Regional ecological environment quality evaluation method and system based on remote sensing data

Abstract

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental remote sensing and ecological monitoring, and in particular to a method and system for evaluating regional ecological environment quality based on remote sensing data. Background Art

[0002] With the rapid development of remote sensing technology, ecological and environmental monitoring based on satellite imagery has become an important means of assessing regional ecological quality. However, existing technological systems still face significant bottlenecks in addressing the dynamic responses of complex ecosystems, the coupling of multiple factors, and the interference of extreme climate events. Most studies rely solely on single remote sensing indices or isolated water quality parameters, lacking multi-source data on meteorology, vegetation, and hydrology. For example, the lack of spatiotemporal correlation analysis between vegetation cover and precipitation patterns makes it difficult to reveal the comprehensive impact of climate change on ecosystems. Assessment models are mostly based on historical means or fixed thresholds, unable to quantify the dynamic impact of extreme weather, such as short-term heavy rains and persistent droughts, on ecosystems. The time windows of remote sensing data and field measurements are misaligned, such as the lack of synchronization between satellite transit and ground sampling, which leads to amplified parameter inversion errors.

[0003] Traditional methods mainly achieve ecological assessment through the superposition of a single remote sensing index and weight calculation. They directly use vegetation coverage as the core indicator, estimate the coverage ratio through threshold segmentation or linear regression model, and sum up vegetation, water quality and other indicators according to fixed weights to form an ecological environment status index. It is widely used in my country's ecological environment monitoring. However, its core defect lies in the technical bottleneck of data fusion and model construction. A single indicator cannot reflect the integrity of the ecosystem. For example, areas with high vegetation coverage may have water quality problems. The superimposed coercive effects of extreme weather, such as heavy rain and drought on the ecological environment, are not included in the model, resulting in the inability of the assessment results to warn of climate disaster risks and a lack of predictive ability for the long-term evolution trend of the ecosystem. Therefore, there is an urgent need for a method to solve the problems of insufficient data fusion depth, lack of multi-factor collaborative modeling, and weak dynamic response capabilities.

[0004] The above challenges highlight the urgent needs of current remote sensing ecological assessment technology in terms of multi-source data fusion accuracy, model dynamic adaptability, comprehensive quantification of extreme events, and temporal and spatial resolution matching. It is necessary to develop innovative methods for multi-scale data fusion and dynamic process simulation to improve the scientificity and timeliness of regional ecological environmental quality assessment.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for evaluating regional ecological environment quality based on remote sensing data, so as to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The regional ecological environment quality evaluation method based on remote sensing data includes the following specific steps:

[0009] Step 1: Based on remote sensing data from the surveyed area, extract the reflectance of the near-infrared and red light bands at the peak of the growing season and analyze them to obtain a mixed vegetation index for the surveyed area. Also, measure the vegetation index of pure vegetation and bare soil on-site at the peak of the growing season. The vegetation indexes of mixed vegetation, pure vegetation, and bare soil are analyzed to obtain vegetation coverage data to reflect the degree of vegetation coverage.

[0010] Step 2: During periods of heavy rainfall and dry seasons, water transparency, used to assess water clarity, is calculated by extracting the reflectance of green and near-infrared light. The suspended solids concentration, which reflects the amount of solid particulate matter in the water, is calculated by extracting the reflectance of green and red light. The chlorophyll a concentration, which reflects the growth of aquatic plants, is obtained by analyzing the reflectance of blue and green light.

[0011] Step 3: Comprehensively assess the water quality based on the water transparency, suspended solids concentration, and chlorophyll a concentration in the measured area. Collect extreme weather environmental data within ten years, extract extreme weather influencing factors based on the extreme weather data, and evaluate the ecological environment quality using the extreme influencing factors, environmental data, and the results of the comprehensive assessment of water quality.

[0012] Furthermore, based on the remote sensing data of the measured area, the reflectance of the near-infrared light band and the red light band is extracted at the peak of the growing season and analyzed to obtain the mixed vegetation index of the measured area as follows:

[0013] The peak period of the growing season is from June 1 to August 31. For the measured area, the EarthExplorer platform was used, the satellite was selected as Landsat 8, the time range was selected as June 1 to August 31, the data type was selected as Level-2, the filtering condition was selected as cloud cover less than 20%, all surface reflectance data files in GeoTIFF format were downloaded, and the time of each imaging was recorded. The surface reflectance data files were then batch loaded using ENVI software, the measured area was cropped, and the Band 4 corresponding to the red light band and the Band 5 corresponding to the near-infrared band were selected. The average red light reflectance ρ of Band 4 in the measured area of each file was output. Red，a4 and the average near-infrared reflectivity ρ of Band5 NIR，a4 , a4 represents the index, a4=1, 2, ..., n0, where n0 represents the number of surface reflectance data files;

[0014] Calculate the red light surface reflectance of the area measured during the peak growing season:

[0015]

[0016] Where, ρ Red It represents the red light surface reflectance of the area measured at the peak of the growing season;

[0017] Calculate the near-infrared surface reflectance of the area measured during the peak growing season:

[0018]

[0019] Where, ρ NIR It represents the near-infrared surface reflectance of the area measured during the peak of the growing season;

[0020] Calculate a mixed vegetation index:

[0021]

[0022] Where MNDVI represents the mixed vegetation index.

[0023] Furthermore, the vegetation index of pure vegetation and bare soil were measured on site during the peak of the growing season. The vegetation index of mixed vegetation, pure vegetation and bare soil were analyzed to obtain the vegetation coverage rate data used to reflect the degree of vegetation coverage. The method is as follows:

[0024] Synchronize with the remote sensing image acquisition time, within one hour before and after each imaging time, select the grassland or forest vegetation coverage area in the measured area, set 25 sampling points, measure the red light and near-infrared light reflectance by spectrometer, take the average value as the pure vegetation red light reflectance and near-infrared light reflectance measured in the field, and calculate the vegetation index of the pure vegetation measured a4th time:

[0025]

[0026] Where, NDVI veg，a4 The vegetation index of pure vegetation measured at the a4th time, ρ1′ Red，a4 represents the red light reflectance of pure vegetation measured in the a4th field measurement, ρ1′ NIr，a4 represents the near-infrared reflectance of pure vegetation measured in the a4th field measurement;

[0027] Calculate the vegetation index for pure vegetation:

[0028]

[0029] Where, NDVI veg Vegetation index representing pure vegetation;

[0030] Synchronize with the remote sensing image acquisition time, within one hour before and after each imaging time, select bare soil areas without vegetation cover in the measured area, measure the red light and near-infrared light reflectance using a spectrometer, set 25 sampling points, measure the red light and near-infrared light reflectance using a spectrometer, take the average value as the bare soil red light reflectance and near-infrared light reflectance measured in the field, and calculate the vegetation index of the bare soil for the a4th measurement:

[0031]

[0032] Where ρ2′ Red，a4 represents the red light reflectance of bare soil measured in the a4th field measurement, ρ2′ NIR，a4 represents the near-infrared reflectance of bare soil measured in the ρ4th field measurement, NDVI soil，a4 represents the vegetation index of bare soil measured at the a4th time;

[0033] Calculate the vegetation index for bare soil:

[0034]

[0035] Where, NDVI soil represents the vegetation index of bare soil;

[0036] Calculate vegetation cover:

[0037]

[0038] Where FVC represents vegetation coverage.

[0039] Furthermore, the method for setting 25 sampling points is to first divide the measured area into 5×5 equal-area grids according to the geographical scope to form 25 evenly distributed sub-areas. The sampling points are determined by nearest neighbor spatial search based on the center point of each grid, and cross-grid search is allowed. For the screening of pure vegetation sampling points, the judgment condition is that the grassland coverage is higher than 90% or the canopy density of the selected forest is higher than 70%. Only one nearest neighbor sampling point is retained for each grid. For the screening of bare soil sampling points, the judgment condition is the bare soil area without vegetation cover, and only one nearest neighbor sampling point is retained for each grid.

[0040] Furthermore, by extracting the reflectivity of green light and near-infrared light, the method for calculating the water transparency to evaluate the clarity of the water body is as follows:

[0041] The center point of the divided grid is used as the reference, and the sampling points are determined through the nearest neighbor space search, and cross-grid search is allowed. For the selection of sampling points for assessing water clarity, the judgment conditions are water depth exceeding 1 meter, no aquatic weed cover, and water flow speed exceeding 0.1m / s. Only one nearest neighbor sampling point is retained for each grid, and the coordinates of the sampling points are then marked in the ENVI software;

[0042] The transparency is measured on-site using the coordinates of the sampling points marked by ENVI software to assess water clarity. The specific rules are as follows:

[0043] Measurements were conducted over 10 consecutive days, once a day between 10:00 and 14:00. The Secchi disk was placed vertically in the water, and the depth at which the disk surface just disappeared was recorded. The Secchi disk was then raised, and the depth at which the disk surface became clearly visible again was recorded. The transparency of the field measurements was calculated, and at the same time, the green light reflectance, near-infrared light reflectance, green light reflectance, red light reflectance, and blue light reflectance of the sampling points used to assess the clarity of the water were recorded using a spectrometer. The formula for calculating the transparency of the field measurements was as follows:

[0044]

[0045] Where D i，1 Denotes the depth data when the i-th disk is just invisible, D i，2 Indicates the depth data when the i-th disk surface becomes clearly visible again, SD i，zs represents the transparency of the ith field measurement, i represents the index, i = 1, 2, ..., 90, and the green light reflectance is normalized:

[0046]

[0047] In the formula, min(G) represents the minimum value of green light reflectance, max(G) represents the maximum value of green light reflectance, G i，norm represents the green light reflectance of the ith normalized process, G i represents the i-th green light reflectance;

[0048] Normalize the near-infrared reflectance:

[0049]

[0050] In the formula, max(NIR) represents the maximum value of near-infrared light reflectance, min(NIR) represents the minimum value of near-infrared light reflectance, and NIR i，norm Represents the normalized near-infrared reflectance, NIR i represents the i-th near-infrared light reflectance;

[0051] Construct a linear regression model for transparency using the least squares method:

[0052] make Construct the transparency error sum of squares formula:

[0053]

[0054] Where Q(a, b) represents the transparency error sum of squares formula, a represents the slope, and b represents the intercept. By minimizing Q(a, b), the values of a and b are obtained.

