A Method and System for Assessing the Drought Impact of Urbanization Based on Multi-Source Data Fusion

Abstract

This invention proposes a method and system for assessing the drought impact of urbanization based on multi-source data fusion, belonging to the technical field of environmental monitoring. The method includes acquiring a first dataset of an urban area, which is a multi-dimensional spatial dataset; performing multi-source data fusion on the first dataset using the entropy weight method to obtain first data corresponding to the first dataset; and generating an ecological risk level distribution map corresponding to the urban area based on the spatial distribution relationship between various types of index data in the first data, wherein the ecological risk level distribution map is a drought impact risk map of urbanization. This invention can objectively and accurately assess the ecological impact of urbanization, providing a direct scientific basis for urban planning and ecological management.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method and system for assessing the impact of urbanization on drought based on multi-source data fusion. Background Technology

[0002] Urbanization is a significant factor influencing the environment and water resource management globally. With rapid urban population growth, urbanization significantly impacts the occurrence and severity of ecological and environmental phenomena by increasing impervious surfaces, altering local climates, and reducing soil moisture retention capacity. Therefore, developing a method to accurately assess the ecological effects of urbanization is crucial for urban planning, water resource management, and sustainable development.

[0003] Traditional drought impact assessment methods primarily rely on meteorological or remote sensing data. However, in urban environments, their accuracy is limited by localized climate change and alterations in the hydrological cycle caused by urbanization. Furthermore, existing research typically focuses on the single impacts of urbanization on issues like the urban heat island effect, rarely conducting quantitative correlation analyses with drought. To address these issues, existing technologies have proposed data fusion methods, often specifically designed to assess the comprehensive impacts of urbanization on drought. However, the weighting allocation often depends on subjective settings and lacks objective basis. This limits the applicability of the assessment results in urban environments and prevents them from providing direct decision support for urban planning.

[0004] Existing technologies also disclose a drought risk assessment method based on a comprehensive drought risk assessment model. This method does not link the impact of urbanization on drought, and it uses a random forest method to calculate the weights of indicators. This weight calculation method depends on the distribution characteristics of sample data, is easily affected by sample selection, and is difficult to achieve accurate assessment. Summary of the Invention

[0005] To address the problem that existing technologies struggle to objectively and accurately assess the ecological impacts of urbanization, this invention proposes a method and system for assessing the drought impacts of urbanization based on multi-source data fusion, which can objectively and accurately evaluate the ecological effects of urbanization.

[0006] To achieve the above-mentioned technical effects, the technical solution of the present invention is as follows:

[0007] A method for assessing the drought impact of urbanization based on multi-source data fusion includes the following steps: Obtain the first dataset of the urban area; the first dataset is a multidimensional spatial dataset. The first dataset is fused using the entropy weighting method to obtain the first data corresponding to the first dataset. Based on the spatial distribution relationship between the various types of index data in the first data, an ecological risk level distribution map corresponding to the urban area is generated, which is an ecological risk level distribution map of the impact risk of drought on urbanization.

[0008] Preferably, the first dataset includes urbanization characteristic data and ecological environment characteristic data; the first data is index data, including urbanization intensity index and ecological environment status index.

[0009] Preferably, the first dataset is fused using the entropy weight method to obtain an urbanization intensity index in the first data corresponding to the first dataset, including: The urbanization indicators of the urbanization feature data in the first dataset are standardized to obtain multiple standardized urbanization indicators. Calculate the first entropy value corresponding to the standardized urbanization index; Based on the first entropy value, calculate the weights of the standardized urbanization indicators; The weights of the standardized urbanization indicators are weighted and fused with their corresponding standardized urbanization indicators to obtain the second data, which is the urbanization intensity index.

[0010] Preferably, the first dataset is fused using the entropy weight method to obtain an ecological environment state index in the first data corresponding to the first dataset, including: The ecological indicators of the ecological environment feature data in the first dataset are standardized to obtain multiple standardized ecological indicators. Calculate the second entropy value corresponding to the standardized ecological indicator; Based on the second entropy value, the weights of the standardized ecological indicators are calculated; The weights of the standardized ecological indicators are weighted and fused with their corresponding standardized ecological indicators to obtain the third data, which is an ecological environment status index that includes a comprehensive drought index.

[0011] Preferably, the urbanization indicators include at least nighttime light data, impermeable surface area ratio data, population density data, and building height data; the ecological indicators include at least drought index, vegetation index, and soil moisture data calculated based on potential evapotranspiration.

[0012] Preferably, based on the spatial distribution relationship between various types of index data in the first data, a bivariate quartile spatial overlay method is used to generate an ecological risk level distribution map corresponding to the urban area. This ecological risk level distribution map is an urban drought impact risk map, including: Obtain raster maps of urbanization intensity index and comprehensive drought index; Calculate the upper quartile of all pixel values ​​in the urbanization intensity index raster map and the comprehensive drought index raster map. Use the upper quartile of all pixel values ​​in the urbanization intensity index raster map as the first threshold for classifying high and low urbanization intensity levels, and use the upper quartile of all pixel values ​​in the comprehensive drought index raster map as the second threshold for classifying high and low drought levels. Based on the first segmentation threshold, an urbanization intensity region discrimination mask is constructed, and based on the second segmentation threshold, an aridity degree region discrimination mask is constructed. Based on the urbanization intensity regional discrimination mask and the drought degree regional discrimination mask, a risk raster map with several risk levels is generated; The risk raster is visualized and rendered, and different colors are assigned to different risk levels in the risk raster to obtain the risk map of the impact of urbanization drought.

[0013] Preferably, the urbanization intensity region discrimination mask includes a high urbanization intensity region mask and a low urbanization intensity region mask; the high urbanization intensity region mask marks regions in the urbanization intensity index raster image with pixel values ​​greater than the first segmentation threshold as true, and the remaining regions as false; the low urbanization intensity region mask marks regions in the urbanization intensity index raster image with pixel values ​​less than or equal to the first segmentation threshold as true, and the remaining regions as false. The drought severity region discrimination mask includes a high drought severity region mask and a low drought severity region mask; the high drought severity region mask marks regions in the comprehensive drought index raster image with pixel values ​​greater than the second division threshold as true and the remaining regions as false; the low drought severity region mask marks regions in the comprehensive drought index raster image with pixel values ​​less than or equal to the second division threshold as true and the remaining regions as false.

[0014] Preferably, the risk levels include at least the highest risk, the second highest risk, the medium risk, and the low risk. The generation of a risk raster map with several risk levels based on the urbanization intensity regional discrimination mask and the drought degree regional discrimination mask includes: Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the high-urbanization-intensity region mask and the high-aridity region mask are marked as regions with the highest risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the high-urbanization-intensity region mask and the low-aridity region mask are marked as regions with the second-highest risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the low-urbanization-intensity region mask and the high-aridity region mask are marked as regions with medium risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the low urbanization intensity region mask and the low drought level region mask are marked as low-risk regions. The risk raster map is obtained by combining the areas with the highest risk, the areas with the second highest risk, the areas with the medium risk, and the areas with the low risk.

[0015] This invention also proposes a system for assessing the impact of urbanization on drought based on multi-source data fusion. The system is used to implement the method for assessing the impact of urbanization on drought based on multi-source data fusion as described above. The system includes: The data acquisition module is used to acquire the first dataset of the urban area, which is a multidimensional spatial dataset. The data processing module is used to perform multi-source data fusion on the first dataset using the entropy weight method to obtain the first data corresponding to the first dataset. The data assessment module is used to generate an ecological risk level distribution map corresponding to the urban area based on the spatial distribution relationship between various types of index data in the first data. The ecological risk level distribution map is a risk map of the impact of drought on urbanization.

