CN111127643A - Digital elevation model upscaling method capable of effectively relieving terrain information loss - Google Patents
Digital elevation model upscaling method capable of effectively relieving terrain information loss Download PDFInfo
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Abstract
The invention discloses a digital elevation model upscaling method for effectively relieving terrain information loss, which belongs to the technical field of geographic information analysis modeling. The invention adopts the grid accumulated flow as the weight factor in the upscaling calculation process, fully considers the river network continuity characteristic in the river basin characteristic analysis, effectively keeps the topographic information of the digital elevation model, slows down the attenuation of DEM information quantity after the upscaling process, ensures the continuity of the main flow and the branch flow river network in the river basin, and can obtain a reasonable river basin range according to the target grid DEM. The determination of reasonable continuous river network and river basin boundary is an important aspect for constructing digital river basins, and river basin characteristic parameters such as the river basin characteristic parameters, slope and the like are important basis for practical application such as hydrological statistics, runoff analysis, water resource evaluation, flood forecasting, hydraulic engineering design and the like.
Description
Technical Field
The invention belongs to the technical field of geographic information analysis modeling, and particularly relates to a digital elevation model upscaling method for effectively relieving topographic information loss.
Background
A Digital Elevation Model (DEM) is basic data of geospatial information science, and topographic information provided by the DEM is widely used in various fields such as domain feature analysis, distributed hydrological modeling, meteorological simulation, ecological environment assessment, landform landscape analysis and the like, and has important application value. With the enhancement of the ground monitoring capability of global satellites, high-resolution digital elevation data such as SRTM DEM, ASTER GDEM, Terras AR-X DEM, HYDRO1K and the like exist at present, and the high-resolution digital elevation data has the characteristics of high horizontal resolution, open shared access and the like.
However, when spatial information analysis based on the DEM, such as terrain analysis and digital river network extraction in large areas, countries or continents, is performed, the high-resolution DEM data has a fine grid and a large data volume, occupies a large amount of computer memory, and causes that the difficulty of software programs such as a geographic information system (such as ArcGIS) in processing DEM data is remarkably increased, and the processing is difficult to be completed on a common stand-alone platform. In addition, a plurality of distributed hydrological models and land models established based on the DEM are widely applied to the fields of flood forecasting, drought and flood forecasting, ecological assessment and the like, the terrain is an important basic parameter in the distributed models, and when the models are used for large-area simulation calculation, the grid resolution is required to be between thousands of meters and tens of kilometers generally so as to meet the requirements on calculation stability, calculation efficiency and the like.
These applications require upscaling of existing fine-grid DEM data products to a suitable grid resolution. In the upscaling process, the numerical average is usually directly performed on the fine grid DEM, which causes a series of losses of terrain information quantity, such as the average gradient of the upscaled DEM is reduced, the digital river network is no longer continuous, and the watershed boundary is distorted, and thus the requirements of scientific research and practical application cannot be met. How to increase the scale to generate DEM data with proper resolution and slow down the attenuation of terrain information quantity, and maintain the accuracy of the boundaries of a digital river network water system and a river basin is an important link for ensuring the DEM data in scientific research and production practice of geographical science such as hydrological weather.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a digital elevation model upscaling method for effectively relieving terrain information loss. The method adopts the grid accumulated flow as a weight factor in the upscaling calculation process, fully considers the continuity characteristic of the river network in the river basin characteristic analysis, effectively keeps the topographic information of the digital elevation model, slows down the attenuation of DEM information quantity after the upscaling process, and ensures the continuity of the main flow and the branch river network in the river basin. The method can provide a group of large-area DEM data with different resolutions, and can provide necessary support for large-scale basin underlying surface information extraction, establishment and application of a distributed hydrological model and a land model, a distributed model space-time scale problem, ecological environment response evaluation under a changing environment and the like.
