Urban river water sensitive landscape reconstruction demand identification method based on GIS

Abstract

The invention relates to a GIS (Geographic Information System)-based urban river water sensitive landscape reconstruction requirement identification method. The method comprises the following steps: firstly, acquiring and preprocessing an urban river vector, a digital elevation model and earth surface coverage data; the method comprises the following steps of: linearly segmenting river channel vector data into river section analysis units, and constructing corresponding shore zone analysis units by expanding a distance to a landside by taking a river channel center line or a shoreline as a reference; and then extracting a multi-dimensional spatial feature factor in each bank zone unit, associating the multi-dimensional spatial feature factor to a corresponding river reach unit, performing direction uniformization and standardization processing on a factor numerical value, and calculating a water-sensitive landscape reconstruction demand index of each river reach according to the direction uniformization and standardization processing. And finally, grading all the river reach units according to the indexes, and outputting a spatial distribution result and a transformation demand grade. Compared with the prior art, the method has the advantages of solving the problem of data acquisition, being high in implementation in the planning stage and the like.

Description

Technical Field

[0001] This invention relates to the field of urban landscape renovation technology, and in particular to a GIS-based method for identifying the needs of urban river water-sensitive landscape renovation. Background Technology

[0002] Urban waterways are crucial carriers of urban stormwater runoff and aquatic ecosystems, with their riverbanks serving multiple functions including drainage safety, water environment improvement, ecological habitat, and public activities. With increasing urbanization, many urban waterways are experiencing problems such as high proportions of hardened riverbanks, large areas of impermeable surfaces, rapid runoff inflow, insufficient ecological buffer zones, fragmented green spaces, and poor accessibility and continuity of public spaces. These issues lead to increased peak runoff during the rainy season, higher overflow risks, and a decline in the water body's self-purification capacity and ecosystem services, further hindering the improvement of waterway landscape quality and the effectiveness of water environment management.

[0003] Water Sensitive Urban Design (WSUD) emphasizes a combined approach of source reduction, process regulation, and end-of-pipe treatment. It enhances urban stormwater management and the resilience of water systems through landscaping measures such as green spaces, wetlands, bioretention, infiltration, and water storage, while also considering ecological and aesthetic value. Therefore, in urban river landscape renewal, identifying river sections in urgent need of water-sensitive landscape transformation, clarifying the spatial differences in transformation needs, and formulating priority outcomes for decision-making are crucial prerequisites for implementing the WSUD concept.

[0004] In existing technologies, one type of method focuses on the design of river flood control and drainage or water quality management projects, usually relying on field surveys, monitoring data, or engineering parameters, making it difficult to achieve rapid screening during the planning stage. Another type of method uses Geographic Information Systems (GIS) to conduct spatial analysis of flood risk, water system connectivity, or land use, but it is mostly oriented towards watershed-scale or urban area risk assessment. The output results often remain at the level of risk distribution or general planning recommendations, lacking a dedicated identification process and indicator system for "water-sensitive landscape transformation needs," especially lacking a demand classification method that is applicable to river segments and riparian units, can be carried out under conditions of publicly available spatial data, and is repeatable. Furthermore, existing methods often suffer from problems in multi-source spatial factor fusion, such as inconsistent indicator definitions, highly subjective thresholds and weights, and difficulty in forming standardized output results, leading to insufficient comparability and generalizability of the results.

[0005] In existing technologies, such as the invention patent with publication number CN117875564A, a method for analyzing the source and sink of surface runoff in urban three-dimensional landscapes is disclosed. This method acquires point cloud data, meteorological and hydrological data, and geospatial data to extract three-dimensional indicators of vegetation and buildings, and uses a hydrological model to perform rainfall-runoff simulation analysis, thereby obtaining the surface runoff depth and source-sink inflection points. However, this method has the following significant shortcomings when practically applied to river landscape renovation planning: The method faces challenges due to its high data acquisition threshold and reliance on complex models: it fundamentally depends on costly point cloud data and detailed meteorological and hydrological data to drive the hydrological model. In the early screening phase of urban river planning, high-precision point cloud data is often lacking, and constructing and calibrating hydrological models is time-consuming and labor-intensive, making rapid and low-cost assessments difficult.

[0006] This method focuses on physical processes while neglecting comprehensive transformation needs. It emphasizes the analysis of physical hydrological mechanisms such as surface runoff response and source-sink inflection points, primarily addressing runoff simulation issues. However, it fails to comprehensively consider the degree of shoreline hardening, social sensitivity, and existing landscape utilization conditions. Consequently, its results can only reflect water flow conditions and cannot directly deduce the urgency and feasibility of landscape transformation.

[0007] The evaluation objects are not suitable for planning management: the output results are mostly technical parameters based on grids or three-dimensional points, lacking physical evaluation of river segments or shoreline units, making it difficult for planners to directly determine the specific river sections for engineering implementation based on the results.

