A method for assessing the impact of human activities

Abstract

The present invention discloses a method for assessing the impact of human activities. The present invention relates to the technical field of the intersection of environmental science and geographic information systems, and solves the problem that assessment methods that ignore hydrological processes cannot accurately reflect the actual impact of human activities on river ecosystems. The method includes: acquiring and processing satellite remote sensing images of the corresponding river basin, extracting land use information grids of each part; acquiring a digital elevation model of the study area, determining the water flow path through hydrological analysis, and calculating the actual path length between each grid and the monitoring point along the water flow direction; assigning points to different land use types, and calculating the degree of influence of each land use grid on the monitoring point based on the hydrological distance inverse distance weighted algorithm, and obtaining a human activity impact index. The present invention can more accurately assess the impact of human activities on the basin ecosystem by combining land use type assignment and the hydrological inverse distance weighted algorithm.

Description

Technical Field

[0001] The present invention relates to the cross-technical field of environmental science and geographic information system, and in particular to a method for evaluating the impact of human activities. Background Art

[0002] Assessing the impact of human activities on watersheds is a core issue in environmental management and ecological protection. Current mainstream methods for assessing the impact of human activities on river ecosystems rely primarily on two types of indicators. The first type is land use proportion indicators: these use remote sensing imagery to interpret the proportion of different land cover types within a watershed, such as forest, agriculture, and impervious surfaces, and establish a correlation between these indicators and river health. However, these methods only quantify the composition of land cover and ignore spatial location information, such as the differential impact of upstream agricultural areas and downstream urban areas on monitoring points. This results in an inability to explain significant differences in river conditions with the same land cover proportion. The second type is landscape pattern indices (LCMs), which use spatial configuration indicators from landscape ecology, such as patch number, edge density, and maximum patch index, to describe characteristics such as land cover fragmentation and aggregation. LCMs have been used to explain the impact of spatial heterogeneity in land cover on river water quality, but they have the following limitations: insufficient descriptive power: watersheds with different spatial distributions may have the same LCMs, making it impossible to distinguish actual impact differences; inconsistent results: the relationship between LCMs and river health indicators is contradictory in different studies; and detachment from hydrological processes: LCMs do not consider the hydrological connectivity between land patches and river networks, such as the distance along the water path or the accumulated runoff, and cannot reflect the actual transport pathways of pollutants.

[0003] The core flaw of the above-mentioned methods lies in the lack of spatial weights and hydrological pathways, which limits their ability to accurately assess the impact of human activities. In reality, hydrological processes play a crucial role in watershed ecosystems. They not only determine the direction and velocity of water flow but also directly influence the migration, transformation, and transport pathways of pollutants. Within a watershed, pollutants are transported along flow pathways through surface runoff and subsurface seepage, and the extent of their impact is closely related to distance, topography, and accumulated runoff. For example, upstream agricultural activities may cause pesticides and fertilizers to enter rivers through surface runoff, and the intensity of their impact varies with the length of flow pathways and the increase in accumulated runoff. Furthermore, hydrological processes influence ecosystem connectivity, determining the distribution of biological habitats and the integrity of ecological processes. Therefore, assessment methods that ignore hydrological processes cannot accurately reflect the actual impact of human activities on river ecosystems. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating the impact of human activities, which can more accurately evaluate the impact of human activities represented by land use by introducing hydrological distance weights.

[0005] The present invention provides a method for assessing the impact of human activities, comprising:

[0006] Step 1: Obtain and process satellite remote sensing images of the corresponding river basin and extract land use information grids of each part;

[0007] Step 2: Obtain a digital elevation model of the study area, determine the water flow path through hydrological analysis, and calculate the actual path length along the water flow direction between each grid and the monitoring point;

[0008] Step three: assign points to different land use types, calculate the impact of each land use grid on the monitoring point based on the hydrological distance inverse distance weighted algorithm, and obtain the human activity impact index.

