High-altitude cold region highway surrounding surface environment long time sequence analysis method and system

Abstract

The invention belongs to the technical field of remote sensing technology and environment monitoring, and discloses a long-time-sequence analysis method and system for a surface environment around a highway in a high-altitude cold region, and the analysis method comprises the following steps: S1, creating a buffer region, and obtaining multi-source remote sensing data and time-space auxiliary data; step S2, performing standardization treatment; s3, environment indexes, a surface water body shape index SI and a fractal dimension FD are calculated, GIS operation is carried out on data obtained through calculation, and the environment indexes comprise the surface temperature LST, the normalized vegetation index NDVI, the temperature vegetation drought index TVDI, the surface water body area SSA and the surface water body number SSN; s4, performing analysis from two dimensions of time trend and spatial distribution based on the environment index; and S5, carrying out visual display and export on an analysis result. The method can accurately reveal the space-time law of environment change, and provides scientific support for safe operation and ecological protection of highway engineering.

Description

Technical Field

[0001] This invention relates to the fields of remote sensing technology and environmental monitoring technology, and in particular to a long-term analysis method and system for the surface environment around highways in high-altitude cold regions. Background Technology

[0002] As crucial linear infrastructure, highways in high-altitude, cold regions are highly susceptible to changes in the surrounding surface environment, making their long-term stable operation vulnerable to these changes. Current research on the environment surrounding highways in these regions largely relies on field surveys, localized ground temperature monitoring, or short-term remote sensing data, which has the following shortcomings: First, the research scope is limited, focusing primarily on highways in the heart of high-altitude, cold regions, with insufficient coverage of highways in the more challenging plateau border areas. Second, the data dimensions are singular, with most studies targeting only a single environmental element (such as permafrost deformation or vegetation cover), lacking multi-dimensional collaborative analysis of surface water, surface temperature, vegetation indices, and drought indices. Third, the time series are short, making it difficult to capture long-term (e.g., over 20 years) environmental change trends, and data continuity and consistency are difficult to guarantee. Fourth, the analytical methods lack systematicity; existing technologies have not formed a complete process of "data acquisition - preprocessing - index calculation - spatiotemporal analysis - result output," failing to efficiently support precise operation and maintenance decisions for highway engineering.

[0003] Remote sensing technology, with its ability to acquire large-scale, long-term, and multi-dimensional data, has become an important tool for environmental monitoring in high-altitude, cold regions. However, existing remote sensing applications mostly remain at the level of single data processing or simple index calculations, lacking integrated analysis methods specifically for highway scenarios in high-altitude, cold regions, and thus failing to meet the needs of long-term, multi-factor collaborative analysis of the environment surrounding highways. Therefore, designing a long-term analysis method for the surface environment surrounding highways in high-altitude, cold regions based on multi-source remote sensing data has significant practical implications and application value. Summary of the Invention

[0004] The purpose of this invention is to address the technical shortcomings of existing technologies, such as limited data dimensions, unsystematic analysis processes, and insufficient long-term monitoring capabilities, by providing a long-term analysis method for the surface environment around highways in high-altitude cold regions based on remote sensing technology.

[0005] Another objective of this invention is to provide an analysis system for implementing the long-term time-series analysis method for the surface environment around highways in high-altitude cold regions.

[0006] The technical solution adopted to achieve the purpose of this invention is: A long-term analysis method for the surface environment around highways in high-altitude cold regions includes the following steps: Step S1: Create a buffer and acquire multi-source remote sensing data and spatiotemporal auxiliary data. The multi-source remote sensing data includes surface water data, surface temperature data and vegetation index data. The spatiotemporal auxiliary data includes terrain data and highway basic data. Step S2: Standardize the data obtained in step S1; Step S3: Based on the multi-source data preprocessed in step S2, calculate the environmental index, surface water shape index SI, and fractal dimension FD, and then perform GIS operations to obtain the annual characteristics of environmental factors in the study area. The environmental index includes surface temperature LST, normalized difference vegetation index NDVI, temperature-vegetation-drought index TVDI, surface water area SSA, and surface water quantity SSN. Step S4: Based on the environmental indices obtained in step S3, analyze them from two dimensions: time trend and spatial distribution. The time trend analysis includes the trend slope and time variability of each environmental index, and the spatial distribution analysis includes buffer analysis, regional hierarchical analysis, and terrain correlation analysis. Step S5: Visualize and export the analysis results from step S4.

