Mountain forest biomass identification method and system based on high-resolution remote sensing data

Abstract

The invention relates to the technical field of remote sensing biomass detection, in particular to a mountain forest biomass recognition method and system based on high-resolution remote sensing data, and the method comprises the steps: dividing all clusters into a high-gradient shady slope cluster and a low-gradient sunny slope cluster based on the gradient feature value of each pixel; based on the pixel values of all the pixels in each low-gradient sunny slope cluster, screening out feature pixels from each low-gradient sunny slope cluster; and based on a result of fusion of a mean value of pixel values of all pixels in each high-gradient shady slope cluster and a texture feature value, and in combination with a confusion degree of coordinates of all feature pixels in each low-gradient sunny slope cluster, determining biological complexity, and identifying the biomass of the mountain forest region to be identified. According to the method, the problem that the biomass in the mountain forest is misestimated due to interference of topographic factors is solved, and the accuracy of identifying the biomass condition of the mountain forest is improved.

Description

Technical Field

[0001] The present application relates to the technical field of remote sensing biomass detection, and specifically to a method and system for identifying forest biomass in mountainous areas based on high-resolution remote sensing data. Background Art

[0002] As a vital component of terrestrial ecosystems, assessment of forest aboveground biomass is a key indicator of forest ecosystem structure, function, and carbon storage, and is crucial for studying carbon cycling, climate change, and the sustainable use of forest resources. Traditional biomass surveys are often costly, time-consuming, and difficult to cover large areas. They are particularly unsuitable for complex terrain or remote forest areas. High-resolution remote sensing technology, however, enables non-destructive assessment of forest biomass over large areas, supporting biomass estimation at multiple scales, from plots to regions.

[0003] Remote sensing feature extraction of forest vegetation is the core step in identifying forest biomass in mountainous areas using high-resolution remote sensing data. Slope affects the vertical flow of water and soil nutrients, and the steepness of the slope has a significant impact on soil thickness. However, when evaluating biomass using traditional high-resolution remote sensing data, the effects of slope and aspect on biomass are ignored, making it impossible to accurately judge mountainous terrain and capture the complex characteristics between terrain factors and biomass. This increases the risk of biomass misestimation due to interference from terrain factors and makes it impossible to accurately identify the biomass status of mountainous forests. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for identifying forest biomass in mountainous areas based on high-resolution remote sensing data. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data, the method comprising the following steps: Obtain an optical remote sensing image of a mountain forest area to be identified, and spectral images of the mountain forest area within a preset first band and a preset second band, respectively, and record them as a first spectral image and a second spectral image; Determine the scattering characteristic value of each pixel based on the mean reflectivity of all pixels in the neighborhood of each pixel in the first spectral image and the difference in reflectivity between each pixel and all pixels in its neighborhood; determine the reflectivity dispersion of each pixel based on the distribution of all pixels in the neighborhood of each pixel in the second spectral image and the degree of dispersion of the reflectivity gradient values of each pixel in all directions in its neighborhood, and determine the slope characteristic value of each pixel in combination with the scattering characteristic value; cluster all pixels in the optical remote sensing image, and divide all clusters into high-slope shady slope clusters and low-slope sunny slope clusters based on the average distribution of the slope characteristic values of all pixels in each cluster; The texture feature values of each high-slope shady slope cluster were determined based on the degree of autocorrelation of all pixel values within each high-slope shady slope cluster. Feature pixels were screened from each low-slope sunny slope cluster based on the pixel values of all pixels within each low-slope sunny slope cluster. The biocomplexity of the mountain forest area to be identified was determined based on the fusion of the pixel value mean and texture feature values of all pixels within each high-slope shady slope cluster, combined with the degree of coordinate confusion of all feature pixels within each low-slope sunny slope cluster. Based on the biological complexity, the biomass of the mountain forest area to be identified is identified.

[0005] Preferably, the method for determining the scattering characteristic value of each pixel is: The cumulative sum of the differences in reflectance between each pixel in the first spectral image and all pixels in its neighborhood is calculated, and the result of forward fusion of the cumulative sum with the mean reflectance of all pixels in the neighborhood of each pixel in the first spectral image is used as the scattering characteristic value of each pixel.

[0006] Preferably, the expression of the reflectance discreteness of each pixel is: Where, represents the reflectance dispersion of pixel i; Indicates the discrete degree of reflectance gradient value of pixel i in all directions within its neighborhood in the second spectrum image; represents the mean reflectance of all pixels in the neighborhood of pixel i in the second spectral image; Indicates a preset constant greater than 0.

[0007] Preferably, the slope characteristic value of each pixel is a result of normalizing the ratio of the scattering characteristic value of each pixel to the reflectance dispersion.

