CN115860487B - Method for evaluating local vegetation change risk based on vegetation stability risk index - Google Patents
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Abstract
The invention discloses a method for evaluating local vegetation change risk based on vegetation stability risk indexes. Aiming at the defects of more influence factors, difficult accurate quantification, lack of quantification of ecological risk space scale and the like of the existing ecological risk evaluation method, the invention focuses on the main factors such as vegetation self status, temperature range leveling, precipitation range leveling, population density, space planning and the like which influence the local vegetation change, provides a vegetation stability risk index model, and improves the space precision for identifying the local vegetation change risk by quantitatively analyzing the space information of risk factors, risk receptors and exposure degree which cause the vegetation change. The method utilizes a space analysis technology to quantify the local vegetation change risk value, grade and space scale on the cell and area scale, makes up the defect that the existing vegetation ecological risk lacks space scale information, and provides more accurate vegetation change risk space distribution information and grade scale information for monitoring and protecting a biological system.
Description
Technical Field
The invention relates to the technical fields of botanic, ecology, weather climate, geography, remote sensing, geographic information systems and the like, in particular to a method for evaluating local vegetation change risk based on vegetation stability risk indexes.
Background
The vegetation change is limited by various factors, and the vegetation change risk is often caused by adverse effects of human activities, environmental changes and the like on the vegetation composition, structure and function, and comprises risks caused by uncertain human accidents or natural disasters, and risks caused by human activities and progressive environmental changes. The risk source of early ecological risk assessment is mainly analyzed by the possibility of accident occurrence, chemical pollution is emphasized more, no explicit risk receptor, exposure assessment and risk characterization exist, and the whole assessment process is mainly analyzed by simple qualitative analysis. After the 90 s of the 20 th century, the risk factors extended to events that could cause ecological risk, gradually evolving from the initial single-factor single-risk assessment to a multi-factor multi-risk assessment. Risk receptors also develop from humans to populations, communities, ecosystems. Traditional ecological risk assessment includes hazard assessment, exposure assessment, receptor analysis, risk characterization, risk comprehensive assessment and the like, but ecological risk assessment emphasizes risk source identification and description of ecological risk. The official promulgation of the ecological risk assessment guidelines in USEPA1998 suggests that ecological risk analysis includes ecological exposure analysis, ecosystem and receptor profiling.
Most of the existing ecological risk evaluation methods only give the risk degree, and quantification of the ecological risk space grade scale is lacking. For example, the 2022 world natural protection alliance (IUCN) issued guidelines for applying ecosystem risk assessment science to ecosystem restoration, suggests that the IUCN ecosystem red directory be applied to ecosystem restoration, reflects the risk of an ecosystem using five assessment metrics (i.e., habitat-wide degradation, abiotic environment degradation, biological process degradation, habitat restriction distribution, threat quantification analysis), and classifies ecosystems into easily understood risk categories (crashes, extremely endangered, fragile, near-endangered, and endangered).
Because the risk source, the ecosystem and the like have spatial heterogeneity in the region, the spatial diversity of the risk of a specific element (vegetation and soil) is difficult to reflect by the simple ecological risk evaluation on the region scale, and the space-time precision is low.
In conclusion, the existing ecological risk evaluation methods are more, but accurate quantification is difficult, and the defects of quantification of the ecological risk space scale and the like are lacked.
Disclosure of Invention
The invention aims to provide a method for evaluating local vegetation change risk based on a vegetation stability risk index. And the spatial precision of vegetation change risk identification is improved by accurately identifying the spatial information of risk factors, risk receptors and exposure. On the basis, the change risk value, the grade and the space scale are quantized, the positive change and the negative change are distinguished, the problem that the existing ecological risk evaluation lacks space information and grade scale information is solved, and more accurate vegetation change risk space distribution information and grade scale information are provided for monitoring and protecting a biological system.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the invention provides a method for evaluating local vegetation change risk based on vegetation stability risk indexes, which comprises the following steps:
s1, selecting a risk receptor, a risk factor and an exposure index of local vegetation change risk in a research area, and constructing a vegetation stability risk index model;
s2, carrying out unified preprocessing on the data format, the data coordinate system and the resolution ratio of different sources and different format index data according to the unified research area range;
s3, carrying out grid operation on the preprocessed data by using spatial data processing software according to different index calculation formulas to obtain different risk assessment indexes; the different indices include: spatial information of risk receptor index, risk factor index, and exposure;
s4, substituting the different indexes into a vegetation stability risk index model, and grading local vegetation change risks in a research area according to an output result of the vegetation stability risk index model;
s5, determining the vegetation change risk scale of the research area according to the relation between the cell grade division and the vegetation area according to the grade division result.
