A remote sensing mapping method for grassland management in karst rocky desertification areas

Abstract

This application relates to the field of remote sensing mapping technology, specifically to a remote sensing mapping method for grasslands in karst rocky desertification control areas. The method includes: acquiring remote sensing images and DEM data of the target karst region; quantifying the shading probability, vegetation confidence, and vegetation density of pixels based on the trends in radiance and spectral reflectance of the remote sensing images and the topographic features of the DEM, and then adaptively constructing an adjustment factor for correcting the soil-adjusted vegetation index; calculating the soil-adjusted vegetation index at each pixel based on the adjustment factor to create a remote sensing vegetation map of the target karst region. This application solves the problem of difficulty in distinguishing between densely shaded vegetation and sparsely lit vegetation in complex karst terrain due to the use of a uniform adjustment factor by constructing shading vegetation feature values ​​to adaptively determine the adjustment factor, thus improving the accuracy and reliability of vegetation mapping in karst rocky desertification control areas.

Description

Technical Field

[0001] This application relates to the field of remote sensing mapping technology, specifically to a remote sensing mapping method for grassland management in karst rocky desertification areas. Background Technology

[0002] Under the dual pressures of natural factors and human activities, karst ecosystems are severely degraded, and rocky desertification is a prominent problem. Grasslands, as a key element in rocky desertification control, have significant ecological benefits, including rapid productivity generation and water and soil conservation. Rapidly and accurately mapping the spatial distribution of grasslands is not only an important means of monitoring ecosystem service processes in the control area, but also provides theoretical support for grassland management decisions and regulation, which is of great significance for consolidating the achievements in rocky desertification control.

[0003] Remote sensing imagery has become a crucial data source for mapping rocky desertification, with existing methods primarily employing the Normalized Difference Vegetation Index (NDVI) or the Soil-Adjusted Vegetation Index (SAVI) for monitoring. However, the exposed bedrock in karst regions makes the NDVI susceptible to interference from soil background. While the traditional SAVI method incorporates an adjustment factor L, its globally uniform parameter makes it ill-suited to the complex topography and uneven background distribution characteristic of karst areas. In particular, topographic shadows significantly reduce the reflectivity of ground features, causing the SAVI values ​​for densely shaded vegetation and sparsely lit vegetation to converge, severely weakening their distinguishing ability. This results in mapping outcomes that fail to accurately reflect grassland cover, reducing the accuracy and reliability of vegetation mapping in karst rocky desertification control areas. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a remote sensing mapping method for grassland management in karst rocky desertification areas, thereby resolving the existing issues.

[0005] The remote sensing mapping method for grassland remediation in karst rocky desertification control proposed in this application adopts the following technical solution: Acquire remote sensing images and DEM data of the target karst region; Based on the radiance of each pixel in the near-infrared band, the probability of each pixel being occluded by shadow is quantified; based on the slope and topographic relief in the DEM data corresponding to each pixel, the shadow topographic feature value of each pixel is determined; the significance of the change trend of the reflectance value of each pixel in the visible light band and near-infrared band, as well as the reflectance in the near-infrared band, are analyzed to calculate the vegetation confidence at each pixel, and combined with the probability of each pixel being occluded by shadow and the shadow topographic feature value, the shadow vegetation feature value of each pixel is constructed. Based on the distribution of vegetation confidence scores of all pixels in the neighborhood of each pixel, the vegetation density of each pixel is determined, and the adjustment factor of each pixel is constructed by combining the shadow vegetation feature value. The soil-adjusted vegetation index at each pixel is calculated based on the adjustment factor to create a remote sensing vegetation map of the target karst region.

[0006] Preferably, the quantification process for the probability that each pixel is occluded by shadow is as follows: The negative value of the radiance of each pixel in the near-infrared band in the remote sensing image is normalized and used as the quantitative result of the probability that each pixel is occluded by shadow.

[0007] Preferably, the shadow terrain feature value of each pixel is the average of the slope normalized value and the terrain relief normalized value in the DEM data corresponding to each pixel.

[0008] Preferably, the calculation process for the vegetation confidence score at each pixel is as follows: The goodness of fit is obtained by fitting all reflectances and corresponding wavelengths of each pixel in the visible and near-infrared bands. Calculate the ratio between the reflectance of each pixel in the near-infrared band and the maximum reflectance value of the pixel in all bands; The vegetation confidence level at each pixel is determined based on the goodness of fit and the ratio.

