CN115983522A - Rural habitat quality evaluation and prediction method - Google Patents
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Abstract
The invention discloses a method for evaluating and predicting the quality of a country habitat, belonging to the field of urban and rural planning; the method couples the FLUS model, the InVEST model and the MaxEnt model, 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 habitat quality of the research area from the aspects of geography and biology respectively; obtaining a future land utilization distribution map of a research area under the RCPs scene through algorithms such as FLUS model land utilization inertia, suitability probability and the like; on the basis, the habitat degradation and habitat quality evaluation of the research area are obtained through an ecosystem service function evaluation module carried by an InVEST model, future habitat quality prediction of the research area is given, and finally the species abundance distribution pattern of the research area under the RCPs situation is predicted by combining a MaxEnt model module, so that the habitat quality comprehensive evaluation of the research area and the future prediction result under the RCPs situation are obtained, and further a habitat optimization strategy of the research area is provided.
Description
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 habitat quality refers to the quality degree of the ecological environment, and is based on the ecological theory, the ecological environment is suitable for human survival and sustainable development of social economy in a specific time and space range reflected from the ecological system level, and the nature and change state of the ecological environment are evaluated according to the specific requirements of human beings. Rural areas tend to have a better ecological environment than cities, but in recent years, due to rapid development of economic society, the ecological quality of rural areas is threatened by excessive interference of human activities. The habitat quality has intrinsic laws, and the habitat quality conditions can be accurately predicted by mastering the laws, so that the habitat quality in the rural area is quantitatively evaluated, the future conditions of the habitat quality are predicted, and a targeted habitat optimization strategy is formulated on the basis, so that an effective way for improving the habitat quality in the rural area is provided.
The current habitat evaluation usually utilizes a single model to evaluate the current situation, and the future cannot be scientifically predicted, so that corresponding policies cannot be reasonably arranged according to future development; or the habitat quality is obtained only according to the land utilization condition without considering the species richness pattern, and the obtained result is more comprehensive. The invention comprehensively simulates and predicts the habitat quality under different future development situations.
The future development condition of the habitat quality can be reasonably predicted, and then a corresponding policy 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 expression of the future development of potential radiation-active emissions (such as greenhouse gases, aerosols, etc.) and is also the basis for the prediction of the future climate change. Based on future atmospheric radiation intensity changes, typical concentration path emission Scenarios (RCPs) of greenhouse gases are proposed in fifth climate change evaluation reports of the inter-government climate change committee (IPCC) of the United nations, and the method can be used for various prediction models such as climate modes, land utilization changes and the like. RCPs are characterized by stable concentration, comprehensively consider future climate change, greenhouse gas emission, socioeconomic change and land utilization change, and provide several different development scenes, including four kinds of RCP2.6, RCP4.5, RCP6.0 and RCP 8.0; both RCP4.5 and RCP6.0 belong to the intermediate development situation under government intervention, but the situation is not considered because the cultivated land area is reduced under the RCP4.5 situation, the lowest red line is possibly exceeded, and the situation is not practical. Three of RCP2.6,RCP 6.0,RCP 8.5 were selected as future scenarios for the study of this patent.
RCP2.6 is a scenario where the concentration of greenhouse gases is very low; the change of energy utilization types in the global range enables the greenhouse gas emission to be obviously reduced, and is the emission scene with the largest increase of the global crop area. RCP6.0 is a climate scenario with government intervention, in which the population has increased to 100 billion by 2100 years; the emission of greenhouse gases is reduced by simulating the formulation of various policies, but compared with RCP2.6, the emission is lower in alleviation degree, and the influence degree of the increase of the cultivated land area on the forest area is small. RCP8.5 is a baseline scenario when no climate change policy intervention, characterized by increasing greenhouse gas emissions and concentrations, under which fossil fuel consumption becomes greater with a large global population increase, slow income growth, and changes in technological and energy efficiency.
At present, methods for evaluating and measuring biodiversity at home and abroad mainly focus on two modes: one is based on a ground investigation method, and the important point is the evaluation research of the biological background investigation; the other is an evaluation method based on Ecological model simulation, such as an EVR model (Ecological Value at Risk) for Ecological Risk analysis, a comprehensive evaluation model (INVEST) for Ecological system service and balance, and the like. However, the accuracy and time span of single model prediction are limited, for example, the InVEST model cannot predict future habitat conditions 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 difficult to predict habitat quality changes in some specific situations in the future.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for evaluating and predicting the quality of the country habitat.