Further, the step S1 includes:
constructing a vegetation stability risk index model:
R=B×C×F
wherein R is vegetation stability risk index; b is a risk receptor index; c is vegetation coverage, and is used as space information of exposure; f is a risk factor index.
Further, the step S2 includes:
the step S2 includes:
s21, generating a cell fishing net with the size of 1000m multiplied by 1000m in geographic information system software according to the spatial range of a research area;
s22, performing spatial cutting, rasterization, coordinate system conversion and resampling on each evaluation index corresponding to the key factors according to the range of the fishing net; and realizing that the data space range, format, coordinate system and spatial resolution of each evaluation index are consistent.
Further, the step S3 includes:
and calculating each index data after pretreatment according to a risk receptor index formula, a risk factor index formula, a spatial information formula of exposure and a vegetation stability risk index model to obtain a corresponding output result.
Further, the risk receptor index formula is as follows:
wherein B is a risk receptor index of vegetation change risk of a research area, A is each grid area of the research area;
vegetation area for each grid.
Further, the risk factor index is obtained by:
1) Obtaining human driving factors of a research area; the human driving factor includes: population density index and space planning index;
wherein the population density index is calculated as follows:
extracting maximum and minimum population density values of the estimated year, and performing grid operation according to the following formula:
wherein D is a population density index;
evaluating annual population density for grid point i;
To assess annual study area population density maxima;
To evaluate annual study area population density minima; />
The space planning factor index DP is assigned as follows: assigning 1 to the range in the town development boundary, assigning 0.1 to the range in the ecological protection red line, and assigning 0.6 to the other ranges;
2) Acquiring natural factors of a research area: the natural factors include: a precipitation factor index and a temperature factor index;
wherein, the precipitation factor index is calculated as follows:
wherein P is a precipitation risk factor index;
evaluating annual growth season precipitation for grid point i;
Average value of precipitation for many years of growth of grid point i,/, for grid point i>
Maximum value of precipitation for many years of growth of grid point i, < >>
The minimum precipitation amount of the grid point i in the growing season for many years;
the temperature factor index is calculated as follows:
wherein T is a temperature factor index;
annual growth is assessed for grid point i Ji Junwen;
For the grid point i years the average value of the temperature, +.>
Ji Junwen maximum for annual growth of grid point i, +.>
Ji Junwen minimum for annual growth of grid point i;
3) Calculating a risk factor index:
adding and calculating each risk factor index according to the following formula to obtain a risk factor index:
F=D+DP+P+T
wherein F is a risk factor index; d is a population density index; DP is the space planning factor index; p is the precipitation factor index; t is a temperature factor index.
Further, the spatial information of the exposure is quantified by vegetation coverage of each grid point according to the following formula:
wherein, C is vegetation coverage; NDVI is a grid point estimated annual growth season normalized vegetation index;
evaluating annual growth season NDVI maximum for the study area;
Annual growth season NDVI minima were assessed for the study area.
Compared with the prior art, the invention has the following beneficial effects:
aiming at the defects of more influence factors, difficult accurate quantification, lack of scale quantification of ecological risk space and the like of the traditional ecological risk evaluation method, the method for evaluating the local vegetation change risk based on the vegetation stability risk index provided by the embodiment of the invention focuses on the main factors influencing the vegetation change, provides a vegetation stability risk index model, and improves the vegetation change risk space precision by accurately identifying the space information of risk factors, risk receptors and exposure. And quantifying vegetation change risk values, grades and space scales on the cell and region scales by using a space analysis technology so as to distinguish positive change risk regions from negative change risk regions on the basis, and formulating a targeted management strategy. The defect that the existing vegetation ecological risk lacks space scale information is overcome, and more accurate vegetation change risk space information is provided for monitoring and protecting a biological system.