The technical scheme is as follows: in order to achieve the purpose, the invention provides the following technical scheme:
a digital elevation model upscaling method for effectively relieving terrain information loss comprises the following steps:
1) and (3) collecting data of the fine grid DEM: collecting and downloading the existing commonly used fine grid DEM data, wherein the grid resolution is usually between dozens of meters and one kilometer;
2) DEM projection conversion: carrying out projection setting on the fine grid DEM which is not projected in an equal area to generate fine grid DEM data projected in an equal area;
3) calculating the gradient: selecting DEM data of a research area range, filling holes by adopting a geographic information system tool, and then calculating the grid gradient of the fine grid;
4) mesh cumulative flow computation of the fine mesh: determining the grid flow direction by adopting a D8 method based on the maximum gradient, calculating the grid accumulated flow of the fine grid, and comparing the grid accumulated flow with the actual river network;
5) grid cumulative flow calculation for the target grid: determining the size of a target grid according to research requirements and practical application requirements, and calculating a grid accumulated flow value FAC of the target grid according to the number of DEMs of the fine grids in the target grid;
6) determining a basin boundary: setting a certain threshold according to the grid accumulated flow value FAC of the target grid, determining the grid smaller than the threshold as a basin boundary grid, and determining the other grids as river network grids;
7) DEM calculation of a basin boundary grid: aiming at a target grid at a drainage basin boundary, calculating the elevation of the target grid by adopting a common grid averaging method so as to ensure that the elevation of the grid at the drainage basin boundary is not excessively reduced and keep the drainage basin boundary reasonable;
8) calculating a river network grid DEM: calculating the elevation of the target river network grid by taking the grid accumulated flow of the fine grid as a weighting weight on the basis of the DEM of the fine grid, thereby better keeping the topographic information quantity of the grid and leading the extracted river network to be continuous;
9) comparing and analyzing the topographic information features: according to the target grid DEM generated by the method, characteristic information such as gradient, terrain index, river network, river basin boundary and the like is extracted and is compared and analyzed with a common grid average method.
Further, in the step 1) -2), the fine mesh DEM data of the equal-area projection is set by projecting the fine mesh DEM (such as ASTER GDEM and SRTM DEM) which is not the equal-area projection, so as to generate the fine mesh DEM data of the equal-area projection, which is used for calculating the characteristic parameters of the drainage basin and performing hydrological modeling.
Further, in the step 3), the grid gradient of the fine grid is S, the grid Flow direction is calculated according to a single Flow direction D8 algorithm based on the maximum gradient, and then a grid cumulative Flow (Flow Accumulation) of the fine grid is generated and compared with the actual river network; if the ground is supposed to be impervious or the soil is supposed to be saturated, the value of rainfall finally reaching a certain grid through the action of a watershed can be represented by the accumulated flow of the grid; thus, the grid accumulated flow can be used to define a river network (a grid with a large accumulated flow) and a river basin boundary (a grid with a small accumulated flow), which contains the required river network and river basin boundary information of the DEM data and is convenient for calculation and acquisition;
wherein S is the grid gradient and the unit degree;
and
elevation variability in the x and y directions, respectively, is dimensionless.
Further, in the step 6), FAC is used as an algorithm control variable, and in the calculation process, FAC is smaller than a certain threshold FAC0The grid is identified as a drainage basin boundary grid, see formula (II), the elevation of the grid is calculated in an upscaling mode by adopting a grid averaging method at the drainage basin boundary, and the drainage basin boundary is kept reasonable; the other grids are river network grids.
In the formula, GRID is a GRID type and is dimensionless; gbdyAnd GrvrThe method is characterized by comprising the following steps of respectively forming a river basin boundary grid and a river network grid without dimension; FAC is the grid accumulated flow of the target grid, and is dimensionless; FAC0The grid type critical threshold is dimensionless.
Further, in the step 7), during the DEM grid upscaling process, a grid averaging method is generally adopted, which is shown in formula (III); in the calculation process of the grid average method, only a single elevation factor is considered, other effective data information is not introduced, the generated large-scale DEM data is usually insufficient to ensure that reasonable continuous water system and basin boundaries are extracted, the quantity of a plurality of terrain information is rapidly attenuated, and the large-scale DEM data is difficult to be used for constructing a large-scale distributed hydrological model and relevant research; different from a common grid average upscaling method, the Digital Elevation Model (DEM) upscaling method for effectively reducing terrain information loss takes generated grid accumulated flow as a weight control factor;
in the formula, the ELEV is the target grid elevation in m; ELEV is the elevation of the initial fine grid DEM in m; n is the ratio of the target grid dimension to the fine grid dimension, and n multiplied by n is the number of the fine grid DEMs contained in the target grid.
