Ecological environment quality detection method and equipment fused with topographic information

Abstract

The invention relates to the technical field of environment detection, in particular to an ecological environment quality detection method and device fused with topographic information, and the method comprises the steps: obtaining a remote sensing image and digital elevation model (DEM) data of a target area; generating first image data representing vegetation growth conditions, second image data representing earth surface humidity and third image data representing earth surface temperature based on the remote sensing image; terrain complexity image data is generated based on digital elevation model (DEM) data, terrain complexity index values in one-to-one correspondence with actual geographic positions in the target area are distributed in the terrain complexity image data, and the terrain complexity index values are calculated and determined based on terrain information; and fusing the first image data, the second image data, the third image data and the terrain complexity image data, and taking a fusion result as an ecological environment quality detection result of the target area. Therefore, the ecological environment quality of complex terrain areas such as mountainous regions and hills can be reflected more truly.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, specifically to a method and equipment for monitoring the ecological environment quality by integrating topographic information. Background Technology

[0002] With the increasing severity of ecological and environmental problems, the scientific, rapid, and objective evaluation of a region's ecological and environmental quality has become an important requirement for ecological and environmental protection and territorial spatial planning. Remote sensing technology provides an effective means for this purpose, among which the Remote Sensing Ecological Index (RSEI) is particularly widely used. RSEI calculates the Normalized Difference Vegetation Index (NDVI), Tasseled Cap Wetness (Wet), Normalized Difference Bare Soil Index (NDBSI), and Land Surface Temperature (LST) based on remote sensing data. Then, through Principal Component Analysis (PCA), the RSEI detection results are generated, enabling the detection and evaluation of ecological and environmental quality.

[0003] However, the aforementioned RSEI model has significant limitations: it relies entirely on two-dimensional spectral information based on remote sensing images. While it can ensure the accuracy of ecological environment quality detection in plain areas, it does not consider the key impact of topography on the ecosystem. This results in inaccurate and unobjective detection results in complex terrain areas such as mountains and hills. For example, when the greenness is the same, the ecological vulnerability of steep slopes is much higher than that of flat areas, valleys have higher humidity than slopes and mountain tops, and areas with high surface dissection have more fragmented natural ecosystems. Therefore, it is difficult to truly reflect the actual ecological situation.

[0004] Therefore, existing technologies are insufficient to provide accurate and reliable ecological environment quality monitoring results in complex terrain areas, and cannot meet the urgent needs for refined monitoring in fields such as ecological protection and land spatial planning. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an ecological environment quality detection method and device that integrates terrain information, so as to overcome the current problem that it is impossible to effectively detect the ecological environment quality of complex terrain.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Firstly, this application provides a method for detecting ecological environment quality by integrating topographic information, including: Acquire remote sensing images and digital elevation model (DEM) data of the target area; Based on the remote sensing imagery, first image data representing vegetation growth status, second image data representing surface humidity, and third image data representing surface temperature are generated. The first image data contains greenness index values ​​that correspond one-to-one with the pixels of the remote sensing image; the second image data contains humidity index values ​​that correspond one-to-one with the pixels of the remote sensing image; and the third image data contains temperature index values ​​that correspond one-to-one with the pixels of the remote sensing image. The pixels of the remote sensing image correspond one-to-one with the actual geographical location in the target area. Based on the digital elevation model (DEM) data, terrain complexity image data is generated. The terrain complexity image data contains terrain complexity index values ​​that correspond one-to-one with the actual geographical locations in the target area. The terrain complexity index values ​​are calculated and determined based on terrain information, which includes at least one of surface roughness, slope, slope variability, surface curvature, surface dissection, terrain relief, and elevation variation coefficient. The first image data, the second image data, the third image data, and the terrain complexity image data are fused together, and the fusion result is used as the ecological environment quality detection result of the target area.

[0008] Furthermore, in some embodiments of this application, the method further includes: preprocessing the remote sensing images and digital elevation model (DEM) data; The preprocessing of the remote sensing imagery includes radiometric calibration, atmospheric correction, geometric correction, mosaicking, and cropping; the preprocessing of the digital elevation model (DEM) data includes projection transformation, smoothing, mosaicking, and cropping.