The purpose of the invention can be realized 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 country habitat quality assessment database, wherein the database comprises: the method comprises the following steps of (1) rural historical land utilization data, current social and economic data, future prediction data under the RCPs situation and environment variable data;
s3, establishing a plus model, taking the historical rural land utilization data as influence data, screening out a training sample by using the plus model, determining a land utilization change factor X, and obtaining the occurrence probability of various land;
s4, taking future climate prediction data and historical land utilization data in the RCPs situations as influence factors, and performing repeated iteration simulation in a Flus model to obtain grid data of land utilization distribution in the future in different RCPs situations;
s5, inputting the land utilization grid data obtained by the plus model into an InVEST model to obtain habitat quality data and threat element 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 environment variables in the S1, and respectively performing simulation prediction on potential distribution areas of wild protection species in the RCPs context;
s8, extracting a model prediction result threshold value, drawing a wild protected plant abundance graph, and obtaining the spatial distribution grade of the abundance of the wild protected species;
and S9, superposing the current situation of the habitat quality distribution, future prediction and species abundance distribution to obtain a habitat quality comprehensive evaluation chart.
Further, current socioeconomic data include: population data, total domestic production value, distance from a city center and road network;
future forecast data includes percentage of land used, 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, social economy, and climate factors are used as influence data, a fitness probability estimation module of an artificial neural network in the model is used to screen out training samples, a land utilization change factor X is determined, and the training samples are input to the neural network to obtain the occurrence probability of various land types.
Further, in S3, the formula for determining the land use variation factor X is:
X＝(x 1 (1),x 2 (1),…,x n (1)) T (1)
in the formula X i And extracting the ith variable for driving the silver for the 1 st sampling point, wherein T is a transposed matrix.
Further, in S4, the step of simulating the future land use distribution of RCPs includes:
s41, taking future climate prediction data and historical land utilization data in the RCPs situation as influence factors to preliminarily obtain the future land utilization requirement in the RCPs situation,
s42, defining a self-adaptive coefficient to automatically adjust the inertia of each land based on a cellular automata module of a self-adaptive inertia mechanism, and performing multiple iterations on the coefficient according to the land utilization requirements under different future scenes and the actual number of the current various lands to finally simulate the future land utilization distribution of the RCPs.
Further, the iterative formula in S42 is:
in the formula: i is t p Representing the inertia coefficient of the land type of the p type in the t time period; d t-1 p The difference between the demand of the p-type land and the current actual land quantity in the t-1 time period.
Further, in S5, the calculating step of the habitat quality data is:
1) And calculating the habitat degradation degree according to the land utilization data and the data in the table, wherein the expression is as follows:
in the formula D xj Representing the habitat degradation degree of the xth habitat pixel in the habitat type j; r is a threat source of the habitat; y is a grid in the threat source r; w is a r A weight of a threat source r; i.e. i rxy Representing the influence of r on each grid of the environment; beta is a x Representing local protection policy impact; s jr Indicating the relative sensitivity of each habitat to different sources of threat;
2) The habitat quality score expression is:
in the formula, Q xj Expressing the habitat quality score of the xth habitat pixel in the habitat type j, and the value rangeIs [0,1]；H j Representing the habitat suitability of habitat type j; k and z adopt model default parameters.
Further, in S7, the step of simulating the potential distribution region of the wild protective species includes:
s71, inputting the collected geographic distribution data set and environment variables of the current species in the research area into a MaxEnt model, calculating constraint conditions of the distribution of target species according to the distribution data and environment characteristic variables of the known species occurrence points, and exploring possible distribution of maximum entropy under ecological requirements;
and S72, projecting the simulation result to a research area through a constructed model, and predicting the potential habitat distribution and suitability of the target species in the research area according to the simulation result.
Further, in S7, on the premise of containing known information, assuming that the random variable α includes n possible results, including A1, A2, A3, …, an, the entropy value thereof is:
where H (α) is the entropy value and P1, P2, P3, …, pn is the probability of each occurrence.
Further, in S8, the step of drawing the wild protected plant abundance map is:
s81, importing the maximum entropy operation threshold result of each wild protection species into an Arc GIS, carrying out interval division on the richness pattern of each protection species through a threshold division function in the layer attribute of the Arc GIS software, processing the richness pattern into 10 intervals, and visualizing each interval into different gray scales which are in negative correlation with the richness of the species;
and S82, overlapping potential distribution image layers of the selected protected species, and finally obtaining a wild protected plant species abundance map of the research area.