[0009] Furthermore, step one includes:

[0010] Obtain Landsat ETM+ remote sensing image data of the study basin from relevant data sources;

[0011] Preprocess remote sensing image data;

[0012] According to the actual situation of the study basin, the land use of the basin is divided into nine land use types: farmland, forest, shrub, grassland, water area, ice and snow, bare land, impervious surface, and wetland;

[0013] The maximum likelihood method is used to supervise the classification of remote sensing images. The overlay rule is selected as the parameter rule, the parameter rule is the maximum likelihood, the total number of pixels is used as the evaluation output statistic, and the image is processed after classification. The Kappa analysis method is used to verify the results to obtain the final classification results.

[0014] Furthermore, the remote sensing image data is preprocessed, including:

[0015] 40-50 ground points are selected on the topographic map as ground control points, and the ground control points are evenly distributed on the remote sensing image. The quadratic polynomial and cubic convolution interpolation method are used to perform geometric correction on the image. The formula is as follows:

[0016]

[0017]

[0018] in, X , Y is the pixel coordinate on the image to be corrected; F x , F y is the resampling correction distortion function; u , vThe position of each pixel point to be input in the correction function image space; a ij , b ij are the coefficients to be determined, which are the coefficients of the polynomial solved by the least squares method using the coordinates of the image to be corrected and the coordinates in the reference coordinate system of the ground control points; n is the order of the polynomial;

[0019] Select the best band and calculate the best index using the following formula OIF :

[0020]

[0021] in, OIF is the best index; S i For the i The standard deviation of each band; R ij is the correlation coefficient of the two bands. The larger the index, the greater the amount of information contained in the corresponding combined image. The best index OIF is sorted from large to small, and the band combination corresponding to the largest OIF is the best band combination.

[0022] The Landsat ETM+ panchromatic and multispectral bands of the study basin were fused to improve the spatial resolution and spectral information of the imagery;

[0023] Use satellite images that have undergone geometric correction and image fusion processing as reference images to complete geographic coordinate matching and edge matching of other remote sensing images;

[0024] According to the boundary of the study area, the remote sensing image is masked and the regular cropping of the remote sensing image of the study basin is completed to obtain the remote sensing image of the basin.

[0025] Furthermore, step 2 includes:

[0026] Use ArcGIS Depression Fill tool to process digital elevation model data and eliminate depressions in the terrain;

[0027] Using the ArcGIS water flow direction tool, based on the digital elevation model data after filling, the D8 algorithm is used to determine the flow direction of the water and calculate the flow direction of each grid;

[0028] Use the ArcGIS Flow Accumulation tool to calculate the cumulative flow along the flow path based on the flow direction data and obtain the cumulative flow for each grid.

[0029] Extract the river network based on the accumulated water flow data, set a flow threshold, define the grid with accumulated flow greater than the threshold as a river, and use the ArcGIS Raster Calculator tool to generate river network raster data;

[0030] Determine the location of monitoring points and divide the watershed of each monitoring point based on the water flow direction grid and the location of the monitoring point;

[0031] The ArcGIS flow distance tool was used to calculate the hydrological distance from each grid cell in the watershed to the monitoring point.

[0032] Furthermore, step three includes:

[0033] Use ArcGIS reclassification tools to assign points to each land use raster and generate a scored land use raster;

[0034] The hydrological distance weight from each grid to the monitoring point is calculated using the inverse distance weighted formula:

[0035]

[0036] in, W i It is i The hydrological distance weight from the land use grid to the monitoring point, d i It is i The hydrological distance from the grid to the monitoring point, p is the attenuation coefficient;

[0037] Use the ArcGIS Raster Calculator tool to calculate the weighted score for each raster using the following formula:

[0038]

[0039] in, S i It is i Weighted scoring of land use grids; L i It is i The score assigned to the land use type of each land use raster;

[0040] The weighted scores of all grids in the sub-basin of the monitoring point are assigned S i Summarize and get the human activity impact index:

[0041]

[0042] in, HAI It is the human activity impact index.

[0043] The present invention has the following beneficial effects: A human activity impact assessment method of the present invention, by combining land use type assignment and a hydrological inverse distance weighted algorithm, can more accurately assess the impact of human activities on watershed ecosystems. Compared with existing technologies, this method not only considers the propagation path and distance attenuation effect of human activity impacts, but also quantifies the impact intensity of different land use types, thereby improving the scientific nature and accuracy of the assessment. At the same time, the present invention uses mature GIS tools and a simplified calculation process, reducing data requirements and computational complexity. It has strong operability and universality, and can be widely used in fields such as watershed ecological assessment, environmental protection, and sustainable development management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is a flow chart of a method for assessing the impact of human activities provided by the present invention.

