Method for assessing influence of human activity on forestry ecological diversity

Abstract

The application discloses a kind of human activity influence assessment methods of forestry ecological diversity, comprising: the historical remote sensing image sequence of target area is collected, the reflectivity of pixel is fitted as continuous harmonic model;Harmonic model is used to calculate the reflectivity of pixel fitting, and the mutation pixel is screened out, and then the mutation point that human activity produces disturbance to target area is identified;The normalized disturbance index of pixel is calculated, and the habitat suitability index of pixel is obtained;The habitat suitability index of pixel is calculated, and the suitable habitat pixel is screened out, and the maximum activity radius and ecological area of organism in suitable habitat patch are calculated;Ecological diversity influence coefficient is calculated, and the influence situation of human activity on the ecological diversity of target area is evaluated.The application uses long time sequence remote sensing to quantize disturbance and recovery track, can identify short-term interference and long-term recovery process, realize the time sequence correlation evaluation of " change process-ecological response ".It provides data support for forestry sustainable management and ecological protection.

Description

Technical Field

[0001] This invention relates to the field of forestry management, and more specifically to a method for assessing the impact of human activities on forest biodiversity. Background Technology

[0002] Forest ecosystems are vital carriers of biodiversity. Human activities (such as selective logging, thinning, road construction, and tourism development) alter forest structure, directly or indirectly affecting the quality and connectivity of species habitats, leading to biodiversity loss. Current impact assessment methods largely rely on long-term, costly, and limited-coverage field surveys, making it difficult to achieve large-scale, high-frequency dynamic monitoring.

[0003] In existing technologies, biodiversity assessment using remote sensing data often employs a single index (such as NDVI) or simple habitat classification, which has the following main shortcomings:

[0004] 1. The temporal characteristics of remote sensing data were not fully explored, resulting in insufficient capture of the dynamic processes of forest structure changes;

[0005] 2. Habitat quality assessment is too static, with highly subjective parameters and a lack of consideration for species' survival and adaptability. Summary of the Invention

[0006] To address the aforementioned shortcomings of existing technologies, this invention provides a method for assessing the impact of human activities on forest biodiversity, enabling high-precision, multi-scale assessment of the impact of human activities on forest biodiversity.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0008] A method for assessing the impact of human activities on forest biodiversity is provided, comprising:

[0009] Step S1: Collect historical remote sensing image sequences of the target area, and preprocess and merge them to obtain monthly composite remote sensing image sequences. Fit the reflectance of pixels in the continuous monthly composite remote sensing images to a continuous harmonic model.

[0010] Step S2: Calculate the reflectance of the fitted pixels using the harmonic model, screen out abruptly changed pixels, obtain the abruptly changed monthly composite remote sensing image, and then identify the abruptly changed points where human activities have disturbed the target area.

[0011] Step S3: Calculate the normalized perturbation index of the pixel using the average reflectance of the pixels in the monthly composite remote sensing image of the year in which the mutation point is located, and then calculate the perturbation intensity of human activities on the target area to obtain the habitat suitability index of the pixel.

[0012] Step S4: Merge the monthly composite remote sensing images of the year in which the mutation point is located, calculate the habitat suitability index of the pixels, screen suitable habitat pixels, merge them into suitable habitat patches, and calculate the maximum activity radius and ecological area of ​​organisms in the suitable habitat patches.

[0013] Step S5: Calculate the animal's survival suitability index using the maximum activity radius, and combine it with the ecological area of ​​suitable habitat patches to calculate the biodiversity impact coefficient, and assess the impact of human activities on the biodiversity of the target area.

[0014] Further, step S1 includes:

[0015] Step S11: Collect historical remote sensing image sequences of the target area, perform radiometric calibration, atmospheric correction and shadow masking on the historical remote sensing images, and synthesize the remote sensing image sequences within a month on a monthly basis to obtain a monthly composite remote sensing image sequence.

[0016] Step S12: Align the monthly composite remote sensing images in the monthly composite remote sensing image sequence, and fit the reflectance of pixels in consecutive monthly composite remote sensing images into a continuous harmonic model.

[0017] ;

[0018] in, Monthly time t Fitted reflectance For the trend term and trend coefficient, k Numbering the harmonic order, These are the sine harmonic coefficient and the cosine harmonic coefficient, respectively. T It is an annual cycle. n It represents the harmonic order.

[0019] Further, step S2 includes:

[0020] Step S21: Calculate the monthly time using the harmonic model. t Fitted reflectivity According to the pixel in the monthly time t Actual reflectivity Calculate the reflectivity residual ;

[0021] Step S22: Set the reflectivity residual threshold ,like If the mutation occurs, the pixel is determined to have mutated; otherwise, the pixel is determined not to have mutated.

