction. In the field of workflow standardization, the BPMN2.0 standard specification has been widely recognized by those in the business process field; its hierarchical nature allows complex geographic problems to be decomposed into smaller, implementable sub-problems, and the BPMN2.0 standard provides a unified process construction notation and syntax, which is suitable for use as a standard for collaborative online remote sensing image processing and analysis workflows. Therefore, this paper designed a collaborative remote sensing image processing and analysis process construction model based on BPMN2.0. The hierarchical structure of the image processing and analysis process is shown in Figure 2.
With respect to the steps of defining the process in the context definition phase, the collaborative remote sensing analysis process is constructed in three steps. In the first step, the participant analyzes the problem, identifies the sub-problems present in the problem, and creates a workspace to deal with these sub-problems; the second step involves the participant constructing a number of sub-processes for the analysis of the sub-problems; in the third step, the participant is responsible for constructing the specific implementation activities associated with each sub-process. Accordingly, the process-building model divides collaborative online remote sensing analysis into sub-problems, sub-processes, and realizations.

3.1.1. The Sub-Problem Layer

The sub-problem layer reflects the participants’ understanding of the problem at a macro scale. At this level, complex geographic problems are divided into sub-problems. The different sub-problems are defined as multiple workspaces. Using the BPMN2.0 unified linking structure, the sub-problem workspace, process start event, and process end event are linked together to form the main process of this remote sensing analysis. In the main process, only the sub-problems included in the collaborative analysis are reflected without directly reflecting the sub-processes involved in the analysis of the sub-problems and the activities associated with the realization of the sub-processes. The analysis sub-processes and specific realization activities for the sub-problems are embedded in the sub-problem workspace; in addition, they follow the BPMN2.0 standard process structure of sub-processes, collapsed sub-processes, and call activities to complete the analysis of the sub-problems.

3.1.2. The Sub-Process Layer

The sub-process layer incorporates the image processing and analysis processes associated with the geographic sub-problem. This layer was designed to assign participants to positions that are appropriate for them, based on the technical areas in which they are proficient. Participants in this layer need to only be proficient in one step of the analysis and are not required to understand the specifics of the other analysis steps. An example would be the extraction of fire areas through the process of classified feature extraction, region extraction, etc. The expert, who is proficient in feature extraction, only needs to be responsible for the feature extraction sub-process, and they can submit the results of the feature extraction without having to study the model for region extraction. In this level, each analysis step of the remote sensing sub-problem is modeled as a BPMN2.0 sub-process, which facilitates this process realization activity to be conducted by the participants.

3.1.3. The Implementation Layer

The implementation layer, as the final level of collaborative online remote sensing analysis, is responsible for the specific processing and analysis activities of sub-processes. Additionally, the implementation layer contains the invocation and integration of remote sensing image processing models. There are generally three steps to a remote sensing analysis step realization activity, namely inputting data, processing data, and outputting data. As an example, the calculation of the NDVI index involves three steps: acquiring remote sensing image data, calculating the NDVI index, and sharing the results of the calculation. Therefore, at this level, the implementation of collaborative online remote sensing analysis sub-processes will consist of three key activities: data acquisition, online remote sensing and processing analysis model implementation, and the sharing of analysis results. These three key activities can be constructed using the standardized process symbols provided via the BPMN2.0 standard. For example, user tasks can be used to enable the integration of image processing models, the construction of analysis activities, and the resolution of issues related to collaboration (such as the collaborative analysis of operating privileges between users and allowing experts from different disciplines to take sole responsibility for the processing and analysis steps, which improves the degree to which tasks are specialized). Analysis activities are constructed using scripting tasks, such that participants can write their own scripts for the processing and analysis of remote sensing images.

