A flood storage spatialization method and system based on remote sensing data

Abstract

This invention discloses a method and system for spatializing flood storage capacity based on remote sensing data, belonging to the field of flood storage capacity spatialization technology. The method includes: determining spatial raster data of rainfall, runoff, and lake / reservoir volumes for the vegetation ecosystem of the target area to be evaluated, based on remote sensing data; and then determining the spatialized flood storage capacity of the target area based on these data. This invention utilizes the rasterizable nature of remote sensing data to achieve the purpose of spatialized calculation of flood storage capacity.

Description

Technical Field

[0001] This invention relates to the field of spatialization technology of flood storage capacity, and in particular to a method and system for spatialization of flood storage capacity based on remote sensing data. Background Technology

[0002] Gross Ecosystem Product (GEP) is an indicator that comprehensively reflects the functional value of an ecosystem, clearly defining the supporting role of ecosystem products and services in economic and social development. Flood regulation and storage indicators are an important component of GEP accounting.

[0003] Currently, the estimation of flood storage capacity is limited by the lack of systematic calculation methods and the difficulty in obtaining data. Therefore, current research cannot fully reflect the overall flood storage capacity and its spatial distribution characteristics in the target area. Thus, a spatialization method for flood storage capacity is necessary. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for spatializing flood storage capacity based on remote sensing data, which can utilize the rasterizable characteristics of remote sensing data to achieve the purpose of spatialized calculation of flood storage capacity.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] In a first aspect, the present invention provides a method for spatializing flood storage capacity based on remote sensing data, comprising:

[0007] Acquire daily rainfall remote sensing raster data of the target area to be evaluated, and based on the daily rainfall remote sensing raster data, determine the storm rainfall spatial raster data of the vegetation ecosystem of the target area to be evaluated;

[0008] Acquire the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor in the target area to be evaluated for each hydrological monitoring station area, and predict the annual storm runoff spatial raster data of each non-hydrological monitoring station area in the target area to be evaluated based on the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor.

[0009] Based on the annual storm runoff spatial raster data of each hydrological monitoring station area and each non-hydrological monitoring station area in the target area to be evaluated, the storm runoff spatial raster data of the vegetation ecosystem in the target area to be evaluated is determined.

[0010] The spatial raster data of lakes and reservoirs in the target area to be evaluated are determined by using a dual-channel extraction method of water body index and vegetation index and a maximum connected component algorithm.

[0011] Based on the spatial raster data of storm rainfall, storm runoff, and lake and reservoir water volume of the target area to be evaluated, the spatialization result of flood storage capacity of the target area is determined.

[0012] Optionally, acquiring the daily rainfall remote sensing raster data of the target area to be evaluated specifically includes:

[0013] Obtain remote sensing data of daily rainfall in the target area to be assessed;

[0014] The remote sensing data of the daily rainfall is preprocessed to obtain remote sensing raster data of the daily rainfall of the target area to be evaluated.

[0015] Optionally, determining the spatial raster data of storm rainfall for the vegetation ecosystem of the target area to be assessed based on the daily rainfall remote sensing raster data specifically includes:

[0016] The daily rainfall remote sensing raster data is filtered to obtain the daily heavy rainfall spatial raster data of the target area to be evaluated;

[0017] The spatial raster data of daily heavy rainfall in the accounting year of the target area to be evaluated are superimposed to obtain the spatial raster data of annual heavy rainfall in the target area to be evaluated.

[0018] Based on the land use type data of the target area to be evaluated, determine the spatial raster data of the vegetation ecosystem of the target area to be evaluated;

[0019] The spatial raster data of annual rainstorm precipitation and spatial raster data of vegetation ecosystem in the target area to be evaluated are resampled;

[0020] Multiplication is performed on the resampled annual storm rainfall spatial raster data and the resampled vegetation ecosystem spatial raster data to obtain the storm rainfall spatial raster data of the vegetation ecosystem in the target area to be evaluated.

[0021] Optionally, acquiring the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor in the target area to be evaluated specifically includes:

[0022] Obtain remote sensing data of daily rainfall and daily surface runoff for each hydrological monitoring station in the target area to be evaluated;

[0023] The remote sensing data of daily surface runoff in the hydrological monitoring station area with daily rainfall of rainstorm are accumulated to obtain the remote sensing data of daily rainstorm runoff in each hydrological monitoring station area.

[0024] The remote sensing data of daily rainstorm runoff are summed to obtain the remote sensing data of annual rainstorm runoff for each hydrological monitoring station area.

[0025] The remote sensing data of annual storm runoff were processed using spatial interpolation to obtain spatial raster data of annual storm runoff for each hydrological monitoring station area.

[0026] Acquire initial spatial raster data for each rainstorm runoff influencing factor; the rainstorm runoff influencing factors include remote sensing data of rainstorm precipitation, remote sensing data of evapotranspiration, remote sensing data of soil moisture content, remote sensing data of vegetation cover, and remote sensing data of slope.

[0027] The initial spatial raster data of each of the aforementioned rainstorm runoff influencing factors are resampled;

[0028] Standard normalization was performed on each of the resampled initial spatial raster data to obtain the final spatial raster data of each rainstorm runoff influencing factor.

[0029] Optionally, predicting the annual storm runoff spatial raster data for each non-hydrological monitoring station area in the target area to be evaluated based on the annual storm runoff spatial raster data for each of the hydrological monitoring station areas and the spatial raster data for each of the storm runoff influencing factors specifically includes:

[0030] Using the hydrological monitoring station area as the sample area, the principal component analysis algorithm is used to perform bivariate analysis on the annual rainstorm runoff spatial raster data and the spatial raster data of each rainstorm runoff influencing factor in each hydrological monitoring station area, so as to obtain the correlation coefficient between the annual rainstorm runoff and each rainstorm runoff influencing factor.

