Method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indexes

Abstract

The invention relates to the technical field of ecological environment quality evaluation, in particular to a method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indexes, which comprises the following steps: S1, collecting remote sensing data such as Landsat, SRTMDEM, MOD11A2, GlobeLand30 and FLDAS, and preprocessing; s2, extracting a man-made pressure index, a biological abundance index, a vegetation coverage index, surface temperature and ground relative humidity from the multi-source remote sensing data set obtained through preprocessing in the step S1; and S3, downscaling the ground relative humidity data by adopting a regression Kriging method so as to match the resolutions of other data. S4, after all indexes are standardized, MCEEI is constructed through principal component analysis, and the comprehensive quality of the ecological environment is reflected. According to the method, the surface ecological environment quality condition is rapidly quantified from multi-source remote sensing data by integrating five indexes, and the problem that a traditional ecological environment quality remote sensing evaluation method cannot effectively balance regional differences in large-scale research is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment quality evaluation, and in particular to a method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators. Background Art

[0003] Early ecological environment quality assessments were conducted using plants as indicators. For example, benthic and phytoplankton were used as evaluation indicators to classify eutrophication at river bifurcations, estuaries, and coastal zones. In addition, in 1969, the United States enacted the National Environmental Policy Act to assess the impact of individual environmental pollutants on the ecological environment. Subsequently, countries such as Sweden and Canada introduced environmental assessment systems into their legal environmental protection frameworks. Due to the widespread need to assess regional ecological environment quality when making protection and restoration decisions, the use of ecological indices has become more frequent in the past three decades. Most of these ecological indices are based on summaries of selected locations to determine regional trends. Therefore, they cannot be directly applied to larger areas and cannot predict the global consequences of human activities.

[0004] In recent years, remote sensing technology has been proven to be an effective means to quickly learn about the spatiotemporal changes of regional ecological environment because it can record and process a wide range of various spatiotemporal data. However, early applications usually focused on a single ecological factor or one aspect of the ecological environment, such as the normalized difference vegetation index (NDVI) or land surface temperature (LST), and the evaluation of ecological environment quality was not systematic and comprehensive enough. Subsequently, some remote sensing ecological quality evaluation indicators were proposed and effectively applied to the evaluation of ecological environment quality in cities or urban agglomerations, including remote sensing based ecological index (RSEI), comprehensive ecological evaluation index (CEI) and comprehensive ecological quality evaluation index (CEEI). These remote sensing ecological quality evaluation methods can quickly and effectively analyze the ecological environment quality of the region and accurately describe the health status of the regional ecological environment.

[0005] However, the existing ecological quality index relies heavily on remote sensing satellite images for calculation. In the study of large-scale, cloudy and rainy areas, it is difficult to obtain remote sensing images with consistent time or even seasons across the entire range (due to meteorological factors such as frequent cloud interference and continuous rainfall in some areas, there are very few historical images available), which directly affects the remote sensing quantitative inversion of ecological environment quality. In addition, if the acquisition time of adjacent images is different (for example, one is from summer and the other is from winter), the extracted key surface parameters such as NDVI or LST will show significant differences at the edge of the image, which will have a significant impact on the inversion results of ecological environment quality, making it unsuitable for remote sensing quantitative evaluation of ecological environment quality at a large regional scale.

[0006] In view of the above problems, it is necessary to develop a new remote sensing quantitative evaluation method of ecological environment quality based on comprehensive multi-source data sets for remote sensing quantitative evaluation of ecological environment quality in large-scale research. Summary of the invention

[0007] The purpose of the present invention is to provide a method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators. By integrating five indicators, the surface ecological environment quality status can be quickly quantified from multi-source remote sensing data, so as to solve the problem that traditional ecological environment quality remote sensing assessment methods cannot effectively balance regional differences in large-scale research.

[0008] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0009] A method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators includes the following steps:

[0010] S1: Collect remote sensing data such as Landsat, SRTMDEM, MOD11A2, GlobeLand30 and FLDAS, and perform preprocessing.