3.2. An Online Remote Sensing Image Processing Analysis Model Based on the OGC Standards

In order to accomplish a collaborative online remote sensing image analysis, it is not sufficient to just provide a standard process-building model. It is also necessary to provide a standard online remote sensing analysis model that can be integrated with the online remote sensing analysis process. The key to a standardized online remote sensing analysis model that supports collaboration lies in standardizing information interactions. Consequently, this paper presented an online remote sensing image processing analysis model that uses the OGC standard, which is widely recognized in the geoprocessing field. This online remote sensing analysis model aims to provide a standardized online remote sensing analysis model that is integrated with a standardized remote sensing analysis process. This is shown in Figure 3.
During the collaborative online remote sensing image processing and analysis process, there are three types of information interactions: between sub-problems and sub-problems, between sub-processes and sub-processes, and between realization activities and realization activities. The design of the sub-problem workspace as a BPMN2.0 sub-process structure not only realizes its role in the collaborative process, but it can also consider the information interactions between sub-problems and sub-problems when they are part of the information interactions between sub-processes and sub-processes. Therefore, the interaction of information only occurs between sub-processes and other sub-processes, and between realization activities and other realization activities. A standardized interaction method for these two types of information is demonstrated in Figure 3 through the use of two sub-processes and three key realization activities.

3.2.1. Information Interactions between Sub-Processes

During collaborative processing and analysis procedures, it is impossible to avoid information interactions between analysts, i.e., between analytical sub-processes; therefore, there needs to be a method of interacting between them. However, due to the large amounts of remote sensing data available, using the process variables provided via the BPMN2.0 standard for remote sensing data transmission will result in a reduction in collaborative online remote sensing processing and analysis interactive performance. In order to support remote sensing data storage and remote sensing information interactions, a remote sensing information sharing platform is required; meanwhile, BPMN2.0 process variables only serve the auxiliary purpose of transferring the parameters between sub-processes; for example, how one can retrieve the results of the remote sensing image processing of the previous process sub-process or parameters that contain other pieces of information. Through the use of this mode of interaction, it is possible to integrate remote sensing analysis with standardized processes, as well as preserve the results of each step of the analysis, thus allowing for the traceability of the results of the analysis. Geospatial applications that are mature at this point already have a perfect data management system, as well as a data-sharing system, which is particularly suitable in serving as a platform for collaborative online remote sensing analyses.
To realize the information interactions between the sub-processes and the data-sharing platform, the analytical activities of data acquisition and result sharing are established at the beginning and end of the sub-processes. By performing these two activities, participants are able to obtain data from the platform, as well as share analytics with it.
Remote sensing image data acquisition can be categorized into elemental data acquisition and raster data acquisition. These data are either raw data or the result of the previous sub-process. By setting the service parameters of WCS and WFS, participants in the data acquisition activity acquire raster and elemental data. It is important to note that the parameters of both the raster data acquisition service and the element data acquisition service contain the same parameters: service, request, and version. These parameters indicate the type of service requested, the operation request, and the version of the requested service protocol, respectively. Moreover, the WCS and WFS services contain their own request parameters. The parameters of the WCS service include the name of the data layer, coordinate system, data format, spatial extent, width, and height. As a result of these parameters, the attributes of a requested raster image can be determined. The parameters of the WFS service include the layer name and metadata information element ID, which determine the attribute values of the requested vector image. Finally, participants may manually enter some of the parameters of the image processing model in conjunction with the data acquisition parameters for the purposes of executing the image processing model and the analysis model. In addition, it should be noted that these engagements are context-specific. As an example, when the SegNet semantic segmentation model was invoked, the total number of feature categories, the category names, the weight file paths, and other parameters were entered.
The main purpose of the result-sharing activity is to share the results of collaborative online processing and analysis when using the OGC services. In this activity, the share activity request parameters are the same for the element data and raster data, and they include the IP address, port number, username, and password of the geospatial application server (GeoServer), the name of the workspace, and the name of the store where the data are to be published. These data are then shared with the GeoServer using these parameters. Different geospatial applications require slightly different parameters.