[0008] In addition, existing evaluation methods often suffer from problems such as inconsistent indicator definitions, highly subjective thresholds and weights, and lack of standardized processing procedures in the fusion of multi-source spatial factors. This leads to poor consistency of analysis results across different regions and makes it difficult to conduct horizontal comparisons and promote their application.

[0009] In summary, current methods for identifying the need for water-sensitive landscape renovation in urban waterways suffer from high data acquisition thresholds and complex calculations, making rapid implementation difficult during the planning and decision-making and preliminary screening stages. Furthermore, existing technologies often lack a clear scale for evaluating objects or focus solely on simulating hydrophysical processes, failing to consider hardened, ecological, and social characteristics, and thus cannot directly generate a priority renovation list that is easy to manage and design. In addition, the existing evaluation system lacks standardized processing and unified dimensions for multi-source spatial factors, resulting in poor consistency and low comparability of analysis results. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a GIS-based method for identifying the needs of urban river water-sensitive landscape renovation.

[0011] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a GIS-based method for identifying the need for urban river water-sensitive landscape renovation is provided, the method comprising the following steps: S1. Acquire and preprocess urban river vector data, digital elevation model data and land cover data of the area to be analyzed; S2. Perform linear segmentation on the preprocessed urban river vector data to obtain multiple river segment analysis units; for each river segment analysis unit, extend a preset distance to the landward side based on the river centerline or shoreline to construct the corresponding shoreline analysis unit. S3. Based on the preprocessed digital elevation model data and land cover data, extract a set of spatial feature factors containing multiple spatial feature factors in each of the aforementioned shoreline analysis units, and associate the values ​​of each extracted spatial feature factor with the corresponding river segment analysis unit. S4. Perform directional consistency and standardization processing on the values ​​of each spatial feature factor in the spatial feature factor set; and calculate the water-sensitive landscape transformation demand index value corresponding to each river section analysis unit based on the standardized spatial feature factor set. S5. Classify all river segment analysis units according to the demand index value, and obtain and output the spatial distribution results and water-sensitive landscape renovation demand levels of each river segment analysis unit.

[0012] As a preferred technical solution, the preprocessing in S1 includes coordinate system unification, spatial registration, and resolution consistency processing.

[0013] As a preferred technical solution, in S2, the specific methods of segmentation include: dividing the river centerline into equal segments according to a preset length; or, identifying preset nodes and segmenting the river using the preset nodes as boundaries; the preset nodes include bridges, sluice gates, tributary confluences, or abrupt changes in shoreline morphology on the river.

[0014] As a preferred technical solution, the construction method of the shoreline analysis unit in S2 includes: setting a fixed preset distance and generating a buffer zone of fixed width based on the fixed preset distance; or, determining a graded preset distance according to the river level, surrounding land use type or riverside road boundary conditions, and generating a buffer zone of preset graded width based on the graded preset distance. After generating the buffer, the water body layer is used to clip the buffer to remove the water area, resulting in the final shoreline analysis unit.

[0015] As a preferred technical solution, the spatial characteristic factors in S3 include the following three categories: shoreline hardening degree factors extracted based on land cover data, ecological or landscape spatial condition factors extracted based on land cover data, and stormwater pressure factors or runoff potential factors obtained based on digital elevation model data analysis.

[0016] As a preferred technical solution, the shoreline hardening factor includes at least one of the following: the proportion of impermeable shoreline surface, the proportion of building and road coverage, and the proportion of hard shoreline.

[0017] The extraction method for the coastal hardening degree factor is as follows: Utilize land cover data to statistically determine the proportion of impervious surface area within the coastal analysis unit; or... Calculate the coverage ratio of hard infrastructure or the length ratio of hard shoreline within the shoreline analysis unit using building and road vector data.

[0018] As a preferred technical solution, ecological or landscape spatial condition factors include at least one of the following: the proportion of riparian green space, vegetation coverage or vegetation index, green patch connectivity index, and the proportion of open space.

[0019] The extraction methods for ecological or landscape spatial condition factors are as follows: the Normalized Difference Vegetation Index (NDVI) is calculated based on multispectral remote sensing images, and the mean NDVI value within the shoreline analysis unit is statistically analyzed; or, the proportion of green area, green patch connectivity index, or proportion of open space within the shoreline analysis unit is statistically analyzed based on green space vector data.

[0020] As a preferred technical solution, the stormwater pressure factor or runoff potential factor is obtained by performing hydro-topographic analysis on digital elevation model data; the hydro-topographic analysis includes at least one of depression filling calculation, flow direction calculation, runoff accumulation calculation and slope calculation.

[0021] As a preferred technical solution, the spatial feature factors also include socially sensitive exposure factors; the socially sensitive exposure factors are extracted by: statistically analyzing the number, density, or weighted density of Points of Interest (POIs) within or near a preset range of the shoreline analysis unit, wherein the POIs include schools, hospitals, residential areas, and elderly care facilities.

