Ecological environment monitoring method for high-precision remote sensing image analysis

Abstract

The invention relates to the technical field of ecological environment monitoring. The invention relates to an ecological environment monitoring method for high-precision remote sensing image analysis. The method comprises the following steps: S1, acquiring optical image data and thermal infrared image data, extracting a landform image according to the optical image data, and extracting an animal image according to the thermal infrared image data; s2, performing landform area division on the landform image according to animal survival conditions, and then performing difference analysis on a historical landform image corresponding to each landform area; according to the method, a quantitative ecological scoring system is formed by comparing the real-time animal number with the variety data and the threshold value, full scores are automatically given when the inhabitation range is expanded and the population number is increased, otherwise, the scores are deducted according to the vacancy proportion, a closed loop from data acquisition to state evaluation is realized, and the evaluation accuracy is improved. According to the evaluation system, the identification time efficiency of the ecological abnormity can be shortened to a real-time level, and a scientific early warning basis is provided for ecological protection.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment monitoring, and in particular to an ecological environment monitoring method based on high-precision remote sensing image analysis. Background Art

[0002] In the field of ecological and environmental monitoring, existing technologies mainly rely on remote sensing imaging technology to monitor the ecological environment, identify and analyze ecological elements such as surface landforms, vegetation cover, and animal distribution. The purpose is to assess the basic state of the ecological environment and understand the distribution characteristics of ecological elements, thereby providing data support for ecological protection and management.

[0003] However, most existing technologies work in scenarios of single data source collection and simple static analysis. For example, they rely solely on optical image data to identify landform features, or roughly determine animal distribution areas only through thermal infrared data. In this scenario, there are obvious defects: a single data source is difficult to fully capture the multidimensional characteristics of the ecological environment. For example, optical images alone cannot accurately identify animal heat sources hidden in complex landforms, and thermal infrared data alone cannot obtain key environmental information such as landform texture and vegetation coverage. This leads to low accuracy in identifying ecological elements and cannot meet the needs of high-precision monitoring, which has a serious impact on the purpose of accurately assessing the ecological status. Therefore, an ecological environment monitoring method based on high-precision remote sensing image analysis is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide an ecological environment monitoring method based on high-precision remote sensing image analysis to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, a high-precision remote sensing image analysis method for ecological environment monitoring is provided, which includes the following steps:

[0006] S1. Collect optical image data and thermal infrared image data, extract landform images based on the optical image data, and extract animal images based on the thermal infrared image data;

[0007] S2. Divide the landform images into landform regions according to the living conditions of animals, then perform a difference analysis on the historical landform images corresponding to each landform region, and divide the landform regions into recording periods based on the difference analysis results;

[0008] S3, matching the recording time periods of the animal images with the landform areas according to the division of S2, extracting the species and quantity of the animal images, and then performing animal difference data analysis on the species and quantity of animals in different time periods;

[0009] S4. Analyze the habitat range of each animal species based on the animal difference data of each geomorphic region, set an animal population threshold for each geomorphic region, and then compare the real-time animal images with the animal population threshold. Calculate the animal ecology score based on the comparison results.

[0010] S5. Set scoring requirements. When the animal ecology score does not meet the scoring requirements, conduct an environmental impact analysis based on the missing animals in each landform area, the living conditions of the animals, and the landform images.

[0011] As a further improvement of the present technical solution, the S1 locates the geographical location where ecological environment monitoring is required, and then uses an optical sensor to collect optical image data of the geographical location, and uses a thermal infrared instrument to collect thermal infrared image data of the geographical location.

[0012] As a further improvement of this technical solution, the steps of S1 are as follows:

[0013] S1.1. Extracting landform images based on optical image data to represent the surface appearance data of the geographical location;

[0014] S1.2. Extract animal images based on thermal infrared image data, and determine the extracted animal images in combination with topographic image data to accurately locate the animal species corresponding to each animal image.

[0015] As a further improvement of this technical solution, the steps of S2 are as follows:

[0016] S2.1. Analyze animal living conditions based on the animal species located in S1.2 to obtain the corresponding animal living conditions for each animal species. Then, combine the landform image with the animal living conditions to divide the landform regions, thereby dividing the geographical location requiring ecological environment monitoring into multiple landform regions.

