CN115660134A - Flood storage regulation spatialization method and system based on remote sensing data - Google Patents
Flood storage regulation spatialization method and system based on remote sensing data Download PDFInfo
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Abstract
The invention discloses a flood regulation and storage capacity spatialization method and a flood regulation and storage capacity spatialization system based on remote sensing data, and relates to the technical field of flood regulation and storage capacity spatialization, wherein the method comprises the following steps: determining rainstorm rainfall space grid data, rainstorm runoff space grid data and lake reservoir space grid data of a vegetation ecosystem of a target area to be evaluated based on the remote sensing data; and then determining a flood storage regulation amount spatialization result of the target area according to the rainstorm rainfall space grid data of the vegetation ecosystem, the rainstorm runoff space grid data of the vegetation ecosystem and the lake reservoir space grid data of the target area to be evaluated. The method can utilize the characteristic that remote sensing data can be rasterized to achieve the purpose of spatial accounting of the flood storage regulation amount.
Description
Technical Field
The invention relates to the technical field of spatialization of flood storage capacity, in particular to a method and a system for spatialization of flood storage capacity based on remote sensing data.
Background
The total production value (GEP) of the Ecosystem is an index which can systematically and comprehensively reflect the relevant functional value of the Ecosystem, and can clearly show the supporting role of products and services provided by the Ecosystem in the development of the economic society. The accounting of the flood regulation index is an important index in the GEP accounting.
At present, the estimation of the flood storage capacity is limited by the defects that a calculation method has no systematicness, data is difficult to obtain and the like, and the current research cannot fully reflect the overall view of the flood storage regulation function of a target area and the spatial distribution characteristics of the flood storage regulation function. Therefore, a method of spatializing the flood storage amount is necessary.
Disclosure of Invention
The invention aims to provide a method and a system for spatializing flood storage capacity based on remote sensing data, which can achieve the purpose of spatializing accounting of the flood storage capacity by utilizing the characteristic that the remote sensing data can be rasterized.
In order to achieve the purpose, the invention provides the following scheme:
in a first aspect, the invention provides a flood storage regulation spatialization method based on remote sensing data, which comprises the following steps:
acquiring daily rainfall remote sensing raster data of a target area to be evaluated, and determining rainstorm rainfall space raster data of a vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing raster data;
acquiring annual rainstorm runoff space grid data of each hydrologic monitoring site area and space grid data of each rainstorm runoff influencing factor in the target area to be evaluated, and predicting annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the space grid data of each rainstorm runoff influencing factor;
determining rainstorm runoff space grid data of a vegetation ecosystem of the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated;
determining lake and reservoir space grid data of the target area to be evaluated by adopting a water body index and vegetation index double-channel extraction method and a maximum connected domain algorithm;
and determining a flood storage regulation amount spatialization result of the target area according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated, the rainstorm runoff space grid data of the vegetation ecosystem and the lake reservoir space grid data.
Optionally, the obtaining of the daily rainfall remote sensing raster data of the target area to be evaluated specifically includes:
acquiring remote sensing data of the daily rainfall of a target area to be evaluated;
and preprocessing the remote sensing data of the daily rainfall to obtain the daily rainfall remote sensing raster data of the target area to be evaluated.
Optionally, the determining, based on the daily rainfall remote sensing raster data, rainstorm rainfall spatial raster data of the vegetation ecosystem of the target area to be evaluated specifically includes:
screening the daily rainfall remote sensing raster data to obtain daily rainfall space raster data of the target area to be evaluated;
superposing the daily rainstorm rainfall space grid data of the target area to be evaluated within the accounting year to obtain the annual rainstorm rainfall space grid data of the target area to be evaluated;
determining space grid data of a vegetation ecosystem of the target area to be evaluated according to the land utilization type data of the target area to be evaluated;
resampling annual rainstorm rainfall spatial grid data of the target area to be evaluated and spatial grid data of a vegetation ecosystem;
and multiplying the re-sampled annual rainstorm rainfall space grid data and the re-sampled space grid data of the vegetation ecosystem to obtain the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated.
Optionally, the acquiring annual rainstorm runoff spatial grid data of each hydrologic monitoring station area in the target area to be evaluated and spatial grid data of each rainstorm runoff influence factor specifically includes:
acquiring remote sensing data of daily rainfall and remote sensing data of daily surface runoff of each hydrological monitoring site area in the target area to be evaluated;
accumulating the remote sensing data of the daily surface runoff of the hydrological monitoring site area with the daily rainfall as the rainstorm to obtain the remote sensing data of the daily rainstorm runoff of each hydrological monitoring site area;
accumulating the remote sensing data of the daily rainstorm runoff to obtain the remote sensing data of the annual rainstorm runoff of each hydrological monitoring site area;
processing the remote sensing data of the annual torrential rainfall runoff by adopting a spatial interpolation calculation mode to obtain the spatial grid data of the annual torrential rainfall runoff of each hydrological monitoring site area;
acquiring initial space grid data of each storm runoff influence factor; the torrential rainfall runoff influence factor comprises torrential rainfall remote sensing data, evapotranspiration remote sensing data, soil water content remote sensing data, vegetation coverage remote sensing data and gradient remote sensing data;
resampling the initial space grid data of each storm runoff influencing factor;
and carrying out standard normalization processing on the initial space grid data after resampling to obtain final space grid data of each rainstorm runoff influence factor.