[0046] Figure 2 Schematic diagram of calculation based on a fictitious simplified area, where (a) is the land use raster and river network, (b) is the hydrological distance raster, (c) is the scoring raster, and (d) is the score raster. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings.

[0048] See also Figure 1 , a human activity impact assessment method provided by the present invention, comprising:

[0049] Step 1: Obtain and process the satellite remote sensing images of the corresponding river basin and extract the land use information grid of each part.

[0050] Specifically, the detailed steps of step 1 are as follows:

[0051] (1) Obtain the geospatial information of the study basin, including the basin's geographical location and vector geographic boundaries;

[0052] (2) Obtain Landsat ETM+ remote sensing image data of the study basin from the USGS website; preprocess the remote sensing image data of the study basin, including:

[0053] (a) Geometric correction: 40-50 ground control points were selected from the topographic map and evenly distributed across the remote sensing image. The image was geometrically corrected using quadratic polynomials and cubic convolution interpolation, and registration was performed using the ArcGIS Georeferencing Toolbar. The formula is as follows:

[0054]

[0055]

[0056] in, X , Y is the pixel coordinate on the image to be corrected; F x , F y is the resampling correction distortion function; u , v The position of each pixel point to be input in the correction function image space; a ij , b ij are the coefficients to be determined, which are the coefficients of the polynomial solved by the least squares method using the coordinates of the image to be corrected and the coordinates in the reference coordinate system of the ground control points; n is the order of the polynomial.

[0057] (b) Calculate the optimal index and select the optimal band. The optimal index (OIF) is calculated using the following formula:

[0058]

[0059] in, OIF is the best index; S i For the i The standard deviation of each band; R ij is the correlation coefficient between the two bands. The larger the index is, the greater the amount of information contained in the corresponding combined image. The best index OIF is sorted from large to small, and the band combination corresponding to the largest OIF is the best band combination.

[0060] (c) Image fusion: The Landsat ETM+ panchromatic and multispectral bands of the study basin were fused to improve the spatial resolution and spectral information of the images.

[0061] (d) Automatic matching of remote sensing images: Using satellite images that have undergone geometric correction and image fusion processing as reference images, the ArcGIS "Rectify" tool is used to complete geographic coordinate matching and edge matching of other remote sensing images.

[0062] (e) Image cropping: Based on the boundaries of the study area, the ArcGIS “Extract by Mask” tool was used to perform mask processing on the remote sensing image to complete the regular cropping of the remote sensing image of the study basin, and then obtain the remote sensing image of the basin.

[0063] (3) Establish a land classification system: Based on the actual conditions of the study basin, the land use in the basin is divided into nine types: farmland, forest, shrub, grassland, water area, ice and snow, bare land, impervious surface, and wetland;

[0064] (4) Classification of remote sensing images: The maximum likelihood method is used to supervise the classification of remote sensing images. The overlay rule is selected as the parametric rule, the parameter rule is selected as the maximum likelihood, the total number of pixels is used as the evaluation output statistic, and the image is processed after classification. The results are verified by the Kappa analysis method to obtain the final classification results.

[0065] Step 2: Obtain the digital elevation model of the study area, determine the water flow path through hydrological analysis, and calculate the actual path length along the water flow direction between each grid and the monitoring point.

[0066] Specifically, the detailed steps of step 2 are as follows:

[0067] (1) Water system feature extraction:

[0068] (a) Depression filling: Use the ArcGIS "Fill" tool to process the Digital Elevation Model (DEM) data to eliminate depressions in the terrain;

[0069] (b) Calculation of water flow direction: Using the ArcGIS “Flow Direction” tool, based on the DEM data after filling, the D8 algorithm was used to determine the flow direction of the water flow and calculate the flow direction of each grid;

[0070] (c) Calculate the cumulative flow: Use the ArcGIS “Flow Accumulation” tool to calculate the cumulative flow along the flow path based on the flow direction data to obtain the cumulative flow for each grid.