Further, in the step 8), the grid accumulated flow fac (i, j) is introduced as a weight control factor to generate a target grid scale DEM, as shown in formula (IV); the method maintains river network and river basin boundary information in the initial DEM, increases the elevation weight of the fine grid with large confluence area (namely, river channel grid), and keeps the elevation weight of the fine grid with small confluence area (namely, at the river basin boundary);
in the formula, FAC is the sum of the fine grid cumulative flow FAC in the target grid scale, and is dimensionless; the ELEV is an obtained elevation value of a target grid scale and is in a unit of m; ELEV is the elevation of the initial fine grid DEM in m; n is the ratio of the target grid scale to the fine grid scale, is dimensionless and is an integer.
Further, in the step 9), a group of DEM data of different target grid resolutions is generated and used for extracting river basin characteristic parameters such as gradient, elevation characteristic value, terrain index, formula (VI), digital river network and river basin range; simultaneously, DEM data of corresponding grid resolution ratios are obtained according to a grid averaging method, the watershed characteristic parameters are extracted, and the results of the upscaling method and the grid averaging method are compared;
where α is the catchment area per unit of contour length at a point on the slope through which the flow passes and tan β is the slope at that point.
The upscaling method is convenient and fast to operate, the influence of terrain complexity is fully considered, the generated large-scale grid DEM can effectively control the continuity of a river network, reasonable river basin boundaries are extracted, and the attenuation speed of the DEM information quantity in the upscaling process can be obviously reduced.
Generating grid accumulated flow data based on the fine grid DEM, taking the grid accumulated flow as a DEM weight factor, and upscaling to obtain DEM data of target grids with different resolutions; the method adopts grid accumulated flow as a weight factor in the upscaling calculation process, and the upscaling is still carried out at the boundary of a flow domain by adopting a grid average method; according to the method, the continuity characteristic of the river network in the river basin hydrological analysis is fully considered, the topographic information of the digital elevation model is effectively kept, the attenuation of DEM information quantity after the upscaling process is slowed down, and the continuity of the river network of the main flow and the branch flow of the river basin is ensured; performing projection setting on the fine grid DEM which is not projected in an equal area to generate fine grid DEM data projected in an equal area, so as to be beneficial to calculating characteristic parameters of a drainage basin and hydrologic modeling; calculating the grid Flow direction according to a single-Flow D8 algorithm, and further generating a grid cumulative Flow (Flow Accumulation) of the fine grid; different from a common grid average upscaling method, the upscaling method takes the generated grid accumulated flow as a weight factor; the grid accumulated flow can be used for defining a river network and a river basin boundary, contains the required river network and river basin boundary information of DEM data, and is convenient to calculate and obtain; the upscaling method introduces grid accumulated flow as a weight control factor to generate a target grid scale DEM, reserves river network and river basin boundary information in the initial DEM, increases the fine grid elevation weight with large confluence area (namely, river channel grid), and keeps the fine grid elevation weight with small confluence area (namely, at the river basin boundary); the scale-up method simultaneously takes FAC as an algorithm control variable, identifies grids at the watershed boundary in the calculation process, adopts a grid averaging method to perform scale-up calculation on the grid elevation at the watershed boundary, and keeps the watershed boundary reasonable; the upscaling method is convenient and fast to operate, the influence of terrain complexity is fully considered, and compared with a common grid averaging method, the generated large-scale grid DEM can effectively control the continuity of a river network, extract a reasonable river basin boundary and obviously slow down the attenuation speed of DEM information quantity in the upscaling process; the determination of reasonable continuous river network and river basin boundary is an important aspect for constructing digital river basins, and river basin characteristic parameters such as the river basin characteristic parameters, slope and the like are important basis for practical application such as hydrological statistics, runoff analysis, water resource evaluation, flood forecasting, hydraulic engineering design and the like.