[0009] Preferably, further determining the vegetation confidence level at each pixel includes: The sum of the goodness of fit and the preset factor is calculated, and the ratio of the sum to the ratio is used as the vegetation confidence of each pixel, wherein the preset factor is a constant greater than 0.

[0010] Preferably, the shadow vegetation feature values ​​are positively correlated with the quantification result of the probability of a pixel being occluded by shadow, the shadow terrain feature values, and the vegetation confidence level.

[0011] Preferably, the vegetation density of each pixel is the proportion of the number of all pixels in the neighborhood with a vegetation confidence level greater than a preset threshold to the total number of pixels in the neighborhood.

[0012] Preferably, the method for constructing the adjustment factor of each pixel is as follows: Calculate the normalized value of the shadow vegetation feature value and the mean of the vegetation density of each pixel, and denot it as the feature mean. Based on the distribution of reflectance of pixels in the near-infrared and red bands in remote sensing images, pixels are classified into low reflectance pixels and high reflectance pixels. For low reflectivity pixels, the adjustment factor is 1 minus the mean of the features; for high reflectivity pixels, the adjustment factor is the mean of the features.

[0013] Preferably, the process of classifying pixels into low-reflectivity pixels and high-reflectivity pixels is as follows: Calculate the sum of the reflectance of each pixel in the remote sensing image in the near-infrared band and the red band; Pixels whose sum of reflectance is less than the preset background separation threshold are denoted as low reflectance pixels, and the remaining pixels are denoted as high reflectance pixels.

[0014] Preferably, the formula for calculating the soil-adjusted vegetation index at each pixel is: In the formula, This represents the soil-adjusted vegetation index at pixel i. , Represents the reflectance of pixel i in the near-infrared band and the reflectance of pixel i in the red band; This represents the adjustment factor for pixel i.

[0015] One embodiment of this application provides a remote sensing mapping method for grassland remediation in karst rocky desertification areas, the method comprising the following steps: This application has at least the following beneficial effects: This application first constructs a shadow vegetation feature value by comprehensively analyzing the radiance characteristics of pixels in the near-infrared band, DEM topographic features, and spectral curve variation trends. This feature value can accurately quantify the possibility that a pixel is in a shadowy, dense vegetation area, effectively solving the problem that a single spectral feature is difficult to distinguish between "shadow vegetation" and "bright bare soil", eliminating the suppression and interference of topographic shadows on vegetation spectral information, and providing a key and accurate quantitative basis for the subsequent calculation of adaptive adjustment factors. Furthermore, this application determines vegetation density by analyzing the distribution of confidence in neighboring vegetation, constructs a feature mean by combining the feature values ​​of shaded vegetation, and then classifies the pixels based on the sum of their reflectance in the near-infrared and red bands. It adaptively assigns a negative correlation adjustment factor to low reflectance pixels to correct the underestimation bias of vegetation index in shaded areas, and assigns a positive correlation adjustment factor to high reflectance pixels to suppress soil background interference in bright areas. This achieves accurate correction of vegetation signals under different lighting and background conditions, and significantly improves the ability to distinguish between dense shaded vegetation and sparse bright vegetation. Finally, this application introduces an adaptively constructed adjustment factor into the soil-adjusted vegetation index calculation formula, and uses pixel-level parameters to replace the traditional globally unified parameters, realizing dynamic correction of vegetation signals under complex lighting and background conditions, effectively eliminating the interference of shadow suppression and soil background reflection; further, linear interpolation smoothing is used to eliminate numerical jumps in classification boundaries, and finally a remote sensing vegetation map that can truly reflect the grassland cover in karst areas is drawn, improving the accuracy and reliability of vegetation mapping in karst rocky desertification control areas. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the steps of a remote sensing mapping method for grassland management in karst rocky desertification, provided in one embodiment of this application; Figure 2 A pixel classification flowchart is provided for one embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a remote sensing mapping method for grassland management in karst rocky desertification according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] The following description, in conjunction with the accompanying drawings, details a specific scheme for a remote sensing mapping method for karst desertification control grasslands provided in this application.