[0031] A regression equation is established based on the correlation coefficients between the annual storm runoff and various storm runoff influencing factors.

[0032] Based on the established regression equation, predict the annual storm runoff spatial raster data for each non-hydrological monitoring station area in the target area to be evaluated.

[0033] Optionally, the step of using a dual-channel extraction method of water index and vegetation index, along with a maximum connected component algorithm, to determine the spatial raster data of lakes and reservoirs in the target area to be evaluated specifically includes:

[0034] Satellite remote sensing data is processed to obtain spatial raster data of the improved normalized water index of the target area to be evaluated, and the normalized vegetation index of each raster is calculated based on the spatial raster data of the improved normalized water index.

[0035] From the spatial raster data of the improved normalized water index, extract spatial raster data with improved normalized water index values ​​greater than normalized vegetation index values, and determine the extracted spatial raster data as the water spatial raster data of the target area to be evaluated.

[0036] The spatial raster data of the water body is processed using the maximum connected component algorithm to obtain the spatial raster data of the lakes and reservoirs in the target area to be evaluated.

[0037] Optionally, determining the spatialization result of flood storage capacity in the target area based on the spatial raster data of storm rainfall in the vegetation ecosystem, the spatial raster data of storm runoff in the vegetation ecosystem, and the spatial raster data of lakes and reservoirs in the target area to be evaluated specifically includes:

[0038] Based on the spatial raster data of storm rainfall and storm runoff of the vegetation ecosystem in the target area to be evaluated, the spatialization result of vegetation flood storage capacity is determined.

[0039] The flood storage capacity of lakes and reservoirs is determined, and the flood storage capacity and spatial raster data of lakes and reservoirs are processed using a pixel assignment method to obtain the spatialized result of the flood storage capacity of lakes and reservoirs.

[0040] The spatial raster data of marsh wetlands and the flood storage capacity of marsh wetlands in the target area to be evaluated are determined. The flood storage capacity and spatial raster data of marsh wetlands are processed by using a cell assignment method to obtain the spatialized result of the flood storage capacity of marsh wetlands.

[0041] The spatialized results of flood storage capacity in vegetation, lakes and reservoirs, and marshes and wetlands are summed to obtain the spatialized results of flood storage capacity in the target area.

[0042] Optionally, determining the spatialization result of vegetation flood storage capacity based on the spatial raster data of storm rainfall and storm runoff of the vegetation ecosystem in the target area to be evaluated specifically includes:

[0043] According to the formula Determine the amount of flood storage capacity that can be achieved through vegetation;

[0044] Using a pixel assignment method, spatial raster data of flood storage capacity, rainstorm rainfall, and rainstorm runoff in vegetation ecosystems are processed to obtain spatialized results of vegetation flood storage capacity.

[0045] in, Indicates the amount of flood storage capacity regulated by vegetation; n This represents the total number of vegetation ecosystem types in the target area to be assessed; Indicates the first i Annual torrential rainfall in vegetation-like ecosystems; Indicates the first i Annual storm runoff in vegetation-like ecosystems; Indicates the first i The area of ​​a unit grid in a vegetation-like ecosystem.

[0046] Optionally, the determination of the marsh wetland spatial raster data for the target area to be evaluated specifically includes:

[0047] Based on the land use type data of the target area to be evaluated, determine the spatial raster data of the marsh wetland in the target area to be evaluated.

[0048] Secondly, the present invention provides a spatialization system for flood storage capacity based on remote sensing data, comprising:

[0049] The vegetation ecosystem storm rainfall spatial raster data calculation module is used to acquire daily rainfall remote sensing raster data of the target area to be evaluated, and to determine the storm rainfall spatial raster data of the vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing raster data.

[0050] The module for predicting annual storm runoff spatial raster data in non-hydrological monitoring station areas is used to acquire the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor in the target area to be evaluated, and to predict the annual storm runoff spatial raster data of each non-hydrological monitoring station area in the target area to be evaluated based on the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor.

[0051] The vegetation ecosystem storm runoff spatial raster data calculation module is used to determine the storm runoff spatial raster data of the vegetation ecosystem in the target area to be evaluated based on the annual storm runoff spatial raster data of each hydrological monitoring station area and the annual storm runoff spatial raster data of each non-hydrological monitoring station area in the target area to be evaluated.

[0052] The lake and reservoir spatial raster data calculation module is used to determine the lake and reservoir spatial raster data of the target area to be evaluated by using a dual-channel extraction method of water body index and vegetation index and a maximum connectivity algorithm.

[0053] The flood storage capacity spatialization result determination module is used to determine the spatialization result of the flood storage capacity of the target area based on the spatial raster data of the rainfall of the vegetation ecosystem, the spatial raster data of the rainfall runoff of the vegetation ecosystem, and the spatial raster data of the lakes and reservoirs in the target area to be evaluated.

[0054] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0055] This invention uses remote sensing technology to calculate and spatialize flood storage capacity, resulting in a spatial distribution map of flood storage capacity in the target area. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating the method for spatializing flood storage capacity based on remote sensing data provided in Embodiment 1 of the present invention.

[0058] Figure 2 This is a schematic diagram of the structure of the flood storage capacity spatialization system based on remote sensing data provided in Embodiment 2 of the present invention;

[0059] Figure 3 This is a flowchart illustrating the spatialization method for flood storage capacity based on remote sensing data provided in Embodiment 3 of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] The purpose of this invention is to provide a method and system for spatializing flood storage capacity based on remote sensing data. By utilizing the rasterizable nature of remote sensing data, the method performs spatial calculation of flood storage capacity, aiming to quantify the spatial distribution characteristics of flood storage capacity in the target area.