[0008] Preferably, the method of dividing all clusters into high-slope shady slope clusters and low-slope sunny slope clusters includes: Calculate the mean of the slope characteristic values of all pixels in each cluster, record it as the characteristic mean, use the characteristic value mean of all clusters as the input of the threshold segmentation algorithm, output the segmentation threshold, and record all clusters with characteristic means greater than or equal to the segmentation threshold as high-slope shady slope clusters, and all other clusters as low-slope sunny slope clusters.

[0009] Preferably, the texture feature value of each high-slope shady slope cluster is the degree of dispersion of all elements in the autocorrelation sequence of all pixel values in each high-slope shady slope cluster.

[0010] Preferably, the step of selecting characteristic pixels from each low-slope sunny slope cluster includes: The pixel values of all pixels in each low-slope sunny slope cluster are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output and recorded as the pixel threshold. All pixels with pixel values greater than or equal to the pixel threshold are recorded as feature pixels.

[0011] Preferably, the expression for the biological complexity of the mountain forest area to be identified is: Where, represents the biological complexity of the mountain forest area to be identified; It represents the reciprocal of the product of the mean value of the pixel values of all pixels in the j-th high-slope shady slope cluster and the texture feature value; represents the Shannon entropy of the coordinates of all pixels in the k-th low-slope sunny slope cluster; 、 They represent the number of all high-slope shady slope clusters and the number of all low-slope sunny slope clusters in the mountain forest area to be identified respectively; norm[ ] represents the normalization function.

[0012] Preferably, identifying the biomass of the mountain forest area to be identified includes: If the biological complexity of the mountain forest area to be identified is greater than a preset threshold, the biomass of the mountain forest area to be identified is rich; otherwise, the biomass of the mountain forest area to be identified is lacking.

[0013] In the second aspect, an embodiment of the present application also provides a mountain forest biomass identification system based on high-resolution remote sensing data, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned methods for identifying mountain forest biomass based on high-resolution remote sensing data.

[0014] This application has at least the following beneficial effects: This application constructs scattering characteristic values and reflectance dispersion by analyzing the reflectance mean, difference and gradient dispersion of pixels in spectral images, and calculates the slope characteristic value based on this, effectively distinguishing between high-slope shady slopes and low-slope sunny slopes, and cleverly combines the terrain slope and aspect information that are difficult to directly quantify in high-resolution optical images with spectral information, overcoming the problem that a single spectrum or texture feature cannot accurately reflect the influence of complex mountainous terrain; further, this application screens out pixels with higher pixel values in optical remote sensing images of mountainous forest areas to be identified, namely, feature pixels, to represent exposed areas in mountainous forests. The rock corresponding areas can focus on analyzing the impact of these key landforms on the overall biomass identification in mountainous forests, rather than being averaged by dense vegetation pixels, thereby improving the accuracy of biomass status identification in mountainous forest areas; further, this application constructs a biological complexity index by analyzing the texture uniformity of high-slope shady slope areas and the discreteness of rock distribution in low-slope sunny slope areas. This index cleverly combines the effects of terrain and light on vegetation growth, and can effectively reflect the distribution status and overall complexity of biomass in mountainous forest areas, thereby improving the accuracy of biomass status identification in mountainous forest areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 A flowchart of the steps of a method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data provided in one embodiment of the present application; Figure 2 A schematic diagram of cluster division provided for one embodiment of the present application. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of the method and system for identifying mountain forest biomass based on high-resolution remote sensing data proposed in this application. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, 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 belongs.

[0019] The specific scheme of the method and system for identifying forest biomass in mountainous areas based on high-resolution remote sensing data provided by this application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flowchart of a method for identifying mountain forest biomass based on high-resolution remote sensing data provided by an embodiment of the present application, the method comprising the following steps: Step S1: Obtain an optical remote sensing image of a mountain forest area to be identified, and spectral images of the mountain forest area in a preset first band and a preset second band, respectively, and record them as a first spectral image and a second spectral image.

[0021] Optical remote sensing images of the mountain forest area to be identified, as well as spectral images of the mountain forest area in a preset first band and a preset second band, are obtained by the Jilin-1KF01A_0.75m optical high-resolution remote sensing satellite, and are respectively recorded as a first spectrum map and a second spectrum map, wherein the size of the preset first band is 450-510nm, and the size of the preset second band is 510-580nm. The 450-510nm is mainly a blue light distribution band containing a small amount of green light, and the 510-580nm is mainly green light and part of yellow light. All the above-obtained images are atmospherically corrected using the FLASH model to correct the spatial heterogeneity interference caused by the aerosol optical thickness in the steep slope area of the mountain forest. The above-obtained images are geometrically corrected using the RPC orthorectification model to eliminate the distortion caused by the terrain undulation.