The invention has the beneficial effects that: the future habitat quality of the research area can be accurately predicted, and the future habitat quality can be comprehensively analyzed by combining with the existing habitat quality and species abundance pattern of the research area, so that a scientific and comprehensive reference basis can be obtained for the habitat optimization strategy of the area.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the overall process of the present invention;
FIG. 2 is a schematic diagram of research area division according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a visualization of regional climate data according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of a visualization of population density data for a study area according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating data visualization of a traffic area in a research area according to an embodiment of the present invention;
FIG. 6 is a schematic view of topographic data for a research area in accordance with an embodiment of the present invention;
FIG. 7 is a graphical representation of research and regional NDVI data in accordance with an embodiment of the present invention;
FIG. 8 is a schematic land use diagram for a future RCP2.6, 6.0, 8.5 scenario of an embodiment of the present invention;
FIG. 9 is a graphical illustration of habitat quality values for current and future RCP2.6 scenarios in accordance with embodiments of the present invention;
FIG. 10 is a schematic diagram of distribution points of wild-type protection plants in a research area according to an embodiment of the present invention;
FIG. 11 is a schematic diagram illustrating the operation result of the MaxEnt model according to the embodiment of the present invention;
FIG. 12 is a chart of comprehensive evaluation of habitat quality in accordance with embodiments of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a method for estimating and predicting the quality of a country habitat includes the following steps:
s1, referring to a ' statistical division code and urban and rural division code compilation rule ' printed by the State statistics Bureau (national system of Chinese characters 2009 ' 91), classifying a three-digit code according to urban and rural areas inquired on the official network of the State statistics Bureau, wherein an area with a first-digit code of 1 is classified as a town, and an area with a first-digit code of 2 is classified as a country; urban and rural areas are divided in arcgis 10.7;
after the study object and the range are determined, equidistant grid division is carried out on a study area, grids 1km × 1km are selected and are equidistantly distributed in each part of the study area, the grids are numbered, the first grid in the northwest corner is taken as a number 1, and numbering is carried out sequentially from left to right and from top to bottom, namely, 1,2, 3, and.
S2, establishing a country habitat quality evaluation database, and performing data preprocessing; the database includes: country historical land utilization data, current socioeconomic data (population, total domestic production value, distance from a city center, road network), future prediction data (percentage of land of various types, climate, economy and population in the context of RCPs) and 23 environment variable data (mainly 19 climate factor data, terrain data (DEM elevation, gradient and slope direction) and NDVI data) of a research area;
the method comprises the steps of obtaining country historical land utilization data from a geographic national condition monitoring cloud platform, downloading past 20-year calendar historical land utilization data of a research area with the precision of 30m, and carrying out rasterization processing in an Arc GIS. Simplifying land utilization types according to different provincial land utilization conditions;
acquiring current population data from population distribution data of the European and air Bureau, downloading current population density utilization data of a research area with the precision of 30m, and rasterizing in an Arc GIS;
the total domestic production value is acquired from the annual book of statistics of each province or a global change scientific research data publishing system, and the GDP is subjected to rasterization processing in an Arc GIS by taking a region as a unit;
obtaining the distance from the city center from World urbanization prospect websites (World urbanization prospects), downloading the data of the distance from the current city center of the research area with the precision of 30m, and performing Euclidean distance operation and rasterization processing in an Arc GIS;
the road network is obtained from a Global road data set website (Global roads open access data set), the current road network data of the research area is downloaded, the precision is 30m, and rasterization processing is carried out in an Arc GIS.
The future climate mode adopts three emission scenes of RCP2.6, RCP6.0 and RCP8.5, and the temperature, the precipitation and the sunshine intensity under the RCPs situation 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 ℃ in the 21 st century, a global average precipitation increase of 2.1%; changing the current temperature and precipitation data in the Arc GIS;
the biological climate factor data covered by the research area is obtained from a world climate database (http:// www.WordClim.org), and 19 climate factors are obtained: bio 1-bio 19, different research areas cover different climate factors in quantity; the current climate model used 19 biological climate factors from 1950-2000, as shown in table 1:
table 1 biological climate factor data
DEM elevation data are acquired from a geospatial data cloud website, and DEM images of a research area are downloaded with the precision of 30m; extracting the Slope and the direction of Slope in an Arc GIS through a spatial analysis tools-Surface-Slope and Aspect command in an Arctolbox to finally obtain the elevation, the Slope and the direction of Slope of a research area, wherein the resolution is 30m multiplied by 30m, and the downloaded climate variable data is subjected to mask extraction to obtain a layer in the range of the research area; the coordinate system is WGS1984, the grid size is set uniformly, and finally the grid size is converted into a file in an ASCII format for model operation;
NDVI (vegetation index) data is combined by using the detection data of the satellite visible light and the near infrared band according to the spectral characteristics of the vegetation, and is helpful for identifying the vegetation existence degree; firstly, downloading Landsat 8 image data from a geographic space data cloud, selecting an image with less cloud amount in the same month in 2020, calculating NDVI in ENVI through band math to obtain an NDVI index raster map, cutting out a research area range, and further obtaining the NDVI raster data of the research area as an environmental factor for model prediction; the coordinate system is WGS1984, the size of the grid is uniformly set, and finally the grid is converted into a file in an ASCII format for model operation.