Has the advantages that: compared with the prior art, the digital elevation model upscaling method for effectively relieving the loss of the topographic information adopts the grid accumulated flow as the weight factor in the upscaling calculation process, fully considers the continuity characteristic of the river network in the river basin characteristic analysis, effectively maintains the topographic information of the digital elevation model, relieves the attenuation of DEM information quantity after the upscaling process, ensures the continuity of the main flow and the branch flow river network of the river basin, and can obtain a reasonable river basin range according to the target grid DEM. The method can be used in the scientific and practical fields of large-scale river network water system extraction, digital river basin construction, river basin distributed type hydrology and water forecasting, water resource prediction evaluation and comprehensive management, climate mode modeling, hydrology and water resource response assessment under global change and the like.
Drawings
FIG. 1 is a schematic diagram showing a structural comparison between the method of the present invention and a conventional method;
FIG. 2 is a flow chart of the steps involved in the method of the present invention;
FIG. 3 the present invention effectively slows down loss of terrain information-average slope;
FIG. 4 is a diagram of the present invention that effectively mitigates loss of topographical information — elevation maxima;
FIG. 5 the present invention effectively mitigates loss of terrain information-terrain index;
figure 6 digital river network continuity and watershed boundary correctness verification.
Detailed Description
The invention will be further described with reference to the following drawings and specific embodiments.
A Digital Elevation Model (DEM) upscaling method for effectively reducing terrain information loss is characterized in that grid accumulated Flow data (Flow Accumulation) is generated based on a fine grid DEM, the grid accumulated Flow is used as an elevation weight factor, and DEM data of target grids with different resolutions are obtained by upscaling. Compared with the traditional upscaling method (as shown in fig. 1), the method of the present invention uses the grid cumulative flow as the weighting factor and the discriminant index in the upscaling calculation process, and the upscaling is performed by using the grid averaging method at the flow domain boundary (i.e. when the grid cumulative flow is smaller than a certain value). According to the method, the continuity characteristic of the river network in the river basin hydrological analysis is fully considered, the topographic information of the digital elevation model is effectively kept, the attenuation of DEM information quantity after the upscaling process is slowed down, and the continuity of the river network of the main flow and the branch flow of the river basin is ensured.
The fine-grid DEM in the upscale method usually uses currently available DEM data which can be downloaded from websites, such as the global digital elevation model (ASTER GDEM) of the AST (advanced satellite thermal emission and reflection radiometer), the terrain mapping plan (SRTM DEM) of the space shuttle radar, and the HYDRO1K DEM manufactured by the American geological survey. Of these, ASTER GDEM has a horizontal resolution of 1 arcsecond (about 30 meters), SRTM DEM has a horizontal resolution of 3 arcseconds (about 90 meters), and HYDRO1K DEM has a horizontal resolution of 1 km. Actual mapping or DEM generated from contours may also be used.
When the common DEM data is used for large-scale digital basin construction and distributed hydrological modeling, upscale processing is required to be carried out so as to carry out efficient data information analysis and modeling calculation.
A digital elevation model upscaling method for effectively relieving terrain information loss comprises the following steps:
1) and (3) collecting data of the fine grid DEM: collecting and downloading the existing commonly used fine grid DEM data, wherein the grid resolution is usually between dozens of meters and one kilometer;
2) DEM projection conversion: carrying out projection setting on the fine grid DEM which is not projected in an equal area to generate fine grid DEM data projected in an equal area;
3) calculating the gradient: selecting DEM data of a research area range, filling holes by adopting a geographic information system tool, and then calculating the grid gradient of the fine grid;
4) mesh cumulative flow computation of the fine mesh: determining the grid flow direction by adopting a D8 method based on the maximum gradient, calculating the grid accumulated flow of the fine grid, and comparing the grid accumulated flow with the actual river network;
5) grid cumulative flow calculation for the target grid: determining the size of a target grid according to research requirements and practical application requirements, and calculating a grid accumulated flow value FAC of the target grid according to the number of DEMs of the fine grids in the target grid;
6) determining a basin boundary: setting a certain threshold according to the grid accumulated flow value FAC of the target grid, determining the grid smaller than the threshold as a basin boundary grid, and determining the other grids as river network grids;
7) DEM calculation of a basin boundary grid: aiming at a target grid at a drainage basin boundary, calculating the elevation of the target grid by adopting a common grid averaging method so as to ensure that the elevation of the grid at the drainage basin boundary is not excessively reduced and keep the drainage basin boundary reasonable;
8) calculating a river network grid DEM: calculating the elevation of the target river network grid by taking the grid accumulated flow of the fine grid as a weighting weight on the basis of the DEM of the fine grid, thereby better keeping the topographic information quantity of the grid and leading the extracted river network to be continuous;
9) comparing and analyzing the topographic information features: according to the target grid DEM generated by the method, characteristic information such as gradient, terrain index, river network, river basin boundary and the like is extracted and is compared and analyzed with a common grid average method.
