Sand landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes

Abstract

The invention discloses a sand landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes, and relates to the technical field of ecological restoration and water resource management, and the method comprises the following steps: 1, collecting a remote sensing image, field ecological data and hydro meteorological data, and carrying out the preprocessing to obtain ecological hydrological element data; 2, calculating the ecological water demand of the semi-arid sand landscape according to the ecological hydrological element data; 3, constructing a nonlinear-multi-target sand landscape pattern optimization model according to the ecological water demand and the ecological hydrological element data; 4, solving the nonlinear-multi-target sand landscape pattern optimization model by adopting a multi-target optimization algorithm according to the condition constraint to obtain a parameter optimal solution; and 5, constructing a sand landscape pattern strategy according to the parameter optimal solution. By comprehensively optimizing water and sand resource allocation and ecological water demand inside and outside rivers and lakes and cooperatively deploying all landscape elements, sustainable utilization of sand landscape water resources and improvement of an ecological hydrological function are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological restoration and water resource management, and more particularly to a method for optimizing sandy land landscape patterns based on ecological water demands inside and outside rivers and lakes. Background Art

[0002] Sandy landscapes (including mobile dunes, semi-fixed dunes, and some fixed dune ecosystems) are among the most ecologically fragile regions on Earth, particularly vulnerable to drought, wind-blown erosion, and land degradation. With global climate change and intensified human activities, the area of sandy lands continues to expand, and desertification is becoming increasingly severe. Sandy lands suffer from scarce and unevenly distributed water resources, and their fragile ecosystems often face problems such as excessive water consumption and exacerbated desertification. This poses significant challenges to their ecological restoration and sustainable development. Furthermore, the water supply of sandy river and lake systems is unstable, creating a significant conflict between ecological water demand and demand. Under the current policy framework of prioritizing ecology and promoting green development, increasing attention is being paid to sandy landscape protection and water resource regulation. Appropriate human intervention can not only mitigate sandy landscape degradation but also promote the coordinated development of eco-hydrological systems, making it an indispensable means of maintaining the sustainability of sandy land ecosystems.

[0003] Traditional sandy land ecological restoration techniques typically focus on single water resource management or vegetation restoration, often focusing only on the needs of local areas or specific water sources. They fail to fully consider the systematic coordination of water resource supply and demand within the sandy land and its interrelationship with surrounding ecosystems (water conservation areas, wetlands, lakes, etc.). During the optimization of sandy land landscape patterns, a lack of comprehensive consideration of the ecological water demand balance within and beyond rivers and lakes has resulted in less than ideal ecological restoration results, even exacerbating water resource waste and making it difficult to achieve the coordinated and sustainable development of the eco-hydrological system. The rational configuration of sandy land landscape patterns (including sand dunes, vegetation, wetlands, lakes, etc.) has a decisive impact on the realization of key ecological functions such as the hydrological cycle, soil moisture conservation, and biodiversity protection.

[0004] Therefore, how to optimize the configuration of sandy landscapes, coordinate the water demand relationship between internal and external ecosystems through comprehensive allocation of water and sand resources, and maintain the healthy eco-hydrological functions of sandy landscapes is an issue that technical personnel in this field urgently need to solve. Summary of the Invention

[0005] In light of this, the present invention provides a method for optimizing sandy landscape patterns based on the ecological water demands within and outside rivers and lakes. This method employs a collaborative optimization model for water and sand resources and ecological water demands. By comprehensively optimizing the allocation of water and sand resources and the ecological water demands within and outside rivers and lakes, and collaboratively allocating landscape elements such as sand dunes, grasslands, wetlands, and rivers and lakes, the method achieves sustainable utilization of sandy landscape water resources and enhances ecohydrological functions. This method addresses the difficulty of existing technologies in protecting and restoring sandy land ecosystems while also addressing the prominent issue of unsustainable ecological governance at the expense of water resources.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for optimizing sandy land landscape pattern based on ecological water demand inside and outside rivers and lakes comprises the following steps:

[0008] Step 1: Collect remote sensing images, field ecological data, and hydrological and meteorological data, and obtain eco-hydrological element data after pre-processing;