[0062] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0063] Example 1

[0064] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for spatializing flood storage capacity based on remote sensing data, which includes the following steps.

[0065] Step 100: Obtain daily rainfall remote sensing raster data of the target area to be evaluated, and based on the daily rainfall remote sensing raster data, determine the storm rainfall spatial raster data of the vegetation ecosystem of the target area to be evaluated.

[0066] Step 200: Obtain the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor in the target area to be evaluated, and predict the annual storm runoff spatial raster data of each non-hydrological monitoring station area in the target area to be evaluated based on the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor.

[0067] Step 300: Based on the annual storm runoff spatial raster data of each hydrological monitoring station area and the annual storm runoff spatial raster data of each non-hydrological monitoring station area in the target area to be evaluated, determine the storm runoff spatial raster data of the vegetation ecosystem in the target area to be evaluated.

[0068] Step 400: Using the dual-channel extraction method of water index and vegetation index and the maximum connected component algorithm, determine the spatial raster data of lakes and reservoirs in the target area to be evaluated.

[0069] Step 500: Based on the spatial raster data of storm rainfall, storm runoff, and lake and reservoir water volume of the target area to be evaluated, determine the spatialization result of flood storage capacity of the target area.

[0070] As a preferred embodiment, step 100 of this invention, "acquiring daily rainfall remote sensing raster data of the target area to be evaluated," specifically includes:

[0071] First, remote sensing data of daily rainfall in the target area to be evaluated is acquired. Then, the remote sensing data of daily rainfall is preprocessed to obtain remote sensing raster data of daily rainfall in the target area to be evaluated.

[0072] As a preferred embodiment, step 100 of this invention, "determining the spatial raster data of rainstorm rainfall of the vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing raster data," specifically includes:

[0073] Step 101: Filter the daily rainfall remote sensing raster data to obtain the daily heavy rainfall spatial raster data of the target area to be evaluated.

[0074] One example is: by setting a threshold, the remote sensing raster data of daily rainfall with a daily precipitation of ≥50mm (according to China's meteorological regulations, the rainfall value corresponding to rainstorm is 50mm or more in 24 hours) is retained, and the remaining raster data is processed to zero, so as to realize the filtering operation of the remote sensing raster data of daily rainfall and obtain the spatial raster data of daily rainstorm rainfall of the target area to be evaluated.

[0075] Step 102: Overlay the daily heavy rainfall spatial raster data of the target area to be evaluated within the calculation year to obtain the annual heavy rainfall spatial raster data of the target area to be evaluated.

[0076] Step 103: Based on the land use type data of the target area to be evaluated, determine the spatial raster data of the vegetation ecosystem of the target area to be evaluated.

[0077] One example is: using land use type data, extract the pixels of the vegetation ecosystem (forest and shrubland) of the target area to be evaluated and assign them a value of 1, while assigning a value of 0 to the remaining raster, thus obtaining the spatial raster data of the vegetation ecosystem.

[0078] Step 104: Resample the spatial raster data of annual rainstorm precipitation and the spatial raster data of vegetation ecosystem in the target area to be evaluated; wherein the resolution is set to be consistent during resampling.

[0079] Step 105: Perform a multiplication operation on the resampled annual storm rainfall spatial raster data and the resampled vegetation ecosystem spatial raster data to obtain the storm rainfall spatial raster data of the vegetation ecosystem of the target area to be evaluated.

[0080] As a preferred embodiment, step 200 of this invention, "obtaining spatial raster data of annual storm runoff from each hydrological monitoring station in the target area to be evaluated," specifically includes:

[0081] First, remote sensing data of daily rainfall and daily surface runoff were acquired for each hydrological monitoring station in the target area to be evaluated. Second, the remote sensing data of daily surface runoff for hydrological monitoring station areas with daily rainfall of ≥50 mm were accumulated to obtain the remote sensing data of daily torrential rain runoff for each hydrological monitoring station area. Then, the remote sensing data of daily torrential rain runoff were accumulated to obtain the remote sensing data of annual torrential rain runoff for each hydrological monitoring station area. Finally, the remote sensing data of annual torrential rain runoff were processed using spatial interpolation to obtain the spatial raster data of annual torrential rain runoff for each hydrological monitoring station area. The watershed to which the hydrological monitoring station belongs is the upstream area.

[0082] As a preferred embodiment, the "spatial raster data of each rainstorm runoff influencing factor" in step 200 of this embodiment specifically includes:

[0083] First, satellite remote sensing data is processed to obtain initial spatial raster data for each rainstorm runoff influencing factor. These factors include remote sensing data for rainfall precipitation, evapotranspiration, soil moisture content, vegetation cover, and slope. Second, the initial spatial raster data for each of these factors is resampled. Finally, the resampled initial spatial raster data is normalized to obtain the final spatial raster data for each rainstorm runoff influencing factor. The resolution is consistently set during resampling.

[0084] As a preferred embodiment, step 200 of this invention, "predicting the annual storm runoff spatial raster data of each non-hydrological monitoring station area in the target area to be evaluated based on the annual storm runoff spatial raster data of each of the hydrological monitoring station areas and the spatial raster data of each of the storm runoff influencing factors," specifically includes:

[0085] Step 201: Using the hydrological monitoring station area as the sample area, the principal component analysis algorithm is used to perform bivariate analysis on the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor in each hydrological monitoring station area to obtain the correlation coefficient between the annual storm runoff and each storm runoff influencing factor.

