Method for evaluating influence of human activities on forestry ecological diversity

Abstract

The invention discloses a method for evaluating the influence of human activities on the ecological diversity of forestry, and the method comprises the steps: collecting a historical remote sensing image sequence of a target region, and fitting the reflectivity of pixels into a continuous harmonic model; calculating the reflectivity of pixel fitting by using a harmonic model, screening out abrupt change pixels, and further identifying abrupt change points where human activities disturb the target area; calculating a normalized disturbance index of the pixel to obtain a habitat suitability index of the pixel; calculating habitat suitability indexes of the pixels, screening suitable habitat pixels, and calculating the maximum activity radius and ecological area of organisms in suitable habitat plaques; and calculating an ecological diversity influence coefficient, and evaluating the ecological diversity influence condition of the human activities on the target area. According to the method, disturbance and recovery tracks are quantified by using long-time sequence remote sensing depth, short-term interference and long-term recovery processes can be identified, and'change process-ecological response 'time sequence correlation evaluation is realized. And data support is provided for sustainable management and ecological protection of forestry.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of forestry management, and in particular to a method for evaluating the influence of human activities on the ecological diversity of forestry. BACKGROUND

[0002] The forestry ecosystem is an important carrier of biodiversity. Changes in forest structure caused by human activities (such as selective logging, thinning, road construction, tourism development, etc.) will directly or indirectly affect the quality and connectivity of species habitats, and thus lead to biodiversity loss. Current evaluation methods rely heavily on long-term, high-cost, and limited-coverage field surveys, making it difficult to achieve dynamic monitoring on a large scale and with high frequency.

[0003] In the prior art, remote sensing data is used to evaluate biodiversity, and single indices (such as NDVI) or simple habitat classification are mainly used, which has the following disadvantages: 1. The temporal characteristics of remote sensing data are not fully exploited, and the dynamic process of forest structure changes is not captured; 2. The habitat quality evaluation is too static, the parameters are highly subjective, and the adaptability of species to survival is not considered. SUMMARY

[0004] To overcome the above-mentioned deficiencies of the prior art, the present application provides a method for evaluating the influence of human activities on the ecological diversity of forestry, which realizes high-precision and multi-scale evaluation of the influence of human activities on the ecological diversity of forestry.

[0005] To achieve the above-mentioned application purposes, the technical solutions adopted by the present application are as follows: A method for evaluating the influence of human activities on the ecological diversity of forestry is provided, which comprises: Step S1: Collecting historical remote sensing image sequences of a target area, and performing preprocessing and merging to obtain monthly synthetic remote sensing image sequences, fitting the reflectivity of pixels in consecutive monthly synthetic remote sensing images into a continuous harmonic model; Step S2: Calculating the fitted reflectivity of pixels using the harmonic model, screening out mutation pixels to obtain monthly synthetic remote sensing images with mutations, and then identifying mutation points disturbed by human activities in the target area; Step S3: Calculating the normalized disturbance index of pixels using the average reflectivity of pixels in the monthly synthetic remote sensing images of the year in which the mutation point is located, and then calculating the disturbance intensity of human activities in the target area, to obtain the habitat suitability index of the pixels; Step S4: Merging the monthly synthetic remote sensing images of the year in which the mutation point is located, calculating the habitat suitability index of the pixels, screening out suitable habitat pixels, and merging them into suitable habitat patches, and calculating the maximum activity radius and ecological area of the 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.

[0006] Further, 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.

[0007] Further, 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 mutation point that causes disturbance to the target area by human activities.

[0008] Further, 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. .

[0009] Further, 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. .

[0010] Further, 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.

[0011] 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

[0012] Figure 1 A flowchart for assessing the impact of human activities on forest biodiversity. Detailed Implementation

[0013] 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.

[0014] like Figure 1 As shown, a method for assessing the impact of human activities on forestry biodiversity is characterized by comprising: 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: 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.

[0015] 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.

[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. ; 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; 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: 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 imagem 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 mutation point that causes disturbance to the target area by human activities.

[0017] 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: 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. ; ; in, This refers to the month and time of the year in which the mutation point occurred. The timeframe is the monthly period 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; Step S32: Based on the normalized perturbation index Calculate the disturbance intensity of human activities on the target area ; ; 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.

[0018] 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.

[0019] 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. ; ; 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.

[0020] 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.

[0021] 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: 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. ; ; 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; 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. ; ; 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; 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. ; ; in, This refers to the set of suitable habitat pixels within a suitable habitat patch.

[0022] 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: 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. ; ; 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.

[0023] 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; 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.

[0024] 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.

[0025] 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 evaluating the influence of human activities on forestry ecological diversity, characterized by, The application relates to a method for evaluating the influence of human activities on the ecological diversity of a target region. Step S1: collecting a historical remote sensing image sequence of a target region, and performing pretreatment and merging to obtain a monthly synthetic remote sensing image sequence, fitting the reflectivity of a pixel in the continuous monthly synthetic remote sensing image into a continuous harmonic model; Step S2: calculating the fitting reflectivity of the pixel by using the harmonic model, screening out a mutation pixel to obtain a monthly synthetic remote sensing image of the mutation, and identifying a mutation point disturbed by human activities in the target region; Step S3: calculating a normalized disturbance index of the pixel by using the average reflectivity of the pixel in the monthly synthetic remote sensing image of the year in which the mutation point is located, calculating the disturbance intensity of the disturbance of human activities on the target region, and obtaining a habitat suitability index of the pixel; Step S4: merging the monthly synthetic remote sensing image of the year in which the mutation point is located, calculating the habitat suitability index of the pixel, screening suitable habitat pixels, merging the suitable habitat pixels into suitable habitat patches, and calculating the maximum activity radius and ecological area of the biological in the suitable habitat patches; Step S5: calculating the survival suitability index of the animal by using the maximum activity radius, combining the ecological area of the suitable habitat patches, calculating an ecological diversity influence coefficient, and evaluating the influence of human activities on the ecological diversity of the target region.