[0007] In the above technical solution, in step S1, the surface water data uses the JRC Monthly WaterHistory dataset, the surface temperature data and vegetation index data use the MODIS series dataset, and the terrain data uses SRTM digital elevation model data.

[0008] In the above technical solution, the standardization process in step S2 includes radiometric calibration and atmospheric correction, spatial registration and resampling, data pruning and stitching, time series standardization, and accuracy optimization.

[0009] In the above technical solution, step S3 includes GIS operations such as classification, computational geometry, and calculated fields.

[0010] In the above technical solution, the method for extracting the surface temperature LST in step S3 is as follows: after extracting LST from the standardized MODIS series dataset, elevation correction is performed based on the terrain data.

[0011] In the above technical solution, the method for extracting the Normalized Difference Vegetation Index (NDVI) in step S3 is as follows: NDVI bands were extracted from the preprocessed MODIS series datasets, and then the Normalized Difference Vegetation Index (NDVI) was calculated accordingly. ; in, For near-infrared reflectivity, The reflectance is in the red light band, and the normalized vegetation index (NDVI) ranges from -1 to 1.

[0012] In the above technical solution, the method for extracting the Temperature Vegetation Drought Index (TVDI) in step S3 is as follows: First, construct the NDVI-LST feature space and calculate the dry boundary. Calculate the wet boundary ,in, For the maximum LST, To be the minimum LST, a , b For wet boundary coefficient, c , d The dry boundary coefficient is used as the basis for calculating the Temperature-Vegetation Aridity Index (TVDI). The value of TVDI ranges from 0 to 1.

[0013] In the above technical solution, the method for extracting the surface water area SSA and the surface water quantity SSN in step S3 is as follows: first, water bodies are extracted from JRC Monthly Water History data, and the extracted water body raster data is vectorized; then, each vectorized water body is traversed to calculate the surface water area SSA, the surface water quantity SSN, and the water body perimeter. The formula for calculating the surface water shape index (SI) is: The formula for calculating the fractal dimension FD is: ,in P The circumference of the water body A The area of ​​the water body.

[0014] In the above technical solution, in step S4, the trend slope The calculation formula is: time variability The calculation formula is: ,in For the year, Average year For each environmental index, the first Year The value of a pixel for The average value of a pixel over time.

[0015] In the above technical solution, the visualization display in step S5 includes time series charts, spatial distribution maps, and trend and variability maps.

[0016] Another aspect of the present invention includes an analysis system for implementing the long-term time-series analysis method of the surface environment around highways in high-altitude cold regions, comprising a data acquisition module, a preprocessing module, an environmental index calculation module, a spatiotemporal analysis module, and a result visualization and output module. The data acquisition module performs step S1, the preprocessing module performs step S2, the environmental index calculation module performs step S3, the spatiotemporal analysis module performs step S4, and the result visualization and output module performs step S5.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention enables long-term (e.g., from 2000 to the present) multi-dimensional collaborative analysis of surface water bodies, surface temperature, vegetation index, and drought index around highways in high-altitude cold regions, accurately revealing the spatiotemporal patterns of environmental changes and providing scientific support for the safe operation and ecological protection of highway projects. Attached Figure Description

[0018] Figure 1 This is a flowchart of the long-term analysis method for the surface environment around highways in high-altitude cold regions according to the present invention.

[0019] Figure 2 This invention relates to the module composition of the long-term time-series analysis system for the surface environment around highways in high-altitude cold regions.

