Method for biodiversity survey and monitoring based on satellite remote sensing and environmental DNA sequencing

Abstract

The application discloses a biodiversity investigation and monitoring method based on satellite remote sensing and environmental DNA sequencing, comprising the following steps: S1, obtaining habitat remote sensing data and environmental DNA data; S2, preprocessing the obtained data and comparing with a public gene database to obtain species distribution data; S3, aligning and fusing the habitat remote sensing data and the environmental DNA data in time and space scales to generate a biodiversity feature data set; and S4, constructing a correlation model and a three-dimensional model between the habitat and the species distribution, and visually displaying the dynamic change of biodiversity. The application improves the comprehensiveness, accuracy and timeliness of investigation and monitoring, and provides a scientific basis for biodiversity protection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biodiversity investigation and monitoring, in particular to a biodiversity investigation and monitoring method based on satellite remote sensing and environmental DNA sequencing. BACKGROUND

[0002] Biodiversity is facing threats caused by human activities and climate change, and many rare and endangered species are disappearing or will soon disappear. Existing biodiversity investigation and monitoring mainly uses manual investigation methods based on sample lines and sample plots, supplemented by species identification methods based on morphology, so as to achieve the purpose of finding out the biodiversity background of a specific area or monitoring the dynamic changes of biodiversity. However, in the biodiversity investigation stage, it is necessary to consume investigation personnel, and some species cannot be investigated and monitored due to reasons such as being unable to reach; in the species identification stage, it is highly dependent on species identification experts and their professionalism. Therefore, it is necessary to combine various data sources and analysis methods to build an accurate biodiversity investigation and monitoring system.

[0003] Satellite remote sensing technology: multi-spectral and hyper-spectral imaging of the monitoring area through satellite remote sensing to obtain habitat remote sensing data, including but not limited to: vegetation coverage type, vegetation index, land surface temperature, water body distribution, terrain features and human activity traces.

[0004] Environmental DNA technology: with the development of gene sequencing technology, environmental DNA (eDNA) technology is currently used to collect biological gene fragments for sequencing, which can achieve efficient species identification. Through the environmental DNA sampling device, water samples, soil samples or air samples are collected in the monitoring area, filtered and DNA extracted, and then high-throughput sequencing is performed to obtain environmental DNA data, including: species composition, species relative abundance, rare and endangered species information and alien species information.

[0005] The existing field investigation or single remote sensing monitoring method has obvious limitations in monitoring efficiency, species identification accuracy and hidden species detection, resulting in unclear biodiversity quantity and distribution in the investigation and monitoring area, which is difficult to meet the growing demand for global biodiversity protection.

[0006] Biodiversity distribution is influenced by habitat environment, human activities and climate change, and other factors, so it is necessary to integrate multi-source data and various technical means to investigate and monitor biodiversity quickly and accurately, and thus provide a biodiversity investigation and monitoring method based on satellite remote sensing and environmental DNA sequencing. SUMMARY

[0007] The present application aims to overcome the existing defects and provide a biodiversity investigation and monitoring method based on satellite remote sensing and environmental DNA sequencing, which improves the comprehensiveness, accuracy and timeliness of investigation and monitoring, and provides a scientific basis for biodiversity protection.

[0008] The technical solution to achieve the above-mentioned purpose is:

[0009] The biodiversity investigation and monitoring method based on satellite remote sensing and environmental DNA sequencing comprises:

[0010] Step S1, obtaining habitat remote sensing data and environmental DNA data;

[0011] Step S2, preprocessing the obtained data and comparing with a public gene database to obtain species distribution data;

[0012] Step S3, aligning and fusing the habitat remote sensing data and the environmental DNA data in time and space scales to generate a biodiversity feature data set;

[0013] Step S4, constructing a correlation model and a three-dimensional model between the habitat and the species distribution, and visualizing the dynamic changes of biodiversity.