[0016] The present invention also proposes a computer device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform operations as described in the method for assessing the impact of urbanization on drought based on multi-source data fusion.

[0017] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention proposes a method and system for assessing the impact of urbanization on drought based on multi-source data fusion. First, a first dataset of urban areas is acquired, which is a multi-dimensional spatial dataset. Then, the entropy weight method is used to perform multi-source data fusion on the first dataset to obtain first data corresponding to the first dataset. Next, based on the spatial distribution relationship between various types of index data in the first data, an ecological risk level distribution map corresponding to the urban area is generated. This ecological risk level distribution map serves as a drought impact risk map for urbanization, effectively avoiding interference from subjective human factors on the data fusion results and ensuring the objectivity of the multi-source fused index data. This not only significantly improves the accuracy and objectivity of the assessment results on the impact of urbanization on drought but also enables the visualization of the assessment results, providing direct scientific decision-making basis for urban planning and ecological management. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for assessing the impact of urbanization on drought based on multi-source data fusion, as proposed in this embodiment of the invention. Figure 2 This diagram illustrates the flowchart of a method for assessing the impact of urbanization on drought based on multi-source data fusion, as proposed in this invention. Figure 3 A bar chart showing the weights of the urbanization intensity index proposed in this embodiment of the invention; Figure 4 This represents a raster map of the urbanization intensity index proposed in this embodiment of the invention; Figure 5 A bar chart showing the distribution of the comprehensive drought index proposed in this embodiment of the invention; Figure 6 This represents the raster chart of the comprehensive drought index proposed in this embodiment of the invention; Figure 7 This represents the risk map of the impact of drought on urbanization proposed in this embodiment of the invention; Figure 8 This is a structural block diagram of an urbanization-drought impact assessment system based on multi-source data fusion proposed in this embodiment of the invention. Figure 9 This is a structural block diagram of a computer device proposed in an embodiment of the present invention.

[0019] 91. Processor; 92. Memory; 93. Communication interface; 94. Communication bus; 95. Executable instructions. Detailed Implementation

[0020] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. It is understandable to those skilled in the art that some well-known details may be omitted from the accompanying drawings; To facilitate understanding of this embodiment, the prior art information of this embodiment is first introduced as follows: Traditional drought assessment methods primarily rely on meteorological data (such as rainfall and temperature) or remote sensing data (such as the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST)) to assess drought conditions by calculating indicators such as the Standardized Precipitation Index (SPI) or the Standardized Precipitation Evapotranspiration Index (SPEI). While these methods perform well in natural environments, their accuracy is limited in urban environments due to localized climate change and altered hydrological cycles caused by urbanization. Furthermore, urbanization-related studies typically focus on the impacts of nighttime light, population density, or impervious surface area on the urban heat island effect, but rarely integrate these factors with drought assessments, lacking a quantitative analysis of the dynamic relationship between urbanization and drought.

[0021] Furthermore, while data fusion technology is gaining popularity in environmental science, generating comprehensive indicators by integrating multi-source data (such as remote sensing, meteorology, and ground observations), studies have shown that fusing NDVI, LST, and soil moisture data can improve the accuracy of drought assessments. However, existing data fusion methods often do not specifically address the impact of urbanization on drought, and weight allocation relies heavily on subjective settings, lacking objective basis. This results in limited applicability of assessment results in urban environments, failing to provide direct decision support for urban planning.

[0022] Existing drought assessment techniques mainly include the following methods: Traditional drought assessment methods: SPI and SPEI, based on meteorological data, are commonly used drought assessment indicators. For example, SPEI is calculated using rainfall and potential evapotranspiration (PET) to reflect the severity of meteorological drought. Its disadvantages include: ignoring urbanization factors, failing to consider the increase in impervious surface area, heat island effect, and decrease in soil moisture caused by urbanization, resulting in significant bias in urban areas; relying on a single data source, mainly meteorological station data, with low spatial resolution, making it difficult to capture the heterogeneity within cities. For remote sensing data fusion methods: Some studies generate comprehensive drought indicators by fusing remote sensing data (such as Landsat's NDVI and LST, and MODIS's soil moisture). For example, studies use PCA to fuse multi-band data to improve drought monitoring accuracy. Its disadvantages include: lack of urbanization variables, failure to integrate urbanization indicators (such as nighttime light pollution and population density), and inability to quantify the impact of urbanization on drought; subjective weighting, with fusion weights often set empirically, lacking objective basis, affecting the scientific validity of the indicators. Urbanization impact assessment methods analyze the effects of urbanization on local climate, such as the urban heat island effect or changes in rainfall runoff, using nighttime light or population density. Their drawbacks include: lack of integration with drought; these methods typically do not correlate urbanization variables with drought indicators, lacking quantitative analysis of the urbanization-drought interaction; and insufficient data integration: they often rely on single-indicator analyses and do not fully utilize multi-source data fusion techniques. Hydrological data fusion methods integrate meteorological, topographical, and urbanization data (such as impervious surfaces) to assess urban flood risk. For example, studies identify flood risk areas through multi-source data fusion and hydrodynamic models. Their drawbacks include: focusing on flood rather than drought; the methods do not address drought issues and ignore the impact of urbanization on reduced soil moisture and increased evapotranspiration. While the aforementioned technical solutions have made some progress in drought monitoring and urbanization research, they suffer from the following problems: Lack of urbanization factors: Traditional methods ignore the impact of urbanization on drought, leading to inaccurate assessment results in urban areas. Subjective weight allocation: Data fusion weights are mostly set based on experience, lacking objective basis and affecting the reliability of the results. Limited applicability: Existing methods are not optimized for urban environments, making it difficult to provide direct support for urban planning and water resource management.

[0023] To facilitate a clearer understanding of the objectives, technical solutions, and advantages of this invention, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of this invention from the content disclosed in this specification.

[0024] This invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of this invention.

[0025] Secondly, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0026] Example 1 This embodiment proposes a method for assessing the impact of urbanization on drought based on multi-source data fusion. It can be applied to terminals or fixed terminals with display functions. The terminals are not limited to personal computers, smartphones, tablets, desktop computers or all-in-one computers with cameras, etc.

[0027] The method for assessing the drought impact of urbanization based on multi-source data fusion can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network (WAN), a metropolitan area network (MAN), or a local area network (LAN). The method for assessing the drought impact of urbanization based on multi-source data fusion proposed in this embodiment can be executed by the server, by the terminal, or by both the server and the terminal.

[0028] For example, for terminals that require assessment and processing of the drought impact of urbanization based on multi-source data fusion, the drought impact assessment function based on multi-source data fusion provided by the method of this invention can be directly integrated into the terminal, or a client for implementing the method of this invention can be installed. Alternatively, the method provided by this invention can also run on servers or other devices in the form of a Software Development Kit (SDK), providing an interface for the drought impact assessment and processing function based on multi-source data fusion. Terminals or other devices can then implement the drought impact assessment and processing function based on multi-source data fusion through the provided interface. The following, in conjunction with the accompanying drawings, further elaborates on the drought impact assessment method based on multi-source data fusion proposed in this embodiment.