[0020] Figure 3 This is a schematic diagram of the multi-gradient buffer partitioning and environmental element distribution of the spatiotemporal analysis module in an embodiment of the present invention. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] Example 1 like Figure 1 As shown, a long-term time-series analysis method for the surface environment around highways in high-altitude cold regions includes the following steps: Step S1: Create a buffer zone and collect multi-source remote sensing data and spatiotemporal auxiliary data around highways in high-altitude cold regions. The multi-source remote sensing data includes surface water data, surface temperature (LST) data, and vegetation index (NDVI) data. The spatiotemporal auxiliary data includes topographic data and highway basic data.

[0023] Specifically: The multi-source remote sensing data is automatically acquired by connecting to data sources such as the Google Earth Engine (GEE) platform and NASA data centers, resulting in long-term (e.g., 2000-present) multi-resolution remote sensing data, including: (1) Surface water data: The JRC Monthly Water History dataset (30m resolution, 1984-2021) was used to extract the distribution, area and morphological characteristics of surface water bodies; (2) Land surface temperature (LST) and vegetation index (NDVI) data: MODIS series datasets (MOD11A2 dataset, 8-day composite, 1km resolution; MOD13A2 dataset, 16-day composite, 1km resolution) were used to calculate LST, NDVI and subsequent temperature-vegetation drought index (TVDI). The MOD11A2 dataset was used to extract LST and the MOD13A2 dataset was used to calculate NDVI.

[0024] The terrain data includes 30m resolution SRTM digital elevation model (SRTM DEM) data, which is used to extract terrain features such as elevation, slope, and aspect along the highway to assist in the spatial distribution analysis of environmental elements. The basic highway data includes highway vector boundaries (such as the start and end coordinates and route orientation of typical highways in high-altitude cold regions) and administrative division data, which are used to delineate the research scope (such as a 10km buffer zone on both sides of the highway).

[0025] Step S2 involves standardizing the data obtained in step S1 to eliminate data differences and ensure the consistency of time, space, and accuracy of multi-source data, providing a dataset with a unified format for subsequent analysis.

[0026] The specific steps are as follows: Radiometric calibration and atmospheric correction: Radiometric calibration is performed on the MODIS series datasets, converting the original DN values ​​into surface reflectance, radiance temperature, and so on. The FLAASH atmospheric correction algorithm is used to eliminate the influence of atmospheric scattering and absorption on the imagery and improve data accuracy. Spatial registration and resampling: All remote sensing data (such as JRC water body data at 30m resolution and MODIS data at 1km resolution) were uniformly projected to the WGS84 coordinate system. The quadratic polynomial transformation method was used to spatially register all remote sensing data, and the root mean square error (RMSE) was controlled within ≤0.5 pixels. The low-resolution data (such as MODIS data at 1km resolution) was resampled to 30m resolution using bilinear interpolation to ensure spatial consistency of different data sources. Data cropping and stitching: Based on the research area defined by the highway basic data (such as a 10km buffer zone on both sides of the highway), the preprocessed remote sensing data and topographic data are cropped; for the research area spanning multiple images, the images are stitched together to form a complete dataset of the research area. Time series standardization: Long-term time series data (such as land surface temperature (LST) and vegetation index (NDVI) LST from 2000 to 2024) are normalized over time to extract effective pixels for each year. The maximum value composite method (MVC) is used to eliminate short-term interference such as clouds and shadows. Data from the same season (such as summer, June to August) in different years are aligned to eliminate the impact of seasonal differences on long-term trend analysis.

[0027] Accuracy optimization: LST data is corrected by combining it with DEM elevation information to eliminate the influence of altitude differences on temperature measurement, and NDVI data is smoothed by "3×3 pixel median filtering".

[0028] Step S3: Based on the multi-source data preprocessed in Step S2, calculate the environmental indices of the surface environment around highways in high-altitude cold regions, including surface temperature (LST), normalized difference vegetation index (NDVI), temperature-vegetation drought index (TVDI), surface water area (SSA), and surface water number (SSN).