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FIG. 1 is a flowchart of a method for evaluating local vegetation change based on a vegetation stability risk index according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a vegetation stability risk identification and assessment index system according to an embodiment of the present invention.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
With the maturation of space analysis technologies such as remote sensing and GIS, a plurality of mathematical modeling are combined, and technical support is provided for simultaneously carrying out ecological risk evaluation on the area and grid scale. Based on this, referring to fig. 1, the method for evaluating local vegetation change based on the vegetation stability risk index provided by the invention comprises the following steps:
s1, selecting a risk receptor, a risk factor and an exposure index of local vegetation change risk in a research area, and constructing a vegetation stability risk index model;
s2, carrying out unified preprocessing on the data format, the data coordinate system and the resolution ratio of different sources and different format index data according to the unified research area range;
s3, carrying out grid operation on the preprocessed data by using spatial data processing software according to different index calculation formulas to obtain different risk assessment indexes; the different indices include: spatial information of risk receptor index, risk factor index, and exposure;
s4, substituting the different indexes into a vegetation stability risk index model, and grading local vegetation change risks in a research area according to an output result of the vegetation stability risk index model;
s5, determining the vegetation change risk scale of the research area according to the relation between the cell grade division and the vegetation area according to the grade division result.
According to the embodiment of the invention, the main influencing factors of vegetation change are focused by utilizing space analysis technologies such as remote sensing, GIS and the like, the vegetation stability risk index is provided, the space precision of the existing vegetation change risk assessment is improved, the number, grade, scale and occurrence area of vegetation change risks are accurately quantified on the grid and area scale, and more accurate vegetation change risk space information is provided for monitoring and protecting a ecological system.
In step S3, grid operation is performed on each index data after pretreatment by using different index calculation formulas, so as to obtain different indexes; comprising the following steps: the risk receptor index, the risk factor index and the exposure index are calculated, and the architecture is shown in figure 2;
1. risk receptor index
Risk receptors refer to the vegetation of the study area. The risk receptor index B is quantified by the vegetation (herein referred to as woodland) area of each grid point occupying the grid area (which can be calculated by equation (1)). The vegetation area of each grid point is obtained by the following steps: generating a unit cell fishing net with the size of 1000m multiplied by 1000m by utilizing ArcGIS; position selection (intersection of fishing net and vegetation distribution); leading out a fishing net layer; obtaining vegetation areas of each grid through regional statistics; and (5) correlating with the fishing net layer to obtain the vegetation area layer of each lattice point.
Wherein B is the risk receptor index of vegetation in the investigation region, A is the grid area (m 2 ），
Vegetation area (m) for each grid point 2 ）。
The specific operation is as follows:
step1, extracting a vegetation distribution range of a research area by using a mask;
step2, establishing a fishing net (a range selection grid research area, setting the pixel height and width according to the needs and selecting a geometric type selection surface);
step3, selecting a required grid (namely eliminating redundant grids) by utilizing the vegetation distribution range of the research area and the intersection of the fishing net according to the position;
step4, deriving a layer (deriving a fishing net);
step5, calculating various vegetation type areas of each grid;
firstly, extracting vegetation distribution data of a research area sequentially by utilizing attribute extraction; and then calculating the vegetation type area of each cell through regional statistical analysis. (the specific operation is as follows, in the Arcmap software, a space analysis-a table display partition statistical method is selected, firstly, a derived fishing net is selected, a field is selected for FID, grid data of different vegetation types in a research area are assigned for grid selection, the table name calculated by a system, namely the vegetation type area of a cell, and the statistical type is selected for ALL);
step6, correlating, namely connecting the vegetation area data of each cell with a fishing net by using the FID number, and calculating the vegetation area of each grid/the grid area of the fishing net by using the attribute to obtain the area percentage of the risk receptor.