3.2.2. Information Interactions between Activities

In the collaborative online remote sensing image processing and analysis process, the interaction of image processing and analysis activities mainly exists between the sub-problem analysis process realization activities, i.e., the interactions between image processing and analysis activities mainly exist between the three key activities of the realization layer. Among the three key activities in the implementation layer, the data acquisition activity is responsible for providing data to the online remote sensing analysis model implementation activity, while the remote sensing activity is responsible for receiving the results of the online remote sensing image processing and analysis model implementation activity. As a result, standardizing the inputs and outputs of the control online processing and analysis model execution activities is essential for standardizing the interactions between these activities. Using the customized WPS process encapsulation and invocation methods provided via the OGC and by encapsulating the model as a WPS process, not only can the remote sensing analysis model be analyzed online, but also its inputs and outputs can be controlled. Therefore, it is only necessary for the participants to control the input parameters of the WPS process, as well as the output parameters during the execution of the model. The input parameters for processing and analyzing the model’s execution activities include the WPS process ID, as well as the other parameters required to invoke the WPS process, which is determined when the construction of the WPS process is conducted; the output parameters are determined according to the requirements.
Depending on the needs of the participants, these three key activities can be augmented with other activities, such as data collection, presentation, and data downloading. These activities can all be accomplished using the OGC standards. As an example, the data results’ presentation activity may use the WMS service provided via the OGC to present data analysis results on the Web; it may also use other services, such as WFS and WCS, to download the analysis results on a local computer.

4. The Case for a Collaborative Online Forest Fire Post-Disaster Assessment

4.1. Background

Extreme droughts and strong winds tend to trigger forest fires. Forest fires not only disrupt the local ecological balance, but they can also exhibit several impacts on human survival. The assessments of forest fires following a disaster can assist in observing the changes in forests before and after a catastrophe, as well as provide effective assistance in the recovery and management of soils following a disaster. Several participants decided to analyze the post-disaster assessment of fires using optical remote sensing and radar remote sensing data.

4.2. Participants, the Study Area, and the Experimental Data

As part of this experiment, participants from different research fields, and with different domains of expertise, collaborated on the post-disaster assessment of the “3.30 forest fire in Xichang”. For example, several experts in optical and radar remote sensing image processing and analysis, a BPMN2.0 process builder, and a project stakeholder were involved in this collaborative disaster analysis project. Participants from different domains collaborated and shared resources during the analysis process to define their needs, goals, and expected results for the forest fire post-disaster assessment.
The research object of this study was a forest fire that occurred in Sichuan Province between 30 March 2020 and 3 April 2020, known as the “Xichang 3.30 Forest Fire”. The forest fire area is located at 102°15′12′′ E, 27°50′30′′ N, and it is surrounded by residential areas, roads, lakes, and other such features. Figure 4 shows the fire area and nearby features.
This experiment was conducted using the raster data downloaded from the Copernicus Open Access Center (https://scihub.copernicus.eu/), accessed on 24 June 2023, which includes four types of image data, the adjacent Sentinel I radar image data before and after the fire, and the Sentinel II optical image data. As opposed to the raster data, the vector data corresponded to the approximate extent of the fire as determined via visual interpretation. This experiment provided one vegetation index calculation, two fire index calculations, and two microwave parameter calculation models, as well as one fire trace discrimination model and one fire class segmentation model to be used by the online fire post-disaster assessment model. In addition, this paper utilized the image-cropping WPS process already available in the GeoServer WPS extension. Table 1 and Table 2 provide details on the raster data and model information used in this experiment.