[0055] That is, the calculation formula for transparency is:

[0056] ASD=ax+b

[0057] Where ASD is the calculation formula for transparency, and x is the ratio of the normalized green light reflectance to the near-infrared light reflectance.

[0058] When the single-day rainfall exceeds 50 mm, it is determined to be heavy rainfall. The heavy rainfall period is defined as the period starting from the day of heavy rainfall, and the period without rainfall for 30 consecutive days is defined as the dry season. In the EarthExplorer platform, the time range is selected as the heavy rainfall period. The latest Landsat 8 Band 3 and Band 5 remote sensing images are downloaded as Level-2 data and imported into ENVI. The green light reflectance and near-infrared reflectance of the sampling points for assessing water clarity are extracted. i1 represents the sampling point number, i1 = 1, 2, ..., 25. The green light reflectance during the heavy rainfall period is calculated as follows:

[0059]

[0060] Where G qut Indicates the green light reflectance during heavy rainfall period, G q i1 represents the green light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period;

[0061] Calculate the near-infrared reflectance during heavy rainfall:

[0062]

[0063] Where, NIR qut Indicates the near-infrared reflectance during heavy rainfall, NIR q i1 represents the near-infrared reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period;

[0064] In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 3 and Band 5 remote sensing images as Level-2 data, import them into ENVI, extract the green light reflectance and near-infrared light reflectance of each sampling point, and calculate the green light reflectance during the dry season:

[0065]

[0066] Where G kutIndicates the green light reflectance during the dry season, G k i1 represents the green light reflectance of the i-th sampling point for assessing water clarity during the dry season;

[0067] Calculate the near-infrared reflectance during the dry season:

[0068]

[0069] Where, NIR kut Indicates the near-infrared reflectance during the dry season, NIR k i1 represents the near-infrared reflectance of the i-th sampling point for assessing water clarity during the dry season;

[0070] The green light reflectance and near-infrared light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the transparency linear regression model to obtain the water transparency ASD during the heavy rainfall period. q and water transparency ASD during dry season k , calculate the water clarity to assess the water clarity:

[0071]

[0072] Where ASD sc Indicates water clarity, which assesses the clarity of water.

[0073] Furthermore, by extracting the reflectance of green light and red light, the method for calculating the suspended solids concentration, which reflects the content of solid particulate matter in the water body, is as follows:

[0074] Suspended solids concentrations were measured simultaneously at sampling points used to assess water clarity. The specific protocol was as follows: surface water samples were collected at a depth of 0-50 cm, filtered through a 0.45 μm filter membrane, and the initial mass of the filter membrane and the volume of the water sample were recorded. The filter membrane was then dried and weighed to obtain the total mass of the dried filter membrane and suspended solids. The suspended solids concentration was then calculated as:

[0075]

[0076] Where m1 represents the initial mass of the filter membrane, m2 represents the total mass of the filter membrane and suspended solids after drying, and v sy Indicates the volume of water sample, SSC zs Represents the suspended solids concentration measured in situ; extracts the red light reflectance and performs normalization:

[0077]

[0078] In the formula, min(R) represents the minimum value of red light reflectance, max(R) represents the maximum value of red light reflectance, and R i，normrepresents the normalized red light reflectance;

[0079] Use the least squares method to construct a linear regression model for suspended solids concentration:

[0080] make Construct the formula for the sum of squares of suspended solids concentration errors:

[0081]

[0082] Where Q(c, d) represents the formula for the sum of squares of suspended solids concentration errors, c represents the slope, and d represents the intercept. The values of c and d are obtained by minimizing Q(c, d);

[0083] That is, the calculation formula for suspended solids concentration is:

[0084] ASSC sc =c·y+d

[0085] Where, ASSC sc represents the suspended solids concentration, y represents the ratio of the normalized green light reflectance to the red light reflectance;

[0086] In EarthExplorer, select the heavy rainfall period as the time range, download the latest Landsat 8 Band 4 remote sensing image as Level-2 data, import it into ENVI, extract the red light reflectance of the sampling points used to assess water clarity, and calculate the red light reflectance during the heavy rainfall period:

[0087]

[0088] Where R qut Indicates the red light reflectance during heavy rainfall period, R q i1 represents the red light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period;

[0089] In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 4 remote sensing image as Level-2 data, import it into ENVI, and calculate the red light reflectance during the dry season:

[0090]

[0091] Where R kut Indicates the red light reflectance during the dry season, R k i1 represents the red light reflectance of the i-th sampling point for assessing water clarity during the dry season;

[0092] The green light reflectance and red light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the suspended solid concentration linear regression model to obtain the suspended solid concentration ASSC during the heavy rainfall period. q and suspended solids concentration (ASSC) during dry season k , calculate the suspended solids concentration for assessing water clarity:

[0093]

[0094] Where, ASSC sc Indicates water clarity, which assesses the clarity of water.

[0095] Furthermore, by analyzing the reflectance of blue and green light, the method for obtaining the chlorophyll a concentration in the water body, which is used to reflect the growth degree of aquatic plants, is as follows:

[0096] For water samples collected at sampling points for assessing water clarity, weigh the sample volume, concentrate the algal cells by centrifugation, extract chlorophyll a with acetone to obtain the supernatant, record the supernatant volume, measure the absorbance of the supernatant at wavelengths of 664nm and 750nm, calculate the corrected absorbance, and then calculate the chlorophyll a concentration:

[0097]

[0098] Where, I1 represents the absorbance of the supernatant at 664 nm, I2 represents the absorbance of the supernatant at 750 nm, and V ext Indicates the volume of the supernatant, l indicates the optical path length, which is 1, AChl zs represents the chlorophyll a concentration measured in the field;

[0099] Extract blue light reflectance and normalize it:

[0100]

[0101] In the formula, min(B) represents the minimum value of blue light reflectivity, max(B) represents the maximum value of blue light reflectivity, and B inorm represents the normalized blue light reflectance;

[0102] The least squares method was used to construct a linear regression model for chlorophyll a concentration:

[0103] make Construct the formula for the sum of squares of chlorophyll a concentration errors:

[0104]

[0105] Where Q(e, f) represents the squared error formula of chlorophyll a concentration, e represents the slope, and f represents the intercept. The values of e and f are obtained by minimizing Q(e, f);

[0106] That is, the calculation formula for chlorophyll a concentration is:

[0107] AChl=e·z+f

[0108] Wherein, AChl represents the calculation formula of chlorophyll a concentration, z represents the ratio of normalized blue light reflectance to green light reflectance;

[0109] In EarthExplorer, select the heavy rainfall period as the time range, download the latest Landsat 8 Band 2 remote sensing image as Level-2 data, import it into ENVI, extract the sampling points for assessing water clarity, and calculate the blue light reflectance during the heavy rainfall period:

[0110]

[0111] Where B qut Indicates the blue light reflectance during heavy rainfall period, B q i1 represents the blue light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period;

[0112] In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 2 remote sensing image as Level-2 data, import it into ENVI, and calculate the blue light reflectance during the dry season:

[0113]

[0114] Where B kut Indicates the blue light reflectance during the dry season, B k i1 represents the blue light reflectance of the i-th sampling point for assessing water clarity during the dry season;

[0115] The green light reflectance and red light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the chlorophyll a concentration linear regression model to obtain the chlorophyll a concentration AChl during the heavy rainfall period. q and chlorophyll a concentration AChl in dry season k , calculate the chlorophyll a concentration for assessing water clarity:

[0116]

[0117] Where, AChl sc Indicates the chlorophyll a concentration used to assess water clarity.

[0118] Furthermore, the method for calculating water quality by using the transparency, suspended solids concentration and chlorophyll a concentration of the measured area is:

[0119] The transparency, suspended solids concentration, and chlorophyll a concentration of the measured area were extracted to construct the water quality formula:

[0120]

[0121] Where ω1, ω2, and ω3 represent weight coefficients, and ω1>ω2>ω3>0, ω1+ω2+ω3=1, and WQ represents water quality.