[0009] Furthermore, in some embodiments of this application, the terrain information is slope, surface relief, surface dissection, and surface curvature.

[0010] Furthermore, in some embodiments of this application, generating terrain complexity image data based on the digital elevation model (DEM) data includes: For each actual geographical location, the slope, surface relief, surface dissection and surface curvature are calculated based on the digital elevation model (DEM) data, and the terrain complexity index value of the actual geographical location is calculated based on the preset weights of various terrain information.

[0011] Furthermore, in some embodiments of this application, the weights of slope, surface relief, surface dissection, and surface curvature are 0.5, 0.3, 0.15, and 0.05, respectively.

[0012] Furthermore, in some embodiments of this application, fusing the first image data, the second image data, the third image data, and the terrain complexity image data includes: The index values ​​corresponding to the same actual geographical location in the first image data, second image data, third image data, and terrain complexity image data are merged into a terrain adjustment remote sensing ecological index value corresponding to that actual geographical location.

[0013] Furthermore, in some embodiments of this application, fusing the first image data, the second image data, the third image data, and the terrain complexity image data includes: Principal component analysis was performed on the first image data, the second image data, the third image data, and the terrain complexity image data, and the first principal component image was used as the fusion result. The fusion results include terrain-adjusting remote sensing ecological index values ​​that correspond one-to-one with the actual geographical locations in the target area.

[0014] Furthermore, in some embodiments of this application, fusing the first image data, the second image data, the third image data, and the terrain complexity image data includes: The first image data, the second image data, the third image data, and the terrain complexity image data are fused using machine learning algorithms or deep learning algorithms to obtain the fusion result; The fusion results include terrain-adjusting remote sensing ecological index values ​​that correspond one-to-one with the actual geographical locations in the target area.

[0015] Furthermore, in some embodiments of this application, the method further includes: normalizing the topographic adjustment remote sensing ecological index values ​​in the first principal component image after principal component analysis.

[0016] Secondly, this application provides an ecological environment quality monitoring device that integrates terrain information, including a processor and a memory, wherein the processor is connected to the memory: The processor is used to call and execute the program stored in the memory; The memory is used to store the program, which is at least used to execute the above-described method for detecting the ecological environment quality by fusing terrain information.

[0017] This invention relates to the field of environmental monitoring technology, specifically to a method and device for monitoring ecological environment quality by integrating topographic information. The method includes: acquiring remote sensing images and digital elevation model (DEM) data of a target area; generating first image data representing vegetation growth, second image data representing surface humidity, and third image data representing surface temperature based on the remote sensing images; generating topographic complexity image data based on the DEM data, wherein the topographic complexity image data contains topographic complexity index values ​​that correspond one-to-one with the actual geographical locations in the target area. These topographic complexity index values ​​are calculated based on topographic information, which includes at least one of surface roughness, slope, slope variability, surface curvature, surface dissection, topographic relief, and elevation variation coefficient; and fusing the first image data, second image data, third image data, and topographic complexity image data, using the fusion result as the ecological environment quality monitoring result for the target area. This allows for a more realistic reflection of the ecological environment quality of complex terrain areas such as mountains and hills. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the ecological environment quality detection method that integrates terrain information provided in this embodiment of the invention. Figure 2 This is a schematic diagram illustrating the principle of the ecological environment quality detection method integrating terrain information provided in this embodiment of the invention; Figure 3 This is a comparison chart of the detection results of the ecological environment quality detection method integrating terrain information provided in this embodiment of the invention and existing detection methods for the same target area; Figure 4 This is a schematic diagram of the structure of the ecological environment quality monitoring device that integrates terrain information provided in this embodiment of the invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] Figure 1 This is a flowchart illustrating the ecological environment quality detection method that integrates terrain information, as provided in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the principle of the ecological environment quality detection method integrating terrain information provided in this embodiment of the invention; Please see Figure 1 and Figure 2 This embodiment may include the following steps: S101. Acquire remote sensing images and digital elevation model (DEM) data of the target area.