[0021] This application provides a remote sensing mapping method for grassland restoration in karst rocky desertification areas, specifically, the following method is provided. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps: Step S1: Acquire remote sensing images and DEM data of the target karst region.

[0022] To acquire remote sensing images and DEM data of the target karst region for which remote sensing mapping is required, the remote sensing images can be obtained from the MODIS remote sensing data network, and the DEM data can be obtained from the geospatial data cloud.

[0023] Furthermore, the acquired remote sensing images and DEM data are preprocessed separately, as follows: The acquired remote sensing images are preprocessed, including atmospheric correction, cloud detection, geometric registration, and remote sensing image enhancement, to obtain the preprocessed remote sensing images. The atmospheric correction, cloud detection, geometric registration, and remote sensing image enhancement processes are all well-known technologies, and the specific processes will not be described in detail. The acquired DEM data undergoes preprocessing, which includes coordinate system transformation, raster format conversion, and resolution resampling based on the geographic projection coordinate system and resolution of the remote sensing image. This ensures a one-to-one correspondence between pixels in the remote sensing image and pixels in the preprocessed DEM data, thereby obtaining the DEM data for each pixel in the remote sensing image, including slope and topographic relief. The coordinate system transformation, raster format conversion, and resolution resampling of the DEM data are all well-known techniques, and their specific processes will not be elaborated upon.

[0024] Step S2: Based on the trends of radiance and spectral reflectance changes in remote sensing images and DEM topographic features, quantify the probability of pixel occlusion, vegetation confidence and vegetation density, and then adaptively construct adjustment factors for correcting soil-adjusted vegetation indices.

[0025] The formula for calculating the soil-adjusted vegetation index is as follows: In the formula, , Let represent the reflectance in the near-infrared band and the red band, respectively, and L represent the adjustment factor, where the values ​​of both reflectance and adjustment factor L are in the range [0,1]. Wherein, when ( When L < 1, increasing L will cause Decrease; when ( When )>1, increasing L will cause Increased. Ground features in bright areas appear in the near-infrared band. and red band The reflectivity of light is usually very high, and shadows will significantly reduce the visibility of ground objects in the shadow area in the near-infrared band. and red band The reflectance of vegetation, especially in the near-infrared band, is severely weakened, making it difficult to calculate the soil-adjusted vegetation index for all pixels in a remotely sensed image using a globally large adjustment factor L. At the same time, soil-adjusted vegetation index can be used to address dense vegetation in shadowed areas of remotely sensed images. The soil-adjusted vegetation index was significantly underestimated, particularly in areas with sparse vegetation in bright regions. The soil-adjusted vegetation index was overestimated, resulting in relatively similar soil-adjusted vegetation indices for both. Therefore, the final remote sensing vegetation map is difficult to reflect the true grassland cover in the target karst area.

[0026] Therefore, based on the above analysis, this embodiment quantifies the occlusion probability, vegetation confidence, and vegetation density of pixels based on the trends of radiance and spectral reflectance changes in remote sensing images and DEM topographic features. Then, it adaptively constructs adjustment factors for correcting the soil-adjusted vegetation index. The specific process is as follows: S2.1 Based on the radiance of each pixel in the near-infrared band, quantify the probability of each pixel being occluded by shadow; based on the slope and topographic relief in the DEM data corresponding to each pixel, determine the shadow topographic feature value of each pixel; analyze the significance of the change trend of the reflectance value of each pixel in the visible light band and near-infrared band, as well as the reflectance in the near-infrared band, calculate the vegetation confidence at each pixel, and combine the probability of each pixel being occluded by shadow and the shadow topographic feature value to construct the shadow vegetation feature value of each pixel.

[0027] Due to the shading effect, features within the shadowed area receive significantly less solar radiation than those in the bright area, resulting in lower spectral radiance in remote sensing images, especially noticeable in the near-infrared band. Furthermore, the typical karst topography of peak clusters and depressions means that shadowed areas are mostly located on the shady sides of peaks or ridges; since the shadow effect is more pronounced on steeper slopes, these areas typically exhibit steep slopes and dramatic local topographic relief in DEM data.

