CN114219872A - Ecological geological map compiling method - Google Patents

Ecological geological map compiling method Download PDF

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CN114219872A
CN114219872A CN202111382338.4A CN202111382338A CN114219872A CN 114219872 A CN114219872 A CN 114219872A CN 202111382338 A CN202111382338 A CN 202111382338A CN 114219872 A CN114219872 A CN 114219872A
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index
habitat
value
vahi
factor
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李生永
王现国
张永安
王西平
王磊
朱洪生
宁立波
张开心
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Gansu Nonferrous Engineering Survey Design And Research Co ltd
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Abstract

The invention provides an ecological geological map compiling method, which comprises the following steps: s1: performing ecological remote sensing interpretation by using ENVI software on the basis of regional remote sensing data; s2: by regional plant habitat survey, representing the ecological geological condition of a region by using the vegetation on-land habitat index and the land environment index in a comprehensive way, reflecting the ecological geological condition into a corresponding map, and constructing the vegetation on-land habitat index and the land environment index; s3: interpolating the data fields by using an interpolation method in Arcgis 10.2 software space analysis-interpolation, and re-classifying interpolation results to obtain classification results of the terrestrial habitat indexes and the terrestrial habitat indexes; s4: and (3) overlapping the vegetation ground habitat index and the regional habitat index by using the regional geological map as a base map and using the Arcgis 10.2 software space overlapping analysis function to draw a regional ecological geological map. Book (I)The invention uses the coupling of the plant terrestrial habitat index and the land environment index to represent the ecological geological condition of a region from multiple dimensions.

Description

Ecological geological map compiling method
Technical Field
The invention relates to the technical field of geological mapping, in particular to an ecological geological map compiling method.
Background
With the emergence of various ecological problems, the industry deeply recognizes that the root of many ecological problems is geological problems, but the traditional single investigation mode and map filling method cannot represent the geological status of the ecological environment in an area from multiple dimensions.
At present, the ecological geological map compiling method reported in the prior art mainly adopts a natural complex multi-attribute superposition filling map method: the main process is as follows:
surveying and drawing the zone characteristics and the differentiation phenomena of geology, landform, hydrogeology, environmental geology and the like of the whole area, and using the zone characteristics and the differentiation phenomena as a basic map layer for surveying and mapping; closely combining soil and vegetation, and simultaneously filling and measuring soil and vegetation type maps; and summarizing basic units for refining the geological map of the ecological environment.
Since plants are the first productivity of ecosystem and the current ecological problems are mainly plants, such as ecological function degradation caused by plant degradation, the ecological geological survey and the compilation of ecological geological maps must be centered on plants, that is, the ecological geological survey should investigate the state of plants and their habitat.
However, the existing method for compiling the ecological geological map has many problems, if vegetation coverage is directly superposed on the geological map and ecological problems are superposed on the geological map, the research scope of the ecological geological map is excessively enlarged, the range from the atmosphere to the rock ring is ascribed to the research content of the ecological geological map, and the true connotation of the ecological geological map is deviated. Therefore, the conventional single investigation mode and mapping method cannot represent the ecological environment geological status in the area from multiple dimensions.
Disclosure of Invention
The invention provides an ecological geological map compiling method, which aims to solve the problem that the geological situation of the ecological environment in an area cannot be represented from multiple dimensions by adopting a traditional single investigation mode and a map filling method in the existing ecological geological map compiling process.
In order to solve the above problems, the present invention provides an ecological geological map compilation method, comprising:
S1: performing ecological remote sensing interpretation by using ENVI software on the basis of regional remote sensing data;
S2: through regional plant habitat survey, comprehensively representing the ecological geological condition of a region by using a vegetation on-land habitat index and a habitat index LI, reflecting the ecological geological condition into a corresponding map, and constructing the vegetation on-land habitat index and the habitat index LI;
S3: interpolating the data fields by using an interpolation method in Arcgis 10.2 software space analysis-interpolation, and re-classifying interpolation results to obtain a ground habitat index and ground habitat index LI classification result;
S4: and (3) overlapping the vegetation ground habitat index and the regional habitat index LI by using the regional geological map as a base map and through an Arcgis 10.2 software space overlapping analysis function, and drawing a regional ecological geological map.
Preferably, the method for characterizing the ecological geological condition of the area by using the vegetation ground habitat index VAHI specifically comprises the following steps:
the vegetation habitat index VAHI is coupled with a humidity factor Wet, a temperature factor Heat and a dryness factor Dry;
extracting the information of the humidity factor Wet, the temperature factor Heat and the dryness factor Dry based on a remote sensing technology, and carrying out automatic weighted value superposition analysis on the humidity factor Wet, the temperature factor Heat and the dryness factor Dry by a principal component analysis method to evaluate the terrestrial habitat conditions of the vegetation.
Preferably, the method for determining the humidity measure index of the humidity factor Wet comprises the following steps: by adopting a Thyscap transformation technology, obtaining a third component which has a direct relation with surface physical parameters and is used as a reflection index of a humidity factor Wet, wherein the specific formula of the humidity factor Wet is as follows:
WetOL1＝(0.1511γblue+0.1973γgreen+0.3283γred+0.3407γnir-0.7117γswir1-0.4559γswir2)
in the formula, gammablue、γgreen、γred、γnir、γswir1、γswir2Respectively the wavelengths of a blue wave band, a green wave band, a red wave band, a near infrared wave band, a middle infrared wave band 1 and a middle infrared wave band 2.
Preferably, the method for determining the Heat measurement index of the temperature factor Heat comprises: and (2) inverting the land surface temperature LST by adopting an atmospheric correction method and utilizing a thermal infrared remote sensing technology, wherein the calculation formula of the land surface temperature LST is as follows:
Figure RE-GDA0003483731590000031
Figure RE-GDA0003483731590000032
Lλ＝gain×DN+bias (3-3)
Figure RE-GDA0003483731590000033
Figure RE-GDA0003483731590000034
Figure RE-GDA0003483731590000035
in the formula, LST represents land surface temperature and the unit is; t is a temperature value at the Landsat-8TIRS sensor; l isλThe reflectivity of a thermal infrared band after radiation calibration is shown, gain represents a gain value, DN represents a pixel brightness value of a remote sensing image, and no unit exists; band 10, K for Landsat-8TIRS sensor1＝774.89W·m-2·sr-1·μm-1，K21321.08K; λ is the central wavelength of the thermal infrared band, λTM＝11.435 μm，λb10＝10.900μm；x＝1.438×10-2m·K；
ζbuildingIs the emissivity, zeta, of the pixels in urban townssurfaceSpecific radiance of natural surface pixel, FvRepresenting vegetation coverage;
NDVIVNDVI value of the Pixel covered by the complete Vegetation, NDVISThe NDVI value for a pixel that is completely bare or vegetation-free covered area.