[0029] The steps for extracting surface temperature (LST) are as follows: extract daytime LST (LST_Day_1km band) directly from the standardized MODIS series datasets (such as MOD11A2 data), and perform elevation correction based on the topographic data (DEM) of the study area to eliminate the influence of altitude differences on LST.

[0030] The steps for extracting the Normalized Difference Vegetation Index (NDVI) are as follows: Extract the NDVI bands from the preprocessed MODIS series datasets (e.g., MOD13A2 data), and the calculation formula is: ,in, For near-infrared reflectivity, NDVI represents the reflectance in the red light band; the value ranges from -1 to 1, and a larger value indicates better vegetation cover.

[0031] The method for calculating the Temperature Vegetation Drought Index (TVDI) is as follows: based on the synergistic relationship between LST and NDVI, an NDVI-LST feature space is constructed, and the dry boundary is calculated. wet boundary ,in a , b For wet boundary coefficient, c , d The dry boundary factor is used; the TVDI calculation formula is: The TVDI value ranges from 0 to 1, with a larger value indicating a drier region.

[0032] The steps for extracting surface water area (SSA) and quantity (SSN) are as follows: First, water bodies are extracted from JRC Monthly Water History data (using a threshold method, threshold=1, where 1 represents a water body), and the extracted water body raster data is vectorized; then, each vectorized water body is traversed using a geographic information database such as Python GeoPandas to calculate basic parameters such as surface water area (SSA), quantity (SSN), and surface water body perimeter, and finally, the shape index (SI) and fractal dimension (FD) are calculated based on the area and perimeter.

[0033] Based on the distribution of surface water bodies, the surface water bodies are vectorized, and the shape index (SI) reflects the irregularity of the water body's shoreline, while the fractal dimension (FD) reflects the complexity of the water body's shape. The formula for calculating SI is: ,in P The circumference of the water body is (m). A The area of ​​the water body (m²) is represented by the SI value; a larger SI value indicates a more irregular shoreline; the FD calculation formula is: The value of FD ranges from 1 to 2. The closer it is to 2, the more complex the shape of the water body.

[0034] After acquiring vector data of LST, NDVI, TVDI, and surface water, a series of GIS operations, including classification, computational geometry, and field calculation, were performed on the data to obtain the annual characteristics of environmental factors in the study area.

[0035] Step S4: Based on the environmental index obtained in step S3, conduct an in-depth analysis of the surface environment around highways in high-altitude cold regions from two dimensions: time trend and spatial distribution.

[0036] Time trend analysis includes trend slope calculation and variability analysis: (1) Trend slope calculation: The least squares method was used to perform linear regression on the LST, NDVI, TVDI, SSA and SSN of long-term time series (e.g., 2000-2024) to calculate the trend slope. : ,in For the year, Average year For each environmental element (environmental index) Year The value of a pixel for The time series average value of a pixel; 0 indicates that the index is trending upward. 0 indicates a downward trend.

[0037] (2) Variation analysis: The standard deviation was used to calculate the pixel values ​​of each environmental element. Time variability The formula is: ; The larger the value, the more drastic the temporal fluctuations of the pixel index and the worse the environmental stability.

[0038] Spatial distribution analysis includes buffer analysis, hierarchical zoning analysis, and terrain correlation analysis: (1) Buffer zone analysis: Based on the center line of the highway, multiple gradient buffer zones of 2km, 4km, 6km, 8km, and 10km are delineated. The average value and area ratio of LST, NDVI, and TVDI in different buffer zones are statistically analyzed to analyze the variation law of environmental elements with highway distance (e.g., LST decreases with increasing distance and TVDI decreases with increasing distance). (2) Regional hierarchical analysis: LST (such as 10℃, 10-15℃, 15-25℃ 25℃), NDVI (such as 0.05, 0.05-0.1, 0.1-0.2 0.2), TVDI (such as) 0.2, 0.2-0.6, 0.6-0.8 0.8) Based on numerical ranges, spatial overlay analysis technology is used to generate graded distribution maps of environmental elements in different years, revealing the spatial clustering characteristics of environmental elements (e.g., LST around highways in high-altitude cold regions is mainly concentrated between 15-25℃, and NDVI is mostly below 0.2). (3) Topographic correlation analysis: Spatial correlation is performed between environmental indices and elevation, slope and aspect data extracted from DEM to analyze the impact of topographic factors on environmental elements (such as lower LST in high-altitude areas and higher NDVI on the south slope than on the north slope).