S3, according to the requirement of a plus (future land utilization simulation model) model, taking historical land utilization, location, natural environment, social economy and climate factors as influence data, screening out a training sample by using an suitability probability estimation module of an artificial neural network in the model, determining a land utilization change factor X, and inputting the training sample into the neural network to obtain the occurrence probability of various land;
after data is input into the Flus model, a training sample is screened out by adopting a random sampling method in an artificial neural network-based suitability probability estimation module, and a factor X for driving land use change is determined to be used as an input layer neuron (X) in the neural network i I =1,2, …, n) with the formula:
X＝(x 1 (1),x 2 (1),…,x n (1)) T (1)
wherein Xi is the variable of the ith driving silver extracted by the 1 st sampling point, and T is a transposed matrix;
each neuron in the output layer generates a value between 0 and 1, the value represents the probability of the pixel developing into a certain type of land, and the higher the value is, the higher the possibility of developing into the land of the type in the future is; images are derived of the probability of occurrence of the plots in future RCP2.6, 4.5, 8.5 scenarios, and the results are used in the next step.
S4, taking future climate prediction data and historical land utilization data in the RCPs (Radar Cross-section) situation as influence factors, preliminarily obtaining future land utilization requirements in the RCPs situation, defining a self-adaptive coefficient to automatically adjust the inertia of each land in a cellular automata module based on a self-adaptive inertia mechanism, and performing multiple iterations on the coefficient according to the land utilization requirements in different future situations and the actual number of the current various lands, wherein the coefficient is shown in a formula (2), and finally simulating the future land utilization distribution of the RCPs:
in the formula: i is t p Representing the inertia coefficient of the land type of the p type in the t time period; d t-1 p The difference between the demand of the p-type land and the current actual land quantity in the t-1 time period.
S5, inputting the grid data of land utilization obtained by the plus model into an InVEST (ecological system service and balance comprehensive evaluation model), defining three influence factors of threat factor type, stress distance and relative sensitivity required by the InVEST model according to the characteristics of various lands in the country, and operating the InVEST to obtain habitat quality data and threat source distribution data; the method comprises the following specific steps:
referring to 'the current land utilization classification' revised by the national resource ministry (GB/T21010-2017), the current land utilization classification types in China comprise cultivated land, garden land, forest land, grassland, business 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. The method is characterized in that 8 types of land such as cultivated land, business land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land and other land (other agricultural land) are taken as non-habitat land types due to severe human activities; the fields of the garden, the forest land, the grassland, the water area and the water conservancy facility land which are less influenced by human activities are used as habitat fields; the non-habitat land types are listed as threat factors in an InVEST model, and the threat factors are quantitatively analyzed:
the first factor is the relative devastation of each threat source to all habitats; the devastating nature of the habitat given a degenerate source weight wr according to the different threat types can take any value from 0 to 1, as shown in table 2:
TABLE 2 threat source weights
The second factor is the influence distance of the threat source to various habitats; the degree of threat decreases as the grid increases in distance from the source of the threat, so those grid cells closest to the threat will be more affected; according to the InVEST specification, there are two functions, linear and exponential distance decay, to describe the decay of the threat in space, 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 susceptibility of threat sources
Making tables (tables 5 and 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 tables and the tif data into an INVEST model;
TABLE 5 threat source data
TABLE 6 impact of threat sources on various types of land use
And calculating the habitat degradation degree according to the land utilization data and the data in the table, wherein the expression is as follows:
in the formula, D xj Representing the habitat degradation degree of the xth habitat pixel in the habitat type j; r is a threat source of the habitat; y is a grid in the threat source r; w is a r A weight of a threat source r; i all right angle rxy Representing the effect r has on each grid of the environment (linear or exponential); beta is a x The influence of local protection policy and the like is shown, and the influence on the final result is small; s jr Indicating the relative sensitivity of each habitat to different sources of threat.