[0011] S2: Extract the anthropogenic pressures index (API), biological richness index (BRI), vegetation coverage index (VCI), land surface temperature (LST), and surface relative humidity (SRH) from the multi-source remote sensing data set pre-processed in step S1;

[0012] S3: Regression kriging is used to downscale the surface relative humidity data to match the resolution of other data, and the universal kriging interpolation model is used to fill in gaps and downscale the surface temperature data to match the resolution of other data.

[0013] S4: After standardizing all indicators, principal component analysis was used to construct the MCEEI, which reflects the comprehensive quality of the ecological environment.

[0014] Further, step S1 specifically includes: obtaining the U.S. Landsat series remote sensing images through the United States Geological Survey (USGS) website (GloVis, https: / / glovis.usgs.gov / ); obtaining SRTMDEM and MOD11A2 product data through the Geospatial Data Cloud Platform of the Computer Network Information Center of the Chinese Academy of Sciences (http: / / www.gscloud.cn); obtaining the surface cover dataset GlobeLand30 through (http: / / www.globallandcover.com / ); and obtaining the FLDAS dataset through the U.S. Earth Science Data and Information Service Center (https: / / disc.gsfc.nasa.gov / datasets / FLDAS_NOAH01_C_GL_M_001 / summary). After collecting multi-source remote sensing data, image preprocessing is performed according to the actual situation of the study area, including but not limited to geometric correction, atmospheric correction, image stitching, and cropping.

[0015] Furthermore, step S2 specifically includes the following sub-steps:

[0016] S2.1: Extract the anthropogenic pressures index (API) from the preprocessed multi-source remote sensing data set. The index is composed of the impervious surface cover (ISC) and the soil and built-up coverage (SBC). The impervious surface cover (ISC) refers to the coverage content of the impervious surface per unit area, which quantifies the percentage of the impervious surface per unit area in the landscape. The calculation formula is as follows:

[0017] API=SBC+ISC

[0018]

[0019] In the formula, A i is the set unit internal area size, a ijThe area of ​​the pixel in the set unit is the impervious surface, SBC is the soil and building coverage in the watershed, and the calculation formula is as follows:

[0020] SBC=1-VC-WC

[0021] rr

[0022]

[0023] Among them, WC r is the water area coverage, VC r is the vegetation coverage rate, A is the regional area, and A w is the area of ​​the regional water body.

[0024] S2.2: Extract the biological richness index (BRI) from the preprocessed multi-source remote sensing dataset. The BRI is calculated based on land use product data (GlobeLand30). The specific method is as follows:

[0025]

[0026] In the formula, A BRI is the normalization coefficient of BRI (value is 692.09602), A is the area of ​​the region, and A f is the forest area, A g is the grassland area, A w is the wetland area, A p is the cultivated land area, A c is the construction land area, A u It is unused area.

[0027] S2.3: Extract vegetation coverage index (VCI) from the preprocessed multi-source remote sensing data set. The specific method is as follows:

[0028]

[0029] Among them, A VCI is the normalized coefficient of VCI (value is 601.110997), A is the area of ​​the region, A f is the forest area, A g is the grassland area, A p is the cultivated land area, A c is the construction land area, A u It is unused area.

[0030] S2.4: Extract the land surface temperature (LST) from the preprocessed land surface temperature product dataset (MOD11A2) and convert the land surface temperature to Celsius using the following formula:

[0031] LST=T b ×0.02-273.15

[0032] Among them, T b is the pixel value of the temperature product data, and LST is the surface temperature in °C. Since there are many null values ​​in the surface temperature product data, the universal Kriging interpolation model is used to fill the vacant values ​​and downscale the data to obtain a spatial resolution consistent with API, BRI, and VCI. The calculation formula is as follows:

[0033]

[0034] In the formula, Z UK (x) is the Kriging estimate at x, λ i is the weight of the ith value, x i is the ith observation value.

[0035] S2.5: Select the specific humidity (SH) from the preprocessed FLDAS data set as the basic data to extract the surface relative humidity (SRH). The specific method is as follows:

[0036]

[0037] p = 100.56 × e -0.000117×H ×1000

[0038] Where w is the mass mixing ratio of water vapor to dry air (dimensionless), w s is the mass mixing ratio of water vapor to dry air in equilibrium (dimensionless), p is the atmospheric pressure (Pa), q is the specific humidity (kg·kg -1 ), T is the Kelvin temperature (K), T 0 is the reference temperature (usually 273.15K) and H is the altitude (m).