[0086] Step 202: Establish a regression equation based on the correlation coefficients between annual storm runoff and various storm runoff influencing factors.

[0087] Step 203: Based on the established regression equation, predict the annual storm runoff spatial raster data for each non-hydrological monitoring station area in the target area to be evaluated.

[0088] As a preferred embodiment, step 300 of this invention specifically includes:

[0089] Step 301: Overlay the annual storm runoff spatial raster data of each hydrological monitoring station area and each non-hydrological monitoring station area in the target area to be evaluated to obtain the annual storm runoff spatial raster data of the entire target area to be evaluated, and resample the annual storm runoff spatial raster data of the target area to be evaluated. The resolution during resampling is the same as the resolution in step 104.

[0090] Step 302: Perform a multiplication operation on the resampled annual storm runoff spatial raster data and the resampled vegetation ecosystem spatial raster data to obtain the storm runoff spatial raster data of the vegetation ecosystem in the target area to be evaluated. The process for determining the resampled vegetation ecosystem spatial raster data is described in steps 103 to 104.

[0091] As a preferred embodiment, step 400 of this invention specifically includes:

[0092] First, satellite remote sensing data is processed to obtain spatial raster data of the improved normalized water index (EMI) for the target area to be evaluated. Based on this EPI spatial raster data, the normalized vegetation index (NDI) for each raster is calculated. Second, spatial raster data with EPI values ​​greater than the NDI values ​​are extracted from the EPI spatial raster data, and these extracted spatial raster data are identified as the water spatial raster data for the target area to be evaluated. Finally, the maximum connected component algorithm is used to process the water spatial raster data to obtain spatial raster data of large and medium-sized lakes and reservoirs in the target area to be evaluated.

[0093] One example is:

[0094] Step 401: Process the satellite remote sensing data to obtain spatial raster data of the improved normalized water index (MNDWI) for the target area to be evaluated.

[0095] Step 402: Based on the improved normalized water index (MNDWI) spatial raster data, calculate the normalized vegetation index (NDVI) for each raster.

[0096] Step 403: Using the Normalized Difference Vegetation Index (NDVI) as a threshold, extract spatial raster data where the Improved Normalized Difference Water Index (MNDWI) value is greater than the Normalized Difference Vegetation Index (NDVI) value, thus obtaining the water body spatial raster data.

[0097] Step 404: Using months as the time unit, repeat steps 401-403 to obtain 12 water body spatial raster data of the target area to be evaluated. Select the water body raster data with the most water body pixels as the water body spatial raster data of the target area to be evaluated.

[0098] Step 405: Using the maximum connected component algorithm, remove small-volume water pixels from the water spatial raster data of the target area to be evaluated, and extract large and medium-sized lakes and reservoirs.

[0099] Step 406: Assign a value of 1 to the pixels in the large and medium-sized lakes and reservoirs area, and a value of 0 to the other grids to obtain the spatial grid data of the large and medium-sized lakes and reservoirs in the target area to be evaluated.

[0100] As a preferred embodiment, step 500 of this invention specifically includes:

[0101] Step 501: Based on the spatial raster data of storm rainfall and storm runoff of the vegetation ecosystem in the target area to be evaluated, determine the spatialization result of vegetation flood storage capacity.

[0102] Step 502: Determine the flood storage capacity of lakes and reservoirs, and process the flood storage capacity and spatial raster data of lakes and reservoirs using a pixel assignment method to obtain the spatialized result of the flood storage capacity of lakes and reservoirs.

[0103] Step 503: Determine the spatial raster data of the marsh wetland and the flood storage capacity of the marsh wetland in the target area to be evaluated, and process the flood storage capacity and spatial raster data of the marsh wetland using a pixel assignment method to obtain the spatialized result of the flood storage capacity of the marsh wetland.

[0104] Step 504: Add the spatialized results of the vegetation flood storage capacity, the spatialized results of the lake and reservoir flood storage capacity, and the spatialized results of the marsh and wetland flood storage capacity to obtain the spatialized results of the flood storage capacity of the target area.

[0105] In this embodiment of the invention, the vegetation ecosystem is divided into forest ecosystem and shrub ecosystem.

[0106] Based on the actual conditions of the target area to be assessed, the flood storage capacity... C fm The calculation is based on the vegetation flood storage capacity. C vfm Lake flood storage capacity C lfm Flood storage capacity of reservoirs C rfm With the flood storage capacity of marsh wetlands Cmfm sum.

[0107] Among them, vegetation flood storage capacity C vfm The calculation formula is:

[0108] ;

[0109] n This represents the total number of vegetation ecosystem types in the target area to be assessed; P i For the first i Rainfall (mm) in vegetation-like ecosystems; R fi For the first i Rainfall runoff (mm) in vegetation-like ecosystems; A i The unit grid area (km²) of the i-th type of vegetation ecosystem 2 ); i For the target area to be evaluated i Vegetated ecosystem type.

[0110] The formula for calculating the flood storage capacity of a reservoir is:

[0111] ;

[0112] C t Reservoir capacity (km) 3 ).

[0113] The formula for calculating the flood storage capacity of marsh wetlands is:

[0114] ;

[0115] S The unit grid area of ​​marshland (km²) 2 ); h The water storage depth of the marsh wetland soil (m); ρ Bulk density of marsh wetland soil (g / cm³) 3 ); ρ w The density of water; F The saturated water content of marsh wetland soil; E The natural moisture content of the marshland before it is submerged by floodwater; H The height of stagnant water on the surface of marsh wetlands (m).