[0029] See Figure 1This embodiment proposes a method for assessing the impact of urbanization on drought based on multi-source data fusion, including the following steps: S1. Obtain the first dataset of the urban area, which is a multidimensional spatial dataset; The first dataset includes urbanization characteristic data and ecological environment characteristic data; the first data is index data, including urbanization intensity index and ecological environment status index; the urbanization characteristic data includes urbanization indicators selected from urban socio-economic activities, land use patterns, population concentration, and three-dimensional spatial structure dimensions; the urbanization indicators include at least nighttime light data. NTL impermeable surface ratio data ISA Population density data PD Building height data BH ; S2. Use the entropy weight method to perform multi-source data fusion on the first dataset to obtain the first data corresponding to the first dataset; In S2, the entropy weight method is used to perform multi-source data fusion on the first dataset to obtain the urbanization intensity index in the first data corresponding to the first dataset, including: S201. Standardize multiple urbanization indicators in the urbanization feature data of the first dataset to obtain multiple standardized urbanization indicators; The standardization process for multiple urbanization indicators in the urbanization feature data includes: The various urbanization indicators in the urbanization characteristic data are dimensionless and standardized using range:

[0030] in, For the i-th sample, the th j The original value of a standardized first indicator. For the i-th sample, the th j The original value of the first indicator, For the first The first sample j The original value of the first indicator, n The total number of samples; here, "sample" refers to urbanization characteristic data. S202. Calculate the first entropy value corresponding to the standardized urbanization index. ;

[0031] in, Represents the i-th sample. j The first weight of the original value of a standardized indicator. The calculation expression is as follows:

[0032] in, Indicates the first The first sample j The original value of a standardized first indicator; like ,but Entropy ∈[0,1], the smaller the value, the greater the information utility of the indicator.

[0033] S203. Based on the first entropy value Calculate the weights of the standardized urbanization indicators. as follows:

[0034] in, m For the total number of indicators, ,and The smaller the entropy value, the greater the weight; the greater the spatial variability of the indicator data, the higher the assigned weight.

[0035] S204. The weights of the standardized urbanization indicators are weighted and fused with their corresponding standardized urbanization indicators to obtain second data, which is the urbanization intensity index; the urbanization intensity index The calculation is as follows:

[0036]

[0037] In this embodiment, urbanization characteristic data is selected as the sample, and the urbanization intensity index is calculated from this sample. as follows:

[0038] in, NTL norm To standardize nighttime light intensity; PD norm Standardized population density; ISA norm To standardize impermeable pavement; BH norm Standardized building height; w 1 、w 2 、w 3 、w 4 represents the corresponding weight; The first dataset is fused using the entropy weight method to obtain an ecological environment state index in the first data corresponding to the first dataset, including: S211. Standardize multiple ecological indicators of the ecological environment characteristic data in the first dataset to obtain multiple standardized ecological indicators; The standardization process for multiple ecological indicators in the ecological environment characteristic data includes: Dimensionless transformation of each ecological indicator in the ecological environment characteristic data is performed, and range standardization is adopted:

[0039] in, For the i-th sample, the th j A standardized second indicator raw value, For the i-th sample, the th j The original value of the second indicator, For the first The first sample j The original value of the second indicator, n The total number of samples; here, "sample" refers to urbanization characteristic data. S212. Calculate the second entropy value corresponding to the standardized ecological indicator. ;

[0040]

[0041] in, Represents the i-th sample. j The second weight of the original value of a standardized indicator. The calculation expression is as follows:

[0042] in, Indicates the first The first sample j A standardized second indicator raw value, S213. Based on the second entropy value Calculate the weights of the standardized ecological indicators. as follows:

[0043] in, m For the total number of indicators, ,and The smaller the entropy value, the greater the weight; the greater the spatial variability of the indicator data, the higher the assigned weight.

[0044] S214. The weights of the standardized ecological indicators are weighted and fused with their corresponding standardized ecological indicators to obtain third data, wherein the third data is an ecological environment status index including a comprehensive drought index. The calculation is as follows:

[0045] It should also be specifically stated that, in addition to using the entropy weight method to perform multi-source data fusion on the first dataset to obtain the first data corresponding to the first dataset, a fourth data corresponding to the first data is also generated and obtained, the fourth data is standardized, and a corresponding fifth data is generated; wherein, the fourth data is the basic indicator data that constitute the first data; and the fifth data is the basic indicator data after standardization. Based on the fifth data, a sixth data corresponding to the fourth data is calculated and generated, and a seventh data corresponding to the fourth data is created based on the sixth data; wherein, the sixth data is the information entropy value of each indicator; the seventh data is the weight coefficient of each indicator, and the greater the spatial variability of the indicator data, the higher the weight is assigned.

[0046] In this embodiment, the sample selected is ecological and environmental characteristic data. The ecological and environmental characteristic data includes indicator data generated and selected based on meteorological conditions, vegetation status, and soil conditions. The ecological indicators include at least the drought index SPEI, vegetation index NDVI, and soil moisture data calculated based on potential evapotranspiration. SM The comprehensive drought index calculated from this sample as follows:

[0047] In the formula, SPEI norm , NDVI norm and SM norm These are standardized drought index SPEI, vegetation index NDVI, and soil moisture data. SM ; w 5 、w 6 、w 7 represents the corresponding weight.

[0048] S3. Based on the spatial distribution relationship between the various types of index data in the first data, generate an ecological risk level distribution map corresponding to the urban area, wherein the ecological risk level distribution map is a risk map of the impact of drought on urbanization.

[0049] The urbanization drought impact risk map has a clear spatial distribution and well-defined levels, used to intuitively identify areas with different risk levels of urbanization's impact on drought. When executing S3, it is necessary to construct the spatial correlation between various types of index data in the multi-source fusion index data; and based on the spatial distribution relationship of these various types of index data, create and generate an urbanization drought impact risk map corresponding to the urban area, including: creating, generating, and acquiring an eighth data point corresponding to the first data point; wherein, the eighth data point is a classification threshold. Based on the eighth data and each spatial pixel corresponding to the first data, and combined with the bivariate quartile spatial overlay method, at least one ecological risk level corresponding to each spatial pixel is generated; wherein, high-risk areas correspond to areas where high urbanization intensity and poor ecological environment status overlap. Specifically, using the bivariate quartile spatial overlay method, an ecological risk level distribution map corresponding to urban areas is generated, the ecological risk level distribution map being an urbanization drought impact risk map, including: S31. Obtain the urbanization intensity index raster map and the comprehensive drought index raster map; specifically, the urbanization intensity index and the comprehensive drought index are spatially stored in the urban area to obtain the urbanization intensity index raster map and the comprehensive drought index raster map; S32. Calculate the upper quartile (i.e., the 75th percentile) of all pixel values ​​in the urbanization intensity index raster map and the comprehensive drought index raster map. Use the upper quartile of all pixel values ​​in the urbanization intensity index raster map as the first threshold for classifying high and low urbanization intensity levels, denoted as UII_T. Use the upper quartile of all pixel values ​​in the comprehensive drought index raster map as the second threshold for classifying high and low drought levels, denoted as CDI_T. The first and second thresholds are dynamic thresholds. The use of dynamic thresholds ensures that the classification of high and low levels is based on the data distribution characteristics of the study area itself, avoiding the subjectivity of manually setting absolute thresholds, and enhancing the adaptability of the method to different regions and the objectivity of the results.

[0050] S33. Based on the first division threshold, construct an urbanization intensity region discrimination mask, and based on the second division threshold, construct an aridity degree region discrimination mask; Based on the first segmentation threshold, constructing an urbanization intensity region discrimination mask includes: The urbanization intensity region discrimination mask includes a high urbanization intensity region mask and a low urbanization intensity region mask; the high urbanization intensity region mask marks regions in the urbanization intensity index raster image with pixel values ​​greater than the first segmentation threshold as true, and the remaining regions as false; the low urbanization intensity region mask marks regions in the urbanization intensity index raster image with pixel values ​​less than or equal to the first segmentation threshold as true, and the remaining regions as false. Based on the second segmentation threshold, a drought level region discrimination mask is constructed, including: The drought severity region discrimination mask includes a high drought severity region mask and a low drought severity region mask; the high drought severity region mask marks regions in the comprehensive drought index raster image with pixel values ​​greater than the second division threshold as true and the remaining regions as false; the low drought severity region mask marks regions in the comprehensive drought index raster image with pixel values ​​less than or equal to the second division threshold as true and the remaining regions as false.