[0028] Therefore, based on the above analysis, this embodiment quantifies the probability of each pixel being occluded by shadows according to the radiance of each pixel in the near-infrared band, and is used to assess whether the ground features corresponding to each pixel have the near-infrared band distribution characteristics of the shadowed areas in the target karst region in the remote sensing image. Specifically: In this embodiment, the negative of the radiance of each pixel in the near-infrared band in the remote sensing image is normalized to a value that is used as the quantification result of the probability that each pixel is occluded by shadow.

[0029] Based on the quantification results of the probability of each pixel being occluded by shadows, it can be understood that the quantification results are used to characterize the probability that a pixel will form a shadowed area in a remote sensing image due to terrain occlusion, reflecting the intensity of solar radiation received by the pixel. Its calculation is mainly affected by the radiance of the pixel in the near-infrared band. The lower the radiance, the larger the quantization result obtained after normalization of its negative number, reflecting that the pixel receives less solar radiation and is more likely to be occluded by shadows. This is more consistent with the spectral characteristics of shadowed areas in karst regions in remote sensing images, which helps to identify vegetation information that is severely weakened by shadows. Conversely, the higher the radiance, the smaller the quantization result, reflecting that the pixel is in a bright area with sufficient light and is minimally affected by shadows.

[0030] Furthermore, in this embodiment, the shadow terrain feature value of each pixel is determined based on the slope and topographic relief in the DEM data corresponding to each pixel. Specifically: In this embodiment, the average of the slope normalization value and the terrain relief normalization value in the DEM data corresponding to each pixel is used as the shadow terrain feature value of each pixel.

[0031] It should be noted that there are many commonly used normalization methods. In this embodiment, the maximum and minimum value normalization method is used to normalize the slope and the topographic relief. In practical applications, as other implementation methods, implementers may also use other normalization methods according to specific circumstances. This embodiment does not impose any special restrictions on the selection of normalization methods.

[0032] The process of normalizing data using the maximum-minimum normalization method is a well-known technique and will not be elaborated further.

[0033] It should be noted that, unless otherwise specified, all normalization processes in this embodiment employ the maximum-minimum value normalization method.

[0034] Based on the shadow topographic feature values ​​of each pixel, it can be understood that the shadow topographic feature values ​​are used to characterize the degree of topographic undulation and steepness of the area where the pixel is located, reflecting the topographic distribution characteristics of shadow-prone areas in typical karst landforms (such as peak clusters and depressions). Its calculation is affected by the slope and topographic relief in the DEM data. If the slope and topographic relief are greater, the shadow topographic feature value is larger, reflecting that the pixel is more likely to be located on the shady steep slope of a mountain peak or ridge, and the topography contributes more to the formation of shadows, thus enhancing the reliability of identifying shadow areas. Conversely, the smaller the feature value, the more it reflects that the pixel is located in a flat or sunny area and is less affected by topographic shadows.

[0035] Furthermore, due to the fact that the main carbonate rocks (such as limestone and dolomite) and natural soils in karst areas have relatively smooth reflectance spectral curves in the visible-near-infrared band, without significant characteristic peaks or valleys, and the reflectance increases gradually with wavelength. In contrast, vegetation, affected by chlorophyll absorption and leaf cell structure, exhibits a small reflectance peak in the visible band and a typical "steep slope" effect and high reflectance characteristics in the near-infrared band; even under shading, where spectral information is weakened, the near-infrared band still maintains relatively high reflectance, which provides a key basis for distinguishing vegetation from background features. Therefore, based on the above analysis, this embodiment analyzes the significance of the reflectance value change trend of each pixel in the visible and near-infrared bands, as well as the reflectance in the near-infrared band, and calculates the vegetation confidence level at each pixel, specifically: In this embodiment, the reflectance and corresponding wavelength of each pixel in the visible light band and near-infrared band are fitted to obtain the goodness of fit. The fitting algorithm uses a univariate linear regression model for fitting. The process of fitting data using a univariate linear regression model is a well-known technique and will not be described in detail here. Calculate the ratio between the reflectance of each pixel in the near-infrared band and the maximum reflectance value of the pixel in all bands; Further, the sum of the goodness of fit and the preset factor is calculated, and the ratio of the sum to the preset factor is used as the vegetation confidence of each pixel, wherein the preset factor is a constant greater than 0.