The habitat quality score expression is:
in the formula, Q xj Representing the habitat quality score of the xth habitat pixel in the habitat type j, and the value range is [0,1]；H j Representing the habitat suitability of habitat type j; k and z adopt model default parameters.
S6, establishing a species geographic distribution data set of a research area by using the selected representative species (wild protection plants), and finally using the species geographic distribution data set for model simulation;
obtaining a wild protection plant directory from a Chinese plant subject database (http:// www.plant.csdb.cn /); geographic distribution data of various species are obtained through a Global Biodiversity Information Facility (https:// www.gbif.org /), species with the number of records less than five are removed, and finally the species meeting the operation requirements of the model are obtained and are introduced into Excel for sorting and repeated points are removed; guiding the sorted wild protection plant species data of the province into an Arc GIS, and obtaining wild protection plant distribution point data in a research area through superposition analysis; and exporting the data, inputting the data into an Excel table, storing the data in a csv format, and forming a geographic distribution data set of the species in the research area, wherein each group of data comprises the academic name and the distribution point of the species, and particularly the latitude and longitude.
S7, respectively carrying out simulation prediction on potential distribution areas of wild protected species under the RCPs (Radar Cross-section) situation by utilizing the collected geographic distribution data set of the current species in the research area and the 23 environment variable driven MaxEnt (maximum entropy model) models;
the model calculates the constraint condition of the distribution of the target species according to a corresponding algorithm through the distribution data and the environment characteristic variables of the known species 'occurrence points', explores the possible distribution of the maximum entropy under the ecological requirement, and the probability distribution of the species when the entropy is maximum meets the space range of the species habitat condition; projecting the simulation result to a research area through a constructed model, and predicting the potential habitat distribution and suitability of the target species in the research area; under the premise of containing known information, when the entropy value is maximum, redundant information is excluded, and if the random variable alpha contains n possible results of A1, A2, A3, … and An, the entropy value is as follows:
where H (α) is the entropy value and P1, P2, P3, …, pn is the probability of each occurrence.
S8, extracting a model prediction result threshold, drawing a wild protection plant abundance pattern, and dividing the wild protection plant abundance pattern into 10 grades by using a natural breakpoint method to obtain the spatial distribution grade of the wild protection species abundance;
firstly, introducing a maximum entropy operation threshold result of each wild protection species into an Arc GIS, carrying out interval division on the richness pattern of each protection species through a threshold division function in the layer attribute of the Arc GIS software, processing the richness pattern into 10 intervals, and visualizing each interval into different gray scales, wherein the gray scales are in negative correlation with the richness of the species; and secondly, overlapping potential distribution image layers of the selected protected species, and finally obtaining a wild protected plant species abundance map of the research area.
S9, comprehensively analyzing evaluation results of an InVEST model and a MaxEnt model, superposing the current situation of habitat quality distribution simulated by the two models, future prediction and species abundance distribution patterns, and dividing the comprehensive evaluation level of the habitat quality into 10 levels from high to low according to a habitat quality comprehensive evaluation chart obtained by superposition, wherein the lower the gray level is, the larger the numerical value is, the higher the level is, and the better the comprehensive condition of biodiversity in a research area is;
and identifying the optimal habitat range and the habitat optimization key area of the research area under different future development scenes, analyzing the habitat space priority protection area, and finally providing a scientific reference basis for the habitat optimization strategy of the future research area.
Example (b):
according to the method, the habitat quality evaluation based on the Maxent and InVEST models is utilized, under three RCP situations, the comprehensive evaluation of the habitat quality of the research area is obtained through the combined analysis of the models, the future conditions of the habitat quality of the research area under different RCP situations are predicted, and the specific habitat optimization strategy of the research area is provided according to the comprehensive evaluation and the future conditions;
taking a certain province as an example, the habitat quality evaluation method based on the Maxent and InVEST models under the RCPs scene comprises the following steps:
s1: according to the urban and rural classified three-digit code inquired on the official network of the national statistics bureau, dividing the code with the first digit of 1 into towns and dividing the code with the first digit of 2 into villages according to the 'statistical division code and urban and rural division code compiling rule' (the national statistics bureau number [ 2009 ] 91) issued by the national statistics bureau; the province city and the country region are divided in Arcgis 10.7; a country land area of a certain province is obtained as shown in fig. 2.
S2: establishing a model database, and performing data preprocessing; the data are divided into three types which are respectively applied to three models of FLUS, inVEST and Maxent.
