CN115983522B - Rural habitat quality assessment and prediction method - Google Patents

Rural habitat quality assessment and prediction method Download PDF

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CN115983522B
CN115983522B CN202211406746.3A CN202211406746A CN115983522B CN 115983522 B CN115983522 B CN 115983522B CN 202211406746 A CN202211406746 A CN 202211406746A CN 115983522 B CN115983522 B CN 115983522B
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徐宁
潘可欣
何雪馨
段皓然
成玉宁
郑琳
池麦
王姁
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Southeast University
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Abstract

The invention discloses a rural habitat quality assessment and prediction method, and belongs to the field of urban and rural planning; the method couples FLUS, inVEST and MaxEnt models, utilizes future climate data under RCPs scene to drive various ecological models, and obtains comprehensive evaluation and future prediction results of the current situation of the ecological environment quality of the research area from the aspects of geography and biology respectively; acquiring a future land utilization distribution map of a research area under the RCPs scene by utilizing algorithms such as inertia, suitability probability and the like of FLUS model land; on the basis, the ecological system service function evaluation module of the InVEST model is used for obtaining the ecological environment degradation and the ecological environment quality evaluation of the research area, giving out the future ecological environment quality prediction of the research area, and finally combining the MaxEnt model module for predicting the species richness distribution pattern of the research area under the RCPs scene to obtain the comprehensive evaluation of the ecological environment quality of the research area and the future prediction result under the RCPs scene, so as to further provide the ecological environment optimization strategy of the research area.

Description

Rural habitat quality assessment and prediction method
Technical Field
The invention belongs to the field of urban and rural planning, and particularly relates to a rural habitat quality assessment and prediction method.
Background
The ecological quality refers to the quality of the ecological environment, which is based on the ecological theory, reflects the suitability of the ecological environment for human survival and the sustainable development of socioeconomic in a specific time and space range from the ecological system level, and evaluates the property of the ecological environment and the result of the change state according to the specific requirements of human. Rural areas often have a better ecological environment than cities, but in recent years, due to the rapid development of economy and society, excessive interference of human activities threatens the quality of habitat in rural areas. The habitat quality has own internal rules, and the rules are mastered, so that the habitat quality condition can be accurately predicted, therefore, the habitat quality of the rural area is quantitatively evaluated, the future condition is predicted, and a targeted habitat optimization strategy is formulated on the basis, so that the habitat quality of the rural area is an effective way for improving.
The current habitat assessment often utilizes a single model to assess the current situation, but cannot scientifically predict the future, so that corresponding policies cannot be reasonably arranged according to the future development; or the habitat quality is obtained only according to the land utilization condition without considering the pattern of species richness, and the obtained result is more unilateral. The invention comprehensively simulates and predicts the quality of the habitat under different development scenes in the future.
Future development conditions of the habitat quality are reasonably predicted, and corresponding policies can be arranged. The future development of the quality of the habitat is closely related to the future climate change, wherein the emission scenario is a possible representation of the future development of potential radiation active emissions (such as greenhouse gases, aerosols, etc.), and is the basis for the prediction of the future climate change. Based on the future atmospheric radiation intensity change, a typical concentration path emission Scenario (RCPs) of greenhouse gases is proposed in a fifth climate change evaluation report of the inter-government climate change specialized committee (IPCC) of the united states, and the method can be used for various prediction models such as climate mode and land utilization change. RCPs are characterized by stable concentrations, comprehensively considering future climate change, greenhouse gas emission, socioeconomic change and land utilization change, and providing several different development scenarios including four types of RCP2.6, RCP4.5, RCP6.0 and RCP 8.0; where both RCP4.5 and RCP6.0 are intermediate developments under government intervention, such scenario is not considered because of reduced tilled area in the context of RCP4.5, possibly exceeding the lowest red line, which is not practical. Three of the RCPs 2.6,RCP 6.0,RCP 8.5 were chosen as future scenarios for the study of this patent.
RCP2.6 is a very low greenhouse gas profile; the change of energy utilization type in the global scope can obviously reduce the emission of greenhouse gases, and is an emission scenario with the largest increase of the global crop area. RCP6.0 is a climate scenario under government intervention, in which population numbers increased to 100 billion up to 2100 years; the establishment of various policies is simulated to reduce the emission of greenhouse gases, but compared with RCP2.6, the emission is relieved to a lower extent, and the influence degree of the increase of the cultivated land area on the forest area is small. RCP8.5 is a baseline scenario without climate change policy intervention, characterized by ever increasing greenhouse gas emissions and concentrations, in which fossil fuel consumption becomes greater as global population increases substantially, revenue grows slowly, and technology changes and energy efficiency changes.