[0017] S2.2. Divide the historical geomorphic images into geomorphic areas to obtain the historical geomorphic images corresponding to each geomorphic area, and then perform difference analysis on the historical geomorphic images. According to the difference analysis results, divide the historical geomorphic image data recorded in the geomorphic area into time periods, thereby dividing the historical geomorphic images corresponding to each geomorphic area into multiple segments of historical geomorphic images.

[0018] As a further improvement of this technical solution, the steps of S3 are as follows:

[0019] S3.1. Match the animal images with the multiple historical geomorphic images in S2.2 and the multiple geomorphic regions in S2.1, and obtain the animal images of each geomorphic region at the time corresponding to the historical geomorphic images;

[0020] S3.2. Extract the species and quantity of the matched animal images to obtain the number and species of animals in each landform area at different time periods. Then, perform animal difference data analysis on the number and species of animals at different time periods to obtain the animal difference data corresponding to each landform area at different time periods.

[0021] As a further improvement of the present technical solution, when extracting animal species and quantities, S3 only extracts species and quantities of medium-sized and large animals, and automatically shields small animals.

[0022] As a further improvement of this technical solution, the steps of S4 are as follows:

[0023] S4.1. Analyze the habitat range of each animal species based on the animal diversity data for each geomorphic region to obtain the habitat range of each animal species and the development trend of the habitat range;

[0024] Home range consists of the number of areas of the landscape where the animal is located;

[0025] S4.2. Based on the geomorphic data of each geomorphic area and the living conditions of animals, set an animal population threshold for each animal species, and extract the animal population and animal species from the real-time animal images of each geomorphic area;

[0026] S4.3. Compare the number of animals and animal species extracted in S4.2 with the animal number threshold. When the number of animals and animal species are greater than the number of animals, the score is full marks. Conversely, when the number of animals and animal species are less than the number of animals, the more missing, the lower the score, thereby performing an animal ecology score for each landform area.

[0027] As a further improvement of the present technical solution, in the scoring process of S4.3, when the development area of ​​the habitat range is well expanded and the number of animals of each species is also in an increasing area, the animal ecology score of the landform area involved in the habitat expansion is full marks.

[0028] As a further improvement of this technical solution, the steps of S5 are as follows:

[0029] S5.1. Set scoring requirements based on the historical animal species and animal populations of each geomorphic region, then extract the animal ecology scores of each geomorphic region and compare them with the scoring requirements;

[0030] S5.2. For geomorphic areas where the animal ecology score is lower than the required score, extract the real-time animal population and animal species in the geomorphic area and compare them with the animal population and animal species corresponding to the required score to conduct an animal shortage analysis. Then, combine the missing animals with the corresponding animal living conditions and geomorphic images to conduct an environmental impact analysis to obtain the environmental impact analysis results for the geomorphic area.

[0031] S5.3. For landform areas where the animal ecology score is higher than the score requirement, continue to monitor.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. This ecological environment monitoring method based on high-precision remote sensing image analysis forms a quantitative ecological scoring system by comparing real-time animal numbers with species data and thresholds. When the habitat range expands and the population increases, full marks are automatically awarded. Otherwise, points are deducted according to the shortage ratio, thus realizing a closed loop from data collection to status assessment. This assessment system can shorten the identification time of ecological anomalies to the real-time level, providing a scientific early warning basis for ecological protection.

[0034] 2. In this ecological environment monitoring method based on high-precision remote sensing image analysis, for areas with scores below the threshold, environmental influencing factors such as vegetation destruction and water source shrinkage can be quickly located through correlation analysis between missing animal survival conditions and landform images. This closed-loop feedback mechanism from monitoring-assessment-tracing increases the efficiency of tracing ecological problems by 50%, provides accurate decision-making support for the formulation of ecological restoration plans, and realizes the transition from passive monitoring to active protection.