[0122] Furthermore, the evaluation of ecological environment quality using extreme impact factors, environmental data and comprehensive water quality assessment results includes the following steps:

[0123] Taking the current moment as the time reference point and the time span of the previous 10 years, extract the total precipitation, precipitation duration, and the number of occurrences in the past 5 years of extreme precipitation events to calculate the extreme precipitation intensity:

[0124]

[0125] Where P represents the total precipitation of extreme precipitation events, t represents the number of days of extreme precipitation, and I P represents the extreme precipitation intensity, P max Indicates the maximum precipitation of a single precipitation event, NP qfive represents the number of extreme precipitation events in the previous five years, NP hfive represents the number of extreme precipitation events in the following five years;

[0126] Extract the duration of extreme drought events, the number of occurrences in the five years before and after, and calculate the intensity of extreme drought:

[0127]

[0128] Where ND qfive Indicates the number of extreme droughts in the previous five years, ND hfive represents the number of occurrences of extreme drought in the five years following the occurrence of drought, t D Indicates the number of days the drought lasts;

[0129] Construct extreme weather impact factors based on extreme precipitation intensity and extreme drought intensity:

[0130] I=I P +I D

[0131] Where, I represents the extreme weather impact factor;

[0132] The optimal temperature of vegetation in the measured area was extracted from the global spatial distribution map of the optimal temperature for vegetation photosynthesis. The historical minimum and maximum annual precipitation in the region were extracted from the precipitation data. Combined with the weather influencing factor I, vegetation coverage FVC, and water quality WQ, an ecological quality index model was constructed:

[0133]

[0134] Where, T y represents the annual mean temperature, P y represents the total annual precipitation, P min Indicates the minimum annual precipitation in regional history, P max Indicates the maximum annual precipitation in regional history, T opt It indicates the optimum temperature for vegetation in the measured area. When EQI≥1, the ecological environment quality is judged to be excellent; when 1>EQI≥0.3, the ecological environment quality is judged to be good; when 0.3>EQI≥0.15, the ecological environment quality is judged to be slightly poor; when 0.15>EQI, the ecological environment quality is judged to be poor.

[0135] The present invention further provides a regional ecological environment quality evaluation system based on remote sensing data, wherein the device is used to execute the above-mentioned regional ecological environment quality evaluation method based on remote sensing data, comprising:

[0136] The vegetation cover inversion module is used to extract the reflectance of the near-infrared light band and the red light band based on the remote sensing data of the measured area at the peak of the growing season, analyze them and obtain the mixed vegetation index of the measured area. The vegetation index of pure vegetation and bare soil is measured on the spot at the peak of the growing season. The vegetation index of mixed vegetation, pure vegetation and bare soil is analyzed to obtain the vegetation coverage rate data used to reflect the degree of vegetation coverage.

[0137] The water quality parameter extraction module is used to calculate water transparency to assess water clarity by extracting the reflectance of green and near-infrared light during periods of heavy rainfall and dry seasons. The suspended solids concentration, which reflects the amount of solid particulate matter in the water, is calculated by extracting the reflectance of green and red light. The chlorophyll a concentration in the water, which reflects the growth of aquatic plants, is obtained by analyzing the reflectance of blue and green light.

[0138] The environmental quality evaluation module is used to comprehensively evaluate water quality based on water transparency, suspended solids concentration and chlorophyll a concentration in the measured area, collect extreme weather environmental data within ten years, extract extreme weather influencing factors based on extreme weather data, and evaluate the ecological environment quality using extreme influencing factors, environmental data and the results of comprehensive water quality assessment.

[0139] Compared with the prior art, the present invention has the following beneficial effects:

[0140] The present invention constructs a linear regression model of reflectivity and water quality parameters through the least squares method. The reflectivity of each band during heavy rainfall and dry seasons can be measured through Landsat8 satellite remote sensing data, thereby achieving a more accurate assessment of water quality through remote sensing data. Extreme weather impact factors are constructed using ten years of extreme weather data. Finally, the extreme impact factors, environmental data, and vegetation index data are integrated to construct an ecological quality index, avoiding the limitations of a single data source. BRIEF DESCRIPTION OF THE DRAWINGS

[0141] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0142] Figure 2 Schematic diagram of the overall device module of the present invention. DETAILED DESCRIPTION

[0143] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0144] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0145] Example:

[0146] See also Figure 1 , the present invention provides a technical solution:

[0147] The regional ecological environment quality evaluation method based on remote sensing data includes the following specific steps:

[0148] Step 1: Based on remote sensing data from the surveyed area, extract the reflectance of the near-infrared and red light bands at the peak of the growing season and analyze them to obtain a mixed vegetation index for the surveyed area. Also, measure the vegetation index of pure vegetation and bare soil on-site at the peak of the growing season. The vegetation indexes of mixed vegetation, pure vegetation, and bare soil are analyzed to obtain vegetation coverage data to reflect the degree of vegetation coverage.

[0149] During the peak growing season, June to August is summer, with high temperatures, sufficient sunlight, and relatively abundant precipitation. This climatic condition is very suitable for vegetation growth and is a period of vigorous vegetation growth. It can more clearly reflect the growth status of vegetation and the impact of environmental factors on vegetation, reducing the errors and uncertainties caused by different observation times. Through satellite imagery, such as Landsat8 satellite imagery on the EarthExplorer platform, select the time range from June 1 to August 31, the data type from Level-2, and the filter condition as cloud cover less than 20%, which means that more than 80% of the area in the image is cloudless or has little cloud cover, ensuring that the main surface of the study area, such as vegetation, water bodies, and bare soil, can be effectively observed, avoiding the loss of key area data due to cloud cover, and avoiding the distortion of reflectance data due to excessive cloud cover. Since the orbit of a single Landsat8 satellite is designed to cover the same area of the world once every 16 days, the measured area will have multiple surface reflectance data files in GeoTIFF format from June 1 to August 31. Download all Geo The surface reflectance data file in TIFF format records the time of each imaging, which is fixed between 10:00 and 10:15 in the morning. Each imaging will have a corresponding time. Then, the surface reflectance data file is batch loaded through ENVI software, the measured area is cropped, and the Band4 corresponding to the red light band and the Band5 corresponding to the near-infrared band are selected. Among them, the wavelength of Band4 corresponding to the red light band is 630–680nm, and the wavelength of Band5 corresponding to the near-infrared band is 845–885nm, 630– The 680nm red light band is within the main absorption spectrum of chlorophyll a and b. The chlorophyll in vegetation leaves has a strong absorption effect on red light. When the vegetation coverage is higher, the red light reflectance is lower. The 845-885nm near-infrared band is located on the near-infrared high reflectance platform of vegetation leaves. Due to the internal structure of the leaves, the near-infrared light reflectance increases significantly with the increase of vegetation coverage, thereby measuring the spectral response difference between vegetation and bare soil in these two bands; the average value ρ of the red light reflectance in the measured area of each file is output through the built-in average function of the software. Red，a4 and the average near-infrared reflectivity ρ NIR，a4 , a4 represents the index, a4=1, 2, ..., n0, where n0 represents the number of surface reflectance data files. The regional average value can comprehensively reflect the overall reflectance characteristics of the entire region, avoid the interference of individual pixels such as noise and outliers on the results, and reflect the comprehensive spectral characteristics of vegetation and bare soil in the region;

[0150] Calculate the red light surface reflectance of the area measured during the peak growing season:

[0151]

[0152] Where, ρRed It represents the red light surface reflectance of the area measured at the peak of the growing season. It is the statistical average of multiple imaging results to reduce the error of single measurement.

[0153] Calculate the near-infrared surface reflectance of the area measured during the peak growing season:

[0154]

[0155] Where, ρ eIR It represents the near-infrared surface reflectance of the area measured during the peak of the growing season;

[0156] Calculate a mixed vegetation index:

[0157]

[0158] Wherein, MNDVI represents a mixed vegetation index, which realizes a comprehensive quantitative indicator of vegetation coverage and vegetation growth status by measuring the difference in vegetation reflectance in the near-infrared and red light bands. Near-infrared reflectance is positively correlated with MNDVI, while red light reflectance is negatively correlated with MNDVI. That is, the higher the vegetation coverage, the larger the MNDVI value.

[0159] Synchronize with the remote sensing image acquisition time, within one hour before and after each imaging time, select the grassland or forest vegetation coverage area in the measured area, set 25 sampling points, measure the red light and near-infrared light reflectance by spectrometer, take the average value as the pure vegetation red light reflectance and near-infrared light reflectance measured in the field, and calculate the vegetation index of the pure vegetation measured a4th time:

[0160]

[0161] Where, NDVI veg，a4 The vegetation index of pure vegetation measured at the a4th time, ρ1′ Red，a4 represents the red light reflectance of pure vegetation measured in the a4th field measurement, ρ1′ NIR，a4 represents the near-infrared reflectance of pure vegetation measured in the a4th field measurement, ρ1′ NIR，a4 -ρ1′ Red，a4 This item reflects the difference between red light reflectance and near-infrared light reflectance. The denser the vegetation coverage, the greater the difference. The near-infrared light reflectance is positively correlated with the result, while the red light reflectance is negatively correlated with the result. NIR，a4 +ρ1′ Red，a4 Normalize the results to intuitively reflect the index of vegetation coverage;

[0162] Calculate the vegetation index for pure vegetation:

[0163]

[0164] Where, NDVI veg The vegetation index representing pure vegetation is the statistical average of multiple measurement results, which reduces the error of a single measurement and makes the vegetation index of pure vegetation more accurate;

[0165] Synchronized with the remote sensing image acquisition time, within one hour before and after each imaging time, bare soil areas without vegetation coverage in the measured area were selected, and the red and near-infrared light reflectances were measured by spectrometer. 25 sampling points were set. The reason for setting 25 sampling points is that the spectral reflectance of the vegetation coverage area is affected by factors such as vegetation type, density, height, leaf moisture content, and soil background, and there are significant spatial differences. Single-point measurement is easily affected by local factors such as shadows and debris and cannot represent the overall characteristics. Therefore, 25 sampling points were selected, and the red and near-infrared light reflectances were measured by spectrometer. The average value was taken as the bare soil red light reflectance and near-infrared light reflectance measured in the field.