[0022] As a preferred technical solution, in S4, the direction consistency processing specifically includes: If an increase in the value of a certain spatial characteristic factor indicates an increase in the need for transformation, then that factor is set as a positive factor. Conversely, if the value of a spatial characteristic factor increases, indicating improved conditions or reduced need for modification, then the factor is set as a reverse factor, and the reverse factor is reversed to convert it into a positive indicator. The standardization process employs at least one of range standardization, Z-score standardization, or quantile standardization.

[0023] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention utilizes publicly available or readily available urban river vector data, digital elevation model data, and land cover data, along with GIS spatial analysis techniques. This allows the solution to identify and classify renovation needs without relying on on-site measured hydrological and water quality monitoring data, making it highly feasible in the planning stage. It solves the problem of existing methods relying on high-cost monitoring data and the difficulty in quickly conducting screening in the early planning stages, making it particularly suitable for planning decision-making and preliminary screening.

[0024] 2. In this invention, river segmentation and shoreline analysis units are used as the basic objects. By using equidistant or nodal segmentation methods that conform to engineering practice, the water area is eliminated to accurately locate the land shoreline space. The invention outputs the river segment-level demand index and grade results, which can directly form a list of priority river segments for renovation and a spatial distribution layer. This makes it easy for management departments and designers to use. The evaluation objects are clear, the spatial positioning is accurate, and the results are directly usable. This invention solves the defects of existing technologies, such as excessively large evaluation scales and inability to guide specific shoreline design.

[0025] 3. In this invention, multi-source data errors are eliminated by coordinate unification and spatial registration, and spatial factors of different sources and dimensions are made comparable by index direction consistency and standardization, thereby improving the consistency and generalizability of cross-regional and cross-river section analysis results. Standardization eliminates data differences, making the results comparable and ensuring the mathematical rigor of the evaluation model.

[0026] 4. In this invention, the spatial characteristic factors also include the stormwater pressure factor or the confluence potential factor obtained from the analysis of digital elevation model data. By analyzing the hydro-topography of the digital elevation model data, waterlogging hazard points can be identified without constructing a complex hydrodynamic model. This method obtains key hydro-topographic features at low cost, effectively making up for the stormwater risk assessment link that is often neglected in landscape planning.

[0027] 5. This invention introduces factors such as the degree of hardening of the shoreline and ecological or landscape spatial conditions. The degree of hardening of the shoreline is used to quantify the proportion of impermeable shoreline, and the ecological or landscape spatial conditions are used to quantify the green space conditions. This accurately identifies areas that have both runoff pressure and potential for landscape improvement. By comprehensively considering hardening and ecological conditions, the identification results can better serve the decision-making on water-sensitive landscape transformation and subsequent measures, achieving a synergy between safety and ecological value.

[0028] 6. The method and process in this invention can be implemented in existing GIS platforms, making it easy to integrate and expand. Furthermore, the invention introduces socially sensitive exposure factors (such as POIs for schools and hospitals), allowing for the expansion of socially sensitive exposure factor or measure type label output based on data conditions. This ensures that the determination of transformation priorities is based not only on natural conditions but also on social disaster prevention needs, thus possessing significant engineering promotion value. Attached Figure Description

[0029] Figure 1 This is a schematic diagram illustrating the steps of a GIS-based method for identifying urban river water-sensitive landscape renovation needs in this invention. Figure 2 This is a flowchart illustrating the specific implementation process for identifying the needs of urban river water-sensitive landscape renovation in the example. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] To address the challenges of quickly and objectively identifying urban river water-sensitive landscape modification targets during the planning stage in existing technologies, and the fact that current GIS analysis methods often focus on flood risk or general water system planning while lacking a standardized demand identification process for river segments and shoreline units, this solution provides a GIS-based method for identifying urban river water-sensitive landscape modification needs. This method utilizes publicly available or accessible multi-source spatial data, including topographic, remote sensing, land use, and built environment data. Through river segmentation, shoreline unit construction, spatial feature factor extraction, standardization, and comprehensive calculation, it identifies and categorizes urban river water-sensitive landscape modification needs, providing a technical basis for subsequent water-sensitive landscape design and renewal decisions.

[0032] This method acquires spatial data such as river channels / shorelines, DEMs, remote sensing imagery, or land use / cover. After coordinate unification and preprocessing on a GIS platform, it segments small and medium-sized rivers and constructs shoreline analysis units. Within these units, spatial characteristic factors reflecting the degree of shoreline hardening, stormwater pressure or runoff potential, and ecological / landscape spatial conditions are extracted. These factors are then oriented and standardized to construct and calculate a water-sensitive landscape renovation demand index. Based on this demand index, each river segment is classified, and the renovation demand level and its spatial distribution are visualized. This method utilizes publicly available or accessible multi-source spatial data, including topographic, remote sensing, land use, and built environment data, to segment and analyze river segments and their shorelines, extract characteristic factors, and make comprehensive judgments without relying on field measurements. It outputs renovation demand levels and spatial distribution results, providing objective and feasible technical support for subsequent water-sensitive landscape design and renewal decisions. This method does not rely on field measurement data and is suitable for preliminary screening and decision support in urban-scale river landscape renewal and water-sensitive urban design.