[0022] Based on monthly time t The number of pixels that produce abrupt changes in the corresponding monthly composite remote sensing image m Calculate the pixel mutation rate , M This represents the number of pixels in the monthly composite remote sensing image.

[0023] Step S23: Set the pixel mutation rate threshold ,like Then determine the monthly time. t The corresponding monthly composite remote sensing imagery exhibits abrupt changes, altering the monthly timeframe. t As a mutation point that causes disturbance to the target area by human activities.

[0024] Further, step S3 includes:

[0025] Step S31: Calculate the average reflectance of pixels in the monthly composite remote sensing image of the year in which the mutation point occurs. and the average reflectance of pixels in monthly composite remote sensing images over the three years preceding the mutation point. Then use average reflectivity and average reflectivity Calculate the normalized perturbation index of the pixel in the year where the mutation point is located. ;

[0026] Step S32: Based on the normalized perturbation index Calculate the disturbance intensity of human activities on the target area ;

[0027] Step S33: Utilize the disturbance intensity Calculate the habitat suitability index of pixels in the monthly composite remote sensing image of the year in which the mutation point occurs. .

[0028] Further, step S4 includes:

[0029] Step S41: Merge the monthly composite remote sensing images of the year in which the mutation point occurs to obtain the annual composite remote sensing image of the year in which the mutation point occurs, and calculate the habitat suitability index of the pixels in the annual composite remote sensing image. ;

[0030] Set a threshold for the habitat suitability index. ,like Then determine the pixel i Suitable habitat pixel; otherwise, judgment pixel. i Pixels representing unsuitable habitats;

[0031] Step S42: Merge consecutive suitable habitat pixels in the annual synthetic remote sensing image of the year in which the mutation point occurs to obtain suitable habitat patches, and calculate the ecological area of ​​each suitable habitat patch. , p Numbering of suitable habitat patches;

[0032] Step S43: Based on the coordinates of suitable habitat pixels Calculate the center coordinates of suitable habitat patches ;

[0033] Step S44: Calculate the distance between suitable habitat pixels and the center coordinates within the suitable habitat patch to obtain the maximum activity radius of organisms within the suitable habitat patch. .

[0034] Further, step S5 includes:

[0035] Step S51: Based on the ideal activity radius of animals within the target area Calculate the survival suitability index of animals in suitable habitat patches. ;

[0036] ;

[0037] Step S52: Based on the survival suitability index and ecological area Calculate the impact coefficient of human activities on the biodiversity of the target area. ;

[0038] ;

[0039] in, S The total ecological area of ​​the target region. P The number of suitable habitat patches;

[0040] Step S53: Set the threshold for the biodiversity impact coefficient ,like If the impact of human activities on the biodiversity of the target area is small, then it is determined that human activities have a relatively large impact on the biodiversity of the target area; otherwise, it is determined that human activities have a relatively large impact on the biodiversity of the target area.

[0041] The beneficial effects of this invention are as follows: This invention utilizes long-term remote sensing depth quantification of perturbation and recovery trajectories, enabling the identification of short-term disturbances and long-term recovery processes, and achieving a time-series correlation assessment of "change process-ecological response." By quantifying the habitat quality of target areas after human activities, it achieves an assessment of the impact on ecological environment quality, significantly improving the accuracy and dynamism of the assessment, and providing data support for sustainable forestry management and ecological protection. Attached Figure Description

[0042] Figure 1 A flowchart for assessing the impact of human activities on forest biodiversity. Detailed Implementation

[0043] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0044] like Figure 1 As shown, a method for assessing the impact of human activities on forestry biodiversity is characterized by comprising:

[0045] Step S1: Acquire historical remote sensing image sequences of the target area, preprocess and merge them to obtain monthly composite remote sensing image sequences, and fit the reflectance of pixels in consecutive monthly composite remote sensing images into a continuous harmonic model. Step S1 specifically includes:

[0046] Step S11: Collect historical remote sensing image sequences of the target area, perform radiometric calibration, atmospheric correction and shadow masking on the historical remote sensing images, and synthesize the remote sensing image sequences within a month on a monthly basis to obtain a monthly composite remote sensing image sequence. The synthesis of remote sensing image sequences mainly adopts the method of taking the best pixel in the remote sensing images within a month as the pixel in the monthly composite remote sensing image, and discarding the other inferior pixels.

