Multi-source remote sensing ecological evaluation method and device considering water quality

Abstract

The invention provides a multi-source remote sensing ecological evaluation method and device considering water quality, and the method comprises the steps: carrying out the preprocessing of satellite image data according to an analysis result of a StateQA wave band and a preset research boundary, and obtaining the processed satellite data; acquiring data through an unmanned aerial vehicle control system according to a preset target area and a preset cruise path to obtain an unmanned aerial vehicle shot image; separating a water body and a land in the satellite image data through a threshold method to obtain land area data and water body area data; evaluating water quality according to the water body area data and the processed satellite data; ecological assessment is carried out according to land area data and processed satellite data; calculating according to the water quality evaluation value and various land index values to obtain a multi-source remote sensing ecological evaluation index considering the water quality; based on the index grade, according to the multi-source remote sensing ecological evaluation index, evaluating the ecological environment of the preset target area to obtain an ecological evaluation value; according to the method, more comprehensive ecological environment quality evaluation is realized by coupling water quality evaluation and land indexes.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring, and in particular to a multi-source remote sensing ecological assessment method and device that takes water quality into account. Background Technology

[0002] The combined effects of global climate change and intense human activities are altering the Earth's ecological environment system on an unprecedented scale and at an unprecedented speed. In the pursuit of economic and social development, the overexploitation and utilization of natural resources has triggered a series of severe ecological crises, including forest and grassland degradation, increased soil erosion, expanded soil salinization, and a sharp decline in biodiversity. Among these, the pressure on river and lake ecosystems is particularly pronounced. To meet the ever-increasing water demands of agricultural irrigation, industrial production, and urban life, large-scale water diversion projects have altered the natural hydrological rhythms and hydraulic connectivity of rivers and lakes, reducing their self-purification capacity and leading to the accumulation of nutrients and pollutants, resulting in water quality deterioration. The fragmentation and loss of aquatic habitats have led to simplified species community structures and functional degradation. Against this backdrop, how to scientifically and accurately assess the quality of the regional ecological environment has become one of the core concerns of ecological civilization construction.

[0003] Traditional ecological assessment methods typically rely on fixed-point sampling and manual measurement, which suffer from low efficiency, high cost, and terrain limitations. Data acquisition cycles are long, failing to reflect dynamic changes in the ecological environment in a timely manner. Furthermore, they lack comprehensive coverage of the entire study area, hindering refined regional assessment. The development of remote sensing technology has greatly facilitated ecological assessment, enabling large-scale, long-term ecological evaluations. Remote sensing-based studies often construct remote sensing ecological indices based on a few spectral indices such as the National Distance Visibility Index (NDVI), Enhanced Vegetation Index (EVI), and Free Fiber Cover (FVC). However, these indices have limited dimensions and cannot comprehensively characterize the synergistic mechanisms of multiple regional factors. Moreover, existing remote sensing ecological assessment methods only consider the distribution characteristics of water bodies, failing to adequately address the impact of water quality on the ecological environment. Summary of the Invention

[0004] This invention provides a multi-source remote sensing ecological assessment method and apparatus that takes water quality into account, in order to solve the shortcomings of existing remote sensing ecological assessment methods that only consider the distribution characteristics of water bodies and do not fully consider the impact of water quality on the ecological environment. It realizes the coupling of water quality with indices such as the vegetation index (NDVI) to form a multi-source remote sensing ecological assessment method that takes water quality into account, thereby achieving a more comprehensive assessment of ecological environment quality.

[0005] This invention provides a multi-source remote sensing ecological assessment method that takes into account water quality, comprising the following steps.

[0006] By using a preset data processing function, the satellite image data is preprocessed based on the resolution results of the StateQA band in the satellite image data and the preset research boundary, resulting in processed satellite data. By using a thresholding method, water and land in satellite imagery data are separated to obtain land area data and water area data; Water quality assessment is conducted based on water area data and processed satellite data to obtain water quality assessment values; ecological assessment is conducted based on land area data and processed satellite data to obtain various land index values; and a multi-source remote sensing ecological evaluation index that takes water quality into account is calculated based on the water quality assessment values ​​and various land index values. Based on the index level, the ecological environment of the preset target area is evaluated according to the multi-source remote sensing ecological evaluation index, and the ecological evaluation value is obtained.

[0007] The multi-source remote sensing ecological assessment method considering water quality provided by the present invention further includes: The drone control system collects data based on the preset target area and preset cruise path to obtain images captured by the drone. The coordinates of the control points are obtained by extracting coordinates from images captured by the drone; the exterior orientation elements are obtained by calculating based on the control point coordinates and the drone positioning data using aerial triangulation. Based on control point coordinates, parallax is calculated from multiple images captured by UAVs to obtain point clouds; the point clouds are then divided into regions and attribute information is extracted to obtain multiple different regional point clouds and regional point cloud attribute data. Based on the exterior orientation elements, region point cloud, and region point cloud attribute data, multiple target object point cloud models and their corresponding absolute positions are obtained; based on the absolute positions of the target objects, the multiple target object point cloud models are combined to obtain a 3D model of the preset target region. The ecological evaluation values ​​are imported as labels into the three-dimensional model of the preset target area to obtain the three-dimensional ecological model of the preset target area.