[0014] Preferably, in step S1, the habitat remote sensing data and the environmental DNA data are obtained, comprising:

[0015] Step S11, multi-spectral, hyper-spectral and thermal infrared imaging monitoring of the monitoring area by satellite remote sensing to obtain habitat remote sensing data, wherein the habitat remote sensing data includes but is not limited to vegetation cover type, normalized vegetation index, enhanced vegetation index, land surface temperature, water body distribution, terrain features and human activity traces;

[0016] Step S12, supervised classification of vegetation cover type using high-resolution multi-spectral images, extraction of vegetation biochemical parameters using hyper-spectral data, and inversion of land surface temperature using thermal infrared band data;

[0017] Step S13, setting sampling points according to ecosystem types in the monitoring area by hierarchical random sampling method, and arranging environmental DNA sampling points by system grid method;

[0018] Step S14, collecting samples in water, soil or air according to the standard environmental DNA sample collection process at each sampling point, collecting environmental DNA samples for protected species and endangered species as needed, and performing DNA extraction and PCR (a method for rapidly amplifying specific genes or DNA sequences in vitro) amplification processing after sample collection.

[0019] Preferably, in step S12, according to the spectral characteristics of the vegetation, the combination of wave bands that can best reflect the differences of the vegetation is selected, and a random forest algorithm, a maximum likelihood classification, a Mahalanobis distance classification or a minimum distance classification algorithm is selected, and the classification rules of the training samples are used to classify the entire image.

[0020] In step S14, environmental DNA samples of protected species and endangered species are collected for analyzing genetic diversity; the amplified DNA samples are subjected to high-throughput sequencing by an Illumina NovaSeq (a high-throughput sequencing system) sequencing platform to obtain high-quality environmental DNA sequence data; after obtaining the environmental DNA sequence data, a bioinformatics tool is used for quality control of the sequence data, low-quality sequences are removed, and noise removal processing is performed.

[0021] Preferably, in step S2, the obtained data is preprocessed and compared with a public gene database to obtain species distribution data, including:

[0022] In step S21, a remote sensing image processing software is used to perform atmospheric correction on the multispectral and hyperspectral images, and a regional digital orthophoto map is used as a reference to perform geometric fine correction by a quadratic polynomial fitting model.

[0023] In step S22, for remote sensing data of different resolutions and time effectiveness, an image fusion algorithm is used to process and integrate the data.

[0024] In step S23, the environmental DNA sequencing data is subjected to strict quality control, and a noise removal algorithm is used to remove redundant and unreliable sequences.

[0025] In step S24, the high-throughput sequencing data after quality control and noise removal is compared with a public gene database, and a BLAST or other sequence alignment tool is used to identify the species in the sequencing data to obtain species distribution data.

[0026] Preferably, in step S23, the environmental DNA sequencing data is subjected to strict quality control, including removing low-quality sequences and trimming adapter sequences, wherein a bioinformatics software is used for quality inspection to remove low-quality and redundant sequences.

[0027] Preferably, in step S3, the biodiversity feature data set includes three levels of biodiversity data, namely, ecosystem diversity, species diversity and genetic diversity.

[0028] Preferably, in step S3, the habitat remote sensing data and the environmental DNA data are time and spatial scale aligned and fused to generate a biodiversity feature data set, including:

[0029] Step S31, the dynamic time warping algorithm is used to synchronize the habitat remote sensing data and species distribution data of different time sequences, and then the time scale alignment and fusion of the habitat remote sensing data and the environmental DNA data are realized;

[0030] Step S32, the position of the environmental DNA sampling point is accurately matched with the pixel center coordinates of the remote sensing image by using a unified spatial reference coordinate system, so that the spatial scale alignment and fusion of the habitat remote sensing data and the environmental DNA data are realized;

[0031] Step S33, based on the aligned and fused habitat remote sensing data and environmental DNA data, the biological diversity at three levels of ecosystem diversity, species diversity and genetic diversity is calculated, and a spatio-temporally aligned biological diversity feature data set is generated.

[0032] Preferably, in step S32, for point-like environmental DNA data, the inverse distance weighted interpolation method is used to interpolate the weighted average of the data of the surrounding known sampling points, so as to calculate the environmental DNA data of the unsampled points in space;

[0033] In step S33, the ecosystem diversity data is generated by calculating the area proportion and spatial distribution pattern of each ecosystem type to generate the Shannon (Shannon-Wiener index) ecosystem diversity index; the species diversity data is calculated by the species composition and abundance to generate the species diversity index; and the genetic diversity data is calculated by using the DnaSP (DNA polymorphism analysis software) software to calculate the genetic diversity index.