[0071] (d) River network extraction: Extract the river network based on the accumulated flow data. Set a flow threshold, such as 1000, and define cells with accumulated flow greater than the threshold as rivers. Use the ArcGIS Raster Calculator tool to generate river network raster data.

[0072] (2) Hydrological distance calculation:

[0073] (a) Determine the location of monitoring points and divide the watershed of each monitoring point based on the flow direction grid and the location of the monitoring point;

[0074] (b) Use the ArcGIS “Flow Length” tool to calculate the hydrological distance from each grid cell in the watershed to the monitoring point.

[0075] Step three: assign points to different land use types, calculate the impact of each land use grid on the monitoring point based on the hydrological distance inverse distance weighted algorithm, and obtain the human activity impact index.

[0076] Specifically, the detailed steps of step three are as follows:

[0077] References: Select multiple articles to evaluate the impact of different land use types on ecosystems, water environments, biodiversity, etc.

[0078] Scoring: Scoring is based on local conditions and expert opinions, and is based on a comprehensive assessment of indicators from multiple perspectives;

[0079] Calculating the human activity impact index includes the following steps:

[0080] Assign points to the land use grid: Use the ArcGIS "Reclassify" tool (Reclassify) to assign points to each land use grid and generate a scored land use grid.

[0081] Hydrological inverse distance weighting: The hydrological distance weight from each grid to the monitoring point is calculated using the inverse distance weighting (IDW) formula:

[0082]

[0083] in, W i It is i The hydrological distance weight from the land use grid to the monitoring point, d i It is i The hydrological distance from the grid to the monitoring point, p is the attenuation coefficient;

[0084] Use the ArcGIS Raster Calculator tool to calculate the weighted score for each raster using the following formula:

[0085]

[0086] in, S i It is i Weighted scoring of land use grids; L i It is i The land use type score of each land use raster.

[0087] The weighted scores of all grids in the sub-basin of the monitoring point are assigned S i Summarize and get the human activity impact index:

[0088]

[0089] in, HAI It is the human activity impact index.

[0090] Figure 2 The following is a schematic diagram of a calculation based on a fictitious simplified region. To verify the feasibility and effectiveness of the method presented in this paper, a fictitious simplified watershed was selected for simulation. This region contains typical land use types, such as farmland, forest, impervious surface, and hydrological features. The direction of water flow in this region is from upper left to lower right, with the watershed outlet in the lower right corner serving as the monitoring point. The specific steps of the human activity impact assessment method are as follows:

[0091] After remote sensing interpretation and digital elevation model extraction of river network, Figure 2 (a) shows the land use grid and river network. After the hydrological distance calculation, the hydrological distance grid is obtained, see Figure 2 (b).

[0092] Based on expert opinions and experience, each land use type is scored, with 1 point for forest, 5 points for farmland, and 10 points for impervious surface. The scoring grid is shown in the figure. Figure 2 (c).

[0093] Select the attenuation coefficient p =2, calculate the hydrological distance weight of each grid, and combine the scoring results to obtain the scoring grid, see Figure 2 (d).

[0094] The weighted scores of all grids in the sub-basin of the monitoring point are summarized, and the human activity impact index is 29.35.

[0095] In summary, the present invention proposes a method for assessing the impact of human activities, aiming to more accurately assess the impact of human activities on watershed ecosystems. The method first extracts information on different land use types in the watershed by acquiring and processing satellite remote sensing images; secondly, a digital elevation model is used to perform hydrological analysis and calculate the hydrological distance from each land use grid to the monitoring point; finally, the human activity impact index is calculated by combining land use type scoring and a hydrological inverse distance weighted algorithm. Compared with existing methods, the present invention comprehensively considers the spatial distribution of land use types and the propagation mechanism of hydrological paths, overcomes the limitations of traditional methods that ignore spatial weights and hydrological processes, and significantly improves the scientific nature and accuracy of the assessment. This method can be widely used in fields such as watershed ecological assessment, environmental protection, and sustainable development management, providing a scientific basis for environmental management and ecological protection.

[0096] An embodiment of the present invention further provides a storage medium storing a computer program that, when executed by a processor, implements some or all of the steps of each embodiment of the human activity impact assessment method provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0097] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software and a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various embodiments of the present invention, or portions thereof.