[0009] Step 2: Calculate the ecological water demand of the semi-arid sandy landscape based on the eco-hydrological element data;

[0010] Step 3: Construct a nonlinear multi-objective sandy land landscape pattern optimization model based on ecological water demand and eco-hydrological element data;

[0011] Step 4: Based on the conditional constraints, a multi-objective optimization algorithm is used to solve the nonlinear multi-objective sandy land landscape pattern optimization model to obtain the optimal solution for the parameters;

[0012] Step 5: Construct sandy land landscape pattern strategy based on the optimal parameter solution.

[0013] Preferably, the eco-hydrological element data include river and lake distribution characteristics, vegetation distribution characteristics and hydrological characteristics.

[0014] Preferably, the river and lake distribution characteristics include the number and area of regional water bodies, and the process of extracting the river and lake distribution characteristics based on remote sensing images includes:

[0015] Step 11: Use the Ostu adaptive threshold segmentation method to perform preliminary segmentation on the remote sensing image to obtain the initial segmentation result;

[0016] Step 12: Build a neural network model, perform depth segmentation based on the initial segmentation results and remote sensing images, identify water body boundaries, and extract water bodies;

[0017] Step 13: Count the number of water bodies in the region based on the extracted water bodies, and calculate the area using the Geographic Information System (GIS) to obtain the water body area.

[0018] Preferably, the water bodies include wetlands, rivers and lakes.

[0019] Preferably, traditional soil and vegetation survey methods are used to obtain field ecological data, and combined with hydrological and meteorological data to extract vegetation distribution characteristics, including vegetation indicators, soil indicators, and factors affecting vegetation growth environment; vegetation indicators include physiological and ecological indicators such as vegetation type, vegetation coverage, and plant transpiration; soil indicators include physical and chemical characteristics such as soil permeability;

[0020] According to the distribution characteristics of rivers and lakes and vegetation, the influencing factors of vegetation growth environment were obtained through redundancy analysis and correlation analysis.

[0021] Preferably, hydrological characteristics are extracted based on hydrological and meteorological data and remote sensing images; hydrological characteristics include precipitation, net flow, water flow rate, water level, water level change, etc.; precipitation includes monthly, quarterly and annual precipitation.

[0022] Preferably, the ecological water demand in step 2 includes the ecological water demand inside rivers and lakes and the ecological water demand outside rivers and lakes;

[0023] Based on the ecological factor data, the ecological flow model is used to calculate the river maintenance base flow and the lake ecological water demand. The sum of the river maintenance base flow and the lake ecological water demand is the internal ecological water demand of the river and lake; it is expressed as:

[0024] W in =W in-t +W in-l =(Q min +L min )·f(H season )

[0025] Among them, W in W is the ecological water demand of rivers and lakes in sandy landscape; in-r 、W in-l represent the river maintenance base flow and lake ecological water demand respectively; Q min It represents the minimum flow rate of the river to maintain the needs of internal biological populations and its sediment transport capacity, obtained based on vegetation indicators and hydrological characteristics; L min represents the minimum flow required for the lake to maintain its internal biological population, obtained based on vegetation indicators and hydrological characteristics; f(H season / ) represents the relationship function between flow and water level, which is usually an empirical relationship or the result of fitting a hydrological model. In semi-arid areas, the calculation of ecological flow needs to take into account seasonal fluctuations;

[0026] The wetland ecological water demand is calculated based on the ecological factor data. The vegetation ecological water demand of each landscape unit is calculated using the biological water demand model based on the ecological factor data. The sum of the wetland ecological water demand and the vegetation ecological water demand of all landscape units is the external ecological water demand of rivers and lakes.