Optionally, the predicting the annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the spatial grid data of each rainstorm runoff influence factor specifically includes:
taking a hydrologic monitoring station area as a sample area, and performing bivariate analysis on the annual rainstorm runoff space grid data of each hydrologic monitoring station area and the space grid data of each rainstorm runoff influencing factor by adopting a principal component analysis algorithm to obtain a correlation coefficient of the annual rainstorm runoff and each rainstorm runoff influencing factor;
establishing a regression equation based on the correlation coefficient of the annual rainstorm runoff and each rainstorm runoff influencing factor;
and predicting the annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the established regression equation.
Optionally, the determining, by using a two-channel extraction method of a water body index and a vegetation index and a maximum connected domain algorithm, the lake and reservoir space grid data of the target area to be evaluated specifically includes:
processing the satellite remote sensing data to obtain spatial grid data of an improved normalized water body index of a target area to be evaluated, and calculating a normalized vegetation index of each grid according to the spatial grid data of the improved normalized water body index;
extracting space grid data with an improved normalized water body index value larger than a normalized vegetation index value from the space grid data of the improved normalized water body index, and determining the extracted space grid data as water body space grid data of a target area to be evaluated;
and processing the water body space grid data by adopting a maximum connected domain algorithm to obtain lake and reservoir space grid data of the target area to be evaluated.
Optionally, determining a flood storage regulation volume spatialization result of the target area according to the rainstorm volume spatial grid data of the vegetation ecosystem of the target area to be evaluated, the rainstorm runoff volume spatial grid data of the vegetation ecosystem, and the lake reservoir spatial grid data, specifically includes:
determining a vegetation flood storage regulation amount spatialization result according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated and the rainstorm runoff space grid data of the vegetation ecosystem;
determining the flood regulation and storage amount of the lake reservoir, and processing the flood regulation and storage amount of the lake reservoir and the space grid data by adopting a pixel assignment mode to obtain a spatialization result of the flood regulation and storage amount of the lake reservoir;
determining the swamp wetland space grid data and the swamp wetland flood regulation and storage amount of a target area to be evaluated, and processing the swamp wetland flood regulation and storage amount and the space grid data by adopting a pixel assignment mode to obtain a swamp wetland flood regulation and storage amount spatialization result;
and accumulating the vegetation flood regulation spatialization result, the lake reservoir flood regulation spatialization result and the marsh wetland flood regulation spatialization result to obtain a flood regulation spatialization result of a target area.
Optionally, determining a vegetation flood storage regulation spatialization result according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated and the rainstorm runoff space grid data of the vegetation ecosystem, specifically including:
according to the formula
Determining the vegetation flood storage regulation amount;
processing flood storage regulation capacity, rainstorm rainfall spatial grid data and rainstorm runoff spatial grid data of a vegetation ecological system by adopting a pixel assignment mode to obtain a vegetation flood storage regulation capacity spatialization result;
wherein, C vfm Representing the regulation and storage amount of vegetation flood; n represents the total number of types of vegetation ecosystems in the target area to be evaluated; p i Representing the annual rainstorm rainfall of the i-th vegetation ecosystem; r is fi Representing the annual rainstorm runoff of the i-th vegetation ecosystem; a. The i Representing the unit grid area of the i-th vegetation ecosystem.
Optionally, the determining the swamp wetland spatial grid data of the target area to be evaluated specifically includes:
and determining the swamp wetland space grid data of the target area to be evaluated according to the land utilization type data of the target area to be evaluated.
In a second aspect, the present invention provides a flood storage regulation spatialization system based on remote sensing data, including:
the vegetation ecosystem rainstorm rainfall space grid data calculation module is used for acquiring daily rainfall remote sensing grid data of a target area to be evaluated and determining the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing grid data;
the non-hydrologic monitoring station area annual rainstorm runoff space grid data prediction module is used for acquiring annual rainstorm runoff space grid data of each hydrologic monitoring station area in the target area to be evaluated and space grid data of each rainstorm runoff influence factor, and predicting annual rainstorm runoff space grid data of each non-hydrologic monitoring station area in the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring station area and the space grid data of each rainstorm runoff influence factor;
the vegetation ecosystem torrential flow space grid data calculation module is used for determining the torrential flow space grid data of the vegetation ecosystem of the target area to be evaluated according to the annual torrential flow space grid data of each hydrologic monitoring site area and the annual torrential flow space grid data of each non-hydrologic monitoring site area in the target area to be evaluated;
the lake reservoir space grid data calculation module is used for determining the lake reservoir space grid data of the target area to be evaluated by adopting a water body index and vegetation index dual-channel extraction method and a maximum connected domain algorithm;
and the flood storage regulation spatialization result determination module is used for determining a flood storage regulation spatialization result of the target area according to the rainstorm rainfall space grid data of the vegetation ecological system of the target area to be evaluated, the rainstorm runoff space grid data of the vegetation ecological system and the lake reservoir space grid data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention carries out accounting and spatial processing on the flood storage regulation amount through the remote sensing technology, forms a flood storage regulation amount spatial distribution map of a target area, provides method support for regional ecological economic development diagnosis and evaluation, provides a new thought for national ecological development evaluation at present, and provides a technical means for regional ecological economic high-quality development.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a flood storage regulation spatialization method based on remote sensing data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a flood storage regulation spatialization system based on remote sensing data according to a second embodiment of the present invention;
fig. 3 is a schematic flow chart of a flood storage regulation spatialization method based on remote sensing data according to a third embodiment of the present invention;
fig. 4 is a rainstorm rainfall spatial grid data diagram of a vegetation ecosystem in a region in Zhejiang province according to a second embodiment of the present invention;
fig. 5 is a rainstorm runoff volume space grid data diagram of a vegetation ecosystem in a region in Zhejiang province according to a second embodiment of the present invention;
fig. 6 is a flood regulation and storage map of lakes and reservoirs in the area of zhejiang province according to the second embodiment of the present invention;
fig. 7 is a result diagram of the spatialization of flood storage regulation in the area of zhejiang province according to the second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a flood regulation and storage capacity spatialization method and a flood regulation and storage capacity spatialization system based on remote sensing data, which utilize the characteristic that the remote sensing data can be rasterized to carry out flood regulation and storage capacity spatialization accounting, aim at quantizing the spatial distribution characteristics of flood regulation and storage capacity in a target area, enable people to recognize the huge benefits and social welfare of ecological system services, and provide scientific basis for relevant systems such as ecological compensation established by governments.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Example one
As shown in fig. 1, a flood regulation spatialization method based on remote sensing data provided by an embodiment of the present invention includes the following steps.