In the steps 1) -2), the fine grid DEM data of the equal-area projection is subjected to projection setting on the fine grid DEM (such as ASTER GDEM and SRTM DEM) which is not the equal-area projection, and the fine grid DEM data of the equal-area projection is generated and used for calculating the characteristic parameters of the drainage basin and performing hydrological modeling.
In the step 3), the grid gradient of the fine grid is S, the grid Flow direction is calculated according to a single-Flow D8 algorithm based on the maximum gradient, and then grid accumulated Flow (Flow Accumulation) of the fine grid is generated and is compared with an actual river network; if the ground is supposed to be impervious or the soil is supposed to be saturated, the value of rainfall finally reaching a certain grid through the action of a watershed can be represented by the accumulated flow of the grid; thus, the grid accumulated flow can be used to define a river network (a grid with a large accumulated flow) and a river basin boundary (a grid with a small accumulated flow), which contains the required river network and river basin boundary information of the DEM data and is convenient for calculation and acquisition;
wherein S is the grid gradient and the unit degree;
and
elevation variability in the x and y directions, respectively, is dimensionless.
Step 6), using the FAC as an algorithm control variable, and in the calculation process, making the FAC smaller than a certain threshold FAC0The grid is identified as a drainage basin boundary grid, see formula (II), the elevation of the grid is calculated in an upscaling mode by adopting a grid averaging method at the drainage basin boundary, and the drainage basin boundary is kept reasonable; the other grids are river network grids.
In the formula, GRID is a GRID type and is dimensionless; gbdyAnd GrvrThe method is characterized by comprising the following steps of respectively forming a river basin boundary grid and a river network grid without dimension; FAC is the grid accumulated flow of the target grid, and is dimensionless; FAC0The grid type critical threshold is dimensionless.
In the step 7), a grid average method is usually adopted in the DEM grid upscaling process, which is shown in a formula (III); in the calculation process of the grid average method, only a single elevation factor is considered, other effective data information is not introduced, the generated large-scale DEM data is usually insufficient to ensure that reasonable continuous water system and basin boundaries are extracted, the quantity of a plurality of terrain information is rapidly attenuated, and the large-scale DEM data is difficult to be used for constructing a large-scale distributed hydrological model and relevant research; different from a common grid average upscaling method, the Digital Elevation Model (DEM) upscaling method for effectively reducing terrain information loss takes generated grid accumulated flow as a weight control factor;
in the formula, the ELEV is the target grid elevation in m; ELEV is the elevation of the initial fine grid DEM in m; n is the ratio of the target grid dimension to the fine grid dimension, and n multiplied by n is the number of the fine grid DEMs contained in the target grid.
In the step 8), introducing the grid accumulated flow fac (i, j) as a weight control factor to generate a target grid scale DEM as shown in a formula (IV); the method maintains river network and river basin boundary information in the initial DEM, increases the elevation weight of the fine grid with large confluence area (namely, river channel grid), and keeps the elevation weight of the fine grid with small confluence area (namely, at the river basin boundary);
in the formula, FAC is the sum of the fine grid cumulative flow FAC in the target grid scale, and is dimensionless; the ELEV is an obtained elevation value of a target grid scale and is in a unit of m; ELEV is the elevation of the initial fine grid DEM in m; n is the ratio of the target grid scale to the fine grid scale, is dimensionless and is an integer.