[0039] Further, the step S3 specifically includes: using the regression Kriging (RK) method to perform spatial downscaling to obtain SRH data consistent with the spatial resolution of API, BRI, and VCI. The calculation formula is as follows:

[0040]

[0041] Among them, Z RK (x) is the estimated x 0 The predicted value of SRH at For x 0 The fitted trend of the location, is the residual value at that location.

[0042] Furthermore, the step S4 specifically includes the following sub-steps:

[0043] S4.1: Standardize the API, BRI, VCI, LST, and SRH data extracted above, because the larger the BRI, VCI, and SRH values, the better the ecological environment quality, while the larger the API and LST values, the worse the ecological environment quality. In order to facilitate calculation, the values ​​of all indicators have a unified direction, that is, the larger the value, the better the ecological environment quality. Therefore, all indicators are standardized as follows:

[0044] Standardization of indicators (BRI, VCI and SRH),

[0045] L i =(l max -l i ) / (l max -l min )

[0046] Indicator standardization (API and LST),

[0047] L i =(l i -l min ) / (l max -l min )

[0048] Among them, L i Yes i The standardized value, l max and l min Respectively represent the maximum and minimum values ​​of each index (API, BRI, VCI, LST, SRH). The standardized values ​​of API, BRI, VCI, LST, and SRH data are between 0 and 1.

[0049] S4.2: Overlay the API, BRI, VCI, LST, and SRH images extracted above to construct the improved comprehensive ecological quality evaluation index MCEEI function:

[0050] MCEEI=f(API,BRI,VCI,LST,SRH)

[0051] Where LDI is the comprehensive land degradation index, and f is the integrated function of API, BRI, VCI, LST, and SRH.

[0052] S4.3: MCEEI function solution. The principal component analysis (PCA) method is used to solve the MCEEI function. The original multidimensional remote sensing data set is compressed by principal component analysis. The first component obtained by principal component analysis, namely the first principal component (PC1), contains most of the information in the original data set. The linear combination of PC1 is used to construct MCEEI:

[0053] MCEEI=(PC1-PC1 min ) / (PC1 max -PC1 min )

[0054] In the formula, PC1 is the first principal component obtained after principal component analysis, PC1 min and PC1 max are the minimum and maximum values ​​of PC1, respectively. The value of MCEEI is between 0 and 1. The larger the value, the better the ecological environment quality in the study area.

[0055] Beneficial effects of the present invention:

[0056] The present invention realizes comprehensive monitoring of different levels of ecological environment by integrating multiple remote sensing data sources such as Landsat, SRTMDEM, MOD11A2, GlobeLand30 and FLDAS. Landsat provides multi-spectral information of the surface with a spatial resolution of 30m, SRTMDEM provides terrain information, MOD11A2 provides surface temperature data that can be used for large-scale research, and GlobeLand30 and FLDAS provide a wide range of detailed information on land use and surface humidity. This fusion of multi-source data makes the evaluation indicators not only rich and complementary, but also able to comprehensively reflect the ecological environment status from multiple angles, thereby improving the comprehensiveness and accuracy of the evaluation.

[0057] There are two major challenges in conducting large-scale ecological environment quality assessment. First, due to the influence of terrain conditions and climate differences in different regions (some areas are affected by cloudy rain), a single data source cannot effectively obtain complete multi-dimensional observation data for the entire region. Second, the humidity (Wet) index used in the existing ecological quality evaluation in large-scale studies has significant regional differences and altitude differences, and the global humidity information is unbalanced, which is not conducive to the monitoring of ecological quality in large-scale studies. The present invention integrates multi-source remote sensing data to effectively obtain five key ecological quality evaluation indicators that are complete in the entire region, among which the surface relative humidity (SRH) can objectively evaluate the humidity environment of different regions in large-scale studies, and can consider the influence of water environment factors (BRI) to a certain extent, without the need to mask and remove water bodies. In addition, the universal kriging method is used to fill in the missing values ​​and downscale the land surface temperature (LST), and the regression kriging method is used to downscale the ground relative humidity (SRH) data to match the spatial resolution of other data. The regression kriging method combines the regression model with spatial interpolation to effectively utilize the trend of meteorological data while maintaining the spatial detail characteristics of the data. Therefore, in the study of large-scale, cloudy and rainy areas, the problem of inconsistent data acquisition can be effectively dealt with, and the accuracy of remote sensing inversion results can be improved.