4.3. Forest Fire Post-Disaster Assessment Case Implementation

Participants are required to go through four stages to complete a collaborative online assessment of fire after fire events, as outlined in the conceptual modeling framework. Participants perform different activities during each phase.
During the context definition phase of the problem, the participants analyzed and discussed the forest fire post-disaster assessment problem, identified the resources required for the problem, and defined the corresponding analysis process. As an example, the participants defined the fire analysis process as a three-step process. As a first step, the participants discussed the needs, objectives, and expected outcomes proposed by stakeholders, as well as identified two sub-problem workspaces for the extraction of fire trace areas and the classification of fires. After identifying the sub-problems, participants should then discuss, in depth, the steps for analyzing the sub-problems. As a result, the participants developed several sub-processes for the two sub-problems in the second step of the collaborative online fire post-disaster assessment. As a final step in the analysis process, the participants identified the basic data and analytical models that were used in this post-fire assessment. Figure 5 illustrates both the sub-problems and the sub-processes developed in this experiment.
Based on the results of the first phase’s discussion, the participants are required to collect, construct, and share analytical resources in the preparation phase of the post-fire assessment. As an example, the participants first collected optical and microwave data for each phase of the fire, and then subsequently shared them within GeoServer, a geospatial application. Secondly, they collected models for calculating the characteristic parameters of fire analysis, extracting fire track areas, and classifying the fire levels. These were then converted into a WPS process, such that they could be integrated into geospatial applications for the purposes of allowing online analytical models to be called. Lastly, although this experiment did not result in the discovery of relevant sub-process models, the sub-processes constructed in this experiment may still be applied in other situations. Figure 6 illustrates the structure of the WPS process for this experiment, and Figure 7 illustrates the built WPS process call reference.
During the construction phase of the fire post-disaster assessment process, the participants constructed an online fire post-disaster assessment analysis process based on the online collaborative remote sensing analysis process construction model, which was based on the BPMN2.0 standard. The participants constructed two sub-problem workspaces for the extraction of fire trace areas and the classification of fires, as well as a number of sub-processes. For example, the fire trail region extraction workspace included an image cropping sub-process, five feature parameter calculation sub-processes, a decision tree extraction sub-process, and three gateways. There is a sub-process in the fire intensity rating sub-problem workspace that performs a fire rating, which is the process of obtaining a dNBR index that is obtained from the extraction of the fire trace regions. In this experiment, the thresholds for fire classification were derived from the forest fire intensity classification criteria developed by Key et al. in the USGS Firewall project [47]. This classification standard includes five classification levels: the unburned area, the lightly burned area, the medium-low burned area, the medium-high burned area, and the highly burned area. Finally, each sub-process established the three realization activities of data acquisition, online remote sensing analysis model implementation, and the sharing of results. Figure 7 illustrates the BPMN2.0 process for the collaborative online analysis of forest fire post-disasters. The workspace, sub-process structure, and realization activities (in the case of the realization activities of the dNBR sub-process) of the BPMN2.0 process extensible document for a standardized online fire post-disaster assessment are outlined in Figure 8. The extensible document consists of events (e.g., startEvent and endEvent), activities (e.g., subProcess and userTask), gateways (e.g., parallelGateway and exclusiveGateway), and process links (subProcess). The events in this experiment were divided into startEvent nodes and endEvent nodes, which control the start and end of the process, respectively; the activities were divided into subProcess nodes and userTask nodes, with subProcess nodes being used to describe the workspace and analyze the sub-processes, and userTask nodes being used to describe the realization of the activities. There are two types of gateways: parallel gateways and exclusive gateways. Parallel gateways include branch gateways and merge gateways, which control the simultaneous beginning and end of the parallel sub-processes for the computation of five parameters, whereas exclusive gateways determine the end of the sub-processes for cropping raster images.
It is worth noting that the activity nodes in this experiment were divided into subProcess nodes and userTask nodes. SubProcess nodes are used to describe the workspace and analyze the sub-processes. UserTask nodes are used to describe the realization activities. In the process implementation stage, the participants in different fields complete the corresponding sub-processes by providing process parameters, including data input and output parameters, as well as WPS process call parameters. For example, the optical remote sensing processing expert obtained the corresponding vegetation index and fire index by processing the optical image; the microwave remote sensing processing expert obtained the SAR-related parameters by processing the microwave image; the analytical model expert extracted the fire track area and classified the fire level according to the provided optical and radar parameters. The analysis results obtained in this experiment are shown in Figure 9.
To quantitatively judge whether the accuracy of the overfire area extraction of this experiment was qualified or not, this experiment involved the use of ENVI 5.3 software to extract the overfire area according to the same steps, as well as compared the accuracy with that extracted via the collaborative online remote sensing analysis. The comparison results of the overfire area and extraction accuracy are shown in Table 3. The accuracy calculation formula is shown in Equation (1):
P = ( 1 − | S o − S c S o | ) × 100 % ,
where P is the extraction precision, S o is the area of the overfire area (3047.7805 hectares) provided by the official website (http://www.sc.gov.cn/), accessed on 24 June 2023, and S c is the area of the extracted overfire area.