Data applied to the FLUS model are:
1) J12000 years of historical land utilization data of the province, J2 2005 of historical land utilization data of the province and J32010 of historical land utilization data of the province;
2) Climate data (Q), as the plus model driving force factor: average temperature in the month Q1, average temperature difference in the year Q2, average precipitation in the year Q3 and solar radiation intensity Q4; as shown in fig. 3;
3) Socioeconomic data (S), as the driving force factor for the plus model: s1, total production value of province and city regions and S2 population density of provinces; as shown in fig. 4
4) T1 traffic area data (T), namely the distance from the provincial road network to the city center, the distance from the city center to the town center, the distance from the city center to the city center, the distance from the city center to the expressway, the distance from the city center to the trunk road and the distance from the city center to the railway; as shown in fig. 5
Future prediction data (W): under the conditions of RCP4.5, RCP6.0 and RCP8.5, W1 temperature changes, W2 precipitation changes and W3 sunshine intensity changes.
Data applied to the InVEST model are:
threat data (X): x1 threat factor, X2 stress distance, and relative sensitivity of X3 types of land to the threat factor.
The data applied to the MAXENT model are:
1) Geographical distribution data of key wild protection plants in the country of the research area;
2) 19 climate factor data;
3) Research area DEM elevation data; as shown in fig. 6;
4) NDVI data in the study area; as shown in fig. 7.
S3: using the database, defining climate, social economy and traffic zone as driving force factors according to the instructions of the FLUS instruction manual, using the annual land utilization data of the province 2000, 2005 and 2010, using an suitability probability estimation module of an artificial neural network in a Flus model, screening out training samples by using a random sampling method, and determining a factor X for driving land utilization change as an input layer neuron (xi, i =1,2, …, n) of the neural network:
X＝(x1(1),x2(1),…,xn(1))T (1)
wherein Xi is the variable of the ith driving silver extracted by the 1 st sampling point, and T is a transposed matrix.
Each neuron in the output layer will generate a value between 0 and 1, the value representing the probability of the pixel developing into a certain type of land, the higher the value, the higher the probability of developing into the type of land in the future. An image is derived of the probability of occurrence of the plots in future RCP2.6, 6.0, 8.5 scenarios, and the result is used for S4.
S4: and defining the percentage of various land types and future population economic data under the RCPs (Radar Cross section) situation according to the instruction of the FLUS instruction manual, and preliminarily obtaining the future land utilization requirement under the RCPs situation by taking the future climate change data and the historical land utilization data as influence factors. And (3) inputting the suitability probability data obtained in the step (3) into a cellular automata module based on a self-adaptive inertia mechanism, inputting the future land utilization requirement under the RCPs situation as a target of land utilization type change quantity, and operating the cellular automata. The cellular automaton carries out multiple iterations (formula 2) according to the land utilization requirements under different future scenes and the actual number of various current land utilization, and finally simulates the land utilization distribution under the scenes of RCP2.6, 6.0 and 8.5 in the future; as shown in fig. 8;
in the formula: i is t p Representing the inertia coefficient of the p-type land usage type in the t time period; d t-1 p The difference between the demand of the p-type land and the current actual land quantity in the t-1 time period.
S5: referring to ' the current land utilization classification ' (GB/T21010-2017) caused by the national Standard administration Committee and the State administration of quality supervision, inspection and quarantine of the people's republic of China, the types of the current land utilization classification of China include cultivated land, garden land, forest land, grassland, business land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land, water area, water conservancy facility land and other land. The method is characterized in that 8 types of land such as cultivated land, business land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land and other land (other agricultural land) are taken as non-habitat land types due to severe human activities; while the fields of gardens, woodlands, lawns, water areas and water conservancy facilities are less affected by human activities as habitats. Selecting the land types widely owned by the province as data, wherein the non-habitat land types are listed as threat factors in an InVEST model, rasterizing historical distribution data and future distribution data of various threat sources in an Arc GIS, exporting tif format files, and inputting the tables and the tif data into the InVEST model. (tables 7 and 8)
Table 7 inputs the threat file of INVEST
Table 8 inputs sendivitu file for InVEST
And calculating the habitat degradation degree according to the land utilization data and the data in the table, wherein the expression is as follows:
in the formula, D xj Representing the habitat degradation degree of the xth habitat pixel in the habitat type j; r is a threat source of the habitat; y is a grid in the threat source r; w is a r A weight of a threat source r; i.e. i rxy Representing the effect r has on each grid of the environment (linear or exponential); beta is a x The influence of local protection policy and the like is shown, and the influence on the final result is small; s jr Indicating the relative sensitivity of each habitat to different sources of threat.