[0034] Preferably, in step S4, the correlation model and three-dimensional model between habitat and species distribution are constructed, and the dynamic changes of biological diversity are visualized and displayed, including:

[0035] Step S41, based on the species occurrence data in the environmental DNA data as the dependent variable and the habitat parameters extracted from the habitat remote sensing data as the independent variable, regression analysis is performed to construct the correlation model between habitat and species distribution, and the spatial distribution map of each species in the monitoring area is generated by establishing the relationship between habitat and species distribution;

[0036] Step S42, a three-dimensional habitat model is constructed by using a high-resolution digital elevation model combined with habitat remote sensing, biological diversity and geographic information data;

[0037] Step S43, the biological diversity feature data is mapped into the three-dimensional grid model, the spatio-temporal heterogeneity of species richness and diversity is represented by the dynamic change of color gradient, and the change is quantitatively evaluated by calculating the biological diversity change rate.

[0038] Preferably, in step S43, the calculation formula of the biological diversity change rate is as follows:

[0039] ;

[0040] In the formula, is a biodiversity index at a certain time point, is a biodiversity index at the next time node;

[0041] In the step S43, a spatial autocorrelation analysis method is used to identify a region with significant biodiversity change, and for the identified biodiversity hotspot region, a color scale marking method is used to highlight in the three-dimensional model visualization diagram.

[0042] The beneficial effects of the present application are:

[0043] 1) Through the data fusion of satellite remote sensing and environmental DNA sequencing, full-scale monitoring from macro-habitat, species to micro-genes is realized, and three levels of biodiversity data, i.e., ecosystem diversity, species diversity and genetic diversity, can be obtained at the same time. The technical bottleneck that a single data source cannot comprehensively reflect the biodiversity status is overcome, and the comprehensiveness and accuracy of the monitoring are greatly improved;

[0044] 2) Through the data fusion technology, multi-source heterogeneous data is effectively integrated, the correlation and consistency of the data are improved, high-quality input data are provided for the subsequent species distribution prediction model, and the prediction accuracy of the model is significantly improved;

[0045] 3) The spatiotemporal distribution pattern of biodiversity is intuitively displayed through three-dimensional visualization technology, combined with dynamic threat assessment and protection priority division, and precise and real-time decision support is provided for biodiversity protection;

[0046] 4) When habitat mutation (such as fire, deforestation) is detected by satellite, environmental DNA sampling and sequencing by unmanned aerial vehicle or ground team are automatically triggered to evaluate the impact of the event on biodiversity.

[0047] In summary, through satellite remote sensing and environmental DNA sequencing, the present application not only greatly reduces the demand for manpower, but also rapidly collects data in remote and inaccessible areas, saves time, collects more biological information in a shorter time, and greatly improves the efficiency and accuracy of biodiversity investigation and monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a flowchart of the biodiversity investigation and monitoring method of the present application based on satellite remote sensing and environmental DNA sequencing;

[0049] Figure 2 is a specific flowchart for obtaining habitat remote sensing data and environmental DNA data in the present application;

[0050] Figure 3 is a specific flowchart of the present application for pre-processing the obtained data and comparing with the public gene database to obtain species distribution data;

[0051] Figure 4 is a specific flowchart of the present application for time and spatial scale alignment and fusion of habitat remote sensing data and environmental DNA data to generate a biological diversity feature data set;

[0052] Figure 5 is a specific flowchart of the present application for constructing a correlation model and a three-dimensional model between habitat and species distribution, and visualizing the dynamic changes of biological diversity. DETAILED DESCRIPTION

[0053] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings. In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the purpose of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying the importance of the opposite.

[0054] The present application will be further described below with reference to the accompanying drawings.

[0055] As shown in Figure 1 , the biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing comprises:

[0056] Step S1, obtaining habitat remote sensing data and environmental DNA data.