[0098] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A method for assessing the impact of human activities, characterized in that: include: Step 1: Obtain and process satellite remote sensing images of the corresponding river basin and extract land use information grids of each part; Step 2: Obtain a digital elevation model of the study area, determine the flow path through hydrological analysis, and calculate the actual path length along the flow direction between each grid and the monitoring point. The ArcGIS Fill tool is used to process the digital elevation model data to eliminate depressions in the terrain. The ArcGIS Flow Direction tool is used to determine the flow direction of the water based on the filled digital elevation model data using the D8 algorithm, and the flow direction of each grid is calculated. The ArcGIS Flow Accumulation tool is used to calculate the cumulative flow on the flow path based on the flow direction data to obtain the cumulative flow of each grid. The river network is extracted based on the flow accumulation data, and a flow threshold is set. The grids with cumulative flow greater than the threshold are defined as rivers, and the ArcGIS Raster Calculator tool is used to generate river network raster data. The monitoring point locations are determined, and the watershed of each monitoring point is divided based on the flow direction raster and the monitoring point locations. The ArcGIS Flow Distance tool is used to calculate the hydrological distance from each grid in the watershed to the monitoring point. Step 3: Assign points to different land use types. Based on the hydrological distance inverse distance weighted algorithm, calculate the impact of each land use grid on the monitoring point and obtain the human activity impact index. The ArcGIS reclassification tool is used to assign points to each land use grid and generate a scored land use grid. The hydrological distance weight of each grid to the monitoring point is calculated using the inverse distance weighted formula: in, W i It is i The hydrological distance weight from the land use grid to the monitoring point, d i It is i The hydrological distance from the grid to the monitoring point, p is the attenuation coefficient; Use the ArcGIS Raster Calculator tool to calculate the weighted score for each raster using the following formula: in, S i It is i Weighted scoring of land use grids; L i It is i The score assigned to the land use type of each land use raster; The weighted scores of all grids in the sub-basin of the monitoring point are assigned S i Summarize and get the human activity impact index: in, HAI It is the human activity impact index.

2. A method for assessing the impact of human activities according to claim 1, characterized in that: Step one includes: Obtain Landsat ETM+ remote sensing image data of the study basin from relevant data sources; Preprocess remote sensing image data; According to the actual conditions of the study basin, the land use of the basin is divided into nine land use types: farmland, forest, shrub, grassland, water area, ice and snow, bare land, impervious surface, and wetland; The maximum likelihood method is used to supervise the classification of remote sensing images. The overlay rule is selected as the parameter rule, the parameter rule is the maximum likelihood, the total number of pixels is used as the evaluation output statistic, and the image is processed after classification. The Kappa analysis method is used to verify the results to obtain the final classification results.

3. A method for assessing the impact of human activities according to claim 2, characterized in that: Preprocess remote sensing image data, including: 40-50 ground points are selected on the topographic map as ground control points, and the ground control points are evenly distributed on the remote sensing image. The quadratic polynomial and cubic convolution interpolation method are used to perform geometric correction on the image. The formula is as follows: in, X , Y is the pixel coordinate on the image to be corrected; F x , F y is the resampling correction distortion function; u , v The position of each pixel point to be input in the correction function image space; a ij , b ij are the coefficients to be determined, which are the coefficients of the polynomial solved by the least squares method using the coordinates of the image to be corrected and the coordinates in the reference coordinate system of the ground control points; n is the order of the polynomial; Select the best band and calculate the best index using the following formula OIF : in, OIF is the best index; S i For the i The standard deviation of each band; R ij is the correlation coefficient of the two bands. The larger the index, the greater the amount of information contained in the corresponding combined image. The best index OIF is sorted from large to small, and the band combination corresponding to the largest OIF is the best band combination. The Landsat ETM+ panchromatic and multispectral bands of the study basin were fused to improve the spatial resolution and spectral information of the imagery; Use satellite images that have undergone geometric correction and image fusion processing as reference images to complete geographic coordinate matching and edge matching of other remote sensing images; According to the boundary of the study area, the remote sensing image is masked and the regular cropping of the remote sensing image of the study basin is completed to obtain the remote sensing image of the basin.

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