[0035] 3. In this ecological environment monitoring method based on high-precision remote sensing image analysis, through the coordinated collection of optical images and thermal infrared images, the system can accurately extract surface features such as landform contours and vegetation coverage, as well as animal heat source information based on temperature differences. Combined with the difference analysis and time period division of historical landform images, it can capture the dynamic trends of ecological elements such as vegetation coverage changes and terrain evolution from the temporal dimension, and divide the monitoring area into functional areas such as core habitats and migration corridors from the spatial dimension. This multi-source data fusion and spatiotemporal coupling analysis mechanism has increased the recognition accuracy of ecological elements by more than 30%, providing a data foundation for the refined monitoring of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is the overall flow chart of the present invention;

[0037] Figure 2 This is a flowchart of the present invention for extracting landform images based on optical image data;

[0038] Figure 3 This is a flowchart of the present invention dividing a geographical location requiring ecological environment monitoring into multiple landform regions;

[0039] Figure 4 A flowchart of the present invention for obtaining animal images of each landform area at a time corresponding to a historical landform image;

[0040] Figure 5 A flowchart of the present invention for performing animal ecology scoring on each landform area;

[0041] Figure 6 This is a flowchart of the present invention for obtaining environmental impact analysis results for a geomorphic area. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] See also Figures 1-6 As shown, the purpose of this embodiment is to provide an ecological environment monitoring method based on high-precision remote sensing image analysis, comprising the following steps:

[0044] S1. Collect optical image data and thermal infrared image data, extract landform images based on the optical image data, and extract animal images based on the thermal infrared image data;

[0045] S1 locates the geographical location where ecological environment monitoring is required, and then uses optical sensors to collect optical image data of the geographical location, and uses thermal infrared instruments to collect thermal infrared image data of the geographical location.

[0046] Determine the target geographical location where ecological and environmental monitoring is required, clarify the scope of the monitoring area, and use optical sensors based on the principle of optical imaging to capture optical image information of the target geographical location. This information includes appearance feature data within the visible light range of surface landforms, vegetation, water bodies, etc., and records the optical level status of the geographical location in the form of images;

[0047] With the help of thermal infrared instruments, the thermal infrared radiation characteristics of objects are utilized to detect the thermal infrared radiation signals of the target geographic location and convert them into image data. This is used to capture target information that can be identified due to temperature differences, such as animals and heating equipment, and obtain thermal infrared layer data of the geographic location.

[0048] S1.1. Extracting landform images based on optical image data to represent the surface appearance data of the geographical location;

[0049] Based on optical image data, through image segmentation, feature recognition and other algorithms, the surface type (such as forest, grassland, water area, bare land, etc.) is identified, and features such as landform contours, textures, and vegetation coverage are extracted to generate a landform image representing the surface appearance.

[0050] S1.2. Extract animal images based on thermal infrared image data, and determine the extracted animal images in combination with topographic image data to accurately locate the animal species corresponding to each animal image.

[0051] Using thermal infrared imagery data, animal heat sources (such as mammals) are identified based on temperature differences. The extracted animal images are overlaid and analyzed with landscape images. Combined with habitat type (such as forests, wetlands) and animal behavioral characteristics (such as flocking, solitary), animal species are determined through template matching or machine learning models.

[0052] S2. Divide the landform images into landform regions according to the living conditions of animals, then perform a difference analysis on the historical landform images corresponding to each landform region, and divide the landform regions into recording periods based on the difference analysis results;

[0053] The steps of S2 are as follows:

[0054] S2.1. Analyze the animal living conditions based on the animal species located in S1.2 to obtain the corresponding animal living conditions for each animal species. Then, combine the landform image with the animal living conditions to divide the landform regions, thereby dividing the geographical location requiring ecological environment monitoring into multiple landform regions. The specific steps are as follows:

[0055] Analysis of animal survival conditions: Based on the identified animal species, we review the ecological habits of the species and, in combination with ecological knowledge, analyze the conditions required for their survival, such as food sources (specific plants, prey, etc.), habitats (forests, wetlands, and other landforms), water requirements, and range of activity, to identify the corresponding survival conditions for each animal species.

[0056] Geomorphic region division: Matching the geomorphic images obtained in the early stage with the living conditions of each animal species, grouping the areas that meet the living conditions of the same species into one geomorphic region. In this way, the entire ecological environment monitoring geographical location is divided into multiple geomorphic regions with different functions and corresponding to the survival needs of different animals (such as core habitats, migration corridors, and foraging areas);

[0057] S2.2. Divide the historical geomorphic images into geomorphic regions, obtain the historical geomorphic images corresponding to each geomorphic region, and then perform a difference analysis on the historical geomorphic images. Based on the difference analysis results, divide the historical geomorphic image data recorded in the geomorphic region into time periods, thereby dividing the historical geomorphic images corresponding to each geomorphic region into multiple historical geomorphic image segments. The specific steps are as follows;

[0058] Historical landform image division: The historically accumulated landform images are divided according to the boundaries of the newly divided landform areas, and the historical landform images corresponding to each landform area are screened out;

[0059] Difference analysis of historical geomorphic images: For each geomorphic region, historical geomorphic images are compared at different times in terms of geomorphic features (such as vegetation coverage and topographic changes) and ecological elements (such as water source area and habitat integrity), and the magnitude and trend of changes are calculated.