2. The method of claim 1, wherein the method is characterized by, The step S1 comprises: Step S11: collecting a historical remote sensing image sequence of a target region, performing radiation calibration, atmospheric correction and shadow masking on the historical remote sensing image, synthesizing the remote sensing image sequence in a month in a month unit to obtain a monthly synthetic remote sensing image sequence; Step S12: aligning the monthly synthetic remote sensing images in the monthly synthetic remote sensing image sequence, and fitting the reflectivity of a pixel in the continuous monthly synthetic remote sensing image into a continuous harmonic model; ； wherein, is a monthly time t is a fitted reflectivity, is a trend term and a trend coefficient, k is a harmonic order number, are respectively a sine harmonic coefficient, a cosine harmonic coefficient, T is an annual cycle, n is a harmonic order.

3. The method for assessing the impact of human activities on forestry biodiversity according to claim 2, characterized in that, The step S2 comprises: Step S21: Calculate the monthly time using the harmonic model t Fitted reflectance , according to the pixel at the monthly time t Actual reflectance , calculate the reflectance residual ; Step S22: setting a reflectivity residual threshold , if , then determining that the pixel generates a mutation; otherwise, determining that the pixel does not generate a mutation; According to the monthly time t The number of pixels that have a mutation in the corresponding monthly composite remote sensing image m , the pixel mutation rate is calculated , M The number of pixels in the monthly composite remote sensing image Step S23: setting a pixel mutation rate threshold , if , it is determined that the monthly time t corresponding to the monthly synthetic remote sensing image produces a mutation, and the monthly time t is taken as the mutation point of the disturbance to the target region by human activities.

4. The method for assessing the impact of human activities on forestry biodiversity according to claim 3, characterized in that, The step S3 comprises: Step S31: Calculate the average reflectivity of the pixel in the monthly composite remote sensing image of the year where the mutation point is located , and the average reflectivity of the pixel in the monthly composite remote sensing image in the three years before the mutation point , and the average reflectivity of the pixel in the monthly composite remote sensing image in the three years before the mutation point , and the average reflectivity of the pixel in the monthly composite remote sensing image in the three years before the mutation point Calculate the normalized disturbance index of the pixel in the year where the mutation point is located ; Step S32: calculating a normalized disturbance index calculating a disturbance intensity of human activities on the target region ; Step S33: using the perturbation intensity The habitat suitability index of the pixel in the monthly composite remote sensing image of the year where the mutation point is located is calculated .

5. The method of claim 4, wherein the method further comprises: The step S4 comprises: Step S41: merge the monthly synthetic remote sensing images of the year in which the mutation point is located to obtain the annual synthetic remote sensing image of the year in which the mutation point is located, and calculate the habitat suitability index of the pixels in the annual synthetic remote sensing image ; Setting a threshold for the habitat suitability index , if , then determining the pixel i to be a suitable habitat pixel, otherwise determining the pixel i to be an unsuitable habitat pixel; Step S42: merge the continuous suitable habitat pixels in the annual synthetic remote sensing image of the year where the mutation point is located to obtain a suitable habitat patch, and calculate the ecological area of each suitable habitat patch , p is the number of the suitable habitat patch Step S43: calculating the center coordinates of the suitable habitat patch according to the coordinates of the suitable habitat pixels Step S43: calculating the center coordinates of the suitable habitat patch according to the coordinates of the suitable habitat pixels Step S43: calculating the center coordinates of the suitable habitat patch according to the coordinates of the suitable habitat pixels Step S44: Calculate the distance between the suitable habitat pixel and the center coordinate in the suitable habitat patch to obtain the maximum activity radius of the organism in the suitable habitat patch .

6. The method of claim 5, wherein the human activity affecting the forestry ecological diversity is evaluated by using the following formula: ###0002### wherein, A: a value of the human activity affecting the forestry ecological diversity; B: a value of the forestry ecological diversity; and C: a value of the forestry ecological diversity. The step S5 comprises: Step S51: calculating the living suitability index of the animal in the suitable habitat patch according to the ideal activity radius of the animal in the target region ;​ ； Step S52: calculating the living suitability index according to the living suitability index and the ecological area Step S53: calculating the ecological diversity influence coefficient of human activities on the target region according to the ecological diversity influence coefficient of human activities on the target region ; ； wherein, S is the total area of the ecological target region, P is the number of suitable habitat patches; Step S53: setting a threshold value of the ecological diversity impact coefficient , if , it is determined that the human activity has a small impact on the ecological diversity of the target region, otherwise, it is determined that the human activity has a large impact on the ecological diversity of the target region.

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