[0022] Specifically, the target area is the area where ecological environment quality monitoring is to be carried out. Remote sensing images can be Landsat data or Sentinel data, or digital elevation model (DEM) data such as ASTER GDEM data (appropriate satellite data can be selected according to actual needs, which will not be listed here).

[0023] S102. Based on remote sensing imagery, generate first image data representing vegetation growth, second image data representing surface humidity, and third image data representing surface temperature.

[0024] The first image data contains greenness index values ​​that correspond one-to-one with the pixels in the remote sensing image; the second image data contains humidity index values ​​that correspond one-to-one with the pixels in the remote sensing image; and the third image data contains temperature index values ​​that correspond one-to-one with the pixels in the remote sensing image. The pixels in the remote sensing image correspond one-to-one with the actual geographical locations in the target area.

[0025] It should be noted that, in this application, the first image data, the second image data, and the third image data mentioned above can be obtained through existing RSEI models.

[0026] Specifically, each pixel in the remote sensing image corresponds one-to-one with an actual geographical location in the target area. When generating the aforementioned image data, for each pixel, a corresponding index value is calculated based on the spectral band information contained within the pixel. Then, index values ​​of the same type for all pixels are arranged according to the original pixel layout to generate various image data sets. For example, when generating the first image data, the greenness index value of each pixel in the remote sensing image is calculated based on the spectral band information of each pixel, and these greenness index values ​​are arranged according to the pixel layout of the remote sensing image to generate the first image data.

[0027] The formula for calculating the greenness index value of each pixel based on its spectral band information is as follows:

[0028] In the formula, NDVI is the normalized difference vegetation index, i.e., the greenness index value. and These represent the surface reflectance values ​​in the red and near-infrared bands of the remote sensing image, respectively. It is understood that this index is used to characterize vegetation growth, with values ​​ranging from -1 to 1. Higher values ​​indicate higher biomass and vegetation vitality in the corresponding area.

[0029] For each pixel, the formula for calculating the pixel's humidity index value based on the pixel's spectral band information is:

[0030]

[0031] In the formula, The humidity component of the tassel transformation, i.e., the humidity index value, is calculated for Landsat 8 OIL remote sensing data. The humidity index value calculated for Landsat TM remote sensing data; , , and These represent the surface reflectance values ​​for blue light, green light, shortwave infrared 1, and shortwave infrared 2 bands, respectively. It can be understood that this index is used to comprehensively quantify surface moisture content, representing the humidity of the soil and environment; high values ​​indicate a humid environment.

[0032] For each pixel, the formula for calculating the pixel's temperature index value based on the pixel's spectral band information is:

[0033] In the formula, DN represents the surface temperature, i.e., the temperature index value. DN is the gray value of the thermal infrared band B10 of Landsat 8 TIRS remote sensing data.

[0034] It should be noted that the above-mentioned indicators and their calculation methods are the same as those in the existing RSEI model. The difference between this application and the indicators based on the RSEI model is that the dryness index in the RSEI model is abandoned (because the dryness index in the existing RSEI model is prone to misjudging the ecological environment quality in areas such as bare soil in urban areas with high spectral reflectance), and the terrain complexity index mentioned later is adopted. This avoids the negative impact of the dryness index under complex terrain, and at the same time, the terrain complexity index further improves the objective accuracy of the detection.

[0035] S103. Generate terrain complexity image data based on digital elevation model (DEM) data.

[0036] Among them, the terrain complexity image data contains terrain complexity index values ​​that correspond one-to-one with the actual geographical locations in the target area. The terrain complexity index values ​​are calculated and determined based on terrain information, which includes at least one of the following: surface roughness, slope, slope variability, surface curvature, surface dissection, terrain relief, and elevation variation coefficient (which can be extracted from digital elevation model (DEM) data using ArcGIS software or other similar GIS software).

[0037] It is understandable that complex terrains such as mountains and hills have characteristics that are significantly different from plains. For example, vegetation is more prone to degradation on steep slopes, water is more likely to accumulate and humidity is higher in valleys, and terrain undulations affect the distribution of heat and light. However, existing RSEI models ignore the impact of this terrain information on the quality of the ecological environment. Therefore, the detection results in complex terrains such as mountains and hills are often inaccurate and cannot objectively reflect the actual quality of the ecological environment.