[0027] The ecological water demand of wetlands is expressed as:

[0028] Q wetland =A·(HH min )·C

[0029] Q wetland is the ecological water demand of the wetland, A is the wetland area, H is the current water level, and H min is the lowest water level of the wetland, C is the flow coefficient calculated based on the soil permeability and water flow rate of the wetland; the wetland area is obtained based on the water body area, the current water level is obtained based on the water level in the hydrological characteristics, and the lowest water level of the wetland is obtained based on the water level change;

[0030] The ecological water demand of vegetation is expressed as:

[0031]

[0032] Q plant represents the ecological water requirement of vegetation; ET is the actual transpiration of plants; K c is the crop transpiration coefficient; P is the precipitation; R is the net flow; ΔS is the change in soil moisture; T is the time period; Indicates the factors affecting vegetation growth environment;

[0033]

[0034] W ex Indicates the external ecological water demand of rivers and lakes; Q plat,s Represents the ecological water demand of vegetation in the sth landscape unit.

[0035] Preferably, the ecological flow model is constructed using the quantitative flow ratio method or the ecological function maintenance flow method.

[0036] Preferably, the objective function of the nonlinear multi-objective sandy land landscape pattern optimization model is constructed to maximize the ecological function and hydrological function of the sandy land landscape constrained by ecological water demand. The objective function is expressed as:

[0037]

[0038] Where F represents the objective function; α1 and α2 represent the trade-off coefficients between hydrological function and ecological function respectively; i represents the i-th landscape unit; j represents the j-th unit grid, and j (a,b) (a, b) represents the spatial location of the unit grid, indicating the precise positioning of the unit grid; n represents the number of landscape unit types; m represents the number of unit grids; F1 and F2 represent the evaluation indicators of hydrological function and ecological function, respectively; R represents the water resource supply of the region; β represents the ecosystem response speed parameter; V ij represents the vegetation coverage of the i-th landscape unit area assigned to the j-th unit grid; γ represents the expected vegetation coverage; Xij Indicates the i-th landscape unit assigned to the j-th unit grid, which is a variable of 0 or 1. ij =1 means allocating the i-th landscape unit, X ij =0 means that the i-th landscape unit is not allocated; W tol Indicates ecological water demand.

[0039] Preferably, the constructed constraints include ecological water demand constraints, water production constraints, binary variable constraints, total land use area constraints, dune type quantity and area constraints, ecological function target constraints, hydrological function target constraints, non-negative constraints, etc.

[0040] Preferably, the multi-objective optimization algorithm includes simplex method, gradient descent, genetic algorithm, particle swarm algorithm, etc.

[0041] Through the above technical solution, it can be known that compared with the prior art, the present invention discloses a method for optimizing the landscape pattern of sandy land based on the ecological water demand inside and outside rivers and lakes, with the coordinated improvement of the ecological hydrological function of sandy land as the core goal, based on the ecological water demand requirements inside and outside rivers and lakes, combined with multiple factors such as sandy land water resource balance, soil moisture balance, total water consumption of trees, shrubs and grasses, total land use area, dune type and area, a sandy land landscape pattern optimization model (water-sand trees, shrubs and grasses coordinated allocation model) is established. Through the optimization model, the sandy land landscape pattern is accurately adjusted to achieve the optimal ratio of water resources and ecological functions, thereby balancing the water demand relationship between river and lake water resources and sandy land ecosystems, providing theoretical and technical support for regional ecological environment protection and sustainable social and economic development. The present invention systematically solves the contradiction between sandy land water resource allocation and ecological water demand, and realizes the improvement of ecological function and sustainable utilization of water resources by comprehensively optimizing water and sand resources and sandy land landscape layout. The present invention has broad application prospects and can be applied to ecological restoration and water resource management of different sandy lands (deserts, sandy land ecosystems), with strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0043] Figure 1 This is a schematic flow chart of a sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes provided by the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] The embodiment of the present invention discloses a method for optimizing the landscape pattern of sandy land based on the ecological water demand inside and outside rivers and lakes, such as Figure 1 As shown, the following steps are included:

[0046] S1: Collect remote sensing images, field ecological data and hydrological and meteorological data, and obtain eco-hydrological element data after pre-processing;

[0047] S2: Calculate the ecological water demand of semi-arid sandy landscape based on eco-hydrological element data;

[0048] S3: Construct a nonlinear multi-objective sandy land landscape pattern optimization model based on ecological water demand and eco-hydrological element data;

[0049] S4: Based on the conditional constraints, a multi-objective optimization algorithm is used to solve the nonlinear multi-objective sandy land landscape pattern optimization model and obtain the optimal solution for the parameters;

[0050] S5: Construct sandy land landscape pattern strategy based on the optimal solution of parameters.