[0022] To prevent the acquired optical remote sensing images of mountain forest areas and the two-band spectral images from being seriously affected by noise interference and subsequent analysis, this embodiment uses a wavelet transform algorithm to denoise the acquired optical remote sensing images and the four-band spectral images.

[0023] Among them, the FLASH model, the RPC orthorectification model and the wavelet transform algorithm are all well-known technologies, and their specific principles are not described in detail here.

[0024] In this embodiment, in order to facilitate analysis, the optical remote sensing image obtained above is grayscaled. Therefore, the pixel value of a pixel in the optical remote sensing image mentioned below is the grayscale value of the pixel.

[0025] Step S2: Determine the scattering characteristic value of each pixel based on the mean reflectivity of all pixels in the neighborhood of each pixel in the first spectral image and the difference in reflectivity between each pixel and all pixels in its neighborhood; determine the reflectivity dispersion of each pixel based on the distribution of all pixels in the neighborhood of each pixel in the second spectral image and the discrete degree of the reflectivity gradient value of each pixel in all directions in its neighborhood, and determine the slope characteristic value of each pixel in combination with the scattering characteristic value; cluster all pixels in the optical remote sensing image, and divide all clusters into high-slope shady slope clusters and low-slope sunny slope clusters based on the average distribution of the slope characteristic values of all pixels in each cluster.

[0026] In the mountain forest area to be identified, light is one of the key factors for plant growth. The areas with sufficient light conditions have healthier vegetation growth conditions. Vegetation in areas with higher slopes, i.e. steep slopes, has significant topographic shadow areas and receives less solar radiation, while vegetation on gentle slopes receives more solar radiation. At the same time, slope direction also has a significant impact on the richness of mountain forest plants. Vegetation in mountain forest areas can enjoy more sufficient light conditions on sunny slopes than on shady slopes.

[0027] Specifically, the 450-510nm band corresponds to the cyan band, that is, the vegetation chlorophyll absorption valley and blue light scattering area. The increase in slope reduces the solar incidence angle, and the more the slope faces the sun, the more exposed the vegetation in the area is, which will lead to an increase in the reflectivity in the spectral image area; while the 510-580nm band corresponds to the vegetation chlorophyll strong absorption area. The decrease in slope and the more the slope faces the sun, the more sufficient the vegetation organisms in the area can obtain light. The dense vegetation in the area makes the chlorophyll photosynthesis more sufficient, the low reflectivity condition inside the area is milder, and the reflectivity gradient direction within the neighborhood is affected by the plant growth light and becomes more regular.

[0028] Based on the above analysis, this embodiment determines the scattering characteristic value of each pixel based on the reflectance mean of all pixels in the neighborhood of each pixel in the first spectral image and the reflectance difference between each pixel and all pixels in its neighborhood. The reflectance dispersion of each pixel is determined based on the distribution of all pixels in the neighborhood of each pixel in the second spectral image and the degree of dispersion of the reflectance gradient value of each pixel in all directions in its neighborhood. In combination with the scattering characteristic value, the slope characteristic value of each pixel is determined to characterize the terrain high slope and the slope direction facing the shade in each area of the mountain forest spectral image, specifically: As an implementation method, in this embodiment, first, the cumulative sum of the differences in reflectivity between each pixel in the first spectrum image and all pixels in its neighborhood is calculated, and the result of forward fusion of the mean reflectivity of all pixels in the neighborhood of each pixel in the first spectrum image and the cumulative sum is used as the scattering characteristic value of each pixel.

[0029] It should be noted that the neighborhood construction process of each pixel is as follows: a neighborhood is constructed with each pixel as the center, and the neighborhood size is In this embodiment, the value of n is 7. In actual application, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0030] It should be understood that forward fusion refers to combining two or more indicators through addition or multiplication to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations and can also include more complex statistical models and analysis methods. Implementers can choose according to their specific circumstances and this embodiment does not impose any special restrictions.

[0031] Preferably, as an implementation manner, the expression of the scattering characteristic value of each pixel is: Where, represents the scattering characteristic value of pixel i; represents the mean reflectance of all pixels in the neighborhood of pixel i in the first spectral image; represents the cumulative sum of the differences in reflectance between pixel i and all pixels in its neighborhood in the first spectrum; exp( ) represents an exponential function with a natural constant as the base.

[0032] It should be noted that there are many methods for measuring the differences between data. In this embodiment, the absolute value of the difference in reflectance between each pixel in the first spectrum and all pixels in its neighborhood is used as the difference in reflectance between pixel i and all pixels in its neighborhood in the first spectrum. In actual application, as other implementation methods, the implementer may also adopt other methods for measuring data differences such as the square or ratio of the difference based on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods for measuring differences between data.