Preferably, the method for determining the city building and bare soil index NDBSI of the dryness factor Dry comprises the following steps: selecting bare soil index SI representing bare soil information and building index IBI representing building land information to synthesize, and taking the average value of the bare soil index SI and the building index IBI, wherein the calculation formula of the urban building and bare soil index NDBSI is as follows:
Figure RE-GDA0003483731590000036
Figure RE-GDA0003483731590000037
Figure RE-GDA0003483731590000041
in the formula, NDBSI represents the index of urban construction and bare soil, gammablue、γgreen、γred、γnir、γswir1、γswir2Respectively the wavelengths of a blue wave band, a green wave band, a red wave band, a near infrared wave band, a middle infrared wave band 1 and a middle infrared wave band 2.
Preferably, the specific process for constructing the habitat index VAHI on vegetation ground comprises:
step 1: creating a remote sensing defined expression according to a mathematical expression of the vegetation habitat index VAHI, wherein the mathematical expression of the vegetation habitat index is VAHI-f (Wet, Heat, Dry), and then the remote sensing defined expression is VAHI*＝f(Wet*,LST,NDBSI),
In the formula, Wet, Heat and Dry respectively represent a humidity factor, a Heat factor and a dryness factor; wet, LST and NDBSI respectively represent humidity index, surface temperature and urban building and bare soil index;
step 2: normalizing the original values of the three factors, and unifying the ranges between [0 and 1], wherein the normalization processing formula is as follows:
Figure RE-GDA0003483731590000042
in the formula, BIiPixel value normalized by a factor, biIs the pixel value i, b of a certain index factormax、 bminThe maximum value and the minimum value of the factor are respectively;
and step 3: after normalization processing, synthesizing 3 normalization factors into an image, performing PCA analysis by utilizing an ENVI (intrinsic differential pressure) self-contained principal component analysis tool, extracting a first principal component, calculating to obtain an initial VAHI value, and recording as VAHI0Said VAHI0The calculation expression of (a) is:
VAHI0＝1-PC1[f(WET,LST,NDBSI)] (3-11)
and 4, step 4: mixing VAHI0And normalizing again to obtain a final VAHI value, wherein the final expression of the final VAHI value is as follows:
Figure RE-GDA0003483731590000043
preferably, in step S2Specifically, the calculating the location index LI includes the following steps:
S21: using SPSS 24.0 statistical softPerforming Pearson correlation analysis, and screening out soil nutrient evaluation factors with significant correlation;
S22: reducing the dimension of each index by adopting a principal component analysis method in factor analysis to obtain an index characteristic value and a characteristic vector which comprehensively reflect the soil quality condition;
S23: selecting key principal components according to the characteristic value lambda being more than or equal to 1, standardizing the numerical values of all factors, calculating the scores of all principal components, and substituting the scores into a comprehensive score formula to obtain the place and environment index values of all sampling points.
Preferably, the normalized calculation formula of each index is as follows:
Figure RE-GDA0003483731590000051
′
in the formula: i is the number of samples; j is the index number; x is the number ofijThe value of the j index in the ith sample is taken as the index value; x is the number ofijIs a standardized value; x is the number ofjIs the average value of the j index; sjIs the standard deviation of the j-th index.
Preferably, in step S23In the formula, each principal component score value is as follows:
yi＝bij×xij′ (3-14)
in the formula: bijThe score coefficient of the principal component of the jth index in the ith sample; y isiThe principal component score values of the ith sample.
Preferably, in step S23The map index value is formulated as follows:
Figure RE-GDA0003483731590000052
in the formula: a isikIs the eigenvalue contribution, LI, of the k-th principal component of the ith sampleiIs the location index LI value of the ith sample.
Compared with the prior art, the invention has obvious advantages and beneficial effects, and is embodied in the following aspects:
1. the ecological geological map compiled by the method can well integrate the plant aboveground habitat and the underground habitat situation into the ecological geological map, combines the interpretation result of the vegetation aboveground habitat index VAHI and the land environment index LI with regional plant land environment survey, well reflects the quality of a regional ecological geological condition and well evaluates the ecological geological condition of a region and reflects the ecological geological condition into a corresponding map, and the ecological geological map compiling method can visually reflect the ecological geological situation.
2. According to the method, the vegetation ground habitat index (VAHI) is coupled with the dryness factor, the humidity factor and the temperature factor, information of each factor is extracted based on a remote sensing technology, and the three factors are subjected to superposition analysis by a principal component analysis method, so that the method can be used for evaluating the ground habitat conditions of the vegetation.
3. The land-environment index LI is influenced by a plurality of factors, and certain correlation exists among the factors, so that information superposition exists among a plurality of indexes reflecting the land-environment index. Factors selected for the context index rating include: hydrolytic nitrogen, organic matter, full salt content, available phosphorus and quick-acting potassium.
4. The ecological geological map is the comprehensive expression of the plant ecological factors and the plant environment factors, and the ecological geological condition of a region can be better reflected by using the coupling of the plant environment indexes and the environment indexes. In the method, L is a coupling value of two indexes, and the level of L reflects the quality of ecological geological conditions of a region.
5. And (3) analyzing results of the overground habitat (vegetation overground habitat index) and the underground habitat (habitat index) comprehensively, drawing a regional ecological geological map by using an Arcgis 10.2 software space superposition analysis function, and analyzing regional ecological geological conditions by combining results under the action of the ecological geological conditions obtained based on the ENVI 5.3 software supervision and classification function.
Drawings
FIG. 1 is a schematic flow chart of a method for compiling an ecological geological map according to an embodiment of the present invention;
FIG. 2 is a spatial distribution diagram of the regional habitat index and the habitat index on the vegetation land in the embodiment of the present invention;
FIG. 3 is a regional ecology geology map and a regional land utilization type map in an embodiment of the present invention;
FIG. 4 is a 2020 habitat index and vegetation land habitat index spatial distribution diagram of a research area in an embodiment of the invention;
fig. 5 is an ecological geology map and a land use type map in 2020 in the embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
As shown in fig. 1 to 5, an embodiment of the present invention provides an ecological geological map compilation method, which includes the following steps:
S1: performing ecological remote sensing interpretation by using ENVI software on the basis of regional remote sensing data;
S2: comprehensively representing the ecological geological condition of the region by using the vegetation ground habitat index VAHI and the habitat index LI through regional plant habitat survey, reflecting the ecological geological condition into a corresponding map, and constructing the vegetation ground habitat index VAHI and the habitat index LI;
S3: interpolating the data fields by using an interpolation method in Arcgis 10.2 software space analysis-interpolation, and reclassifying interpolation results to obtain classification results of the terrestrial habitat indexes VAHI and the terrestrial habitat indexes LI;
S4: and (3) overlapping the vegetation ground habitat index VAHI and the habitat index LI by using the regional geological map as a base map and through an Arcgis 10.2 software space overlapping analysis function, and drawing a regional ecological geological map.