[0038] Therefore, when conducting ecological environment quality testing, this application retains the greenness, humidity, and temperature indicators in the RSEI while discarding the dryness indicator, and adds the topographic complexity indicator.

[0039] The calculation process for the terrain complexity index is as follows: Based on the digital elevation model (DEM) data, the terrain information of each actual geographical location in the target area is determined. Then, for each actual geographical location, the terrain complexity index value is calculated, and the terrain complexity index values ​​of all actual geographical locations are arranged (in practical applications, since the digital elevation model (DEM) data corresponds one-to-one with each actual geographical location, arranging the calculated terrain complexity index values ​​according to the arrangement order of the DEM data can achieve a one-to-one correspondence between each terrain complexity index value and the actual geographical location), generating terrain complexity image data.

[0040] The terrain information may include multiple parameters such as surface roughness, slope, slope variability, surface curvature, surface dissection, topographic relief, and elevation variation coefficient.

[0041] S104. The first image data, the second image data, the third image data, and the terrain complexity image data are fused together, and the fusion result is used as the ecological environment quality detection result of the target area.

[0042] Specifically, image data can be fused using methods such as principal component analysis, machine learning algorithms, deep learning algorithms, or weighted linear summation to obtain detection results for target areas that include greenness, humidity, temperature, and terrain complexity.

[0043] Furthermore, in some embodiments of this application, after acquiring remote sensing images and digital elevation model (DEM) data, preprocessing can be performed to ensure the accuracy of subsequent detection results.

[0044] Specifically, preprocessing for remote sensing images includes radiometric calibration, atmospheric correction, geometric correction, mosaicking, and cropping (where mosaicking and cropping specifically involve mosaicking and cropping of the target area within the remote sensing image based on preset vector boundary information); preprocessing for digital elevation model (DEM) data includes projection transformation, smoothing, mosaicking, and cropping (similar to the mosaicking and cropping of remote sensing images described above).

[0045] Furthermore, in some embodiments of this application, the terrain information may specifically include slope, surface relief, surface dissection, and surface curvature. Based on this, terrain complexity image data is generated using digital elevation model (DEM) data, including: For each actual geographical location, slope, surface relief, surface dissection, and surface curvature are calculated based on the digital elevation model (DEM) data. The terrain complexity index value is then calculated based on preset weights for each type of terrain information. The weights for slope, surface relief, surface dissection, and surface curvature can be 0.5, 0.3, 0.15, and 0.05, respectively.

[0046] Thus, the weighted sum of the slope, surface relief, surface dissection, and surface curvature of an actual geographical location is used as the topographic complexity index value of that actual geographical location.

[0047] Based on this, this application integrates first image data, second image data, third image data and terrain complexity image data, including: integrating the index values ​​corresponding to the same actual geographical location in the first image data, second image data, third image data and terrain complexity image data into a terrain adjustment remote sensing ecological index value corresponding to the actual geographical location, which can also be called a terrain adjustment remote sensing ecological index.

[0048] For example, by using AHP, entropy method or factor analysis, the fusion weights corresponding to the four types of image data are determined. Then, the four index values ​​corresponding to the same actual geographical location are generated by weighted linear summation to produce the terrain adjustment remote sensing ecological index value of the actual geographical location.

[0049] Furthermore, in some embodiments of this application, principal component analysis can be performed on the first image data, the second image data, the third image data, and the terrain complexity image data, and the first principal component image can be used as the fusion result. It is understood that the fusion result contains terrain-adjusting remote sensing ecological index values ​​that correspond one-to-one with the actual geographical locations in the target area.

[0050] In practical applications, the first image data, second image data, third image data, and terrain complexity image data can be input into a preset remote sensing software to perform PCA principal component analysis. The first principal component image (i.e., PC1) is then selected as the fusion result. It is understood that the first principal component image contains terrain-adjusted remote sensing ecological index values ​​that correspond one-to-one with the actual geographical locations in the target area (based on the above principle, they also correspond one-to-one with the pixels in the remote sensing image).