[0051] S34. Based on the urbanization intensity regional discrimination mask and the drought degree regional discrimination mask, generate a risk raster map with several risk levels; the steps include: spatial overlay and risk level assignment, that is, combining the four logical masks obtained in S33 in pairs to construct a risk raster map with at least four risk levels. The aforementioned risk levels include at least the highest risk, the second highest risk, the medium risk, and the low risk. The generation of a risk raster map with several risk levels based on the urbanization intensity regional discrimination mask and the drought degree regional discrimination mask includes: Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the high-urbanization-intensity region mask and the high-aridity region mask are marked as regions with the highest risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the high-urbanization-intensity region mask and the low-aridity region mask are marked as regions with the second-highest risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the low-urbanization-intensity region mask and the high-aridity region mask are marked as regions with medium risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the low urbanization intensity region mask and the low drought level region mask are marked as low-risk regions. The risk raster map is obtained by combining the areas with the highest risk, the areas with the second highest risk, the areas with the medium risk, and the areas with the low risk. Each pixel in the risk raster is then assigned a value according to the following rules: Risk Level 4 (Highest Risk): Pixels that simultaneously meet the criteria of being located within both the high urbanization intensity area mask and the high drought level area mask are assigned a value of 4; this area represents a high degree of spatial overlap between high-intensity urbanization and severe drought, and is the core area where urbanization exerts a significant negative impact on drought; Risk Level 3 (Second Highest Risk): Pixels that simultaneously meet the criteria of being located within both the high urbanization intensity area mask and the low drought level area mask are assigned a value of 3; this area represents a region with a high level of urbanization but a low degree of drought, suggesting that there may be effective water resource management measures or good ecological planning that have mitigated the drought effect; Risk Level 2 (Medium Risk): Pixels that simultaneously meet the criteria of being located within both the low urbanization intensity area mask and the high drought level area mask are assigned a value of 2; this area represents drought mainly driven by natural climate factors or non-urbanized agricultural activities, with a weak correlation to urbanization. Risk Level 1 (Low Risk): Pixels that simultaneously meet the requirements of the low urbanization intensity area mask and the low drought level area mask are assigned a value of 1; this area represents that the impact of both urbanization and drought is at a low level.

[0052] Here, based on the first and second classification thresholds and each spatial pixel corresponding to the multi-source fusion index data, and combined with the bivariate quartile spatial superposition method, at least one ecological risk level corresponding to each spatial pixel is generated; wherein, the high-risk area corresponds to the area where high urbanization intensity and poor ecological environment status overlap; that is, the pixel classified as high-risk corresponds to the case where its urbanization intensity index and ecological environment status index are both higher than their respective classification thresholds.

[0053] S35. Visualize and render the risk raster map, and assign different colors to different risk levels in the risk raster map. For example, green, yellow, orange, and red are used to represent risk levels from low to high in sequence, resulting in an easily interpretable risk map of the impact of urbanization drought. The risk map of the impact of urbanization drought directly displays the distribution of risk levels through spatial location, which can provide accurate decision support for urban planning, water resource management, and drought disaster prevention.

[0054] In this embodiment, firstly, a multidimensional spatial dataset of the urban area is obtained, and then the entropy weight method is used to analyze the multidimensional data. Spatial datasets undergo multi-source data fusion, effectively avoiding interference from subjective human factors in the data fusion results and ensuring the objectivity of multi-source fusion index data. This not only significantly improves the accuracy and objectivity of the assessment results on the impact of urbanization on drought, but also enables the visualization of the assessment results, providing direct scientific decision-making basis for urban planning and ecological management.

[0055] This invention objectively integrates multidimensional urbanization indicators and ecological data using the entropy weight method to construct an urbanization intensity index and an ecological environment status index. It also utilizes an innovative bivariate quartile spatial superposition method to generate an intuitive risk level distribution map, thereby achieving an objective, accurate, and visualized assessment of the ecological effects of urbanization and providing a direct scientific basis for urban planning and ecological management.

[0056] In other words, urbanization is a significant factor influencing environmental and water resource management globally. With rapid urban population growth, the global urban population is projected to exceed 70% by 2050 (UN Urbanization Report). Urbanization significantly impacts the occurrence and severity of drought by increasing impervious surface area, altering local climate (such as the heat island effect), and reducing soil moisture retention capacity. Particularly in rapidly urbanizing regions, the interaction between drought risk and urbanization is becoming increasingly prominent. Therefore, the method for assessing the impact of urbanization on drought based on multi-source data fusion, as provided in this invention, is of great significance for urban planning, water resource management, and sustainable development.

[0057] In summary, the urbanization-based drought impact assessment method proposed in this embodiment, based on multi-source data fusion, has the following advantages: It integrates urbanization factors, constructs an urbanization intensity index by fusing multi-source fusion index data, and combines this with a comprehensive drought index to assess the impact of urbanization on drought. This accurately captures the cumulative effect of urbanization on drought, improving the accuracy of urban area assessments. Furthermore, it employs objective weight allocation: the entropy weight method is used to assign weights to the urbanization intensity index and the comprehensive drought index, determining the weights based on the information content of the data. This avoids subjective setting and improves the scientific rigor and reliability of the indicators. Additionally, a risk distribution raster map is generated through correlation analysis between the urbanization intensity index and the comprehensive drought index. This invention, through multi-source data fusion and the entropy weight method, generates an urbanization intensity index and a comprehensive drought index, analyzes their correlation, addresses the shortcomings of drought assessment in the context of urbanization, and, through a bivariate quartile spatial overlay method, draws a risk map of the urbanization-induced drought impact, providing a scientific basis for urban planning, water resource management, and climate adaptation.

[0058] Example 2 This embodiment proposes a method for assessing the impact of urbanization on drought based on multi-source data fusion, aiming to address the shortcomings of existing methods for assessing the impact of urbanization on drought. Traditional drought assessment methods, such as the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), mainly rely on meteorological data and ignore urbanization factors such as impermeable surfaces and the urban heat island effect, leading to inaccurate drought assessments in urban areas and difficulty in guiding water resource management. Although existing data fusion methods integrate remote sensing data, such as the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), they do not fully incorporate urbanization indicators, such as nighttime light pollution and population density, affecting the reliability of the results.

[0059] See Figure 2 The method in this embodiment generates the Urbanization Intensity Index (UII) and the Comprehensive Drought Index (CDI) as an ecological environment status index through multi-source data fusion. It adopts the entropy weight method to objectively assign weights, quantifies the impact of urbanization on drought, and provides a scientific basis for urban planning and water resource management.

[0060] This embodiment constructs an Urbanization Intensity Index (UII) by integrating multiple urbanization indicators from urbanization characteristic data, such as Nighttime Light Level (NTL), Population Density (PD), Impervious Surface Area Ratio (ISA), and Building Height / Volume (BH). It also integrates four ecological indicators from ecological environment characteristic data, such as Nighttime Light Level (NTL), Population Density (PD), Impervious Surface Area Ratio (ISA), and Building Height / Volume (BH). This approach aims to provide a comprehensive, multi-dimensional, and complementary characterization of urbanization from four key dimensions, surpassing the limitations of traditional single-indicator methods and achieving high-precision, universally applicable drought risk assessment. This solution aims to overcome the limitations of existing technologies, such as insufficient consideration of urbanization factors, single data sources, and poor regional adaptability, providing a scientific basis for urban planning and water resource management.