[0039] Step S5: Visualize the analysis results from step S4. Present the data in an intuitive and easy-to-understand format, and support data export. Specific functions include: (1) Time series charts: Generate line charts and bar charts showing the annual changes in the average values ​​of SSA, SSN, LST, NDVI, and TVDI (e.g., a chart showing the growth trend of surface water area from 2000 to 2024). (2) Spatial distribution map: Using ArcGIS Engine components, generate hierarchical thematic maps of environmental elements (such as LST spatial classification map, TVDI spatial classification map) and buffer analysis comparison map (such as NDVI distribution box plot of buffers at different distances). (3) Trend and Variability Maps: Generate spatial distribution maps of trend slopes and spatial distribution maps of standard deviation of variability for LST, NDVI, and TVDI, which visually demonstrate the spatial differences in environmental changes. (4) Data export: Supports exporting preprocessed remote sensing data and environmental index data (such as annual NDVI raster data and TVDI vector data) in common formats (such as GeoTIFF and Shapefile).

[0040] Example 2 like Figure 2 As shown, this embodiment provides an analysis system that can implement the analysis method described in Embodiment 1, including a data acquisition module, a preprocessing module, an environmental index calculation module, a spatiotemporal analysis module, and a result visualization and output module. The data acquisition module is used to collect multi-source remote sensing data and spatiotemporal auxiliary data around highways in high-altitude cold regions. The preprocessing module is used to standardize the multi-source data collected by the data acquisition module. The environmental index calculation module automatically calculates the environmental index of the surface environment around highways in high-altitude cold regions based on the standardized multi-source data. The spatiotemporal analysis module performs in-depth analysis of the surface environment around highways in high-altitude cold regions from two dimensions: temporal trend and spatial distribution, based on the long-time series index data output by the environmental index calculation module. The result visualization and output module is used to visualize the results of the spatiotemporal analysis module.

[0041] The data acquisition module has automatic data update and filtering functions, and can automatically remove data with cloud cover exceeding a threshold (e.g., cloud cover percentage) based on the user-defined time range (e.g., 2000-2024) and spatial range (e.g., highway buffer zone). 20% of invalid images were removed to ensure data quality.

[0042] The environmental index calculation module has an automatic index verification function. When the calculation result exceeds a reasonable range (such as NDVI), it will automatically verify the index. -1 or 1. TVDI 0 or 1) When outliers are automatically marked, they are corrected using adjacent cell interpolation to ensure index accuracy.

[0043] Example 3 This embodiment is a further explanation based on embodiments 1 and 2.

[0044] This invention provides a study of a typical cold-region highway on the Qinghai-Tibet Plateau with an average altitude of over 4000m, traversing seasonally and permafrost areas. Based on a modular system architecture and actual surface environmental characteristics, a long-term time-series analysis from 2000 to 2024 is achieved. These embodiments are implemented under the premise of the technical solution of this invention and should be understood as illustrative only and not intended to limit the scope of the invention.

