Ecological restoration effect evaluation method and system based on multi-dimensional remote sensing data

Abstract

The invention discloses an ecological restoration effect evaluation method and system based on multi-dimensional remote sensing data, and the method comprises the steps: carrying out the integration and correction of the multi-source remote sensing data, and completing a preprocessing process; based on the preprocessed data, respectively quantifying vegetation recovery, soil stability and hydrological regulation capabilities; integrating the indexes to construct an ecological comprehensive restoration index; and carrying out spatial visualization on the comprehensive repair index. The system comprises a preprocessing module, a core index calculation module, a comprehensive index calculation module and a data visualization module. According to the method, ecological weak links can be accurately positioned, a spatial decision basis is provided for differentiated ecological management, the restoration efficiency is effectively improved, and the cost is reduced. The method can be widely applied to the field of ecological environment assessment.

Description

Technical Field

[0001] This invention relates to the field of ecological environment assessment, and in particular to a method and system for assessing the effectiveness of ecological restoration based on multidimensional remote sensing data. Background Technology

[0002] Low mountain and hilly areas, as an important component of terrestrial ecosystems, are not only key zones for biodiversity enrichment but also core areas for water conservation, soil and water conservation, and climate regulation. Ecological restoration in these areas has become a crucial guarantee for regional ecological security and sustainable development. Scientifically assessing the effectiveness of ecological restoration in such areas not only provides a basis for precise regulation of restoration projects but also offers data support for the formulation of ecological compensation policies and the optimization of land use, thus holding significant strategic importance for advancing ecological civilization.

[0003] However, existing methods for evaluating the effectiveness of ecological restoration rely on assessment models based on a single remote sensing data source (such as optical images), lacking the ability to perform multi-dimensional (vegetation-soil-hydrology) collaborative inversion and topographic correction, resulting in low parameter extraction accuracy and severe topographic shadow interference. Summary of the Invention

[0004] In view of this, in order to address the problem that most existing methods for evaluating the effectiveness of micro-ecological restoration only use a single remote sensing data source, thus making it difficult to achieve spatially refined and dynamic evaluation of the effectiveness of ecological restoration, firstly, this invention proposes an ecological restoration effectiveness evaluation method based on multi-dimensional remote sensing data, which includes: Multi-source remote sensing data preprocessing involves integrating and correcting multi-source remote sensing data to complete the preprocessing process; the multi-source remote sensing data includes optical remote sensing data, radar data, and lidar or elevation data.

[0005] The core indicators for ecological restoration are calculated based on preprocessed data, quantifying vegetation restoration, soil stability, and hydrological regulation capacity.

[0006] The comprehensive restoration index is calculated by integrating the above indicators to construct an ecological comprehensive restoration index.

[0007] Index visualization mapping allows for spatial visualization of the comprehensive restoration index, generating thematic maps of restoration effects.

[0008] Analysis and diagnosis of restoration effectiveness: Analyzing the spatial differentiation characteristics of the comprehensive restoration index to accurately identify weak points in the restoration process. Based on the above method, in a second aspect, the present invention also provides an ecological restoration effectiveness evaluation system based on multidimensional remote sensing data, including a preprocessing module, a core indicator calculation module, a comprehensive indicator calculation module, and a data visualization module.

[0009] Based on the above scheme, the present invention provides a method and system for evaluating the effectiveness of ecological restoration based on multi-dimensional remote sensing data. By integrating multi-source remote sensing data and introducing a terrain correction mechanism, the present invention effectively overcomes the influence of terrain undulation in low mountain and hilly areas on the accuracy of remote sensing inversion, and improves the accuracy and reliability of ecological parameter extraction. By constructing a vegetation-soil-hydrology collaborative evaluation model, it can accurately identify weak links in ecological restoration. Attached Figure Description

[0010] Figure 1 This is a flowchart of the steps of an ecological restoration effectiveness evaluation method based on multidimensional remote sensing data according to the present invention; Figure 2 This is a structural block diagram of an ecological restoration effectiveness evaluation system based on multidimensional remote sensing data according to the present invention. Detailed Implementation

[0011] In addition to the lack of multi-dimensional data application mentioned in the background technology, some assessment methods rely on manual ground surveys, which are constrained by terrain accessibility, have sparse distribution of survey points and insufficient spatial representativeness, making it difficult to capture the spatial heterogeneity of restoration effectiveness, and especially unable to effectively characterize the regulatory role of topographic factors such as slope aspect and slope on ecological restoration.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0013] It should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0014] It should be understood that the terms "system," "apparatus," "unit," and / or "module" used in this application are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0015] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "a," and / or "the" are not specifically singular and may include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements. An element defined by the phrase "comprising an..." does not exclude the presence of other identical elements in the process, method, product, or apparatus that includes the element.