[0051] Furthermore, S1 collects remote sensing images and field ecological data to identify the ecological elements of the research area and its landscape, obtains the distribution characteristics of rivers and lakes and vegetation, determines the number and area of rivers and lakes in the area, and other ecological elements, such as vegetation types (trees, shrubs, grasslands, etc.); collects hydrological and meteorological data to identify hydrological characteristics;

[0052] Based on remote sensing imagery (synthetic aperture radar (SAR)) such as Sentinel-1 and ALOS PALSAR, which are not affected by weather, the Ostu adaptive threshold segmentation method is first used to automatically calculate the optimal water segmentation threshold to identify water bodies. Based on the backscatter coefficient (Backscatter) of the SAR image, low-value (dark) areas correspond to water bodies, improving classification accuracy. Deep learning methods such as U-Net or DeepLabv3+ models are then applied to train neural network models based on large-scale remote sensing image data to improve water boundary detection and extract water bodies. After water body extraction, a geographic information system (GIS) is used to calculate area and perform time series analysis to monitor changes in the area of rivers and lakes, obtaining water area as a characteristic of river and lake distribution.

[0053] Traditional soil and vegetation survey methods were used to conduct in-situ field ecological surveys to obtain field ecological data. The extracted vegetation indicators mainly included vegetation type, vegetative / reproductive height, vegetation cover, plant transpiration, vegetation index (NDVI), leaf area index (LAI), net primary productivity (NPP), aboveground and underground biomass and other physiological and ecological indicators; the extracted soil indicators included physical and chemical characteristics such as soil permeability, soil bulk density, soil hydraulic characteristics, and soil nutrients; the extracted vegetation indicators and soil indicators were used as vegetation distribution characteristics; the growth characteristic indicators of different vegetation communities and their relationship with environmental factors were analyzed through statistical methods such as redundancy analysis and correlation analysis to obtain the factors affecting the vegetation growth environment;

[0054] Hydrometeorological data are historical precipitation data of the study area, including monthly, quarterly and annual precipitation. Precipitation is the main factor affecting ecological water demand. Meteorological data such as evaporation and transpiration are obtained using hydrometeorological data or remote sensing images. Evaporation and transpiration directly affect water consumption. For rivers and lakes, water depth, net flow, water flow rate and water level change data are collected. In particular, the base flow demand of rivers and water level changes of lakes are crucial to the ecosystem. Soil moisture data are collected, especially during the vegetation growing season. The moisture retention of the soil will affect plant growth and ecological water demand. The above-mentioned precipitation, evaporation, transpiration, net flow, water depth, water flow rate, water level change, soil moisture, etc. are used as hydrological characteristics.

[0055] Furthermore, the ecological water demand of semi-arid sandy landscapes is calculated. The calculation of ecological water demand in semi-arid areas usually relies on some hydrological and ecological models. Environmental flow models are used to estimate the minimum flow required for rivers and lakes to maintain ecological health. Common models include the quantitative flow ratio method (Q ratio method) and the ecological function flow method (EFM). Biological water demand models estimate the water requirements of various biological populations (such as aquatic plants and fish). These models take into account factors such as plant transpiration and habitat humidity. Water balance models calculate regional water changes based on factors such as precipitation, evaporation, soil infiltration rate, and net flow. The specific process of calculating ecological water demand is as follows:

[0056] (1) Calculation of ecological water demand within rivers and lakes

[0057] River baseflow demand calculations typically use data such as precipitation, flow, and evaporation, combined with historical flow records, to calculate the amount of water needed to maintain the river's baseflow, also known as the river maintenance baseflow. This calculation needs to take into account seasonal variations in ecological water flows and maintain a certain flow to meet the habitat and reproduction needs of aquatic organisms.