[0047] The target area includes areas with human activity, such as forestry operation areas, road networks, settlements, and tourism development areas. The historical period corresponding to the historical remote sensing image sequence includes the time from the abrupt point before the onset of human activity to the present time of forestry ecological recovery. Taking a selective logging activity in a temperate natural forest area as an example, the collected data is a historical remote sensing image sequence of the target area from 2000 to 2023, with the selective logging occurring in 2015.

[0048] Step S12: Align the monthly composite remote sensing images in the monthly composite remote sensing image sequence, and fit the reflectance of pixels in consecutive monthly composite remote sensing images into a continuous harmonic model.

[0049] ;

[0050] in, Monthly time t (For example, the reflectance fitted in June 2015) For the trend term and trend coefficient, k Numbering the harmonic order, These are the sine harmonic coefficient and the cosine harmonic coefficient, respectively. T It is an annual cycle. n The harmonic order, generally the harmonic order. n Take 3;

[0051] Step S2: Calculate the reflectance of the fitted pixels using a harmonic model, screen out pixels with abrupt changes, obtain the monthly composite remote sensing image with abrupt changes, and then identify the abrupt change points where human activities have disturbed the target area. Step S2 specifically includes:

[0052] Step S21: Calculate the monthly time using the harmonic model. t Fitted reflectivity According to the pixel in the monthly time t Actual reflectivity Calculate the reflectivity residual ;

[0053] ;

[0054] Step S22: Set the reflectivity residual threshold ,like If the mutation occurs, the pixel is determined to have mutated; otherwise, the pixel is determined not to have mutated.

[0055] Based on monthly time t The number of pixels that produce abrupt changes in the corresponding monthly composite remote sensing image m Calculate the pixel mutation rate , M This represents the number of pixels in the monthly composite remote sensing image.

[0056] Step S23: Set the pixel mutation rate threshold ,like Then determine the monthly time. t The corresponding monthly composite remote sensing imagery exhibits abrupt changes, altering the monthly timeframe. t As a mutation point that causes disturbance to the target area by human activities.

[0057] Step S3: Calculate the normalized perturbation index of the pixel using the average reflectance of pixels in the monthly composite remote sensing image of the year in which the mutation point occurs. Then, calculate the perturbation intensity of human activities affecting the target area to obtain the habitat suitability index of the pixel. Step S3 specifically includes:

[0058] Step S31: Calculate the average reflectance of pixels in the monthly composite remote sensing image of the year in which the mutation point occurs. and the average reflectance of pixels in monthly composite remote sensing images over the three years preceding the mutation point. Then use average reflectivity and average reflectivity Calculate the normalized perturbation index of the pixel in the year where the mutation point is located. ;

[0059] ;

[0060] in, This refers to the month and time of the year in which the mutation point occurred. The monthly timeframes are within the three years preceding the mutation point. The year and month of the mutation point The average reflectance of abruptly changed pixels in the corresponding monthly composite remote sensing image. Monthly timeframe for the three years preceding the mutation point The average reflectance of abruptly changed pixels in the corresponding monthly composite remote sensing image; The standard deviation of reflectance of the mutation pixels in the years preceding the mutation point. The monthly time required for the reflectance of a mutation pixel fitted based on a continuous harmonic model to recover to the ideal reflectance;

[0061] Step S32: Based on the normalized perturbation index Calculate the disturbance intensity of human activities on the target area ;

[0062] ;

[0063] in, This represents the proportion of aberration pixels in the monthly composite remote sensing image of the third year after the perturbation that have recovered to their ideal reflectance; through proportion It represents the proportion of damaged forest vegetation that has been restored.

[0064] Disturbance intensity Characterizing the extent and intensity of human activities that damage vegetation in a target area. A higher reflectance indicates greater damage to vegetation in the target area caused by human activities, and vice versa. The recovery of reflectance of a mutant pixel to its ideal reflectance indicates that the vegetation damaged by human activities has recovered, proportionally... The larger the value, the less damage human activities cause to the forest vegetation in the target area, or the stronger the vegetation recovery capacity.

[0065] Step S33: Utilize the disturbance intensity Calculate the habitat suitability index of pixels in the monthly composite remote sensing image of the year in which the mutation point occurs. ;

[0066] ;

[0067] in, For pixels i Environmental resource factors corresponding to the location For biological species j In pixels i habitat adaptation function, J The number of biological species used to characterize ecological diversity. It is the optimal environmental resource factor for biological species. The tolerance range of a species to environmental resource factors; tolerance range It reflects the tolerance of biological species. For the disturbance sensitivity coefficient, this embodiment takes... The value can be determined based on the recovery capacity of the forest vegetation in the target area after damage; the greater the recovery capacity, the smaller the disturbance sensitivity coefficient. For pixels i The intensity of the disturbance.