[0008] According to the present invention, a multi-source remote sensing ecological assessment method considering water quality is provided. By using a preset data processing function, and based on the analytical results of the StateQA bands in the satellite image data and a preset research boundary, data preprocessing is performed on the satellite image data to obtain processed satellite data, including: The StateQA bands in satellite imagery data are analyzed using a cloud mask extraction function to obtain the analysis results. Based on the analysis results, cloud information is extracted from the satellite imagery data. A cloudless mask is then generated based on the cloud information. The cloud information includes cloud status, cloud shadows, and cirrus cloud information. By using a cloudless image function and based on a cloudless mask, satellite image data is processed to obtain cloudless area image data; geometric correction, radiometric calibration, and atmospheric correction are then performed on the cloudless area image data to obtain corrected cloudless image data. The corrected cloudless imagery data is cropped according to the preset research boundaries to obtain the processed satellite data.

[0009] According to the present invention, a multi-source remote sensing ecological assessment method considering water quality is provided, which assesses water quality based on regional water body data and processed satellite data to obtain water quality assessment values, including: The near-infrared reflectance value was obtained by calculating based on data from the water body area. The phytoplankton index is obtained by comparing the near-infrared reflectance value with the clean baseline value. The water quality assessment value is obtained by evaluating the value of the phytoplankton index.

[0010] According to the present invention, a multi-source remote sensing ecological assessment method considering water quality is provided. Ecological assessment is performed based on land area data and processed satellite data to obtain multiple land index values. A multi-source remote sensing ecological assessment index considering water quality is calculated based on the water quality assessment value and the multiple land index values, including: By using a pre-set independent inversion model, an ecological assessment of the land area data is conducted based on land area data and processed satellite data, resulting in various types of land index values. These land index values ​​include: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EDI), Chlorophyll Content, Vegetation Coverage, and Bare Soil Index. Principal component analysis was used to calculate and normalize the water quality assessment value and the land index value to obtain a multi-source remote sensing ecological evaluation index that takes into account water quality.

[0011] This invention also provides a multi-source remote sensing ecological assessment device that takes into account water quality, comprising the following modules: The satellite data preprocessing module is used to preprocess satellite image data based on the resolution results of the StateQA band in the satellite image data and the preset research boundary through a preset data processing function, so as to obtain processed satellite data. The land-water separation module is used to separate water and land in satellite imagery data using a threshold method, to obtain land area data and water area data. The water quality and terrestrial ecological assessment module is used to conduct water quality assessment based on water area data and processed satellite data to obtain water quality assessment values; to conduct ecological assessment based on terrestrial area data and processed satellite data to obtain various terrestrial index values; and to calculate a multi-source remote sensing ecological evaluation index that takes water quality into account based on the water quality assessment values ​​and various terrestrial index values. The evaluation value generation module is used to evaluate the ecological environment of a preset target area based on the index level and the multi-source remote sensing ecological evaluation index, and obtain the ecological evaluation value.

[0012] According to the present invention, a multi-source remote sensing ecological assessment device that takes into account water quality is provided, wherein the module further includes: The 3D ecological model building module is used to collect data from drone images via a drone control system based on a preset target area and a preset cruise path. Coordinate extraction is performed on these images to obtain control point coordinates. Exterior orientation elements are calculated using aerial triangulation based on the control point coordinates and drone positioning data. Based on the control point coordinates, parallax is calculated from multiple drone images to obtain point clouds. These point clouds are then divided into regions and attribute information is extracted to obtain multiple distinct regional point clouds and their attribute data. Based on the exterior orientation elements, regional point clouds, and their attribute data, multiple target object point cloud models and their corresponding absolute positions are obtained. These target object point cloud models are then combined based on their absolute positions to obtain a 3D model of the preset target area. Finally, ecological evaluation values ​​are imported as labels into the 3D model of the preset target area to obtain a 3D ecological model of the target area.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-source remote sensing ecological assessment method for water quality consideration as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-source remote sensing ecological assessment method for water quality consideration as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the multi-source remote sensing ecological assessment method for water quality consideration as described above.