[0033] Example 1 In this embodiment, a GIS-based method for identifying urban river water-sensitive landscape renovation needs is adopted, characterized by the following steps: S1. Acquire and preprocess urban river vector data, digital elevation model data and land cover data of the area to be analyzed; S2. Perform linear segmentation on the preprocessed urban river vector data to obtain multiple river segment analysis units; for each river segment analysis unit, extend a preset distance to the landward side based on the river centerline or shoreline to construct the corresponding shoreline analysis unit. S3. Based on the preprocessed digital elevation model data and land cover data, extract a set of spatial feature factors containing multiple spatial feature factors in each of the aforementioned shoreline analysis units, and associate the values ​​of each extracted spatial feature factor with the corresponding river segment analysis unit. S4. Perform directional consistency and standardization processing on the values ​​of each spatial feature factor in the spatial feature factor set; and calculate the water-sensitive landscape transformation demand index value corresponding to each river section analysis unit based on the standardized spatial feature factor set. S5. Classify all river segment analysis units according to the demand index value, and obtain and output the spatial distribution results and water-sensitive landscape renovation demand levels of each river segment analysis unit.

[0034] Data Acquisition and Preprocessing: Acquire spatial data of urban small and medium-sized rivers in the area to be analyzed, as well as multi-source GIS data for spatial analysis; the multi-source GIS data includes at least Digital Elevation Model (DEM) data and remote sensing imagery or land use / cover data, and may optionally include road, building, green space / open space and point of interest (POI) data; perform coordinate system unification, spatial registration, data clipping and resolution consistency processing on the above data in the GIS platform.

[0035] River segmentation processing: The urban small and medium-sized rivers are segmented to obtain multiple river segment analysis units; the segmentation processing can be based on equal intervals of preset length, or segmentation based on at least one node among bridges, sluice gates, tributary confluences, and abrupt changes in shoreline morphology.

[0036] Shoreline analysis unit structure: A shoreline analysis unit is constructed for each river segment analysis unit. The shoreline analysis unit is a buffer analysis area formed by extending from the river segment analysis unit to the land side or both sides. The shoreline buffer width is a fixed width or a graded variable width. The graded variable width is determined according to the river class, surrounding land use type or road boundary conditions. The effective range of the shoreline analysis unit can be effectively trimmed to remove water surface or unusable areas.

[0037] Spatial Feature Factor Extraction: Within the shoreline analysis unit, a set of spatial feature factors is extracted to characterize the needs for water-sensitive landscape modification. This set of spatial feature factors includes at least the following: Shoreline hardening factor: used to reflect at least one of the following: the proportion of impermeable shoreline, the proportion of building and road coverage, and the proportion of hard shoreline. Rainfall pressure or runoff potential factor: obtained from hydrogeomorphic analysis based on DEM, wherein the hydrogeomorphic analysis includes at least one or more of the following: depression filling, flow direction calculation, runoff accumulation calculation, and slope calculation; Ecological or landscape spatial condition factors: used to reflect at least one of the following: proportion of riparian green space, vegetation coverage or vegetation index, green patch connectivity index, and proportion of open space. It can also optionally extract socially sensitive exposure factors, which are one of the number, density, or weighted density of points of interest (POIs) such as schools, hospitals, residential areas, and elderly care facilities within or near the shoreline analysis unit.

[0038] Indicator processing and standardization: The set of spatial feature factors is subjected to directional consistency processing and standardization processing; directional consistency processing includes: setting factors that "increase in value indicates increase in transformation demand" as positive factors, and reversing factors that "increase in value indicates improvement in conditions or decrease in transformation demand"; standardization processing adopts at least one of range standardization, Z-score standardization or quantile standardization.

[0039] Construction and Calculation of the Transformation Demand Index: A water-sensitive landscape transformation demand index is constructed based on a standardized set of spatial characteristic factors, and the index value corresponding to each river segment analysis unit is calculated. The set of spatial characteristic factors includes at least: indicators characterizing the degree of bank hardening, indicators characterizing stormwater pressure or runoff potential, and indicators characterizing ecological or landscape spatial conditions, and optionally includes indicators characterizing the degree of social sensitivity exposure. The transformation demand index is obtained by weighted summation or multi-indicator fusion of the standardized values ​​corresponding to the above indicators. Specifically, this involves weighted summation of the standardized values ​​of the bank hardening degree indicator, the standardized values ​​of the stormwater pressure or runoff potential indicator, the standardized values ​​of the ecological or landscape spatial condition indicator (and optionally the standardized values ​​of the social sensitivity exposure indicator), or by synthesizing the above indicators through a multi-indicator fusion model to obtain the transformation demand index. The weights are determined by the entropy weight method, or by a combination of the entropy weight method and preset weights. Demand Classification and Result Output: Based on the water-sensitive landscape renovation demand index value, the water-sensitive landscape renovation demand level of each river segment analysis unit is obtained; the classification adopts at least one of natural breakpoint classification, equidistant classification, quantile classification or cluster classification, and the demand level is divided into at least three levels; the demand level and its spatial distribution result are output, and the spatial distribution result is output in at least one of the following forms: river segment vector layer, raster layer, statistical table or list, and each river segment records its corresponding renovation demand index value and demand level.