Step 100: the method comprises the steps of obtaining daily rainfall remote sensing raster data of a target area to be evaluated, and determining rainstorm rainfall space raster data of a vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing raster data.
Step 200: acquiring annual rainstorm runoff space grid data of each hydrologic monitoring site area and space grid data of each rainstorm runoff influencing factor in the target area to be evaluated, and predicting annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the space grid data of each rainstorm runoff influencing factor.
Step 300: and determining the rainstorm runoff space grid data of the vegetation ecosystem of the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated.
Step 400: and determining the lake reservoir space grid data of the target area to be evaluated by adopting a water body index and vegetation index dual-channel extraction method and a maximum connected domain algorithm.
Step 500: and determining a flood storage regulation amount spatialization result of the target area according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated, the rainstorm runoff space grid data of the vegetation ecosystem and the lake reservoir space grid data.
As a preferred implementation manner, the "acquiring daily rainfall remote sensing raster data of a target area to be evaluated" in step 100 according to the embodiment of the present invention specifically includes:
the method comprises the steps of firstly obtaining remote sensing data of the daily rainfall of a target area to be evaluated, and then preprocessing the remote sensing data of the daily rainfall to obtain the remote sensing raster data of the daily rainfall of the target area to be evaluated.
As a preferred implementation manner, the "determining rainstorm spatial grid data of a vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing grid data" in step 100 according to the embodiment of the present invention specifically includes:
step 101: and screening the daily rainfall remote sensing raster data to obtain the daily rainfall spatial raster data of the target area to be evaluated.
One example is: by setting a threshold value, daily rainfall remote sensing grid data with the daily rainfall being more than or equal to 50mm (the rainfall value corresponding to rainstorm is 50mm or more in 24 hours according to Chinese meteorological regulations) are reserved, and the rest grid data are subjected to zero returning processing, so that the screening operation of the daily rainfall remote sensing grid data is realized, and the daily rainstorm rainfall space grid data of a target area to be evaluated are obtained.
Step 102: and superposing the daily rainstorm rainfall space grid data in the accounting year of the target area to be evaluated to obtain the annual rainstorm rainfall space grid data of the target area to be evaluated.
Step 103: and determining the space grid data of the vegetation ecosystem of the target area to be evaluated according to the land utilization type data of the target area to be evaluated.
One example is: and extracting pixel points of a vegetation ecosystem (forest and brush) of the target area to be evaluated according to the land utilization type data, assigning the pixel points to be 1, and assigning the rest grids to be 0 to obtain space grid data of the vegetation ecosystem.
Step 104: resampling annual rainstorm rainfall spatial grid data of the target area to be evaluated and spatial grid data of a vegetation ecosystem; wherein the resolution at the time of resampling is set to be uniform.
Step 105: and multiplying the re-sampled annual rainstorm rainfall space grid data and the re-sampled space grid data of the vegetation ecosystem to obtain the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated.
As a preferred implementation manner, the step 200 of the embodiment of the present invention of "obtaining annual torrential rain runoff space grid data of each hydrologic monitoring site area in the target area to be evaluated" specifically includes:
firstly, acquiring remote sensing data of daily rainfall and remote sensing data of daily surface runoff of each hydrological monitoring site area in a target area to be evaluated; secondly, accumulating the remote sensing data of the daily surface runoff of the hydrological monitoring site areas with the daily rainfall being rainstorm (the daily rainfall is more than or equal to 50 mm) to obtain the remote sensing data of the daily rainstorm runoff of each hydrological monitoring site area; then accumulating the remote sensing data of the daily rainstorm runoff to obtain the remote sensing data of the annual rainstorm runoff of each hydrological monitoring site area; finally, processing the remote sensing data of the annual rainstorm runoff by adopting a spatial interpolation calculation mode to obtain the spatial grid data of the annual rainstorm runoff of each hydrological monitoring site area; wherein, the basin that hydrology monitoring station belongs to is the upstream area.
As a preferred implementation manner, the "spatial grid data of each storm runoff influencing factor" in step 200 according to the embodiment of the present invention specifically includes:
firstly, processing satellite remote sensing data to obtain initial space grid data of each storm runoff influence factor; the torrential rainfall runoff influence factor comprises torrential rainfall remote sensing data, evapotranspiration remote sensing data, soil water content remote sensing data, vegetation coverage remote sensing data and gradient remote sensing data; secondly, resampling the initial space grid data of each torrential rainfall runoff influence factor; and finally, performing standard normalization processing on each resampled initial space grid data to obtain the final space grid data of each storm runoff influencing factor. Wherein the resolution at the time of resampling is set to be uniform.