Specifically, in an embodiment of the present invention, the characterizing the ecological geological condition of the area by using the vegetation ground habitat index VAHI specifically includes:
the vegetation habitat index VAHI is coupled with a humidity factor Wet, a temperature factor Heat and a dryness factor Dry;
extracting the information of the humidity factor Wet, the temperature factor Heat and the dryness factor Dry based on a remote sensing technology, and carrying out automatic weighted value superposition analysis on the humidity factor Wet, the temperature factor Heat and the dryness factor Dry by a principal component analysis method to evaluate the terrestrial habitat conditions of the vegetation.
Therefore, in the method, the vegetation habitat index VAHI is coupled with the humidity factor, the temperature factor and the dryness factor, the information of each factor is extracted based on a remote sensing technology, and the three factors are subjected to automatic weighting value superposition analysis by a principal component analysis method to evaluate the vegetation habitat conditions on the ground.
Specifically, the method for determining the humidity measurement index of the humidity factor Wet comprises the following steps: by adopting a Thyscap transformation technology, obtaining a third component which has a direct relation with surface physical parameters and is used as a reflection index of a humidity factor Wet, wherein the specific formula of the humidity factor Wet is as follows:
WetOL1＝(0.1511γblue+0.1973γgreen+0.3283γred+0.3407γnir-0.7117γswir1-0.4559γswir2)
in the formula, gammablue、γgreen、γred、γnir、γswir1、γswir2Respectively the wavelengths of a blue wave band, a green wave band, a red wave band, a near infrared wave band, a middle infrared wave band 1 and a middle infrared wave band 2.
The tassel-cap transform is a technique of compressing data and orthogonally transforming the original data, and the transform result includes "luminance", "greenness", "third component", and the like. The brightness, the greenness and the third component in the Thysanocap transformation have direct relation with the surface physical parameters, wherein the third component reflects the humidity of water, soil and vegetation, and therefore the third component is usually used as an index of a humidity factor.
Specifically, in the embodiment of the present invention, the method for determining the Heat measure of the temperature factor Heat includes: and (2) inverting the land surface temperature LST by adopting an atmospheric correction method and utilizing a thermal infrared remote sensing technology, wherein the calculation formula of the land surface temperature LST is as follows:
Figure RE-GDA0003483731590000081
Figure RE-GDA0003483731590000082
Lλ＝gain×DN+bias(3-3)
Figure RE-GDA0003483731590000083
Figure RE-GDA0003483731590000084
Figure RE-GDA0003483731590000085
in the formula, LST represents land surface temperature and the unit is; t is a temperature value at the Landsat-8TIRS sensor; l isλThe reflectivity of a thermal infrared band after radiation calibration is shown, gain represents a gain value, DN represents a pixel brightness value of a remote sensing image, and no unit exists; band 10, K for Landsat-8TIRS sensor1＝774.89W·m-2·sr-1·μm-1，K21321.08K; λ is the central wavelength of the thermal infrared band, λTM＝11.435 μm，λb10＝10.900μm；x＝1.438×10-2m·K；
ζbuildingIs the emissivity, zeta, of the pixels in urban townssurfaceSpecific radiance of natural surface pixel, FvRepresenting vegetation coverage;
NDVIVNDVI value of the Pixel covered by the complete Vegetation, NDVISBeing completely bare soil or vegetation-free coverageNDVI value of the picture element.
Therefore, the heat index is represented by the Surface Temperature (LST), the inversion of the Surface Temperature has important significance in the aspects of urban heat islands, natural disaster monitoring and the like, and the inversion of the Surface Temperature by using the thermal infrared remote sensing technology is a main mode for obtaining the Surface Temperature. There are currently 3 major types of Land Surface Temperature (LST) inversion, including: atmospheric correction methods (also called radiative transfer equation methods), single window algorithms, and split window algorithms (also known as split window algorithms). The research adopts an atmospheric correction method to carry out the inversion of the LST, so that the land surface temperature can be more accurately reflected, and the calculation is simpler and more convenient.
Specifically, in the embodiment of the present invention, the method for determining the city building and bare soil index NDBSI of the dryness factor Dry includes: selecting bare soil index SI representing bare soil information and building index IBI representing building land information to synthesize, and taking the average value of the bare soil index SI and the building index IBI, wherein the calculation formula of the urban building and bare soil index NDBSI is as follows:
Figure RE-GDA0003483731590000091
Figure RE-GDA0003483731590000092
Figure RE-GDA0003483731590000093
in the formula, NDBSI represents the index of urban construction and bare soil, gammablue、γgreen、γred、γnir、γswir1、γswir2Respectively the wavelengths of a blue wave band, a green wave band, a red wave band, a near infrared wave band, a middle infrared wave band 1 and a middle infrared wave band 2.
It should be noted that, in the process of water and soil loss, the soil fertility is damaged, so that the vegetation on the ground surface is reduced, bare land can appear, and the bare land is in direct proportion to the land degradation, i.e., the more bare the ground surface is, the more severe the land degradation is, and thus the corresponding bare soil index is also higher, so the bare soil index has an important meaning for monitoring the water and soil loss. In urban and rural development, some land is used as construction land, and the land is also dried to influence the ecological environment. Therefore, the dryness index is synthesized by selecting the bare soil index SI representing bare soil information and the building index IBI representing building land information, and the average value of the bare soil index SI and the building index IBI is more accurate.
Specifically, in an embodiment of the present invention, the specific process of constructing the vegetation ground habitat index VAHI includes:
step 1: creating a remote sensing defined expression according to a mathematical expression of the vegetation habitat index VAHI, wherein the mathematical expression of the vegetation habitat index is VAHI-f (Wet, Heat, Dry), and then the remote sensing defined expression is VAHI*＝f(Wet*,LST,NDBSI),
In the formula, Wet, Heat and Dry respectively represent a humidity factor, a Heat factor and a dryness factor; wet, surface temperature and city building and bare soil indices are represented by Wet, LST, NDBSI, respectively.