[0033] According to the scattering characteristic value of each pixel, it can be understood that if the reflectivity mean value of all pixels in the neighborhood of pixel i in the first spectrum is larger, it means that the vegetation surface in the corresponding mountain forest area of pixel i and its neighborhood reflects more blue-green light. This indicates that the slope of the area is higher and the angle of incidence of the sun is reduced, making it easier for light to be scattered to the sensor by the rough ground or exposed vegetation surface instead of being absorbed by chlorophyll. In addition, the area is steep and may also be facing the shade, because the relative coefficient of vegetation facing the shade reduces the absorption of light and increases the scattering of light. Therefore, , the larger the corresponding scattering characteristic value; at the same time, if the cumulative sum of the differences in reflectance between pixel i and all pixels in its neighborhood in the first spectrum is larger, it means that the spectral reflectance of different positions in the neighborhood of pixel i is very different. Combined with the influence of the large mean reflectance of all pixels in the neighborhood of pixel i in the first spectrum, it means that due to the geographical conditions of steep slope and shady slope, the reflectance difference between pixel i and different pixels in its neighborhood is larger. Therefore, the larger the corresponding scattering characteristic value, the more likely the current pixel is to correspond to a high-slope shady slope area. On the contrary, if the mean reflectivity of all pixels in the neighborhood of pixel i in the first spectrum is smaller, it means that the vegetation surface in the corresponding mountain forest area of pixel i and its neighborhood reflects less blue light, which means that the area is unlikely to have a high slope and a small solar incidence angle, and the light is not easily scattered by rough ground or exposed surfaces, or the vegetation itself has a strong absorption of blue light. Moreover, it is unlikely that the area is both steep and shady, because scattering will usually increase under such conditions. Therefore, the smaller the corresponding scattering characteristic value is. At the same time, if the cumulative sum of the differences in reflectance between pixel i and all pixels in its neighborhood in the first spectral map is smaller, it means that the spectral reflectances at different positions in the neighborhood of pixel i are not much different, and the overall spectral characteristics are relatively uniform. This indicates that not only is the blue light reflectance in the area where pixel i is located relatively low overall, but this low state is also distributed relatively consistently in the neighborhood without drastic changes. Therefore, the smaller the corresponding scattering characteristic value is, the more likely the current pixel is not in a high-slope shady area, and its terrain and vegetation conditions tend to be more gentle and sunny, or have uniform and dense vegetation coverage.

[0034] Furthermore, this embodiment determines the reflectance dispersion of each pixel based on the distribution of all pixels in the neighborhood of each pixel in the second spectrum and the dispersion of the reflectance gradient values of each pixel in all directions in its neighborhood, specifically: In this embodiment, the reflection discreteness of pixel i is The expression is: Where, Indicates the discrete degree of reflectance gradient value of pixel i in all directions within its neighborhood in the second spectrum image; represents the mean reflectance of all pixels in the neighborhood of pixel i in the second spectral image; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is set artificially. The value of is 0.01. Under the premise of ensuring that the denominator is not 0 and does not excessively affect the calculation results, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0035] It should be noted that there are many methods to measure the degree of discreteness of a set of data. In this embodiment, the variance of the reflectance gradient values of pixel i in the second spectrum in all directions within its neighborhood is used as the degree of discreteness of the reflectance gradient values of pixel i in the second spectrum in all directions within its neighborhood. In actual application, as other implementation methods, the implementer may also adopt other methods of measuring the degree of discreteness of data, such as standard deviation or dispersion coefficient, in combination with specific circumstances. This embodiment does not impose any special restrictions on the selection of methods for measuring the degree of discreteness of data.

[0036] It should be noted that, in this embodiment, all the contents related to measuring the degree of data dispersion adopt the calculation method of variance.

[0037] In addition, it should be noted that in this embodiment, when calculating the reflectivity gradient value of pixel i in each direction within its neighborhood, the Laplacian operator is used to calculate the gradient value. In actual application, as other implementation methods, the implementer may also use the Sobel operator to calculate the specific gradient value based on the specific situation. Regarding the selection of the gradient value calculation method, this embodiment does not impose any special restrictions.

[0038] Among them, the Lablas operator is a well-known technology, and the specific process of using it to calculate the gradient value will not be repeated.