Step 9), generating a group of DEM data with different target grid resolutions for extracting slope, elevation characteristic values, terrain indexes, formula (VI), digital river network, river basin range and other river basin characteristic parameters; simultaneously, DEM data of corresponding grid resolution ratios are obtained according to a grid averaging method, the watershed characteristic parameters are extracted, and the results of a scale-up method and a grid averaging method are compared;
where α is the catchment area per unit of contour length at a point on the slope through which the flow passes and tan β is the slope at that point.
The flow of the above implementation steps of the method of the present invention is shown in fig. 2.
In the embodiment, a watershed above the Tangmieh (100 degrees 09 'E and 35 degrees 30' N) upstream of the yellow river is selected as a research area, the HYDRO1K is used as initial DEM elevation data, four sets of DEM data with different grid scales of 5km, 10km, 15km and 20km of the two research areas are respectively generated by using the algorithm and the grid averaging method, and the DEM data are used for extracting characteristic parameters of the watershed and carrying out comparative analysis.
As the DEM grid scale increases, the slope will be flattened. The slope average values obtained by the two methods are in a rapid reduction trend, the slope average value obtained by the algorithm is obviously larger than the result obtained by the grid average method, and as shown in fig. 3, the slope information quantity attenuation speed of the algorithm is smaller than that of the grid average method in the calculation process. Meanwhile, the gradient mean square error of the two methods is reduced, and DEM information quantity attenuation caused by grid scale increase is reflected.
The change of the grid scale causes the change of the elevation extreme value of the drainage basin, and the elevation of the drainage basin obtained by calculation of the DEM data of the same grid scale obtained by the two methods is also different. The algorithm of the invention increases the weight of the grid value with large confluence area, and the obtained minimum elevation value and average value are both smaller than those of the grid average method; the algorithm also ensures the elevation at the watershed boundary, and the maximum obtained elevation is greater than that of the grid averaging method, as shown in fig. 4. By taking the elevation mean square error as the measurement standard of the terrain complexity, the algorithm can better reserve the terrain complexity and has the tendency of slowly increasing the terrain complexity. The grid averaging method tends to homogenize the elevation distribution, reflecting that the elevation mean square error is rapidly reduced.
The terrain index ln (α/tan β) represents the spatial distribution of the soil moisture condition in the flow area, and provides a reasonable physical explanation and mathematical description for the full productive flow mechanism, as the DEM grid scale increases, the terrain index average value shows a decreasing trend along with the increase of the DEM grid scale, generally, the decreasing speed of the algorithm of the invention is smaller than that of the grid average method, as shown in FIG. 5, correspondingly, the mean square deviations obtained by the two methods are gradually reduced, and the root mean square value of the terrain index obtained by the algorithm of the invention is larger than that of the grid average method on different grid scales.
And generating a digital river network and a river basin range of different grid scales of the river basin by using ArcGIS software. Set critical threshold FAC of river network for comparison under different grid scales0Are all 400km2. Taking the river network extracted by the two methods with the resolution of 5km DEM as an example, the result is shown in figure 6, the algorithm can generate a continuous river network under different grid scales, a grid averaging method cannot generate a reasonable continuous river network and has too many parallel river channels, and a river basin has a plurality of unreasonable outlets.
Table 1 lists the river basin parameters such as the river basin area, the river network density, the number of river sections, the main river channel length and the like under different DEM grid scales. The river network density is the ratio of the total length of all rivers in the river basin to the area of the river basin, represents the space density degree of the river channel, and is an index reflecting the relative proportion of ground runoff and river runoff. As the grid size is larger, the river network density and the river reach number are gradually reduced, and the reduction rates of the river network density and the river reach number are consistent. The main river length is the length from the highest river to the watershed, and the value generally has a decreasing trend as the grid scale becomes larger. The grid averaging method cannot obtain reasonable watershed parameters, and the method is invalid.