[0058] Based on the pressure-state-response (PSR) framework, the present invention extracts and standardizes the anthropogenic pressure index, biological abundance index, vegetation cover index, surface temperature and ground relative humidity from five key ecological dimensions. Through the principal component analysis (PCA) method, an improved comprehensive ecological quality evaluation index (MCEEI) is constructed, which can integrate the advantages of multi-source remote sensing information and effectively capture the contribution of each indicator in the comprehensive ecological quality. PCA integrates various ecological indicators through dimensionality reduction technology and extracts the first principal component (PC1) containing the largest amount of information, thereby ensuring the scientific integration and comprehensive weighing of multiple indicators in the evaluation process.

[0059] With the high temporal and spatial resolution and global coverage of remote sensing data, the present invention can effectively track the dynamic changes in ecological environment quality. Especially in areas with dense human activities and sensitive areas of climate change where the ecosystem is easily affected, the macro-monitoring and rapid response capabilities of this method provide important support for effective early warning and management decisions of the ecological environment. Through standardization and adjustment of indicator directions, the results are intuitive and easy to understand, helping managers to quickly identify potential ecological degradation areas and formulate corresponding protection measures.

[0060] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0063] Figure 2 Schematic diagram of land surface temperature data; (a) LST data after MOD11A2 unit conversion; (b) LST data after using universal Kriging interpolation;

[0064] Figure 3 Schematic diagram of surface humidity of the present invention; (a) SH data in FLDAS data set; (b) SRH data calculated by SRH conversion formula; (c) SRH after RK prediction interpolation;

[0065] Figure 4 (a) is a true color display diagram of OLI images, and a comparison diagram of RSEI (b) and MCEEI (c); (d) is a correlation analysis; (e) is an RSEI index diagram;

[0066] Figure 5 This is a schematic diagram of the comparison of the EI data released by government departments in 2020 with the RSEI, CEEI, and MCEEI in different urban areas;

[0067] Figure 6 This is a schematic diagram of the spatiotemporal characteristics of the surface ecological environment quality in the Pearl River Basin from 2000 to 2020;

[0068] Figure 7 Schematic diagram of Google Earth image samples of the surface ecological environment of the Pearl River Basin; (a) The grade distribution of surface EEQ of the Pearl River Basin in 2020, (b) The extension of artificial surface of mountain forests obtained by Google Earth Pro (23°25'17"N, 113°08'58"E, December 21, 2017), (c) The forest land obtained by Google Earth Pro (23°26'02"N, 108°27'59"E, December 05, 2019), (d) The urban center area obtained by Google Earth Pro (23°25'17"N, 113°08'58"E, January 26, 2019) DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] Example 1

[0071] The method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators described in this embodiment, the construction process of the improved comprehensive ecological evaluation index (MCEEI) is as follows: Figure 1 As shown in the figure, it mainly includes: multi-source remote sensing data acquisition and preprocessing, anthropogenic pressures index (API) extraction, biological richness index (BRI) extraction, vegetation coverage index (VCI) extraction, land surface temperature (LST) extraction, surface relative humidity (SRH) extraction, and the integration of improved comprehensive ecological evaluation index (MCEEI).

[0072] 1) Obtain multi-source remote sensing data: Obtain the U.S. Landsat series of remote sensing images through the United States Geological Survey (USGS) website (GloVis, https: / / glovis.usgs.gov / ); obtain SRTMDEM and MOD11A2 product data through the Geospatial Data Cloud Platform of the Computer Network Information Center of the Chinese Academy of Sciences (http: / / www.gscloud.cn); obtain the surface cover dataset GlobeLand30 through (http: / / www.globallandco ver.com / ); and obtain the FLDAS dataset through the U.S. Earth Science Data and Information Service Center (https: / / disc.gsfc.nasa.gov / datasets / FLDAS_NOAH01_C_GL_M_001 / summary). After collecting multi-source remote sensing data, image preprocessing was performed according to the actual situation of the study area, mainly including geometric correction, atmospheric correction, image stitching, and cropping.