[0058] The calculation of lake ecological water demand requires the calculation of lake ecological water demand based on parameters such as lake depth, water level, and water flow rate, combined with the needs of the biological community. During the dry season, the lake may face a drop in water level, so water replenishment is needed to maintain ecological balance.

[0059] The ecological water demand within rivers and lakes can be calculated using the ecological flow model. Taking the maintenance of ecological flow as an example, the commonly used calculation method is the estimation based on the flow ratio method:

[0060] W in =W in-r +W in-l =(Q min +L min )·f(H season )

[0061] Among them, W in is the ecological water demand in sandy landscape rivers and lakes (m 3 / s); W in-r 、W in-l represent the water volume requirements for rivers and lakes to maintain their minimum eco-hydrological functions, Q min It represents the minimum flow required for a river to maintain its biological population and its sediment transport capacity, and is obtained by combining the net flow with vegetation distribution characteristics and hydrological characteristics; L min It represents the minimum flow required for the lake to maintain its internal biological population, and is obtained by combining the net flow of vegetation distribution characteristics and hydrological characteristics; f(H season ) represents the relationship between flow and water level, which is usually an empirical relationship or the result of fitting a hydrological model. In semi-arid areas, the calculation of ecological flow needs to take into account seasonal fluctuations;

[0062] (2) Calculation of external ecological water demand for rivers and lakes

[0063] a. Calculation of wetland ecological water demand

[0064] Wetlands typically require a certain water level and flow rate to maintain their ecological functions, especially during dry seasons. Ecological water demand primarily considers changes in water level and flow rate. The water flow calculation formula based on wetland water level and hydrological environment is as follows:

[0065] Q wetland =A·(HH min )·C

[0066] Among them, Q wetland The amount of water required for wetlands (m 3 / s); A is the wetland area (m 2 ); H is the current water level (m); H minis the lowest water level of the wetland (m); C is the water flow coefficient, which represents the permeability and water flow rate of the wetland; the wetland area is obtained according to the water body area, and the lowest water level of the wetland at the current water level is obtained according to the water level in the hydrological characteristics;.

[0067] b. Calculation of vegetation ecological water demand

[0068] Calculate the water requirements of different vegetation types (such as trees, shrubs, and grasslands) based on their transpiration. Estimate the ecological water requirements of different vegetation types by comparing their growth cycles and water resource changes. Transpiration is the amount of water required by vegetation and is usually determined by plant type and growth conditions. The formula used is:

[0069]

[0070] Q plant Indicates the ecological water demand of vegetation (m 3 / s); ET is the actual transpiration of plants (mm), obtained based on the collected plant transpiration; K c is the crop transpiration coefficient, which is related to plant type, growth stage and climatic conditions and is an empirical value; P is precipitation (mm); R is net flow (mm); ΔS is the change in soil moisture (mm); T is the time period (day, month, year), which is used to calculate the change in water volume within a specific time period; Represents factors affecting the vegetation growth environment (such as sand dunes, grasslands, wetlands, etc.), including soil physical and chemical characteristics and hydrological and meteorological data.

[0071] c. Balance between ecological flow demand and ecological water demand

[0072] The ecological flow model is used to calculate the minimum water flow required for different landscape units to maintain ecological functions. Regional balance analysis is then conducted, combining ecological water demand and hydrological processes to ensure that the river and lake system can support the health of the entire sandy ecosystem. A comprehensive assessment of the ecological water demand of the sandy landscape is usually expressed by summing the ecological water demand of each landscape unit:

[0073] W tol =W in +W ex

[0074]

[0075] W tol The total ecological water demand in the region, W ex W represents the external ecological water demand of rivers and lakes; in Indicates the ecological water demand within rivers and lakes; Q plat,s represents the ecological water demand of vegetation in the s-th landscape unit;

[0076] Furthermore, the objective function of the nonlinear-multi-objective sandy land landscape pattern optimization model is to maximize the ecological and hydrological functions of the sandy land landscape constrained by the ecological water demand inside and outside rivers and lakes; the objective function is:

[0077]