[0116] Based on the above, step 501 specifically includes:

[0117] First, according to the formula The vegetation flood storage capacity is determined; then, using a pixel assignment method, the spatial raster data of the vegetation ecosystem flood storage capacity, rainstorm rainfall, and rainstorm runoff are processed to obtain the spatialized result of the vegetation flood storage capacity.

[0118] The flood storage capacity of each lake and the reservoir capacity are all from official statistics, therefore step 502 specifically includes:

[0119] First, the reservoir flood storage capacity is determined based on the calculation formulas for reservoir capacity and reservoir flood storage capacity. Second, the reservoir flood storage capacity and the spatial raster data of large and medium-sized reservoirs in the target area to be evaluated are spatialized and assigned values ​​to obtain the reservoir flood storage capacity.

[0120] First, determine the flood storage capacity of the lake; then, spatialize the flood storage capacity of the lake and the spatial raster data of large and medium-sized lakes to obtain the flood storage capacity of the lake.

[0121] The parameters required for calculating the flood storage capacity of marsh wetlands come from official statistical data and measured data; therefore, step 503 specifically includes:

[0122] First, based on the land use type data of the target area to be evaluated, the swamp wetland pixels of the target area to be evaluated are extracted and assigned a value of 1, while the other grids are assigned a value of 0, thus obtaining the swamp wetland spatial grid data.

[0123] Secondly, based on the aforementioned official statistics and measured data, as well as the calculation formula for the flood storage capacity of marsh wetlands, the flood storage capacity of marsh wetlands is calculated.

[0124] Finally, the flood storage capacity of the marsh wetland and the spatial raster data of the marsh wetland are spatialized to obtain the spatialized result of the flood storage capacity of the marsh wetland.

[0125] Example 2

[0126] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a spatialization system for flood storage capacity based on remote sensing data is provided below.

[0127] like Figure 2 As shown in the figure, an embodiment of the present invention provides a spatialization system for flood storage capacity based on remote sensing data, comprising:

[0128] The vegetation ecosystem storm rainfall spatial raster data calculation module 1 is used to acquire daily rainfall remote sensing raster data of the target area to be evaluated, and to determine the storm rainfall spatial raster data of the vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing raster data.

[0129] The annual storm runoff spatial raster data prediction module 2 for non-hydrological monitoring station areas is used to acquire the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor of each hydrological monitoring station area in the target area to be evaluated, and to predict the annual storm runoff spatial raster data of each non-hydrological monitoring station area in the target area to be evaluated based on the annual storm runoff spatial raster data and the spatial raster data of each storm runoff influencing factor of each hydrological monitoring station area.

[0130] The vegetation ecosystem storm runoff spatial raster data calculation module 3 is used to determine the storm runoff spatial raster data of the vegetation ecosystem in the target area to be evaluated based on the annual storm runoff spatial raster data of each hydrological monitoring station area and the annual storm runoff spatial raster data of each non-hydrological monitoring station area in the target area to be evaluated.

[0131] The lake and reservoir spatial raster data calculation module 4 is used to determine the lake and reservoir spatial raster data of the target area to be evaluated by using a dual-channel extraction method of water body index and vegetation index and a maximum connectivity algorithm.

[0132] The flood storage capacity spatialization result determination module 5 is used to determine the spatialization result of the flood storage capacity of the target area based on the spatial raster data of the rainfall of the vegetation ecosystem, the spatial raster data of the rainfall runoff of the vegetation ecosystem, and the spatial raster data of the lakes and reservoirs in the target area to be evaluated.

[0133] Example 3

[0134] This invention relates to a spatialization method for flood storage capacity based on remote sensing data, which aims to quantify the spatial distribution characteristics of flood storage capacity in a target area to be evaluated.

[0135] like Figure 3 As shown in the figure, this invention proposes a method for spatializing flood storage capacity based on remote sensing data, which specifically includes the following steps:

[0136] S1: Preprocess the remote sensing data of rainfall, combine it with land use type data, and use the threshold method to obtain the spatial raster data of rainstorm rainfall of the vegetation ecosystem of the target area to be evaluated.

[0137] S2: Principal component analysis is used to establish a spatial model of rainstorm runoff and its multiple influencing factors. Combined with land use type data, spatial raster data of rainstorm runoff of vegetation ecosystem in the target area to be evaluated are obtained.

[0138] S3: Using a dual-channel extraction method of water index and vegetation index and the maximum connectivity algorithm, spatial raster data of large and medium-sized lakes and reservoirs in the target area to be evaluated are obtained.

[0139] S4: Perform pixel-based calculations on flood storage capacity to obtain the spatialized results of flood storage capacity for the target area to be evaluated.

[0140] In this embodiment, step S1 specifically includes:

[0141] Using 2020 as the accounting year and a certain province as the research area, daily rainfall remote sensing raster data was obtained through preprocessing using satellite remote sensing data UCSB-CHG / CHIRPS / DAILY and the administrative boundary of the province on the GEE cloud platform.

[0142] By setting a threshold, the raster values ​​with daily rainfall ≥ 50 mm (according to China's meteorological regulations, the rainfall value corresponding to rainstorm is 50 mm or more in 24 hours) are retained, and the remaining raster values ​​are set to zero, thus obtaining the spatial raster data of daily rainstorm rainfall.

[0143] By overlaying the daily heavy rainfall data of a certain province in 2020, spatial raster data of annual heavy rainfall was obtained.

[0144] The pixels of the vegetation ecosystem (forest and shrubland) in a certain province were extracted using Modis land use type data and assigned a value of 1, while the rest of the raster was assigned a value of 0, thus obtaining the spatial raster data of the vegetation ecosystem.

[0145] The annual torrential rainfall data and the spatial raster data of vegetation ecosystems were resampled, and the resolution was set to be consistent.