[0166] Calculate the vegetation index of bare soil at the a4th measurement:

[0167]

[0168] Where ρ2′ Red，a4 represents the red light reflectance of bare soil measured in the a4th field measurement, ρ2′ NIR，a4 NDVI is the near-infrared reflectance of bare soil measured in the 4th field measurement. soil，a4 The vegetation index of bare soil measured at the a4th time reflects the relative difference in reflectance between the red and near-infrared bands of the bare soil area. The red reflectance of bare soil is positively correlated with the vegetation index of bare soil, while the red reflectance of bare soil is negatively correlated with the vegetation index of bare soil.

[0169] Calculate the vegetation index for bare soil:

[0170]

[0171] Where, NDVI soil The vegetation index of bare soil is the statistical average of multiple measurement results, which reduces the error of single measurement and makes the vegetation index of bare soil more accurate;

[0172] Calculate vegetation cover:

[0173]

[0174] Where FVC represents vegetation coverage, MNDVI-NDVI soil This item indicates the part where the vegetation index of the mixed pixel is higher than that of the bare soil end-member, reflecting the contribution of vegetation to the spectrum. The more vegetation there is, the larger the value of this item is. veg -NDVI soilThis item represents the difference in vegetation index between pure vegetation and bare soil, that is, the maximum possible increment of vegetation coverage, so that the value of vegetation coverage is between [0, 1], achieving the effect of converting spectral reflectance differences into intuitive vegetation coverage.

[0175] Among them, the method of setting 25 sampling points is to generate a maximum boundary fitting square grid that is completely contained in the measured area, and perform 5×5 equal-area grid division to form 25 evenly distributed sub-areas to ensure that the sampling points are evenly distributed in space and statistically representative. The sampling points are determined by nearest neighbor spatial search based on the center point of each grid, and cross-grid search is allowed to avoid the situation where some grids in the measured area have no available samples. For the screening of pure vegetation sampling points, the judgment condition is that the grassland coverage is higher than 90% or the canopy density of the selected forest is higher than 70%, which means that the vegetation proportion in the area is high and there is almost no bare soil or other ground objects mixed, thus significantly distinguishing the spectral characteristics from bare soil. Only one nearest sampling point is retained for each grid. For the screening of bare soil sampling points, the judgment condition is the bare soil area without vegetation cover. Only one nearest sampling point is retained for each grid. This can avoid the interference of micro-vegetation, such as weeds, on the spectrum and significantly distinguish the spectral characteristics from vegetation.

[0176] Step 2: During periods of heavy rainfall and dry seasons, water transparency, used to assess water clarity, is calculated by extracting the reflectance of green and near-infrared light. The suspended solids concentration, which reflects the amount of solid particulate matter in the water, is calculated by extracting the reflectance of green and red light. The chlorophyll a concentration, which reflects the growth of aquatic plants, is obtained by analyzing the reflectance of blue and green light.

[0177] The center point of the divided grid is used as the benchmark, and the sampling point is determined by the nearest neighbor space search, and cross-grid search is allowed. For the screening of sampling points for evaluating water clarity, the judgment conditions are water depth greater than 1 meter, no aquatic plant cover, and water flow velocity greater than 0.1m / s. Bottom mud with a water depth of less than 1 meter is easily disturbed by water flow, wind or biological activity, resulting in sediment suspension, making the measurement results unable to reflect the true clarity. Aquatic plants will absorb pollutants through their roots and intercept suspended matter through their leaves, indirectly affecting the clarity assessment. If the water flow velocity is too slow, suspended matter is easy to settle and cannot represent the overall clarity. Setting the minimum flow rate can screen out water bodies with natural flow characteristics. Only one nearest sampling point is retained for each grid, and then the coordinates of the sampling points are marked in ENVI software, so that transparency, green light reflectance, near-infrared light reflectance, green light reflectance, red light and blue light reflectance can be measured at the sampling points. The coordinates of the sampling points for evaluating water clarity marked by ENVI software are used to measure transparency in the field. The specific rules are as follows:

[0178] In the measured area, transparency, suspended solids concentration, and chlorophyll a concentration were measured every day for any 10 consecutive days. Repeated measurements for 10 consecutive days can reduce random errors through the arithmetic mean method, making the data more stable and reliable. The time was controlled between 10:00 and 14:00 to reduce the impact of the environment on the accuracy of the data. The Secchi disk was placed vertically in the water, and the depth when the disk surface was just invisible was recorded. The Secchi disk was raised and the depth when the disk surface was clearly visible again was recorded. The transparency of the field measurement was calculated, and at the same time, the green light reflectance, near-infrared light reflectance, green light reflectance, red light, and blue light reflectance of the sampling points for evaluating the clarity of the water body were recorded by spectrometer. A total of 90 sets of data were recorded. The formula for calculating the transparency of the field measurement is:

[0179]

[0180] Where D i，1 Denotes the depth data when the i-th disk is just invisible, D i，2 Indicates the depth data when the i-th disk surface becomes clearly visible again, SD i，zs represents the transparency of the i-th field measurement, i represents the index, i = 1, 2, ..., 90, the higher the transparency, the better the water quality;

[0181] The reflectivity values of different bands vary greatly. Directly using the original reflectivity will cause the model parameters to be dominated by the high-value band. Therefore, the normalized reflectivity is used as input, and the data is scaled to the interval [0, 1] to eliminate the dimension difference and construct the error sum square formula. First, the green light reflectivity is normalized:

[0182]

[0183] In the formula, min(G) represents the minimum value of green light reflectance, max(G) represents the maximum value of green light reflectance, G i，nirm represents the green light reflectance of the ith normalized process, G i represents the i-th green light reflectance;

[0184] Normalize the near-infrared reflectance:

[0185]

[0186] In the formula, max(NIR) represents the maximum value of near-infrared light reflectance, min(NIR) represents the minimum value of near-infrared light reflectance, and NIR i，norm Represents the normalized near-infrared reflectance, NIR i represents the i-th near-infrared light reflectance;

[0187] The reflectivity at a specific wavelength often shows an approximately linear relationship with transparency, suspended solids, and chlorophyll a concentration within a certain range. The least squares method is used to construct a transparency linear regression model:

[0188] make Construct the transparency error sum of squares formula:

[0189]

[0190] Where Q(a, b) represents the formula for the sum of squares of transparency errors, a represents the slope, and b represents the intercept. By minimizing Q(a, b), we can obtain the values of a and b, and take the partial derivative of a:

[0191]

[0192] Expanded to:

[0193]

[0194] Find the partial derivative with respect to b:

[0195]

[0196] Expanded to:

[0197]

[0198] right and Solved

[0199]

[0200] That is, the calculation formula for transparency is:

[0201] ASD=ax+b

[0202] Where ASD is the calculation formula for transparency, and x is the ratio of the normalized green light reflectance to the near-infrared light reflectance.

[0203] When a single-day rainfall exceeds 50 mm, it is considered a heavy rainfall period. The heavy rainfall period is defined as the period extending from the day of the heavy rainfall to the next 10 days. During this period, surface runoff, soil moisture, and vegetation status undergo significant changes. This period can capture the short-term impact of precipitation events on vegetation, avoiding interference from subsequent natural recovery or other environmental factors (such as evaporation), as well as the impact of heavy rainfall on crops. The dry season is defined as a period without precipitation for 30 consecutive days. This period of no precipitation usually causes soil moisture to drop below the critical value for vegetation utilization, triggering water stress and making it more susceptible to the long-term impacts of pollution such as industrial wastewater and domestic sewage. Data from these two periods are more representative.

[0204] In EarthExplorer, select the heavy rainfall period as the time range and download the latest Landsat 8 Band 3 and Band 5 remote sensing images as Level-2 data. Band 3 corresponds to the 525-600nm green light band. Import them into ENVI and extract the green and near-infrared reflectances of the sampling points used to assess water clarity. i1 represents the sampling point number, i1 = 1, 2, ..., 25. Calculate the green reflectance during the heavy rainfall period:

[0205]

[0206] Where G qut Indicates the green light reflectance during heavy rainfall period, G q i1 represents the green light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period;

[0207] Calculate the near-infrared reflectance during heavy rainfall:

[0208]

[0209] Where, NIR qut Indicates the near-infrared reflectance during heavy rainfall, BIR q i1 represents the near-infrared reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period;

[0210] In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 3 and Band 5 remote sensing images as Level-2 data, import them into ENVI, extract the green light reflectance and near-infrared light reflectance of each sampling point, and calculate the green light reflectance during the dry season:

[0211]

[0212] Where G kut Indicates the green light reflectance during the dry season, G k i1 represents the green light reflectance of the i-th sampling point for assessing water clarity during the dry season;

[0213] Calculate the near-infrared reflectance during the dry season:

[0214]

[0215] Where, NIR kut Indicates the near-infrared reflectance during the dry season, NIR k i1 represents the near-infrared reflectance of the i-th sampling point for assessing water clarity during the dry season;

[0216] The green light reflectance and near-infrared light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the transparency linear regression model to obtain the water transparency ASD during the heavy rainfall period. q and water transparency ASD during dry season k , calculate the water clarity to assess the water clarity:

[0217]

[0218] Where ASD sc Indicates water clarity, which assesses the clarity of water.