At present, methods for evaluating and measuring biological diversity at home and abroad are mainly focused on two modes: one is based on ground investigation methods, focusing on evaluation study of biological background investigation; another is an evaluation method based on ecological model simulation, such as an EVR model (Ecological Value at Risk) for ecological risk analysis, an integrated evaluation model (invent) for ecosystem services and trade-offs, and the like. However, the accuracy and time span of single model prediction are limited, for example, the InVEST model cannot predict the future habitat condition and cannot be well used for evaluating land utilization changes and ecological environment influences caused by land utilization changes, so that the InVEST model is used for predicting the habitat quality changes in some specific future situations.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a rural habitat quality assessment and prediction method.
The aim of the invention can be achieved by the following technical scheme:
a rural habitat quality assessment and prediction method comprises the following steps:
s1, carrying out equidistant grid division on a research area;
s2, establishing a rural habitat quality assessment database, wherein the database comprises: rural historical land utilization data, current socioeconomic data, future prediction data in the context of RCPs, and environmental variable data;
s3, establishing a plus model, taking rural historical land utilization data as influence data, screening out training samples by using the plus model, determining land utilization change factors X, and obtaining occurrence probabilities of various lands;
s4, taking future climate forecast data and historical land utilization data under the RCPs situation as influencing factors, repeatedly simulating in a plus model for many times, and obtaining land utilization distributed grid data under different RCPs situations in the future;
s5, inputting the grid data of land utilization obtained by the plus model into the InVEST model to obtain habitat quality data and threat metadata distribution data;
s6, selecting representative species, and establishing a species geographic distribution data set of a research area;
s7, driving a MaxEnt model by using the species geographic distribution data set obtained in the S6 and the environmental variables in the S1, and respectively carrying out simulation prediction on potential distribution areas of wild protection species under the condition of RCPs;
s8, extracting a model prediction result threshold value, and drawing a wild protection plant richness chart to obtain the level of spatial distribution of the richness of the wild protection species;
and S9, superposing the current situation of the quality distribution of the habitat, the future prediction and the species richness distribution map to obtain a comprehensive evaluation chart of the quality of the habitat.
Further, current socioeconomic data includes: population data, domestic production total value, distance from the city center and road network;
future forecast data includes percentage of land usage, climate, economy, and population;
the environment variable data includes: 19 climate factor data, DEM elevation data, grade data, slope data, and NDVI data.
Further, in the step S3, historical land utilization, location, natural environment, socioeconomic performance and climate factors are used as influence data, a training sample is screened out by using a suitability probability estimation module of an artificial neural network in the model, a land utilization change factor X is determined, and the occurrence probability of various lands is obtained by inputting the training sample into the neural network.
Further, in S3, the formula for determining the land use change factor X is:
X＝(x 1 (1),x 2 (1),…,x n (1)) T (1)
wherein X is i The variable of the ith driving silver extracted for the 1 st sampling point, T is the transposed matrix.
Further, in S4, the step of simulating the future land use distribution of RCPs includes:
s41, taking future climate forecast data and historical land utilization data under the condition of the RCPs as influencing factors to preliminarily obtain future land utilization requirements under the condition of the RCPs,
s42, defining an adaptive coefficient to automatically adjust the inertia of each land based on a cellular automaton module of an adaptive inertia mechanism, iterating for a plurality of times according to land utilization requirements in different future scenes and the actual number of the current land of each type, and finally simulating future land utilization distribution of RCPs.
Further, the iterative formula in S42 is:
wherein: i t p An inertia coefficient representing the type of p-type land used in the t time period; d (D) t-1 p Is the difference between the p-type land requirement and the current actual land quantity of the t-1 time period.
Further, in the step S5, the calculating step of the habitat quality data includes:
1) And calculating the habitat degradation degree according to land utilization data and table data, wherein the expression is as follows:
wherein D is xj Representing the habitat degradation degree of an xth habitat pixel in the habitat type j; r is a threat source of habitat; y is a grid in threat source r; w (w) r The weight of the threat source r; i.e rxy Representing the influence of r on each grid of the habitat; beta x Representing local protection policy impact; s is S jr Representing the relative sensitivity of each habitat to different threat sources;
2) The habitat quality score expression is:
in which Q xj Representing the x-th habitat pixel in habitat type jThe quality score of the habitat is 0,1]；H j A habitat fitness value representing a habitat type j; k and z employ model default parameters.