[0146] By multiplying the resampled annual rainstorm precipitation spatial raster data with the vegetation ecosystem spatial raster data, the rainstorm precipitation spatial raster data of a certain province's vegetation ecosystem is obtained.

[0147] In this embodiment, step S2 specifically includes:

[0148] By acquiring daily rainfall and daily surface runoff data from various hydrological monitoring stations in a certain province, the surface runoff of hydrological monitoring stations with daily rainfall ≥ 50 mm (heavy rain) is summed to obtain the daily heavy rain runoff of each station.

[0149] The daily torrential rain runoff is summed up to obtain the annual torrential rain runoff data for each hydrological monitoring station.

[0150] Spatial interpolation was used to obtain the spatial raster data of annual rainstorm runoff in the areas of each hydrological monitoring station.

[0151] Multiple data points were selected as influencing factors for rainstorm runoff, including rainstorm precipitation data, evapotranspiration data, soil moisture content data, vegetation cover data, and slope data.

[0152] Spatial raster data of each influencing factor were acquired using satellite remote sensing data and then resampled; the resolution was set to be consistent.

[0153] The rainfall data for the rainstorm was obtained in step S1.

[0154] Evapotranspiration data are from satellite remote sensing data NASA / GLDAS / V021 / NOAH / G025 / T3H.

[0155] Vegetation cover data were obtained from Sentinel-2 MSI: MultiSpectral Instrument, Level-2A band satellite remote sensing data.

[0156] Soil moisture content data are from NASA-USDA Enhanced SMAP Global Soil Moisture satellite remote sensing data.

[0157] The slope data was calculated using NASA SRTM Digital Elevation 30m satellite DEM data.

[0158] Due to the different units of measurement, the raster data of each of the above-mentioned influencing factors need to be standardized and normalized.

[0159] Using the areas of each hydrological monitoring station as the sample area, the raster data of each influencing factor were selected from the same locations as the areas of each hydrological monitoring station, listed in a table, and imported into SPSS software.

[0160] Principal component analysis was performed using SPSS software. Bivariate analysis was conducted on the storm runoff data of each hydrological monitoring station area and the sample data of each influencing factor to obtain the correlation coefficient between the storm runoff data and the data of each influencing factor. Based on this correlation coefficient, a regression equation was established.

[0161] Based on the established regression equation, the annual storm runoff in the non-hydrological monitoring station area is estimated, and spatial raster data of the annual storm runoff in the non-hydrological monitoring station area is obtained.

[0162] The annual storm runoff spatial raster data of the hydrological monitoring station area and the non-hydrological monitoring station area are overlaid to obtain the annual storm runoff spatial raster data of the entire target area to be evaluated.

[0163] By multiplying the annual storm runoff spatial raster data with the vegetation ecosystem spatial raster data, we can obtain the storm runoff spatial raster data of the vegetation ecosystem in a certain province.

[0164] In this embodiment, step S3 specifically includes:

[0165] On the GEE cloud platform, spatial raster data of the improved normalized water index (MNDWI) for a certain province were obtained using Landsat 8 satellite remote sensing data.

[0166] Based on the spatial raster data of MNDWI, the normalized vegetation index (NDVI) value of each raster is calculated.

[0167] Using NDVI as a threshold, extract the raster data with MNDWI values ​​greater than NDVI values ​​to obtain water body raster data.

[0168] Using months as the time unit, 12 water body raster data sets for a certain province were obtained. The raster data set with the most water body pixels was selected as the water body raster data for that province.

[0169] By using the maximum connected component algorithm, small-volume water pixels in the water raster data are removed to obtain large and medium-sized lakes and reservoirs.

[0170] By assigning a value of 1 to the pixels of lakes and reservoirs and a value of 0 to other raster cells, spatial raster data of large and medium-sized lakes and reservoirs in a certain province is obtained.

[0171] In this embodiment, step S4 specifically includes:

[0172] Based on the actual conditions of the target area to be assessed, the flood storage capacity... C fm The calculation is based on the vegetation flood storage capacity. C vfm Lake flood storage capacity C lfm Flood storage capacity of reservoirs C rfm With the flood storage capacity of marsh wetlands C mfm sum.

[0173] Among them, vegetation flood storage capacity C vfm The calculation formula is:

[0174] ;

[0175] n This represents the total number of vegetation ecosystem types in the target area to be assessed; P i For the firsti Rainfall (mm) in vegetation-like ecosystems; R fi For the first i Rainfall runoff (mm) in vegetation-like ecosystems; A i The unit grid area (km²) of the i-th type of vegetation ecosystem 2 ); i For the target area to be evaluated i Vegetated ecosystem type.

[0176] The formula for calculating the flood storage capacity of a reservoir is:

[0177] ;

[0178] C t Reservoir capacity (km) 3 ).

[0179] The formula for calculating the flood storage capacity of marsh wetlands is:

[0180] ;

[0181] S The unit grid area of ​​marshland (km²) 2 ); h The water storage depth of the marsh wetland soil (m); ρ Bulk density of marsh wetland soil (g / cm³) 3 ); ρ w The density of water; F The saturated water content of marsh wetland soil; E The natural moisture content of the marshland before it is submerged by floodwater; H The height of stagnant water on the surface of marsh wetlands (m).

[0182] Based on the above calculation formula, the spatialized results of vegetation flood storage capacity in the target area to be evaluated are obtained.