4.4. Discussion of Experimental Results

A comprehensive online collaborative forest fire post-disaster assessment process was demonstrated in this paper to validate the effectiveness of this collaborative online remote sensing image processing framework for solving geographical problems. In the collaborative assessment process, participants identified the assessment process, as well as the resources needed by discussing the post-fire assessment process; the collection and sharing of the four image datasets and the WPS model formalization of the seven models were accomplished, thereby reducing the difficulty of reusing the online remote sensing image processing and analysis models. The construction of a collaborative online fire post-disaster assessment process based on the BPMN2.0 and OGC standards was completed using the process construction model and the online remote sensing analysis model. Through the implementation of this process, the extraction of fire trace areas and the classification of fire intensity levels were accomplished, and the analytical results of the intermediate processing steps were shared, thereby assisting in the post-disaster assessment process and in the analysis of fire traceability. In addition, this paper proposed a collaborative online fire analysis process that uses the BPMN2.0 and OGC standards to solve the problems related to poor sharing and reusing in the collaborative online remote sensing process, as well as processing difficulties, high hardware requirements, and the difficult information interactions due to the characteristics of remote sensing data. In the standardized collaborative online fire analysis process construction, the participants constructed two sub-problem workspaces, several analysis sub-processes, sub-process realization activities based on the process construction model, and standardized information interactions based on the online fire analysis model. This standardized collaboration framework not only realized a multifaceted collaboration among participants, but also significantly reduced the amount of time occupied by individual participants in the analysis process. In addition, it also enhanced the manageability and reusability of the collaborative online fire analysis process.
Compared to a non-collaborative and offline analysis, collaborative online processing and analysis offers significant advantages. In collaborative online fire analysis, stakeholders and researchers from various disciplines can cooperate to construct and implement the analysis process to ensure that the analysis’ purpose is achieved, and that technical feasibility is ensured, thus achieving an interdisciplinary fire analysis. Online fire analysis can reduce the requirements for computer hardware and can solve the problem of difficult remote sensing data processing when compared with offline fire analysis.