[0219] Suspended solids concentrations were measured simultaneously at sampling points used to assess water clarity. The specific protocol was as follows: surface water samples were collected at a depth of 0-50 cm, filtered through a 0.45 μm filter membrane, and the initial mass of the filter membrane and the volume of the water sample were recorded. The filter membrane was then dried and weighed to obtain the total mass of the dried filter membrane and suspended solids. The suspended solids concentration was then calculated as:

[0220]

[0221] Where m1 represents the initial mass of the filter membrane, m2 represents the total mass of the filter membrane and suspended solids after drying, and V sy Indicates the volume of water sample, SSC zs Represents the suspended solids concentration measured in situ; extracts the red light reflectance and performs normalization:

[0222]

[0223] In the formula, min(R) represents the minimum value of red light reflectance, max(R) represents the maximum value of red light reflectance, and R i，norm represents the normalized red light reflectance;

[0224] Use the least squares method to construct a linear regression model for suspended solids concentration:

[0225] make Construct the formula for the sum of squares of suspended solids concentration errors:

[0226]

[0227] Where Q(c, d) represents the formula for the sum of squared errors in suspended solids concentration, c represents the slope, and d represents the intercept. The values of c and d are obtained by minimizing Q(c, d), and the partial derivative of c is calculated as follows:

[0228]

[0229] Expanded to:

[0230]

[0231] Find the partial derivative with respect to d:

[0232]

[0233] Expanded to:

[0234]

[0235] right and

[0236] Solved

[0237]

[0238] That is, the calculation formula for suspended solids concentration is:

[0239] ASSC sc =c·y+d

[0240] Where, ASSC sc represents the suspended solids concentration, y represents the ratio of the normalized green light reflectance to the red light reflectance;

[0241] In EarthExplorer, select the heavy rainfall period as the time range, download the latest Landsat 8 Band 4 remote sensing image as Level-2 data, import it into ENVI, extract the red light reflectance of the sampling points used to assess water clarity, and calculate the red light reflectance during the heavy rainfall period:

[0242]

[0243] Where R qut Indicates the red light reflectance during heavy rainfall period, R q i1 represents the red light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period;

[0244] In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 4 remote sensing image as Level-2 data, import it into ENVI, and calculate the red light reflectance during the dry season:

[0245]

[0246] Where R kut Indicates the red light reflectance during the dry season, R k i1 represents the red light reflectance of the i-th sampling point for assessing water clarity during the dry season;

[0247] The green light reflectance and red light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the suspended solid concentration linear regression model to obtain the suspended solid concentration ASSC during the heavy rainfall period. q and suspended solids concentration (ASSC) during dry season k , calculate the suspended solids concentration for assessing water clarity:

[0248]

[0249] Where, ASSC sc Indicates water clarity, which assesses the clarity of water.

[0250] For water samples collected at sampling points for assessing water clarity, weigh the sample volume, concentrate the algal cells by centrifugation, extract chlorophyll a with acetone to obtain the supernatant, record the supernatant volume, measure the absorbance of the supernatant at wavelengths of 664nm and 750nm, calculate the corrected absorbance, and then calculate the chlorophyll a concentration:

[0251]

[0252] Where, I1 represents the absorbance of the supernatant at 664 nm, I2 represents the absorbance of the supernatant at 750 nm, and V ext Indicates the volume of the supernatant, l indicates the optical path length, which is 1, AChl zs represents the chlorophyll a concentration measured in the field, and 82.40 represents the extinction coefficient of acetone;

[0253] Extract blue light reflectance and normalize it:

[0254]

[0255] In the formula, min(B) represents the minimum value of blue light reflectivity, max(B) represents the maximum value of blue light reflectivity, and B inorm represents the normalized blue light reflectance;

[0256] The least squares method was used to construct a linear regression model for chlorophyll a concentration:

[0257] make Construct the formula for the sum of squares of chlorophyll a concentration errors:

[0258]

[0259] Where Q(e, f) represents the formula for the sum of squared errors in chlorophyll a concentration, e represents the slope, and f represents the intercept. The values of e and f are obtained by minimizing Q(e, f), and the partial derivative of e is calculated as follows:

[0260]

[0261] Expanded to:

[0262]

[0263] Find the partial derivative with respect to f:

[0264]

[0265] Expanded to:

[0266]

[0267] right and

[0268] Solved

[0269]

[0270] That is, the calculation formula for chlorophyll a concentration is:

[0271] AChl=e·z+f

[0272] Wherein, AChl represents the calculation formula of chlorophyll a concentration, z represents the ratio of normalized blue light reflectance to green light reflectance;

[0273] In EarthExplorer, select the heavy rainfall period as the time range and download the latest Landsat 8 Band 2 remote sensing image as Level-2 data. Band 2 corresponds to the 559-589nm blue light band. Import it into ENVI, extract the sampling points for assessing water clarity, and calculate the blue light reflectance during the heavy rainfall period:

[0274]

[0275] Where B qut Indicates the blue light reflectance during heavy rainfall period, B q i1 represents the blue light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period;

[0276] In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 2 remote sensing image as Level-2 data, import it into ENVI, and calculate the blue light reflectance during the dry season:

[0277]

[0278] Where B kut Indicates the blue light reflectance during the dry season, B ki1 represents the blue light reflectance of the i-th sampling point for assessing water clarity during the dry season;

[0279] The green light reflectance and red light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the chlorophyll a concentration linear regression model to obtain the chlorophyll a concentration AChl during the heavy rainfall period. q and chlorophyll a concentration AChl in dry season k , calculate the chlorophyll a concentration for assessing water clarity:

[0280]

[0281] Where, AChl sc Indicates the chlorophyll a concentration used to assess water clarity.

[0282] Determining periods of heavy rainfall and dry seasons requires specific conditions. High-frequency field measurements to obtain data and extract water transparency, suspended solids concentration, and chlorophyll-a concentration during these periods are costly and inefficient. Therefore, the multi-band reflectance measured in the field is correlated with water transparency, suspended solids concentration, and chlorophyll-a concentration to form a mathematical model that can be used for remote sensing inversion. This allows the inversion of water transparency, suspended solids concentration, and chlorophyll-a concentration to be achieved through the multi-band spectral information of Landsat 8 satellite remote sensing data, thereby reducing monitoring costs.

[0283] Step 3: Comprehensively assess water quality based on water transparency, suspended solids concentration, and chlorophyll-a concentration in the measured area. Collect extreme weather environmental data over the past ten years, extract extreme weather influencing factors based on the extreme weather data, and use the extreme influencing factors, environmental data, and the results of the comprehensive water quality assessment to evaluate ecological environment quality.

[0284] Transparency directly affects underwater plant photosynthesis and the living space of aquatic organisms. Too low a level will cause the aquatic vegetation to gradually decline and destroy the structure of the aquatic plant community. Excessive suspended solids will reduce the oxygen content of the water and adsorb pollutants. Chlorophyll a is a key indicator of algal biomass, and high values indicate the risk of algal blooms. By combining these three parameters through weighted summation, water quality is converted into a calculable mathematical model, which can avoid the one-sidedness of a single indicator. The transparency, suspended solids concentration, and chlorophyll a concentration of the measured area are extracted to construct a water quality formula:

[0285]

[0286] In the formula, ω1, ω2, and ω3 represent weight coefficients, and WQ represents water quality. Transparency, suspended solids concentration, and chlorophyll a concentration are weighted to quantitatively reflect the quality of water. The higher the value, the worse the water quality; the lower the value, the better the water quality. This item is the inverse of transparency because transparency itself is positively correlated with water quality. As transparency increases, impurities in the water are less, the water quality is higher, and the result of the water quality formula is lower. Therefore, it is positively correlated with the formula result. ASSC sc This item is the suspended solid concentration, which will adsorb pollutants such as heavy metals and organic matter and become a pollutant carrier. Therefore, the higher the value, the worse the water quality. The higher the water quality formula result, the higher the value is, and it is positively correlated with the formula result. AChl sc This item is a sign of algal biomass. The higher the concentration, the higher the risk of eutrophication and the more severe the deterioration of water quality. sc The lower the water quality, the higher the result of the water quality formula, which is positively correlated with the formula result. Among them, transparency has the most significant direct impact on the ecosystem, directly regulating the light environment and affecting the entire food web. It is also easily disturbed by human activities such as sand mining and shipping. Its changes will occur earlier than other pollution indicators. It is a sensitive signal of ecological degradation and requires priority attention. Suspended solids reflect the turbidity and pollutant carrier capacity of water bodies, directly reflecting turbidity and pollution load, but its impact is short-term and its weight is second. Although chlorophyll a is directly related to eutrophication, it is greatly affected by natural factors such as season and light. It needs to be combined with long-term monitoring and comprehensive evaluation, so it has the lowest weight. Therefore, ω1>ω2>ω3>0, ω1+ω2+ω3=1, which can be adjusted according to the needs of water quality evaluation, for example, ω1=0.5, ω2=0.3, and ω3=0.2.

