WO2024098445A1 - 一种乡村生境质量评估及预测方法 - Google Patents

一种乡村生境质量评估及预测方法 Download PDF

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WO2024098445A1
WO2024098445A1 PCT/CN2022/132095 CN2022132095W WO2024098445A1 WO 2024098445 A1 WO2024098445 A1 WO 2024098445A1 CN 2022132095 W CN2022132095 W CN 2022132095W WO 2024098445 A1 WO2024098445 A1 WO 2024098445A1
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habitat
land use
future
species
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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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东南大学
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  • the invention belongs to the field of urban and rural planning, and specifically relates to a rural habitat quality assessment and prediction method.
  • Habitat quality refers to the quality of the ecological environment. It is based on ecological theory and reflects the suitability of the ecological environment for human survival and sustainable social and economic development at the ecosystem level within a specific time and space range. It evaluates the nature and changing state of the ecological environment according to the specific requirements of humans. Compared with cities, rural areas often have better ecological environments. However, in recent years, due to the rapid development of the economy and society, excessive interference from human activities has also threatened the quality of habitats in rural areas. Habitat quality has its own inherent laws. If these laws are mastered, the status of habitat quality can be correctly predicted. Therefore, quantitative assessment of habitat quality in rural areas, prediction of its future status, and formulation of targeted habitat optimization strategies on this basis are an effective way to improve the quality of habitats in rural areas.
  • the future development of habitat quality is closely related to future climate change.
  • 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 estimating future climate change.
  • IPCC Intergovernmental Panel on Climate Change
  • RCPs Typical Concentration Pathway Emission Scenarios
  • RCP2.6, RCP4.5, RCP6.0 and RCP8.0 are both intermediate development situations under government intervention, but due to the reduction of cultivated land area under the RCP4.5 scenario, it is possible to exceed the minimum red line, which is not in line with reality, so such scenarios are not considered.
  • RCP 2.6, RCP 6.0 and RCP 8.5 were selected as the future scenarios of this patent study.
  • RCP2.6 is a scenario model with very low greenhouse gas concentrations; changes in energy utilization types on a global scale have significantly reduced greenhouse gas emissions, and it is the emission scenario with the largest increase in global crop area.
  • RCP6.0 is a climate scenario under government intervention. In this scenario, the population will increase to 10 billion by 2100; the formulation of various simulated policies has reduced greenhouse gas emissions, but compared with RCP2.6, the degree of emission mitigation is lower, and the increase in cultivated land has little impact on forest area.
  • RCP8.5 is a baseline scenario in the absence of climate change policy intervention, characterized by increasing greenhouse gas emissions and concentrations. In this scenario, with the substantial increase in global population, slow income growth, and increased fossil fuel consumption caused by technological changes and energy efficiency changes.
  • the methods for assessing and measuring biodiversity at home and abroad are mainly concentrated in two ways: one is based on ground survey methods, focusing on the assessment research of biological background surveys; the other is an evaluation method based on ecological model simulation, such as the EVR model (Ecological Value at Risk) of ecological risk analysis, the integrated assessment model of ecosystem services and trade-offs (InVEST), etc.
  • ecological model simulation such as the EVR model (Ecological Value at Risk) of ecological risk analysis, the integrated assessment model of ecosystem services and trade-offs (InVEST), etc.
  • the accuracy and time span of single model predictions are limited.
  • the InVEST model cannot predict future habitat conditions and cannot be used well to assess land use changes and their resulting ecological and environmental impacts, making it difficult to use the InVEST model to predict changes in habitat quality in some specific future scenarios.
  • the purpose of the present invention is to provide a method for assessing and predicting rural habitat quality.
  • a rural habitat quality assessment and prediction method comprises the following steps:
  • S2 establish a rural habitat quality assessment database, which includes: rural historical land use data, current socioeconomic data, future prediction data under RCPs scenarios, and environmental variable data;
  • current socio-economic data include: population data, gross domestic product, distance from the city center, and road network;
  • Future forecast data include percentage of various land uses, climate, economy, and population
  • Environmental variable data include: 19 climate factor data, DEM elevation data, slope data, aspect data and NDVI data.
  • the suitability probability estimation module of the artificial neural network in the model is used to screen out training samples, determine the land use change factor X, and input it into the neural network to obtain the occurrence probability of various types of land use.
  • Xi is the variable of the i-th driving element extracted at the first sampling point
  • T is the transposed matrix
  • the step of simulating the future land use distribution of RCPs includes:
  • a cellular automaton module based on the adaptive inertia mechanism defines an adaptive coefficient to automatically adjust the inertia of each type of land use.
  • the coefficient is iterated multiple times according to the land use demand under different future scenarios and the actual number of each type of land use at present, and finally simulates the future land use distribution of RCPs.
  • I t p represents the inertia coefficient of land use type p in time period t
  • D t-1 p is the difference between the land demand of type p in time period t-1 and the current actual land quantity.
  • the steps of calculating the habitat quality data are:
  • Dxj represents the habitat degradation degree of the x-th habitat pixel in habitat type j; r is the threat source of the habitat; y is the grid in threat source r; wr is the weight of threat source r; i rxy represents the impact of r on each grid of the habitat; ⁇ x represents the impact of local protection policies; Sjr represents the relative sensitivity of each habitat to different threat sources;
  • Qxj represents the habitat quality score of the x-th habitat pixel in habitat type j, and its value range is [0,1]; Hj represents the habitat suitability of habitat type j; k and z use the default parameters of the model.
  • the step of simulating and predicting the potential distribution area of wild protected species is:
  • H( ⁇ ) is the entropy value
  • P1, P2, P3, ..., Pn are the probabilities of each occurrence.
  • the step of drawing a wild protected plant richness map is:
  • the beneficial effects of the present invention are as follows: it can make a relatively accurate prediction of the future habitat quality of the study area, and can combine it with the existing habitat quality and species richness pattern of the study area for comprehensive analysis, thereby obtaining a relatively scientific and comprehensive reference basis for the habitat optimization strategy of the region.
  • FIG1 is a schematic diagram of the overall method flow of the present invention.
  • FIG2 is a schematic diagram of the division of the research area according to an embodiment of the present invention.
  • FIG3 is a schematic diagram of the visualization of climate data in a research area according to an embodiment of the present invention.
  • FIG4 is a schematic diagram of visualization of population density data in a study area according to an embodiment of the present invention.
  • FIG5 is a schematic diagram of visualization of traffic location data in a research area according to an embodiment of the present invention.
  • FIG6 is a schematic diagram of terrain data of a research area according to an embodiment of the present invention.
  • FIG7 is a schematic diagram of NDVI data of a research area according to an embodiment of the present invention.
  • FIG8 is a schematic diagram of land use under future RCP2.6, 6.0, and 8.5 scenarios according to an embodiment of the present invention.
  • FIG9 is a numerical diagram of habitat quality under the current and future RCP2.6 scenario according to an embodiment of the present invention.