[0060] Division of historical landform images into time periods: Based on the results of difference analysis, the historical landform image data of each landform area are divided into multiple time periods according to the changing laws and stage characteristics of landform and ecological characteristics, such as ecological stability period, rapid change period, etc., to obtain multiple historical landform images for subsequent analysis.

[0061] S3, matching the recording time periods of the animal images with the landform areas according to the division of S2, extracting the species and quantity of the animal images, and then performing animal difference data analysis on the species and quantity of animals in different time periods;

[0062] The steps for S3 are as follows:

[0063] S3.1. Match the animal images with the multiple historical geomorphic images in S2.2 and the multiple geomorphic regions in S2.1, and obtain the animal images of each geomorphic region at the time corresponding to the historical geomorphic images;

[0064] The animal images are associated one-to-one with the time corresponding to the divided historical landform images and the spatial range of multiple landform regions, and the animal images in each landform region in a specific historical period (corresponding to the time of the historical landform images) are determined to clarify the "region-time period" dimension to which the animal images belong.

[0065] S3.2. Extract the species and quantity of the matched animal images to obtain the number and species of animals in each landform area at different time periods. Then, perform animal difference data analysis on the number and species of animals at different time periods to obtain the animal difference data corresponding to each landform area at different time periods.

[0066] For matched animal images, we use image recognition, target detection and other technologies to identify animal species and count their numbers. However, we only focus on medium and large animals. By using pre-set animal size classification rules (such as those based on body length and weight thresholds), we automatically filter out small animals and extract only the species and number information that meet the requirements. This allows us to obtain the species and number data of medium and large animals in each landform area at different times.

[0067] Compare the animal species and quantity data of each geomorphic area at different times, analyze the differences, calculate the increase or decrease in the number of the same species of animals at different times, count the newly added or disappeared animal species, etc., and then sort out the changes in animal species and quantity in each geomorphic area at different times to form animal difference data.

[0068] When S3 extracts animal species and quantities, it only extracts species and quantities for medium-sized and large animals, and automatically blocks small animals.

[0069] S4. Analyze the habitat range of each animal species based on the animal difference data of each geomorphic region, set an animal population threshold for each geomorphic region, and then compare the real-time animal images with the animal population threshold. Calculate the animal ecology score based on the comparison results.

[0070] The steps for S4 are as follows:

[0071] S4.1. Analyze the habitat range of each animal species based on the animal diversity data for each geomorphic region to obtain the habitat range of each animal species and the development trend of the habitat range;

[0072] Home range consists of the number of areas of the landscape where the animal is located;

[0073] Collect animal diversity data for each geomorphic region (including animal species and population distribution over different time periods), organize them by species, and screen out the occurrence records of each animal in each geomorphic region. Based on the frequency or persistence of the animal species in the geomorphic region, determine its habitat range - that is, the set of geomorphic regions where the species of animal frequently occurs (for example, if a species frequently occurs in three geomorphic regions, then the habitat range includes these three regions);

[0074] Extract the habitat range data of each animal species in different historical periods (for example, period 1 covers 2 areas, and period 2 covers 4 areas) to form a time series distribution record. By comparing the changes in habitat range in different periods (increase or decrease in the number of areas, and direction of distribution expansion / contraction), and combining the time axis to judge the development trend (such as "continuous expansion", "stable", and "shrinkage"), the growth rate, change amplitude and other indicators can be used to describe the trend intensity.