[0183] The flood storage capacity of each lake and the reservoir capacity are all obtained from official statistics. The flood storage capacity of the reservoir is determined based on the reservoir capacity. The flood storage capacity of the reservoir and the flood storage capacity of the lake are combined with the spatial raster data of large and medium-sized lakes and reservoirs in the target area to be evaluated, and the water storage capacity of each large and medium-sized lake and reservoir is spatially assigned to obtain the flood storage capacity of the lakes and reservoirs.

[0184] The swamp wetland pixels in the target area to be evaluated are extracted using Modis land use type data and assigned a value of 1, while the other raster pixels are assigned a value of 0, thus obtaining the spatial raster data of the swamp wetland.

[0185] The parameters required for calculating the flood storage capacity of marsh wetlands come from official statistical data and measured data. That is, the flood storage capacity of marsh wetlands can be determined according to the above formula. By combining the flood storage capacity of marsh wetlands with remote sensing spatial raster data of marsh wetlands, the water storage capacity of marsh wetlands is spatially assigned to obtain the spatialized result of the flood storage capacity of marsh wetlands.

[0186] By summing up, the spatialized result of flood storage capacity for a certain province is obtained.

[0187] Analysis of the spatial distribution map of flood storage capacity in a certain province reveals that the overall flood storage capacity gradually increases from the north to the south. Within the province, areas with higher flood storage capacity are mainly concentrated in marshland wetlands containing lakes and reservoirs. This demonstrates the crucial role of marshland wetland ecosystems in flood regulation.

[0188] This invention quantifies the spatial distribution characteristics of flood storage capacity in the target area.

[0189] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0190] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A flood storage spatialization method based on remote sensing data, characterized in that, The method comprises the following steps: acquire daily rainfall remote sensing grid data of a target region to be evaluated, and determine storm rainfall spatial grid data of a vegetation ecosystem of the target region to be evaluated based on the daily rainfall remote sensing grid data; acquire annual storm runoff spatial grid data of each hydrological monitoring station region in the target region to be evaluated and spatial grid data of each storm runoff influencing factor, and predict annual storm runoff spatial grid data of each non-hydrological monitoring station region in the target region to be evaluated according to the annual storm runoff spatial grid data of each hydrological monitoring station region and the spatial grid data of each storm runoff influencing factor; determine storm runoff spatial grid data of a vegetation ecosystem of the target region to be evaluated according to the annual storm runoff spatial grid data of each hydrological monitoring station region and the annual storm runoff spatial grid data of each non-hydrological monitoring station region; determine lake and reservoir spatial grid data of the target region to be evaluated by using a water body index and vegetation index dual-channel extraction method and a maximum connected domain algorithm; determine a flood storage capacity spatialization result of the target region according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated, the storm runoff spatial grid data of the vegetation ecosystem and the lake and reservoir spatial grid data; the determination of the flood storage capacity spatialization result of the target region according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated, the storm runoff spatial grid data of the vegetation ecosystem and the lake and reservoir spatial grid data specifically comprises the following steps: determining a vegetation flood storage capacity spatialization result according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated and the storm runoff spatial grid data of the vegetation ecosystem; determining a lake and reservoir flood storage capacity, and processing the lake and reservoir flood storage capacity and spatial grid data by using a pixel assignment method to obtain a lake and reservoir flood storage capacity spatialization result; determining marsh wetland spatial grid data and a marsh wetland flood storage capacity of the target region to be evaluated, and processing the marsh wetland flood storage capacity and spatial grid data by using a pixel assignment method to obtain a marsh wetland flood storage capacity spatialization result; and adding the vegetation flood storage capacity spatialization result, the lake and reservoir flood storage capacity spatialization result and the marsh wetland flood storage capacity spatialization result to obtain a flood storage capacity spatialization result of the target region to be evaluated.

2. The flood storage spatialization method based on remote sensing data according to claim 1, characterized in that, The acquisition of the daily rainfall remote sensing grid data of the target region to be evaluated specifically comprises the following steps: acquire remote sensing data of daily rainfall of the target region to be evaluated; perform preprocessing on the remote sensing data of daily rainfall to obtain daily rainfall remote sensing grid data of the target region to be evaluated.

3. The method of claim 1, wherein, The determination of the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated based on the daily rainfall remote sensing grid data specifically comprises the following steps: Filtering the daily rainfall remote sensing grid data to obtain daily storm rainfall spatial grid data of the target region to be evaluated; Superimposing the daily storm rainfall spatial grid data of the target region to be evaluated in the accounting year to obtain annual storm rainfall spatial grid data of the target region to be evaluated; According to the land use type data of the target region to be evaluated, the spatial grid data of the vegetation ecosystem of the target region to be evaluated is determined; Resampling the annual storm rainfall spatial grid data and the spatial grid data of the vegetation ecosystem of the target region to be evaluated; The resampled annual storm rainfall spatial grid data and the resampled spatial grid data of the vegetation ecosystem are subjected to multiplication operation to obtain the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated.

4. The method of claim 1, wherein, The obtaining of the annual storm runoff spatial grid data of each hydrological monitoring station region in the target region to be evaluated and the spatial grid data of each storm runoff influencing factor comprises: Obtaining remote sensing data of daily rainfall and remote sensing data of daily surface runoff in each hydrological monitoring station region in the target region to be evaluated; The remote sensing data of daily surface runoff of the hydrological monitoring station region with daily rainfall as storm rainfall is accumulated to obtain remote sensing data of daily storm runoff of each hydrological monitoring station region; The remote sensing data of daily storm runoff is accumulated to obtain remote sensing data of annual storm runoff of each hydrological monitoring station region; The remote sensing data of annual storm runoff is processed by spatial interpolation calculation to obtain annual storm runoff spatial grid data of each hydrological monitoring station region; Obtaining initial spatial grid data of each storm runoff influencing factor; the storm runoff influencing factors include storm precipitation remote sensing data, evapotranspiration remote sensing data, soil moisture content remote sensing data, vegetation coverage remote sensing data and slope remote sensing data; Resampling each initial spatial grid data of the storm runoff influencing factor; Standard normalization processing is performed on each resampled initial spatial grid data to obtain final spatial grid data of each storm runoff influencing factor.