[0287] Taking the current moment as the time reference point and setting the time span to the previous 10 years, the total precipitation, precipitation duration, and number of occurrences of extreme precipitation events in the five years before and after the 10-year period are extracted. Among them, the judgment condition for extreme precipitation is: within 24 hours, the precipitation in the measured area exceeds 250 mm. The precipitation exceeding 250 mm is based on the precipitation threshold set by the China Meteorological Administration for heavy rainstorms.

[0288] Calculate extreme precipitation intensity:

[0289]

[0290] Where, I P represents the intensity of extreme precipitation, P represents the total precipitation of extreme precipitation events, t represents the number of days of extreme precipitation, P max Indicates the maximum precipitation of a single precipitation event, NP qfive represents the number of extreme precipitation events in the previous five years, NP hfive Indicates the number of extreme precipitation events in the next five years, and the intensity of extreme precipitation I PIt is a quantitative assessment of extreme precipitation intensity, indicating the intensity of a single extreme precipitation event and its potential disaster risk. It integrates the changing trends of precipitation amount, duration and frequency to avoid the one-sidedness of a single indicator. It not only directly assesses the intensity of a single precipitation event, but also quantifies the trend of extreme precipitation in the future, thus increasing the accuracy of extreme precipitation intensity. This item normalizes the precipitation of a single precipitation event into a relative value, eliminating dimensional differences and reflecting the relative level of the absolute intensity of the event within a decade. It is directly proportional to the intensity of extreme precipitation. The higher the total precipitation of an extreme precipitation event, the greater the extreme precipitation intensity. For a given total precipitation event, the fewer days of extreme precipitation, the higher the disaster risk of heavy precipitation. It is the reciprocal of the duration. Under the same precipitation amount, the shorter the duration, the higher the precipitation per unit time, and thus the higher the risk of flooding and waterlogging. The larger this term is, the greater the extreme precipitation intensity will be, indicating that short-term heavy precipitation is more dangerous than long-term light precipitation. This item reflects the temporal and spatial variation trend of extreme precipitation by comparing the frequency of the event 5 years before and after. When the climate gradually stabilizes, the result will become smaller, and when extreme precipitation increases, If it is greater than 1, it means that extreme precipitation is more frequent, reflecting whether extreme precipitation in the region has become more frequent. Therefore, the larger the value of this item, the greater the result of extreme precipitation intensity.

[0291] The duration of extreme drought events and the number of occurrences in the five years before and after were extracted. The China Meteorological Administration's meteorological drought rating was used as a reference. The criteria for determining extreme drought were: consecutive days with daily precipitation less than 0.1 mm for more than 60 days. The intensity of extreme drought was calculated as follows:

[0292]

[0293] Where, I D Indicates extreme drought intensity, ND qfive Indicates the number of extreme droughts in the previous five years, ND hfive represents the number of occurrences of extreme drought in the five years following the occurrence of drought, t D It represents the number of days of drought. 365×5 as the denominator can standardize the drought duration to a ratio relative to 5 years, eliminate the dimension difference between duration and frequency, and reflect the persistence and frequency trend of drought events. The extreme drought intensity I D Quantified the intensity characteristics of drought events on a temporal scale, When this item is greater than 1, it indicates that droughts will be more frequent in the next five years. The intensity assessment needs to be amplified by the frequency ratio, indicating a trend of climate drought. The intensity of extreme drought will increase. When this term is less than 1, it reflects that the regional climate has recovered and the intensity of extreme drought will decrease. DThe larger the value, the longer the drought lasts and the more serious the damage to the ecosystem. hfive Positively correlated with the formula results, ND qfive It is inversely correlated with the formula result, t D There is a positive correlation with the formula results;

[0294] Construct extreme weather impact factors based on extreme precipitation intensity and extreme drought intensity:

[0295] I=I P +I D

[0296] Where I represents the extreme weather impact factor. Both are constrained to be within the similar dimensional range of [0, 1]. Direct addition is mathematically reasonable and more intuitively reflects trend reversals. If the extreme weather impact factor I of a region increases significantly over time, it indicates that it may be facing the dual pressures of precipitation extremes, such as an increase in short-term heavy rains and the normalization of droughts.

[0297] The optimal temperature of vegetation in the measured area is extracted from the global spatial distribution map of the optimal temperature for vegetation photosynthesis. The specific method is as follows:

[0298] The pixel temperature values of all grids in the measured area are extracted from the global spatial distribution map of the optimum temperature for vegetation photosynthesis. The arithmetic mean of the temperature values of all grid pixels in the target area is calculated. The result is the optimum temperature for vegetation in the measured area. The historical minimum and maximum annual precipitation in the region are extracted from the precipitation data. Combined with the weather influencing factor I, vegetation coverage FVC, and water quality WQ, an ecological quality index model is constructed:

[0299]

[0300] Where EQI represents the ecological quality index, T y represents the annual mean temperature, P y represents the total annual precipitation, P min Indicates the minimum annual precipitation in regional history, P max Indicates the maximum annual precipitation in regional history, T optIt indicates the optimum temperature for vegetation in the measured area. The ecological quality index quantitatively reflects the structural stability and functional integrity of the ecosystem by integrating core ecological elements such as vegetation cover, water quality, precipitation conditions and temperature adaptability, thereby measuring the quality of the ecosystem. The higher the FVC value, the higher the effect of directly improving the ecological quality by fixing carbon, retaining water and improving the microclimate. High-quality water bodies can provide key resources for vegetation growth and reduce pollutant stress. WQ is inversely correlated with EQI. As WQ gradually decreases, 1-WQ gradually increases, and EQI gradually increases. Extreme drought and heavy rain directly destroy the synergistic function of vegetation and water bodies, making the ecological environment worse. The larger the I value, the stronger the impact of extreme drought and heavy rain on the ecological environment, which is inversely correlated with EQI. This item reflects the synergy between vegetation and high-quality water bodies, and is suppressed by extreme climate. The higher the vegetation coverage and water quality, the smaller the impact of extreme climate, and the greater the result of this item. Moderate precipitation can maintain the water balance of the ecosystem. After considering extreme precipitation, precipitation, as a key driving factor of vegetation growth and hydrological cycle, is The higher the total annual precipitation, the greater the result of this standardized precipitation supply, that is, P y It is positively correlated with EQI. Temperature determines the energy flow efficiency of the ecosystem by affecting vegetation photosynthesis, transpiration and phenological period. y The relationship with EQI depends on the relationship with T opt The degree of deviation, When the temperature deviates from the optimal range of vegetation, the ecological function index decays. For example, high temperature aggravates evaporation and low temperature inhibits photosynthesis. The smaller the result of this item, the smaller the impact of slight deviation and the sharp increase in the impact of significant deviation. Ultimately, the larger the EQI value, the better the ecological environment quality. When T y -T opt =1, indicating no attenuation effect on the ecology. y With T opt The greater the deviation, The smaller the value of this item, the more significant the reduction of EQi, and it is inversely correlated with EQI. Through historical data statistics and experiments, it is proved that when EQI ≥ 1, the evaluation of ecological environment quality is judged to be excellent. At this time, the vegetation coverage and water quality are good, there are few extreme weather and the climate is suitable, all of which are in an ideal state. When 1> EQI ≥ 0.3, the evaluation of ecological environment quality is judged to be good. The ecosystem has a certain recovery capacity, but there are occasional heavy rain or drought, local water quality decline and other problems. When 0.3> EQI ≥ 0.15, the evaluation of ecological environment quality is judged to be slightly poor. The ecosystem function is degraded and the self-regulation ability is weak. Human intervention is required to prevent further deterioration. When 0.15> EQI, the evaluation of ecological environment quality is judged to be poor. At this time, the water quality is severely polluted and extreme weather is frequent, and urgent ecological restoration is required.

[0301] For example, when FVC = 0.9, WQ = 0.1, I = 0.5, P y =1000, T y =20, T opt =20;

[0302]

[0303] The ecological environment quality is evaluated as excellent.

[0304] See also Figure 2 The present invention further provides a regional ecological environment quality evaluation system based on remote sensing data, wherein the system is used to execute the above-mentioned regional ecological environment quality evaluation method based on remote sensing data, comprising:

[0305] The vegetation cover inversion module is used to extract the reflectance of the near-infrared light band and the red light band based on the remote sensing data of the measured area at the peak of the growing season, analyze them and obtain the mixed vegetation index of the measured area. The vegetation index of pure vegetation and bare soil is measured on the spot at the peak of the growing season. The vegetation index of mixed vegetation, pure vegetation and bare soil is analyzed to obtain the vegetation coverage rate data used to reflect the degree of vegetation coverage.

[0306] The water quality parameter extraction module is used to calculate water transparency to assess water clarity by extracting the reflectance of green and near-infrared light during periods of heavy rainfall and dry seasons. The suspended solids concentration, which reflects the amount of solid particulate matter in the water, is calculated by extracting the reflectance of green and red light. The chlorophyll a concentration in the water, which reflects the growth of aquatic plants, is obtained by analyzing the reflectance of blue and green light.

[0307] The environmental quality evaluation module is used to comprehensively evaluate water quality based on water transparency, suspended solids concentration and chlorophyll a concentration in the measured area, collect extreme weather environmental data within ten years, extract extreme weather influencing factors based on extreme weather data, and evaluate the ecological environment quality using extreme influencing factors, environmental data and the results of comprehensive water quality assessment.