[0016] The multi-source remote sensing ecological assessment method and apparatus that takes water quality into account provided by this invention preprocesses satellite image data based on the analysis results of the StateQA bands in the satellite image data and a preset research boundary using a preset data processing function, obtaining processed satellite data. A threshold method is then used to separate water bodies and land in the satellite image data, yielding land area data and water body area data. Water quality is assessed based on the water body area data and the processed satellite data, resulting in a water quality assessment value. An ecological assessment is performed based on the land area data and the processed satellite data, obtaining multiple land index values. A multi-source remote sensing ecological assessment index that takes water quality into account is calculated based on the water quality assessment value and the multiple land index values. Based on the index level, the ecological environment of a preset target area is evaluated according to the multi-source remote sensing ecological assessment index, yielding an ecological assessment value. Compared with existing remote sensing ecological assessment methods that only consider the distribution characteristics of water bodies and do not fully consider the impact of water quality on the ecological environment, this invention can couple water quality assessment values ​​with multiple land indices to form a multi-source remote sensing ecological assessment method that takes water quality into account, achieving a more comprehensive assessment of ecological environment quality. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the multi-source remote sensing ecological assessment method for water quality provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of the multi-source remote sensing ecological assessment device that takes into account water quality provided by the present invention.

[0020] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The following is combined Figures 1-3 This invention is described.

[0023] Figure 1 This is a flowchart illustrating the multi-source remote sensing ecological assessment method for water quality considerations provided by the present invention, as shown below. Figure 1 As shown, the method includes the following: Step 101: Using a preset data processing function, preprocess the satellite image data based on the analysis results of the StateQA band in the satellite image data and the preset research boundary to obtain the processed satellite data.

[0024] In step 101 above, satellite imagery data refers to satellite remote sensing data.

[0025] The preprocessing of satellite remote sensing data includes: using MODIS imagery data and leveraging the Google Earth Engine (GEE) platform for data preprocessing. This involves extracting cloud, cloud shadow, and cirrus cloud information from the MODIS imagery data through computation, generating a cloud-free mask, removing pixels affected by atmospheric pollution, and ensuring the reliability of surface reflectance data. Based on the GEE cloud platform, the acquisition, processing, and analysis of MODIS imagery data are completed by calling GEE's APIs and functions.

[0026] The acquisition of MODIS imagery data was defined based on the time series and the time scale of the study. This project sets the study period (2018-2020) with one-year intervals. The study season (July-September) was also set to reduce the impact of seasonality. A DateRange list (2018-2020, one time period per year) was generated.

[0027] The preset research boundaries are defined by the vector dataset uploaded by the user. For example, if the research object is Province A, a boundary file of the administrative divisions of Hebei Province needs to be uploaded, i.e., vector data. GEE will only process the image within this boundary file, just like using "scissors" to cut the image according to the shape of Province A.

[0028] Optionally, step 101 includes steps A1 to A3: Step A1: Analyze the StateQA band in the satellite image data using the cloud mask extraction function to obtain the analysis results; extract cloud information from the satellite image data based on the analysis results; generate a cloudless mask based on the cloud information; the cloud information includes: cloud status, cloud shadows, and cirrus cloud information.

[0029] Step A2: Using the cloudless image function and based on the cloudless mask, process the satellite image data to obtain cloudless area image data; perform geometric correction, radiometric calibration, and atmospheric correction on the cloudless area image data to obtain corrected cloudless image data.

[0030] Step A3: Crop the corrected cloudless image data according to the preset research boundary to obtain the processed satellite data.

[0031] In steps A1 to A3 above, cloud removal and image preprocessing define a cloud mask extraction function: parsing the StateQA band to extract cloud state, cloud shadow, and cirrus cloud information. A cloud-free image function is created, retaining only image data of cloud-free, cloud-free, and cirrus-free regions, removing the effects of atmospheric pollution to ensure cloud-free, cloud-free, and cirrus-free areas; a mask is applied to preserve cloud-free regions. Geometric correction, radiometric calibration, and atmospheric correction are performed on the image; finally, the image is cropped based on the administrative division vector data of the study area.

[0032] Specifically, when satellites take photos, if there are clouds in the sky, the ground will not be clearly visible. Clouds, cloud shadows, and thin high-altitude cirrus clouds all contaminate the image, making it impossible to accurately analyze the true condition of the ground, i.e., surface reflectivity. MODIS data includes a quality description band, the StateQA band. This band acts like a diagnostic report for the image, using code to record whether each pixel contains clouds, cloud shadows, etc. The program interprets this diagnostic report and generates a map mask. This mask is black and white: white areas represent good, clean, cloudless pixels, and black areas represent bad pixels containing clouds or cloud shadows. Finally, the mask is applied, placing this black and white mask over the original color image. All the bad pixels in the black areas are masked out, leaving only the clean white pixels, thus obtaining a cloudless image.

[0033] Step 102: Using the thresholding method, separate the water and land in the satellite image data to obtain land area data and water area data.

[0034] In step 102 above, the improved Normalized Difference Water Index (MNDWI) is calculated for the study area to construct a water mask to distinguish between land and water, so that subsequent operations can be performed on land and in water respectively, which greatly improves the accuracy of the results and the calculation speed.

[0035] MNDWI=(Green-SWIR1) / (Green+SWIR1) In this designation, Green represents the green band, and SWIR1 represents the shortwave infrared band 1. A threshold method is used to separate water bodies from land, ensuring that MNDWI < 0 is used to identify water bodies and generate a water body mask.