[0057] As shown in Figure 2 , obtaining habitat remote sensing data and environmental DNA data comprises:

[0058] Step S11, multi-spectral, hyper-spectral and thermal infrared imaging monitoring of the monitoring area by satellite remote sensing to obtain habitat remote sensing data, wherein the habitat remote sensing data includes but is not limited to: vegetation cover type, normalized vegetation index, enhanced vegetation index, land surface temperature, water body distribution, topographic features and human activity traces.

[0059] In the embodiment, the remote sensing image data covers a complete ecological cycle (at least 12 months) to comprehensively monitor seasonal changes, and the spatial range of the remote sensing image should include the monitoring area and a buffer zone of 1 km outside the monitoring area to obtain relevant information of the surrounding environment. In order to ensure the high quality of the data, cloud screening should ensure that qualified images with a cloud cover of less than 20% are retained.

[0060] In step S12, high-resolution multispectral images are used for supervised classification of vegetation coverage types, hyperspectral data are used to extract vegetation biochemical parameters, and thermal infrared band data are used to retrieve land surface temperature.

[0061] In the embodiment, according to the spectral characteristics of the vegetation, a band combination that can best reflect the differences between the vegetation is selected, wherein a commonly used band combination includes near-infrared, red and green bands, and a random forest algorithm, maximum likelihood classification, Mahalanobis distance classification or minimum distance classification algorithm is selected. The classification rules of the training samples are used to classify the entire image, and different vegetation types and vegetation growth states are accurately extracted. Hyperspectral data are used to extract vegetation biochemical parameters, especially important ecological indicators such as chlorophyll content, to further evaluate the health status of the vegetation. Thermal infrared band data are used to retrieve land surface temperature (LST), which provides an important reference for subsequent ecological models and environmental monitoring. The spatial resolution of the remote sensing image is required to be not less than 30 meters, and the temporal resolution is required to be not less than 16 days, to ensure the timeliness and accuracy of the monitoring.

[0062] In step S13, sampling points are set according to the ecosystem type by using the stratified random sampling method in the monitoring area, and the environmental DNA sampling points are arranged by using the system grid method.

[0063] In the embodiment, samples in water, soil or air are collected at each sampling point according to the standard environmental DNA sample collection process, and environmental DNA samples can also be collected for some specific species such as protected species and endangered species for analyzing the genetic diversity of the species.

[0064] In step S14, samples in water, soil or air are collected at each sampling point according to the standard environmental DNA sample collection process, and environmental DNA samples can also be collected for some specific species such as protected species and endangered species according to the needs. After the samples are collected, DNA extraction and PCR amplification processing are performed.

[0065] In the embodiment, environmental DNA samples of certain specific species such as protected species and endangered species are collected for analyzing genetic diversity; the amplified DNA samples are subjected to high-throughput sequencing by an Illumina NovaSeq sequencing platform to obtain high-quality environmental DNA sequence data; after obtaining the environmental DNA sequence data, bioinformatics tools are used for quality control of the sequence data, low-quality sequences are removed, and noise removal processing is performed to ensure the reliability of the data.

[0066] In step S2, the obtained data is preprocessed and compared with a public gene database to obtain species distribution data.

[0067] As shown in Figure 3 the obtained data is preprocessed and compared with a public gene database to obtain species distribution data, including:

[0068] In step S21, remote sensing image processing software is used to perform atmospheric correction on multispectral and hyperspectral images, and geometric fine correction is performed by a quadratic polynomial fitting model based on regional digital orthophoto maps.

[0069] In the embodiment, remote sensing image processing software is used to perform atmospheric correction on multispectral and hyperspectral images to eliminate the influence of aerosols and other atmospheric factors, thereby improving the authenticity and accuracy of remote sensing images. Radiometric correction helps to correct image quality problems caused by atmospheric scattering, absorption and other phenomena, ensuring the accuracy of reflecting ground features; geometric fine correction is performed by a quadratic polynomial fitting model based on regional digital orthophoto maps; this step ensures that each pixel point in the remote sensing image can accurately correspond to the geographic coordinate system on the earth's surface, thereby eliminating geometric errors and improving the spatial accuracy of the image.

[0070] In step S22, image fusion algorithms are used to process and integrate remote sensing data of different resolutions and timeliness; for example, high-resolution multispectral data is fused with low-resolution hyperspectral data; by this method, not only higher resolution spectral information can be obtained, but also the timeliness and spatial coverage of the data can be further improved, which provides more detailed and comprehensive data support for subsequent habitat feature extraction and biodiversity analysis.