2. Risk factor index
The risk factors are various factors that cause vegetation to change, i.e., risk factors. Key risk factors for each grid point include human driving factors (population density, space planning) and natural driving factors (precipitation factor index, temperature factor). The human driving force is characterized by two factors, namely human mouth density and space planning, and is calculated by a formula (2) under the condition that population quality is consistent, the population density is higher, and the human interference risk is higher. The interference risk of town space and agricultural space in space planning is large, and the interference risk of ecological space is small. The natural driving factors are quantified by the absolute value of the distance between precipitation in the growing season and the temperature in the growing season, and the larger the absolute value of the distance is, the larger the risk is; obtained by the formula (3) and the formula (4), respectively. The spatial interpolation method of the point data can be adopted to obtain the related raster data. And adding the risk factor indexes to obtain the risk factor index.
The human driving factors comprise population density indexes and space planning indexes, and are obtained by respectively carrying out correlation processing on population density data and space planning data, and the specific steps are as follows:
step1 population Density raster data production
Population density: average population living on thousands of meters of land per square. In this embodiment, kilometer grid demographics data is referred to. The statistical population of the administrative area serving as a basic unit is expanded to a grid with a certain spatial scale (1 km), the spatial characteristics and the regional differences of population distribution are comprehensively analyzed, and the population division is calculated. And establishing a multivariate population space model in each partitioned subarea to carry out population space. Specifically referring to the population density space data set production method provided by the national science and technology foundation condition platform-national earth system science data center.
Step2: population density factor index calculation
Population density data of the estimated year is extracted, population density maximum values and minimum values of the research area are extracted, and then grid operation is carried out in software according to a formula (2).
Wherein D is a population density index;
evaluating annual population density for grid point i;
To assess annual study area population density maxima;
To evaluate annual study area population density minima;
step3: space planning factor index DP (Development Plan)
In the process of integrating multiple regulations, the domestic land space is uniformly planned according to the urban development boundary, the permanent basic farmland red line and the ecological protection red line, wherein the range in the urban development boundary in the domestic land space planning of the research area is assigned with 1, the range in the ecological protection red line is assigned with 0.1, and other assignments are assigned with 0.6. The specific operation is that vector data such as town development boundaries, ecological protection red line boundaries and the like are merged into a grid or vector layer in geographic information system software, grid reclassification or vector attribute assignment is carried out according to different planning space types, and then reclassified grid or vector data conversion process is consistent with 1000m multiplied by 1000m grid data of a research area range and a data base.
Step1: precipitation factor index calculation
Spatial interpolation is carried out on the annual growth season precipitation estimated by the research area and the annual precipitation average value of the annual growth season, the spatial interpolation is processed into gridding processing with the resolution of 1000m multiplied by 1000m, the maximum value and the minimum value in the annual precipitation average value of each cell growth season are extracted, then grid operation is carried out, and the operation is carried out according to the formula (3):
wherein P is an annual precipitation factor index;
evaluating annual growth season precipitation for grid point i;
Average value of years precipitation for growing season for grid point i, < >>
Maximum value of years precipitation for growing season for grid point i, < >>
Growing a minimum amount of precipitation for a plurality of years for the grid point i;
step2: temperature factor index calculation
Spatial interpolation is carried out on the estimated annual growth season temperature value of the research area and the annual temperature average value of the growth season, the spatial interpolation is processed into gridding processing with the resolution of 1000m multiplied by 1000m, the maximum value and the minimum value in the annual average value are extracted, then grid operation is carried out, and the operation is carried out according to the formula (4):
wherein T is a temperature factor index;
annual growth is assessed for grid point i Ji Junwen;
For the average of the annual temperature of the grid points, +.>
For annual growth of grid pointsMaximum temperature->
Ji Junwen minimum for grid point annual growth;
And (3) adding and calculating each risk factor index according to the formula (5) to obtain the risk factor index.
Using the formula
F=D+DP+P+T （5）
Wherein F is a risk factor index; d is a population density index; DP is the space planning factor index; p is the precipitation factor index; t is a temperature factor index.