[0039] According to the reflectance dispersion of each pixel, it can be understood that the greater the dispersion of the reflectance gradient values of pixel i in all directions in its neighborhood in the second spectral map, the greater the corresponding reflectance dispersion, indicating that the reflectance changes in different directions in the neighborhood of pixel i are very different, that is, the reflectance gradient direction trend is irregular. This may be because the reflectance in the mountain forest area corresponding to the pixel neighborhood is complex, resulting in drastic spatial variations in the reflectance, while the vegetation canopy structure in the low-slope sunny slope area is more complex and the vegetation species are richer. Therefore, the corresponding reflectance dispersion is larger. At the same time, due to the local shadows of the steep and irregular terrain, the reflectance change patterns of different areas in the neighborhood are inconsistent. Therefore, the corresponding reflectance dispersion is larger. At the same time, if the mean reflectance of all pixels in the neighborhood of pixel i in the second spectral map is smaller, it means that the vegetation in the mountain forest area corresponding to the neighborhood of pixel i is more dense and complex, which indicates that the mountain forest area corresponding to the neighborhood of pixel i is more likely to be a low-slope sunny slope area, because the photosynthesis in the low-slope sunny slope area is strong and the vegetation is dense and rich. On the contrary, if the dispersion degree of the reflectance gradient values of pixel i in all directions in its neighborhood in the second spectral map is smaller, the corresponding reflectance dispersion is smaller, indicating that the reflectance changes in different directions in the neighborhood of pixel i are not much different, that is, the reflectance gradient direction trend is relatively regular and consistent. This may be because the vegetation canopy structure in the mountain forest area corresponding to the pixel neighborhood is relatively simple or uniform, resulting in a gentle change in the spatial reflectance, while in the high-slope shady slope area, the vegetation canopy structure may be relatively sparse or more single-affected by the terrain, or although the terrain is steep, the lighting conditions are consistent, resulting in a single shadow pattern, so the corresponding reflectance dispersion is smaller; at the same time, if the mean reflectance of all pixels in the neighborhood of pixel i in the second spectral map is larger, it means that the vegetation in the mountain forest area corresponding to the pixel i neighborhood is relatively sparse, or there are more exposed parts of the surface, which absorbs relatively less light, and this indicates that the mountain forest area corresponding to the pixel i neighborhood is more likely to be a high-slope shady slope area, because the lighting conditions in the high-slope shady slope area are poor and vegetation growth is restricted.

[0040] Furthermore, this embodiment determines the slope characteristic value of each pixel based on the reflectance dispersion of each pixel and in combination with the scattering characteristic value, specifically: In this embodiment, the result of normalizing the ratio of the scattering characteristic value to the reflectance dispersion of each pixel is used as the slope characteristic value of each pixel.

[0041] According to the slope characteristic value of each pixel, it can be understood that if the scattering characteristic value of pixel i is larger, it means that pixel i is more likely to correspond to an area with relatively exposed vegetation, such as a steep slope or shady slope, and therefore, the corresponding slope characteristic value is larger; at the same time, if the reflectance discreteness of pixel i is smaller, it means that the corresponding mountain forest area of pixel i is more likely to be a high-slope shady slope area, because the lighting conditions in the high-slope shady slope area are poor and vegetation growth is restricted, so the corresponding slope characteristic value is larger; conversely, if the scattering characteristic value of pixel i is smaller, it means that pixel i is more likely to correspond to an area with relatively dense vegetation, such as a low-slope sunny slope, and therefore, the corresponding slope characteristic value is smaller; at the same time, if the reflectance discreteness of pixel i is larger, it means that the corresponding mountain forest area of pixel i is more likely to be a low-slope sunny slope area, because the vegetation canopy structure in the low-slope sunny slope area is complex and rich in species, resulting in drastic spatial variations in reflectance, so the corresponding slope characteristic value is smaller.

[0042] Furthermore, this embodiment clusters all pixels in the optical remote sensing image and divides all clusters into high-slope shady slope clusters and low-slope sunny slope clusters based on the average distribution of the slope characteristic values of all pixels in each cluster. Specifically: In this embodiment, the k-means clustering algorithm is used to cluster all pixels in the optical remote sensing image, wherein the metric distance is set to the absolute value of the difference in slope eigenvalues between pixels, the number of clusters is obtained by the elbow method, and finally all clusters are output.

[0043] Among them, the k-means clustering algorithm and the elbow method are both well-known technologies, and their specific principles are not described in detail here.

[0044] Furthermore, this embodiment calculates the mean of the slope characteristic values of all pixels in each cluster, records it as the characteristic mean, uses the characteristic value mean of all clusters as the input of the threshold segmentation algorithm, outputs the segmentation threshold, and records all clusters with characteristic means greater than or equal to the segmentation threshold as high-slope shady slope clusters, and all other clusters are recorded as low-slope sunny slope clusters.

[0045] It should be noted that there are many commonly used threshold segmentation algorithms. In this embodiment, the maximum inter-class variance algorithm is used to classify clusters. In actual application, as other implementation methods, implementers can also use other threshold segmentation algorithms based on specific circumstances. This embodiment does not impose any special restrictions.