5. Conclusions

In order to solve complex geographic problems for experts, it is difficult for one expert to complete the analysis and processing tasks alone with their, in comparison, limited energy, expertise, and resources; as such, the development of collaborative online processing and analysis by multidisciplinary experts is essential. A collaborative online analysis and processing process can help experts from different disciplines collaborate with each other to resolve complex geographical problems, but an effective model remains to be developed in order to assist participants in developing a collaborative online remote sensing image processing and analysis process that is effective. Therefore, this study constructed a conceptual framework of collaborative online remote sensing image processing. This framework was divided into four phases, in which a range of activities, from the definition of the collaborative process context to the gathering of resources to the design of standardized collaborative online processing and analysis procedures, and finally to the completion of the implementation process, was encompassed.
By standardizing the collaborative online analysis and processing process, it is possible to address the problems of poor sharing and reuse in collaborative analysis, as well as in the difficulty in processing data, the high demands of analysis hardware, and the difficulty in interacting with information due to the characteristics of remote sensing data. Therefore, in this study, on the basis of constructing a complete conceptual model framework for the collaborative online remote sensing image processing and analysis process, we developed a collaborative remote sensing image processing and analysis procedure based on the BPMN2.0 standard, as well as an online remote sensing analysis model based on the OGC standards. Participants can construct a standardized collaborative online remote sensing processing and analysis procedure using the process construction model. By standardizing the process, errors and repetitive construction work can be reduced, and the process of collaborative remote sensing analysis can become more controlled and standardized. As such, the problem of the inadequate sharing of remote sensing analysis resources will be solved; thus, participants will have a better understanding of each other’s work and will be able to collaborate more effectively. Additionally, the online remote sensing processing and analysis model provides a standardized method for the interactions between sub-processes and other sub-processes, as well as between activities and other activities, which reduces unnecessary communication between participants and enhances the collaboration capability of collaborative online remote sensing analysis. Last but not least, the online remote sensing analysis model can effectively combine online remote sensing processing and analysis with collaborative processes in order to achieve collaborative online remote sensing image processing and analysis.
This study provided a conceptual framework for collaborative online remote sensing processing and analysis, as well as a standardized methodology for collaborative online remote sensing analysis. However, certain issues still require further investigation. In the case of the remote sensing processing and analysis model, WPS processualization, sub-process model integration, and professional personnel are still required to operate this model, and there is a lack of automated and effective methods available. A methodology that enables an automatic WPS processing of the remote sensing processing analysis model and the integration of sub-process models into the analysis process is necessary in order to solve the above problems and to improve participants’ resource management efficiency. The purpose of this method is to allow participants to complete a WPS processing operation by providing the relevant models without requiring manual intervention, thereby indirectly enhancing the efficiency of collaborative online processing and analysis.

Author Contributions

Conceptualization, X.Z. and F.Z.; methodology, X.Z.; software, X.S., S.W. and X.C.; validation, F.Z., Q.W. and S.W.; formal analysis, H.W., F.Z. and X.S.; investigation, X.Z. and X.S.; resources, Q.W.; data curation, H.W. and S.W.; writing—original draft preparation, X.S., F.Z. and X.Z.; writing—review and editing, H.W., Q.W. and S.W.; visualization, S.W.; supervision, Q.W.; project administration, Q.W.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a grant from the following project: (1) the National Science and Technology Innovation 2030 “New Generation Artificial Intelligence” Major Project under grant number 2021ZD0113604. (2) Major Science and Technology Innovation Program of Shandong Province under grant number 2022CXGC010609.

Data Availability Statement

The datasets in this paper are available at https://scihub.copernicus.eu/, accessed on 24 June 2023.