[0036] Step 103: Conduct water quality assessment based on water area data and processed satellite data to obtain water quality assessment values; conduct ecological assessment based on land area data and processed satellite data to obtain multiple land index values; calculate the multi-source remote sensing ecological evaluation index that takes water quality into account based on the water quality assessment values ​​and multiple land index values.

[0037] Optionally, step 103 involves conducting a water quality assessment based on the water body area data and processed satellite data to obtain a water quality assessment value, including steps B1 to B3: Step B1: Calculate the near-infrared reflectance value based on the water area data.

[0038] Step B2: Obtain the phytoplankton index based on the difference between the near-infrared reflectance value and the clean baseline value.

[0039] Step B3: Evaluate the water quality based on the value of the phytoplankton index to obtain the water quality assessment value.

[0040] In steps B1 to B3 above, the Floating Algae Index (FAI) is used to assess the water quality of the water body area and is named T1. T1 = FAI = NIR - NIR′; NIR′ = Red + (SWIR - Red)(λnir - λRed) / (λSWIR - λRed).

[0041] Among them, NIR is the near-infrared band, SWIR is the short-wave infrared band, and Red is the red band.

[0042] T1 is the reflectance value actually measured by the satellite sensor in the near-infrared band, which includes the total reflectance information of the water body and any algae or other substances that may be present in it.

[0043] NIR′ is a simulated or baseline value. It represents the reflectance value that the near-infrared band “should” have under the same conditions, assuming the water body is clean and free from phytoplankton.

[0044] FAI stands for Phytoplankton Index. Its physical meaning is quite intuitive. If FAI ≈ 0, it means the actual observed value (NIR) and the simulated clean water value (NIR') are roughly the same, indicating clean water with no obvious algae. If FAI > 0, it means the actual observed value (NIR) is greater than the simulated clean water value (NIR'), indicating enhanced near-infrared reflectance, which strongly suggests the presence of phytoplankton on the water surface. A higher FAI value usually means higher algae density and a more severe algal bloom.

[0045] Optionally, in step 103, an ecological assessment is performed based on land area data and processed satellite data to obtain multiple land index values; based on water quality assessment values ​​and multiple land index values, a multi-source remote sensing ecological evaluation index that takes water quality into account is calculated, including steps C1 to C2: Step C1: Using a pre-set independent inversion model, an ecological assessment is performed on the land area data based on the land area data and processed satellite data, yielding various types of land index values. These land index values ​​include: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EDI), Chlorophyll Content, Vegetation Coverage, and Bare Soil Index.

[0046] Step C2: Using principal component analysis, principal component calculations and normalization are performed on the water quality assessment value and the land index value to obtain a multi-source remote sensing ecological evaluation index that takes into account water quality.

[0047] In steps C1 and C2 above, this invention employs five core indicators for comprehensive evaluation of the terrestrial ecological environment. The inversion of each indicator is based on multi-source remote sensing data, including multispectral imagery, field measurement data, and relevant statistical data. By referencing existing inversion models and introducing the random forest algorithm, an independent inversion model suitable for each indicator is constructed. Based on this, the model is validated and its parameters optimized using synchronously measured data, ultimately obtaining accurate and reliable indicator inversion results, providing a scientific basis for regional ecological environment assessment.

[0048] The specific inversion process for each indicator is as follows: Normalized Difference Vegetation Index (NDVI): T2 = NDVI = (NIR - Red) / (NIR + Red). Where NIR is the near-infrared band and Red is the red band. Enhanced Vegetation Index (EVI): T3 = EVI = 2.5 (NIR - Red) / (NIR + 6Red - 7.5Blue + 1). Where NIR is the near-infrared band, Red is the red band, and Blue is the blue band. Chlorophyll Content: T4 = ReCI = NIR / Red - 1. Where NIR is the near-infrared band and Red is the red band. Vegetation Cover (FVC): T5 = FVC = (NDVI - NDVIsoil) / (NDVIveg + NDVIsoil). Wherein, NDVIveg and NDVIsoil are the NDVI values ​​of clean vegetation pixels and clean soil pixels in the remote sensing image, respectively; the bare soil index T6 = BSI = {(Red + SWIR1) - (NIR + Blue)} / {(Red + SWIR1) + (NIR + Blue)}. Wherein, Red represents the red band, Blue represents the blue band, NIR represents the near-infrared band, and SWIR1 represents the shortwave infrared band 1.

[0049] To eliminate the comparability barriers caused by differences in numerical range and dimensions of different ecological indices, this invention uses the range standardization method to normalize each index, uniformly converting its values ​​to the range of 0 to 1, so as to achieve dimensionless and direct comparability of each ecological parameter.