[0061] For nighttime light NTL, this indicator dimension is human activity and economy, which can dynamically, macroscopically and directly reflect urban energy consumption and economic activity. The data is easy to obtain and has a long time series. Its purpose is to capture the intensity of urban functions and energy input, and it is an intuitive reflection of the level of urbanization.

[0062] For population density (PD), this indicator dimension is population agglomeration and society. It can reflect the population agglomeration and social density of a city, and is the fundamental driving force and carrier of urban functions. It is used to make up for the lack of socio-economic information in remote sensing data and ensure that the urbanization intensity index has both physical and human attributes.

[0063] The Impermeable Surface Area (ISA) index, which is based on land use and hardening, accurately reflects the degree to which urban land is hardened by buildings, roads, etc. It is a direct physical indicator of urban ecological environment changes and the heat island effect. Its purpose is to characterize the irreversible impact of urbanization on natural land cover and is a key environmental stress factor.

[0064] For building height / volume (BH), this indicator dimension is spatial structure and three-dimensionality, which can reflect the degree of vertical expansion and three-dimensional development of the city in three dimensions. It is a unique symbol of high-density and compact urbanization. Its purpose is to provide vertical information that traditional two-dimensional remote sensing (such as ISA) cannot provide, so that the dimension of the urbanization intensity index is improved from two-dimensional to three-dimensional, which reflects the innovation of high-dimensional integration.

[0065] In the technical solution of this invention, data collection and preprocessing are defined as follows: Remote sensing data refers to Earth surface information acquired through satellites or airborne platforms, including normalized vegetation index (NDVI), soil moisture (SM), meteorological data (daily maximum temperature Tmax, daily minimum temperature Tmin, relative humidity RH, total solar radiation Rs, wind speed u2 at 2m altitude, and rainfall P); and urbanization indicators (nighttime light level (NTL), population density (PD), impermeable surface area ratio (ISA), and building height (BH). The specific operation involves acquiring remote sensing data, meteorological data, and urbanization indicators for the study area, and processing the data, such as using upsampling to bilinearly interpolate all low-resolution data onto a high-resolution reference raster to achieve data fusion.

[0066] The principles involved in data collection and preprocessing are explained below: The basic principle of data fusion is to determine a reference raster based on all indicators of the urbanization intensity index with an original spatial resolution of 30m: nighttime light intensity (NTL), population density (PD), impermeable surface area (ISA), and building height (BH). The reference raster is standardized using the georeferenced information R of the 30m indicators (especially NTL) and the raster size (NTL).

[0067] The principle of bilinear interpolation: For each new pixel in the target grid (30m), its value is calculated by weighted averaging the values ​​of its four nearest (2×2) neighboring pixels in the original low-resolution grid (1km). The weights are inversely proportional to the distance from the target point to the center of these four original pixels. The closer the target point is to the center of an original pixel, the greater the weight of that original pixel.

[0068] For feature extraction of urbanization process, the following definition is provided: Urbanization Intensity Index. The comprehensive index, ranging from 0 to 1, is a weighted average calculated based on nighttime light data and population density to reflect the level of urbanization.

[0069]

[0070] in, NTL norm To standardize nighttime light intensity; PD norm Standardized population density; ISA norm To standardize impermeable pavement; BH norm Standardized building height; w 1 、w 2 、w 3 、w4 represents the corresponding weight; the data on nighttime lighting, population density, proportion of impermeable ground, and building height are standardized to 0~1 using the entropy weight method.

[0071] The comprehensive drought index, which serves as the ecological environment characteristic index data, is defined as follows: Comprehensive Drought Index The drought comprehensive index, calculated by weighted average of the standardized precipitation evapotranspiration index (SPEI), normalized vegetation index (NDVI), and soil moisture (SM), reflects the overall drought status.

[0072] Operation: The Standardized Precipitation Evapotranspiration Index (SPEI) is a standardized drought index based on the difference between rainfall and potential evapotranspiration (PET), used to quantify the intensity, duration, and spatial distribution of drought. SPEI combines rainfall and PET, providing a more comprehensive picture than the Standardized Precipitation Index (SPI) and reflecting evapotranspiration-driven drought under climate change. In this invention, SPEI is a comprehensive drought index. One of the core input variables, combined with standardized NDVI and soil moisture, characterizes medium- to long-term meteorological and hydrological drought. The lower the SPEI value, the more severe the drought. This invention uses the FAO-56 Penman-Monteith (PM) equation, based on energy balance and aerodynamic principles, to calculate the reference crop potential evapotranspiration, and combines this with precipitation to calculate the standardized precipitation evapotranspiration index, used to characterize drought conditions at different time scales.

[0073] Methods for calculating Potential Evaporation Capacity (PET):

[0074] Where Δ is the slope of the temperature-vapor pressure curve; γ is the hygrometer constant; G is the soil heat flux density; T is the daily average temperature; Rn is the difference between the incident net shortwave radiation and the outgoing net longwave radiation; U is the daily average wind speed; es is the saturated vapor pressure; ea is the measured vapor pressure; es, ea and Rn can be calculated using the following formulas.

[0075]

[0076]

[0077] in, This represents the saturated vapor pressure corresponding to the daily average maximum temperature. This represents the saturated vapor pressure corresponding to the daily average minimum temperature. This represents the saturated vapor pressure corresponding to the daily average temperature. The average relative humidity; Net shortwave radiation; This is net longwave radiation.

[0078] SPEI assesses regional drought conditions by measuring the difference between standardized monthly precipitation (Pre) and monthly potential evapotranspiration (PET), calculated using the following formula:

[0079] in, Let be the rainfall (mm) in the i-th month. The potential evapotranspiration (mm) for the i-th month. The value represents the moisture balance (mm), with a positive value indicating wet conditions and a negative value indicating drought.

[0080] The Standardized Precipitation Evapotranspiration Index (SPEI) encompasses multiple time scales. SPEI indices at different time scales can identify drought variations in different time units; for example, SPEI-3 identifies seasonal drought, while SPEI-12 identifies interannual drought. When calculating the SPEI index at different time scales, it is necessary to calculate the cumulative difference between precipitation and potential evapotranspiration at the corresponding time scale. Where i represents the year, j represents the month, and k represents the time scale. For example, the cumulative difference over a 12-month timescale can be calculated using the following formula:

[0081]

[0082] In obtaining the cumulative difference sequence Then, the SPEI is calculated using a three-parameter Log-Logistic distribution, and its probability density function and distribution function are shown in the following formulas:

[0083]

[0084] Where α is the scale parameter, β is the shape parameter, and γ is the position parameter, and their calculation method is shown in the following formula:

[0085]

[0086]

[0087] Where Γ(β) is the gamma function; ws is the s-order probability weight moment, which can be estimated using the following formula:

[0088]

[0089] Where i is the observation number arranged in ascending order, and N is the total number of data points.

[0090] Finally, the SPEI index can be calculated using the following formula:

[0091]

[0092] Where P is the probability of a given value of x; when F(x)>0.5, P=1-F(x), otherwise P=F(x). =2.515517, =0.802853, =0.010328, =1.432788, =0.189269, =0.001308.

[0093] Combine SPEI, NDVI, and SM to build The formula is:

[0094] In the formula, SPEI norm , NDVI norm and SM norm These are standardized drought index SPEI, vegetation index NDVI, and soil moisture data. SM ; w 5 、w 6 、w 7 represents the corresponding weight.

[0095] Urbanization Intensity Index The validity verification adopts a spatial rationality test, requiring... The spatial distribution should conform to the understanding of urban core areas, secondary centers, suburbs, and rural areas in urban planning and common sense.