[0036] It should be noted that the preset factor is set manually. In this embodiment, the preset factor is set to 0.01. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0037] The calculation process for goodness of fit is a well-known technique and will not be elaborated further.

[0038] Based on the vegetation confidence score at each pixel, it can be understood that the vegetation confidence score is used to characterize the probability that the corresponding ground feature of the pixel is vegetation, reflecting the similarity between the spectral curve characteristics of the pixel and the spectral characteristics of typical vegetation. Its calculation is affected by the significance of the trend of reflectance change in the visible-near infrared band (goodness of fit) and the relative magnitude of the reflectance in the near infrared band. The more significant the trend of reflectance change (the lower the goodness of fit, i.e., the less smooth the curve) and the greater the relative reflectance in the near infrared band, the higher the vegetation confidence score, reflecting that the pixel has a typical vegetation "red edge" steep slope effect and high reflectance characteristics, and can effectively distinguish vegetation from background ground features even in shadow. Conversely, the lower the vegetation confidence score, the more gentle the pixel's spectral characteristics tend to be, and the closer they are to the spectral characteristics of exposed rock or soil.

[0039] Furthermore, in this embodiment, based on the vegetation confidence at each pixel and combined with the probability of each pixel being occluded by shadows and the shadow terrain feature value, a shadow vegetation feature value for each pixel is constructed. Specifically: In this embodiment, the shadow vegetation feature value is positively correlated with the quantification result of the probability of a pixel being occluded by shadow, the shadow terrain feature value, and the vegetation confidence level.

[0040] It should be understood that a positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. The specific relationship can be additive or multiplicative, etc., and is determined by the actual application. This application does not impose any special restrictions.

[0041] Preferably, as one implementation method, in this embodiment, the shadow vegetation feature value of each pixel is the average of the quantification result of the probability of each pixel being occluded by shadow, the shadow terrain feature value, and the vegetation confidence level. In practical applications, as other implementation methods, implementers may also use other positive correlation calculation methods such as sum or product in combination with specific circumstances. This embodiment does not impose any special restrictions.

[0042] Based on the shading vegetation feature value, it can be understood that the shading vegetation feature value is used to characterize the comprehensive degree to which a pixel possesses both shading area characteristics and vegetation spectral characteristics, reflecting the probability that the pixel is located in a vegetated area within a shading region. Its calculation is affected by the probability of the pixel being shaded, the shading terrain feature value, and the vegetation confidence level. The larger these three factors are, the larger the shading vegetation feature value is, reflecting that the pixel is highly likely to be covered by dense vegetation under shading. This allows subsequent calculations to specifically adjust its vegetation index to eliminate the influence of shading suppression. Conversely, the smaller the probability of the pixel being shaded, the shading terrain feature value, and the vegetation confidence level are, the smaller the shading vegetation feature value is, reflecting that the pixel is more likely to be a non-vegetated or non-shading area, requiring different adjustment strategies.

[0043] Thus, this embodiment constructs a shadow vegetation feature value by comprehensively analyzing the radiance characteristics of pixels in the near-infrared band, DEM topographic features, and spectral curve variation trends. This feature value can accurately quantify the possibility that a pixel is located in a shadowy, dense vegetation area, effectively solving the problem that a single spectral feature is difficult to distinguish between "shadow vegetation" and "bright bare soil," eliminating the suppression and interference of topographic shadows on vegetation spectral information, and providing a key and accurate quantitative basis for the subsequent calculation of adaptive adjustment factors.

[0044] S2.2: Based on the distribution of vegetation confidence scores of all pixels in the neighborhood of each pixel, determine the vegetation density of each pixel, and combine it with the shadow vegetation feature value to construct the adjustment factor for each pixel.

[0045] Based on the vegetation density and shaded vegetation characteristic values ​​obtained in step S2.1, an adjustment factor is constructed, specifically: In this embodiment, the normalized value of the shadow vegetation feature value of each pixel and the mean value of the vegetation density are calculated and denoted as the feature mean. Furthermore, based on the distribution of reflectance of pixels in the near-infrared and red bands in the remote sensing image, pixels are classified into low-reflectance pixels and high-reflectance pixels. Specifically, the sum of reflectance of each pixel in the remote sensing image in the near-infrared and red bands is calculated; pixels whose sum of reflectance is less than a preset background separation threshold are recorded as low-reflectance pixels, and the remaining pixels are recorded as high-reflectance pixels; wherein, the preset background separation threshold ranges from [0.9, 1.1]. In this embodiment, the preset background separation threshold is set to 1.0. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0046] For low reflectivity pixels, the adjustment factor is 1 minus the mean of the features; for high reflectivity pixels, the adjustment factor is the mean of the features.