[0073] 2) Extract the anthropogenic pressures index (API) from the preprocessed multi-source remote sensing data set. The API is composed of the impervious surface cover (ISC) and the soil and building coverage (SBC). The impervious surface coverage (ISC) refers to the coverage content of the impervious surface per unit area, which quantifies the percentage of the impervious surface per unit area in the landscape. The calculation formula is as follows:

[0074] API=SBC+ISC

[0075]

[0076] In the formula, A i is the set unit internal area size, a ij The area of ​​the pixel in the set unit is the impervious surface, SBC is the soil and building coverage in the watershed, and the calculation formula is as follows:

[0077] SBC=1-VC-WC

[0078] rr

[0079]

[0080] Among them, WC r is the water area coverage, VC r is the vegetation coverage rate, A is the regional area, and A w is the area of ​​the regional water body.

[0081] 3) Extract the biological richness index (BRI) from the preprocessed multi-source remote sensing data set. The BRI is calculated based on land use product data (GlobeLand30). The specific method is as follows:

[0082]

[0083] In the formula, A BRI is the normalization coefficient of BRI (value is 692.09602), A is the area of ​​the region, and A f is the forest area, A g is the grassland area, A w is the wetland area, A p is the cultivated land area, A c is the construction land area, A u It is unused area.

[0084] 4) Extract vegetation coverage index (VCI) from the preprocessed multi-source remote sensing data set. The specific method is as follows:

[0085]

[0086] Among them, A VCI is the normalized coefficient of VCI (value is 601.110997), A is the area of ​​the region, A f is the forest area, A g is the grassland area, A p is the cultivated land area, A c is the construction land area, A u It is unused area.

[0087] 5) Extract the land surface temperature (LST) from the preprocessed land surface temperature product dataset (MOD11A2) and convert the land surface temperature to Celsius using the following formula:

[0088] LST=T b ×0.02-273.15

[0089] Among them, T b is the pixel value of the temperature product data, and LST is the surface temperature in °C. Since there are many null values ​​in the surface temperature product data, the universal Kriging interpolation model is used to fill the vacant values. The calculation formula is as follows:

[0090]

[0091] In the formula, Z UK (x) is the Kriging estimate at x, λ i is the weight of the ith value, x i is the ith observation value.

[0092] 6) Select the specific humidity (SH) from the preprocessed FLDAS data set as the basic data to extract the surface relative humidity (SRH). The specific method is as follows:

[0093]

[0094] p = 100.56 × e -0.000117×H ×1000

[0095] Where w is the mass mixing ratio of water vapor to dry air (dimensionless), w sis the mass mixing ratio of water vapor to dry air in equilibrium (dimensionless), p is the atmospheric pressure (Pa), q is the specific humidity (kg·kg -1 ), T is the Kelvin temperature (K), T 0 is the reference temperature (usually 273.15K) and H is the altitude (m).

[0096] 7) Since SRH is calculated using the FLDAS dataset with a spatial resolution of 0.1°, the regression Kriging (RK) method is used to perform spatial downscaling to obtain a spatial resolution consistent with that of API, BRI, VCI, and LST. The calculation formula is as follows:

[0097]

[0098] Among them, Z RK (x) is the estimated x 0 The predicted value of SRH at For x 0 The fitted trend of the location, is the residual value at that location.

[0099] 8) MCEEI integration, the specific method is as follows:

[0100] The API, BRI, VCI, LST, and SRH data extracted above are standardized, because the larger the BRI, VCI, and SRH values, the better the ecological environment quality, while the larger the API and LST values, the worse the ecological environment quality. In order to facilitate calculation, the values ​​of all indicators have a unified direction, that is, the larger the value, the better the ecological environment quality. Therefore, all indicators are standardized as follows:

[0101] Standardization of indicators (BRI, VCI and SRH),

[0102] L i =(l max -l i ) / (l max -l min )

[0103] Indicator standardization (API and LST),

[0104] L i =(l i -l min ) / (l max -l min )

[0105] Among them, L i Yes i The standardized value, l maxand l min Respectively represent the maximum and minimum values ​​of each index (API, BRI, VCI, LST, SRH). The standardized values ​​of API, BRI, VCI, LST, and SRH data are between 0 and 1.