[0078] Where: F represents the objective function; α1 and α2 represent the trade-off coefficients of hydrological function and ecological function respectively; i represents the i-th landscape unit type (sand dunes, grasslands, wetlands, rivers and lakes, etc.); j represents the j-th unit grid, j (a,b) (a, b) represents the spatial location of the unit grid, indicating the precise positioning of the unit grid; n represents the number of landscape unit types; m represents the number of unit grids; F1 and F2 represent the evaluation indicators of hydrological function and ecological function, respectively; R represents the water resource supply of the region, which is obtained by combining hydrological characteristics and river and lake distribution characteristics; β represents the ecosystem response speed parameter; V ij represents the vegetation coverage of the i-th landscape unit area assigned to the j-th unit grid; γ represents the expected vegetation coverage; X ij Indicates the i-th landscape unit assigned to the j-th unit grid, which is a 0-1 variable. ij =1 indicates that the i-th landscape unit type is allocated, X ij =0 means that the i-th landscape unit type is not allocated; W tol Represents the total ecological water demand of semi-arid sandy landscape.

[0079] Furthermore, starting from the constraints of ecological water demand, water production, binary variables, total land use area, number and area of dune types, ecological function target, hydrological function target, and non-negative constraints, the constraints of the above-mentioned sandy landscape optimization configuration model are determined. Specifically,

[0080] (1) Ecological water demand constraints:

[0081] E water，t +C t ≤W t

[0082]

[0083] W t =W t0 +P t

[0084] Where, E water，t represents the total evaporation of water in the sandy landscape area during period t, obtained based on hydrological characteristics; C t represents the total water consumption of vegetation in the area during period t, mm, obtained based on vegetation distribution characteristics; W tRepresents the soil moisture content in the region during period t, including the effective precipitation P in the region during period t t The soil water storage capacity W in the plant root layer at the beginning of period t t0 , mm, obtained based on the soil bulk density in soil indicators; γ represents the natural grassland abundance coefficient (the coefficient of vegetation affected by rainfall); C ij,t It represents the water consumption of the i-th type landscape unit at the j-th unit grid in period t, expressed as a function of potential evapotranspiration, soil moisture and vegetation growth stage, that is, C ij,t (E m ,W,s), mm; E m represents the potential evapotranspiration of the jth unit grid in period t, mm; s represents the growth stage of grassland vegetation; W t0 represents the soil water storage capacity of the plant root layer at the beginning of grassland period t, mm; P t It represents the effective precipitation of grassland in period t, in mm, obtained based on hydrological characteristics.

[0085] (2) Water production constraints:

[0086] R t +D t ≥W total，t

[0087]

[0088] Where R t represents the total water production of the landscape during period t, m 3 ;D t represents the amount of water resources allocated from other areas to the landscape during period t; W total，t represents the total ecological water demand of the landscape area during period t, m 3 ; R ij represents the water yield of the i-th type landscape unit at the j-th unit grid during period t, m 3 .

[0089] (3) Binary variable constraints ensure that each plot of land is allocated to only one use:

[0090]

[0091] (4) Constraints on total land use area:

[0092]

[0093] Where: A ij represents the area of the jth grid cell allocated to the i-th landscape unit; S LT Represents the total land use area.

[0094] (5) Constraints on the number and area of dune types:

[0095]

[0096] Y d ≥0, for all d

[0097] Where: Y d represents an integer variable, indicating the number of different types of sand dunes, where d represents the number of different types of sand dunes (fixed, semi-fixed, semi-mobile, mobile), which is obtained from the vegetation type data in the vegetation distribution characteristics; A d Indicates the area of different types of sand dunes, m 2 ; N t Indicates the total number of sand dunes; S total Indicates the total dune area, m 2 .

[0098] (6) Ecological function target constraints:

[0099]

[0100] Where: B bio represents the biodiversity protection coefficient; B min represents the minimum biodiversity requirement; α veg ,β eco Represents vegetation coverage V i The weight coefficient and ecosystem transpiration E vi The weight coefficient of Eco target represents the ecological function target; ε represents the vegetation coverage change rate threshold; A 乔木i +、A 灌木i 、A 草地i ) represent the area allocated to trees, shrubs and grasslands in the i-th landscape unit respectively; n represents the total number of landscape unit types.