[0040] This method can be implemented on a GIS (Geographic Information System) platform, preferably in the Chinese version of ArcGIS, through its data management, spatial analysis, and cartographic output modules.

[0041] like Figure 2 As shown, the overall process of this scheme includes: input data preparation, data acquisition and preprocessing, river segmentation processing, construction of shoreline analysis units, extraction of spatial feature factors, index processing and standardization, construction and calculation of transformation demand index, demand classification, and result output and visualization.

[0042] In this embodiment, the data used is publicly available data or data obtained with authorization, and all of it can be converted into data formats recognizable by ArcGIS (such as Shapefile, FileGDB, GeoTIFF, etc.). To ensure the operability and stability of the spatial analysis results, it is recommended that the data accuracy meet the following requirements (which are not limitations): (1) River / water system vector data: including river centerline or shoreline features, with a recommended location accuracy of no less than 1:25000; can be obtained from public geographic data (such as open map water system features) or local data open platforms.

[0043] (2) Digital Elevation Model (DEM): The recommended resolution is 10–30m (e.g., 30m DEM or higher precision DEM); used for hydro-topographic derivation calculations.

[0044] (3) Remote sensing images or land use / cover data: The recommended resolution is 10–30m; used to extract indicators related to impervious surfaces, vegetation cover or green spaces.

[0045] (4) Road and building vector data: used for extracting indicators related to the degree of hardening of the shoreline; publicly available road and building outline data can be used.

[0046] (5) Green space / open space vector data (optional): used for extracting ecological / landscape space condition indicators; can be obtained from public green space, land use or surface cover products.

[0047] (6) POI data (optional): used for extracting socially sensitive exposure factors; publicly available point of interest data such as schools, hospitals, and elderly care facilities can be used.

[0048] After creating a project in the Chinese version of ArcGIS, load the above data into the same map project and perform the following preprocessing operations: (1) Unified coordinate system: Unify vector and raster data to the same projected coordinate system (projected coordinate system in meters). Vector projection conversion can be completed in "Data Management Tools, Projection and Transformation, Projection"; raster projection can be completed using "Data Management Tools, Raster, Raster Projection" or equivalent tools.

[0049] (2) Cropping the research scope: Using administrative boundaries or study area boundaries as cropping masks, crop the DEM and remote sensing images / land cover rasters respectively. You can use "data management tools, raster, raster processing, cropping"; for vector layers, you can use "analysis tools, extraction, cropping".

[0050] (3) Unify raster resolution and alignment: If the resolution of the DEM and the remote sensing image are different, use "Data Management Tools, Raster, Raster Processing, Resampling" to unify the raster to the preset cell size. At the same time, set environmental parameters such as "Processing Range, Cell Size, Align Raster, Mask" in "Geographic Processing Environment" to ensure the consistency of subsequent overlay and statistics.

[0051] (4) Vector cleaning and topology check: Check the river line elements for breaks, overlaps, pseudo nodes, etc., to ensure that subsequent segmentation and buffer construction can be executed smoothly; if necessary, “repair geometry” and “merge / delete duplicates” can be processed.

[0052] correspond Figure 1 The "S2 River Channel Segmentation Processing" is described in this embodiment. This embodiment provides a stable and reproducible method for river channel segmentation in ArcGIS: first, segmentation points are generated along the line, and then the river line is divided according to these points.

[0053] (1) Generate segment points along the line: Generate point features along the river centerline according to a preset segment length L, for example, 100–500m, preferably 200m. You can use “Data Management Tools, Feature Class, Generate Points along the Line” and set the “Distance” to L.

[0054] (2) Divide the river line by point: Use “Data Management Tools, Feature Class, Divide by Point” to divide the river centerline into multiple river segment analysis units.

[0055] (3) River segment numbering and attribute organization: Add a field to the attribute table of the segmented river segment layer and use the "field calculator" to assign a unique number to each river segment so that subsequent statistical results can be backfilled and visualized.

[0056] correspond Figure 2 The construction of shoreline analysis units. In the Chinese version of ArcGIS, a shoreline buffer zone is constructed as a spatial analysis unit for each river segment.