[0071] In step S23, strict quality control is performed on the environmental DNA sequencing data, and a denoising algorithm is used to denoise the environmental DNA sequencing data to remove redundant and unreliable sequences, thereby improving the accuracy of species classification and abundance estimation.

[0072] In this embodiment, the environmental DNA sequencing data undergoes rigorous quality control, including the removal of low-quality sequences and the trimming of adapter sequences. Quality checks are performed using bioinformatics software (such as FastQC, Trimmomatic, etc.) to remove low-quality and redundant sequences, thereby eliminating errors or contamination during the sequencing process.

[0073] In step S24, the high-throughput sequencing data, after quality control and noise reduction, will be compared with public gene databases. Using BLAST or other sequence alignment tools, species in the sequencing data will be identified to obtain species distribution data.

[0074] In this embodiment, the species composition information of each sample can be obtained through a comparison process, and then the relative abundance data of each species can be extracted. This process provides detailed information such as species composition, relative abundance of species, and invasive species, providing a basis for subsequent biodiversity assessment. This process helps to accurately identify species diversity within the monitoring area.

[0075] Step S3 involves aligning and fusing habitat remote sensing data and environmental DNA data at temporal and spatial scales to generate a biodiversity feature dataset.

[0076] In this embodiment, the biodiversity feature dataset includes three levels of biodiversity data: ecosystem diversity, species diversity, and genetic diversity.

[0077] like Figure 4 As shown, habitat remote sensing data and environmental DNA data are aligned and fused at temporal and spatial scales to generate a biodiversity feature dataset, including:

[0078] Step S31: Since the acquisition times of remote sensing data and environmental DNA data may differ, a dynamic time warping algorithm is needed to synchronize habitat remote sensing data and species distribution data from different time series, thereby achieving time scale alignment and fusion of habitat remote sensing data and environmental DNA data. The dynamic time warping algorithm can minimize the time differences in time series data, so that the time series data achieves optimal temporal matching.

[0079] Step S32: Using a unified spatial reference coordinate system, the location of environmental DNA sampling points is precisely matched with the pixel center coordinates of remote sensing images to achieve spatial scale alignment and fusion of habitat remote sensing data and environmental DNA data.

[0080] In this embodiment, for point-like environmental DNA data, an inverse distance weighted interpolation method is used to interpolate the weighted average of surrounding known sampling point data to infer the environmental DNA data of unsampled points in space. This inverse distance weighted interpolation method, by interpolating the weighted average of surrounding known sampling point data, infers the environmental DNA data of unsampled points in space. This process generates smooth and continuous environmental DNA data in space, thus avoiding the appearance of blank areas. Subsequently, all environmental DNA data are uniformly resampled to the reference resolution of the remote sensing image to ensure spatial consistency, and a standard grid system is constructed. This unified spatial grid facilitates data standardization and comparability in subsequent analyses, ensuring that all data can be fused and compared within the same spatial framework.

[0081] Step S33: Calculate biodiversity at three levels—ecosystem diversity, species diversity, and genetic diversity—based on the aligned and fused habitat remote sensing data and environmental DNA data, and generate a spatiotemporally aligned biodiversity feature dataset.

[0082] In this embodiment, the Shannon Ecosystem Diversity Index is generated by calculating the area proportion and spatial distribution pattern of each ecosystem type from the ecosystem diversity data; the species diversity index is calculated from the species composition and abundance of the species diversity data; and the genetic diversity index is calculated using the DnaSP software from the genetic diversity data.

[0083] Step S4: Construct a correlation model and a 3D model between the generated habitat and species distribution, and visualize the dynamic changes in biodiversity.

[0084] like Figure 5 As shown, a correlation model and a 3D model between habitat and species distribution are constructed, and the dynamic changes in biodiversity are visualized, including:

[0085] Step S41: Based on species occurrence data in environmental DNA data as the dependent variable and habitat parameters extracted from habitat remote sensing data as the independent variable, regression analysis is performed to construct a correlation model between habitat and species distribution. By establishing the relationship between habitat and species distribution, a spatial distribution map of each species in the monitoring area is generated.