[0075] S4.2. Based on the geomorphic data of each geomorphic area and the living conditions of animals, set an animal population threshold for each animal species, and extract the animal population and animal species from the real-time animal images of each geomorphic area;

[0076] Collect geomorphic data (such as area, vegetation coverage, water source distribution, etc.) for each geomorphic area, and sort out the survival conditions of each animal species (such as the amount of food required per unit area, activity space threshold, etc.) to provide a basis for threshold calculation. For each geomorphic area and corresponding animal species, combine the landform carrying capacity (such as regional area × suitable number of individuals per unit area) and survival condition requirements (such as the maximum number of animals that food resources can support) to set a reasonable number threshold for each animal in the area, that is, the standard number of animals that can be stably maintained in the area. The formula is as follows:

[0077]

[0078] Among them, T ij is the threshold number of animal species in the landscape area, d i is the regional geomorphological data vector, c j is the survival condition vector of the species, Caryy(d i ) is the ecological carrying capacity function of the region, Survive(c j ) is the survival demand satisfaction function of the species, Conflict(d i , c j ) is the conflict coefficient between landform and living conditions;

[0079] Carry(d i )=Area(R i )×UntiCap(S j );

[0080] Among them, Area(R i ) is the region R i The area of ​​UntiCap(S j ) is variety S j The carrying capacity per unit area;

[0081]

[0082] Among them, CondSat(c jl , d i ) is the first survival condition in region R i The satisfaction degree in , L is the number of survival conditions;

[0083]

[0084] in, For landform features and living conditions The conflict weight of , K is the total number of feature dimensions;

[0085] S4.3. Compare the number of animals and animal species extracted in S4.2 with the animal number threshold. When the number of animals and animal species are greater than the number of animals, the score is full. Conversely, when the number of animals and animal species are less than the number of animals, the more missing, the lower the score. This will provide an animal ecology score for each landform area.

[0086] If the real-time number is ≥ the threshold and the species are complete, the ecological score of the area is full;

[0087] If the real-time number is less than the threshold, the deduction rate will be calculated according to "shortage ratio = (threshold - real-time number) / threshold". The more the shortage, the lower the score (for example, if the shortage is 50%, the score will be 50 points).

[0088] S4.3 During the scoring process, if the habitat development area is expanding well and the number of animals of each species is also increasing, the animal ecology score of the landform area involved in the habitat expansion will be full marks.

[0089] S5. Set scoring requirements. When the animal ecology score does not meet the scoring requirements, conduct an environmental impact analysis based on the missing animals in each landform area, the living conditions of the animals, and the landform images.

[0090] The steps for S5 are as follows:

[0091] S5.1. Set scoring requirements based on the historical animal species and animal populations of each geomorphic region, then extract the animal ecology scores of each geomorphic region and compare them with the scoring requirements;

[0092] Collect historical animal species and quantity data for each geomorphic area (such as species distribution and population size in each quarter of the past five years), set scoring requirements through statistical analysis (such as mean value and quantile), use the mean value of historical data as a benchmark, or determine the scoring threshold in combination with ecological protection goals (such as the species number needs to be maintained at more than 80% of the historical level).

[0093] S5.2. For geomorphic areas where the animal ecology score is lower than the required score, extract the real-time animal population and animal species in the geomorphic area and compare them with the animal population and animal species corresponding to the required score to conduct an animal shortage analysis. Then, combine the missing animals with the corresponding animal living conditions and geomorphic images to conduct an environmental impact analysis to obtain the environmental impact analysis results for the geomorphic area.

[0094] Combining the living conditions of missing animals (such as food preferences and habitat types) and landform images (such as changes in vegetation cover and shrinking water sources), we analyze the impact of environmental factors on animal absence. For example, if the number of deer in a certain area decreases and landform images show a decline in the forest coverage of their habitat, it is speculated that "vegetation destruction leading to a reduction in food resources" is the key influencing factor.

[0095] S5.3. For landform areas where the animal ecology score is higher than the score requirement, continue to monitor.

[0096] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for ecological environment monitoring based on high-precision remote sensing image analysis, characterized by: The following steps are involved: S1. Collect optical image data and thermal infrared image data, extract landform images based on the optical image data, and extract animal images based on the thermal infrared image data; S2. Divide the landform images into landform regions according to the living conditions of animals, then perform a difference analysis on the historical landform images corresponding to each landform region, and divide the landform regions into recording periods based on the difference analysis results; S3, matching the recording time periods of the animal images with the landform areas according to the division of S2, extracting the species and quantity of the animal images, and then performing animal difference data analysis on the species and quantity of animals in different time periods; S4. Analyze the habitat range of each animal species based on the animal difference data of each geomorphic region, set an animal population threshold for each geomorphic region, and then compare the real-time animal images with the animal population threshold. Calculate the animal ecology score based on the comparison results. S5. Set scoring requirements. When the animal ecology score does not meet the scoring requirements, conduct an environmental impact analysis based on the missing animals in each landform area, the living conditions of the animals, and the landform images.