[0057] (1) Generate shoreline buffer zone: Use “Analysis Tools, Proximity Analysis, Buffer Zone”, input the river segment layer, and set the buffer distance to W (e.g., 10–50m, preferably 30m). To ensure that each river segment is statistically analyzed independently, it is preferable to set the “Dissolution Type” to “Non-dissolution”.

[0058] (2) Trimming the effective shoreline range (optional): If it is necessary to exclude water surfaces or unusable areas, the buffer zone, water surface, building surface, etc. can be erased / trimmed to form the final shoreline analysis unit. This can be achieved using "Analysis Tools, Overlay Analysis, Erasure" or "Analysis Tools, Extraction, Trimming".

[0059] correspond Figure 2Spatial feature factor extraction. This embodiment uses the shoreline analysis unit as the partitioning object and extracts at least three types of spatial factors through ArcGIS spatial analysis and partition statistics functions: shoreline hardening degree, stormwater pressure / confluence potential, and ecological / landscape spatial conditions; and optionally extracts socially sensitive exposure factors.

[0060] Rainfall pressure / confluence potential factor: (1) Filling depressions in DEM: Use “Spatial Analysis Tools, Hydrological Analysis, Filling Depressions” to fill depressions in DEM to eliminate the influence of local depressions on flow direction calculation.

[0061] (2) Flow direction calculation: Use "Spatial analysis tools, hydrological analysis, flow direction" to obtain the flow direction grid.

[0062] (3) Confluence accumulation calculation: Use "Spatial Analysis Tools, Hydrological Analysis, Confluence Accumulation" to obtain the confluence accumulation grid, which is used to represent the spatial proxy of topographic confluence potential.

[0063] (4) Slope calculation: Use “Spatial analysis tools, surface analysis, slope” to obtain a slope grid, which is used to characterize the runoff generation and confluence conditions of the slope.

[0064] (5) Zonal statistics: Using “Spatial Analysis Tools, Zonal, Zonal Statistics Table”, the “slope grid” and “current accumulation grid” are statistically analyzed using the shoreline analysis unit as the zonal data, and the statistics table is output and backfilled into the attribute table of the shoreline analysis unit.

[0065] Factors affecting the degree of coastal hardening: This embodiment provides two possible implementation methods, which can be selected or used in combination. Method 1: Based on land use / cover raster: Reclassify the corresponding categories such as "impermeable / construction land / road and building" in the land use / cover raster to 1, and the rest to 0. This can be achieved using "Spatial Analysis Tools, Reclassification" or "Raster Calculator". Then, generate a zonal statistical table based on the shoreline analysis unit to obtain the proportion of impermeable pixels or the total amount of impermeable material within the shoreline, and convert it to an impermeable ratio based on the shoreline area.

[0066] Method 2: Based on road and building vectors: Intersect / trim road, building, and other vectors with the shoreline analysis unit (e.g., using analysis tools, overlay analysis, intersection), calculate the area of ​​hard features after intersection, and then calculate the hard cover ratio by comparing it with the area of ​​the shoreline analysis unit. Area calculation can be achieved by adding an area field to the attribute table and using computational geometry or the field calculator.

[0067] Landscape spatial condition factors: (1) Green space ratio: If there is a green space vector, the proportion of green space area can be calculated after the intersection of the green ground and the shoreline analysis unit; if there is a land cover grid, the green space category can be reclassified as 1 and the rest as 0, and then the green space ratio can be obtained by performing zoning statistics.

[0068] (2) Vegetation index: If multispectral images are used, vegetation index, such as NDVI, can be calculated by a raster calculator. Then, the average NDVI can be obtained by performing zonal statistics on the riparian analysis unit as a proxy indicator for vegetation coverage.

[0069] Socially sensitive exposure factors: If POI data is used, the number or density of sensitive points can be counted within the shoreline analysis unit. The "Analysis Tools, Overlay Analysis, Spatial Connection" function can be used to aggregate POI points into the shoreline analysis unit according to their spatial inclusion relationships, obtaining POI counts or density indices normalized by area.

[0070] correspond Figure 2 This embodiment describes the processing, standardization, and transformation of indicators, as well as the construction and calculation of the demand index. The indicator processing and index calculation are performed at the ArcGIS attribute table level, primarily through adding new fields and a field calculator.