Further, in S7, the step of predicting the potential distribution region of the wild protection species by simulation is as follows:
s71, inputting a collected geographical distribution data set and environment variables of the current species in a research area into a MaxEnt model, calculating constraint conditions of target species distribution through distribution data of known species occurrence points and environment characteristic variables, and exploring possible distribution of maximum entropy under ecological requirements;
and S72, projecting a simulation result to a research area by constructing a model, so as to predict the potential habitat distribution and suitability of the target species in the research area.
Further, in the step S7, assuming that the random variable α includes n possible results of A1, A2, A3, …, an on the premise of including the known information, the entropy value is:
where H (α) is the entropy, and P1, P2, P3, …, pn is the probability of each occurrence.
Further, in S8, the step of drawing the wild protection plant richness map includes:
s81, importing a maximum entropy operation threshold result of each wild protection species into an Arc GIS, dividing the richness pattern of each protection species into 10 intervals by a threshold dividing function in the Arc GIS software layer attribute, visualizing each interval into different gray scales, wherein the gray scales are inversely related to the richness of the species;
s82, overlapping potential distribution diagram layers of the selected protective species to finally obtain a wild protective plant species richness diagram of the research area.
The invention has the beneficial effects that: the method can accurately predict the future habitat quality of the research area, and can comprehensively analyze the future habitat quality of the research area by combining the future habitat quality of the research area with the patterns of the existing habitat quality and the species richness, so that a scientific and comprehensive reference basis is obtained for the habitat optimization strategy of the area.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort.
FIG. 1 is a schematic flow diagram of the overall process of the present invention;
FIG. 2 is a schematic diagram of the division of a research area according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a visualization of climate data for a research area in accordance with an embodiment of the present invention;
FIG. 4 is a graphical representation of visualization of regional population density data in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a visualization of regional traffic location data according to an embodiment of the present invention;
FIG. 6 is a schematic view of topographic data of an investigation region in accordance with an embodiment of the present invention;
FIG. 7 is a diagram of exemplary research and area NDVI data;
FIG. 8 is a schematic illustration of land utilization in future RCP2.6, 6.0, 8.5 scenarios in accordance with an embodiment of the invention;
FIG. 9 is a graphical representation of the current and future values of the quality of life in the RCP2.6 scenario, in accordance with an embodiment of the present invention;
FIG. 10 is a schematic view of distribution points of wild protected plants in a research area according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of the results of the MaxEnt model operation in accordance with an embodiment of the present invention;
FIG. 12 is a graph of a comprehensive evaluation of quality of habitat according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a rural habitat quality assessment and prediction method includes the following steps:
s1, referring to a statistics area code and an urban and rural division code compiling rule (national system word [ 2009 ] 91) issued by a national statistics office, according to the urban and rural classification three-digit code queried on the national statistics office network, a region with a first code of 1 is defined as a town, and a region with a first code of 2 is defined as a country; urban and rural areas are divided in arcgis 10.7;
after the study object and the scope are defined, equidistant grid division is carried out on the study area, grids of 1km x 1km are selected and distributed on each part of the study area at equal intervals, the grids are numbered, the first grid at the northwest angle is used as a number 1, and the numbers 1,2, 3, and n are sequentially carried out from left to right and from top to bottom.
S2, establishing a rural habitat quality assessment database, and performing data preprocessing; the database comprises: country historical land utilization data, current socioeconomic data (population, total domestic production, distance from the center of the city, road network), future prediction data (percentage of land, climate, economy, population in the case of RCPs), 23 environmental variable data of the study area (mainly 19 climate factor data, topography data (DEM elevation, slope) and NDVI data);
the rural historical land utilization data are acquired from a geographic national condition monitoring cloud platform, the historical land utilization data of a research area in the past 20 years are downloaded, the precision is 30m, and the rasterization processing is performed in an Arc GIS. According to different provincial land utilization conditions, land utilization type simplification is carried out;
the current population data is obtained from the population distribution data of the European space agency, the current population density utilization data of the research area is downloaded, the precision is 30m, and the rasterization processing is carried out in the Arc GIS;
the total domestic production value is obtained from a statistical annual-differentiation or global-change scientific research data publishing system of each province, and GDP is used as a unit to carry out rasterization treatment in Arc GIS;
the distance from the center of the city is obtained from a world city prospect website (World UrbanizationProspects), the current distance data from the center of the city of a research area is downloaded, the precision is 30m, and Euclidean distance calculation and rasterization processing are carried out in an Arc GIS;
the road network is obtained from a global road data set website (Global roads open assess data set), current road network data of a research area is downloaded, the accuracy is 30m, and rasterization processing is performed in Arc GIS.