  • FIG. 10 is a schematic diagram of the distribution points of wild protected plants in the study area according to an embodiment of the present invention.
  • FIG. 11 is a schematic diagram of the operation results of the MaxEnt model according to an embodiment of the present invention.
  • FIG. 12 is a graph showing a comprehensive evaluation of habitat quality according to an embodiment of the present invention.
  • a rural habitat quality assessment and prediction method includes the following steps:
  • the study area was divided into equidistant grids. 1km*1km grids were selected and evenly distributed in various parts of the study area. The grids were numbered, with the first grid in the northwest corner numbered 1, and the grids were numbered from left to right and from top to bottom, 1, 2, 3, ..., n.
  • the database includes: rural historical land use data, current socioeconomic data (population, GDP, distance from the city center, road network), future prediction data (percentage of various land uses, climate, economy, population under the RCPs scenario), and 23 environmental variable data of the study area (mainly 19 climate factor data, terrain data (DEM elevation, slope, aspect) and NDVI data);
  • the rural historical land use data was obtained from the geographic national conditions monitoring cloud platform, and the historical land use data of the study area in the past 20 years was downloaded with an accuracy of 30m and rasterized in Arc GIS.
  • the land use types were simplified according to the land use conditions in different provinces;
  • the current population data were obtained from the ESA population distribution data, and the current population density utilization data of the study area was downloaded with an accuracy of 30 m and rasterized in Arc GIS;
  • the gross domestic product was obtained from the statistical yearbooks of each province or the global change scientific research data publishing system.
  • the GDP was based on districts and was rasterized in Arc GIS.
  • the distance to the city center was obtained from the World Urbanization Prospects website.
  • the current distance data of the study area to the city center was downloaded with an accuracy of 30m.
  • Euclidean distance calculation and rasterization were performed in Arc GIS.
  • the road network was obtained from the Global roads open assess data set website.
  • the current road network data of the study area was downloaded with an accuracy of 30m and rasterized in Arc GIS.
  • the future climate model adopts three emission scenarios: RCP2.6, RCP6.0 and RCP8.5.
  • the temperature, precipitation and sunshine intensity under the RCPs scenario are obtained from the WorldClim website; for example, the RCP2.6 scenario assumes that the global average temperature will rise by 0.2-1.8°C in the 21st century and the global average precipitation will increase by 2.1%; the current temperature and precipitation data are changed in ArcGIS;
  • the bioclimatic factor data covered by the study area were obtained from the World climate Database (http://www.WordClim.org), with a total of 19 climate factors: bio1 to bio19. The number of climate factors covered in different study areas varies. The current climate model uses 19 bioclimatic factors from 1950 to 2000, as shown in Table 1:
  • DEM elevation data was obtained from the geospatial data cloud website, and the DEM image of the study area was downloaded with an accuracy of 30m.
  • the slope and aspect were extracted through the Spatital Analyst tools-Surface-Slope and Aspect commands in Arctoolbox, and finally the altitude, slope and aspect of the study area were obtained, with a resolution of 30m ⁇ 30m.
  • the downloaded climate variable data mask was extracted to obtain the layer within the study area.
  • the coordinate system was WGS1984, and the grid size was uniformly set. Finally, it was converted into an ASCII format file for model operation.
  • NDVI (vegetation index) data is a combination of satellite visible light and near-infrared band detection data based on the spectral characteristics of vegetation, which helps to identify the presence of vegetation; first, download Landsat 8 image data from the geospatial data cloud, select images with less cloud cover in the same month of 2020, calculate NDVI through band math in ENVI, and obtain the NDVI index raster map.
  • the study area is cropped out to obtain the NDVI raster data of the study area, which is used as an environmental factor for model prediction; the coordinate system is WGS1984, the grid size is set uniformly, and finally converted into an ASCII format file for model operation.
  • the formula is:
  • Each neuron in the output layer will generate a value between 0 and 1, which represents the probability of the pixel developing into a certain type of land use. The higher the value, the greater the possibility of developing into this type of land use in the future. An image of the probability of occurrence of various types of land use under the future RCP2.6, 4.5, and 8.5 scenarios is obtained, and the result is used in the next step.
  • an adaptive coefficient is defined to automatically adjust the inertia of each type of land use. This coefficient is iterated multiple times according to the land use demand under different future scenarios and the actual number of each type of land use at present, as shown in formula (2), and finally simulates the future land use distribution of RCPs:
  • I t p represents the inertia coefficient of land use type p in time period t
  • D t-1 p is the difference between the land demand of type p in time period t-1 and the current actual land quantity.
  • the current land classification types in China include cultivated land, gardens, forests, grasslands, commercial and service land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land, water and water conservancy facilities land, and other land.
  • the first factor is the relative destructiveness of each threat source to all habitats; the degradation source weight wr is given according to the destructiveness of different threat types to the habitat, and any value from 0 to 1 can be selected, as shown in Table 2:
  • the second factor is the distance of the threat source to each type of habitat; the degree of threat decreases as the distance between the grid and the threat source increases, so those grid cells closest to the threat will be more affected; according to InVEST regulations, there are two functions to describe the attenuation of threats in space: linear and exponential distance decay, as shown in Table 3:
  • the third factor is the relative sensitivity of each habitat type to each threat source, which is used to correct the total impact calculated in the previous step; as shown in Table 4:
  • Tables (Tables 5 and 6) were prepared based on the threat sources and habitat types in the study area. The historical and future distribution data of various threat sources were rasterized in ArcGIS, and the tif format data were exported. The tables and tif data were input into the InVEST model together.
  • the habitat degradation degree is calculated based on the land use data and the data in the table.
  • the expression is:
  • Dxj represents the habitat degradation degree of the x-th habitat pixel in habitat type j; r is the threat source of the habitat; y is the grid in threat source r; wr is the weight of threat source r; i rxy represents the impact of r on each grid of the habitat (linear or exponential); ⁇ x represents the impact of local protection policies, which has little effect on the final result; Sjr represents the relative sensitivity of each habitat to different threat sources.
  • habitat quality score is:
  • Qxj represents the habitat quality score of the x-th habitat pixel in habitat type j, and its value range is [0,1]; Hj represents the habitat suitability of habitat type j; k and z use the default parameters of the model.
  • the list of wild protected plants was obtained from the China Plant Thematic Database (http://www.plant.csdb.cn/).
  • the geographical distribution data of each species were obtained through the Global Biodiversity Information Facility (https://www.gbif.org/). Species with less than five records were eliminated, and finally the species that met the model operation requirements were obtained. They were imported into Excel for sorting and duplicate points were removed.
  • the sorted wild protected plant species data of the province were imported into Arc GIS, and the distribution point data of wild protected plants in the study area were obtained through overlay analysis.
  • the data were exported, entered into an Excel table, and saved as *.csv format to form a species geographical distribution data set for the study area.