Acknowledgments

The authors would like to thank the referees for their careful reading and helpful comments.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. A conceptual model framework for collaborative online remote sensing analysis.
Figure 1. A conceptual model framework for collaborative online remote sensing analysis.
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Figure 2. Modeling of the collaborative remote sensing image processing and analysis procedure based on BPMN2.0.
Figure 2. Modeling of the collaborative remote sensing image processing and analysis procedure based on BPMN2.0.
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Figure 3. A collaborative online remote sensing analysis information exchange model based on the OGC standard.
Figure 3. A collaborative online remote sensing analysis information exchange model based on the OGC standard.
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Figure 4. Fire area and the adjacent features.
Figure 4. Fire area and the adjacent features.
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Figure 5. The sub-problems and sub-processes in the post-disaster assessment of forest fires.
Figure 5. The sub-problems and sub-processes in the post-disaster assessment of forest fires.
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Figure 6. Example of the WPS process structure in this experiment.
Figure 6. Example of the WPS process structure in this experiment.
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Figure 7. Case application of the fire post-disaster assessment based on the process-building model and the online remote sensing analysis model.
Figure 7. Case application of the fire post-disaster assessment based on the process-building model and the online remote sensing analysis model.
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Figure 8. The workspace, sub-processes, and realization activities in the standardized online analysis process for the fire post-disaster assessment scalable document.
Figure 8. The workspace, sub-processes, and realization activities in the standardized online analysis process for the fire post-disaster assessment scalable document.
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Figure 9. (a) A map showing the results of the extraction of the fire trail area, and (b) a map showing the intensity levels classified.
Figure 9. (a) A map showing the results of the extraction of the fire trail area, and (b) a map showing the intensity levels classified.
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Table 1. The names and descriptions of the data used in this experiment.
Table 1. The names and descriptions of the data used in this experiment.
Satellite NameImaging TimeImage Description
Sentinel-123 March 2020 In this experiment, the radar data were SLC (single-look complex) products from the Sentinel 1 satellite imagery in the IW mode.
Sentinel-116 April 2020
Sentinel-2A25 March 2020There are two sub-satellites within the Sentinel 2 satellite, 2A and 2B, and the present experiment used the pre-fire optical image acquired via Satellite 2A and the post-fire optical image acquired via Satellite 2B.
Sentinel-2B19 April 2020
Table 2. The models provided for this experiment.
Table 2. The models provided for this experiment.
Model NameModel Description
dSAVIThe soil adjusted vegetation index (SAVI) is calculated based on the normalized vegetation index (NDVI), which effectively reduces the image of the extracted vegetation from the soil’s background. dSAVI is defined as the difference between the SAVI indices acquired after the fire and those acquired before the fire. This model uses two periods of optical image data before and after fire events to obtain the dSAVI index.
dBAIThe burned area index (BAI) measures the burned area by taking advantage of the strong absorption properties of the red and near-infrared bands of the ash and other materials that may have been generated after the fire. The difference between the BAI indexes before and after the fire is known as the dBAI.
dNBRThe normalized burn index (NBR) is calculated with the two bands (near-infrared, as well as long- and short-wave infrared) that exhibit the greatest response to burning. The dNBR index is the difference between the NBR indices of the images taken before and after the fire events. This model was fed optical image data from both before and after the fire event periods, and the dNBR index was calculated as a result.
dSpanThe difference in the total backscattered power (span) was calculated by inputting the microwave data before and after the fire.
drvv-vhThe input microwave data before and after the fire was used to calculate the difference in the cross-polarization ratio (rvv-vh).
Fire trail discrimination modelA panel of experts discuss the sub-problems of fire trace extraction and decide to use a decision tree approach based on the calculated differences between the pre- and post-fire feature parameters to extract the fire trace.
Flame rank segmentation modelA threshold segmentation method is employed in the model for fire classification, which is based on the dNBR index.
Table 3. Comparison of the extraction accuracy of the overfire areas.
Table 3. Comparison of the extraction accuracy of the overfire areas.
Overfire   Area   S c (ha) Extraction   Accuracy   P
Overfire area extracted via collaborative online analysis2749.7690.22%
Overfire area extracted using ENVI2776.3591.09%
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MDPI and ACS Style

Zhang, X.; Wu, Q.; Zhang, F.; Sun, X.; Wu, H.; Wu, S.; Chen, X. A Novel Standardized Collaborative Online Model for Processing and Analyzing Remotely Sensed Images in Geographic Problems. Electronics 2023, 12, 4394. https://doi.org/10.3390/electronics12214394

AMA Style

Zhang X, Wu Q, Zhang F, Sun X, Wu H, Wu S, Chen X. A Novel Standardized Collaborative Online Model for Processing and Analyzing Remotely Sensed Images in Geographic Problems. Electronics. 2023; 12(21):4394. https://doi.org/10.3390/electronics12214394

Chicago/Turabian Style

Zhang, Xueshen, Qiulan Wu, Feng Zhang, Xiang Sun, Huarui Wu, Shumin Wu, and Xuefei Chen. 2023. "A Novel Standardized Collaborative Online Model for Processing and Analyzing Remotely Sensed Images in Geographic Problems" Electronics 12, no. 21: 4394. https://doi.org/10.3390/electronics12214394

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