The future climate mode adopts three emission situations of RCP2.6, RCP6.0 and RCP8.5, and the temperature, precipitation and sunlight intensity under the circumstance of RCPs are all obtained from a WorldClim website; for example, where the RCP2.6 scenario assumes a global average temperature rise of 0.2-1.8 ℃ for the 21 st century, a global average precipitation increase of 2.1%; changing the current temperature and precipitation data in the Arc GIS;
biological climate factor data covered by the study area were obtained from the world climate database (http:// www.WordClim.org) for a total of 19 climate factors: bio 1-bio 19, the number of climate factors covered by different research areas is different; the current climate pattern uses 19 biological climate factors from 1950-2000 as shown in table 1:
TABLE 1 biological climate factor data
The DEM elevation data are acquired from a geospatial data cloud website, and DEM images of a research area are downloaded with the accuracy of 30m; extracting gradient and Slope direction in Arc GIS through Spatital Analyst tools-Surface-Slope and Aspect command in Arc GIS, and finally obtaining elevation, gradient and Slope direction of research area, extracting downloaded climate variable data mask with resolution of 30m×30m to obtain image layer in research area; the coordinate system is WGS1984, the grid size is set uniformly, and finally the grid size is converted into a file in ASCII format for model operation;
NDVI (vegetation index) data are combined by utilizing satellite visible light and near infrared band detection data according to the spectral characteristics of vegetation, so that the vegetation existence degree can be identified; firstly, downloading Landsat 8 image data from a geospatial data cloud, selecting images with small cloud quantity in the same month in 2020, calculating an NDVI (non-uniform visual inspection) in ENVI through band math to obtain an NDVI index grid map, cutting out a range of a research area, and further obtaining the NDVI grid data of the research area as an environmental factor for model prediction; the coordinate system is WGS1984, the grid size is set uniformly, and finally the grid size is converted into a file in ASCII format for model operation.
S3, according to the requirements of a plus (future land utilization simulation model) model, historical land utilization, location, natural environment, socioeconomic and climatic factors are used as influence data, a training sample is screened out by using a suitability probability estimation module of an artificial neural network in the model, a land utilization change factor X is determined, and the occurrence probability of various lands is obtained by inputting the neural network;
after data is input into the plus model, a training sample is screened out by adopting a random sampling method in a suitability probability estimation module based on an artificial neural network, and a factor X for driving land utilization change is determined and used as an input layer neuron (X i I=1, 2, …, n), the formula is:
X＝(x 1 (1),x 2 (1),…,x n (1)) T (1)
wherein Xi is the variable of the ith driving silver extracted from the 1 st sampling point, and T is the transposed matrix;
each neuron in the output layer will generate a value between 0 and 1, which indicates the probability of the pixel developing into a type of land, the higher the value of which indicates the greater the likelihood of developing into that type of land in the future; an image is derived of the probability of occurrence of various kinds of land in the context of future RCPs 2.6, 4.5, 8.5, the result being used in the next step.
S4, future climate forecast data and historical land utilization data under the situation of the RCPs are taken as influencing factors, future land utilization requirements under the situation of the RCPs are preliminarily obtained, the inertia of each type of land is automatically adjusted by defining self-adaptive coefficients in a cellular automaton module based on a self-adaptive inertia mechanism, the coefficients are iterated for a plurality of times according to the land utilization requirements under different situations in the future and the actual quantity of various current lands, and as shown in a formula (2), the future land utilization distribution of the RCPs is finally simulated:
wherein: i t p An inertia coefficient representing the type of p-type land used in the t time period; d (D) t-1 p Is the difference between the p-type land requirement and the current actual land quantity of the t-1 time period.