  • Each set of data included the scientific name of the species and its distribution point, specific to longitude and latitude.
  • the model uses the distribution data of the "appearance points" of known species and environmental characteristic variables, calculates the constraints of the target species distribution according to the corresponding algorithm, and explores the possible distribution of the maximum entropy under ecological needs.
  • the probability distribution of species when the entropy is maximum is the spatial range that meets the species habitat conditions; by constructing a model, the simulation results are projected to the study area, and the potential habitat distribution and suitability of the target species in the study area are predicted; under the premise of containing known information, when the entropy value is maximum, redundant information is excluded.
  • the random variable ⁇ contains A1, A2, A3, ..., An, a total of n possible results, its entropy value is:
  • H( ⁇ ) is the entropy value
  • P1, P2, P3, ..., Pn are the probabilities of each occurrence.
  • S8 extract the threshold of the model prediction results, draw the pattern of wild protected plant richness, divide it into 10 levels using the natural breakpoint method, and obtain the level of spatial distribution of wild protected species richness;
  • the maximum entropy operation threshold results of each wild protected species were imported into Arc GIS, and the richness pattern of each protected species was divided into 10 intervals through the threshold division function in the layer properties of Arc GIS software. Each interval was visualized as a different grayscale, and the grayscale was negatively correlated with species richness.
  • the potential distribution layers of the selected protected species were superimposed, and finally the species richness map of wild protected plants in the study area was obtained.
  • This paper uses the habitat quality assessment based on Maxent and InVEST models. Under three RCP scenarios, the combined analysis of the models is used to obtain a comprehensive assessment of the habitat quality of the study area, predict the future status of the habitat quality of the study area under different RCP scenarios, and propose specific habitat optimization strategies for the study area based on this.
  • the methods for assessing habitat quality based on the Maxent and InVEST models under the RCPs scenario include:
  • the data applied to the FLUS model are:
  • Historical land use data of the province J1 historical land use data of the province in 2000, J2 historical land use data of the province in 2005, J3 historical land use data of the province in 2010;
  • T1 is the provincial road network
  • T2 is the distance to the city center
  • T3 is the distance to the town center
  • T4 is the distance to the highway
  • T5 is the distance to the main road
  • T6 is the distance to the railway; as shown in Figure 5
  • the data applied to the MAXENT model are:
  • Xi is the variable of the i-th driving element extracted from the first sampling point
  • T is the transposed matrix
  • Each neuron in the output layer will generate a value between 0 and 1, which represents the probability of the pixel developing into a certain type of land use. The higher the value, the greater the possibility of developing into this type of land use in the future.
  • the image of the probability of various types of land use under the future RCP 2.6, 6.0, and 8.5 scenarios is obtained, and the result is used in S4.
  • S4 Define the percentage of various types of land use under the RCPs scenario, future population and economic data, future climate change data, and historical land use data as influencing factors according to the FLUS user manual, and preliminarily derive the future land use demand under the RCPs scenario.
  • the cellular automaton module based on the adaptive inertia mechanism, input the suitability probability data obtained in step 3, input the future land use demand under the RCPs scenario as the target of the number of land use type changes, and run the cellular automaton.
  • the cellular automaton will perform multiple iterations based on the land use demand under different future scenarios and the actual number of various types of land use at present (Formula 2), and finally simulate the land use distribution under the future RCP 2.6, 6.0, and 8.5 scenarios; as shown in Figure 8;
  • I t p represents the inertia coefficient of land use type p in time period t
  • D t-1 p is the difference between the land demand of type p in time period t-1 and the current actual land quantity.
  • the habitat degradation degree is calculated based on the land use data and the data in the table.
  • the expression is:
  • Dxj represents the habitat degradation degree of the x-th habitat pixel in habitat type j; r is the threat source of the habitat; y is the grid in threat source r; wr is the weight of threat source r; i rxy represents the impact of r on each grid of the habitat (linear or exponential); ⁇ x represents the impact of local protection policies, which has little effect on the final result; Sjr represents the relative sensitivity of each habitat to different threat sources.
  • habitat quality score is:
  • S6 Obtain the list of wild protected plants in the province from the Chinese Plant Thematic Database (http://www.plant.csdb.cn/), a total of 13 species. Obtain the geographical distribution data of the 13 species through the Global Biodiversity Information Facility (https://www.gbif.org/), remove species with less than five records, and finally obtain 11 species that meet the model operation requirements, which are imported into Excel for sorting. Import the sorted wild protected plant species data of the province into Arc GIS, and obtain the distribution point data of wild protected plants in the study area through overlay analysis, as shown in Figure 10. Export the data, enter it into an Excel table, and save it as *.csv format to form a species geographical distribution data set for the study area. Each set of data includes the scientific name of the species and its distribution point, specific to longitude and latitude.
  • S7 Input the current bioclimatic variable data, elevation data and NDVI data collected in the study area into the MaxEnt model.
  • the model uses the distribution data of the "appearance points" of known species and environmental characteristic variables, and calculates the constraints of the target species distribution according to the corresponding algorithm, and explores the possible distribution of maximum entropy under ecological needs.
  • the probability distribution of species when the entropy is maximum is the spatial range that meets the habitat conditions of the species.
  • the simulation results are projected to the study area, and the potential habitat distribution and suitability of the target species in the study area are predicted. Under the premise of containing known information, when the entropy value is the largest, redundant information is excluded. Assuming that the random variable ⁇ contains A1, A2, A3, ..., An, a total of n possible results, its entropy value is:
  • H( ⁇ ) is the entropy value
  • P1, P2, P3, ..., Pn are the probabilities of each occurrence.
  • S9 The InVEST model was processed in the same way as S8, and grayscale was superimposed on the wild protected plant species richness map of the study area as a representative of biodiversity.
  • the comprehensive evaluation chart of habitat quality obtained by superposition is shown in Figure 12; the comprehensive evaluation level of habitat quality is divided into 10 levels from high to low. The smaller the grayscale, the larger the value, and the higher the level, the better the comprehensive biodiversity status of the study area.