As a preferred implementation manner, the "predicting the annual rainstorm runoff spatial grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the annual rainstorm runoff spatial grid data of each hydrologic monitoring site area and the spatial grid data of each rainstorm runoff influencing factor" in step 200 according to the embodiment of the present invention specifically includes:
step 201: and taking the hydrologic monitoring station areas as sample areas, and performing bivariate analysis on the annual rainstorm runoff space grid data of each hydrologic monitoring station area and the space grid data of each rainstorm runoff influencing factor by adopting a principal component analysis algorithm to obtain a correlation coefficient of the annual rainstorm runoff and each rainstorm runoff influencing factor.
Step 202: and establishing a regression equation based on the correlation coefficient of the annual rainstorm runoff and each rainstorm runoff influence factor.
Step 203: and predicting annual rainstorm runoff spatial grid data of each non-hydrologic monitoring station area in the target area to be evaluated according to the established regression equation.
As a preferred implementation manner, the step 300 in the embodiment of the present invention specifically includes:
step 301: and superposing the annual heavy rain runoff space grid data of each hydrologic monitoring site area in the target area to be evaluated and the annual heavy rain runoff space grid data of each non-hydrologic monitoring site area to obtain the annual heavy rain runoff space grid data of the whole target area to be evaluated, and resampling the annual heavy rain runoff space grid data of the target area to be evaluated. The resolution at the time of resampling is the same as that at the time of step 104.
Step 302: and multiplying the resampled annual rainstorm runoff space grid data and the resampled space grid data of the vegetation ecosystem to obtain the rainstorm runoff space grid data of the vegetation ecosystem of the target area to be evaluated. The determination process of the spatial grid data of the re-sampled vegetation ecosystem refers to steps 103 to 104.
As a preferred implementation manner, the step 400 described in the embodiment of the present invention specifically includes:
firstly, processing satellite remote sensing data to obtain spatial grid data of an improved normalized water body index of a target area to be evaluated, and calculating a normalized vegetation index of each grid according to the spatial grid data of the improved normalized water body index; secondly, extracting space grid data with an improved normalized water body index value larger than a normalized vegetation index value from the space grid data of the improved normalized water body index, and determining the extracted space grid data as water body space grid data of a target area to be evaluated; and finally, processing the water body space grid data by adopting a maximum connected domain algorithm to obtain the space grid data of large and medium lakes and reservoirs of the target area to be evaluated.
One example is:
step 401: and processing the satellite remote sensing data to obtain spatial grid data of an improved normalized water body index (MNDWI) of the target area to be evaluated.
Step 402: on the basis of spatial grid data of the improved normalized water body index (MNDWI), a normalized vegetation index (NDVI) of each grid is calculated.
Step 403: and taking the normalized vegetation index (NDVI) value as a threshold value, and extracting the space grid data of which the improved normalized water body index (MNDWI) value is greater than the normalized vegetation index (NDVI) value to obtain the water body space grid data.
Step 404: and (3) repeating the steps 401-403 by taking a month as a time calculation unit to obtain 12 parts of water space grid data of the target area to be evaluated, and selecting the water space grid data with the most water pixels as the water space grid data of the target area to be evaluated.
Step 405: and eliminating small-volume water pixel points in the water space grid data of the target area to be evaluated through a maximum connected domain algorithm, and extracting large and medium lake and reservoir areas.
Step 406: and assigning the pixel points of the large and medium lakes and reservoir areas as 1 and assigning other grids as 0 to obtain the space grid data of the large and medium lakes and reservoirs of the target area to be evaluated in the target area to be evaluated.
As a preferred implementation manner, the step 400 described in the embodiment of the present invention specifically includes:
step 501: and determining a vegetation flood storage regulation amount spatialization result according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated and the rainstorm runoff space grid data of the vegetation ecosystem.
Step 502: determining the flood regulation amount of the lake reservoir, and processing the flood regulation amount of the lake reservoir and the space grid data by adopting a pixel assignment mode to obtain a spatialization result of the flood regulation amount of the lake reservoir.
Step 503: determining the swamp wetland space grid data and the swamp wetland flood regulation and storage amount of a target area to be evaluated, and processing the swamp wetland flood regulation and storage amount and the space grid data by adopting a pixel assignment mode to obtain a swamp wetland flood regulation and storage amount spatialization result.
Step 504: and accumulating the vegetation flood regulation spatialization result, the lake reservoir flood regulation spatialization result and the marsh wetland flood regulation spatialization result to obtain a flood regulation spatialization result of a target area.
In the embodiment of the invention, the vegetation ecosystem is divided into a forest ecosystem and a bush ecosystem.
Regulating the storage capacity C of flood according to the actual situation of the target area to be evaluated fm Accounting for regulating storage capacity C of vegetation flood vfm Lake flood regulation and storage capacity C lfm Reservoir flood regulation and storage capacity C rfm Regulating storage capacity C with marsh wetland flood mfm And (4) summing.
Wherein, the vegetation flood storage quantity C vfm The calculation formula of (c) is:
n represents the total number of types of vegetation ecosystems in the target area to be evaluated; p is i Rainstorm rainfall (mm) for the ith vegetation ecosystem; r fi Storm runoff (mm) for the ith vegetation ecosystem; a. The i Is the unit grid area (km) of the i-th vegetation ecosystem 2 ) (ii) a And i is the type of the ith vegetation ecosystem of the target area to be evaluated.
The calculation formula of the reservoir flood regulation amount is as follows:
C rfm ＝0.35×C t ；
C t for the storage capacity (km) of the reservoir 3 )。
The calculation formula of the storage regulation amount of the marsh wetland flood is as follows:
C mfm ＝S×h×ρ×(F-E)×10 -2 /ρ W +S×H×10 -2 ；
s is the unit grid area (km) of the marsh wetland 2 ) (ii) a h is the soil water storage depth (m) of the marsh wetland; rho is the volume weight (g/cm) of the soil in the marsh wetland 3 )；ρ w Is the density of water; f is the saturated water content of the soil of the marsh wetland; e is the natural water content of the marsh wetland before flooding; h is the height (m) of the surface stagnant water of the marsh wetland.