The actually measured drainage basin area of the research area is 121972 km2. Table 1 shows the drainage basin area values extracted by the DEM of different grid scales obtained by the algorithm of the present invention, and the data shows that the drainage basin area remains very good and has no obvious variation trend with the increase of the grid scale. The area of the drainage basin extracted by the DEM of 5km, 10km, 15km and 20km generated by the algorithm of the inventionThe errors are respectively 0.638%, 0.433%, 0.720% and 0.023%, and the distribution of the basin boundary is basically consistent. The grid averaging method cannot generate a reasonable continuous river network, cannot extract a river basin area value, and is ineffective.
Generally, the mean square deviations of the elevation, the gradient and the terrain index obtained by the algorithm are all larger than the parameter mean square deviation value of the grid average method, and the fact that the mean square deviation value can obviously slow down the attenuation speed of the information quantity is shown; the algorithm can effectively control the generation of the continuous river network and obtain a reasonable digital river basin, which shows that the method correctly keeps the information quantity related to the generation of the river network and the range of the river basin in the process of increasing the scale of the grid, and the grid averaging method loses the original information of the river network and the range of the river basin in the DEM. In addition, the algorithm of the invention can show the superior performance of the algorithm in the region with gentle terrain gradient, and meets the requirement of constructing a large-scale distributed hydrological model to extract a digital watershed.
The determination of reasonable continuous river network and river basin boundary is an important aspect for constructing digital river basins, and river basin characteristic parameters such as the river basin characteristic parameters, slope and the like are important basis for practical application such as hydrological statistics, runoff analysis, water resource evaluation, flood forecasting, hydraulic engineering design and the like.
Table 1 watershed parameters extracted according to different DEM grid scales generated by the method of the invention
Claims (7)
1. A digital elevation model upscaling method for effectively relieving terrain information loss is characterized by comprising the following steps: the method comprises the following steps:
1) and (3) collecting data of the fine grid DEM: collecting and downloading the existing commonly used fine grid DEM data, wherein the grid resolution is usually between dozens of meters and one kilometer;
2) DEM projection conversion: carrying out projection setting on the fine grid DEM which is not projected in an equal area to generate fine grid DEM data projected in an equal area;
3) calculating the gradient: selecting DEM data of a research area range, filling holes by adopting a geographic information system tool, and then calculating the grid gradient of the fine grid;
4) mesh cumulative flow computation of the fine mesh: determining the grid flow direction by adopting a D8 method based on the maximum gradient, calculating the grid accumulated flow of the fine grid, and comparing the grid accumulated flow with the actual river network;
5) grid cumulative flow calculation for the target grid: determining the size of a target grid according to research requirements and practical application requirements, and calculating a grid accumulated flow value FAC of the target grid according to the number of DEMs of the fine grids in the target grid;
6) determining a basin boundary: setting a certain threshold according to the grid accumulated flow value FAC of the target grid, determining the grid smaller than the threshold as a basin boundary grid, and determining the other grids as river network grids;
7) DEM calculation of a basin boundary grid: aiming at a target grid at a drainage basin boundary, calculating the elevation of the target grid by adopting a common grid averaging method so as to ensure that the elevation of the grid at the drainage basin boundary is not excessively reduced and keep the drainage basin boundary reasonable;
8) calculating a river network grid DEM: calculating the elevation of the target river network grid by taking the grid accumulated flow of the fine grid as a weighting weight on the basis of the DEM of the fine grid, thereby better keeping the topographic information quantity of the grid and leading the extracted river network to be continuous;
9) comparing and analyzing the topographic information features: according to the target grid DEM generated by the method, characteristic information such as gradient, terrain index, river network, river basin boundary and the like is extracted and is compared and analyzed with a common grid average method.
2. The method of claim 1, wherein the digital elevation model upscaling method is effective in mitigating loss of terrain information, and comprises: in the step 1) -2), the fine grid DEM data of the equal-area projection is subjected to projection setting on the fine grid DEM (such as ASTER GDEM and SRTM DEM) which is not the equal-area projection, so as to generate the fine grid DEM data of the equal-area projection, and the fine grid DEM data is used for calculating characteristic parameters of the drainage basin and performing hydrological modeling.