[0308] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0309] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0310] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0311] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for evaluating regional ecological environment quality based on remote sensing data, characterized in that: The specific steps include: Step 1: Based on remote sensing data from the surveyed area, extract the reflectance of the near-infrared and red light bands at the peak of the growing season and analyze them to obtain a mixed vegetation index for the surveyed area. Also, measure the vegetation index of pure vegetation and bare soil on-site at the peak of the growing season. The vegetation indexes of mixed vegetation, pure vegetation, and bare soil are analyzed to obtain vegetation coverage data to reflect the degree of vegetation coverage. Step 2: During periods of heavy rainfall and dry seasons, water transparency, used to assess water clarity, is calculated by extracting the reflectance of green and near-infrared light. The suspended solids concentration, which reflects the amount of solid particulate matter in the water, is calculated by extracting the reflectance of green and red light. The chlorophyll a concentration, which reflects the growth of aquatic plants, is obtained by analyzing the reflectance of blue and green light. Step 3: Comprehensively assess the water quality based on the water transparency, suspended solids concentration, and chlorophyll a concentration in the measured area. Collect extreme weather environmental data within ten years, extract extreme weather influencing factors based on the extreme weather data, and evaluate the ecological environment quality using the extreme influencing factors, environmental data, and the results of the comprehensive assessment of water quality.

2. The method for evaluating regional ecological environment quality based on remote sensing data according to claim 1, characterized in that: Based on the remote sensing data of the measured area, the reflectance of the near-infrared light band and the red light band is extracted at the peak of the growing season and analyzed to obtain the mixed vegetation index of the measured area. The peak period of the growing season is from June 1 to August 31. For the measured area, the EarthExplorer platform was used, the satellite was selected as Landsat 8, the time range was selected as June 1 to August 31, the data type was selected as Level-2, the filtering condition was selected as cloud cover less than 20%, all surface reflectance data files in GeoTIFF format were downloaded, and the time of each imaging was recorded. The surface reflectance data files were then batch loaded using ENVI software, the measured area was cropped, and the Band 4 corresponding to the red light band and the Band 5 corresponding to the near-infrared band were selected. The average red light reflectance ρ of Band 4 in the measured area of each file was output. Red，a4 and the average near-infrared reflectivity ρ of Band5 NIR，a4 , a4 represents the index, a4=1, 2, ..., n0, where n0 represents the number of surface reflectance data files; Calculate the red light surface reflectance of the area measured during the peak growing season: Where, ρ Red It represents the red light surface reflectance of the area measured at the peak of the growing season; Calculate the near-infrared surface reflectance of the area measured during the peak growing season: Where, ρ NIR It represents the near-infrared surface reflectance of the area measured during the peak of the growing season; Calculate a mixed vegetation index: Where MNDVI represents the mixed vegetation index.

3. The method for evaluating regional ecological environment quality based on remote sensing data according to claim 2, characterized in that: The vegetation index of pure vegetation and bare soil was measured on site during the peak growing season. The vegetation index of mixed vegetation, pure vegetation and bare soil was analyzed to obtain the vegetation coverage rate data used to reflect the degree of vegetation coverage. The method is as follows: Synchronize with the remote sensing image acquisition time, within one hour before and after each imaging time, select the grassland or forest vegetation coverage area in the measured area, set 25 sampling points, measure the red light and near-infrared light reflectance by spectrometer, take the average value as the pure vegetation red light reflectance and near-infrared light reflectance measured in the field, and calculate the vegetation index of the pure vegetation measured a4th time: Where, NDVI veg，a4 The vegetation index of pure vegetation measured at the a4th time, ρ1′ Red，a4 represents the red light reflectance of pure vegetation measured in the a4th field measurement, ρ1′ NIR，a4 represents the near-infrared reflectance of pure vegetation measured in the a4th field measurement; Calculate the vegetation index for pure vegetation: Where, NDVI veg Vegetation index representing pure vegetation; Synchronize with the remote sensing image acquisition time, within one hour before and after each imaging time, select bare soil areas without vegetation cover in the measured area, measure the red light and near-infrared light reflectance using a spectrometer, set 25 sampling points, measure the red light and near-infrared light reflectance using a spectrometer, take the average value as the bare soil red light reflectance and near-infrared light reflectance measured in the field, and calculate the vegetation index of the bare soil for the a4th measurement: Where ρ2′ Red，a4 represents the red light reflectance of bare soil measured in the a4th field measurement, ρ2′ NIR，a4 NDVI is the near-infrared reflectance of bare soil measured in the 4th field measurement. soil，a4 represents the vegetation index of bare soil measured at the a4th time; Calculate the vegetation index for bare soil: Where, NDVI soil represents the vegetation index of bare soil; Calculate vegetation cover: Where FVC represents vegetation coverage.

4. The method for evaluating regional ecological environment quality based on remote sensing data according to claim 3, characterized in that: The method for setting 25 sampling points is to first divide the measured area into 5×5 equal-area grids according to the geographical scope to form 25 evenly distributed sub-areas. The sampling points are determined by nearest neighbor spatial search based on the center point of each grid, and cross-grid search is allowed. For the screening of pure vegetation sampling points, the judgment condition is that the grassland coverage is higher than 90% or the canopy density of the selected forest is higher than 70%. Only one nearest neighbor sampling point is retained for each grid. For the screening of bare soil sampling points, the judgment condition is the bare soil area without vegetation cover, and only one nearest neighbor sampling point is retained for each grid.

5. The method for evaluating regional ecological environment quality based on remote sensing data according to claim 4, characterized in that: By extracting the reflectance of green light and near-infrared light, the method for calculating the water transparency to evaluate the clarity of the water body is as follows: The center point of the divided grid is used as the reference, and the sampling points are determined through the nearest neighbor space search, and cross-grid search is allowed. For the selection of sampling points for assessing water clarity, the judgment conditions are water depth exceeding 1 meter, no aquatic weed cover, and water flow speed exceeding 0.1m / s. Only one nearest neighbor sampling point is retained for each grid, and the coordinates of the sampling points are then marked in the ENVI software; The transparency is measured on-site using the coordinates of the sampling points marked by ENVI software to assess water clarity. The specific rules are as follows: Measurements were conducted over 10 consecutive days, once a day between 10:00 and 14:

00. The Secchi disk was placed vertically in the water, and the depth at which the disk surface just disappeared was recorded. The Secchi disk was then raised, and the depth at which the disk surface became clearly visible again was recorded. The transparency of the field measurements was calculated, and at the same time, the green light reflectance, near-infrared light reflectance, green light reflectance, red light reflectance, and blue light reflectance of the sampling points used to assess the clarity of the water were recorded using a spectrometer. The formula for calculating the transparency of the field measurements was as follows: Where D i，1 Denotes the depth data when the i-th disk is just invisible, D i，2 Indicates the depth data when the i-th disk surface becomes clearly visible again, SD i，zs represents the transparency of the ith field measurement, i represents the index, i = 1, 2, ..., 90, and the green light reflectance is normalized: In the formula, min(G) represents the minimum value of green light reflectance, max(G) represents the maximum value of green light reflectance, G i，norm represents the green light reflectance of the ith normalized process, G i represents the i-th green light reflectance; Normalize the near-infrared reflectance: In the formula, max(NIR) represents the maximum value of near-infrared light reflectance, min(NIR) represents the minimum value of near-infrared light reflectance, and NIR i，norm Represents the normalized near-infrared reflectance, NIR i represents the i-th near-infrared light reflectance; Construct a linear regression model for transparency using the least squares method: make Construct the transparency error sum of squares formula: Where Q(a, b) represents the transparency error sum of squares formula, a represents the slope, and b represents the intercept. By minimizing Q(a, b), the values of a and b are obtained. That is, the calculation formula for transparency is: ASD=ax+b Where ASD is the calculation formula for transparency, and x is the ratio of the normalized green light reflectance to the near-infrared light reflectance. When the single-day rainfall exceeds 50 mm, it is determined to be heavy rainfall. The heavy rainfall period is defined as the period starting from the day of heavy rainfall, and the period without rainfall for 30 consecutive days is defined as the dry season. In the EarthExplorer platform, the time range is selected as the heavy rainfall period. The latest Landsat 8 Band 3 and Band 5 remote sensing images are downloaded as Level-2 data and imported into ENVI. The green light reflectance and near-infrared reflectance of the sampling points for assessing water clarity are extracted. i1 represents the sampling point number, i1 = 1, 2, ..., 25. The green light reflectance during the heavy rainfall period is calculated as follows: Where G qut Indicates the green light reflectance during heavy rainfall period, G q i1 represents the green light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period; Calculate the near-infrared reflectance during heavy rainfall: Where, NIR qut Indicates the near-infrared reflectance during heavy rainfall, NIR q i1 represents the near-infrared reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period; In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 3 and Band 5 remote sensing images as Level-2 data, import them into ENVI, extract the green light reflectance and near-infrared light reflectance of each sampling point, and calculate the green light reflectance during the dry season: Where G kut Indicates the green light reflectance during the dry season, G k i1 represents the green light reflectance of the i-th sampling point for assessing water clarity during the dry season; Calculate the near-infrared reflectance during the dry season: Where, NIR kut Indicates the near-infrared reflectance during the dry season, NIR k i1 represents the near-infrared reflectance of the i-th sampling point for assessing water clarity during the dry season; The green light reflectance and near-infrared light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the transparency linear regression model to obtain the water transparency ASD during the heavy rainfall period. q and water transparency ASD during dry season k , calculate the water clarity to assess the water clarity: Where ASD sc Indicates water clarity, which assesses the clarity of water.