Based on the above, step 501 specifically includes:
firstly according to the formula
Determining the vegetation flood storage regulation amount; and secondly, processing the flood storage regulation amount, rainstorm rainfall spatial grid data and rainstorm runoff spatial grid data of the vegetation ecological system by adopting a pixel assignment mode to obtain a vegetation flood storage regulation amount spatialization result.
The regulation amount of flood in each lake and the storage capacity of the reservoir are from official statistical data, so step 502 specifically comprises:
firstly, determining the storage regulation amount of the reservoir flood according to a calculation formula of the storage capacity of the reservoir and the storage regulation amount of the reservoir flood; and then carrying out spatial assignment on the reservoir flood storage capacity and the large and medium reservoir space grid data of the target area to be evaluated to obtain the reservoir flood storage capacity.
Firstly, determining the lake flood storage regulation amount; and then carrying out spatial assignment on the lake flood storage capacity and the large and medium lake space grid data to obtain the lake flood storage capacity.
The parameters required for calculating the flood storage regulation amount of the marsh wetland come from official statistical data and measured data, so the step 503 specifically comprises the following steps:
firstly, extracting pixel points of the marsh wetland in the target area to be evaluated according to the land utilization type data of the target area to be evaluated, assigning the pixel points to be 1, and assigning the rest grids to be 0 to obtain the space grid data of the marsh wetland.
And secondly, calculating the regulating and storing amount of the marsh wetland flood based on the official statistical data and the measured data and a calculation formula of the regulating and storing amount of the marsh wetland flood.
And finally, carrying out spatial assignment on the marsh wetland flood regulation and storage capacity and the marsh wetland spatial grid data to obtain a flood regulation and storage capacity spatial result of the marsh wetland.
Example two
In order to implement the corresponding method of the above embodiments to achieve the corresponding functions and technical effects, a flood regulation spatialization system based on remote sensing data is provided below.
As shown in fig. 2, an embodiment of the present invention provides a flood regulation and storage spatialization system based on remote sensing data, including:
the vegetation ecosystem rainstorm rainfall space grid data calculation module 1 is used for acquiring daily rainfall remote sensing grid data of a target area to be evaluated, and determining the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing grid data.
The non-hydrologic monitoring site area annual rainstorm runoff space grid data prediction module 2 is used for acquiring annual rainstorm runoff space grid data of each hydrologic monitoring site area and space grid data of each rainstorm runoff influence factor in the target area to be evaluated, and predicting annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the space grid data of each rainstorm runoff influence factor.
And the vegetation ecosystem torrential rain runoff space grid data calculation module 3 is used for determining the torrential rain runoff space grid data of the vegetation ecosystem of the target area to be evaluated according to the annual torrential rain runoff space grid data of each hydrological monitoring site area and the annual torrential rain runoff space grid data of each non-hydrological monitoring site area in the target area to be evaluated.
And the lake reservoir space grid data calculation module 4 is used for determining the lake reservoir space grid data of the target area to be evaluated by adopting a water body index and vegetation index dual-channel extraction method and a maximum connected domain algorithm.
And the flood storage regulation amount spatialization result determining module 5 is used for determining a flood storage regulation amount spatialization result of the target area according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated, the rainstorm runoff space grid data of the vegetation ecosystem and the lake reservoir space grid data.
EXAMPLE III
The invention relates to a flood regulation and storage capacity spatialization method based on remote sensing data, which aims to quantify the spatial distribution characteristics of the flood regulation and storage capacity of a target area to be evaluated and provide scientific basis for relevant systems such as government formulated ecological compensation and the like.
As shown in fig. 3, an embodiment of the present invention provides a flood regulation and storage capacity spatialization method based on remote sensing data, which specifically includes the following steps:
s1: and preprocessing the rainfall remote sensing data, and acquiring the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated by combining with the land utilization type data and adopting a threshold value method.
S2: and (3) establishing a rainstorm runoff spatialization model by adopting a principal component analysis method for the rainstorm runoff and a plurality of influence factors thereof, and acquiring rainstorm runoff space grid data of a vegetation ecosystem of a target area to be evaluated by combining with land utilization type data.
S3: and acquiring space grid data of large and medium lakes and reservoirs of a target area to be evaluated by adopting a water body index and vegetation index dual-channel extraction method and a maximum connected domain algorithm.
S4: and carrying out pixel assignment accounting on the flood storage capacity to obtain a flood storage capacity spatialization result of the target area to be evaluated.
In this embodiment, step S1 specifically includes:
and with 2020 as an accounting year and Zhejiang province as a research area, obtaining DAILY rainfall remote sensing raster data through preprocessing on the GEE cloud platform through the administrative boundary of satellite remote sensing data UCSB-CHG/CHIRPS/DAILY and Zhejiang province.
By setting a threshold value, the grid numerical value of which the daily rainfall is more than or equal to 50mm (the rainfall value corresponding to the rainstorm is 50mm or more in 24 hours according to the Chinese meteorological regulation) is reserved, and the rest grid numerical values are subjected to zeroing treatment to obtain the daily rainstorm rainfall space grid data.
And superposing the daily rainstorm rainfall data of the region in Zhejiang province in 2020 to obtain the annual rainstorm rainfall spatial grid data.