The habitat quality score expression is:
in the formula, Q xj Representing the habitat quality score of the xth habitat pixel in the habitat type j, and the value range is [0,1]；H j Representing the habitat suitability of the habitat type j; k and z adopt model default parameters; and finally, the distribution of the habitat quality values in the future situation is obtained, as shown in fig. 9.
S6: the catalogue of the wild protection plant in the province is obtained from a Chinese plant subject database (http:// www.plant.csdb.cn /), 13 types in total. Geographic distribution data of the 13 species are obtained through a Global Biodiversity Information Facility (https:// www.gbif.org /), the species with the number of records less than five are removed, and finally 11 species meeting the operation requirements of the model are obtained and are introduced into Excel for sorting. And (3) introducing the sorted species data of the wild protection plants in the province into an Arc GIS, and obtaining the distribution point data of the wild protection plants in the research area through superposition analysis, wherein the data is shown in figure 10. And exporting the data, inputting the data into an Excel table, storing the data in a csv format, and forming a species geographic distribution data set of the research area, wherein each group of data comprises the academic name and the distribution point of the species, and particularly the latitude and longitude.
S7: inputting the collected current biological climate variable data, elevation data and NDVI data of the research area into a MaxEnt model, calculating a constraint condition of target species distribution according to a corresponding algorithm through the distribution data and environment characteristic variables of known species 'occurrence points' by the model, and exploring the possible distribution of maximum entropy under ecological requirements, wherein the probability distribution of the species when the entropy is maximum meets the spatial range of species habitat conditions. And (4) projecting the simulation result to the research area by constructing a model, and predicting the potential habitat distribution and suitability of the target species in the research area. Under the premise of containing known information, when the entropy value is maximum, redundant information is excluded, and if the random variable alpha contains n possible results of A1, A2, A3, … and An, the entropy value is as follows:
where H (α) is the entropy value and P1, P2, P3, …, pn is the probability of each occurrence.
S8: firstly, a maximum entropy operation threshold result (as shown in fig. 11) of each wild protection species is imported into an Arc GIS, the richness pattern of each protection species is partitioned into 10 intervals through a self-contained threshold partitioning function in the layer attribute of the Arc GIS software, each interval is visualized into different gray levels, and the gray levels are negatively correlated with the richness of the species. And secondly, overlapping potential distribution layers of the selected protective species, and finally obtaining a wild protective plant species abundance map of the research area.
S9: the same treatment as S8 was performed on the InVEST model, which was gray-scale overlaid with the wild protection plant species abundance map of the study area as representative of biodiversity. A habitat quality comprehensive evaluation chart obtained according to the superposition is shown in fig. 12; the comprehensive evaluation grade of the habitat quality is divided into 10 grades from high to low, the smaller the gray scale is, the larger the numerical value is, the higher the grade is, and the better the comprehensive condition of the biodiversity in the research area is.
On the basis of the chart, the biodiversity protection priority region is obtained by combining the existing rural region screening, the habitat space priority protection region is analyzed, and finally, a direct and effective reference basis is provided for the rural region habitat optimization.
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., 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 invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. 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 shows and describes the general principles, essential 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, which are given by way of illustration of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, and such changes and modifications are within the scope of the invention as claimed.
Claims (10)
1. A rural habitat quality assessment and prediction method is characterized by comprising the following steps:
s1, carrying out equidistant grid division on a research area;
s2, establishing a country habitat quality assessment database, wherein the database comprises: the method comprises the following steps of obtaining country historical land utilization data, current social and economic data, future prediction data under the RCPs situation and environment variable data;
s3, establishing a plus model, taking the country historical land utilization data as influence data, screening out a training sample by using the plus model, determining a land utilization change factor X, and obtaining the occurrence probability of various land;
s4, taking future climate prediction data and historical land utilization data in the RCPs situations as influence factors, and iteratively simulating in the Flus model for multiple times to obtain grid data of land utilization distribution in the future in different RCPs situations;
s5, inputting the land utilization grid data obtained by the plus model into an InVEST model to obtain habitat quality data and threat element 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 environment variables in the S1, and respectively performing simulation prediction on potential distribution areas of wild protection species in the RCPs context;
s8, extracting a model prediction result threshold value, drawing a wild protected plant abundance graph, and obtaining the spatial distribution grade of the abundance of the wild protected species;
and S9, superposing the current situation of the habitat quality distribution, future prediction and species abundance distribution to obtain a habitat quality comprehensive evaluation chart.