[0071] Specifically, the numerical objects involved in weighted superposition or fusion calculations include the following categories: Standardized values ​​for shoreline hardening factors: For example, the standardized value of the "percentage of impermeable surface in the shoreline". The higher this value, the more severe the shoreline hardening, and the greater the need for remediation. This factor is used to quantify the degree of shoreline hardening and typically includes the percentage of impermeable surface, the percentage of building and road coverage, and the percentage of hard shoreline. Standardized values ​​for stormwater pressure or runoff potential factors: such as standardized values ​​for "runoff accumulation" or "slope". The larger the value, the greater the stormwater risk or runoff pressure, and the higher the need for improvement. This factor is derived from hydro-topographic analysis of digital elevation model data (e.g., calculations of runoff accumulation, flow direction, slope, etc.), reflecting the region's catchment capacity and stormwater risk. Standardized values ​​of ecological or landscape spatial condition factors (after reverse processing): For example, the "percentage of green space along the shoreline" or "mean NDVI" values ​​after reverse and standardization processing. The smaller the original value, the worse the ecological base and the higher the need for restoration. This factor is mainly used to assess the ecological environment status of the shoreline and usually includes the percentage of green space, vegetation cover (such as NDVI), and green patch connectivity. Standardized values ​​for socially sensitive exposure factors (optional): For example, the standardized value of "sensitivity point (POI) density". The higher the value, the more people are affected by the disaster and the greater the need for renovation. This factor is used to quantify the density of socially sensitive points (such as schools, hospitals, and elderly care facilities) within the shoreline, reflecting the vulnerability of the population to flooding or water environment deterioration.

[0072] The specific process of weighted superposition / calculation: (1) Directional Consistency Processing: In the indicator fields, indicators that "indicate a higher demand for renovation" are treated as positive indicators, such as impermeability, slope, and cumulative runoff statistics; indicators that "indicate better conditions and lower demand for renovation" are treated as negative indicators, such as green space ratio and average NDVI. Negative indicators can be processed by textual description to take their complementary or reverse values, so that they are consistent with the direction of demand.

[0073] (2) Standardization: Standardize the values ​​of each factor to make different factors have the same dimensions and comparability. Common standardization methods include: range standardization: convert the value of each factor into a proportion relative to the maximum and minimum values ​​to obtain a value in the range of [0, 1]; Z-score standardization: subtract the mean from the value of each factor and then divide by the standard deviation to obtain the standardized factor value; quantile standardization: convert the value into quantile representation and standardize it according to the percentage range of the data.

[0074] (3) Weight Calculation: Preset Weights: Based on expert experience or engineering practice, the contribution of each factor is subjectively set, and appropriate weight values ​​are assigned. Entropy Weight Method: By calculating the uncertainty (entropy value) of each factor's data, the weight of each factor is obtained. The larger the entropy value, the lower the weight, indicating that the factor contributes less to the demand index. Construction m Analysis units for each river section n The evaluation matrix of the _n spatial feature factors. Calculate the _n ... i Information entropy of each factor E i ： in, For the first The first section of the river The characteristic weight of each factor. The difference coefficient is calculated based on information entropy. Finally, determine the objective weights. .

[0075] (4) Index calculation: Perform weighted overlay calculation for each river segment analysis unit. Extract the corresponding standardized factor values.Y ij Using the field calculation function or raster calculator of the GIS platform, calculate the water-sensitive landscape modification demand index value for this river section according to the following formula: In the formula: For the first Water-sensitive landscape renovation demand index for each river section analysis unit; The total number of spatial characteristic factors participating in the evaluation; For the first The weights of each factor; Y ij For the first The first section of the river Standardized values ​​on each factor.

[0076] (5) Assigning the result: Assign the calculated result The values ​​are written into the attribute table of each river segment analysis unit, serving as the direct basis for subsequent classification.

[0077] correspond Figure 2 The requirements hierarchy and output results are presented in ArcGIS. This example uses attribute field hierarchy and symbolic representation to obtain visualization results and outputs spatial layers and lists, as detailed below: (1) Demand Classification: Create a new "LEVEL" field in the attribute table to store demand levels. The classification method can be natural breakpoint, quantile, or equal interval classification. To ensure reproducibility, quantile classification can be used: for example, the river section with the demand index in the top 30% can be marked as "high", the middle 40% as "medium", and the bottom 30% as "low". The level assignment can be completed through "select, field calculator assignment".

[0078] (2) Layer symbolization: On the river section layer, open the layer properties → symbol system, select "classification", use the "LEVEL" field as the classification field, and set three types of symbols: "high / medium / low" to realize the spatial visualization of the river section transformation needs level.

[0079] (3) Mapping and export: Overlay the base map, administrative boundary and river section level layers in the layout view (or ArcGIS Pro layout), add legend, scale bar and north arrow, and then export the result map as PNG / PDF, etc.

[0080] (4) Output of results data: Export the river section layer or shoreline analysis unit layer with "demand index and level fields" as Shapefile or FileGDB feature class; at the same time, the attribute table can be exported as CSV / Excel to form a river section priority list, whose fields include at least river section number, demand index, demand level and key factor value.