[0047] Preferably, the pixel classification flowchart provided in this embodiment is as follows: Figure 2 As shown.

[0048] Based on the process of dividing high and low reflectance pixels and their corresponding adjustment factors, it can be understood that this process achieves differentiated processing of the spectral background of ground objects through threshold segmentation, reflecting the effect of adjustment factors on the soil-adjusted vegetation index under different reflectance backgrounds. The opposite mechanism of action: For low-reflectivity pixels (typically corresponding to densely shaded vegetation) where the sum of reflectance is less than a preset background separation threshold, the adjustment factor is negatively correlated with the shaded vegetation feature value and vegetation density. The aim is to avoid the soil-adjusted vegetation index of shaded vegetation by reducing the adjustment factor L. It is undervalued; for high reflectance pixels (usually corresponding to bright sparse vegetation), the adjustment factor is positively correlated, which aims to suppress the interference of bright bare soil background by increasing the adjustment factor L, thereby maximizing the difference in SAVI between the two types of land cover and improving the ability to distinguish them.

[0049] Thus, this embodiment determines vegetation density by analyzing the distribution of confidence in neighboring vegetation, constructs a feature mean by combining the feature values ​​of shaded vegetation, and then classifies pixels based on the sum of reflectance in the near-infrared and red bands. It adaptively assigns a negative correlation adjustment factor to low reflectance pixels to correct the underestimation bias of vegetation index in shaded areas, and assigns a positive correlation adjustment factor to high reflectance pixels to suppress soil background interference in bright areas. This achieves accurate correction of vegetation signals under different lighting and background conditions, and significantly improves the ability to distinguish between dense shaded vegetation and sparse bright vegetation.

[0050] Step S3: Calculate the soil-adjusted vegetation index at each pixel based on the adjustment factor to create a remote sensing vegetation map of the target karst region.

[0051] Furthermore, based on the adjustment factor obtained in step S2, and combined with the reflectance values ​​of each pixel in the remote sensing image across all bands, the soil-adjusted vegetation index (SDI) for each pixel is calculated. The specific process is as follows: Soil-adjusted vegetation index at pixel i The calculation formula is: In the formula, , Represents the reflectance of pixel i in the near-infrared band and the reflectance of pixel i in the red band; This represents the adjustment factor for pixel i.

[0052] Based on the soil-adjusted vegetation index, it can be understood that by introducing an adaptively constructed adjustment factor into the vegetation index calculation, accurate correction of vegetation signals under complex lighting and background conditions is achieved, reflecting the final output effect of the core algorithm of this application; by using a pixel-level adjustment factor L to replace the traditional globally unified parameter, the soil-adjusted vegetation index... It can dynamically respond according to the lighting conditions (shadow or bright) and background features (dense or sparse) of the pixel, effectively eliminating the interference of shadow suppression and soil background reflection, and ensuring that the final remote sensing vegetation map can truly reflect the grassland coverage in the karst region.

[0053] Furthermore, a remote sensing vegetation map is drawn based on the soil-adjusted vegetation index of all pixels in the remote sensing image. The size of the remote sensing vegetation map is consistent with that of the remote sensing image. The soil-adjusted vegetation index of each pixel in the remote sensing image is used as the vegetation index of the corresponding pixel in the remote sensing vegetation map. Linear interpolation is used to smooth the vegetation index of all pixels in the remote sensing vegetation map whose sum of reflectance values ​​in the near-infrared and red bands is close to 1 (e.g., in the interval [0.9, 1.1]) to avoid numerical jumps, thus completing the remote sensing mapping of the target karst region.

[0054] Thus, this embodiment introduces an adaptively constructed adjustment factor into the soil-adjusted vegetation index calculation formula, and uses pixel-level parameters to replace the traditional globally unified parameters, realizing dynamic correction of vegetation signals under complex lighting and background conditions, effectively eliminating the interference of shadow suppression and soil background reflection; further, linear interpolation smoothing process eliminates numerical jumps in classification boundaries, and finally draws a remote sensing vegetation map that can truly reflect the grassland cover in karst areas, significantly improving the accuracy and reliability of vegetation mapping in karst rocky desertification control areas.