2. The method as claimed in claim 1, wherein in S2, the current socioeconomic data includes: population data, total domestic production value, distance from a city center and road network;
future forecast data includes percentage of land used, climate, economy, and population;
the environment variable data includes: 19 climate factor data, DEM elevation data, grade data, slope data and NDVI data.
3. The method according to claim 1, wherein in step S3, historical land use, location, natural environment, socioeconomic, climate factors are used as influence data, a suitability probability estimation module of an artificial neural network in a model is used to screen out training samples, a land use change factor X is determined, and the training samples are input to the neural network to obtain the occurrence probability of each land.
4. The method for estimating and predicting the quality of the rural habitat according to claim 3, wherein 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)
in the formula X i And the ith variable for driving the silver extracted for the 1 st sampling point, wherein T is a transposed matrix.
5. The method according to claim 1, wherein the step of simulating future land use distribution of RCPs in S4 comprises:
s41, taking future climate prediction data and historical land utilization data in the RCPs situation as influence factors to preliminarily obtain the future land utilization requirement in the RCPs situation,
s42, defining a self-adaptive coefficient to automatically adjust the inertia of each land based on a cellular automata module of a self-adaptive inertia mechanism, and performing multiple iterations on the coefficient according to the land utilization requirements under different future scenes and the actual number of the current various lands to finally simulate the future land utilization distribution of the RCPs.
6. The method as claimed in claim 5, wherein the iterative formula in S42 is:
in the formula: I.C. A t p Representing the inertia coefficient of the land type of the p type in the t time period; d t-1 p The difference between the demand of the p-type land and the current actual land quantity in the t-1 time period.
7. The method as claimed in claim 1, wherein the step of calculating the habitat quality data in S5 comprises:
1) And calculating the habitat degradation degree according to the land utilization data and the data in the table, wherein the expression is as follows:
in the formula, D xj Representing the habitat degradation degree of the xth habitat pixel in the habitat type j; r is a threat source of the habitat; y is a grid in the threat source r; w is a r A weight of a threat source r; i all right angle rxy Representing the effect r has on each grid of the environment; beta is a beta x Representing local protection policy impact; s. the jr Indicating the relative sensitivity of each habitat to different sources of threat;
2) The habitat quality score expression is:
in the formula Q xj The habitat quality score of the xth habitat pixel in the habitat type j is represented, and the value range is [0,1 ]]；H j Representing the habitat suitability of the habitat type j; k and z adopt model default parameters.
8. The method for estimating and predicting the quality of the rural habitat according to claim 1, wherein the step of simulating and predicting the potential distribution area of the wild protected species in S7 comprises:
s71, inputting the collected geographic distribution data set and environment variables of the current species in the research area into a MaxEnt model, calculating constraint conditions of the distribution of target species according to the distribution data and environment characteristic variables of known species occurrence points, and exploring the possible distribution of the maximum entropy under ecological requirements;
and S72, projecting the simulation result to a research area through a constructed model, and predicting the potential habitat distribution and suitability of the target species in the research area according to the simulation result.
9. The method according to claim 8, wherein in the step S7, under the premise of containing known information, assuming n possible results including A1, A2, A3, …, an, for a random variable α, entropy thereof is:
where H (α) is the entropy value and P1, P2, P3, …, pn is the probability of each occurrence.
10. The method for estimating and predicting the quality of the rural habitat according to claim 9, wherein in the step S8, the step of drawing the abundance map of the wild protected plants comprises:
s81, importing the maximum entropy operation threshold result of each wild protection species into an Arc GIS, carrying out interval division on the richness pattern of each protection species through a threshold division function in the layer attribute of the Arc GIS software, processing the richness pattern into 10 intervals, and visualizing each interval into different gray scales which are in negative correlation with the richness of the species;
and S82, superposing potential distribution layers of the selected protected species, and finally obtaining a wild protected plant species abundance map of the research area.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116628519A (en) * | 2023-07-17 | 2023-08-22 | 河海大学 | Ecological fragile area identification method and system |
| CN116628519B (en) * | 2023-07-17 | 2023-09-29 | 河海大学 | Ecological fragile area identification method and system |
| CN117974401A (en) * | 2024-03-29 | 2024-05-03 | 南京大学 | Ecological restoration area intelligent identification method based on multi-source data and model integration |
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| WO2024098445A1 (en) | 2024-05-16 |
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