[0016] In the description of the embodiments of this application, "a plurality of" refers to two or more. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0017] Furthermore, flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Additionally, other operations can be added to these processes, or one or more steps can be removed from them.

[0018] Reference Figure 1 This is a flowchart illustrating an optional example of the ecological restoration effectiveness evaluation method based on multidimensional remote sensing data proposed in this invention. The method can be applied to computer equipment, and the evaluation method proposed in this embodiment may include, but is not limited to, the following steps: Step S1: Acquire multi-source remote sensing data and perform corrections to obtain preprocessed data; Step S2: Calculate the vegetation restoration index, soil stability index, and hydrological regulation index based on the preprocessed data; Step S3: Generate a comprehensive restoration index based on the vegetation restoration index, soil stability index, and hydrological regulation index; Step S4: Visualize and record based on the comprehensive repair index; Step S5: Calculate the spatial heterogeneity based on the comprehensive repair index and identify the repair shortcomings.

[0019] In some feasible embodiments, step S1 specifically includes: Based on the study area and assessment needs, acquire time-series multi-source remote sensing data, including optical remote sensing data, radar data, and lidar or elevation data; The optical remote sensing data includes Landsat 8 / 9 or Sentinel-2 data, used to extract vegetation indices (NDVI, EVI) and land surface temperature (LST). The radar data includes: Sentinel-1 data, used to retrieve soil moisture content, surface deformation, and topographic relief; The lidar or elevation data includes: LiDAR data or AW3D30 elevation data, used to generate a high-precision digital elevation model (DEM) and calculate slope and aspect. Topographic radiometric correction is performed on optical remote sensing data to eliminate topographic shading effects and cloud cover filling is applied to generate a spatiotemporally continuous remote sensing data sequence. The specific method for topographic radiometric correction of optical remote sensing data is as follows: in, For the corrected radiance; Original radiance; The solar zenith angle; is the slope angle; k is an empirical coefficient, which is 0.7 by default.

[0020] also, Radar data processing: Using techniques such as polarization decomposition, soil moisture content and clay content loss rates are retrieved to assess soil stability; Elevation data processing: Slope and aspect topographic factors are extracted based on the DEM for topographic correction in subsequent ecological index calculations; In some feasible embodiments, step S2 specifically includes: Based on the processed data, three types of spatial explicit evaluation indicators were constructed: vegetation restoration index, soil stability index, and hydrological regulation index.

[0021] Vegetation restoration index The calculation formula is expressed as follows: in, To correct the difference in NDVI values ​​before and after, terrain normalization was applied. The topographic moisture index, calculated based on the DEM, reflects the redistribution of moisture by topography. The formula is: ,in, The upstream catchment area, Slope; The slope attenuation factor, ; This is the slope aspect deviation coefficient, with 0 for sunny slopes and 1 for shady slopes; Canopy coverage correction factor, Where CC is the current canopy coverage (inverted from LiDAR or Sentinel-2), and CC0 is the ideal canopy coverage for the region; This is the first adjustment factor, with a default value of 0.5; The vegetation restoration index is calculated using the above formula, where an exponential function is used. The pattern allows vegetation restoration to increase rapidly at low values ​​and tend to saturate at high values, which aligns with the actual characteristics of ecosystem response; simultaneously, it introduces... , and Topographic correction factor and canopy cover dynamic correction factor effectively eliminate the influence of topography and canopy structure on vegetation index.

[0022] Soil stability index The calculation formula is expressed as follows: in, The soil erosion modulus is calculated based on RUSLE. It is a rainfall erosivity factor, calculated based on meteorological data or CHIRPS precipitation data, reflecting the catalytic effect of rainfall on soil erosion; The loss rate of clay content retrieved by radar polarization decomposition; Organic matter correction factor OM represents soil organic matter content (retrieved via Sentinel-2 or Hyperion), and OM0 represents the ideal value for the region. Take 0.1; This is the second adjustment factor, with a default value of 0.8; This is the third adjustment factor, with a default value of 0.6; Taking into account the impacts of soil erosion and clay loss on the ecosystem, the soil stability index is calculated using the above formula, employing an exponential function. To reflect the negative impacts of soil erosion on the ecosystem, a reciprocal function is used. This indicates that the higher the clay loss rate, the worse the soil stability. The organic matter dynamic correction is calculated, where OM is the soil organic matter content (inverted by Sentinel-2 or Hyperion), and OM0 is the regional ideal value, which can reflect the impact of soil fertility on stability.