[0086] In the embodiments, common modeling methods for constructing a correlation model between habitat and species distribution include the MaxEnt model (maximum entropy model) or the random forest regression model.

[0087] Step S42: Construct a three-dimensional habitat model by using a high-resolution digital elevation model (spatial resolution of not less than 30 meters) combined with habitat remote sensing, biodiversity (such as ecosystem type, species diversity, genetic diversity, etc.) and geographic information data.

[0088] In an embodiment, by integrating terrain features, ecosystem types and biodiversity data, the three-dimensional model can intuitively show the spatial distribution characteristics of habitats and their relationship with species distribution. For the years when no environmental DNA samples are collected, species diversity and genetic diversity data can be generated by habitat-species distribution correlation modeling to generate the species distribution map for that year. In this way, the distribution of species and its trend can be inferred without direct DNA samples.

[0089] In step S43, the biodiversity feature data (such as ecosystem type, species distribution, species diversity data) is mapped into the three-dimensional grid model, and the spatiotemporal heterogeneity of species richness and diversity is represented by dynamic changes in color gradient, and the change is quantitatively evaluated by calculating the biodiversity change rate.

[0090] In an embodiment, the formula for calculating the biodiversity change rate is as follows:

[0091] ;

[0092] In the formula, is the biodiversity index at a certain time point, is the biodiversity index at the next time node.

[0093] In an embodiment, spatial autocorrelation analysis method or other methods are used to identify areas with significant biodiversity changes. For the identified biodiversity hotspots, they are highlighted in the three-dimensional model visualization graph by color scale annotation.

[0094] For example, when a habitat mutation (such as fire, deforestation) is detected by satellite, the environmental DNA sampling and sequencing of unmanned aerial vehicles or ground teams are automatically triggered to evaluate the impact of the event on biodiversity.

[0095] The present application uses satellite remote sensing and environmental DNA sequencing, which not only greatly reduces the demand for manpower, but also quickly collects data in remote and inaccessible areas, saves time, and collects more biological information in a shorter time, greatly improving the efficiency and accuracy of biodiversity investigation and monitoring.

[0096] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent replacements for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing, characterized in that, Includes the following steps: Step S1: Acquire habitat remote sensing data and environmental DNA data; Step S2: Preprocess the acquired data and compare it with a public gene database to obtain species distribution data; Step S3: Align and fuse habitat remote sensing data and environmental DNA data at temporal and spatial scales to generate a biodiversity feature dataset; Step S4 involves constructing a correlation model and a 3D model between habitat and species distribution, and visualizing the dynamic changes in biodiversity. In step S1, acquiring habitat remote sensing data and environmental DNA data includes the following steps: Step S11: Multispectral, hyperspectral and thermal infrared imaging monitoring of the monitoring area is carried out by satellite remote sensing to obtain habitat remote sensing data. The habitat remote sensing data includes, but is not limited to: vegetation cover type, normalized vegetation index, enhanced vegetation index, surface temperature, water distribution, topographic features and traces of human activities. Step S12: Supervised classification of vegetation cover type is performed using high-resolution multispectral images, vegetation biochemical parameters are extracted using hyperspectral data, and land surface temperature is retrieved using thermal infrared band data. Step S13: Within the monitoring area, sampling points are set up according to ecosystem type using stratified random sampling, and environmental DNA sampling points are deployed using a system grid method. Step S14: At each sampling point, samples are collected from water, soil or air according to the standard environmental DNA sample collection procedure. As needed, environmental DNA samples are collected for protected species and endangered species. After sample collection, DNA extraction and PCR amplification are performed. In step S3, the habitat remote sensing data and environmental DNA data are aligned and fused according to time and spatial scales to generate a biodiversity feature dataset, including the following steps: Step S31: The dynamic time warping algorithm is used to synchronize habitat remote sensing data and species distribution data of different time series, thereby realizing the time scale alignment and fusion of habitat remote sensing data and environmental DNA data. Step S32: Using a unified spatial reference coordinate system, the location of environmental DNA sampling points is precisely matched with the pixel center coordinates of remote sensing images to achieve spatial scale alignment and fusion of habitat remote sensing data and environmental DNA data; Step S33: Calculate biodiversity at three levels—ecosystem diversity, species diversity, and genetic diversity—based on the aligned and fused habitat remote sensing data and environmental DNA data, and generate a spatiotemporally aligned biodiversity feature dataset.