[0046] It is supplemented that, in this embodiment, all contents related to threshold segmentation adopt the maximum inter-class contrast algorithm.

[0047] Preferably, the cluster division diagram provided in this embodiment is as follows Figure 2 shown.

[0048] Thus, this embodiment constructs the scattering characteristic value and reflectance discreteness by analyzing the reflectance mean, difference and gradient discreteness of the pixels in the spectral image, and calculates the slope characteristic value based on this, effectively distinguishing between high-slope shady slopes and low-slope sunny slopes. This method utilizes the significant influence of terrain and light on the spectral characteristics of vegetation, providing a strong quantitative basis for the refined management of mountainous forest areas, and thus helping to improve the accuracy of identifying the biomass status of mountainous forests.

[0049] Step S3: Based on the degree of autocorrelation of the pixel values of all pixels in each high-slope shady slope cluster, the texture feature value of each high-slope shady slope cluster is determined; based on the pixel values of all pixels in each low-slope sunny slope cluster, the feature pixels are screened out from each low-slope sunny slope cluster; based on the result of the fusion of the pixel value mean and texture feature value of all pixels in each high-slope shady slope cluster, and combined with the degree of confusion of the coordinates of all feature pixels in each low-slope sunny slope cluster, the biological complexity of the mountain forest area to be identified is determined.

[0050] In the process of using high-resolution remote sensing images to identify forest biomass in mountainous areas, the high-slope shady slope areas have weak light evaporation and high soil moisture, creating more favorable conditions for the growth of vegetation such as trees; while the vegetation in the low-slope sunny slope areas can enjoy sufficient photosynthesis, the strong light evaporation effect is not conducive to the growth of non-drought-tolerant vegetation, and may also cause vegetation exposure and low biomass density. Simple texture feature extraction cannot accurately capture the high biomass characteristics of mountainous forest areas.

[0051] Specifically, in the optical remote sensing grayscale images of mountain forests, when the vegetation biomass in the high-slope shady slope area is denser, the continuous coverage of the crowns of suitable plants such as trees will produce a homogenized canopy layer, and the texture details in the optical remote sensing grayscale image will be more blurred. At the same time, due to the lush crown, the shadow coverage inside the mountain forest is wide, and the average pixel value is darker; when the vegetation biomass in the low-slope sunny slope area is greater, due to the high vegetation coverage, the plants are arranged closely and the plants are usually phototropic, the rock or vegetation exposed areas, that is, the areas with higher pixel values, account for a smaller proportion and are more discretely distributed.

[0052] Therefore, based on the above analysis, this embodiment determines the texture feature value of each high-slope shady slope cluster based on the degree of autocorrelation of the pixel values of all pixels in each high-slope shady slope cluster; screens out feature pixels from each low-slope sunny slope cluster based on the pixel values of all pixels in each low-slope sunny slope cluster; and determines the biocomplexity of the mountain forest area to be identified based on the fusion result of the pixel value mean of all pixels in each high-slope shady slope cluster and the texture feature value, combined with the degree of confusion of the coordinates of all feature pixels in each low-slope sunny slope cluster. Specifically, As an implementation method, in this embodiment, the degree of discreteness of all elements in the autocorrelation sequence of all pixel values in each high-slope shady slope cluster is used as the texture feature value of each high-slope shady slope cluster, where the degree of discreteness here is calculated using variance.

[0053] The method for obtaining the autocorrelation sequence is a well-known technology, and the specific acquisition process will not be described in detail.

[0054] It should be noted that, in this embodiment, all element values in the autocorrelation sequence are used to measure the degree of autocorrelation of all pixel values in each high-slope shady slope cluster.

[0055] Furthermore, this embodiment uses the pixel values of all pixels in each low-slope sunny slope cluster as the input of the threshold segmentation algorithm, outputs the segmentation threshold and records it as the pixel threshold, and records all pixels with pixel values greater than or equal to the pixel threshold as feature pixels for characterizing the exposed rock area.

[0056] Furthermore, this embodiment determines the biocomplexity of the mountain forest area to be identified based on the fusion result of the pixel value mean and texture feature value of all pixels in each high-slope shady slope cluster, combined with the degree of confusion of the coordinates of all feature pixels in each low-slope sunny slope cluster. Specifically, In this embodiment, the biocomplexity of the mountain forest area to be identified is The expression is: Where, It represents the reciprocal of the product of the mean value of the pixel values of all pixels in the j-th high-slope shady slope cluster and the texture feature value; represents the Shannon entropy of the coordinates of all pixels in the k-th low-slope sunny slope cluster; 、 They represent the number of all high-slope shady slope clusters and the number of all low-slope sunny slope clusters in the mountain forest area to be identified respectively; norm[ ] represents the normalization function.