3. Exposure index
The exposure is quantified by vegetation coverage of each grid point and calculated by formula (6). Vegetation coverage refers to vegetation (including leaves, stems, branches) on the ground
The percentage of the total area of the statistical zone. The exposure degree is expressed by the vegetation coverage, so that the defect that the tiny open land in the vegetation area is also used as a disaster-bearing body when the whole grid is a vegetation area is avoided, and the exposure degree of the vegetation can be reflected more truly.
And extracting vegetation coverage by adopting a classical pixel bipartite model. Namely, the pixel normalization vegetation index NDVI under the condition of covering all vegetation is utilized
) And picture element NDVI (& lt) in the case of a full bare soil background>
) And (3) linearly decomposing the pixel NDVI to obtain vegetation coverage (FVC). The specific operation method comprises the following steps: obtaining the maximum value and the minimum value of NDVI in a research area (or in a period of time in the research area) by means of an Arcmap area statistical method, and taking the maximum value and the minimum value as +.>
Value sum->
Values.
Wherein, C is vegetation coverage; NDVI is normalized vegetation index;
NDVI maximum for the study area;
is the minimum NDVI for the study area.
4. Vegetation stability risk index
The vegetation stability risk index of each grid point is quantified by the product of a risk receptor, exposure and a risk factor, and is obtained through calculation of a formula (7), so that an ecological risk distribution map can be manufactured.
R=B×C×F （7）
Wherein R is vegetation stability risk index (threshold [0, 4 ]), B is risk receptor index, C is vegetation coverage (exposure), and F is risk factor index.
In the step S4, the local vegetation change risk level
Substituting different indexes into the vegetation stability risk index model, and grading the local vegetation change risk of the research area according to the output result of the vegetation stability risk index model. And comprehensively determining according to a natural breakpoint method and historical experience values (grading boundary points can be adjusted according to actual risk values of a research area), dividing ecological risks into 4 risk levels of no risk, low risk, medium risk and high risk, and then manufacturing a vegetation stability risk level distribution map.
In the above step S5, the space scale of the vegetation change risk
The spatial scale of vegetation change is the risk zone area and the spatial distribution range. Quantified by the area of distribution of the different risk areas and the percentage of area of the vegetation distribution area (table 1). On the area scale, the area statistics function (Zonal statistics table) of Arcmap can be utilized to count the distribution areas of the risk-free, low-risk, medium-risk and high-risk areas, and then dividing the distribution areas by the total area of vegetation in the research area to obtain the proportions of the vegetation high-risk area, the medium-risk area and the low-risk area in the research area (table 1).
Table 1. Ecological risk scale statistics table:
| risk level | Space scale (area km) 2 ) | The proportion of the total vegetation area |
| No risk | ||
| Low risk | ||
| Risk in | ||
| High risk | ||
| Totals to |
In the following, beijing city is taken as an example, and based on a vegetation stability risk index model, the classification of the local vegetation change risk level and the judgment of the risk scale are realized through the processing and operation of various data.
S1, establishing a vegetation stability risk index model
S1.1: constructing vegetation stability risk index model
The main factors influencing vegetation change are researched, a vegetation stability risk index model is provided, and the possibility and damage degree of regional vegetation influenced by natural factors or artificial activities are comprehensively reflected. The model is as follows:
R=B×C×F （7）
wherein R is vegetation stability risk index (threshold [0, 4 ]), B is risk receptor index, C is vegetation coverage (exposure), and F is risk factor index.
S1.2: construction of vegetation stability risk index system
And constructing a 3-layer Beijing city vegetation stability risk assessment index system. Layer 1 is a target layer, and vegetation stability risk indexes (Risk Index of vegetation quality stability, RIVQS) comprehensively reflect the possibility and damage degree of regional vegetation affected by natural factors or artificial activities; layer 2 is an index layer, and the vegetation change risk is measured from three aspects of risk receptors, risk factors and exposure; the 3 rd layer is an index layer and comprises specific indexes, wherein the receptor indexes comprise the vegetation area proportion of the unit cells; risk factor indicators include population density, space planning, precipitation, temperature, and the like; the exposure index is vegetation coverage.