Because the three index dimensions are not uniform, in order to avoid the weight unbalance of each index in the principal component analysis, the original values of the three factors need to be normalized, and the ranges are uniform between [0,1 ].
Step 2: normalizing the original values of the three factors, and unifying the ranges between [0 and 1], wherein the normalization processing formula is as follows:
Figure RE-GDA0003483731590000101
in the formula, BIiPixel value normalized by a factor, biIs the pixel value i, b of a certain index factormax、 bminThe maximum and minimum values of the factor are respectively.
The VAHI values are different for different periods and different regions, and for the convenience of comparative study, the VAHI values are compared0Go back toAnd (6) normalizing.
And step 3: after normalization processing, synthesizing 3 normalization factors into an image, performing PCA analysis by utilizing an ENVI (intrinsic differential pressure) self-contained principal component analysis tool, extracting a first principal component, calculating to obtain an initial VAHI value, and recording as VAHI0Said VAHI0The calculation expression of (a) is:
VAHI0＝1-PC1[f(WET,LST,NDBSI)] (3-11)
and 4, step 4: mixing VAHI0And normalizing again to obtain a final VAHI value, wherein the final expression of the final VAHI value is as follows:
Figure RE-GDA0003483731590000111
therefore, the final VAHI value range is between [0 and 1], the higher the VAHI value is, the better the ecological condition is represented, and otherwise, the worse the ecological condition is.
Specifically, in an embodiment of the present invention, the calculating the location index LI specifically includes the following steps:
S21: performing Pearson correlation analysis by using SPSS 24.0 statistical software to screen out soil nutrient evaluation factors with significant correlation;
S22: reducing the dimension of each index by adopting a principal component analysis method in factor analysis to obtain an index characteristic value and a characteristic vector which comprehensively reflect the soil quality condition;
S23: selecting key principal components according to the characteristic value lambda being more than or equal to 1, standardizing the numerical values of all factors, calculating the scores of all principal components, and substituting the scores into a comprehensive score formula to obtain the place and environment index values of all sampling points.
The land-environment index LI is influenced by a plurality of factors, and certain correlation exists among the factors, so that information superposition exists among a plurality of indexes reflecting the land-environment index. Factors selected for the context index rating include: hydrolytic nitrogen, organic matter, full salt content, available phosphorus and quick-acting potassium.
The method comprises the steps of carrying out Pearson correlation analysis by using SPSS 24.0 statistical software, screening out soil nutrient evaluation factors with obvious correlation, then carrying out dimensionality reduction on each index by using a principal component analysis method in factor analysis to obtain an index characteristic value and a characteristic vector which comprehensively reflect the soil quality condition, selecting the index characteristic value and the characteristic vector as key principal components according to the characteristic value lambda being more than or equal to 1, standardizing the numerical value of each factor, calculating the score of each principal component, and substituting the score into a comprehensive score formula to obtain the place and place index value of each sample point.
In step S23In the method, the standardized calculation formula of each index is as follows:
Figure RE-GDA0003483731590000112
′
in the formula: i is the number of samples; j is the index number; x is the number ofijThe value of the j index in the ith sample is taken as the index value; x is the number ofijIs a standardized value; x is the number ofjIs the average value of the j index; sjIs the standard deviation of the j-th index.
In step S23In the formula, each principal component score value is as follows:
yi＝bij×xij′ (3-14)
in the formula: bijThe score coefficient of the principal component of the jth index in the ith sample; y isiThe principal component score values of the ith sample.
In step S23The map index value is formulated as follows:
Figure RE-GDA0003483731590000121
in the formula: a isikIs the eigenvalue contribution, LI, of the k-th principal component of the ith sampleiThe place index value of the ith sample.
Referring to fig. 1, the regional habitat index and the regional habitat index obtained by the above method are interpolated by using an interpolation method in the Arcgis 10.2 software spatial analysis-interpolation. And re-classifying interpolation results to obtain the ground habitat index and ground habitat index classification results.
The regional context index classification result is 6 types: 0.0-0.167, 0.167-0.333, 0.333-0.499, 0.499-0.666, 0.666-0.833, 0.833-1. The vegetation ground habitat indexes are classified into 5 types: 0 to 0.2, 0.2 to 0.4, 0.4 to 0.6, 0.6 to 0.8 and 0.8 to 1.0.
According to the ecological geological map compiling method, a regional geological map is used as a base map, the vegetation ground habitat index and the regional habitat index are overlapped through the Arcgis 10.2 software space overlapping analysis function, and the regional ecological geological map is drawn. By integrating the analysis results of the overground habitat (vegetation overground habitat index) and the underground habitat (land habitat index), the ecological geological condition of a region can be better reflected by coupling the plant overground habitat index and the land habitat index, L is a coupling value of the two indexes, and the level of L reflects the quality of the ecological geological condition of the region.
The ecological geological map is divided into 10 levels: lx: LI (a-b) & VAHI (c-d) means a combination of a habitat index (LI) range of (a-b) and a habitat index on Vegetation (VAHI) range of (c-d), obtained as follows:
L1：LI(0.333-0.5)&VAHI(0.8-1.0)；
L2：LI(0.333-0.5)&VAHI(0.6-0.8)；
L3：LI(0.333-0.5)&VAHI(0.4-0.6)；
L4：LI(0.5-0.7)&VAHI(0.6-0.8)；
L5：LI(0.5-0.7)&VAHI(0.4-0.6)；
L6：LI(0.167-0.333)&VAHI(0.8-1.0)；
L7：LI(0.167-0.333)&VAHI(0.6-0.8)；
L8：LI(0.167-0.333)&VAHI(0.4-0.6)；
L9：LI(0-0.167)&VAHI(0.6-0.8)；
L10：LI(0-0.167)&VAHI(0.4-0.6)。
combining the ecological geological map with the result under the action of the ecological geological condition obtained based on the supervision and classification function in the ENVI 5.3 software, reclassifying the ecological geological map into 5 types: the method is used for analyzing the regional ecological geological conditions of forest lands, grasslands, construction lands, cultivated lands and water bodies. The current situation of the ecological geology can be visually reflected from the ecological geology map, and the land utilization types, the difference degrees of human interference, the vegetation growth conditions of the regions and the soil fertility level of different regions can be seen.