[0106] The API, BRI, VCI, LST, and SRH images extracted above are superimposed to construct the improved comprehensive ecological quality evaluation index MCEEI function:

[0107] MCEEI=f(API,BRI,VCI,LST,SRH)

[0108] Where LDI is the comprehensive land degradation index, and f is the integrated function of API, BRI, VCI, LST, and SRH.

[0109] MCEEI function is solved by principal component analysis (PCA). The original multidimensional remote sensing data set is compressed by principal component analysis. The first component obtained by principal component analysis, namely the first principal component (PC1), contains most of the information in the original data set. The linear combination of PC1 is used to construct MCEEI:

[0110] MCEEI=(PC1-PC1 min ) / (PC1 max -PC1 min )

[0111] In the formula, PC1 is the first principal component obtained after principal component analysis, PC1 min and PC1 max are the minimum and maximum values ​​of PC1, respectively. The value of MCEEI is between 0 and 1. The larger the value, the better the ecological environment quality in the study area.

[0112] In this embodiment, the constructed MCEEI is not limited to being applicable to datasets such as SRTMDEM, MOD11A2, GlobeLand30, and FLDAS, but can also be applied to other remote sensing datasets that have approximate surface environmental parameter information, and is not limited to the method described in this embodiment.

[0113] In this embodiment, the three indicators API, BRI, and VCI may be replaced by indicators having similar functions.

[0114] In this embodiment, MCEEI can be used to classify the ecological environment quality status into five levels (poor, relatively poor, medium, good, and excellent) according to the MCEEI value with a step size of 0.2. Other classification methods are also possible.

[0115] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators, characterized in that: The following steps are involved: S1: Collect and preprocess Landsat, SRTMDEM, MOD11A2, GlobeLand30 and FLDAS remote sensing data; S2: Extracting the anthropogenic pressure index API, biological abundance index BRI, vegetation cover index VCI, land surface temperature LST, and ground relative humidity SRH from the multi-source remote sensing data set obtained by preprocessing in step S1; S3: Regression kriging was used to downscale the surface relative humidity data to match the resolution of other data, and the universal kriging interpolation model was used to fill in the gaps and downscale the surface temperature data to match the resolution of other data; S4: After standardizing all indicators, principal component analysis was used to construct the MCEEI, which reflects the comprehensive quality of the ecological environment.

2. The method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators as claimed in claim 1, characterized in that: Step S1 specifically includes: obtaining the U.S. Land Resources Satellite series remote sensing images through the website of the United States Geological Survey; obtaining SRTMDEM and MOD11A2 product data through the geospatial data cloud platform of the Computer Network Information Center of the Chinese Academy of Sciences; obtaining the surface cover dataset GlobeLand30; obtaining the FLDAS dataset through the U.S. Earth Science Data and Information Service Center; after collecting multi-source remote sensing data, image preprocessing is performed according to the actual situation of the study area, including but not limited to geometric correction, atmospheric correction, image stitching, and cropping.