S5, inputting grid data of land utilization obtained by the Flus model into an InVEST (comprehensive evaluation model of ecological system service and balance) model, defining three influencing factors of threat factor type, stress distance and relative sensitivity required by the InVEST model according to the characteristics of various lands in the village, and operating the InVEST to obtain habitat quality data and threat source distribution data; the method comprises the following specific steps:
referring to the "land use State Classification" revised by the State resource department (GB/T21010-2017), the state classification types of the land in China include cultivated land, garden land, woodland, grasslands, commercial land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land, water area and water conservancy facilities land, and other lands. 8 kinds of land such as cultivated land, business administration land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land and other lands (other agricultural lands) are used as non-habitat land types because of intense human activities; while the land which is less affected by human activities, such as garden, woodland, grassland, water area and water conservancy facilities, is used as habitat land; non-habitat classes are listed in the InVEST model as threat factors, which are quantitatively analyzed:
the first factor is the relative destructiveness of each threat source to all habitats; the degradation source weight wr can be chosen to be any of values from 0 to 1, as shown in table 2, depending on the destructiveness of different threat types to the habitat:
TABLE 2 threat source weights
The second factor is the influence distance of threat sources on various habitats; the degree of threat decreases with increasing grid-to-threat source distance, so those grid cells closest to the threat will be more affected; there are two functions of linear and exponential distance decay to describe the decay of the threat in space, as specified by the invent, as shown in table 3:
TABLE 3 influence distance of threat sources on various habitats
The third factor is the relative sensitivity of each habitat type to each threat source, used to correct the total impact of the previous calculation; as shown in table 4:
TABLE 4 relative sensitivity of threat sources
Making a table (table 5 and table 6) according to threat sources and habitat types existing in a research area, rasterizing historical distribution data and future distribution data of various threat sources in an Arc GIS, deriving tif format data, and inputting the table and tif data into an InVEST model;
TABLE 5 threat source data
TABLE 6 influence of threat sources on various land usages
And calculating the habitat degradation degree according to land utilization data and table data, wherein the expression is as follows:
wherein D is xj Representing the habitat degradation degree of an xth habitat pixel in the habitat type j; r is a threat source of habitat; y is a grid in threat source r; w (w) r The weight of the threat source r; i.e rxy Representing the effect r has on each grid of the habitat (linear or exponential); beta x The influence of local protection policies and the like is expressed, and the influence on the final result is small; s is S jr Representing the relative sensitivity of each habitat to different threat sources.
The habitat quality score expression is:
in which Q xj The habitat quality score of the xth habitat pixel in the habitat type j is represented by the range of [0,1 ]]；H j A habitat fitness value representing a habitat type j; k and z employ model default parameters.
S6, establishing a species geographic distribution data set of a research area by using the selected representative species (wild protected plants), and finally using the species geographic distribution data set for model simulation;
obtaining a wild protection plant directory from a Chinese plant theme database (http:// www.plant.csdb.cn /); obtaining geographic distribution data of each species through a global biodiversity information network (Global Biodiversity Information Facility, https:// www.gbif.org /), eliminating species with record numbers less than five, finally obtaining species meeting the operation requirement of a model, introducing the species into Excel for arrangement, and removing repeated points; the tidied wild protected plant species data are imported into an Arc GIS, and wild protected plant distribution point data in a research area are obtained through superposition analysis; the data are exported and input into an Excel table and are stored in a csv format to form a species geographic distribution data set of a research area, and each group of data comprises the academic name of the species and distribution points thereof, in particular to longitude and latitude.
S7, driving a MaxEnt (maximum entropy model) model by utilizing the collected geographical distribution data set of the current species in the research area and the 23 environmental variables, and respectively carrying out simulation prediction on potential distribution areas of wild protection species under the condition of RCPs;
the model calculates constraint conditions of the distribution of the target species according to the distribution data of the known species 'occurrence points' and the environment characteristic variables and searches possible distribution of maximum entropy under ecological requirements, and the probability distribution of the species meets the spatial range of the species habitat condition when the entropy is maximum; projecting a simulation result to a research area by constructing a model, so as to predict potential habitat distribution and suitability of target species in the research area; on the premise of containing known information, when the entropy value is maximum, redundant information is eliminated, and assuming that the random variable alpha comprises n possible results of A1, A2, A3, … and An, the entropy value is:
where H (α) is the entropy, and P1, P2, P3, …, pn is the probability of each occurrence.
S8, extracting a model prediction result threshold value, drawing a pattern of the richness of the wild protection plants, and dividing the pattern into 10 levels by using a natural breakpoint method to obtain the levels of the spatial distribution of the richness of the wild protection species;
firstly, importing a maximum entropy operation threshold result of each wild protection species into an Arc GIS, dividing the richness pattern of each protection species into 10 intervals by a threshold dividing function in the attribute of an Arc GIS software layer, visualizing each interval into different gray scales, wherein the gray scales are inversely related to the richness of the species; and secondly, superposing potential distribution diagram layers of the selected protective species to finally obtain a wild protective plant species richness diagram of the research area.
S9, comprehensively analyzing evaluation results of an InVEST model and a MaxEnt model, superposing the current situation of the environmental quality distribution simulated by the two models and the distribution pattern of future prediction and species richness, dividing the comprehensive evaluation level of the environmental quality into 10 levels from high to low according to a comprehensive evaluation chart of the environmental quality obtained by superposition, wherein the smaller the gray scale is, the larger the numerical value is, and the higher the level is, the better the comprehensive situation of the biological diversity of a research area is;
and identifying the optimal habitat range and the habitat optimization key areas of the research area under different development scenes in the future, analyzing the habitat space priority protection areas, and finally providing a scientific reference basis for the habitat optimization strategies of the research area in the future.