[0045] The analytical method of this invention is based on the core logic of "target-driven - data flow - result feedback," and is designed to address the environmental analysis needs of highways in high-altitude, cold regions. This process mainly consists of five steps. Step S1 primarily involves acquiring data for the study area, which can be obtained through platforms such as SGS Earth Explorer, NASA Data Center, the National Geographic Information Public Service Platform, and GEE. Step S2 performs spatiotemporal alignment processing on the data acquired in Step S1 to ensure consistency in time, space, and geocoding. Subsequently, in Step S3, specific values ​​of environmental indices such as surface water, vegetation, and surface temperature within the study area are calculated to quantify environmental changes. In Step S4, data analysis methods such as trend slope calculation and variability analysis are used to analyze the spatiotemporal patterns of environmental changes within the study area. In Step S5, the processed data is visualized and exported.

[0046] Step S1: For specific high-altitude cold-region highways, taking typical high-altitude cold-region highways as the core, based on the highway vector boundary data obtained by the data acquisition module, create multi-gradient buffer zones (e.g., 2km, 4km, 6km, 8km, 10km on both sides of the highway) in ArcGIS Pro software. Figure 3 As shown in (a), in conjunction with research needs (such as the analysis of the variation of environmental elements with highway distance), a suitable range (usually 10km on both sides of the highway) is selected as the final research area to ensure that the research area can cover the key environmental impact zone around the highway and avoid interference from irrelevant area data, thus laying a spatial foundation for subsequent accurate analysis.

[0047] After the study area was delineated, long-term remote sensing data since 2000 were acquired through the Google Earth Engine (GEE) platform connected to the data acquisition module. These included MODIS series datasets (such as the 8-day composite, 1km resolution MOD11A2 data) for extracting land surface temperature (LST), MODIS datasets (such as the 16-day composite, 1km resolution MOD13A2 data) for calculating the Normalized Difference Vegetation Index (NDVI), and the JRC Monthly Water History dataset (30m resolution) for extracting surface water distribution. Simultaneously, 30m resolution SRTM DEM data of the study area were acquired through the USGS Earth Explorer platform. Using ArcGIS spatial analysis tools, topographic information such as elevation, slope, and aspect was extracted from the DEM data to support subsequent environmental index elevation correction and topographic correlation analysis.

[0048] Step S2: After data acquisition, the preprocessing module is started to perform data preprocessing to ensure the temporal, spatial, and accuracy consistency of multi-source data. In the temporal dimension, effective pixels for each year are extracted from LST and NDVI data since 2000, and the "maximum composite method" (MVC) is used to eliminate short-term interference such as clouds and shadows, ensuring seasonal alignment of data from different years. In the spatial dimension, SRTM at a 30m resolution is used. Using DEM data as a baseline, spatial registration of all remote sensing data was performed using a quadratic polynomial transform method, controlling the root mean square error (RMSE) to within ≤0.5 pixels. Simultaneously, MODIS data with a 1km resolution was resampled to 30m using bilinear interpolation to unify the resolution with JRC water body data and DEM data, meeting the requirements for pixel-level overlay analysis. For accuracy optimization, LST data was corrected by combining DEM elevation information to eliminate the influence of altitude differences on temperature calculation. NDVI data was smoothed for noise using a "3×3 pixel median filter". After preprocessing, data quality was verified by "pixel integrity ≥98%" and "radiometric accuracy error ≤5%". Once the standards were met, the data was pushed to the subsequent index calculation stage.

[0049] Step S3: After data preprocessing, following the environmental index calculation logic of the environmental index calculation module, import the preprocessed summer LST and NDVI data into the MATLAB or Python analysis environment to construct an LST-NDVI feature space. Then, use a linear fitting algorithm to fit the dry and wet boundaries of the feature space (where the dry boundary corresponds to the LST-NDVI relationship under extreme drought conditions, and the wet boundary corresponds to the LST-NDVI relationship under extreme wet conditions). Finally, based on the fitted dry and wet boundaries, calculate the Temperature Vegetation Drought Index (TVDI) for the study area. The TVDI value ranges from 0 to 1 and reflects the degree of drought in the area (the higher the value, the more severe the drought). Outliers in the calculation results (such as TVDI) are removed. 0 or (Pixels of 1 are corrected using interpolation between adjacent pixels) to ensure that TVDI accuracy meets analysis requirements. The calculated LST, NDVI, and TVDI are as follows: Figure 3 As shown in (b), (c) and (d).