[0023] Hydrological Regulation Index The calculation formula is expressed as follows: in, This represents the soil volumetric water content retrieved by radar. Ideal soil moisture content; The infiltration capacity index is calculated based on the flow curvature of the DEM. This represents the normalized value of runoff accumulation under a rainstorm event. The vegetation roughness factor is calculated by combining NDVI and surface roughness (derived from radar data) and reflects the obstructive effect of vegetation on runoff. This is a slope stability correction factor. I is an indicator function, with a value of 1 when the slope is greater than 30 degrees, and 0 otherwise.

[0024] Taking into account the impact of soil moisture content, infiltration capacity, and flood risk on hydrological regulation function, the hydrological regulation index is calculated using the above formula, where, This reflects a decline in hydrological regulation function when soil moisture content deviates from the ideal level. The stronger the infiltration capacity, the better the hydrological regulation function. This indicates that the lower the flood risk, the stronger the hydrological regulation function; The vegetation roughness factor is calculated by combining NDVI and surface roughness (derived from radar data) and reflects the obstructive effect of vegetation on runoff. This represents the slope stability correction factor, which corrects for the attenuation of hydrological regulation capacity in steep slope areas.

[0025] In some feasible embodiments, in step S3, the comprehensive repair index The calculation formula is expressed as follows: in, These represent different weighting coefficients, with default values ​​of 0.35, 0.25, 0.25, and 0.15. This is a terrain adjustment factor; it is set to 1.2 when the elevation is greater than 500 meters, and 1.0 otherwise. This is the coordination adjustment coefficient, which is set to 0.2 by default.

[0026] To more accurately reflect the contributions of vegetation, soil, and hydrology to the effectiveness of ecological restoration, a comprehensive restoration index is calculated using the formula described above. In the formula, the index weights are... It reflects the fundamental role of vegetation restoration and the synergistic effect of vegetation-soil, and its influence is affected by topographic conditions ( Modulation, linear weighting , and It reflects the importance of soil stability, hydrological regulation function, and the synergistic effect of the soil-hydrological system.

[0027] In some feasible embodiments, step S4 specifically includes: The restoration effectiveness grading and mapping method divides the restoration effectiveness into three levels according to the CRI value: [0, 0.3) is poor, represented by red; [0.3, 0.6) is medium, represented by yellow; and [0.6, 1.0] is excellent, represented by green. A CRI spatial distribution heatmap and time-series animation are generated, and abnormal grids with a CRI change rate greater than 10% are automatically marked.

[0028] By employing spatial explicit output and visualization technology, heat maps and dynamic change animations of restoration effectiveness are generated, providing intuitive and scientific decision support for ecological restoration projects.

[0029] This embodiment provides a method for visualizing restoration effectiveness. By dividing the comprehensive restoration index into three levels and representing them with different colors, the spatial differentiation pattern of ecological restoration effectiveness can be displayed intuitively. By generating time-series animations, the temporal trend of ecological restoration effectiveness can be dynamically displayed. By automatically marking abnormal grids with a change rate greater than 10%, problem areas in the ecological restoration process can be identified in a timely manner.

[0030] In some feasible embodiments, step S5 specifically includes: Based on the spatial differentiation of the comprehensive restoration index, priority areas for governance are identified, and differentiated ecological governance measures are matched accordingly.

[0031] Calculate spatial heterogeneity: in, These are local raster values; This is the regional average. This is the smoothing factor, with a default value of 0.01. The gradient magnitude of CRI is used to characterize the rate of spatial change. This is the gradient weight coefficient, with a default value of 0.3.

[0032] Grid areas that simultaneously meet the criteria of spatial differentiation greater than 0.2 and CRI less than 0.5 are selected as priority governance areas, and corresponding ecological governance measures are matched according to areas with low VRI, SSI, and HRI values.

[0033] Differentiated management measures include: replanting native species and optimizing vegetation communities in areas with low VRI values; setting up terraces, vegetation mats, or soil stabilizers in areas with low SSI values; and constructing rain gardens, infiltration wells, or runoff interception facilities in areas with low HRI values.

[0034] By identifying and addressing shortcomings and matching them with differentiated governance measures, the targetedness and effectiveness of ecological restoration can be significantly improved.