[0051] Furthermore, in some embodiments of this application, after obtaining the first principal component image, the topographic adjustment remote sensing ecological index values ​​in the first principal component image can be normalized to [0, 1] (in other embodiments of this application, [0, 1] normalization can be replaced by standard deviation standardization, quantile normalization or fuzzy membership function mapping) to obtain the final topographic adjustment remote sensing ecological index values.

[0052] Furthermore, in other embodiments of this application, the fusion of the above-mentioned image data can also be achieved through machine learning algorithms or deep learning algorithms. The final fusion result is in the same form as the fusion result based on the PCA principal component analysis principle, that is, it is distributed with terrain-modulated remote sensing ecological index values ​​that correspond one-to-one with the actual geographical locations in the target area.

[0053] In addition, in some embodiments of this application, for specific regions such as arid areas, the dryness index in the existing RSEI model can be added, and in the subsequent fusion stage, factor screening methods such as correlation verification can be used to determine whether to fuse it with other indicators in order to meet the feature detection requirements.

[0054] The ecological environment quality detection method integrating topographic information provided in this application avoids misjudgments caused by the dryness index in high-reflectivity areas such as bare soil and water bodies by using greenness, humidity, and temperature indicators in the RSEI model, in addition to the dryness index, thus making the detection results more objective and reliable. Simultaneously, it introduces a topographic complexity index, which is composed of slope, topographic relief, surface dissection, and curvature, thus addressing the shortcomings of traditional methods in reflecting ecological environment quality under complex terrain conditions. Furthermore, it uses PCA principal component analysis on the four indicators, utilizing the first principal component (PC1) to comprehensively reflect ecological and topographic characteristics, avoiding subjectivity caused by artificial weighting and improving the scientific rigor and universality of the results. Compared to traditional methods applicable only to plains or gentle slopes, the method provided in this application is also applicable to complex terrain areas such as mountains and hills. The ecological environment quality detection results are more consistent with reality, and the final detection results, including topographically adjusted remote sensing ecological index values, can directly serve fields such as ecological environment quality detection and analysis, ecotourism resource development potential analysis, ecological protection zoning, and land spatial planning management, demonstrating promising application prospects.

[0055] Figure 3 This is a comparison chart of the detection results of the ecological environment quality detection method integrating terrain information provided in this embodiment of the invention and existing detection methods for the same target area. Figure 3 In the figures, (a) represents the normalized detection results obtained from the RSEI model in the prior art, while (b) represents the normalized detection results obtained from the ecological environment quality detection method based on the fused terrain information provided in this application. It should be noted that in both figures, green and yellow represent areas with poor ecological environment quality; the lighter the color, the worse the ecological environment quality. Orange and red represent areas with good ecological environment quality; the darker the red, the better the ecological environment quality.

[0056] like Figure 3 As shown, compared with the detection results of the method provided in this application, the normalized detection value of RSEI is generally larger in hilly and mountainous areas, and the red color is prone to oversaturation, reflecting that it has low differentiation of the ecological environment quality of complex hilly and mountainous areas, while the ecological environment quality of complex hilly and mountainous areas is more accurate and reasonable in this application.

[0057] Based on the same inventive concept, this application also provides an ecological environment quality detection device that integrates terrain information, used to implement the above-described method embodiments. Figure 4 This is a schematic diagram of the ecological environment quality monitoring device that integrates terrain information provided in an embodiment of the present invention, as shown below. Figure 4As shown, the ecological environment quality detection device integrating terrain information in this embodiment includes a processor 11 and a memory 12, with the processor 11 connected to the memory 12. The processor 11 is used to call and execute the program stored in the memory 12; the memory 12 is used to store the program, which is at least used to execute the ecological environment quality detection method integrating terrain information in the above embodiments.

[0058] The specific implementation scheme of the ecological environment quality detection equipment that integrates terrain information provided in this application embodiment can refer to the implementation scheme of the ecological environment quality detection method that integrates terrain information in any of the above embodiments, and will not be repeated here.