[0101] (7) Hydrological function objective constraints:

[0102]

[0103] Where: W source represents the water conservation coefficient, which is obtained according to the water conservation area; W min Indicates the minimum requirement for water conservation; R water,total Indicates the total water resource allocation set; V water,total Indicates the total amount of wetland and lake water; α gw ,β sw Respectively represent the groundwater level weight coefficient and surface water weight coefficient; GW i represents the groundwater level of the i-th landscape unit; SW i Hydro represents the surface water level of the i-th landscape unit; targetIndicates hydrological function target; A 湿地i and A 湖泊i Represent the area allocated to wetlands and lakes for the i-th landscape unit respectively; obtain the groundwater level and landmark water level based on the water level in the hydrological characteristics; obtain the total wetland and lake water volume based on the water body area in the river and lake distribution characteristics; R i represents the total water yield of the i-th landscape unit; D i represents the amount of water resources allocated from other areas to the i-th landscape unit; P i represents the effective precipitation of the i-th landscape unit;

[0104] (8) Non-negative constraints:

[0105] All variables in the above formula are ≥0.

[0106] Furthermore, based on the objective function and constraints of the above nonlinear-multi-objective sandy land landscape pattern optimization model, a suitable optimization algorithm is selected according to the nature of the problem. For linear programming problems, linear programming algorithms (such as the simplex method) can be used. For nonlinear programming problems, gradient descent, genetic algorithms, particle swarm algorithms, etc. need to be used. In the process of solving the objective function, initial values are assigned to the parameters in the model, which may need to be adjusted according to the actual situation. The constraints corresponding to each area are selected, for example, weight coefficients, ecosystem dynamics parameters, etc. are determined. The mathematical model is solved using the selected algorithm to find the optimal solution of the parameters that meets the constraints. The optimal solution of the parameters is analyzed, the meaning of each variable and constraint is explained, and it is verified whether the results meet the actual needs.

[0107] In a specific embodiment, taking a certain sandy land as an example, the water and sand resources and ecological water demand of the sandy land were comprehensively optimized. Through simulation and optimization, the water resource allocation of the sandy land became more scientific and reasonable, the vegetation coverage rate increased by 12%, the water resource utilization efficiency increased by 15%, and the ecological functions of wetlands and lakes were significantly enhanced.

[0108] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0109] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing sandy land landscape pattern based on ecological water demand inside and outside rivers and lakes, characterized in that: The following steps are involved: Step 1: Collect remote sensing images, field ecological data, and hydrological and meteorological data, and obtain eco-hydrological element data after pre-processing; Step 2: Calculate the ecological water demand of the semi-arid sandy landscape based on the eco-hydrological element data; Step 3: Construct a nonlinear multi-objective sandy land landscape pattern optimization model based on ecological water demand and eco-hydrological element data; Step 4: Based on the conditional constraints, a multi-objective optimization algorithm is used to solve the nonlinear multi-objective sandy land landscape pattern optimization model to obtain the optimal solution for the parameters; Step 5: Construct sandy land landscape pattern strategy based on the optimal parameter solution.

2. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 1 is characterized in that: Ecological hydrological element data include river and lake distribution characteristics, vegetation distribution characteristics and hydrological characteristics.

3. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 2 is characterized in that: The distribution characteristics of rivers and lakes include the number and area of regional water bodies. The process of extracting river and lake distribution characteristics based on remote sensing images includes: Step 11: Use the Ostu adaptive threshold segmentation method to perform preliminary segmentation on the remote sensing image to obtain the initial segmentation result; Step 12: Build a neural network model, perform depth segmentation based on the initial segmentation results and remote sensing images, identify water body boundaries, and extract water bodies; Step 13: Count the number of water bodies in the region based on the extracted water bodies, and calculate the area using the geographic information system to obtain the water body area.

4. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 3 is characterized in that: Soil and vegetation survey methods were used to obtain field ecological data, and hydrological and meteorological data were combined to extract vegetation distribution characteristics, including vegetation indicators, soil indicators, and factors affecting vegetation growth environment. Vegetation indicators included vegetation type, vegetation coverage, and plant transpiration. Soil indicators included soil permeability. Based on the distribution characteristics of rivers and lakes and vegetation distribution characteristics, redundancy analysis and correlation analysis were used to obtain factors affecting vegetation growth environment.

5. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 4 is characterized in that: Hydrological characteristics are extracted based on hydrological and meteorological data and remote sensing images; hydrological characteristics include precipitation, net flow, water flow rate, water level and water level change; precipitation includes monthly, quarterly and annual precipitation.

6. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 5 is characterized in that: The ecological water demand in step 2 includes the ecological water demand inside rivers and lakes and the ecological water demand outside rivers and lakes; Based on the ecological factor data, the ecological flow model is used to calculate the river maintenance base flow and the lake ecological water demand. The sum of the river maintenance base flow and the lake ecological water demand is the internal ecological water demand of the river and lake; it is expressed as: W in =W in-r +W in-l =(Q min +L min )·f(H season ) Among them, W in W is the ecological water demand of rivers and lakes in sandy landscape; in-r 、W in-l represent the river maintenance base flow and lake ecological water demand respectively; Q min It represents the minimum flow rate of the river to maintain the needs of internal biological populations and its sediment transport capacity, obtained based on vegetation indicators and hydrological characteristics; L min represents the minimum flow required for the lake to maintain its internal biological population, obtained based on vegetation indicators and hydrological characteristics; f(H season ) represents the relationship function between flow and water level; The wetland ecological water demand is calculated based on the ecological factor data. The vegetation ecological water demand of each landscape unit is calculated using the biological water demand model based on the ecological factor data. The sum of the wetland ecological water demand and the vegetation ecological water demand of all landscape units is the external ecological water demand of rivers and lakes. The ecological water demand of wetlands is expressed as: Q wetland =A·(H-H min )·C Q wetland is the ecological water demand of the wetland, A is the wetland area, H is the current water level, and H min is the lowest water level of the wetland, C is the flow coefficient calculated based on the soil permeability and water flow rate of the wetland; the wetland area is obtained based on the water body area, the current water level is obtained based on the water level, and the lowest water level of the wetland is obtained based on the water level change; The ecological water demand of vegetation is expressed as: Q plant represents the ecological water requirement of vegetation; ET is the actual transpiration of plants; K c is the crop transpiration coefficient; P is the precipitation; R is the net flow; ΔS is the change in soil moisture; T is the time period; Indicates the factors affecting vegetation growth environment; W ex represents the external ecological water demand of rivers and lakes; Q plat,s Represents the ecological water demand of vegetation in the sth landscape unit.

7. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 6 is characterized in that: The ecological flow model is constructed using the quantitative flow ratio method or the ecological function maintenance flow method.

8. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 5 is characterized in that: The objective function of constructing a nonlinear multi-objective sandy land landscape pattern optimization model is expressed as: Where F represents the objective function; α1 and α2 represent the trade-off coefficients between hydrological and ecological functions, respectively; i represents the i-th landscape unit; j represents the unit grid; n represents the number of landscape unit types; m represents the number of unit grids; F1 and F2 represent the evaluation indicators of hydrological and ecological functions, respectively; R represents the water supply of the region; β represents the ecosystem response speed parameter; V ij represents the vegetation coverage of the i-th landscape unit area assigned to the j-th unit grid; γ represents the expected vegetation coverage; X ij W represents the i-th landscape unit assigned to the j-th unit grid; tol Indicates ecological water demand.

9. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 1 is characterized in that: The constructed constraints include ecological water demand constraint, water production constraint, binary variable constraint, total land use area constraint, dune type quantity and area constraint, ecological function target constraint, hydrological function target constraint, and non-negative constraint.

10. The sandy land landscape pattern optimization method based on ecological water demand inside and outside rivers and lakes according to claim 1 is characterized in that: Multi-objective optimization algorithms include the simplex method, gradient descent, genetic algorithm, or particle swarm optimization.

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