2. The biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing according to claim 1, characterized in that, In step S12, based on the spectral characteristics of vegetation, the combination of bands that best reflects the differences in vegetation is selected, and a random forest algorithm, maximum likelihood classification, Mahalanobis distance classification, or minimum distance classification algorithm is selected to classify the entire image using the classification rules of the training samples. In step S14, environmental DNA samples are collected for protected and endangered species to analyze their genetic diversity. The amplified DNA samples are then subjected to high-throughput sequencing using the Illumina NovaSeq sequencing platform to obtain high-quality environmental DNA sequence data. After obtaining the environmental DNA sequence data, bioinformatics tools are used to perform quality control of the sequence data, remove low-quality sequences, and perform noise reduction.

3. The biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing according to claim 1, characterized in that, In step S2, the acquired data is preprocessed and compared with a public gene database to obtain species distribution data, including: Step S21: Atmospheric correction is performed on multispectral and hyperspectral images using remote sensing image processing software. Geometric fine correction is performed using a quadratic polynomial fitting model with regional digital orthophotos as the reference. Step S22: For remote sensing data with different resolutions and timeliness, image fusion algorithms are used to process and integrate the data; Step S23 involves rigorous quality control of the environmental DNA sequencing data and the use of a denoising algorithm to remove redundant and unreliable sequences. In step S24, the high-throughput sequencing data, after quality control and noise reduction, will be compared with public gene databases. Using BLAST or other sequence alignment tools, species in the sequencing data will be identified to obtain species distribution data.

4. The biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing according to claim 3, characterized in that, In step S23, the environmental DNA sequencing data undergoes strict quality control, including the removal of low-quality sequences and the trimming of adapter sequences. This is achieved through quality checks using bioinformatics software to remove low-quality and redundant sequences.

5. The biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing according to claim 3, characterized in that, In step S3, the biodiversity feature dataset includes three levels of biodiversity data: ecosystem diversity, species diversity, and genetic diversity.

6. The biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing according to claim 1, characterized in that, In step S32, for point-like environmental DNA data, the inverse distance weighted interpolation method is used to interpolate the weighted average of the data of the surrounding known sampling points, thereby calculating the environmental DNA data of the unsampled points in the space. In step S33, the ecosystem diversity data is used to generate the Shannon ecosystem diversity index by calculating the area proportion and spatial distribution pattern of each ecosystem type; the species diversity data is used to calculate the species diversity index by calculating the species composition and abundance; and the genetic diversity data is used to calculate the genetic diversity index using DnaSP software.

7. The biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing according to claim 1, characterized in that, In step S4, a correlation model and a three-dimensional model between habitat and species distribution are constructed, and the dynamic changes in biodiversity are visualized, including the following steps: Step S41: Based on species occurrence data in environmental DNA data as the dependent variable and habitat parameters extracted from habitat remote sensing data as the independent variable, regression analysis is performed to construct a correlation model between habitat and species distribution. By establishing the relationship between habitat and species distribution, a spatial distribution map of each species in the monitoring area is generated. Step S42: Construct a three-dimensional habitat model by combining a high-resolution digital elevation model with habitat remote sensing, biodiversity and geographic information data; Step S43: Map biodiversity characteristic data onto a three-dimensional grid model, use dynamic changes in color gradients to represent the spatiotemporal heterogeneity of species richness and diversity, and quantitatively assess changes by calculating the rate of biodiversity change.

8. The biodiversity survey and monitoring method based on satellite remote sensing and environmental DNA sequencing according to claim 7, characterized in that, In step S43, the formula for calculating the rate of change in biodiversity is as follows: ； In the formula, The biodiversity index at a specific point in time. The biodiversity index for the next time point; In step S43, spatial autocorrelation analysis is used to identify areas with significant changes in biodiversity. For the identified biodiversity hotspots, they are highlighted in the 3D model visualization using color-coded annotations.

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