[0059] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0060] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0061] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0062] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0063] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0064] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0065] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0066] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0067] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for detecting ecological environment quality by integrating topographic information, characterized in that, include: Acquire remote sensing images and digital elevation model (DEM) data of the target area; Based on the remote sensing imagery, first image data representing vegetation growth status, second image data representing surface humidity, and third image data representing surface temperature are generated. The first image data contains greenness index values ​​that correspond one-to-one with the pixels of the remote sensing image; the second image data contains humidity index values ​​that correspond one-to-one with the pixels of the remote sensing image; and the third image data contains temperature index values ​​that correspond one-to-one with the pixels of the remote sensing image. The pixels of the remote sensing image correspond one-to-one with the actual geographical location in the target area. Based on the digital elevation model (DEM) data, terrain complexity image data is generated. The terrain complexity image data contains terrain complexity index values ​​that correspond one-to-one with the actual geographical locations in the target area. The terrain complexity index values ​​are calculated and determined based on terrain information, which includes at least one of surface roughness, slope, slope variability, surface curvature, surface dissection, terrain relief, and elevation variation coefficient. The first image data, the second image data, the third image data, and the terrain complexity image data are fused together, and the fusion result is used as the ecological environment quality detection result of the target area.

2. The ecological environment quality detection method integrating topographic information according to claim 1, characterized in that, Also includes: The remote sensing images and digital elevation model (DEM) data are preprocessed. The preprocessing of the remote sensing imagery includes radiometric calibration, atmospheric correction, geometric correction, mosaicking, and cropping; the preprocessing of the digital elevation model (DEM) data includes projection transformation, smoothing, mosaicking, and cropping.

3. The method for detecting ecological environment quality by integrating topographic information according to claim 1, characterized in that, The terrain information includes slope, surface relief, surface dissection, and surface curvature.

4. The ecological environment quality detection method integrating topographic information according to claim 3, characterized in that, The process of generating terrain complexity image data based on the digital elevation model (DEM) data includes: For each actual geographical location, the slope, surface relief, surface dissection and surface curvature are calculated based on the digital elevation model (DEM) data, and the terrain complexity index value of the actual geographical location is calculated based on the preset weights of various terrain information.

5. The ecological environment quality detection method integrating topographic information according to claim 4, characterized in that, The weights for slope, surface relief, surface dissection, and surface curvature are 0.5, 0.3, 0.15, and 0.05, respectively.

6. The method for detecting ecological environment quality by integrating topographic information according to claim 1, characterized in that, The fusion of the first image data, the second image data, the third image data, and the terrain complexity image data includes: The index values ​​corresponding to the same actual geographical location in the first image data, second image data, third image data, and terrain complexity image data are merged into a terrain adjustment remote sensing ecological index value corresponding to that actual geographical location.

7. The method for detecting ecological environment quality by integrating topographic information according to claim 1, characterized in that, The fusion of the first image data, the second image data, the third image data, and the terrain complexity image data includes: Principal component analysis was performed on the first image data, the second image data, the third image data, and the terrain complexity image data, and the first principal component image was used as the fusion result. The fusion results include terrain-adjusting remote sensing ecological index values ​​that correspond one-to-one with the actual geographical locations in the target area.

8. The method for detecting ecological environment quality by integrating topographic information according to claim 1, characterized in that, The fusion of the first image data, the second image data, the third image data, and the terrain complexity image data includes: The first image data, the second image data, the third image data, and the terrain complexity image data are fused using machine learning algorithms or deep learning algorithms to obtain the fusion result; The fusion results include terrain-adjusting remote sensing ecological index values ​​that correspond one-to-one with the actual geographical locations in the target area.

9. The method for detecting ecological environment quality by integrating topographic information according to claim 7, characterized in that, Also includes: The topographic adjustment remote sensing ecological index values ​​in the first principal component image after principal component analysis are normalized.

10. An ecological environment quality monitoring device integrating topographic information, characterized in that, It includes a processor and a memory, wherein the processor is connected to the memory: The processor is used to call and execute the program stored in the memory; The memory is used to store the program, which is at least used to execute the ecological environment quality detection method that integrates terrain information as described in any one of claims 1-9.

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