Examples:
according to the invention, the comprehensive evaluation of the habitat quality of the research area is obtained through the combined analysis of the models under three RCP scenes by utilizing the habitat quality evaluation based on Maxen and InVEST models, the future conditions of the habitat quality of the research area under different RCP scenes are predicted, and a specific habitat optimization strategy of the research area is proposed according to the comprehensive evaluation;
taking a certain province as an example, the method for evaluating the quality of the habitat based on Maxen and InVEST models in the RCPs scene comprises the following steps:
s1: referring to the code of the statistical region and the code of the urban and rural division, which are issued by the national statistical office (national statistics [ 2009 ] 91), according to the code of the urban and rural classification, which is inquired on the national statistical office network, the code is divided into towns with the first bit of 1 and is divided into villages with the first bit of 2; dividing the province and country areas in arcgis 10.7; a rural area of a certain province is obtained as shown in fig. 2.
S2: establishing a model database, and preprocessing data; the data was divided into three classes, applied to the FLUS, inVEST, maxent model respectively.
The data applied to the FLUS model were:
1) The province history land use data (J) is J12000 years of the province history land use data, J2 2005 year of the province history land use data and J32010 years of the province history land use data;
2) Climate data (Q) as FLUS model driving force factor: average air temperature in Q1 month, average temperature difference in Q2 years, precipitation in Q3 years and solar radiation intensity in Q4; as shown in fig. 3;
3) Socioeconomic data (S) as FLUS model driving force factor: s1, producing a total value in the provincial and regional division area, and S2, saving population density; as shown in fig. 4
4) Traffic location data (T) T1 the provincial network, T2 to city center distance, T3 to town center distance, T4 to highway distance, T5 to arterial road distance, T6 to railway distance; as shown in fig. 5
Future prediction data (W): w1 temperature change, W2 precipitation change, W3 sunlight intensity change in the context of RCP4.5, RCP6.0 and RCP 8.5.
The data applied to the InVEST model were:
threat data (X): x1 threat factors, X2 stress distance, and the relative sensitivity of various X3 lands to threat factors.
The data applied to the MAXENT model are:
1) The geographical distribution data of the wild protection plants are emphasized in the country of the research area;
2) 19 climate factor data;
3) DEM elevation data of a research area; as shown in fig. 6;
4) Study area NDVI data; as shown in fig. 7.
S3: the database is utilized to define climate, social economy and traffic zone position as driving force factors according to FLUS (flow manual) using manual description, 2000, 2005 and 2010 annual land utilization data are utilized to screen training samples by adopting a random sampling method in a suitability probability estimation module of an artificial neural network in a Flus model, and factor X for driving land utilization change is determined and is used as an input layer neuron (xi, i=1, 2, …, n) of the neural network:
X＝(x1(1),x2(1),…,xn(1))T (1)
where Xi is the variable of the ith driving silver extracted from the 1 st sampling point, and T is the transposed matrix.
Each neuron in the output layer will generate a value between 0 and 1, which value indicates the probability that the pixel will develop into a type of land, the higher the value of which indicates the greater the likelihood of developing into that type of land in the future. An image is derived regarding the probability of occurrence of various kinds of land in the future RCP2.6, 6.0, 8.5 scenarios, the result being used for S4.
S4: and defining various land percentages, future population economic data and future climate change data under the condition of the RCPs according to the FLUS manual description, and preliminarily obtaining future land utilization requirements under the condition of the RCPs by taking the historical land utilization data as influencing factors. And (3) inputting the suitability probability data obtained in the step (3) into a cellular automaton module based on the self-adaptive inertia mechanism, and inputting future land use demands under the condition of RCPs as targets of land use type change quantity to operate the cellular automaton. The cellular automaton carries out iteration for a plurality of times according to land utilization requirements in different future scenes and the actual quantity of various current lands (formula 2), and finally simulates land utilization distribution in future RCP2.6, 6.0 and 8.5 scenes; as shown in fig. 8;
wherein: i t p An inertia coefficient representing the type of p-type land used in the t time period; d (D) t-1 p Is the difference between the p-type land requirement and the current actual land quantity of the t-1 time period.