S2, data processing
S2.1: index data preprocessing
S2.1.1: generating a unified range: in order to unify the research area range and the uniform grid precision, firstly, according to the Beijing city space range, a fishing net with the size of 1000m multiplied by 1000m is generated by utilizing geographic information software.
S2.1.2: and (3) index treatment: and performing spatial cutting, rasterization, coordinate system conversion, resampling and other processing operations on each evaluation index according to the fishing net range, and ensuring uniformity of all index data ranges, formats, spatial resolutions and the like.
S2.2: index calculation processing
S2.2.1: cell vegetation area ratio: firstly, according to vegetation type classification, combining with a 'Chinese multi-period land utilization/land coverage remote sensing monitoring data classification system' established by the national academy of sciences of China and resource research, 2 and 3 of 2-level classification in LUCC of Beijing city in 2018 are extracted, namely two primary classes of woodland and grassland, and 7 secondary classes. And then carrying out regional statistics calculation by utilizing the fishing net and the extracted data to obtain vegetation areas of all grid points of the fishing net, and then obtaining vegetation area proportion of all grid points of the fishing net by utilizing the vegetation areas/grid areas of all grid points.
S2.2.1: space planning index: in the range of the fishing net grid, the range of the Beijing urban ecological protection red line is directly assigned with 0.1, the range of the Beijing urban development boundary is assigned with 1, and other areas are assigned with 0.6.
S2.2.3: other indices: and carrying out space grid operation on the related data in the Arcmap software according to the range of the fishing net.
S3, calculating vegetation stability risk index results and grading
Calculating each index according to the vegetation stability risk index evaluation model, outputting a vegetation stability risk index result, grading the evaluation result, and displaying the drawing in a visual mode. The vegetation change risk is classified into 4 grades of a no risk zone, a lower risk zone, a medium risk zone, and a high risk zone according to a natural classification method. The threshold value of the vegetation change risk level classification in Beijing city is as follows:
table 2 threshold for risk classification of 2021 year vegetation change in beijing
| Risk level | Risk-free (I) | Low risk (II) | Risk in (III) | High risk (IV) |
| Threshold range | 0-0.213 | 0.213-0.589 | 0.589-0.994 | 0.994-1.811 |
And drawing a receptor risk index grade chart, a risk factor index, an exposure index and a vegetation stability risk grade thematic chart by means of ArcGIS10.7 visual drawing software.
S4, determining vegetation stability change risk scale
Through statistics, the vegetation distribution area of 2021 year vegetation (forest land) in Beijing city is 11088 square kilometers. The vegetation change is free of risk, low in risk, medium in risk and high in risk areas of 1588 square kilometers, 1265 square kilometers, 4502 square kilometers and 3733 square kilometers respectively, the vegetation change risk scale is 3733 square kilometers, and the vegetation change risk scale accounts for 33.67% of the total vegetation area and accounts for 22.75% of the Beijing city area.