5. The method of claim 1, wherein, The prediction of annual storm runoff spatial grid data of each non-hydrological monitoring station region in the target region to be evaluated according to the annual storm runoff spatial grid data of each hydrological monitoring station region and the spatial grid data of each storm runoff influencing factor comprises: Taking the hydrological monitoring station region as a sample region, using principal component analysis algorithm, performing bivariate analysis on the annual storm runoff spatial grid data of each hydrological monitoring station region and the spatial grid data of each storm runoff influencing factor to obtain correlation coefficients of annual storm runoff and each storm runoff influencing factor; Based on the correlation coefficients of annual storm runoff and each storm runoff influencing factor, a regression equation is established; According to the established regression equation, the annual storm runoff spatial grid data of each non-hydrological monitoring station region in the target region to be evaluated is predicted.

6. The method of claim 1, wherein, The lake and reservoir spatial grid data of the target region to be evaluated is determined by using the water body index and vegetation index dual-channel extraction method and the maximum connected domain algorithm, and specifically includes: The satellite remote sensing data is processed to obtain the spatial grid data of the improved normalized water body index of the target region to be evaluated, and the normalized vegetation index of each grid is calculated according to the spatial grid data of the improved normalized water body index; The spatial grid data of the improved normalized water body index with a value greater than the normalized vegetation index value is extracted from the spatial grid data of the improved normalized water body index, and the extracted spatial grid data is determined as the water body spatial grid data of the target region to be evaluated; The water body spatial grid data is processed by using the maximum connected domain algorithm to obtain the lake and reservoir spatial grid data of the target region to be evaluated.

7. The method of claim 1, wherein, The vegetation flood storage capacity spatialization result is determined according to the storm rainfall spatial grid data and the storm runoff spatial grid data of the vegetation ecosystem of the target region to be evaluated, and specifically includes: According to the formula determining a vegetation flood storage volume; The storm rainfall spatial grid data, the storm runoff spatial grid data and the vegetation flood storage capacity spatialization result are processed by using the pixel assignment method to obtain the vegetation flood storage capacity spatialization result; wherein, represents the floodwater storage capacity of the vegetation; n represents the total number of types of vegetation ecosystems in the target area to be evaluated; represents the annual rainfall of the vegetation ecosystem of the i class; represents the annual runoff of the vegetation ecosystem of the i class; represents the annual runoff of the vegetation ecosystem of the i class per unit grid area.

8. The flood storage spatialization method based on remote sensing data according to claim 1, wherein, The determination of the marsh wetland spatial grid data of the target region to be evaluated specifically includes: The marsh wetland spatial grid data of the target region to be evaluated is determined according to the land use type data of the target region to be evaluated.

9. A system for spatializing flood storage based on remote sensing data, characterized in that, It includes: The vegetation ecosystem storm rainfall spatial grid data calculation module is used to obtain the daily rainfall remote sensing grid data of the target region to be evaluated, and determine the vegetation ecosystem storm rainfall spatial grid data of the target region to be evaluated based on the daily rainfall remote sensing grid data; The non-hydrological monitoring site area annual storm runoff spatial grid data prediction module is used to obtain the annual storm runoff spatial grid data of each hydrological monitoring site area in the target region to be evaluated and the spatial grid data of each storm runoff influencing factor, and predict the annual storm runoff spatial grid data of each non-hydrological monitoring site area in the target region to be evaluated according to the annual storm runoff spatial grid data of each hydrological monitoring site area and the spatial grid data of each storm runoff influencing factor; The vegetation ecosystem storm runoff spatial grid data calculation module is used to determine the vegetation ecosystem storm runoff spatial grid data of the target region to be evaluated according to the annual storm runoff spatial grid data of each hydrological monitoring site area and the annual storm runoff spatial grid data of each non-hydrological monitoring site area in the target region to be evaluated; The lake and reservoir spatial grid data calculation module is used to determine the lake and reservoir spatial grid data of the target region to be evaluated by using the water body index and vegetation index dual-channel extraction method and the maximum connected domain algorithm. The flood storage spatialization result determination module is configured to determine a flood storage spatialization result of the target region according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated, the storm runoff spatial grid data of the vegetation ecosystem, and the lake and reservoir spatial grid data. The flood storage spatialization result determination module is configured to determine a flood storage spatialization result of the target region according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated, the storm runoff spatial grid data of the vegetation ecosystem, and the lake and reservoir spatial grid data. The flood storage spatialization result determination module is configured to determine a flood storage spatialization result of the target region according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated, the storm runoff spatial grid data of the vegetation ecosystem, and the lake and reservoir spatial grid data. The flood storage spatialization result determination module is configured to determine a flood storage spatialization result of the target region according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated, the storm runoff spatial grid data of the vegetation ecosystem, and the lake and reservoir spatial grid data. The flood storage spatialization result determination module is configured to determine a flood storage spatialization result of the target region according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated, the storm runoff spatial grid data of the vegetation ecosystem, and the lake and reservoir spatial grid data. The flood storage spatialization result determination module is configured to determine a flood storage spatialization result of the target region according to the storm rainfall spatial grid data of the vegetation ecosystem of the target region to be evaluated, the storm runoff spatial grid data of the vegetation ecosystem, and the lake and reservoir spatial grid data.

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