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Abstract

本发明公开一种乡村生境质量评估及预测方法，属于城乡规划领域；该方法将FLUS、InVEST和MaxEnt模型耦合，利用RCPs情景下未来气候数据，驱动多种生态模型，分别从地理学和生物学的角度得到研究区生境质量现状的综合评估及未来预测结果；通过FLUS模型土地利用惯性、适宜性概率等算法，获得RCPs情景下的研究区未来土地利用分布图；在此基础上，通过InVEST模型自带的生态系统服务功能评估模块得出研究区的生境退化和生境质量评估，并给出研究区的未来生境质量预测，最后结合MaxEnt模型模块预测RCPs情景下研究区的物种丰富度分布格局，得到研究区域的生境质量综合评价及RCPs情景下的未来预测结果，进而提出研究区的生境优化策略。

Description

一种乡村生境质量评估及预测方法 技术领域
本发明属于城乡规划领域，具体涉及一种乡村生境质量评估及预测方法。
背景技术
生境质量是指生态环境的优劣程度，它以生态学理论为基础，在特定的时间和空间范围内，从生态系统层次上反映生态环境对人类生存及社会经济持续发展的适宜程度，根据人类的具体要求对生态环境的性质及变化状态的结果进行评定。乡村相对于城市往往拥有更优良的生态环境，但近年来，由于经济社会的快速发展，人类活动的过度干扰使乡村区域生境质量也面临威胁。生境质量有其自身内在的规律，掌握了这些规律，必然可正确的预测生境质量状况，因此，对乡村区域的生境质量进行量化评估，预测其未来状况，在此基础上制定针对性的生境优化策略不失为一种提升乡村地区生境质量的有效途径。
当前生境评估常常利用单一模型评估现状，而无法科学预测未来，因此无法根据未来发展合理安排对应政策；或是仅根据土地利用情况得出生境质量，而不考虑物种丰富度格局，得出的结果较为片面。本发明将综合模拟预测未来不同发展情景下的生境质量。
合理预测生境质量的未来发展状况，才能安排对应政策。而生境质量的未来发展状况与未来气候变化息息相关，其中排放情景是对潜在的辐射活跃排放物(如温室气体、气溶胶等)未来发展的一种可能的表述，也是预估未来气候变化的基础。基于未来大气辐射强度变化，联合国政府间气候变化专门委员会(IPCC)第五次气候变化评估报告中提出温室气体的典型浓度路径排放情景(RCPs)， 可用于气候模式和土地利用变化等各项预测模型。RCPs以稳定浓度为特征，综合考虑未来气候变化、温室气体排放、社会经济变化和土地利用变化，提供了几种不同的发展情景，包括RCP2.6、RCP4.5、RCP6.0和RCP8.0四种；其中RCP4.5和RCP6.0均属于在政府干预下的中间发展情形，但由于RCP4.5情境下耕地面积减少，有可能超过最低红线，不符合实际，故不考虑此类情景。选择了其中RCP 2.6,RCP 6.0,RCP 8.5三种作为本专利研究的未来情景。
RCP2.6是温室气体浓度非常低的情景模式；全球范围内能源利用类型的改变，使温室气体排放显著减少，是全球作物面积增加最大的排放情景。RCP6.0是政府干预下的气候情景，在这种情境下至2100年，人口数目增至100亿；模拟各种政策的制定减少了温室气体的排放，但与RCP2.6相比，排放量的缓解程度较低，耕地的面积增长对森林面积的影响程度小。RCP8.5是在无气候变化政策干预时的基线情景，特点是温室气体排放和浓度不断增加，在此情景下，随着全球人口大幅增长、收入缓慢增长以及技术变革和能源效率改变导致的化石燃料消耗变大。
目前，国内外对生物多样性的评估和测算的方法主要集中于两种方式：一种是基于地面调查方法，重点是关注生物本底调查的评估研究；另一种是基于生态模型模拟的评价方法，如生态风险分析的EVR模型(Ecological Value at Risk)、生态系统服务和权衡的综合评估模型(InVEST)等。但单个模型预测的精度及时间跨度有所局限，例如InVEST模型无法预测未来生境状况，无法较好地用于评估土地利用变化及其导致的生态环境影响，使得InVEST模型用于预测未来一些特定情境下的生境质量变化成为了难题。
发明内容
针对现有技术的不足，本发明的目的在于提供一种乡村生境质量评估及预 测方法。
本发明的目的可以通过以下技术方案实现：
一种乡村生境质量评估及预测方法，包括以下步骤：
S1，对研究区域进行等距网格划分；
S2，建立乡村生境质量评估数据库，数据库包括：乡村历史土地利用数据、当前社会经济数据、RCPs情境下的未来预测数据以及环境变量数据；
S3，建立Flus模型，将乡村历史土地利用数据作为影响数据，利用Flus模型筛选出训练样本，确定土地利用变化因子X，并得出各类用地的出现概率；
S4，将RCPs情境下未来气候预测数据和历史土地利用数据作为影响因素，在Flus模型内多次迭代模拟出，未来不同RCPs情境下的土地利用分布的栅格数据；
S5，将Flus模型得出的土地利用的栅格数据输入InVEST模型，来得出生境质量数据及威胁元分布数据；
S6，选取代表物种，并建立研究区域的物种地理分布数据集；
S7，利用S6得到的物种地理分布数据集及S1中的环境变量驱动MaxEnt模型，在RCPs情境下，分别对野生保护物种的潜在分布区进行模拟预测；
S8，提取模型预测结果阈值，绘制野生保护植物丰富度图，得到野生保护物种丰富度的空间分布的等级；
S9，将生境质量分布现状及未来预测和物种丰富度分布图进行叠加，得到生境质量综合评价图表。
进一步地，当前社会经济数据包括：人口数据、国内生产总值、距市中心距离以及路网；
未来预测数据包括各类用地百分比、气候、经济以及人口；
环境变量数据包括：19个气候因子数据、DEM高程数据、坡度数据、坡向数据以及NDVI数据。
进一步地，所述S3中，将历史土地利用、区位、自然环境、社会经济、气候因素作为影响数据，利用模型中人工神经网络的适宜性概率估算模块，筛选出训练样本，确定土地利用变化因子X，输入神经网络得出各类用地的出现概率。
进一步地，所述S3中，确定土地利用变化因子X的公式为：
X＝(x 1(1),x 2(1),…,x n(1)) T    (1)
式中X i为第1个采样点抽取的第i个驱动银子的变量，T为转置矩阵。
进一步地，所述S4中，模拟出RCPs未来土地利用分布的步骤包括：
S41，将RCPs情境下未来气候预测数据、历史土地利用数据作为影响因素，初步得出RCPs情境下未来土地利用需求，
S42，基于自适应惯性机制的元胞自动机模块，定义自适应系数自动调整每类用地的惯性，该系数根据未来不同情景下的土地利用需求和当前各类用地的实际数量进行多次迭代，最终模拟出RCPs未来土地利用分布。
进一步地，所述S42中的迭代公式为：
Figure PCTCN2022132095-appb-000001
式中：I t p表示t时间段内p类用地类型的惯性系数；D t-1 p为t-1时间段的p类用地需求和当前实际用地数量之差。
进一步地，所述S5中，生境质量数据的计算步骤为：
1)根据土地利用数据和表中数据进行生境退化度计算，表达式为：