[0068] The biological species in this embodiment include animals, plants, and microorganisms. This embodiment obtains environmental resource factors for different biological species through inversion of remote sensing image data or from other data sources; for example, for plant populations, based on pixels... i The Leaf Area Index (LAI) is obtained by inverting remote sensing image data; a higher LAI indicates a richer plant population. For animal populations, a higher LAI indicates denser vegetation, providing better shelter and food sources. The Topographic Moisture Index (TWI) can be obtained from the DEM data of the target area, yielding pixel values. i The topographic humidity index (TWI) of the location is used to assess the habitat adaptability of underground microorganisms.

[0069] Step S4: Merge the monthly composite remote sensing images of the year in which the mutation point occurs, calculate the habitat suitability index of the pixels, screen suitable habitat pixels, merge them into suitable habitat patches, and calculate the maximum activity radius and ecological area of ​​organisms in the suitable habitat patches. Step S4 specifically includes:

[0070] Step S41: Merge the monthly composite remote sensing images of the year in which the mutation point occurs to obtain the annual composite remote sensing image of the year in which the mutation point occurs, and calculate the habitat suitability index of the pixels in the annual composite remote sensing image. ;

[0071] ;

[0072] in, This represents the sum of the habitat suitability indices of pixels in the monthly composite remote sensing image of the year in which the mutation point occurred;

[0073] Set a threshold for the habitat suitability index. ,like Then determine the pixel i Suitable habitat pixel; otherwise, judgment pixel. i Pixels representing unsuitable habitats;

[0074] Step S42: Merge consecutive suitable habitat pixels in the annual synthetic remote sensing image of the year in which the mutation point occurs to obtain suitable habitat patches, and calculate the ecological area of ​​each suitable habitat patch. ;

[0075] ;

[0076] in, P To determine the number of suitable habitat pixels in suitable habitat patches, p Numbering of suitable habitat patches, The ecological area corresponding to a pixel;

[0077] Step S43: Based on the coordinates of suitable habitat pixels Calculate the center coordinates of suitable habitat patches ;

[0078] ;

[0079] Step S44: Calculate the distance between suitable habitat pixels and the center coordinates within the suitable habitat patch to obtain the maximum activity radius of organisms within the suitable habitat patch. ;

[0080] ;

[0081] in, This refers to the set of suitable habitat pixels within a suitable habitat patch.

[0082] Step S5: Calculate the animal's survival suitability index using its maximum activity radius, and combine this with the ecological area of ​​suitable habitat patches to calculate the biodiversity impact coefficient, assessing the impact of human activities on the biodiversity of the target area. Step S5 specifically includes:

[0083] Step S51: Based on the ideal activity radius of animals within the target area Calculate the survival suitability index of animals in suitable habitat patches. ;

[0084] ;

[0085] Survival Suitability Index The survival suitability index represents the proportion of animals whose range of activity is compressed within suitable habitat patches. The larger the value, the better the animal's adaptability to living in suitable habitat patches, and the more conducive it is to its survival and reproduction.

[0086] Step S52: Based on the survival suitability index and ecological area Calculate the impact coefficient of human activities on the biodiversity of the target area. ;

[0087] ;

[0088] in, S The total ecological area of ​​the target region; This indicates the proportion of suitable habitat patches in the target area. The larger the proportion, the less damage to the forest ecosystem caused by human activities, and vice versa.

[0089] Step S53: Set the threshold for the biodiversity impact coefficient ,like If the impact of human activities on the biodiversity of the target area is small, then it is determined that human activities have a relatively large impact on the biodiversity of the target area; otherwise, it is determined that human activities have a relatively large impact on the biodiversity of the target area.

[0090] This invention utilizes long-term remote sensing depth quantification of perturbation and recovery trajectories to identify short-term disturbances and long-term recovery processes, enabling a time-series correlation assessment of "change process-ecological response." By quantifying the habitat quality of target areas after human activities, it assesses the impact on ecological environment quality, significantly improving the accuracy and dynamism of the assessment and providing data support for sustainable forestry management and ecological protection.