S5: referring to the national standards and management committee and the national quality supervision, inspection and quarantine bureau of the people's republic of China (GB/T21010-2017), the current classification types of the land in China include cultivated land, garden land, forest land, grassland, commercial land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land, water area and water conservancy facility land and other lands. 8 kinds of land such as cultivated land, business administration land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land and other lands (other agricultural lands) are used as non-habitat land types because of intense human activities; while the land types of the garden, the woodland, the grassland, the water area and the water conservancy facilities, which are less affected by the human activities, are used as habitats. The widely owned land type of the province is selected as data, wherein non-habitat land types are listed as threat factors in an InVEST model, historical distribution data and future distribution data of various threat sources are subjected to rasterization in an Arc GIS, tif format files are derived, and tables and tif data are input into the InVEST model together. (Table 7, table 8)
TABLE 7 Heat File input into InVEST
TABLE 8 input of sendtvitu file for InVEST
And calculating the habitat degradation degree according to land utilization data and table data, wherein the expression is as follows:
wherein D is xj Representing the habitat degradation degree of an xth habitat pixel in the habitat type j; r is a threat source of habitat; y is a grid in threat source r; w (w) r The weight of the threat source r; i.e rxy Representing the effect r has on each grid of the habitat (linear or exponential); beta x The influence of local protection policies and the like is expressed, and the influence on the final result is small; s is S jr Representing the relative sensitivity of each habitat to different threat sources.
The habitat quality score expression is:
in which Q xj The habitat quality score of the xth habitat pixel in the habitat type j is represented by the range of [0,1 ]]；H j A habitat fitness value representing a habitat type j; k and z adopt model default parameters; finally, the distribution of the values of the quality of the habitat in the future scenario is obtained as shown in fig. 9.
S6: the wild protection plant list of the province is obtained from a Chinese plant theme database (http:// www.plant.csdb.cn /) and 13 species are obtained. Geographic distribution data of 13 species are obtained through a global biodiversity information network (Global Biodiversity Information Facility, https:// www.gbif.org /), species with less than five records are removed, 11 species which meet the running requirement of a model are finally obtained, and the species are imported into Excel for arrangement. The tidied wild protected plant species data are imported into Arc GIS, and wild protected plant distribution point data in the research area are obtained through superposition analysis, as shown in figure 10. The data are exported and input into an Excel table and are stored in a csv format to form a species geographic distribution data set of a research area, and each group of data comprises the academic name of the species and distribution points thereof, in particular to longitude and latitude.
S7: and inputting the collected current biological climate variable data, elevation data and NDVI data of the research area into a MaxEnt model, calculating constraint conditions of target species distribution by the model through distribution data of known species 'appearance points' and environment characteristic variables according to a corresponding algorithm, and exploring possible distribution of maximum entropy under ecological requirements, wherein the probability distribution of the species meets the spatial range of the species habitat conditions when the entropy is maximum. The simulation results are projected to the research area through a built model, so that the potential habitat distribution and suitability of the target species in the research area are predicted. On the premise of containing known information, when the entropy value is maximum, redundant information is eliminated, and assuming that the random variable alpha comprises n possible results of A1, A2, A3, … and An, the entropy value is:
where H (α) is the entropy, and P1, P2, P3, …, pn is the probability of each occurrence.
S8: firstly, a maximum entropy operation threshold result (shown in fig. 11) of each wild protection species is imported into the Arc GIS, the richness pattern of each protection species is divided into 10 intervals by the threshold dividing function in the Arc GIS software layer attribute, each interval is visualized into different gray scales, and the gray scales are inversely related to the richness of the species. And secondly, superposing potential distribution diagram layers of the selected protective species to finally obtain a wild protective plant species richness diagram of the research area.
S9: the same treatment as S8 is carried out on the InVEST model, and gray scale superposition is carried out on the InVEST model and a wild protection plant species richness map of a research area, so that the InVEST model is used as a representation of biodiversity. The obtained comprehensive evaluation chart of the quality of the habitat is overlapped, as shown in fig. 12; the comprehensive evaluation grade of the habitat quality is divided into 10 grades from high to low, and the smaller the gray scale is, the larger the numerical value is, and the higher the grade is, the better the comprehensive condition of the biodiversity of the research area is.
On the basis of the chart, the prior rural area is combined with screening to obtain a biodiversity protection priority area, the habitat space priority protection area is analyzed, and finally a direct and effective reference basis is provided for the habitat optimization of the rural area.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.