Figure PCTCN2022132095-appb-000002
式中，D xj表示生境类型j中第x个生境像元的生境退化度；r为生境的威胁源；y为威胁源r中的栅格；w r为威胁源r的权重；i rxy表示r对生境每个栅格产生的影响；β x表示地方保护政策影响；S jr表示每种生境对不同威胁源的相对敏感程度；
2)生境质量得分表达式为：
Figure PCTCN2022132095-appb-000003
式中，Q xj表示生境类型j中第x个生境像元的生境质量得分，取值范围为[0,1]；H j表示生境类型j的生境适宜度；k和z采用模型默认参数。
进一步地，所述S7中，模拟预测野生保护物种的潜在分布区步骤为：
S71，将收集到的研究区当前的物种的地理分布数据集及环境变量输入MaxEnt模型，通过已知物种出现点的分布数据和环境特征变量，算出目标物种分布的约束条件，探索生态需求下最大熵的可能分布；
S72，通过构建模型将模拟结果投射到研究区域，据此来预测目标物种在研究地的潜在生境分布及适宜性。
进一步地，所述S7中，在包含已知信息的前提下，假设随机变量α，包含A1，A2，A3，…，An共n种可能结果，则其熵值：
Figure PCTCN2022132095-appb-000004
式中，H(α)为熵值，P1，P2，P3，…，Pn为每种出现的概率。
进一步地，于，所述S8中，绘制野生保护植物丰富度图的步骤为：
S81，将每个野生保护物种的最大熵运行阈值结果导入Arc GIS中，通过Arc GIS软件图层属性中自带的阈值划分功能对每个保护物种的丰富度格局进行区间划分，处理为10个区间，将每个区间可视化为不同灰度，灰度与物种丰富度呈负相关；
S82，对所选保护物种的潜在分布图层进行叠加，最后得出研究区野生保护植物物种丰富度图。
本发明的有益效果：能够对研究区域的未来生境质量作出较为准确的预测，并能将其与研究区现有生境质量与物种丰富度格局结合进行综合分析，从而为该区域生境优化策略得出较为科学全面的参考依据。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明的整体方法流程示意图；
图2是本发明实施例研究区域划分示意图；
图3是本发明实施例研究区域气候数据可视化示意图；
图4是本发明实施例研究区域人口密度数据可视化示意图；
图5是本发明实施例研究区域交通区位数据可视化示意图；
图6是本发明实施例研究区域地形数据示意图；
图7是本发明实施例研究与区NDVI数据示意图；
图8是本发明实施例未来RCP2.6、6.0、8.5情景下的土地利用示意图；
图9是本发明实施例当前和未来在RCP2.6情景下的生境质量数值示意图；
图10是本发明实施例研究区域野生保护植物分布点示意图；
图11是本发明实施例MaxEnt模型运行结果示意图；
图12是本发明实施例生境质量综合评价图表。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
如图1所示，一种乡村生境质量评估及预测方法包括以下步骤：
S1，参照国家统计局印发的《统计用区划代码和城乡划分代码编制规则》(国统字〔2009〕91号)，根据国家统计局官网上查询的城乡分类三位数代码，首位代码为1的区域划定为城镇，首位代码为2的区域划定为乡村；在Arcgis10.7中对城市和乡村区域进行划分；
明确研究对象与范围之后，对研究区域进行等距网格划分，选取1km*1km的网格，等距分布于研究区域各部分，并对网格进行编号，以西北角第一个网格为编号1，从左向右、从上向下依次进行编号，1、2、3、...、n。
S2，建立乡村生境质量评估数据库，进行数据预处理；数据库包括：乡村历史土地利用数据、当前社会经济数据(人口、国内生产总值、距市中心距离、路网)、未来预测数据(RCPs情境下各类用地百分比、气候、经济、人口)以及研究区域的23个环境变量数据(主要为19个气候因子数据、地形数据(DEM 高程、坡度、坡向)和NDVI数据)；
其中，乡村历史土地利用数据从地理国情监测云平台获取，下载研究区过去20年历史土地利用数据，精度为30m，在Arc GIS中进行栅格化处理。根据不同省份土地利用情况进行土地利用类型简化；
当前人口数据从欧空局人口分布数据获取，下载研究区目前人口密度利用数据，精度为30m，在Arc GIS中进行栅格化处理；
国内生产总值从各省统计年鉴或全球变化科学研究数据出版系统获取，GDP以区为单位，在Arc GIS中进行栅格化处理；
距市中心距离从世界城市化前景网站(World Urbanization Prospects)获取，下载研究区目前距市中心距离数据，精度为30m，在Arc GIS中进行欧氏距离运算和栅格化处理；
路网从全球道路数据集网站(Global roads open assess data set)获取，下载研究区目前路网数据，精度为30m，在Arc GIS中进行栅格化处理。
未来气候模式采用RCP2.6、RCP6.0和RCP8.5三种排放情景，RCPs情境下温度、降水、日照强度均从WorldClim网站获取；例如，其中RCP2.6情景假设21世纪全球平均温度上升0.2-1.8℃，全球平均降水量增加2.1％；在Arc GIS中对当前温度、降水数据进行更改；
研究区所覆盖的生物气候因子数据从世界气候数据库(http：//www.WordClim.org)获取，共19个气候因子：bio1～bio19，不同研究区覆盖的气候因子数量不同；当前气候模式采用1950-2000年19个生物气候因子，如表1：
表1 生物气候因子数据
Figure PCTCN2022132095-appb-000005
DEM高程数据从地理空间数据云网站获取，下载研究区DEM影像，精度为30m；在Arc GIS中通过Arctoolbox中的Spatital Analyst tools-Surface-Slope和Aspect命令提取坡度、坡向，最终获得研究区域的海拔、坡度、坡向，分辨率均为30m×30m对下载的气候变量数据掩膜提取得到研究区范围内的图层；坐标系统一为WGS1984，栅格大小统一设置，最后转化成ASCII格式的文件用于模型运行；
NDVI(植被指数)数据是根据植被的光谱特征利用卫星可见光和近红外波段探测数据组合而来的，有助于识别植被存在度；首先从地理空间数据云下载Landsat 8影像数据，选取2020年同月份云量少的影像，在ENVI中通过band math计算NDVI，得到NDVI指数栅格图，裁剪出研究区范围，进而得到研究区的NDVI栅格数据，作为环境因子用于模型预测；坐标系统一为WGS1984，栅格大小统一设置，最后转化成ASCII格式的文件用于模型运行。
S3，根据Flus(未来土地利用模拟模型)模型要求，将历史土地利用、区位、自然环境、社会经济、气候因素作为影响数据，利用模型中人工神经网络的适宜性概率估算模块，筛选出训练样本，确定土地利用变化因子X，输入神经网络得出各类用地的出现概率；