And extracting pixel points of a vegetation ecosystem (forest and bush) in the Zhejiang province area through the Modis land utilization type data, assigning the pixel points to be 1, and assigning the rest grids to be 0 to obtain space grid data of the vegetation ecosystem.
Re-sampling the annual rainstorm rainfall and the space grid data of the vegetation ecosystem, and setting the resolution ratio to be consistent.
Multiplication operation is performed on the resampled annual rainstorm rainfall space grid data and the space grid data of the vegetation ecosystem, and rainstorm rainfall space grid data of the vegetation ecosystem in the Zhejiang province area shown in fig. 4 is obtained.
In this embodiment, step S2 specifically includes:
by acquiring the daily rainfall and daily surface runoff data of each hydrological monitoring station in the Zhejiang province, the surface runoff of the hydrological monitoring stations with the daily rainfall being more than or equal to 50mm (storm) is accumulated to obtain the daily rainstorm runoff of each station.
And accumulating the daily rainstorm runoff to obtain annual rainstorm runoff data of each hydrological monitoring station.
And (4) acquiring annual torrential rain runoff space grid data of each hydrological monitoring station area through spatial interpolation calculation.
Selecting a plurality of data as influence factors of the torrential rainfall, wherein the influence factors comprise torrential rainfall data, evapotranspiration data, soil water content data, vegetation coverage data and gradient data.
Acquiring space raster data of each influence factor through satellite remote sensing data, and then resampling; wherein the resolution is set to be uniform.
Wherein the rainstorm rainfall data is obtained by step S1.
The evapotranspiration data is from satellite telemetry data NASA/GLDAS/V021/NOAH/G025/T3H.
The vegetation coverage data is obtained by calculating the satellite remote sensing data Sentinel-2 MSI.
The Soil water content data come from NASA-USDA Enhanced SMAP Global Soil Moisture satellite remote sensing data.
The slope data is obtained by calculation from the satellite DEM data NASA SRTM Digital Elevation 30 m.
Due to different dimensions, the grid data of each influence factor needs to be subjected to standard normalization processing.
And selecting sample data at the same position as each hydrologic monitoring site area for the raster data of each influence factor by taking each hydrologic monitoring site area as a sample area, listing the sample data into a table, and importing SPSS software.
Performing principal component analysis by using SPSS software, performing bivariate analysis on the rainstorm runoff and the sample data of each influence factor of each hydrological monitoring station area to obtain a correlation coefficient of the rainstorm runoff and the data of each influence factor, and establishing a regression equation based on the correlation coefficient.
And according to the established regression equation, estimating the annual rainstorm runoff of the non-hydrologic monitoring site area to obtain the annual rainstorm runoff spatial grid data of the non-hydrologic monitoring site area.
And superposing the annual torrential rainfall runoff space grid data of the hydrologic monitoring site area and the non-hydrologic monitoring site area to obtain the annual torrential rainfall runoff space grid data of the whole target area to be evaluated.
Multiplication operation is performed on the annual rainstorm runoff space grid data and the space grid data of the vegetation ecosystem to obtain rainstorm runoff space grid data of the vegetation ecosystem in the Zhejiang province as shown in fig. 5.
In this embodiment, step S3 specifically includes:
and acquiring space raster data of an improved normalized water body index (MNDWI) in the Zhejiang province region by Landsat8 satellite remote sensing data on the GEE cloud platform.
On the basis of the spatial grid data of the MNDWI, the normalized vegetation index (NDVI) value of each grid is calculated.
And taking the NDVI value as a threshold value, and extracting the grids with the MNDWI value larger than the NDVI value to obtain the water body grid data.
And taking months as a time calculation unit to obtain 12 parts of water grid data of the Zhejiang province region, and selecting the grid data with the most water pixels as the water grid data of the Zhejiang province region.
And eliminating small-volume water pixel points in the water body grid data through a maximum connected domain algorithm to obtain large and medium lake and reservoir areas.
And assigning the pixel points of the lakes and the reservoirs as 1, and assigning other grids as 0 to obtain the space grid data of the large and medium lakes and the medium reservoirs in the Zhejiang province.
In this embodiment, step S4 specifically includes:
regulating the storage capacity C of flood according to the actual situation of the target area to be evaluated fm Accounting for regulating storage volume C of vegetation flood vfm Lake flood regulation volume C lfm Reservoir flood regulation amount C rfm Regulating storage capacity C with marsh wetland flood mfm And (4) summing.
Wherein, the vegetation flood storage quantity C vfm The calculation formula of (2) is as follows:
n represents the total number of types of vegetation ecosystems in the target area to be evaluated; p i Rainstorm rainfall (mm) for the ith vegetation ecosystem; r fi Storm runoff (mm) for the ith vegetation ecosystem; a. The i Is the unit grid area (km) of the i-th vegetation ecosystem 2 ) (ii) a And i is the type of the ith vegetation ecosystem of the target area to be evaluated.
The calculation formula of the reservoir flood regulation amount is as follows:
C rfm ＝0.35×C t ；
C t is the reservoir capacity (km) 3 )。
The calculation formula of the flood storage regulation amount of the marsh wetland is as follows:
C mfm ＝S×h×ρ×(F-E)×10 -2 /ρ W +S×H×10 -2 ；
s is the unit grid area (km) of the marsh wetland 2 ) (ii) a h is the water storage depth (m) of the soil of the marsh wetland; rho is the volume weight (g/cm) of the soil in the marsh wetland 3 )；ρ w Is the density of water; f is the saturated water content of the soil of the marsh wetland; e is marsh wetlandNatural water content before flooding; h is the height (m) of the surface stagnant water of the marsh wetland.
And obtaining a vegetation flood storage regulation amount spatialization result of the target area to be evaluated according to the calculation formula.