[0055] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0056] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0057] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A remote sensing mapping method for grassland management in karst rocky desertification areas, characterized in that, The method includes the following steps: Acquire remote sensing images and DEM data of the target karst region; Based on the radiance of each pixel in the near-infrared band, the probability of each pixel being occluded by shadow is quantified; based on the slope and topographic relief in the DEM data corresponding to each pixel, the shadow topographic feature value of each pixel is determined; the significance of the change trend of the reflectance value of each pixel in the visible light band and near-infrared band, as well as the reflectance in the near-infrared band, are analyzed to calculate the vegetation confidence at each pixel, and combined with the probability of each pixel being occluded by shadow and the shadow topographic feature value, the shadow vegetation feature value of each pixel is constructed. Based on the distribution of vegetation confidence scores of all pixels in the neighborhood of each pixel, the vegetation density of each pixel is determined, and combined with the shadow vegetation feature values, an adjustment factor for each pixel is constructed, including: Calculate the normalized value of the shadow vegetation feature value and the mean of the vegetation density of each pixel, and denot it as the feature mean. Based on the distribution of reflectance of pixels in the near-infrared and red bands in remote sensing images, pixels are classified into low reflectance pixels and high reflectance pixels. This includes: calculating the sum of reflectance of each pixel in the remote sensing image in the near-infrared and red bands; and recording pixels whose sum of reflectance is less than a preset background separation threshold as low reflectance pixels and the remaining pixels as high reflectance pixels. For low reflectivity pixels, the adjustment factor is 1 minus the feature mean; for high reflectivity pixels, the adjustment factor is the feature mean. The soil-adjusted vegetation index at each pixel is calculated based on the adjustment factor to create a remote sensing vegetation map of the target karst region.

2. The remote sensing mapping method for grassland management in karst rocky desertification as described in claim 1, characterized in that, The quantification process for the probability that each pixel is occluded by a shadow is as follows: The negative value of the radiance of each pixel in the near-infrared band in the remote sensing image is normalized and used as the quantitative result of the probability that each pixel is occluded by shadow.

3. The remote sensing mapping method for grassland management in karst rocky desertification as described in claim 1, characterized in that, The shadow terrain feature value of each pixel is the average of the slope normalization value and the terrain relief normalization value in the DEM data corresponding to each pixel.

4. The remote sensing mapping method for grassland management in karst rocky desertification as described in claim 1, characterized in that, The calculation process for the vegetation confidence score at each pixel is as follows: The goodness of fit is obtained by fitting all reflectances and corresponding wavelengths of each pixel in the visible and near-infrared bands. Calculate the ratio between the reflectance of each pixel in the near-infrared band and the maximum reflectance value of the pixel in all bands; The vegetation confidence level at each pixel is determined based on the goodness of fit and the ratio.

5. The remote sensing mapping method for grassland management in karst rocky desertification as described in claim 4, characterized in that, Further determine the vegetation confidence level at each pixel, including: The sum of the goodness of fit and the preset factor is calculated, and the ratio of the sum to the ratio is used as the vegetation confidence of each pixel, wherein the preset factor is a constant greater than 0.

6. The remote sensing mapping method for grassland management in karst rocky desertification as described in claim 1, characterized in that, The shadow vegetation feature values ​​are positively correlated with the quantification result of the probability of a pixel being occluded by shadow, the shadow terrain feature values, and the vegetation confidence level.

7. The remote sensing mapping method for grassland management in karst rocky desertification as described in claim 1, characterized in that, The vegetation density of each pixel is the percentage of all pixels in the neighborhood with a vegetation confidence level greater than a preset threshold.

8. The remote sensing mapping method for grassland management in karst rocky desertification as described in claim 1, characterized in that, The formula for calculating the soil-adjusted vegetation index at each pixel is as follows: In the formula, This represents the soil-adjusted vegetation index at pixel i. , Represents the reflectance of pixel i in the near-infrared band and the reflectance of pixel i in the red band; This represents the adjustment factor for pixel i.

Images