[0050] Principal Component Analysis (PCA) specifically calculates the covariance matrix of six standardized ecological parameters to identify the main directions of variation in the data. The first principal component (PC1) is extracted, capturing the most significant variation information of the six indicators and representing the ecological environment characteristics to the greatest extent. By subtracting the result of the first principal component from PC1, a multi-source remote sensing ecological evaluation index that takes water quality into account is constructed, namely: W-MRSEI0=1-{PC1[ρ(T1, T2, T3, T4, T5, T6)]} After normalization, a multi-source remote sensing ecological evaluation index that takes water quality into account is obtained: W-MRSEI = (W-MRSEI0 - W-MRSEI) min ) / (W-MRSEI max +W-MRSEI min ) This ensures that the final calculated remote sensing ecological index (W-MRSEI) has a consistent scale, facilitating comparisons between different regions.

[0051] This invention constructs a multi-source remote sensing ecological assessment index (W-MRSEI) that takes water quality into account. This index is a multi-dimensional comprehensive evaluation index, and quantitative analysis has been performed on it. It solves the two core defects of existing technologies: the index has only one dimension, making it difficult to comprehensively represent the synergistic mechanism of multiple factors in a region, and it does not fully consider the impact of water quality on the ecological environment.

[0052] This invention innovatively introduces a water quality parameter (FAI phytoplankton index) on top of traditional vegetation indices and surface parameters. FAI can effectively detect eutrophication information such as algal blooms in water bodies, thereby elevating "water" from a simple geographical outline to an evaluation dimension with ecological quality connotations through MNDWI identification, realizing the quantitative characterization of the synergistic effects of multiple factors such as water quality, vegetation, and soil.

[0053] Step 104: Based on the index level, evaluate the ecological environment of the preset target area according to the multi-source remote sensing ecological evaluation index to obtain the ecological evaluation value.

[0054] In step 104 above, the ecological environment status of the study area is evaluated based on the multi-source remote sensing ecological evaluation index that takes water quality into account. Specifically, the W-MRSEI value is taken from 0 to 1, and the W-MRSEI is divided into 5 levels with an interval of 0.2. The first level is [0, 0.2], which indicates "very poor" ecological environment quality; the second level is (0.2, 0.4], which indicates "poor" ecological environment quality; the third level is (0.4, 0.6], which indicates "moderate" ecological environment quality; the fourth level is (0.6, 0.8], which indicates "good" ecological environment quality; and the fifth level is (0.8, 1.0], which indicates "excellent" ecological environment quality.

[0055] Optionally, the multi-source remote sensing ecological assessment method for water quality considerations in this embodiment of the invention further includes... The multi-source remote sensing ecological assessment method considering water quality provided by the present invention further includes steps D1 to D4: Step D1: The UAV control system collects data based on the preset target area and preset cruise path to obtain images captured by the UAV; coordinate extraction is performed on the images captured by the UAV to obtain the coordinates of the control points; the exterior orientation elements are obtained by calculating based on the control point coordinates and the UAV positioning record data using aerial triangulation.

[0056] Step D2: Based on the control point coordinates, perform parallax calculation on multiple images taken by the UAV to obtain point clouds; divide the point clouds into regions and extract attribute information to obtain multiple different regional point clouds and regional point cloud attribute data.

[0057] Step D3: Based on the exterior orientation elements, region point cloud, and region point cloud attribute data, obtain multiple target object point cloud models and their corresponding absolute positions; based on the absolute positions of the target objects, merge the multiple target object point cloud models to obtain a 3D model of the preset target region.

[0058] Step D4: Import the ecological evaluation values ​​as labels into the three-dimensional model of the preset target area to obtain the three-dimensional ecological model of the preset target area.

[0059] In steps D1 to D4 above, visible light data of the study area is collected using a DJI Matrice M350 RTK high-precision industrial drone equipped with a Zenmuse P1 camera, which integrates 45 megapixels, and collects visible light data according to a preset cruise path.

[0060] Specifically: Determine the target area and collect data: Select the target area, and according to the preset cruise path and speed, use a drone equipped with a visible light camera to collect multi-directional target image data of the ecological resources in the target area.

[0061] Furthermore, the control system onboard the UAV integrates multiple functional modules, including navigation, positioning, time acquisition, and battery detection. The navigation unit plans and maintains the flight path and direction; the positioning unit acquires the UAV's geographical location information in real time; the time acquisition unit records image capture times and assists in mission timing control; and the battery detection unit continuously monitors battery status to ensure flight safety. When the battery level drops below 20%, the system automatically triggers a return-to-home procedure, and the UAV returns to its takeoff point. After the operator replaces the battery, the UAV can resume and continue executing unfinished missions, achieving effective management of the operational process and support for continued flight after interruptions.

[0062] This invention establishes an automated and highly efficient data processing workflow based on a cloud platform and drones. It overcomes the problems of low efficiency, high cost, terrain limitations, long data acquisition cycles, and lack of comprehensive coverage in existing technologies.