Claims

1. A method for assessing the impact of human activities on forestry biodiversity, characterized in that, include: Step S1: Collect historical remote sensing image sequences of the target area, and preprocess and merge them to obtain monthly composite remote sensing image sequences. Fit the reflectance of pixels in the continuous monthly composite remote sensing images to a continuous harmonic model. Step S2: Calculate the reflectance of the fitted pixels using the harmonic model, screen out abruptly changed pixels, obtain the abruptly changed monthly composite remote sensing image, and then identify the abruptly changed points where human activities have disturbed the target area. Step S3: Calculate the normalized perturbation index of the pixel using the average reflectance of the pixels in the monthly composite remote sensing image of the year in which the mutation point is located, and then calculate the perturbation intensity of human activities on the target area to obtain the habitat suitability index of the pixel. Step S4: Merge the monthly composite remote sensing images of the year in which the mutation point is located, calculate the habitat suitability index of the pixels, screen suitable habitat pixels, merge them into suitable habitat patches, and calculate the maximum activity radius and ecological area of ​​organisms in the suitable habitat patches. Step S5: Calculate the animal's survival suitability index using the maximum activity radius, and combine it with the ecological area of ​​suitable habitat patches to calculate the biodiversity impact coefficient, and assess the impact of human activities on the biodiversity of the target area. Step S2 includes: Step S21: Calculate the monthly time using the harmonic model. t Fitted reflectivity According to the pixel in the monthly time t Actual reflectivity Calculate the reflectivity residual ; Step S22: Set the reflectivity residual threshold ,like If the mutation occurs, the pixel is determined to have mutated; otherwise, the pixel is determined not to have mutated. Based on monthly time t The number of pixels that produce abrupt changes in the corresponding monthly composite remote sensing image m Calculate the pixel mutation rate , M This represents the number of pixels in the monthly composite remote sensing image. Step S23: Set the pixel mutation rate threshold ,like Then determine the monthly time. t The corresponding monthly composite remote sensing imagery exhibits abrupt changes, altering the monthly timeframe. t As a point of sudden change that causes disturbance to the target area by human activities; Step S3 includes: Step S31: Calculate the average reflectance of pixels in the monthly composite remote sensing image of the year in which the mutation point occurs. and the average reflectance of pixels in monthly composite remote sensing images over the three years preceding the mutation point. Then use average reflectivity and average reflectivity Calculate the normalized perturbation index of the pixel in the year where the mutation point is located. ; Step S32: Based on the normalized perturbation index Calculate the disturbance intensity of human activities on the target area ; Step S33: Utilize the disturbance intensity Calculate the habitat suitability index of pixels in the monthly composite remote sensing image of the year in which the mutation point occurs. ; Step S4 includes: Step S41: Merge the monthly composite remote sensing images of the year in which the mutation point occurs to obtain the annual composite remote sensing image of the year in which the mutation point occurs, and calculate the habitat suitability index of the pixels in the annual composite remote sensing image. ; Set a threshold for the habitat suitability index. ,like Then determine the pixel i Suitable habitat pixel; otherwise, judgment pixel. i Pixels representing unsuitable habitats; Step S42: Merge consecutive suitable habitat pixels in the annual synthetic remote sensing image of the year in which the mutation point occurs to obtain suitable habitat patches, and calculate the ecological area of ​​each suitable habitat patch. , p Numbering of suitable habitat patches; Step S43: Based on the coordinates of suitable habitat pixels Calculate the center coordinates of suitable habitat patches ; Step S44: Calculate the distance between suitable habitat pixels and the center coordinates within the suitable habitat patch to obtain the maximum activity radius of organisms within the suitable habitat patch. ; Step S5 includes: Step S51: Based on the ideal activity radius of animals within the target area Calculate the survival suitability index of animals in suitable habitat patches. ; ； Step S52: Based on the survival suitability index and ecological area Calculate the impact coefficient of human activities on the biodiversity of the target area. ; ； in, S The total ecological area of ​​the target region. P The number of suitable habitat patches; Step S53: Set the threshold for the biodiversity impact coefficient ,like If the impact of human activities on the biodiversity of the target area is small, then it is determined that human activities have a relatively large impact on the biodiversity of the target area; otherwise, it is determined that human activities have a relatively large impact on the biodiversity of the target area.

2. The method for assessing the impact of human activities on forestry biodiversity according to claim 1, characterized in that, Step S1 includes: Step S11: Collect historical remote sensing image sequences of the target area, perform radiometric calibration, atmospheric correction and shadow masking on the historical remote sensing images, and synthesize the remote sensing image sequences within a month on a monthly basis to obtain a monthly composite remote sensing image sequence. Step S12: Align the monthly composite remote sensing images in the monthly composite remote sensing image sequence, and fit the reflectance of pixels in consecutive monthly composite remote sensing images into a continuous harmonic model. ； in, Monthly time t Fitted reflectance For the trend term and trend coefficient, k Numbering the harmonic order, These are the sine harmonic coefficient and the cosine harmonic coefficient, respectively. T It is an annual cycle. n It represents the harmonic order.

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