[0057] The calculation method of Shannon entropy is a well-known technology, and its specific calculation process will not be described in detail.

[0058] According to the biocomplexity of the mountain forest area to be identified, it can be understood that if the reciprocal of the product of the mean pixel value of all pixels in the high-slope shady slope cluster and the texture feature value is larger, it means that the texture details in the optical remote sensing grayscale image of the corresponding high-slope shady slope area are more blurred and the overall grayscale value is lower, which usually reflects that the vegetation in the area grows densely, forming a continuous canopy layer, resulting in homogenization of the image texture. Therefore, the larger the reciprocal of the multiplication result, the smaller the corresponding biocomplexity; at the same time, if the Shannon entropy of the coordinates of all pixels in the low-slope sunny slope cluster is larger, it means that in the corresponding low-slope sunny slope area, the two-dimensional coordinate distribution of the characteristic pixels is more discrete and uniform, with no obvious concentrated area, which usually means that the overall impact of rock exposure on vegetation growth is relatively small and widely distributed. Therefore, the larger the Shannon entropy, the higher the corresponding biocomplexity, reflecting that the rock exposure area in the low-slope sunny slope area is widely and dispersed, and the local inhibitory effect on vegetation growth is relatively small, which is conducive to the maintenance of biomass as a whole or indicates that the biomass itself is relatively uniform; On the contrary, if the reciprocal of the product of the mean pixel value of all pixels in the high-slope shady slope cluster and the texture eigenvalue is smaller, it means that the texture details in the optical remote sensing grayscale image of the corresponding high-slope shady slope area are clearer and the overall grayscale value is higher, which usually reflects that the vegetation growth in the area is sparse and the crown cover is discontinuous, resulting in complex image texture. Therefore, the smaller the reciprocal of the multiplication result, the greater the corresponding biological complexity; at the same time, if the Shannon entropy of the coordinates of all pixels in the low-slope sunny slope cluster is smaller, it means that in the corresponding low-slope sunny slope area, the two-dimensional coordinate distribution of the characteristic pixels is more concentrated and more uneven, and there is an obvious concentrated area, which usually means that the overall impact of rock exposure on vegetation growth is relatively large and not widely distributed. Therefore, the smaller the Shannon entropy, the lower the corresponding biological complexity, reflecting that the rock exposure area in the low-slope sunny slope area is concentrated, and the local inhibitory effect on vegetation growth is relatively large, which is not conducive to the maintenance of biomass as a whole or indicates that the biomass itself is relatively uneven.

[0059] Thus, this embodiment has constructed a biocomplexity index by analyzing the texture uniformity of high-slope shady slope areas and the discreteness of rock distribution in low-slope sunny slope areas. This index cleverly combines the effects of terrain and light on vegetation growth, and can effectively reflect the distribution status and overall complexity of biomass in mountain forest areas, providing a quantitative basis for refined ecological assessment.

[0060] Step S4: Based on the biological complexity, the biomass of the mountain forest area to be identified is identified.

[0061] Based on the biocomplexity obtained in step S3, this embodiment further identifies the biomass of the mountain forest area to be identified based on the biocomplexity, specifically: A preset threshold U is set. If the biological complexity of the mountain forest area to be identified is greater than or equal to the preset threshold U, it means that the vegetation in the high-slope shady slope area of the current mountain forest area to be identified is dense, and the crown coverage is relatively wide, while the exposed rocks in the low-slope sunny slope area are discretely distributed, which has a relatively slight impact on the plant organisms suitable for growing in the low-slope sunny slope area, that is, the biomass of the mountain forest area to be identified is relatively rich; on the contrary, if the biological complexity of the mountain forest area to be identified is less than the preset threshold U, it means that the vegetation in the high-slope shady slope area of the current mountain forest area is sparse, and the plant crown coverage is small, while the exposed rocks in the low-slope sunny slope area are concentrated, which has a relatively serious impact on the plant organisms suitable for growing in the low-slope sunny slope area, that is, the biomass of the mountain forest area to be identified is relatively lacking.

[0062] It should be noted that the preset threshold value U in this embodiment is 0.6. In actual application, as other implementation methods, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0063] Thus, this embodiment has constructed a biological complexity index by analyzing the remote sensing image features of different slope areas of mountain forests, and associated it with biomass, thereby achieving effective identification of biomass. This method integrates multiple aspects of information such as terrain, lighting, and vegetation structure, overcomes the limitations of single texture feature extraction, and improves the accuracy of identifying the biomass status of mountain forests.

[0064] Based on the same inventive concept as the above-mentioned method, an embodiment of the present application also provides a mountain forest biomass identification system based on high-resolution remote sensing data, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned mountain forest biomass identification methods based on high-resolution remote sensing data.