数据输入Flus模型后，在基于人工神经网络的适宜性概率估算模块，采用 随机采样的方法筛选出训练样本，确定驱动土地利用变化的因子X，作为神经网络中的输入层神经元(x i，i＝1,2,…,n)，公式为：
X＝(x 1(1),x 2(1),…,x n(1)) T    (1)
式中Xi为第1个采样点抽取的第i个驱动银子的变量，T为转置矩阵；
输出层中每个神经元都将生成一个0～1之间的值，该数值表示该像元发展为某类用地类型的概率，其值越高表示未来发展成该类用地的可能性越大；得出关于未来RCP2.6、4.5、8.5情境下各类用地出现概率的图像，该结果用于下一步骤。
S4，将RCPs情境下未来气候预测数据、历史土地利用数据作为影响因素，初步得出RCPs情境下未来土地利用需求，在基于自适应惯性机制的元胞自动机模块，定义自适应系数自动调整每类用地的惯性，该系数根据未来不同情景下的土地利用需求和当前各类用地的实际数量进行多次迭代，如式(2)所示，最终模拟出RCPs未来土地利用分布：
Figure PCTCN2022132095-appb-000006
式中：I t p表示t时间段内p类用地类型的惯性系数；D t-1 p为t-1时间段的p类用地需求和当前实际用地数量之差。
S5，将Flus模型得出的土地利用的栅格数据输入InVEST(生态系统服务和权衡的综合评估模型)模型，根据乡村各类土地的特点，定义InVEST模型所需的威胁因子类型、胁迫距离、相对敏感度三个影响因素，运行InVEST得出生境质量数据及威胁源分布数据；具体步骤为：
参照国土资源部修订的《土地利用现状分类》(GB/T 21010—2017)，我 国土地现状分类类型包括耕地、园地、林地、草地、商服用地、工矿仓储用地、住宅用地、公共管理与公共服务用地、特殊用地、交通运输用地、水域及水利设施用地、其他土地。因耕地、商服用地、工矿仓储用地、住宅用地、公共管理与公共服务用地、特殊用地、交通运输用地、其他土地(其他农用地)8种地类人类活动剧烈，将其作为非生境地类；而园地、林地、草地和水域及水利设施用地受人类活动影响较小的地类则作为生境地类；非生境地类在InVEST模型中列为威胁因子，对其量化分析：
第一个因子是每一种威胁源的对所有生境的相对破坏性；根据不同威胁类型对生境的破坏性给予退化源权重wr，能够选取0到1的任何一个数值，如表2所示：
表2 威胁源权重
Figure PCTCN2022132095-appb-000007
第二个因子是威胁源对各类生境的影响距离；威胁的程度随栅格与威胁源距离的增加而减小，因此距离威胁最近的那些栅格单元将受到较高的影响；根据InVEST规定，有线性和指数距离衰减两个函数来描述威胁在空间的衰减，如表3所示：
表3 威胁源对各类生境的影响距离
Figure PCTCN2022132095-appb-000008
第三个因子是每一种生境类型对每一种威胁源的相对敏感性，用于修正上一步计算的总影响；如表4所示：
表4 威胁源的相对敏感性
Figure PCTCN2022132095-appb-000009
根据研究区域存在的威胁源和生境类型制作表格(表5、表6)，并将各类威胁源的历史分布数据和未来分布数据在Arc GIS中进行栅格化处理，导出tif格式数据，将表格和tif数据一起输入InVEST模型；
表5 威胁源数据
Figure PCTCN2022132095-appb-000010
表6 威胁源对各类用地影响
Figure PCTCN2022132095-appb-000011
根据土地利用数据和表中数据进行生境退化度计算，表达式为：
Figure PCTCN2022132095-appb-000012
式中，D xj表示生境类型j中第x个生境像元的生境退化度；r为生境的威胁源；y为威胁源r中的栅格；w r为威胁源r的权重；i rxy表示r对生境每个栅格产生的影响(线性或指数)；β x表示地方保护政策等影响，对最终结果影响较小；S jr表示每种生境对不同威胁源的相对敏感程度。
生境质量得分表达式为：
Figure PCTCN2022132095-appb-000013
式中，Q xj表示生境类型j中第x个生境像元的生境质量得分，取值范围为[0,1]；H j表示生境类型j的生境适宜度；k和z采用模型默认参数。
S6，以选取的代表物种(野生保护植物)建立研究区域的物种地理分布数据集，最终用于模型模拟；
从中国植物主题数据库(http://www.plant.csdb.cn/)获取野生保护植物名录；通过全球生物多样性信息网络(Global Biodiversity Information Facility，https://www.gbif.org/)获得各个物种的地理分布数据，将记录数少于五条的物种剔除，最终得到达到模型运行要求的物种，导入Excel中进行整理，去除重复点；将整理好的该省野生保护植物物种数据导入Arc GIS中，通过叠加分析得到研究区域内的野生保护植物分布点数据；将该数据导出，输入到Excel表中，并 另存为*.csv格式，形成研究区域的物种地理分布数据集，每组数据包括该物种的学名及其分布点，具体到经纬度。
S7，利用收集到的研究区当前的物种的地理分布数据集及上述23个环境变量驱动MaxEnt(最大熵模型)模型，在RCPs情境下，分别对野生保护物种的潜在分布区进行模拟预测；
模型通过已知物种“出现点”的分布数据和环境特征变量，依据相应算法运算出目标物种分布的约束条件，探索生态需求下最大熵的可能分布，熵最大时物种的概率分布即满足物种生境条件的空间范围；通过构建模型将模拟结果投射到研究区域，据此来预测目标物种在研究地的潜在生境分布及适宜性；在包含已知信息的前提下，熵值最大时，冗余信息被排除，假设随机变量α，包含A1，A2，A3，…，An共n种可能结果，则其熵值：
Figure PCTCN2022132095-appb-000014
式中，H(α)为熵值，P1，P2，P3，…，Pn为每种出现的概率。
S8，提取模型预测结果阈值，绘制野生保护植物丰富度格局，利用自然断点法分为10个等级，得到野生保护物种丰富度的空间分布的等级；
首先，将每个野生保护物种的最大熵运行阈值结果导入Arc GIS中，通过Arc GIS软件图层属性中自带的阈值划分功能对每个保护物种的丰富度格局进行区间划分，处理为10个区间，将每个区间可视化为不同灰度，灰度与物种丰富度呈负相关；其次，对所选保护物种的潜在分布图层进行叠加，最后得出研究区野生保护植物物种丰富度图。
S9，综合分析InVEST模型和MaxEnt模型评价结果，将两种模型模拟的生境质量分布现状及未来预测和物种丰富度分布格局进行叠加，按照叠加所得的生境质量综合评价图表，将生境质量的综合评价等级由高到低共划分为10个 等级，灰度越小，数值越大，等级越高，则说明研究区生物多样性综合状况越好；
识别在未来不同的发展情景下，研究区最佳生境范围和生境优化重点区域，分析生境空间优先保护区域，最终为未来研究区生境优化策略提供较为科学的参考依据。
实施例：