[0081] In summary, this method utilizes publicly available or readily available multi-source spatial data and GIS spatial analysis techniques to identify and classify renovation needs without relying on on-site measured hydrological and water quality monitoring data, making it suitable for planning decisions and preliminary screening stages. Using river segmentation and bank zone analysis units as basic objects, it outputs river segment-level demand indices and classification results, directly generating a list of priority renovation river segments and a spatial distribution layer, facilitating use by management departments and designers. Through consistency and standardization of indicator directions, spatial factors from different sources and with different dimensions are made comparable, improving the consistency and generalizability of cross-regional and cross-river segment analysis results. Introducing bank zone hardening degree and ecological / landscape spatial condition factors based on stormwater pressure proxy indicators allows the identification results to better serve decisions regarding water-sensitive landscape renovation and subsequent measure configuration.

[0082] Furthermore, this method can be implemented in existing GIS platforms, and the output of socially sensitive exposure factors or measure type labels can be expanded according to data conditions, which has good engineering promotion value.

[0083] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A GIS-based method for identifying urban river water-sensitive landscape modification needs, characterized in that, The method steps include: S1. Acquire and preprocess urban river vector data, digital elevation model data and land cover data of the area to be analyzed; S2. Perform linear segmentation on the preprocessed urban river vector data to obtain multiple river segment analysis units; for each river segment analysis unit, extend a preset distance to the landward side based on the river centerline or shoreline to construct the corresponding shoreline analysis unit. S3. Based on the preprocessed digital elevation model data and land cover data, extract a set of spatial feature factors containing multiple spatial feature factors in each of the aforementioned shoreline analysis units, and associate the values ​​of each extracted spatial feature factor with the corresponding river segment analysis unit. S4. Perform directional consistency and standardization processing on the values ​​of each spatial feature factor in the spatial feature factor set; and calculate the water-sensitive landscape transformation demand index value corresponding to each river section analysis unit based on the standardized spatial feature factor set. S5. Classify all river segment analysis units according to the demand index value, and obtain and output the spatial distribution results and water-sensitive landscape renovation demand levels of each river segment analysis unit.

2. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 1, characterized in that, The preprocessing in S1 includes coordinate system one, spatial registration, and resolution consistency processing.

3. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 1, characterized in that, In S2, the specific methods of segmentation include: dividing the river centerline into equidistant segments according to a preset length; or, identifying preset nodes and segmenting the river using the preset nodes as boundaries; the preset nodes include bridges, sluice gates, tributary confluences, or abrupt changes in shoreline morphology on the river.

4. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 1, characterized in that, The construction methods of the shoreline analysis unit in S2 include: setting a fixed preset distance and generating a buffer zone of fixed width based on the fixed preset distance; or, determining a graded preset distance based on the river level, surrounding land use type or riverside road boundary conditions, and generating a buffer zone of preset graded width based on the graded preset distance. After generating the buffer, the water body layer is used to clip the buffer to remove the water area, resulting in the final shoreline analysis unit.

5. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 1, characterized in that, The spatial characteristic factors in S3 include the following three categories: shoreline hardening degree factors extracted based on land cover data, ecological or landscape spatial condition factors extracted based on land cover data, and stormwater pressure factors or runoff potential factors obtained based on digital elevation model data analysis.

6. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 5, characterized in that, The aforementioned shoreline hardening factor includes at least one of the following: the proportion of impermeable shoreline surface, the proportion of building and road coverage, and the proportion of hard shoreline. The ecological or landscape spatial condition factors mentioned include at least one of the following: the proportion of green space along the shoreline, vegetation coverage or vegetation index, green patch connectivity index, and the proportion of open space.

7. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 5, characterized in that, The aforementioned stormwater pressure factor or runoff potential factor is obtained through hydro-topographic analysis of digital elevation model data; the hydro-topographic analysis includes at least one of depression filling calculation, flow direction calculation, runoff accumulation calculation, and slope calculation.

8. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 5, characterized in that, The spatial characteristic factors also include socially sensitive exposure factors; the socially sensitive exposure factors are extracted by: statistically analyzing the number, density, or weighted density of points of interest (POIs) within or near a preset range of the shoreline analysis unit, where the POIs include schools, hospitals, residential areas, and elderly care facilities.

9. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 1, characterized in that, The specific process of S4 includes: The values ​​of each spatial feature factor in the set of spatial feature factors are subjected to orientation unification and standardization processing, and the standardized values ​​of each spatial feature factor are output. The standardized values ​​of each spatial characteristic factor are weighted and summed, or the standardized values ​​of each spatial characteristic factor are combined through a multi-index fusion model. The result of the weighted summation or combination is the calculated water-sensitive landscape transformation demand index value corresponding to each river section analysis unit.

10. The method for identifying urban river water-sensitive landscape renovation needs based on GIS according to claim 1, characterized in that, In step S4, the direction unification process specifically includes: If an increase in the value of a certain spatial characteristic factor indicates an increase in the need for transformation, then that factor is set as a positive factor. Conversely, if the value of a spatial characteristic factor increases, indicating improved conditions or reduced need for modification, then the factor is set as a reverse factor, and the reverse factor is reversed to convert it into a positive indicator. The standardization process employs at least one of range standardization, Z-score standardization, or quantile standardization.

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