[0096] Comprehensive drought index The validity of the study was verified using external validity verification, which involved comparative analysis with authoritative drought-related data. The ultimate goal of constructing the comprehensive drought index was to accurately reflect the actual drought status and impact of drought in the study area. The specific verification process was as follows: during the study period, the study area was collected from reports of actual meteorological drought levels, drought-affected crop areas, or other authoritative drought indices (such as SC-PDSI) issued by the meteorological bureau.

[0097] The present invention addresses the construction of an urbanization intensity index. and comprehensive drought index In this process, weight allocation is a crucial step in determining the effectiveness of the index. To ensure the objectivity and reliability of the evaluation results, this invention selects the Entropy Weight Method (EWM) and compares it with existing mainstream weighting methods: The Analytic Hierarchy Process (AHP) is based on expert experience and judgment. It calculates weights by constructing a matrix. The disadvantage of this method is that the weights are highly dependent on subjective judgment, easily limited by expert experience, lack data support, and are difficult to adapt to the fusion of large-scale, multivariate remote sensing data.

[0098] Principal component analysis (PCA) works by assigning weights to indicators based on their variance contribution rate; the larger the variance, the higher the weight. However, this method is disadvantageous because it focuses heavily on the magnitude of variance, which does not necessarily mean a large amount of information. Furthermore, it may lose some residual information that influences the final index.

[0099] The principle of the CRITIC method is to consider both the variability of indicators and the conflict between indicators. The disadvantage of this method is that it is computationally complex and very sensitive to the correlation between indicators. If there is a high degree of multicollinearity among indicators, it may lead to an imbalance in weight allocation.

[0100] The present invention ultimately selects Entropy Weight Method (EWM) as the core weighting tool because its advantage lies in its ability to best reflect the information content and objective value of the data itself. The Entropy Weight Method used in this invention is a purely objective weighting method that relies entirely on the spatial distribution characteristics of the data itself, i.e., the information entropy of the indicators. It avoids any subjective human intervention, ensuring... and The construction is based on scientific data rather than expert experience. From an information theory perspective, information entropy is used to measure the degree of uncertainty or disorder in data. The smaller the information entropy, the greater the dispersion or variability of the data; the greater the variability, the greater the spatial differentiation information provided by the indicator. This invention's entropy weighting method assigns weights to the indicators with the greatest variability, thereby ensuring the final... and This invention maximizes the utilization of the spatial heterogeneity information inherent in the data itself. The entropy weighting method of this invention is also adapted to the characteristics of remote sensing data: remote sensing and geographic information data exhibit strong spatial heterogeneity. By identifying the indicators with the greatest spatial differences, this entropy weighting method ensures that these differences are effectively integrated into the final index, making it particularly suitable for processing large-scale, multi-dimensional remote sensing raster data. The calculation process of the entropy weighting method of this invention is as follows: Entropy weight method for calculating weights: Dimensionless representation of each indicator (such as NTL, PD, SPEI, NDVI, and SM) is applied, followed by range standardization.

[0101]

[0102] in, For the i-th sample, the th j The original value of a standardized first indicator. For the i-th sample, the th j The original value of the first indicator, For the first The first sample j The original value of the first indicator, n The total number of samples; here, "sample" refers to urbanization characteristic data. For the i-th sample, the th j A standardized second indicator raw value, For the i-th sample, the th j The original value of the second indicator, For the first The first sample j The original value of the second indicator, n The total number of samples; here, "sample" refers to urbanization characteristic data. Calculate the weight of each indicator:

[0103] in, Represents the i-th sample. j The first weight of the original value of a standardized indicator. Indicates the first The first sample j A standardized second indicator's original value; Calculate the entropy value of the j-th index:

[0104]

[0105] Calculate the weight of the j-th indicator:

[0106]

[0107] Where m is the total number of indicators. ,and , ,and The smaller the entropy value, the greater the weight.

[0108] Weight Applied to and calculate:

[0109] in, The weight of the j-th indicator , and y These are the standardized values ​​of the i-th sample and the j-th indicator, respectively.

[0110] Assessing the impact of urbanization on drought through correlation and regression analysis. and The relationship.

[0111] Correlation analysis:

[0112] in, and This represents the value at the i-th time step or spatial unit. and , where are the mean and n is the number of samples.

[0113] To further quantify urbanization (in) Characterization) of drought (in the form of) Based on the direction and intensity of the influence (characterized by the variate), the following univariate linear regression model is established:

[0114] a: Regression coefficient (slope), representing When each additional unit is added a: average change; b: intercept term; : The random error term of the i-th sample.

[0115] Perform a significance test (t-test), and calculate the t-statistic based on the standard error SEa of the regression coefficients:

[0116]

[0117] Calculate the p-value using a two-tailed t-test. If p < 0.05, then it is considered... right The effect is significant at the 95% confidence level.

[0118] By combining correlation and regression coefficients, the driving effect of urbanization on drought is quantitatively described; key areas, such as high-altitude areas, are identified. With Gao Overlapping areas provide targeted suggestions for urban planning.

[0119] Finally, an urbanization-drought impact risk map is drawn. The urbanization-drought impact risk map is generated by using an urbanization intensity index raster map and a comprehensive drought index raster map, and by using a bivariate quartile spatial overlay method. This generates an urbanization-drought impact risk map with a clear spatial distribution and a clear classification of levels, which is used to intuitively identify different risk levels of urbanization's impact on drought.

[0120] The drawing steps include: spatial overlay and risk level assignment, that is, combining the four logical masks obtained by S33 in pairs to construct a risk grid map with at least four risk levels; The aforementioned risk levels include at least the highest risk, the second highest risk, the medium risk, and the low risk. The generation of a risk raster map with several risk levels based on the urbanization intensity regional discrimination mask and the drought degree regional discrimination mask includes: Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the high-urbanization-intensity region mask and the high-aridity region mask are marked as regions with the highest risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the high-urbanization-intensity region mask and the low-aridity region mask are marked as regions with the second-highest risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the low-urbanization-intensity region mask and the high-aridity region mask are marked as regions with medium risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the low urbanization intensity region mask and the low drought level region mask are marked as low-risk regions. The risk raster map is obtained by combining the areas with the highest risk, the areas with the second highest risk, the areas with the medium risk, and the areas with the low risk. Each pixel in the risk raster is then assigned a value according to the following rules: Risk Level 4 (Highest Risk): Pixels that simultaneously meet the criteria of being located within both the high urbanization intensity area mask and the high drought level area mask are assigned a value of 4; this area represents a high degree of spatial overlap between high-intensity urbanization and severe drought, and is the core area where urbanization exerts a significant negative impact on drought; Risk Level 3 (Second Highest Risk): Pixels that simultaneously meet the criteria of being located within both the high urbanization intensity area mask and the low drought level area mask are assigned a value of 3; this area represents a region with a high level of urbanization but a low degree of drought, suggesting that there may be effective water resource management measures or good ecological planning that have mitigated the drought effect; Risk Level 2 (Medium Risk): Pixels that simultaneously meet the criteria of being located within both the low urbanization intensity area mask and the high drought level area mask are assigned a value of 2; this area represents drought mainly driven by natural climate factors or non-urbanized agricultural activities, with a weak correlation to urbanization. Risk Level 1 (Low Risk): Pixels that simultaneously meet the requirements of the low urbanization intensity area mask and the low drought level area mask are assigned a value of 1; this area represents that the impact of both urbanization and drought is at a low level.

[0121] Finally, the risk raster map is visualized and rendered, and different colors are assigned to different risk levels in the risk raster map. For example, green, yellow, orange and red are used to represent risk levels from low to high, resulting in an easily interpretable risk map of the impact of urbanization on drought. The risk map of the impact of urbanization on drought directly displays the distribution of risk levels through spatial location, which can provide accurate decision support for urban planning, water resource management and drought disaster prevention.