本发明利用基于Maxent和InVEST模型的生境质量评估，在三种RCP情景下，通过模型的组合分析以得出研究区域生境质量的综合评估、预测研究区域生境质量在不同RCP情景下的未来状况，并以此为依据提出研究区具体的生境优化策略；
下面以某省为例，RCPs情景下基于Maxent和InVEST模型的生境质量评估的方法包括：
S1：参照国家统计局印发的《统计用区划代码和城乡划分代码编制规则》(国统字〔2009〕91号)，根据国家统计局官网上查询的城乡分类三位数代码，代码第一位为1的划分为城镇，第一位为2的划分为乡村；在Arcgis10.7中对该省城市和乡村区域进行划分；得到某省乡村用地区域，如图2所示。
S2：建立模型数据库，进行数据预处理；数据分为三类，分别应用于FLUS、InVEST、Maxent三个模型。
应用于FLUS模型的数据有：
1)该省历史土地利用数据(J):J12000年该省历史土地利用数据、J22005年该省历史土地利用数据、J32010年该省历史土地利用数据；
2)气候数据(Q)，作为FLUS模型驱动力因子：Q1月平均气温、Q2年平均温差、Q3年均降水量、Q4太阳辐射强度；如图3所示；
3)社会经济数据(S)，作为FLUS模型驱动力因子：S1该省分市地区生产总值、S2该省人口密度；如图4所示
4)交通区位数据(T):T1该省路网、T2到市中心距离，T3到城镇中心距离、T4到高速公路距离、T5到主干道距离、T6到铁路距离；如图5所示
未来预测数据(W)：RCP4.5、RCP6.0和RCP8.5情境下W1温度变化、W2降水变化、W3日照强度变化。
应用于InVEST模型的数据有：
威胁数据(X)：X1威胁因子，X2胁迫距离,X3各类土地对威胁因子的相对敏感度。
应用于MAXENT模型的数据有：
1)研究区国家重点野生保护植物地理分布数据；
2)19个气候因子数据；
3)研究区DEM高程数据；如图6所示；
4)研究区NDVI数据；如图7所示。
S3：利用上述数据库，按照FLUS使用手册说明定义气候、社会经济、交通区位定义为驱动力因子，利用该省2000、2005、2010年历土地利用数据，在Flus模型中人工神经网络的适宜性概率估算模块，采用随机取样的方法筛选出训练样本，确定驱动土地利用变化的因子X，作为神经网络的输入层神经元(xi，i＝1,2,…,n)：
X＝(x1(1),x2(1),…,xn(1))T      (1)
式中Xi为第1个采样点抽取的第i个驱动银子的变量，T为转置矩阵。
输出层中每个神经元都将生成一个0～1之间的值，该数值表示该像元发展为某类用地类型的概率，其值越高表示未来发展成该类用地的可能性越大。得 出关于未来RCP 2.6、6.0、8.5情境下各类用地出现概率的图像，该结果用于S4。
S4：按照FLUS使用手册说明定义RCPs情境下各类用地百分比、未来人口经济数据，未来气候变化数据、历史土地利用数据作为影响因素，初步得出RCPs情境下未来土地利用需求。在基于自适应惯性机制的元胞自动机模块，输入步骤3得出的适宜性概率数据，输入RCPs情境下未来土地利用需求作为土地利用类型变化数量的目标，运行元胞自动机。元胞自动机将根据未来不同情景下的土地利用需求和当前各类用地的实际数量进行多次迭代(式2)，最终模拟出未来RCP 2.6、6.0、8.5情景下的土地利用分布；如图8所示；
Figure PCTCN2022132095-appb-000015
式中：I t p表示t时间段内p类用地类型的惯性系数；D t-1 p为t-1时间段的p类用地需求和当前实际用地数量之差。
S5：参照国家标准化管理委员会和中华人民共和国国家质量监督检验检疫局引发的《土地利用现状分类》(GB/T 21010—2017)，我国土地现状分类类型包括耕地、园地、林地、草地、商服用地、工矿仓储用地、住宅用地、公共管理与公共服务用地、特殊用地、交通运输用地、水域及水利设施用地、其他土地。因耕地、商服用地、工矿仓储用地、住宅用地、公共管理与公共服务用地、特殊用地、交通运输用地、其他土地(其他农用地)8种地类人类活动剧烈，将其作为非生境地类；而园地、林地、草地和水域及水利设施用地受人类活动影响较小的地类则作为生境。选择该省广泛拥有的用地类型作为数据，其中非生境地类在InVEST模型中列为威胁因子，将各类威胁源的历史分布数据和未来分布数据在Arc GIS中进行栅格化处理，导出tif格式文件，将表格和tif 数据一起输入InVEST模型。(表7、表8)
表7 输入InVEST的threat文件
Figure PCTCN2022132095-appb-000016
表8 输入InVEST的sensitivitu文件
Figure PCTCN2022132095-appb-000017
根据土地利用数据和表中数据进行生境退化度计算，表达式为：
Figure PCTCN2022132095-appb-000018
式中，D xj表示生境类型j中第x个生境像元的生境退化度；r为生境的威胁源；y为威胁源r中的栅格；w r为威胁源r的权重；i rxy表示r对生境每个栅格产生的影响(线性或指数)；β x表示地方保护政策等影响，对最终结果影响较小；S jr表示每种生境对不同威胁源的相对敏感程度。
生境质量得分表达式为：
Figure PCTCN2022132095-appb-000019
式中，Q xj表示生境类型j中第x个生境像元的生境质量得分，取值范围为[0,1]；H j表示生境类型j的生境适宜度；k和z采用模型默认参数；最终得出该未来情景下的生境质量数值分布，如图9所示。
S6：从中国植物主题数据库(http://www.plant.csdb.cn/)获取该省野生保护 植物名录，共13种。通过全球生物多样性信息网络(Global Biodiversity Information Facility，https://www.gbif.org/)获得该13个物种的地理分布数据，将记录数少于五条的物种剔除，最终得到达到模型运行要求的物种共11种，导入Excel中进行整理。将整理好的该省野生保护植物物种数据导入Arc GIS中，通过叠加分析得到研究区域内的野生保护植物分布点数据，如图10所示。将该数据导出，输入到Excel表中，并另存为*.csv格式，形成研究区域的物种地理分布数据集，每组数据包括该物种的学名及其分布点，具体到经纬度。
S7：将所收集的研究区域当前的生物气候变量数据、高程数据和NDVI数据，输入MaxEnt模型，模型通过已知物种“出现点”的分布数据和环境特征变量，依据相应算法运算出目标物种分布的约束条件，探索生态需求下最大熵的可能分布，熵最大时物种的概率分布即满足物种生境条件的空间范围。通过构建模型将模拟结果投射到研究区域，据此来预测目标物种在研究地的潜在生境分布及适宜性。在包含已知信息的前提下，熵值最大时，冗余信息被排除，假设随机变量α，包含A1，A2，A3，…，An共n种可能结果，则其熵值：
Figure PCTCN2022132095-appb-000020
式中，H(α)为熵值，P1，P2，P3，…，Pn为每种出现的概率。