Therefore, the ecological geological map is the comprehensive expression of the plant ecological factors and the plant environment factors, and the ecological geological condition of a region can be better reflected by the coupling of the plant environment indexes and the environment indexes. In the method, L is a coupling value of two indexes, and the level of L reflects the quality of ecological geological conditions of a region.
And (3) analyzing results of the overground habitat (vegetation overground habitat index VAHI) and the underground habitat (habitat index LI) comprehensively, drawing a regional ecological geological map by using an Arcgis 10.2 software space superposition analysis function, and analyzing regional ecological geological conditions by combining results under the action of the ecological geological conditions obtained based on a supervision and classification function in ENVI 5.3 software.
The method for compiling the ecological geological map is applied to southern Taihang areas of Henan province, and the specific compiling method is as follows:
1. research method
(1) Habitat index on vegetation land
The vegetation ground habitat index VAHI coupling dryness factor, humidity factor and temperature factor is used for extracting information of each factor based on a remote sensing technology, and the coupling dryness factor, the humidity factor and the temperature factor are subjected to automatic weighting value superposition analysis through a principal component analysis method to evaluate the ground habitat conditions of the vegetation.
1) Humidity index
The tassel-cap transform is a technique of compressing data and orthogonally transforming the original data, and the transformation result includes "brightness", "greenness", "third component", and the like. The brightness, the greenness and the third component in the Thysanocap transformation have direct relation with the surface physical parameters, wherein the third component reflects the humidity of water, soil and vegetation, therefore, the third component is usually taken as a humidity factor Wet, and the specific formula is as follows:
WetOL1＝(0.1511γblue+0.1973γgreen+0.3283γred+0.3407γnir-0.7117γswir1-0.4559γswir2)
in the formula, gammablue、γgreen、γred、γnir、γswir1、γswir2Respectively a blue wave band, a green wave band, a red wave band, a near infrared wave band, a middle infrared wave band 1 and a middle infrared wave band 2.
2) Index of heat
The heat index is represented by Land Surface Temperature (LST), the inversion of the Land Surface Temperature has important significance on the aspects of urban heat islands, natural disaster monitoring and the like, the inversion of the Land Surface Temperature by using a thermal infrared remote sensing technology is a main mode for obtaining the Land Surface Temperature, and the inversion of the Land Surface Temperature (LST) mainly comprises 3 types at present, including: atmospheric correction methods (also called radiative transfer equation methods), single window algorithms, and split window algorithms (also known as split window algorithms). The study adopts an atmospheric correction method to carry out LST inversion, and the calculation formula for deducing the surface temperature is as follows:
Figure RE-GDA0003483731590000141
Figure RE-GDA0003483731590000142
L6＝gain×DN+bias
Figure RE-GDA0003483731590000143
Figure RE-GDA0003483731590000144
Figure RE-GDA0003483731590000151
wherein LST is the surface temperature and the unit is; t is the temperature value at the sensor; l is6The thermal infrared band reflectivity after radiation calibration;
for Landsat-5TM sensor, K1＝607.76W·m-2·sr-1·μm-1，K2＝1260.56K；
For Landsat-8TIRS sensor 10 th wave band, K1＝774.89W·m-2·sr-1·μm-1， K2＝1321.08K；
λ is the central wavelength of the thermal infrared band, λTM＝11.435μm，λb10＝10.900μm； x＝1.438×10-2m.K; ε is the surface emissivity, NDVIV＝0.7，NDVISWhen a certain pixel value NDVI > 0.7, F is 0VTaking 1, when NDVI is less than 0, FVTake 0.
3) Dryness index
In the process of water and soil loss, the soil fertility is damaged, so that the vegetation on the earth surface is reduced, bare land can appear, the bare land exposure degree is in direct proportion to the land degradation, namely the more the bare land is, the more the land degradation is, and the corresponding bare soil index is also higher, so that the bare soil index has important significance for monitoring the water and soil loss. In urban and rural development, some land is used as construction land, and the land is also dried to influence the ecological environment. Therefore, the dryness index is synthesized by selecting a bare soil index SI representing bare soil information and a building index IBI representing building land information, the average value of the bare soil index SI and the building index IBI is taken, and the formula is calculated as follows:
Figure RE-GDA0003483731590000152
Figure RE-GDA0003483731590000153
Figure RE-GDA0003483731590000154
in the formula, gammablue、γgreen、γred、γnir、γswir1、γswir2Respectively a blue wave band, a green wave band, a red wave band, a near infrared wave band, a middle infrared wave band 1 and a middle infrared wave band 2.
The NDBSI index is characterized by:
(1) the NDBSI index is between [ -1,1 ];
(2) the enhanced information is greater than zero and the suppressed information is less than zero. Namely, the pixel values of the buildings and the bare soil are more than zero, and the pixel values of the vegetation and the water body are less than zero.
4) Construction of habitat index on vegetation land
The mathematical expression of the habitat index on the vegetation ground is as follows:
VAHI＝f(Wet,Heat,Dry) (3-31)
remote sensing is defined as:
VAHI＝f(Wet,LST,NDBSI) (3-32)
in the formula, Wet, Heat and Dry respectively represent a humidity factor, a Heat factor and a dryness factor; wet, LST and NDBSI respectively represent humidity index, surface temperature and urban building and bare soil index;
because the three index dimensions are not uniform, in order to avoid the weight unbalance of each index in the principal component analysis, the original values of the three factors need to be normalized, the ranges are uniform between [0,1], and the normalization formula is as follows:
Figure RE-GDA0003483731590000161
in the formula, BIiPixel value normalized by a factor, biIs the pixel value i, b of a certain index factormax、 bminThe maximum and minimum values of the factor are respectively. After normalization, 3 normalization factors are synthesized into an image, and ENVI is utilized to carry out principal component analysis (Forward PCA Rotation New Statistics and Rotate) tool, performing PCA analysis, extracting first main component, calculating to obtain initial VAHI value, and recording as VAHII0；
VAHI0＝1-PC1[f(WET,LST,NDBSI)] (3-34)
The VAHI values are different for different periods and different regions, and for the convenience of comparative study, the VAHII is used0And (4) normalizing again:
Figure RE-GDA0003483731590000171
the final VAHI is obtained with values ranging between [0,1], the higher the VAHI value, the better the ecological conditions, and vice versa, the worse the ecological conditions.