3. The method of claim 1, wherein the digital elevation model upscaling method is effective in mitigating loss of terrain information, and comprises: in the step 3), the grid gradient of the fine grid is S, the grid Flow direction is calculated according to a single-Flow D8 algorithm based on the maximum gradient, and then grid cumulative Flow (Flow Accumulation) of the fine grid is generated and compared with an actual river network; if the ground is supposed to be impervious or the soil is supposed to be saturated, the value of rainfall finally reaching a certain grid through the action of a watershed can be represented by the accumulated flow of the grid; thus, the grid accumulated flow can be used to define a river network (a grid with a large accumulated flow) and a river basin boundary (a grid with a small accumulated flow), which contains the required river network and river basin boundary information of the DEM data and is convenient for calculation and acquisition;
wherein S is the grid gradient and the unit degree;
and
elevation variability in the x and y directions, respectively, is dimensionless.
4. The method of claim 1, wherein the digital elevation model upscaling method is effective in mitigating loss of terrain information, and comprises: in the step 6), FAC is used as an algorithm control variable, and is smaller than a certain threshold value FAC in the calculation process0The grid is identified as a drainage basin boundary grid, see formula (II), the elevation of the grid is calculated in an upscaling mode by adopting a grid averaging method at the drainage basin boundary, and the drainage basin boundary is kept reasonable; the other grids are river network grids.
In the formula, GRID is a GRID type and is dimensionless; gbdyAnd GrvrAre respectively a river basin boundary grid and a river network grid, and have no quantityA head line; FAC is the grid accumulated flow of the target grid, and is dimensionless; FAC0The grid type critical threshold is dimensionless.
5. The method of claim 1, wherein the digital elevation model upscaling method is effective in mitigating loss of terrain information, and comprises: in the step 7), a grid average method is generally adopted in the DEM grid upscaling process, which is shown in formula (III); in the calculation process of the grid average method, only a single elevation factor is considered, other effective data information is not introduced, the generated large-scale DEM data is usually insufficient to ensure that reasonable continuous water system and basin boundaries are extracted, the quantity of a plurality of terrain information is rapidly attenuated, and the large-scale DEM data is difficult to be used for constructing a large-scale distributed hydrological model and relevant research; different from a common grid average upscaling method, the Digital Elevation Model (DEM) upscaling method for effectively reducing terrain information loss takes generated grid accumulated flow as a weight control factor;
in the formula, the ELEV is the target grid elevation in m; ELEV is the elevation of the initial fine grid DEM in m; n is the ratio of the target grid dimension to the fine grid dimension, and n multiplied by n is the number of the fine grid DEMs contained in the target grid.
6. The method of claim 1, wherein the digital elevation model upscaling method is effective in mitigating loss of terrain information, and comprises: in the step 8), the grid accumulated flow fac (i, j) is introduced as a weight control factor to generate a target grid scale DEM, as shown in a formula (IV); the method maintains river network and river basin boundary information in the initial DEM, increases the elevation weight of the fine grid with large confluence area (namely, river channel grid), and keeps the elevation weight of the fine grid with small confluence area (namely, at the river basin boundary);
in the formula, FAC is the sum of the fine grid cumulative flow FAC in the target grid scale, and is dimensionless; the ELEV is an obtained elevation value of a target grid scale and is in a unit of m; ELEV is the elevation of the initial fine grid DEM in m; n is the ratio of the target grid scale to the fine grid scale, is dimensionless and is an integer.
7. The method of claim 1, wherein the digital elevation model upscaling method is effective in mitigating loss of terrain information, and comprises: in the step 9), a group of DEM data with different target grid resolutions is generated and used for extracting river basin characteristic parameters such as gradient, elevation characteristic values, terrain indexes, formula (VI), digital river network and river basin range; simultaneously, DEM data of corresponding grid resolution ratios are obtained according to a grid averaging method, the watershed characteristic parameters are extracted, and the results of the upscaling method and the grid averaging method are compared;
where α is the catchment area per unit of contour length at a point on the slope through which the flow passes and tan β is the slope at that point.
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