[0050] Step S4: For the three environmental elements LST, NDVI, and TVDI, based on the time trend analysis method of the spatiotemporal analysis module, first classify and segment them according to numerical intervals (e.g., LST is divided into...). 15℃, 15-20℃ At 20℃, NDVI is divided into 0.05, 0.05-0.1, 0.1-0.2 0.2, TVDI is divided into 0.4, 0.4-0.6, 0.6-0.8 0.8); then extract the classification data for each summer since 2000, construct time series datasets for different classification intervals of each element, and characterize the dynamic changes of LST, NDVI, and TVDI on a long time scale by calculating indicators such as annual mean and rate of change.

[0051] For surface water bodies, the JRC Monthly Water History data acquired by the data acquisition module was first used to extract water bodies (using a threshold method, threshold=1, where 1 represents a water body), and the extracted water body raster data was vectorized. Then, each vectorized water body was traversed using a geographic information database such as Python GeoPandas to calculate basic parameters such as area and perimeter. Based on the area and perimeter, the shape index (SI) and fractal dimension (FD) were calculated. The shape index reflects the irregularity of the shoreline (the larger the value, the more irregular the shoreline), and the fractal dimension reflects the complexity of the water body shape (the closer the value is to 2, the more complex the shape). Based on the annual water body parameter dataset since 2000, a time series of water body area, quantity, SI, and FD was constructed to reveal the long-term temporal variation patterns of surface water bodies around highways in high-altitude cold regions (such as area growth trends and changes in morphological complexity).

[0052] This study integrates long-term time-series datasets of surface water, LST, NDVI, and TVDI, and conducts comprehensive analysis based on the spatiotemporal analysis logic of the spatiotemporal analysis module. Spatially, it combines multi-gradient buffers and topographic data to analyze the spatial distribution differences of the four elements in different spatial buffers (e.g., 2km vs. 10km) and different topographic units (e.g., high-altitude vs. low-altitude areas, sunny slopes vs. shady slopes), revealing the superimposed effects of highway influence and topographic regulation on environmental elements. Temporally, it calculates the annual trend slope of the four elements through linear regression to analyze the long-term evolution patterns of each element under the background of climate warming. Finally, through cross-analysis (e.g., the correlation between water body expansion and LST changes, and the response relationship between TVDI rise and NDVI changes), it systematically studies the synergistic change characteristics of four typical surface environments—surface water, surface temperature, vegetation, and drought—around highways in high-altitude cold regions.

[0053] Step S5: After the comprehensive spatiotemporal analysis is completed, the results are presented and output in multiple forms through the results visualization and output module. For visualization, time-series charts are generated (such as line graphs showing the increase in surface water area since 2000, and bar charts showing the annual changes in LST mean, with trend lines marked), spatial thematic maps (such as LST graded heat maps, TVDI drought zoning maps, and NDVI vegetation cover maps, overlaid with highway buffer zone boundaries and high-risk area markers), and trend maps (such as spatial distribution maps of LST change slopes, visually presenting areas of significant temperature increases). For report generation, the "Surface Environment Report for Highways in High-Altitude Cold Regions" is automatically compiled. The "Environmental Long-Term Analysis Report" covers an overview of the study area, data sources and processing methods, environmental factor change patterns, core conclusions, and engineering recommendations (such as roadbed seepage prevention measures for water body expansion areas and sand control and stabilization schemes for drought-intensified areas). For data export, it supports exporting pre-processed data (such as 30m resolution LST and NDVI raster data), environmental index calculation results (such as annual TVDI statistical tables), and vector boundary data (such as study area range and water body vectors) in common formats (GeoTIFF, CSV, Shapefile) for secondary applications in highway engineering design, ecological assessment, and other work.