(2) Map index LI
The land-environment index LI is influenced by a plurality of factors, and certain correlation exists among the factors, so that information superposition exists among a plurality of indexes reflecting the land-environment index, and a Principal Component Analysis (PCA) can perform dimensionality reduction analysis on the factors, extract principal components, weaken errors caused by autocorrelation among variables, and can be applied to quantitative research of the land-environment index. The land conditions are the comprehensive reflection of a plurality of factors such as soil, partial matrix, water and salt, the factors related to fertility are considered as much as possible by the evaluation factors, and the factors selected by the evaluation of the land condition indexes in the research area mainly comprise: hydrolytic nitrogen, organic matter, full salt content, available phosphorus and quick-acting potassium.
Performing Pearson correlation analysis by using SPSS 24.0 statistical software to screen out soil nutrient evaluation factors with significant correlation;
reducing the dimension of each index by using a principal component analysis method in factor analysis to obtain an index characteristic value and a characteristic vector which comprehensively reflect the soil quality condition;
selecting key principal components according to the characteristic value lambda being more than or equal to 1, standardizing the numerical values of all factors, calculating the scores of all principal components, and substituting the scores into a comprehensive score formula to obtain the place and environment index value of each sampling point.
The standardization formula of each index is as follows:
Figure RE-GDA0003483731590000172
′
in the formula: i is the number of samples; j is the index number; x is the number ofijThe value of the j index in the ith sample is taken as the index value; x is the number ofijIs a standardized value; x is the number ofjIs the average value of the j index; sjIs the standard deviation of the j-th index.
The score value formula of each principal component is as follows:
yi＝bij×xij′ (3-37)
in the formula: y isiFor each principal component score value of the ith sample, bijThe score coefficient of the principal component of the jth index in the ith sample;
map index value formula:
Figure RE-GDA0003483731590000181
in the formula: a isikIs the eigenvalue contribution, LI, of the k-th principal component of the ith sampleiIs the location index LI value of the ith sample.
2. Results of the study
The territorial index LI of the southern tai-pan area totally analyzes 71 sampling points throughout the research area, and totally 230 soil samples. Correlation analysis (table 3-1) is carried out on the original data of indexes such as soil organic matter, hydrolyzable nitrogen, available phosphorus, quick-acting potassium and total salt content, and it can be seen that 5 indexes have significant correlations in different degrees, which indicates that partial information is overlapped.
The association between the various indices of the situation in this study is further illustrated by the KMO and Bartlett sphericity test using the factorial analysis in the SPSS 24.0 software, with KMO values of 0.745>0.5 and a probability of concomitance with the Bartlett sphericity test of P <0.01 (very significant level) (see tables 3-2). Therefore, it is feasible to evaluate the environmental conditions of the area by a principal component analysis method.
TABLE 3-1 soil index correlation matrix
Item Hydrolyzable nitrogen Quick-acting potassium Available phosphorus Total salt content Organic matter
Hydrolyzable nitrogen 1.000 0.554 0.716 0.059 0.932
Quick-acting potassium 0.554 1.000 0.593 0.383 0.609
Available phosphorus 0.716 0.593 1.000 0.141 0.734
Total salt content 0.059 0.383 0.141 1.000 0.143
Organic matter 0.932 0.609 0.734 0.143 1.000
TABLE 3-2 KMO and Batterit test Table
Figure RE-GDA0003483731590000182
Figure RE-GDA0003483731590000191
The eigenvalues, eigenvectors, contribution rates and cumulative contribution rates of the matrix were further solved using SPSS 24.0 software (tables 3-3). According to the principle that the characteristic value lambda is larger than or equal to 1, 2 principal components are extracted, the variance contribution rates of the 2 principal components are 62.816% and 21.398%, the cumulative variance contribution rate is 84.214%, and the basic information of the selected index can be reflected.
According to the relation between the load factor and the principal component score coefficient, the weight of each index on the principal component is different; the first main component, organic matter, available phosphorus, quick-acting potassium and hydrolyzable nitrogen have larger positive values; the second major component, total salt, has a large positive value.
TABLE 3-3 principal component feature vectors and cumulative contributions
Figure RE-GDA0003483731590000192
The 5-factor raw data were normalized using SPSS (ZX for each case)1、ZX2、…、ZX5Expressed), from the principal component score coefficient matrix (tables 3-4), the composite score linear expression for each principal component:
F1＝0.349ZX1+0.157ZX2+0.294ZX3-0.154ZX4+0.333ZX5 (3-39)
F2＝-0.164ZX1+0.358ZX2-0.028ZX3+0.851ZX4-0.078ZX5 (3-40)
then according to LI ═ λ1F1+λ2F2+…+λmFm(λ is the principal component variance contribution rate) calculating the score of the locality index LI, and obtaining an expression: LI (0.628F)1+0.214F2
The location index LI of each sampling point in the research area can be obtained by calculation of SPSS and Execl software, and meanwhile, for the convenience of comparative analysis, the location index LI is normalized by the formula (2-22)
Figure RE-GDA0003483731590000201
In the formula, LIi' is a normalized value; LI (lithium ion) powderminAnd LImaxThe minimum value and the maximum value of the environment index LI are respectively; LI (lithium ion) powderiAnd the location index LI value of each point is obtained.
The calculation results are shown in tables 3 to 5:
tables 3-5 normalized values for each sample point and context index
Figure RE-GDA0003483731590000202
Figure RE-GDA0003483731590000211
Figure RE-GDA0003483731590000221
3. Information expression and analysis result compiled by ecological geological map
As shown in fig. 3-4, the research method is compiled according to the ecological geological map, and the interpretation result of the vegetation ground habitat index VAHI and the habitat index LI is obtained.
3-4, the terrain index of most regions in the study area is in the range of 0.167-0.333, the terrain index value is generally low, and the sample pit fertilizer analysis result shows that:
the organic quality of soil in a research area is in an extremely low level as a whole;
the quantity of the soil hydrolyzable nitrogen is 10.03-193.87mg/Kg, and the soil hydrolyzable nitrogen is in positive correlation with organic matters;
the quick-acting potassium amount of the soil is 38.75-280.77mg/Kg, and the whole body is at a medium to high level;
the effective phosphorus content of the soil is 0.05-11.32mg/Kg, and the whole soil is at a medium and low level;
and by integrating the analysis results of the 4 indexes, the soil fertility of most areas in the research area is smaller. The index value of the habitat of vegetation in a region with less human interference activity at the northwest side and the northeast side of the research area is higher than that of other regions, and the vegetation growth vigor of the region is better than that of other regions due to small human interference strength under the similar soil fertility background.