The flood regulation amount of each lake and the reservoir capacity are from official statistical data. And determining the flood regulation amount of the reservoir based on the storage capacity of the reservoir. Combining the reservoir flood storage capacity and the lake flood storage capacity with the acquired large and medium lake and reservoir space grid data of the target area to be evaluated, and performing spatial assignment on the water storage capacity of each large and medium lake and reservoir to obtain the lake and reservoir flood storage capacity shown in fig. 6.
And extracting pixel points of the marsh wetland in the target area to be evaluated through the Modis land utilization type data, assigning the pixel points to be 1, and assigning the rest grids to be 0 to obtain space grid data of the marsh wetland.
Parameters required by calculation of the marsh wetland flood storage capacity come from official statistical data and measured data, namely the marsh wetland flood storage capacity can be determined according to the formula; combining the flood storage regulation amount of the marsh wetland with remote sensing space grid data of the marsh wetland, and carrying out spatial assignment on the water storage amount of the marsh wetland to obtain a spatial result of the flood storage regulation amount of the marsh wetland.
Through accumulation, the spatialization result of the flood regulation amount in the area in Zhejiang province is obtained as shown in FIG. 7.
The flood regulation volume of the Zhejiang province region can be analyzed and obtained from a spatial result graph of the flood regulation volume, and the flood regulation volume of the Zhejiang province region integrally shows a trend of gradually increasing from the north region to the south region. From the perspective of each area inside, the places with high flood regulation capacity are mainly concentrated in swamp wetland areas including lakes and reservoirs. Therefore, the marsh wetland ecosystem plays an important role in regulating and storing flood.
The method quantifies the spatial distribution characteristics of the flood regulation and storage amount of the target area, and can provide scientific basis for relevant systems such as government-made ecological compensation.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A flood regulation and storage volume spatialization method based on remote sensing data is characterized by comprising the following steps:
acquiring daily rainfall remote sensing raster data of a target area to be evaluated, and determining rainstorm rainfall space raster data of a vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing raster data;
acquiring annual rainstorm runoff space grid data of each hydrologic monitoring site area and space grid data of each rainstorm runoff influencing factor in the target area to be evaluated, and predicting annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the space grid data of each rainstorm runoff influencing factor;
determining rainstorm runoff space grid data of a vegetation ecosystem of the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the annual rainstorm runoff space grid data of each non-hydrologic monitoring site area in the target area to be evaluated;
determining lake reservoir space grid data of the target area to be evaluated by adopting a water body index and vegetation index dual-channel extraction method and a maximum connected domain algorithm;
and determining a flood storage regulation amount spatialization result of the target area according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated, the rainstorm runoff space grid data of the vegetation ecosystem and the lake reservoir space grid data.
2. The flood storage regulation spatialization method based on remote sensing data according to claim 1, wherein the obtaining of daily rainfall remote sensing raster data of a target area to be evaluated specifically comprises:
acquiring remote sensing data of the daily rainfall of a target area to be evaluated;
and preprocessing the remote sensing data of the daily rainfall to obtain the daily rainfall remote sensing raster data of the target area to be evaluated.
3. The flood regulation and storage capacity spatialization method based on remote sensing data according to claim 1, wherein the determining of rainstorm rainfall space raster data of a vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing raster data specifically comprises:
screening the daily rainfall remote sensing raster data to obtain daily rainfall space raster data of the target area to be evaluated;
superposing the daily rainstorm rainfall space grid data in the accounting year of the target area to be evaluated to obtain the annual rainstorm rainfall space grid data of the target area to be evaluated;
determining spatial grid data of a vegetation ecosystem of the target area to be evaluated according to the land utilization type data of the target area to be evaluated;
resampling annual rainstorm rainfall spatial grid data of the target area to be evaluated and spatial grid data of a vegetation ecosystem;
and multiplying the re-sampled annual rainstorm rainfall space grid data and the re-sampled space grid data of the vegetation ecosystem to obtain the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated.
4. The flood regulation and storage capacity spatialization method based on remote sensing data according to claim 1, wherein the acquiring of annual rainstorm path flow rate spatial grid data and spatial grid data of each rainstorm path flow rate influence factor of each hydrologic monitoring site area in the target area to be evaluated specifically comprises:
acquiring remote sensing data of daily rainfall and remote sensing data of daily surface runoff of each hydrological monitoring site area in the target area to be evaluated;
accumulating the remote sensing data of the daily surface runoff of the hydrological monitoring site areas with the daily rainfall as rainstorm to obtain the remote sensing data of the daily rainstorm runoff of each hydrological monitoring site area;
accumulating the remote sensing data of the daily rainstorm runoff to obtain the remote sensing data of the annual rainstorm runoff of each hydrological monitoring site area;
processing the remote sensing data of the annual torrential rainfall runoff by adopting a spatial interpolation calculation mode to obtain the spatial grid data of the annual torrential rainfall runoff of each hydrological monitoring site area;
acquiring initial space grid data of each storm runoff influencing factor; the torrential rainfall runoff influence factor comprises torrential rainfall remote sensing data, evapotranspiration remote sensing data, soil water content remote sensing data, vegetation coverage remote sensing data and gradient remote sensing data;
resampling the initial space grid data of each stormwater runoff influencing factor;
and performing standard normalization processing on the initial space grid data after resampling to obtain final space grid data of each rainstorm runoff influencing factor.