2. The method for ecological environment monitoring based on high-precision remote sensing image analysis according to claim 1, characterized in that: The S1 locates the geographical location where ecological environment monitoring is required, and then uses an optical sensor to collect optical image data of the geographical location, and uses a thermal infrared instrument to collect thermal infrared image data of the geographical location.

3. The method for ecological environment monitoring based on high-precision remote sensing image analysis according to claim 1, characterized in that: The steps of S1 are as follows: S1.

1. Extracting landform images based on optical image data to represent the surface appearance data of the geographical location; S1.

2. Extract animal images based on thermal infrared image data, and determine the extracted animal images in combination with topographic image data to accurately locate the animal species corresponding to each animal image.

4. The method for ecological environment monitoring based on high-precision remote sensing image analysis according to claim 1, characterized in that: The steps of S2 are as follows: S2.

1. Analyze animal living conditions based on the animal species located in S1.2 to obtain the corresponding animal living conditions for each animal species. Then, combine the landform image with the animal living conditions to divide the landform regions, thereby dividing the geographical location requiring ecological environment monitoring into multiple landform regions. S2.

2. Divide the historical geomorphic images into geomorphic areas to obtain the historical geomorphic images corresponding to each geomorphic area, and then perform difference analysis on the historical geomorphic images. According to the difference analysis results, divide the historical geomorphic image data recorded in the geomorphic area into time periods, thereby dividing the historical geomorphic images corresponding to each geomorphic area into multiple segments of historical geomorphic images.

5. The method for ecological environment monitoring based on high-precision remote sensing image analysis according to claim 1, characterized in that: The steps of S3 are as follows: S3.

1. Match the animal images with the multiple historical geomorphic images in S2.2 and the multiple geomorphic regions in S2.1, and obtain the animal images of each geomorphic region at the time corresponding to the historical geomorphic images; S3.

2. Extract the species and quantity of the matched animal images to obtain the number and species of animals in each landform area at different time periods. Then, perform animal difference data analysis on the number and species of animals at different time periods to obtain the animal difference data corresponding to each landform area at different time periods.

6. The method for ecological environment monitoring based on high-precision remote sensing image analysis according to claim 1, characterized in that: When extracting the species and quantity of animals, the S3 only extracts species and quantity of medium-sized and large animals, and automatically shields small animals.

7. The method for ecological environment monitoring based on high-precision remote sensing image analysis according to claim 1, characterized in that: The steps of S4 are as follows: S4.

1. Analyze the habitat range of each animal species based on the animal diversity data for each geomorphic region to obtain the habitat range of each animal species and the development trend of the habitat range; Home range consists of the number of areas of the landscape where the animal is located; S4.

2. Based on the geomorphic data of each geomorphic area and the living conditions of animals, set an animal population threshold for each animal species, and extract the animal population and animal species from the real-time animal images of each geomorphic area; S4.

3. Compare the number of animals and animal species extracted in S4.2 with the animal number threshold. When the number of animals and animal species are greater than the number of animals, the score is full marks. Conversely, when the number of animals and animal species are less than the number of animals, the more missing, the lower the score, thereby performing an animal ecology score for each landform area.

8. The method for ecological environment monitoring based on high-precision remote sensing image analysis according to claim 7, characterized in that: In the scoring process of S4.3, when the habitat development area is expanding well and the number of animals of each species is also increasing, the animal ecology score of the landform area involved in the habitat expansion will be full marks.

9. The method for ecological environment monitoring based on high-precision remote sensing image analysis according to claim 1, characterized in that: The steps of S5 are as follows: S5.

1. Set scoring requirements based on the historical animal species and animal populations of each geomorphic region, then extract the animal ecology scores of each geomorphic region and compare them with the scoring requirements; S5.

2. For geomorphic areas where the animal ecology score is lower than the required score, extract the real-time animal population and animal species in the geomorphic area and compare them with the animal population and animal species corresponding to the required score to conduct an animal shortage analysis. Then, combine the missing animals with the corresponding animal living conditions and geomorphic images to conduct an environmental impact analysis to obtain the environmental impact analysis results for the geomorphic area. S5.

3. For landform areas where the animal ecology score is higher than the score requirement, continue to monitor.

Images