The habitat index value and the vegetation habitat index value of a small part of area in northwest are higher than those of other areas; the field investigation result shows that: most of the areas are protogenic forests which are slightly influenced by human activities, forest age groups are reasonable in structure, arbor-shrub-grass biological communities are complete in structure, and the ecological system has a healthy and complete structure and function; according to the analysis result of the root layer sheet of the sample pit, the area has high herbage density, large trees and shrubs occupy a certain proportion, the planting types are rich, the area with the depth of 0-30cm is a place and environment stable layer of the herbage such as green bristlegrass, sweet wormwood and the like, the area with the depth of 30-100 cm is a place and environment stable layer of the trees and shrubs such as paper mulberry, elm, negundo chastetree, wild jujube and the like, the root system is vertically distributed reasonably, and the water source conservation and soil conservation function is strong.
The habitat index values of partial areas in the middle and south and the habitat index values of vegetation land are lower than those of other areas, the human activities in the areas are strong, activities such as mining and land reclamation are carried out, vegetation degradation, species diversity reduction, serious water and soil loss and even stony desertification occur; the field sample pit survey shows that: the soil in the area is mostly brown soil, the soil layer is thinner, the thickness is mostly 20-50cm, the gravel content is higher, and the root systems are mostly distributed in the underground space of 0-40cm under the stress of rock strata, temperature, water, nutrients and other factors.
Referring to fig. 5, the analysis results of the ground habitat (vegetation ground habitat index) and the underground habitat (vegetation ground habitat index) are combined, the left side is a regional ecological geological map (fig. 5 left) drawn by the spatial superposition analysis function of the Arcgis 10.2 software, and a result map under the effect of the ecological geological condition is obtained by combining the supervision classification function based on the ENVI 5.3 software (fig. 5 right)
It should be noted that: the graph is labeled Lx: LI (a-b) & VAHI (c-d) means that the combined range of the habitat index (LI) range (a-b) and the vegetation habitat index (VAHI) range (c-d) is as follows:
L1：LI(0.333-0.5)&VAHI(0.8-1.0)；
L2：LI(0.333-0.5)&VAHI(0.6-0.8)；
L3：LI(0.333-0.5)&VAHI(0.4-0.6)；
L4：LI(0.5-0.7)&VAHI(0.6-0.8)；
L5：LI(0.5-0.7)&VAHI(0.4-0.6)；
L6：LI(0.167-0.333)&VAHI(0.8-1.0)；
L7：LI(0.167-0.333)&VAHI(0.6-0.8)；
L8：LI(0.167-0.333)&VAHI(0.4-0.6)；
L9：LI(0-0.167)&VAHI(0.6-0.8)；
L10：LI(0-0.167)&VAHI(0.4-0.6)。
as shown in FIG. 5, the current situation of the ecological geology can be intuitively reflected, which is specifically shown in the following steps:
1. the overall ecological geological conditions of the research area show a trend that the human activity degree is gradually decreased from the regions with weak human activity degree in the northwest to the regions with strong human activity degree in the southeast and the south.
Particularly, the west, the northwest and the northeast are large woodland, trees are used as main trees, towns are distributed sporadically, the activity intensity of human beings is low, the ecological geological condition level can reach the level of L7, and the local areas can reach the levels of L1, L2 and L4. Most of the areas are native forests, plants grow well, the variety of the plants is rich, forest age groups are stable in structure, plant community structures are complete, and the water and soil conservation and water source conservation functions are strong, so that the soil fertilizer level of the areas is high, local microclimate is appropriate, and the ecological geological conditions are good. Corresponding to a large piece of forest land in the land use type map.
The areas of south China and southeast China are Weihui city, Hui county city and Fengquan city areas, the roads are dense, the cities and towns, mining areas and farmlands are concentrated, the intensity of human activities such as mining, engineering construction and agricultural production is high, the vegetation types mainly comprise herbs and shrubs, trees are few, and the arbors are mostly artificially planted arborvitae with high tolerance. Most of the ecological geological condition grades are L8 and L9, the periphery of the south part of the mining area is affected by long-term mineral resource development and utilization activities, the soil erosion is serious, the water and soil retention capacity is extremely low, and the ecological geological condition grade is L10. Reflects that the local soil fertility level is low and the climate environment is severe, and corresponds to the stagger distribution of the forest land and the grassland in the southeast of the land utilization type chart.
The ecological geological condition grade around farmlands and towns in the south can reach the levels of L2 and L3, namely the habitat index is higher and the habitat index on vegetation land is lower. The local land vegetation ecosystem is unbalanced due to the influence of unreasonable cultivation behaviors of human beings such as transition grazing, transition reclamation, excessive cutting and disorder cutting in the area, but the influence on the land conditions is small, so the soil fertility level is high. The land utilization type map reflects the alternate distribution of the forest land, the grass-irrigating land and the farmland.
The analysis results are combined, so that the ecological geological map compiling method can intuitively reflect the ecological geological problems of the area.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to be within the scope of the present disclosure.

Claims (10)

1. An ecological geological map compilation method is characterized by comprising the following steps:
S1: performing ecological remote sensing interpretation by using ENVI software on the basis of regional remote sensing data;
S2: comprehensively representing the ecological geological condition of the region by using the vegetation ground habitat index VAHI and the habitat index LI through regional plant habitat survey, reflecting the ecological geological condition into a corresponding map, and constructing the vegetation ground habitat index VAHI and the habitat index LI;
S3: interpolating the data fields by using an interpolation method in Arcgis 10.2 software space analysis-interpolation, and reclassifying interpolation results to obtain a ground habitat index and a ground habitat index classification result;
S4: and (3) overlapping the vegetation ground habitat index VAHI and the habitat index LI by using the regional geological map as a base map and through an Arcgis 10.2 software space overlapping analysis function, and drawing a regional ecological geological map.
2. The method of claim 1, wherein the step S is performed by using a computer to generate the ecological geological map2The method for representing the ecological geological condition of the area by using the vegetation ground habitat index VAHI specifically comprises the following steps:
the vegetation habitat index VAHI is coupled with a humidity factor Wet, a temperature factor Heat and a dryness factor Dry;
extracting the information of the humidity factor Wet, the temperature factor Heat and the dryness factor Dry based on a remote sensing technology, and carrying out automatic weighted value superposition analysis on the humidity factor Wet, the temperature factor Heat and the dryness factor Dry by a principal component analysis method to evaluate the terrestrial habitat conditions of the vegetation.