[0054] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions, characterized in that, Includes the following steps: Step S1: Create a buffer and acquire multi-source remote sensing data and spatiotemporal auxiliary data. The multi-source remote sensing data includes surface water data, surface temperature data and vegetation index data. The spatiotemporal auxiliary data includes terrain data and highway basic data. Step S2: Standardize the data obtained in step S1; Step S3: Based on the multi-source data preprocessed in step S2, calculate the environmental index, surface water shape index SI, and fractal dimension FD, and then perform GIS operations to obtain the annual characteristics of environmental factors in the study area. The environmental index includes surface temperature LST, normalized difference vegetation index NDVI, temperature-vegetation-drought index TVDI, surface water area SSA, and surface water quantity SSN. Step S4: Based on the environmental indices obtained in step S3, analyze them from two dimensions: time trend and spatial distribution. The time trend analysis includes the trend slope and time variability of each environmental index, and the spatial distribution analysis includes buffer analysis, regional hierarchical analysis, and terrain correlation analysis. Step S5: Visualize and export the analysis results from step S4.

2. The long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in claim 1, characterized in that, In step S1, the surface water data uses the JRC Monthly Water History dataset, the surface temperature data and vegetation index data use the MODIS series dataset, and the topographic data uses SRTM digital elevation model data.

3. The long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in claim 1, characterized in that, In step S2, the standardization process includes radiometric calibration and atmospheric correction, spatial registration and resampling, data pruning and stitching, time series standardization, and accuracy optimization.

4. The long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in claim 1, characterized in that, In step S3, the GIS operation includes classification, computational geometry, and calculated fields.

5. The long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in claim 1, characterized in that, In step S3, the method for extracting the surface temperature (LST) is as follows: after extracting the LST from the standardized MODIS series dataset, elevation correction is performed based on the terrain data.

6. The long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in claim 1, characterized in that, In step S3, the method for extracting the Normalized Difference Vegetation Index (NDVI) is as follows: NDVI bands were extracted from the preprocessed MODIS series datasets, and then the Normalized Difference Vegetation Index (NDVI) was calculated accordingly. ； in, For near-infrared reflectivity, The reflectance is in the red light band, and the normalized vegetation index (NDVI) ranges from -1 to 1.

7. The long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in claim 1, characterized in that, In step S3, the method for extracting the Temperature Vegetation Drought Index (TVDI) is as follows: First, construct the NDVI-LST feature space and calculate the dry boundary. Calculate the wet boundary ,in, For the maximum LST, To be the minimum LST, a , b For wet boundary coefficient, c , d The dry boundary coefficient is used as the basis for calculating the Temperature-Vegetation Aridity Index (TVDI). The value of TVDI ranges from 0 to 1.

8. The long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in claim 1, characterized in that, In step S3, the method for extracting the surface water area SSA and the surface water quantity SSN is as follows: first, water bodies are extracted from the JRC Monthly WaterHistory data, and the extracted water body raster data is vectorized; then, each vectorized water body is traversed to calculate the surface water area SSA, the surface water quantity SSN, and the water body perimeter. The formula for calculating the surface water shape index (SI) is: The formula for calculating the fractal dimension FD is: ,in P The circumference of the water body A The area of ​​the water body.

9. The long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in claim 1, characterized in that, In step S4, the trend slope The calculation formula is: time variability The calculation formula is: ,in For the year, Average year For statistical years, For each environmental index, the first Year The value of a pixel for The average value of a pixel over time.

10. An analysis system for implementing the long-term time-series analysis method for the surface environment surrounding highways in high-altitude cold regions as described in any one of claims 1 to 9, characterized in that, It includes a data acquisition module, a preprocessing module, an environmental index calculation module, a spatiotemporal analysis module, and a result visualization and output module. The data acquisition module performs step S1, the preprocessing module performs step S2, the environmental index calculation module performs step S3, the spatiotemporal analysis module performs step S4, and the result visualization and output module performs step S5.

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