[0065] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0067] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data, characterized in that: The method comprises the following steps: Obtain an optical remote sensing image of a mountain forest area to be identified, and spectral images of the mountain forest area within a preset first band and a preset second band, respectively, and record them as a first spectral image and a second spectral image; Determine the scattering characteristic value of each pixel based on the mean reflectivity of all pixels in the neighborhood of each pixel in the first spectral image and the difference in reflectivity between each pixel and all pixels in its neighborhood; determine the reflectivity dispersion of each pixel based on the distribution of all pixels in the neighborhood of each pixel in the second spectral image and the degree of dispersion of the reflectivity gradient values of each pixel in all directions in its neighborhood, and determine the slope characteristic value of each pixel in combination with the scattering characteristic value; cluster all pixels in the optical remote sensing image, and divide all clusters into high-slope shady slope clusters and low-slope sunny slope clusters based on the average distribution of the slope characteristic values of all pixels in each cluster; The texture feature values of each high-slope shady slope cluster were determined based on the degree of autocorrelation of all pixel values within each high-slope shady slope cluster. Feature pixels were screened from each low-slope sunny slope cluster based on the pixel values of all pixels within each low-slope sunny slope cluster. The biocomplexity of the mountain forest area to be identified was determined based on the fusion of the pixel value mean and texture feature values of all pixels within each high-slope shady slope cluster, combined with the degree of coordinate confusion of all feature pixels within each low-slope sunny slope cluster. Based on the biological complexity, the biomass of the mountain forest area to be identified is identified.

2. The method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data according to claim 1, wherein: The method for determining the scattering characteristic value of each pixel is: The cumulative sum of the differences in reflectance between each pixel in the first spectral image and all pixels in its neighborhood is calculated, and the result of forward fusion of the cumulative sum with the mean reflectance of all pixels in the neighborhood of each pixel in the first spectral image is used as the scattering characteristic value of each pixel.

3. The method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data according to claim 1, wherein: The expression of the reflectance discreteness of each pixel is: Where, represents the reflectance dispersion of pixel i; Indicates the discrete degree of reflectance gradient value of pixel i in all directions within its neighborhood in the second spectrum image; represents the mean reflectance of all pixels in the neighborhood of pixel i in the second spectral image; Indicates a preset constant greater than 0.

4. The method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data according to claim 1, wherein: The slope characteristic value of each pixel is a result of normalizing the ratio of the scattering characteristic value of each pixel to the reflection dispersion.

5. The method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data according to claim 1, wherein: The method divides all clusters into high-slope shady slope clusters and low-slope sunny slope clusters, including: Calculate the mean of the slope characteristic values of all pixels in each cluster, record it as the characteristic mean, use the characteristic value mean of all clusters as the input of the threshold segmentation algorithm, output the segmentation threshold, and record all clusters with characteristic means greater than or equal to the segmentation threshold as high-slope shady slope clusters, and all other clusters as low-slope sunny slope clusters.

6. The method for identifying mountain forest biomass based on high-resolution remote sensing data according to claim 1, wherein: The texture feature value of each high-slope shady slope cluster is the discrete degree of all elements in the autocorrelation sequence of all pixel values in each high-slope shady slope cluster.

7. The method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data according to claim 1, wherein: The characteristic pixels are selected from each low-slope sunny slope cluster, including: The pixel values of all pixels in each low-slope sunny slope cluster are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output and recorded as the pixel threshold. All pixels with pixel values greater than or equal to the pixel threshold are recorded as feature pixels.

8. The method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data according to claim 1, wherein: The expression of the biological complexity of the mountain forest area to be identified is: Where, represents the biological complexity of the mountain forest area to be identified; It represents the reciprocal of the product of the mean value of the pixel values of all pixels in the j-th high-slope shady slope cluster and the texture feature value; represents the Shannon entropy of the coordinates of all pixels in the k-th low-slope sunny slope cluster; 、 They represent the number of all high-slope shady slope clusters and the number of all low-slope sunny slope clusters in the mountain forest area to be identified respectively; norm[ ] represents the normalization function.

9. The method for identifying forest biomass in mountainous areas based on high-resolution remote sensing data according to claim 1, wherein: The identifying of the biomass of the mountain forest area to be identified includes: If the biological complexity of the mountain forest area to be identified is greater than a preset threshold, the biomass of the mountain forest area to be identified is rich; otherwise, the biomass of the mountain forest area to be identified is lacking.

10. A mountain forest biomass identification system based on high-resolution remote sensing data, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for identifying mountain forest biomass based on high-resolution remote sensing data as described in any one of claims 1 to 9 are implemented.

Images