[0063] This invention provides batch processing on a cloud computing platform. Batch processing utilizes the cloud data storage and computing capabilities of a pre-defined Earth engine to automatically perform a series of preprocessing steps on satellite imagery such as MODIS, including atmospheric correction, cloud masking, time-series composites, and cropping, by calling its API. The pre-defined Earth engine includes the Google Earth engine.

[0064] The principle of batch processing in cloud computing platforms transforms traditional local, manual, and time-consuming data processing into parallel, on-demand services in the cloud, greatly shortening the data preparation cycle and enabling rapid processing of large-scale, long-term data.

[0065] This invention also supplements its approach with intelligent UAV surveying. For key areas, this invention uses high-precision UAVs for visible light data acquisition. The underlying technology involves an integrated control system that combines navigation, positioning, and battery detection to enable automatic flight along preset routes and resume flight from interrupted points. This solves the problem of complex terrain areas and provides higher spatial resolution data, serving as a valuable supplement to satellite data. Together, they form a highly efficient data acquisition network combining wide-area satellite coverage with detailed UAV coverage for specific areas.

[0066] The construction of the 3D model of the study area includes: The construction begins with extracting control point coordinates from multi-view target images and conducting aerial triangulation using GPS and POS data to obtain high-precision exterior orientation elements. Based on this, high-density point clouds are generated using preprocessed multi-view images, and semantically segmented into several regions. Then, point cloud data of target objects are extracted from each region, their attribute information is extracted after stitching, and exterior orientation elements are fused to construct independent target object point cloud models. Subsequently, the point cloud models of all target objects are integrated to form a complete 3D model of the study area. The ecological environment assessment results are imported into the 3D model as tags to obtain a 3D ecological model of the study area.

[0067] This invention achieves the integrated fusion and visualization of ecological assessment results with 3D reality models. It overcomes the shortcomings of traditional assessment results, which are mostly 2D models, resulting in poor spatial intuitiveness and difficulty in directly linking them to management decisions.

[0068] This invention is a process of transforming images into 3D models. Specifically, based on multi-view images captured by a drone, the precise position and attitude of the camera, i.e., exterior orientation elements, are recovered through an aerial triangulation algorithm, and then a high-density 3D point cloud is generated through a multi-view stereo matching algorithm.

[0069] This invention performs semantic information injection. Specifically, the multi-source remote sensing ecological assessment index (W-MRSEI) calculated above is used as an attribute label and assigned to the corresponding 3D point cloud model. This makes the 3D model not only have a geometric shape, but also contain semantic information about the ecological quality of each region. The technical principle is to associate and map attribute data with spatial coordinates, thereby transforming an abstract "index" into an "ecological status" that can be directly observed, measured, and interacted with in 3D space, providing managers with unprecedented intuitive decision support.

[0070] This invention systematically solves a series of bottleneck problems in the comprehensiveness, timeliness and practicality of existing ecological assessment methods through three core technologies: multidimensional index construction, automated process innovation and three-dimensional visualization integration.

[0071] This application proposes a multi-source remote sensing ecological assessment method that considers water quality. By coupling water quality parameters with various vegetation and surface parameters, a multi-dimensional ecological assessment index system is constructed, effectively overcoming the inherent shortcomings of traditional methods, such as reliance on ground sampling, limited coverage, single indicators, and neglect of water quality impact. Utilizing Google Earth engine and UAV collaborative operations, large-scale, long-term, and high-precision automated data acquisition and processing are achieved, significantly improving efficiency and reducing costs. Furthermore, based on principal component analysis and range standardization, a comprehensive W-MRSEI index is generated by integrating multi-source remote sensing indicators, enabling scientific and consistent quantification of ecological environment quality. Simultaneously, by constructing a labeled three-dimensional ecological model, the evaluation results are visualized and spatially managed, providing comprehensive, dynamic, and operable technical support for regional ecological monitoring, assessment, and decision-making.

[0072] This invention provides a multi-source remote sensing ecological assessment method that considers water quality. It preprocesses satellite imagery data using a preset data processing function, based on the analytical results of the StateQA bands and a preset research boundary, to obtain processed satellite data. A drone control system then collects data according to a preset target area and a preset flight path, obtaining drone-captured images. A threshold method is used to separate water and land areas from the satellite imagery data, yielding land area data and water area data. Water quality is assessed based on the water area data and the processed satellite data to obtain a water quality assessment value. Finally, based on the land area data… Ecological assessments are performed on processed satellite data to obtain various land index values. Based on water quality assessment values ​​and these land index values, a multi-source remote sensing ecological evaluation index that considers water quality is calculated. Based on the index levels and the multi-source remote sensing ecological evaluation index, the ecological environment of a preset target area is evaluated to obtain an ecological evaluation value. Compared with existing remote sensing ecological evaluation methods that only consider the distribution characteristics of water bodies and do not fully consider the impact of water quality on the ecological environment, this invention can couple water quality assessment values ​​with multiple land indices to form a multi-source remote sensing ecological evaluation method that considers water quality, achieving a more comprehensive assessment of ecological environment quality.