3. The method for compiling the ecological geological map according to claim 2, wherein the humidity measure of the humidity factor Wet is determined by the following method: by adopting a Thyscap transformation technology, obtaining a third component which has a direct relation with surface physical parameters and is used as a reflection index of a humidity factor Wet, wherein the specific formula of the humidity factor Wet is as follows:
WetOL1＝(0.1511γblue+0.1973γgreen+0.3283γred+0.3407γnir-0.7117γswir1-0.4559γswir2)
in the formula, gammablue、γgreen、γred、γnir、γswir1、γswir2Respectively the wavelengths of a blue wave band, a green wave band, a red wave band, a near infrared wave band, a middle infrared wave band 1 and a middle infrared wave band 2.
4. The method for constructing an ecological geological map according to the claim 2, characterized in that the Heat measure of the temperature factor Heat is determined by the following method: and (2) inverting the land surface temperature LST by adopting an atmospheric correction method and utilizing a thermal infrared remote sensing technology, wherein the calculation formula of the land surface temperature LST is as follows:
Figure FDA0003366024690000021
Figure FDA0003366024690000022
Lλ＝gain×DN+bias (3-3)
Figure FDA0003366024690000023
Figure FDA0003366024690000024
Figure FDA0003366024690000025
in the formula, LST represents land surface temperature and the unit is; t is a temperature value at the Landsat-8TIRS sensor; l isλThe reflectivity of a thermal infrared band after radiation calibration is shown, gain represents a gain value, DN represents a pixel brightness value of a remote sensing image, and no unit exists; band 10, K for Landsat-8TIRS sensor1＝774.89W·m-2·sr-1·μm-1，K21321.08K; λ is the central wavelength of the thermal infrared band, λTM＝11.435μm，λb10＝10.900μm；x＝1.438×10-2m·K；
ζbuildingIs the emissivity, zeta, of the pixels in urban townssurfaceSpecific radiance of natural surface pixel, FvRepresenting vegetation coverage;
NDVIVNDVI value of the Pixel covered by the complete Vegetation, NDVISThe NDVI value for a pixel that is completely bare or vegetation-free covered area.
5. The method for compiling the eco-geological map according to claim 2, wherein the method for determining the index NDBSI of the city building and the bare soil of the dryness factor Dry comprises the following steps: selecting bare soil index SI representing bare soil information and building index IBI representing building land information to synthesize, and taking the average value of the bare soil index SI and the building index IBI, wherein the calculation formula of the urban building and bare soil index NDBSI is as follows:
Figure FDA0003366024690000026
Figure FDA0003366024690000027
Figure FDA0003366024690000031
in the formula, NDBSI represents the index of urban construction and bare soil, gammablue、γgreen、γred、γnir、γswir1、γswir2Respectively the wavelengths of a blue wave band, a green wave band, a red wave band, a near infrared wave band, a middle infrared wave band 1 and a middle infrared wave band 2.
6. The method for compiling the ecological geological map according to claim 1, wherein the specific process for constructing the vegetation ground habitat index VAHI comprises the following steps:
step 1: creating a remote sensing defined expression according to a mathematical expression of the vegetation habitat index VAHI, wherein the mathematical expression of the vegetation habitat index VAHI is VAHI-f (Wet, Heat, Dry), and then the remote sensing defined expression is VAHI*＝f(Wet*,LST,NDBSI)；
In the formula, Wet, Heat and Dry respectively represent a humidity factor, a Heat factor and a dryness factor; wet*LST and NDBSI respectively represent a humidity index, a land surface temperature and an urban building and bare soil index;
step 2: normalizing the original values of the three factors, and unifying the ranges between [0 and 1], wherein the normalization processing formula is as follows:
Figure FDA0003366024690000032
in the formula, BIiPixel value normalized by a factor, biIs the pixel value i, b of a certain index factormax、bminThe maximum value and the minimum value of the factor are respectively;
and step 3:after normalization processing, synthesizing 3 normalization factors into an image, performing PCA analysis by utilizing an ENVI (intrinsic differential pressure) self-contained principal component analysis tool, extracting a first principal component, calculating to obtain an initial VAHI value, and recording as VAHI0Said VAHI0The calculation expression of (a) is:
VAHI0＝1-PC1[f(WET,LST,NDBSI)] (3-11)
and 4, step 4: mixing VAHI0And normalizing again to obtain a final VAHI value, wherein the final expression of the final VAHI value is as follows:
Figure FDA0003366024690000033
7. the method of claim 1, wherein the step S is performed by using a computer to generate the ecological geological map2Specifically, the calculation of the context index includes the following steps:
S21: performing Pearson correlation analysis by using SPSS 24.0 statistical software to screen out soil nutrient evaluation factors with significant correlation;
S22: reducing the dimension of each index by adopting a principal component analysis method in factor analysis to obtain an index characteristic value and a characteristic vector which comprehensively reflect the soil quality condition;
S23: selecting key principal components according to the characteristic value lambda being more than or equal to 1, standardizing the numerical values of all factors, calculating the scores of all principal components, and substituting the scores into a comprehensive score formula to obtain the place and environment index values of all sampling points.
8. The method of claim 7, wherein the step S is performed by using a computer to generate the ecological geological map23In the method, the standardized calculation formula of each index is as follows:
Figure FDA0003366024690000041
in the formula: i is the number of samples; j is the index number; x is the number ofijThe value of the j index in the ith sample is taken as the index value; x'ijIs a standardized value; x is the number ofjIs the average value of the j index; sjIs the standard deviation of the j-th index.
9. The method of claim 8, wherein the step S is performed by using a computer to generate the ecological geological map23In the formula, each principal component score value is as follows:
yi＝bij×xij′ (3-14)
in the formula: bijThe score coefficient of the principal component of the jth index in the ith sample; y isiThe principal component score values of the ith sample.
10. The method of claim 9, wherein the step S is performed by using a computer to generate the ecological geological map23The map index value is formulated as follows:
Figure FDA0003366024690000042
in the formula: a isikIs the eigenvalue contribution, LI, of the k-th principal component of the ith sampleiThe place index value of the ith sample.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115860517A (en) * 2022-11-15 2023-03-28 清华大学 Ecological environment quality evaluation method and product based on geographic computing language
CN116975503A (en) * 2023-09-22 2023-10-31 临沂大学 Soil erosion information management method and system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115860517A (en) * 2022-11-15 2023-03-28 清华大学 Ecological environment quality evaluation method and product based on geographic computing language
CN115860517B (en) * 2022-11-15 2023-10-20 清华大学 Ecological environment quality evaluation method and product based on geographic computing language
CN116975503A (en) * 2023-09-22 2023-10-31 临沂大学 Soil erosion information management method and system
CN116975503B (en) * 2023-09-22 2023-12-05 临沂大学 Soil erosion information management method and system

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