TABLE 3 Beijing city vegetation change ecological risk Scale statistics
| Risk level | Space scale (area km) 2 ) | The proportion of the total vegetation area |
| No risk | 1588 | 14.32% |
| Low risk | 1265 | 11.41% |
| Risk in | 4502 | 40.60% |
| High risk | 3733 | 33.67% |
| Totals to | 11088 | 100% |
In the embodiment of the invention, the spatial information of the risk receptor and the exposure degree is accurately identified on the grid point scale by establishing the vegetation stability risk index model; fusing key risk factors such as a risk receptor, exposure, population density, space planning, precipitation level, temperature level and the like into a vegetation stability risk assessment model on a grid point scale; and then quantifying the risk value, the grade, the scale and the occurrence area on the grid and area scale respectively by using a GIS space analysis technology. The space precision of the existing vegetation change risk assessment is improved, the number, grade, scale and occurrence area of the local vegetation change risk are accurately quantized on the grid and area scale, and more accurate vegetation change space information is provided for monitoring and protecting a biological system.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (5)
1. A method of assessing the risk of a local vegetation change based on a vegetation stability risk index, comprising the steps of:
s1, selecting a risk receptor, a risk factor and an exposure index of local vegetation change risk in a research area, and constructing a vegetation stability risk index model;
s2, carrying out unified preprocessing on the data format, the data coordinate system and the resolution ratio of different sources and different format index data according to the unified research area range;
s3, carrying out grid operation on the preprocessed data by using spatial data processing software according to different index calculation formulas to obtain different risk assessment indexes; the different indices include: spatial information of risk receptor index, risk factor index, and exposure;
s4, substituting the different indexes into a vegetation stability risk index model, and grading local vegetation change risks in a research area according to an output result of the vegetation stability risk index model;
s5, determining the vegetation change risk scale of the research area according to the relation between the grade division of the cell and the vegetation area according to the grade division result;
wherein the risk receptor index formula is as follows:
wherein B is a risk receptor index of vegetation change risk of a research area, A is each grid area of the research area; a is that veg Vegetation areas for each grid;
the risk factor index is obtained by the following steps:
1) Obtaining human driving factors of a research area; the human driving factor includes: population density index and space planning index;
wherein the population density index is calculated as follows:
extracting maximum and minimum population density values of the estimated year, and performing grid operation according to the following formula:
wherein D is a population density index; d (D) i Evaluating annual population density for grid point i; d (D) max To assess annual study area population density maxima; d (D) min To evaluate annual study area population density minima;
the space planning factor index DP is assigned as follows: assigning 1 to the range in the town development boundary, assigning 0.1 to the range in the ecological protection red line, and assigning 0.6 to the other ranges;
2) Acquiring natural factors of a research area: the natural factors include: a precipitation factor index and a temperature factor index;
wherein, the precipitation factor index is calculated as follows:
wherein P is a precipitation risk factor index; p (P) i Evaluating annual growth season precipitation for grid point i;P mean average value of precipitation amount for grid point i growing for many years, P max Maximum precipitation amount P for growth of grid point i for many years min The minimum precipitation amount of the grid point i in the growing season for many years;
the temperature factor index is calculated as follows:
wherein T is a temperature factor index; t (T) i Annual growth is assessed for grid point i Ji Junwen; t (T) mean For years, the average value of the temperature of the grid point i is T max For the annual growth Ji Junwen maximum of grid point i, T min Ji Junwen minimum for annual growth of grid point i;
3) Calculating a risk factor index:
adding and calculating each risk factor index according to the following formula to obtain a risk factor index:
F＝D+DP+P+T
wherein F is a risk factor index; d is a population density index; DP is the space planning factor index; p is the precipitation factor index; t is a temperature factor index.
2. The method of assessing the risk of local vegetation change based on a vegetation stability risk index according to claim 1, wherein step S1 comprises:
constructing a vegetation stability risk index model:
R＝B×C×F
wherein R is vegetation stability risk index; b is a risk receptor index; c is vegetation coverage, and is used as space information of exposure; f is a risk factor index.
3. The method of assessing the risk of local vegetation change based on a vegetation stability risk index according to claim 1, wherein step S2 comprises:
s21, generating a cell fishing net with the size of 1000m multiplied by 1000m in geographic information system software according to the spatial range of a research area;
s22, performing spatial cutting, rasterization, coordinate system conversion and resampling on each evaluation index corresponding to the key factors according to the range of the fishing net; and realizing that the data space range, format, coordinate system and spatial resolution of each evaluation index are consistent.
4. The method of assessing the risk of local vegetation change based on a vegetation stability risk index according to claim 1, wherein step S3 comprises:
and calculating each index data after pretreatment according to a risk receptor index formula, a risk factor index formula, a spatial information formula of exposure and a vegetation stability risk index model to obtain a corresponding output result.
5. The method of assessing the risk of a local vegetation change based on a vegetation stability risk index according to claim 4 wherein the spatial information of exposure is quantified by the vegetation coverage of each grid point according to the following formula:
wherein, C is vegetation coverage; NDVI is a grid point estimated annual growth season normalized vegetation index; NDVI veg Evaluating annual growth season NDVI maximum for the study area; NDVI soil Annual growth season NDVI minima were assessed for the study area.
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