[0122] This embodiment proposes a method for assessing the impact of urbanization on drought based on multi-source data fusion, which has the following advantages: Highly intuitive: It transforms the complex results of multi-source data fusion into a clear and concise spatial risk level map, enabling decision-makers to quickly identify problem areas and success case areas.

[0123] Objective and scientific: The upper quartile based on the data distribution is used as a dynamic threshold, which avoids the bias set by human experience and ensures the objectivity and scientific nature of the classification results.

[0124] The guiding significance is clear: the four risk levels have clear physical significance and guiding role, especially in effectively distinguishing between areas where "urbanization exacerbates drought" and areas where "urbanization does not exacerbate drought", providing a direct basis for the formulation of differentiated policies.

[0125] Repeatability and universality: This method is procedural and standardized, does not depend on absolute values ​​of a specific region, and is applicable to similar assessments at different geographical scales and in different environmental contexts, thus having broad application prospects.

[0126] Compared with existing technologies, this invention employs the FAO-56 Penman-Monteith (PM) equation, based on energy balance and aerodynamic principles, to calculate the potential evapotranspiration of a reference crop and combines it with precipitation to calculate a standardized precipitation evapotranspiration index, used to characterize drought conditions at different time scales. The quantification of the impact of urbanization is achieved by integrating nighttime light and population density through an urbanization intensity index, combined with Pearson correlation, to assess the driving role of urbanization in drought. The scientific nature of multi-source data fusion is ensured by using entropy weighting to weight and fuse multi-source data, covering remote sensing, meteorological, and urbanization information, overcoming the limitations of existing technologies that rely on data from a single meteorological station. This invention overcomes the shortcomings of traditional methods—low accuracy, limited coverage, and lack of urbanization factors—through multi-dimensional data fusion and statistical verification.

[0127] The following section will use City A in 2020 as a case study to further illustrate the urbanization-based drought impact assessment method proposed in this embodiment, which is based on multi-source data fusion. The method includes the following steps: In this embodiment, the satellite precipitation data for the study area was collected and preprocessed using CHIRPS (Climate Hazards Center Infrared Precipitation with Stations version 3.CHIRPS3 Data Repository https: / / doi.org / 10.15780 / G2JQ0P (2025)), with a temporal resolution of monthly and a spatial resolution of 0.05°. The normalized difference vegetation index was obtained using EARTHDATA SEARCH (https: / / search.earthdata.nasa.gov / search), with a temporal resolution of monthly and a spatial resolution of 0.05°. Soil moisture data are available at: GLEAM4: global land evaporation and soil moisture dataset at 0.1°; resolution from 1980 to near present. Scientific Data, 12,416. https: / / doi.org / 10.1038 / s41597-025-04610-y, with a temporal resolution of years and a spatial resolution of 0.1°. Nighttime light data is available from: Y. Wu, K. Shi, Z. Chen, S. Liu and Z. Chang, "Developing Improved Time-Series DMSP-OLS-Like Data (1992–2019) in China by Integrating DMSP-OLS and SNPP-VIIRS," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 4407714, doi: 10.1109 / TGRS.2021.3135333. The temporal resolution is in years, and the spatial resolution is 0.1°. Population density data are from WorldPop Hub (WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Department deGeographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global HighResolution Population Denominators Project - Funded by The Bill and MelindaGates Foundation (OPP1134076). https: / / dx.doi.org / 10.5258 / SOTON / WP00675), with a temporal resolution of years and a spatial resolution of 1 km.

[0128] The impervious surface ratio data is: Gong P, Li X, Wang J, et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018[J]. Remote Sensing of Environment, 2020, 236: 111510, with a temporal resolution of years and a spatial resolution of 30 meters; Building height data is as follows: Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., & Dai, Y. (2024). Buildingheight of Asia in 3D-GloBFP [Data set]. Zenodo. https: / / doi.org / 10.5281 / zenodo.11397015, with a time resolution of years and data type as shapefile vector. SPEI uses the FAO-56 Penman-Monteith (PM) equation based on energy balance and aerodynamic principles to calculate the reference crop potential evapotranspiration (PET), and combines it with precipitation to calculate the Standardized Precipitation-Evapotranspiration Index (SPEI).

[0129] In constructing an urbanization intensity index and comprehensive drought index During this process, ensuring that all input metrics have a consistent spatial resolution is crucial. The method in this embodiment employs... A data resolution-based strategy was adopted, uniformly resampling all raster data to a uniform resolution of 30m. This was done to calculate the urbanization intensity index at the same spatial scale. and comprehensive drought index In the construction and subsequent spatial overlay analysis, all input indicators were uniformly resampled to a baseline resolution of 30m. Specifically, the urbanization intensity index was... Aligned to a uniform 30m grid size; while the original resolution of the composite drought index was lower. The system then upsamples to a 30m grid using bilinear interpolation. All indicators are only used to calculate weights and construct the final index using the entropy weight method after the spatial resolution is unified, ensuring pixel-level consistency of the risk assessment matrix.

[0130] Calculate the urbanization intensity index The information entropy weighting method was used to determine the weights of four indicators: nighttime light, population density, proportion of impervious surfaces, and building height. Simultaneously, Min-Max standardization was applied to the four types of data to unify indicators of different units and magnitudes into a dimensionless range of [0,1], making them comparable. The calculated... Indicator weight distribution, such as Figure 3 The image shows the index weights for the final weighted fusion to generate the urbanization intensity index, and a raster map of the urbanization intensity index is plotted as follows. Figure 4 As shown; similarly, the entropy weight method was used to construct a comprehensive drought index by integrating three indicators: SPEI, vegetation status, and soil moisture. The weight distribution of the calculated comprehensive drought index is shown in the figure. Figure 5 As shown, a raster map of the comprehensive drought index, which serves as an index of the ecological environment status, is plotted, as follows. Figure 6 As shown.

[0131] After obtaining two core raster layers, spatial correlation analysis and statistical tests were performed. The Pearson correlation coefficient between the urbanization intensity index and the comprehensive drought index on all valid pixels was calculated, and a t-test was conducted to calculate the p-value. The final Pearson correlation coefficient between the urbanization intensity index and the comprehensive drought index was 0.6787, and the p-value was close to 0, indicating that urbanization is an important factor that exacerbates the risk of regional drought.

[0132] Next, the urbanization intensity index and the comprehensive drought index were divided into high and low categories using the quartile method. Then, through spatial overlay analysis, a final urbanization drought impact risk map was generated, as shown below. Figure 7 As shown in the figure, each pixel is divided into four types: 1) low urbanization - low drought risk; 2) low urbanization - high drought risk; 3) high urbanization - low drought risk; 4) high urbanization - high drought risk.

[0133] Example 3 See Figure 8 This embodiment proposes a system for assessing the impact of urbanization on drought based on multi-source data fusion. The system is used to implement the method for assessing the impact of urbanization on drought based on multi-source data fusion described in the above embodiment. The system includes: The data acquisition module is used to acquire multidimensional spatial datasets of urban areas; The data processing module is used to perform multi-source data fusion on the multidimensional spatial dataset using the entropy weight method to obtain multi-source fusion index data; The data evaluation module is used to generate an urbanization drought impact risk map based on the multi-source fusion index data and using an improved bivariate quartile spatial overlay method.

[0134] In this embodiment, firstly, a multidimensional spatial dataset of the urban area is obtained, and then the entropy weight method is used to analyze the multidimensional data. Spatial datasets undergo multi-source data fusion, effectively avoiding interference from subjective human factors in the data fusion results and ensuring the objectivity of multi-source fusion index data. This not only significantly improves the accuracy and objectivity of the assessment results on the impact of urbanization on drought, but also enables the visualization of the assessment results, providing direct scientific decision-making basis for urban planning and ecological management.