3. The method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators as claimed in claim 1, characterized in that: Step S2 specifically includes the following sub-steps: S2.1: Extract the anthropogenic pressure index API from the preprocessed multi-source remote sensing data set. The index is composed of the impervious surface coverage ISC and the soil and building coverage SBC. The impervious surface coverage ISC refers to the coverage content of the impervious surface per unit area. It quantifies the percentage of the impervious surface per unit area in the landscape. The calculation formula is as follows: API=SBC+ISC In the formula, A i is the set unit internal area size, a ij The area of ​​the pixel in the set unit is the impervious surface, SBC is the soil and building coverage in the watershed, and the calculation formula is as follows: SBC=1-VC-WC r r Among them, WC r is the water area coverage, VC r is the vegetation coverage rate, A is the regional area, and A w is the area of ​​the regional water body; S2.2: Extract the biological abundance index (BRI) from the preprocessed multi-source remote sensing dataset. BRI is calculated based on the land use product data GlobeLand30. The specific method is as follows: In the formula, A BRI is the normalization coefficient of BRI, A is the area of ​​the region, and A f is the forest area, A g is the grassland area, A w is the wetland area, A p is the cultivated land area, A c is the construction land area, A u It is unused area; S2.3: Extract the vegetation cover index VCI from the preprocessed multi-source remote sensing data set as follows: Among them, A VCI is the normalized coefficient of VCI, A is the area of ​​the region, and A f is the forest area, A g is the grassland area, A p is the cultivated land area, A c is the construction land area, A u It is unused area; S2.4: Extract the land surface temperature from the preprocessed land surface temperature product dataset MOD11A2 and convert the land surface temperature to degrees Celsius using the following formula: LST=T b ×0.02-273.15 Among them, T b is the pixel value of the temperature product data, and LST is the surface temperature in °C. Since there are many null values ​​in the surface temperature product data, the universal Kriging interpolation model is used to fill the gaps and downscale the data to obtain a spatial resolution consistent with API, BRI, and VCI. The calculation formula is as follows: In the formula, Z UK (x) is the Kriging estimate at x, λ i is the weight of the ith value, x i is the i-th observation value; S2.5: Select the ground specific humidity SH from the preprocessed FLDAS data set as the basic data SRH to extract the ground relative humidity. The specific method is as follows: p=100.56×e -0.000117×H ×1000 Where w is the mass mixing ratio of water vapor to dry air, w s is the mass mixing ratio of water vapor to dry air in equilibrium, p is the atmospheric pressure, q is the specific humidity, T is the Kelvin temperature, T0 is the reference temperature, and H is the altitude.

4. The method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators as claimed in claim 1, characterized in that: The step S3 specifically includes: using the regression kriging method to perform spatial downscaling to obtain data consistent with the spatial resolution of API, BRI, and VCI. The calculation formula is as follows: Among them, Z RK (x) is the predicted value of SRH at x0, is the fitting trend at x0, is the residual value at that location.

5. The method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators as claimed in claim 1, characterized in that: The step S4 specifically includes the following sub-steps: S4.1: Standardize the API, BRI, VCI, LST, and SRH data extracted above, because the larger the BRI, VCI, and SRH values, the better the ecological environment quality, while the larger the API and LST values, the worse the ecological environment quality; in order to facilitate calculation, the values ​​of all indicators have a unified direction, that is, the larger the value, the better the ecological environment quality; so all indicators are standardized, the method is as follows: Indicator standardization BRI, VCI and SRH, L i ＝(l max -l i ) / (l max -l min ) Indicator standardization (API and LST), L i ＝(l i -l min ) / (l max -l min ) Among them, L i Yes i The standardized value, l max and l min Respectively represent the maximum and minimum values ​​of indicators API, BRI, VCI, LST, and SRH; the values ​​of API, BRI, VCI, LST, and SRH after standardization are between 0 and 1; S4.2: Overlay the API, BRI, VCI, LST, and SRH images extracted above to construct the improved comprehensive ecological quality evaluation index MCEEI function: MCEEI=f(API,BRI,VCI,LST,SRH) In the formula, LDI is the comprehensive land degradation index, and f is the integrated function of the five indicators of API, BRI, VCI, LST, and SRH; S4.3: MCEEI function solution, the principal component analysis method PCA is used to solve the MCEEI function. The original multidimensional remote sensing data set is compressed by principal component analysis. The first component obtained by principal component analysis, that is, the first principal component, contains most of the information in the original data set. The linear combination of the first principal component is used to construct MCEEI: MCEEI=(PC1-PC1 min ) / (PC1 max -PC1 min ) In the formula, PC1 is the first principal component obtained after principal component analysis, PC1 min and PC1 max are the minimum and maximum values ​​of PC1 respectively; the value of MCEEI is between 0 and 1, and the larger the value, the better the ecological environment quality in the study area.

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