[0073] The following describes the multi-source remote sensing ecological assessment device that takes water quality into account provided by the present invention. The multi-source remote sensing ecological assessment device that takes water quality into account described below can be referred to in correspondence with the multi-source remote sensing ecological assessment method that takes water quality into account described above.

[0074] Figure 2 This is a schematic diagram of the process of the multi-source remote sensing ecological assessment device for water quality provided by the present invention, as shown below. Figure 2 As shown, the device includes the following: The satellite data preprocessing module 201 is used to preprocess satellite image data according to the analysis results of the StateQA band in the satellite image data and the preset research boundary through a preset data processing function, so as to obtain processed satellite data.

[0075] The land-water separation module 202 is used to collect data through the UAV control system based on the preset target area and preset cruise path to obtain images captured by the UAV; and to separate the water and land in the satellite image data by using a threshold method to obtain land area data and water area data.

[0076] The water quality and terrestrial ecological assessment module 203 is used to conduct water quality assessment based on water area data and processed satellite data to obtain water quality assessment values; conduct ecological assessment based on terrestrial area data and processed satellite data to obtain various terrestrial index values; and calculate a multi-source remote sensing ecological evaluation index that takes water quality into account based on the water quality assessment values ​​and various terrestrial index values.

[0077] The evaluation value generation module 204 is used to evaluate the ecological environment of a preset target area based on the index level and the multi-source remote sensing ecological evaluation index, and obtain the ecological evaluation value.

[0078] Optionally, the multi-source remote sensing ecological assessment device that takes water quality into account provided by the present invention further includes: The 3D ecological model building module is used to collect data from drone images via a drone control system based on a preset target area and a preset cruise path. Coordinate extraction is performed on these images to obtain control point coordinates. Exterior orientation elements are calculated using aerial triangulation based on the control point coordinates and drone positioning data. Based on the control point coordinates, parallax is calculated from multiple drone images to obtain point clouds. These point clouds are then divided into regions and attribute information is extracted to obtain multiple distinct regional point clouds and their attribute data. Based on the exterior orientation elements, regional point clouds, and their attribute data, multiple target object point cloud models and their corresponding absolute positions are obtained. These target object point cloud models are then combined based on their absolute positions to obtain a 3D model of the preset target area. Finally, ecological evaluation values ​​are imported as labels into the 3D model of the preset target area to obtain a 3D ecological model of the target area.

[0079] This invention provides a multi-source remote sensing ecological assessment device that considers water quality. It preprocesses satellite image data using a preset data processing function, based on the analysis results of the StateQA bands in the satellite image data and a preset research boundary, to obtain processed satellite data. A drone control system then collects data according to a preset target area and a preset cruise path, obtaining drone-captured images. A threshold method is used to separate water and land areas in the satellite image data, obtaining land area data and water area data. Water quality is assessed based on the water area data and the processed satellite data to obtain a water quality assessment value. Finally, based on the land area data… Ecological assessments are performed on processed satellite data to obtain various land index values. Based on water quality assessment values ​​and these land index values, a multi-source remote sensing ecological evaluation index that considers water quality is calculated. Based on the index levels and the multi-source remote sensing ecological evaluation index, the ecological environment of a preset target area is evaluated to obtain an ecological evaluation value. Compared with existing remote sensing ecological evaluation methods that only consider the distribution characteristics of water bodies and do not fully consider the impact of water quality on the ecological environment, this invention can couple water quality assessment values ​​with multiple land indices to form a multi-source remote sensing ecological evaluation method that considers water quality, achieving a more comprehensive assessment of ecological environment quality.

[0080] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions from the memory 830 to execute a multi-source remote sensing ecological assessment method that considers water quality.

[0081] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the multi-source remote sensing ecological assessment method for water quality that takes into account the above methods.

[0083] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the multi-source remote sensing ecological assessment method for water quality consideration provided by the methods described above.

[0084] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0085] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-source remote sensing ecological assessment method that takes water quality into account, characterized in that, include: By using a preset data processing function, the satellite image data is preprocessed based on the analysis results of the StateQA band in the satellite image data and the preset research boundary, resulting in processed satellite data. The water and land areas in the satellite image data are separated by a threshold method to obtain land area data and water area data. Water quality assessment is performed based on the water body area data and the processed satellite data to obtain water quality assessment values; ecological assessment is performed based on the land area data and the processed satellite data to obtain multiple land index values; and a multi-source remote sensing ecological evaluation index that takes water quality into account is calculated based on the water quality assessment values ​​and the multiple land index values. Based on the index level, the ecological environment of the preset target area is evaluated according to the multi-source remote sensing ecological evaluation index to obtain the ecological evaluation value.