S8：首先，将每个野生保护物种的最大熵运行阈值结果(如图11所示)，导入Arc GIS中，通过Arc GIS软件图层属性中自带的阈值划分功能对每个保护物种的丰富度格局进行区间划分，处理为10个区间，将每个区间可视化为不同灰度，灰度与物种丰富度呈负相关。其次，对所选保护物种的潜在分布图层进行叠加，最后得出研究区野生保护植物物种丰富度图。
S9：对InVEST模型进行与S8相同的处理，与研究区野生保护植物物种丰富度图进行灰度叠加，作为生物多样性的代表。按照叠加所得的生境质量综合 评价图表，如图12所示；将生境质量的综合评价等级由高到低共划分为10个等级，灰度越小，数值越大，等级越高，则说明研究区生物多样性综合状况越好。
在此图表基础上，结合现有乡村区域筛选得到生物多样性保护优先地区，分析生境空间优先保护区域，最终为乡村区域生境优化提供直接有效的参考依据。
在本说明书的描述中，参考术语“一个实施例”、“示例”、“具体示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
以上显示和描述了本发明的基本原理、主要特征和本发明的优点。本行业的技术人员应该了解，本发明不受上述实施例的限制，上述实施例和说明书中描述的只是说明本发明的原理，在不脱离本发明精神和范围的前提下，本发明还会有各种变化和改进，这些变化和改进都落入要求保护的本发明范围内。

Claims (10)

  1. 一种乡村生境质量评估及预测方法，其特征在于，包括以下步骤：
    S1，对研究区域进行等距网格划分；
    S2，建立乡村生境质量评估数据库，数据库包括：乡村历史土地利用数据、当前社会经济数据、RCPs情境下的未来预测数据以及环境变量数据；
    S3，建立Flus模型，将乡村历史土地利用数据作为影响数据，利用Flus模型筛选出训练样本，确定土地利用变化因子X，并得出各类用地的出现概率；
    S4，将RCPs情境下未来气候预测数据和历史土地利用数据作为影响因素，在Flus模型内多次迭代模拟出，未来不同RCPs情境下的土地利用分布的栅格数据；
    S5，将Flus模型得出的土地利用的栅格数据输入InVEST模型，来得出生境质量数据及威胁元分布数据；
    S6，选取代表物种，并建立研究区域的物种地理分布数据集；
    S7，利用S6得到的物种地理分布数据集及S1中的环境变量驱动MaxEnt模型，在RCPs情境下，分别对野生保护物种的潜在分布区进行模拟预测；
    S8，提取模型预测结果阈值，绘制野生保护植物丰富度图，得到野生保护物种丰富度的空间分布的等级；
    S9，将生境质量分布现状及未来预测和物种丰富度分布图进行叠加，得到生境质量综合评价图表。
  2. 根据权利要求1所述的一种乡村生境质量评估及预测方法，其特征在于，所述S2中，当前社会经济数据包括：人口数据、国内生产总值、距市中心距离以及路网；
    未来预测数据包括各类用地百分比、气候、经济以及人口；
    环境变量数据包括：19个气候因子数据、DEM高程数据、坡度数据、坡向数据以及NDVI数据。
  3. 根据权利要求1所述的一种乡村生境质量评估及预测方法，其特征在于，所述S3中，将历史土地利用、区位、自然环境、社会经济、气候因素作为影响数据，利用模型中人工神经网络的适宜性概率估算模块，筛选出训练样本，确定土地利用变化因子X，输入神经网络得出各类用地的出现概率。
  4. 根据权利要求3所述的一种乡村生境质量评估及预测方法，其特征在于，所述S3中，确定土地利用变化因子X的公式为：
    X＝(x 1(1),x 2(1),…,x n(1)) T   (1)
    式中X i为第1个采样点抽取的第i个驱动银子的变量，T为转置矩阵。
  5. 根据权利要求1所述的一种乡村生境质量评估及预测方法，其特征在于，所述S4中，模拟出RCPs未来土地利用分布的步骤包括：
    S41，将RCPs情境下未来气候预测数据、历史土地利用数据作为影响因素，初步得出RCPs情境下未来土地利用需求，
    S42，基于自适应惯性机制的元胞自动机模块，定义自适应系数自动调整每类用地的惯性，该系数根据未来不同情景下的土地利用需求和当前各类用地的实际数量进行多次迭代，最终模拟出RCPs未来土地利用分布。
  6. 根据权利要求5所述的一种乡村生境质量评估及预测方法，其特征在于，所述S42中的迭代公式为：
    Figure PCTCN2022132095-appb-100001
    式中：I t p表示t时间段内p类用地类型的惯性系数；D t-1 p为t-1时间段的p类用地需求和当前实际用地数量之差。
  7. 根据权利要求1所述的一种乡村生境质量评估及预测方法，其特征在于，所述S5中，生境质量数据的计算步骤为：
    1)根据土地利用数据和表中数据进行生境退化度计算，表达式为：
    Figure PCTCN2022132095-appb-100002
    式中，D xj表示生境类型j中第x个生境像元的生境退化度；r为生境的威胁源；y为威胁源r中的栅格；w r为威胁源r的权重；i rxy表示r对生境每个栅格产生的影响；β x表示地方保护政策影响；S jr表示每种生境对不同威胁源的相对敏感程度；
    2)生境质量得分表达式为：
    Figure PCTCN2022132095-appb-100003
    式中Q xj表示生境类型j中第x个生境像元的生境质量得分，取值范围为[0,1]；H j表示生境类型j的生境适宜度；k和z采用模型默认参数。
  8. 根据权利要求1所述的一种乡村生境质量评估及预测方法，其特征在于，所述S7中，模拟预测野生保护物种的潜在分布区步骤为：
    S71，将收集到的研究区当前的物种的地理分布数据集及环境变量输入 MaxEnt模型，通过已知物种出现点的分布数据和环境特征变量，算出目标物种分布的约束条件，探索生态需求下最大熵的可能分布；
    S72，通过构建模型将模拟结果投射到研究区域，据此来预测目标物种在研究地的潜在生境分布及适宜性。
  9. 根据权利要求8所述的一种乡村生境质量评估及预测方法，其特征在于，所述S7中，在包含已知信息的前提下，假设随机变量α，包含A1，A2，A3，…，An共n种可能结果，则其熵值：
    Figure PCTCN2022132095-appb-100004
    式中，H(α)为熵值，P1，P2，P3，…，Pn为每种出现的概率。
  10. 根据权利要求9所述的一种乡村生境质量评估及预测方法，其特征在于，所述S8中，绘制野生保护植物丰富度图的步骤为：
    S81，将每个野生保护物种的最大熵运行阈值结果导入Arc GIS中，通过Arc GIS软件图层属性中自带的阈值划分功能对每个保护物种的丰富度格局进行区间划分，处理为10个区间，将每个区间可视化为不同灰度，灰度与物种丰富度呈负相关；
    S82，对所选保护物种的潜在分布图层进行叠加，最后得出研究区野生保护植物物种丰富度图。
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