[0135] Example 4 See Figure 9 This embodiment also proposes a computer device, see [link to relevant documentation]. Figure 9 It includes: a processor 91, a memory 92, a communication interface 93 and a communication bus 94, wherein the processor 91, the memory 92 and the communication interface 93 communicate with each other through the communication bus 94; The processor 91, memory 92, and communication interface 93 communicate with each other via a communication bus 94. The communication interface 93 is used for network communication with other devices, such as clients or other servers. The processor 91 executes executable instructions 95, specifically performing the operations of the described digital photo frame interaction method. Specifically, the executable instructions 95 may include program code. The processor 91 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computer device includes one or more processors, which can be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0136] Memory 92 is used to store executable instructions 95. Memory 92 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0137] Executable instruction 95 can be invoked by processor 91 to cause the computer device to perform the following operations: S1. Obtain a multidimensional spatial dataset of the urban area; S2. Use the entropy weight method to perform multi-source data fusion on the multi-dimensional spatial dataset to obtain multi-source fusion index data; S3. Based on the multi-source fusion index data, an improved bivariate quartile spatial overlay method is used to generate a risk map of the impact of drought on urbanization.

[0138] In this embodiment, a multi-dimensional spatial dataset of urban areas is first obtained, and then the entropy weight method is used to perform multi-source data fusion on the multi-dimensional spatial dataset. This effectively avoids the interference of human subjective factors on the data fusion results and ensures the objectivity of the multi-source fusion index data. This not only greatly improves the accuracy and objectivity of the assessment results of the impact of urbanization on drought, but also realizes the visualization of the assessment results, providing a direct scientific basis for urban planning and ecological management.

[0139] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the impact of urbanization on drought based on multi-source data fusion, characterized in that, Includes the following steps: Obtain the first dataset of the urban area; the first dataset is a multidimensional spatial dataset. The first dataset is fused using the entropy weighting method to obtain the first data corresponding to the first dataset. Based on the spatial distribution relationship between the various types of index data in the first data, an ecological risk level distribution map corresponding to the urban area is generated, which is an ecological risk level distribution map of the impact risk of drought on urbanization.

2. The method for assessing the impact of urbanization on drought based on multi-source data fusion according to claim 1, characterized in that, The first dataset includes urbanization characteristic data and ecological environment characteristic data; the first data is index data, including urbanization intensity index and ecological environment status index.

3. The method for assessing the impact of urbanization on drought based on multi-source data fusion according to claim 2, characterized in that, The first dataset is fused using the entropy weight method to obtain an urbanization intensity index in the first data corresponding to the first dataset, including: The urbanization indicators of the urbanization feature data in the first dataset are standardized to obtain multiple standardized urbanization indicators. Calculate the first entropy value corresponding to the standardized urbanization index; Based on the first entropy value, calculate the weights of the standardized urbanization indicators; The weights of the standardized urbanization indicators are weighted and fused with their corresponding standardized urbanization indicators to obtain the second data, which is the urbanization intensity index.

4. The method for assessing the impact of urbanization on drought based on multi-source data fusion according to claim 3, characterized in that, The first dataset is fused using the entropy weight method to obtain an ecological environment state index in the first data corresponding to the first dataset, including: The ecological indicators of the ecological environment feature data in the first dataset are standardized to obtain multiple standardized ecological indicators. Calculate the second entropy value corresponding to the standardized ecological indicator; Based on the second entropy value, the weights of the standardized ecological indicators are calculated; The weights of the standardized ecological indicators are weighted and fused with their corresponding standardized ecological indicators to obtain the third data, which is an ecological environment status index that includes a comprehensive drought index.

5. The method for assessing the impact of urbanization on drought based on multi-source data fusion according to claim 4, characterized in that, The urbanization indicators include at least nighttime light data, impermeable surface area ratio data, population density data, and building height data; the ecological indicators include at least drought index, vegetation index, and soil moisture data calculated based on potential evapotranspiration.

6. The method for assessing the impact of urbanization on drought based on multi-source data fusion according to claim 4, characterized in that, Based on the spatial distribution relationship between various types of index data in the first data, a bivariate quartile spatial overlay method is used to generate an ecological risk level distribution map corresponding to the urban area. This ecological risk level distribution map is a map of the impact risk of urbanization drought, including: Obtain raster maps of urbanization intensity index and comprehensive drought index; Calculate the upper quartile of all pixel values ​​in the urbanization intensity index raster map and the comprehensive drought index raster map. Use the upper quartile of all pixel values ​​in the urbanization intensity index raster map as the first threshold for classifying high and low urbanization intensity levels, and use the upper quartile of all pixel values ​​in the comprehensive drought index raster map as the second threshold for classifying high and low drought levels. Based on the first segmentation threshold, an urbanization intensity region discrimination mask is constructed, and based on the second segmentation threshold, an aridity degree region discrimination mask is constructed. Based on the urbanization intensity regional discrimination mask and the drought degree regional discrimination mask, a risk raster map with several risk levels is generated; The risk raster is visualized and rendered, and different colors are assigned to different risk levels in the risk raster to obtain the risk map of the impact of urbanization drought.

7. The method for assessing the impact of urbanization on drought based on multi-source data fusion according to claim 6, characterized in that, The urbanization intensity region discrimination mask includes a high urbanization intensity region mask and a low urbanization intensity region mask; the high urbanization intensity region mask is that regions with pixel values ​​greater than the first segmentation threshold in the urbanization intensity index raster image are marked as true, and the remaining regions are marked as false; The low urbanization intensity area mask is defined as follows: regions in the urbanization intensity index raster image with pixel values ​​less than or equal to the first segmentation threshold are marked as true, and the remaining regions are marked as false. The drought severity region discrimination mask includes a high drought severity region mask and a low drought severity region mask; The high drought level region mask is defined as regions in the comprehensive drought index raster image where the pixel value is greater than the second division threshold and the remaining regions are marked as false; the low drought level region mask is defined as regions in the comprehensive drought index raster image where the pixel value is less than or equal to the second division threshold and the remaining regions are marked as false.

8. The method for assessing the impact of urbanization on drought based on multi-source data fusion according to claim 7, characterized in that, The aforementioned risk levels include at least the highest risk, the second highest risk, the medium risk, and the low risk. The generation of a risk raster map with several risk levels based on the urbanization intensity regional discrimination mask and the drought degree regional discrimination mask includes: Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the high-urbanization-intensity region mask and the high-aridity region mask are marked as regions with the highest risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the high-urbanization-intensity region mask and the low-aridity region mask are marked as regions with the second-highest risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the low-urbanization-intensity region mask and the high-aridity region mask are marked as regions with medium risk. Regions in the risk raster map that simultaneously satisfy the conditions of being located within both the low urbanization intensity region mask and the low drought level region mask are marked as low-risk regions. The risk raster map is obtained by combining the areas with the highest risk, the areas with the second highest risk, the areas with the medium risk, and the areas with the low risk.

9. A system for assessing the impact of urbanization on drought based on multi-source data fusion, the system being used to implement the method for assessing the impact of urbanization on drought based on multi-source data fusion as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition module is used to acquire the first dataset of the urban area, which is a multidimensional spatial dataset. The data processing module is used to perform multi-source data fusion on the first dataset using the entropy weight method to obtain the first data corresponding to the first dataset. The data assessment module is used to generate an ecological risk level distribution map corresponding to the urban area based on the spatial distribution relationship between various types of index data in the first data. The ecological risk level distribution map is a risk map of the impact of drought on urbanization.

10. A computer device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the urbanization-drought impact assessment method based on multi-source data fusion as described in any one of claims 1-8.

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