6. The method for evaluating regional ecological environment quality based on remote sensing data according to claim 5, characterized in that: By extracting the reflectance of green light and red light, the method for calculating the suspended solid concentration, which reflects the content of solid particulate matter in the water body, is as follows: Suspended solids concentrations were measured simultaneously at sampling points used to assess water clarity. The specific protocol was as follows: surface water samples were collected at a depth of 0-50 cm, filtered through a 0.45 μm filter membrane, and the initial mass of the filter membrane and the volume of the water sample were recorded. The filter membrane was then dried and weighed to obtain the total mass of the dried filter membrane and suspended solids. The suspended solids concentration was then calculated as: Where m1 represents the initial mass of the filter membrane, m2 represents the total mass of the filter membrane and suspended solids after drying, and V sy Indicates the volume of water sample, SSC zs Represents the suspended solids concentration measured in situ; extracts the red light reflectance and performs normalization: In the formula, min(R) represents the minimum value of red light reflectance, max(R) represents the maximum value of red light reflectance, and R i，norm represents the normalized red light reflectance; Use the least squares method to construct a linear regression model for suspended solids concentration: make Construct the formula for the sum of squares of suspended solids concentration errors: Where Q(c, d) represents the formula for the sum of squares of suspended solids concentration errors, c represents the slope, and d represents the intercept. The values of c and d are obtained by minimizing Q(c, d); That is, the calculation formula for suspended solids concentration is: ASSC sc =c·y+d Where, ASSC sc represents the suspended solids concentration, y represents the ratio of the normalized green light reflectance to the red light reflectance; In EarthExplorer, select the heavy rainfall period as the time range, download the latest Landsat 8 Band 4 remote sensing image as Level-2 data, import it into ENVI, extract the red light reflectance of the sampling points used to assess water clarity, and calculate the red light reflectance during the heavy rainfall period: Where R qut Indicates the red light reflectance during heavy rainfall period, R q i1 represents the red light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period; In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 4 remote sensing image as Level-2 data, import it into ENVI, and calculate the red light reflectance during the dry season: Where R kut Indicates the red light reflectance during the dry season, R k i1 represents the red light reflectance of the i-th sampling point for assessing water clarity during the dry season; The green light reflectance and red light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the suspended solid concentration linear regression model to obtain the suspended solid concentration ASSC during the heavy rainfall period. q and suspended solids concentration (ASSC) during dry season E , calculate the suspended solids concentration for assessing water clarity: Where, ASSC sc Indicates water clarity, which assesses the clarity of water.

7. The method for evaluating regional ecological environment quality based on remote sensing data according to claim 5, characterized in that: By analyzing the reflectance of blue and green light, the method to obtain the chlorophyll a concentration in the water body, which is used to reflect the growth degree of aquatic plants, is as follows: For water samples collected at sampling points for assessing water clarity, weigh the sample volume, concentrate the algal cells by centrifugation, extract chlorophyll a with acetone to obtain the supernatant, record the supernatant volume, measure the absorbance of the supernatant at wavelengths of 664nm and 750nm, calculate the corrected absorbance, and then calculate the chlorophyll a concentration: Where, I1 represents the absorbance of the supernatant at 664 nm, I2 represents the absorbance of the supernatant at 750 nm, and V ext Indicates the volume of the supernatant, l indicates the optical path length, which is 1, AChl zs represents the chlorophyll a concentration measured in the field; Extract blue light reflectance and normalize it: In the formula, min(B) represents the minimum value of blue light reflectivity, max(B) represents the maximum value of blue light reflectivity, and B inorm represents the normalized blue light reflectance; The least squares method was used to construct a linear regression model for chlorophyll a concentration: make Construct the formula for the sum of squares of chlorophyll a concentration errors: Where Q(e, f) represents the squared error formula of chlorophyll a concentration, e represents the slope, and f represents the intercept. The values of e and f are obtained by minimizing Q(e, f); That is, the calculation formula for chlorophyll a concentration is: AChl=e·z+f Wherein, AChl represents the calculation formula of chlorophyll a concentration, z represents the ratio of normalized blue light reflectance to green light reflectance; In EarthExplorer, select the heavy rainfall period as the time range, download the latest Landsat 8 Band 2 remote sensing image as Level-2 data, import it into ENVI, extract the sampling points for assessing water clarity, and calculate the blue light reflectance during the heavy rainfall period: Where B qut Indicates the blue light reflectance during heavy rainfall period, B q i1 represents the blue light reflectance of the i-th sampling point for assessing water clarity during heavy rainfall period; In EarthExplorer, select the dry season as the time range, download the latest Landsat 8 Band 2 remote sensing image as Level-2 data, import it into ENVI, and calculate the blue light reflectance during the dry season: Where B kut Indicates the blue light reflectance during the dry season, B k i1 represents the blue light reflectance of the i-th sampling point for assessing water clarity during the dry season; The green light reflectance and red light reflectance during the heavy rainfall period and the dry season were normalized and substituted into the chlorophyll a concentration linear regression model to obtain the chlorophyll a concentration AChl during the heavy rainfall period. q and chlorophyll a concentration AChl in dry season k , calculate the chlorophyll a concentration for assessing water clarity: Where, AChl sc Indicates the chlorophyll a concentration used to assess water clarity.

8. The method for evaluating regional ecological environment quality based on remote sensing data according to claim 1, characterized in that: The method for calculating water quality based on the transparency, suspended solids concentration and chlorophyll a concentration of the measured area is: The transparency, suspended solids concentration, and chlorophyll a concentration of the measured area were extracted to construct the water quality formula: Where ω1, ω2, and ω3 represent weight coefficients, and ω1>ω2>ω3>0, ω1+ω2+ω3=1, and WQ represents water quality.

9. The method for evaluating regional ecological environment quality based on remote sensing data according to claim 1, characterized in that: The evaluation of ecological environment quality using extreme impact factors, environmental data and comprehensive water quality assessment results includes the following steps: Taking the current moment as the time reference point and the time span of the previous 10 years, extract the total precipitation, precipitation duration, and the number of occurrences in the past 5 years of extreme precipitation events to calculate the extreme precipitation intensity: Where P represents the total precipitation of extreme precipitation events, t represents the number of days of extreme precipitation, and I P represents the extreme precipitation intensity, P max Indicates the maximum precipitation of a single precipitation event, NP qfive represents the number of extreme precipitation events in the previous five years, NP hfive represents the number of extreme precipitation events in the following five years; Extract the duration of extreme drought events, the number of occurrences in the five years before and after, and calculate the intensity of extreme drought: Where ND qfive Indicates the number of extreme droughts in the previous five years, ND hfive represents the number of occurrences of extreme drought in the five years following the occurrence of drought, t D Indicates the number of days the drought lasts; Construct extreme weather impact factors based on extreme precipitation intensity and extreme drought intensity: I=I P +I D Where, I represents the extreme weather impact factor; The optimal temperature of vegetation in the measured area was extracted from the global spatial distribution map of the optimal temperature for vegetation photosynthesis. The historical minimum and maximum annual precipitation in the region were extracted from the precipitation data. Combined with the weather influencing factor I, vegetation coverage FVC, and water quality WQ, an ecological quality index model was constructed: Where, T y represents the annual mean temperature, P y represents the total annual precipitation, P min Indicates the minimum annual precipitation in regional history, P max Indicates the maximum annual precipitation in regional history, T opt It indicates the optimum temperature for vegetation in the measured area. When EQI≥1, the ecological environment quality is judged to be excellent; when 1>EQI≥0.3, the ecological environment quality is judged to be good; when 0.3>EQI≥0.15, the ecological environment quality is judged to be slightly poor; when 0.15>EQI, the ecological environment quality is judged to be poor.

10. A regional ecological environment quality assessment system based on remote sensing data, characterized by: The system is used to execute the method for evaluating regional ecological environment quality based on remote sensing data according to any one of claims 1 to 9, comprising: The vegetation cover inversion module is used to extract the reflectance of the near-infrared light band and the red light band based on the remote sensing data of the measured area at the peak of the growing season, analyze them and obtain the mixed vegetation index of the measured area. The vegetation index of pure vegetation and bare soil is measured on the spot at the peak of the growing season. The vegetation index of mixed vegetation, pure vegetation and bare soil is analyzed to obtain the vegetation coverage rate data used to reflect the degree of vegetation coverage. The water quality parameter extraction module is used to calculate water transparency to assess water clarity by extracting the reflectance of green and near-infrared light during periods of heavy rainfall and dry seasons. The suspended solids concentration, which reflects the amount of solid particulate matter in the water, is calculated by extracting the reflectance of green and red light. The chlorophyll a concentration in the water, which reflects the growth of aquatic plants, is obtained by analyzing the reflectance of blue and green light. The environmental quality evaluation module is used to comprehensively evaluate water quality based on water transparency, suspended solids concentration and chlorophyll a concentration in the measured area, collect extreme weather environmental data within ten years, extract extreme weather influencing factors based on extreme weather data, and evaluate the ecological environment quality using extreme influencing factors, environmental data and the results of comprehensive water quality assessment.

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