5. The flood regulation volume spatialization method based on remote sensing data according to claim 1, wherein the prediction of the annual rainstorm path flow volume spatial grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the annual rainstorm path flow volume spatial grid data of each hydrologic monitoring site area and the spatial grid data of each rainstorm path flow impact factor specifically comprises:
taking a hydrologic monitoring site area as a sample area, and performing bivariate analysis on the annual rainstorm runoff space grid data of each hydrologic monitoring site area and the space grid data of each rainstorm runoff influencing factor by adopting a principal component analysis algorithm to obtain a correlation coefficient of the annual rainstorm runoff and each rainstorm runoff influencing factor;
establishing a regression equation based on the correlation coefficient of the annual rainstorm runoff and each rainstorm runoff influencing factor;
and predicting annual rainstorm runoff spatial grid data of each non-hydrologic monitoring site area in the target area to be evaluated according to the established regression equation.
6. The flood regulation and storage capacity spatialization method based on remote sensing data according to claim 1, wherein the lake and reservoir space grid data of the target area to be evaluated are determined by adopting a water body index and vegetation index dual-channel extraction method and a maximum connected domain algorithm, and the method specifically comprises the following steps:
processing the satellite remote sensing data to obtain spatial grid data of an improved normalized water body index of a target area to be evaluated, and calculating a normalized vegetation index of each grid according to the spatial grid data of the improved normalized water body index;
extracting space grid data with an improved normalized water body index value larger than a normalized vegetation index value from the space grid data of the improved normalized water body index, and determining the extracted space grid data as water body space grid data of a target area to be evaluated;
and processing the water body space grid data by adopting a maximum connected domain algorithm to obtain lake and reservoir space grid data of the target area to be evaluated.
7. The flood storage regulation spatialization method based on remote sensing data according to claim 1, wherein the determination of the flood storage regulation spatialization result of the target area according to the rainstorm volume space grid data of the vegetation ecosystem, the rainstorm runoff volume space grid data of the vegetation ecosystem, and the lake reservoir space grid data of the target area to be evaluated specifically comprises:
determining a vegetation flood regulation and storage capacity spatialization result according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated and the rainstorm runoff space grid data of the vegetation ecosystem;
determining the flood regulation and storage amount of the lake reservoir, and processing the flood regulation and storage amount of the lake reservoir and the space grid data by adopting a pixel assignment mode to obtain a spatialization result of the flood regulation and storage amount of the lake reservoir;
determining the swamp wetland space grid data and the swamp wetland flood regulation and storage amount of a target area to be evaluated, and processing the swamp wetland flood regulation and storage amount and the space grid data by adopting a pixel assignment mode to obtain a swamp wetland flood regulation and storage amount spatialization result;
and accumulating the vegetation flood regulation and storage quantity spatialization result, the lake reservoir flood regulation and storage quantity spatialization result and the marsh wetland flood regulation and storage quantity spatialization result to obtain a flood regulation and storage quantity spatialization result of the target area to be evaluated.
8. The flood storage regulation spatialization method based on remote sensing data according to claim 7, wherein the determining of the vegetation flood storage regulation spatialization result according to the rainstorm rainfall space grid data of the vegetation ecosystem and the rainstorm runoff space grid data of the vegetation ecosystem of the target area to be evaluated specifically comprises:
according to the formula
Determining vegetationRegulating storage capacity by flood;
processing flood storage regulation capacity, rainstorm rainfall spatial grid data and rainstorm runoff spatial grid data of a vegetation ecological system by adopting a pixel assignment mode to obtain a vegetation flood storage regulation capacity spatialization result;
wherein, C vfm Indicating the regulation and storage amount of vegetation flood; n represents the total number of types of vegetation ecosystems in the target area to be evaluated; p i Representing the annual rainstorm rainfall of the i-th vegetation ecosystem; r fi Representing the annual storm runoff of the i-th vegetation ecosystem; a. The i Represents the unit grid area of the i-th vegetation ecosystem.
9. The flood storage regulation spatialization method based on remote sensing data according to claim 7, wherein the determining of the swamp wetland space grid data of the target area to be evaluated specifically comprises:
and determining the swamp wetland space grid data of the target area to be evaluated according to the land utilization type data of the target area to be evaluated.
10. The utility model provides a flood regulation volume spatialization system based on remote sensing data which characterized in that includes:
the vegetation ecosystem rainstorm rainfall space grid data calculation module is used for acquiring daily rainfall remote sensing grid data of a target area to be evaluated and determining the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated based on the daily rainfall remote sensing grid data;
the non-hydrologic monitoring station area annual rainstorm runoff space grid data prediction module is used for acquiring annual rainstorm runoff space grid data of each hydrologic monitoring station area in the target area to be evaluated and space grid data of each rainstorm runoff influence factor, and predicting annual rainstorm runoff space grid data of each non-hydrologic monitoring station area in the target area to be evaluated according to the annual rainstorm runoff space grid data of each hydrologic monitoring station area and the space grid data of each rainstorm runoff influence factor;
the vegetation ecosystem torrential flow space grid data calculation module is used for determining the torrential flow space grid data of the vegetation ecosystem of the target area to be evaluated according to the annual torrential flow space grid data of each hydrologic monitoring site area and the annual torrential flow space grid data of each non-hydrologic monitoring site area in the target area to be evaluated;
the lake reservoir space grid data calculation module is used for determining lake reservoir space grid data of the target area to be evaluated by adopting a water body index and vegetation index double-channel extraction method and a maximum connected domain algorithm;
and the flood storage regulation amount spatialization result determination module is used for determining the flood storage regulation amount spatialization result of the target area according to the rainstorm rainfall space grid data of the vegetation ecosystem of the target area to be evaluated, the rainstorm runoff space grid data of the vegetation ecosystem and the lake reservoir space grid data.
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