The Construction of Ecological Corridors under the Impact of Traffic: A Case Study of Ningxia Hui Autonomous Region, China

Description

Research Assumptions This study posits that transportation infrastructure significantly impacts the Ningxia region's ecosystem in terms of landscape, processes, and functions. Specifically, it may alter land use patterns, disrupt ecosystem processes, and change ecosystem functions. By strategically planning and constructing ecological corridors, we can mitigate these negative effects, enhance ecosystem connectivity, and boost biodiversity. Data Display Content The dataset encompasses: Model Screenshots: Software modeling visually illustrates processing methods, aiding interested scholars. Process Screenshots: These depict the full workflow, from data collection and model building to result analysis, including constructing a comprehensive resistance surface with factors like land use, elevation, and slope, as well as extracting and grading ecological corridors. Raw Data: Comprises Ningxia's land use, elevation, slope, and transportation network data, sourced from remote sensing, GIS databases, and government statistics. Result Data: Features ecological corridor distribution maps, ecological source area identification, connectivity and sensitivity assessments, and land use predictions. Optimized corridor layouts notably facilitate species exchange and migration, supporting sustainable development. Data Explanation and Usage Data Explanation: Interpretation requires considering Ningxia's unique ecology and transportation context. For instance, when assessing corridor connectivity, account for the region's complex terrain and dense transportation network. Also, recognize varying impacts on different ecosystems, like forests versus grasslands. Data Usage: The dataset informs ecological corridor planning in Ningxia. Planners can use corridor distribution maps to avoid conflicts with transportation infrastructure and leverage ecological source area data to bolster protection, ensuring corridor effectiveness and sustainability.

Steps to reproduce

To obtain the data for this study, we followed a systematic approach that involved several key steps. 1. Identifying Data Sources: We began by identifying the necessary data types for our research, which included land use data, elevation data, slope data, and transportation network data. For land use data, we targeted remote sensing imagery that could provide detailed information on different land cover types in the Ningxia region. Elevation and slope data were sourced from digital elevation models (DEMs), which are widely available and offer accurate topographical information. Transportation network data, including roads and railways, were obtained from government departments responsible for transportation infrastructure, as well as from publicly accessible GIS databases. 2. Data Collection: Remote Sensing Imagery: We acquired high-resolution satellite imagery covering the Ningxia region. This imagery was processed to classify different land use types, such as forests, grasslands, urban areas, and water bodies. The classification was performed using image processing software and machine learning algorithms to ensure accuracy. DEM Data: Digital elevation models were downloaded from reputable sources that provide global or regional topographic data. These models were used to generate slope data, which is crucial for understanding the terrain's influence on ecological processes and the feasibility of ecological corridor placement. Transportation Data: Detailed transportation network data were collected, including the location, type, and connectivity of roads and railways. This information was essential for analyzing the spatial relationship between transportation infrastructure and ecological elements. 3. Data Integration and Preprocessing**: Once all the data were collected, they were integrated into a GIS platform. This allowed us to overlay different datasets and perform spatial analyses. Preprocessing steps included data cleaning to remove any errors or inconsistencies, data transformation to ensure compatibility between different datasets, and data normalization to facilitate comparative analysis. 4. Modeling and Analysis: With the integrated and preprocessed data, we constructed models to simulate and analyze the ecological corridors. This involved using software tools to create resistance surfaces based on the collected data and applying algorithms to identify optimal corridor routes that minimized ecological resistance while considering transportation impacts. By following this structured approach, we were able to collect comprehensive and reliable data, which formed the foundation for our research on ecological corridor planning in the context of transportation development in Ningxia. This methodology can be replicated by others interested in conducting similar studies, provided they have access to the necessary data sources and analytical tools.

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