2. The multi-source remote sensing ecological assessment method considering water quality as described in claim 1, characterized in that, Also includes: The drone control system collects data based on the preset target area and preset cruise path to obtain images captured by the drone. The coordinates of the control points are obtained by extracting coordinates from the images captured by the UAV; the exterior orientation elements are obtained by calculating based on the control point coordinates and the UAV positioning data using aerial triangulation. Based on the coordinates of the control points, parallax calculation is performed on multiple images captured by the UAV to obtain a point cloud; The point cloud is divided into regions and its attribute information is extracted to obtain multiple different regional point clouds and regional point cloud attribute data. Based on the exterior orientation elements, the region point cloud, and the region point cloud attribute data, multiple target object point cloud models and corresponding absolute positions of the target objects are obtained; based on the absolute positions of the target objects, the multiple target object point cloud models are combined to obtain a three-dimensional model of the preset target region. The ecological evaluation value is imported as a label into the three-dimensional model of the preset target area to obtain the three-dimensional ecological model of the preset target area.

3. The multi-source remote sensing ecological assessment method considering water quality as described in claim 1, characterized in that, The process involves using a preset data processing function to preprocess the satellite image data based on the resolution results of the StateQA bands in the satellite image data and a preset research boundary, resulting in processed satellite data, including: The StateQA band in satellite imagery data is analyzed using a cloud mask extraction function to obtain the analysis result; cloud information is then extracted from the satellite imagery data based on the analysis result; a cloudless mask is generated based on the cloud information; wherein, the cloud information includes: cloud state, cloud shadow, and cirrus cloud information; By using the cloudless image function and based on the cloudless mask, the satellite image data is processed to obtain cloudless area image data; geometric correction, radiometric calibration, and atmospheric correction are then performed on the cloudless area image data to obtain corrected cloudless image data. The corrected cloudless image data is cropped according to a preset research boundary to obtain processed satellite data.

4. The multi-source remote sensing ecological assessment method considering water quality according to claim 1, characterized in that, The step of conducting a water quality assessment based on the water body area data and the processed satellite data to obtain a water quality assessment value includes: The near-infrared reflectance value is calculated based on the data of the water body area. The phytoplankton index is obtained based on the difference between the near-infrared band reflectance value and the clean baseline value. The water quality assessment value is obtained by evaluating the value of the phytoplankton index.

5. The multi-source remote sensing ecological assessment method considering water quality as described in claim 1, characterized in that, The process involves performing an ecological assessment based on the land area data and the processed satellite data to obtain multiple land index values; and calculating a multi-source remote sensing ecological evaluation index that takes water quality into account based on the water quality assessment value and the multiple land index values, including: By using a preset independent inversion model, an ecological assessment is performed on the land area data based on the land area data and the processed satellite data to obtain various types of land index values; among which, the land index values ​​include: normalized vegetation index, enhanced vegetation index, chlorophyll content, vegetation coverage and bare soil index. Principal component analysis was used to calculate and normalize the water quality assessment value and the land index value to obtain a multi-source remote sensing ecological evaluation index that takes water quality into account.

6. A multi-source remote sensing ecological assessment device that takes into account water quality, characterized in that, include: The satellite data preprocessing module is used to preprocess the satellite image data according to the parsing results of the StateQA band in the satellite image data and the preset research boundary through a preset data processing function, so as to obtain processed satellite data. The land-water separation module is used to separate the water and land in the satellite image data using a threshold method to obtain land area data and water area data. The water quality and terrestrial ecology assessment module is used to perform water quality assessment based on the water body area data and the processed satellite data to obtain water quality assessment values; to perform ecological assessment based on the land area data and the processed satellite data to obtain multiple land index values; and to calculate a multi-source remote sensing ecological evaluation index that takes water quality into account based on the water quality assessment values ​​and the multiple land index values. The evaluation value generation module is used to evaluate the ecological environment of the preset target area based on the index level and the multi-source remote sensing ecological evaluation index to obtain the ecological evaluation value.

7. The multi-source remote sensing ecological assessment device considering water quality according to claim 1, characterized in that, Also includes: The 3D ecological model building module is used to collect data and obtain images captured by the drone through the drone control system based on the preset target area and preset cruise path. Coordinate extraction is performed on the images captured by the UAV to obtain control point coordinates. Using aerial triangulation, exterior orientation elements are calculated based on the control point coordinates and UAV positioning data. Based on the control point coordinates, parallax is calculated on multiple UAV images to obtain point clouds. The point clouds are then divided into regions and attribute information is extracted to obtain multiple different regional point clouds and regional point cloud attribute data. Based on the exterior orientation elements, the regional point clouds, and the regional point cloud attribute data, multiple target object point cloud models and their corresponding absolute positions are obtained. Based on the absolute positions of the target objects, the multiple target object point cloud models are combined to obtain a three-dimensional model of the preset target region. The ecological evaluation value is imported as a label into the three-dimensional model of the preset target area to obtain the three-dimensional ecological model of the preset target area.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the multi-source remote sensing ecological assessment method that takes water quality into account as described in any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-source remote sensing ecological assessment method that takes water quality into account as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-source remote sensing ecological assessment method that takes water quality into account as described in any one of claims 1 to 5.

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