[0035] This embodiment proposes a method for identifying shortcomings in ecological restoration and optimizing governance strategies, specifically including three core steps: spatial heterogeneity calculation, priority governance zone delineation, and matching of differentiated governance measures. First, spatial heterogeneity is calculated to identify grids whose ecological restoration effectiveness is significantly lower than the regional average. Then, grid areas that simultaneously meet the criteria of spatial heterogeneity greater than 0.2 and CRI less than 0.5 are selected as priority governance zones. Finally, corresponding ecological governance measures are matched to areas with low VRI, SSI, and HRI values ​​to achieve precise governance.

[0036] like Figure 2 As shown, based on the overall steps of the above method, an ecological restoration effectiveness evaluation system based on multidimensional remote sensing data includes the following modules: The preprocessing module is used to perform step S1; The core indicator calculation module is used to execute step S2; The comprehensive index calculation module is used to execute step S3; The data visualization module is used to execute step S4.

[0037] The content of the above method embodiments is applicable to the system embodiments. The specific functions implemented in the system embodiments are the same as those in the above structural embodiments, and the beneficial effects achieved are also the same as those achieved in the above structural embodiments.

[0038] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for evaluating the effectiveness of ecological restoration based on multidimensional remote sensing data, characterized in that, The method includes the following steps: Acquire multi-source remote sensing data and perform corrections to obtain preprocessed data; Based on the preprocessed data, calculate the vegetation restoration index, soil stability index, and hydrological regulation index; A comprehensive restoration index is generated based on the vegetation restoration index, the soil stability index, and the hydrological regulation index. Visual records are generated based on the comprehensive repair index.

2. The method for evaluating the effectiveness of ecological restoration based on multidimensional remote sensing data according to claim 1, characterized in that, Also includes: Spatial heterogeneity is calculated based on the comprehensive repair index, and repair shortcomings are identified.

3. The method for evaluating the effectiveness of ecological restoration based on multidimensional remote sensing data according to claim 1, characterized in that, The step of acquiring and correcting multi-source remote sensing data specifically includes: The multi-source remote sensing data includes optical remote sensing data, radar data, and elevation data; The optical remote sensing data is subjected to topographic radiometric correction and cloud cover filling to generate a spatiotemporally continuous remote sensing data sequence.

4. The method for evaluating the effectiveness of ecological restoration based on multidimensional remote sensing data according to claim 3, characterized in that, The correction formula for topographic radiation correction is expressed as follows: in, For the corrected radiance; Original radiance; The solar zenith angle; is the slope angle; k is an empirical coefficient.

5. The method for evaluating the effectiveness of ecological restoration based on multidimensional remote sensing data according to claim 1, characterized in that: The formula for calculating the vegetation restoration index is as follows: in, To repair the difference in NDVI values ​​before and after terrain normalization; The topographic humidity index; This is the slope attenuation factor; This is the slope deviation coefficient; Canopy coverage correction factor; This is the first adjustment coefficient; The formula for calculating the soil stability index is as follows: in, Soil erosion modulus; It is the erosivity factor of rainfall; The loss rate of clay content retrieved by radar polarization decomposition; Organic matter correction factor; This is the second adjustment coefficient; This is the third adjustment coefficient; The formula for calculating the hydrological regulation index is as follows: in, This represents the soil volumetric water content retrieved by radar. Ideal soil moisture content; The infiltration capacity index is calculated based on the flow curvature of the DEM. This represents the normalized value of runoff accumulation under a rainstorm event. Vegetation roughness factor; This is the slope stability correction factor.

6. The method for evaluating the effectiveness of ecological restoration based on multidimensional remote sensing data according to claim 1, characterized in that, The formula for calculating the comprehensive repair index is as follows: in, Indicates different weighting coefficients. As a terrain adjustment factor, This is the coordination adjustment coefficient.

7. The method for evaluating the effectiveness of ecological restoration based on multidimensional remote sensing data according to claim 1, characterized in that, The formula for calculating the spatial heterogeneity is as follows: in, These are local raster values; This is the regional average. It is a smoothing factor; The gradient magnitude of CRI; These are the gradient weight coefficients.

8. An ecological restoration effectiveness evaluation system based on multidimensional remote sensing data, characterized in that, include: The preprocessing module is used to acquire and correct multi-source remote sensing data to obtain preprocessed data. The core index calculation module is used to calculate the vegetation restoration index, soil stability index and hydrological regulation index based on the preprocessed data. The comprehensive index calculation module is used to generate a comprehensive restoration index based on the vegetation restoration index, the soil stability index, and the hydrological regulation index. The data visualization module records data visually based on the comprehensive repair index.

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