Claims (3)

1. The rural habitat quality assessment and prediction method is characterized by comprising the following steps of:
s1, carrying out equidistant grid division on a research area;
s2, establishing a rural habitat quality assessment database, wherein the database comprises: rural historical land utilization data, current socioeconomic data, future prediction data in the context of RCPs, and environmental variable data;
s3, establishing a plus model, taking rural historical land utilization data as influence data, screening out training samples by using the plus model, determining land utilization change factors X, and obtaining occurrence probabilities of various lands;
s4, taking future climate forecast data and historical land utilization data under the RCPs situation as influencing factors, repeatedly simulating in a plus model for many times, and obtaining land utilization distributed grid data under different RCPs situations in the future;
s5, inputting the grid data of land utilization obtained by the plus model into the InVEST model to obtain habitat quality data and threat metadata distribution data;
s6, selecting representative species, and establishing a species geographic distribution data set of a research area;
s7, driving a MaxEnt model by using the species geographic distribution data set obtained in the S6 and the environmental variables in the S2, and respectively carrying out simulation prediction on potential distribution areas of wild protection species under the condition of RCPs;
s8, extracting a model prediction result threshold value, and drawing a wild protection plant richness chart to obtain the level of spatial distribution of the richness of the wild protection species;
s9, superposing the current situation of the quality distribution of the habitat, future prediction and a species richness distribution map to obtain a comprehensive evaluation chart of the quality of the habitat;
in the step S2, the current socioeconomic data includes: population data, domestic production total value, distance from the city center and road network;
future forecast data includes percentage of land usage, climate, economy, and population;
the environment variable data includes: 19 climate factor data, DEM elevation data, grade data, slope data, and NDVI data;
in the S3, historical land utilization, location, natural environment, socioeconomic performance and climate factors are used as influence data, a training sample is screened out by using a suitability probability estimation module of an artificial neural network in the model, a land utilization change factor X is determined, and the occurrence probability of various lands is obtained by inputting the neural network;
in the step S3, the formula for determining the land use change factor X is as follows:
X＝(x 1 (1),x 2 (1),…,x n (1)) T (1)
wherein X is i The variable of the ith driving silver extracted for the 1 st sampling point is T which is the transposed matrix;
in the step S4, the step of simulating the future land use distribution of the RCPs comprises the following steps:
s41, taking future climate forecast data and historical land utilization data under the condition of the RCPs as influencing factors to preliminarily obtain future land utilization requirements under the condition of the RCPs,
s42, defining an adaptive coefficient to automatically adjust the inertia of each type of land based on a cellular automaton module of an adaptive inertia mechanism, iterating for a plurality of times according to land utilization requirements in different future scenes and the actual number of the current various lands, and finally simulating future land utilization distribution of RCPs;
the iterative formula in S42 is:
wherein: i t p An inertia coefficient representing the type of p-type land used in the t time period; d (D) t-1 p The difference between the p-type land requirement and the current actual land quantity in the t-1 time period;
in the step S5, the steps of calculating the habitat quality data are as follows:
1) And calculating the habitat degradation degree according to land utilization data and table data, wherein the expression is as follows:
wherein D is xj Representing the habitat degradation degree of an xth habitat pixel in the habitat type j; r is a threat source of habitat; y is a grid in threat source r; w (w) r The weight of the threat source r; i.e rxy Representing the influence of r on each grid of the habitat; beta x Representing local protection policy impact; s is S jr Representing the relative sensitivity of each habitat to different threat sources;
2) The habitat quality score expression is:
q in xj The habitat quality score of the xth habitat pixel in the habitat type j is represented by the range of [0,1 ]]；H j A habitat fitness value representing a habitat type j; k and z adopt model default parameters;
in the step S7, the step of simulating and predicting the potential distribution area of the wild protection species is as follows:
s71, inputting a collected geographical distribution data set and environment variables of the current species in a research area into a MaxEnt model, calculating constraint conditions of target species distribution through distribution data of known species occurrence points and environment characteristic variables, and exploring possible distribution of maximum entropy under ecological requirements;
and S72, projecting a simulation result to a research area by constructing a model, so as to predict the potential habitat distribution and suitability of the target species in the research area.
2. The method for evaluating and predicting the quality of a rural habitat according to claim 1, wherein in S7, assuming that the random variable α includes n possible results A1, A2, A3, …, an, on the premise of including the known information, the entropy value is:
where H (α) is the entropy, and P1, P2, P3, …, pn is the probability of each occurrence.
3. The method for evaluating and predicting the quality of a rural habitat according to claim 2, wherein in S8, the step of drawing a rich map of wild protected plants comprises:
s81, importing a maximum entropy operation threshold result of each wild protection species into an Arc GIS, dividing the richness pattern of each protection species into 10 intervals by a threshold dividing function in the Arc GIS software layer attribute, visualizing each interval into different gray scales, wherein the gray scales are inversely related to the richness of the species;
s82, overlapping potential distribution diagram layers of the selected protective species to finally obtain a wild protective plant species richness diagram of the research area.
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