CN-121684337-B - Method for evaluating influence of human activities on ecological diversity of forestry

CN121684337BCN 121684337 BCN121684337 BCN 121684337BCN-121684337-B

Abstract

The invention discloses an influence assessment method of human activities on forestry ecological diversity, which comprises the steps of collecting a historical remote sensing image sequence of a target area, fitting reflectivities of pixels into a continuous harmonic model, calculating the reflectivities of pixel fitting by using the harmonic model, screening out mutation pixels, further identifying mutation points of disturbance of the human activities on the target area, calculating normalized disturbance indexes of the pixels to obtain a habitat suitability index of the pixels, calculating the habitat suitability index of the pixels, screening suitable habitat pixels, calculating the maximum activity radius and ecological area of organisms in suitable habitat patches, calculating ecological diversity influence coefficients, and assessing the influence condition of the human activities on the ecological diversity of the target area. The invention utilizes the long-time sequence remote sensing depth quantization disturbance and recovery track, can identify short-term disturbance and long-term recovery process, and realizes time sequence association evaluation of 'change process-ecological response'. And data support is provided for sustainable management and ecological protection of forestry.

Inventors

YANG JINLIANG
DENG XIAOBING
WU XUEXIAN
HE JIAMIN
HUANG RUI
Diao Xue

Assignees

四川省林业科学研究院（四川省林产工业研究设计所）

Dates

Publication Date: 20260512
Application Date: 20260210

Claims (2)

1. A method for evaluating the impact of human activity on ecological diversity in forestry, comprising: Step S1, acquiring a historical remote sensing image sequence of a target area, preprocessing and merging to obtain a monthly synthesized remote sensing image sequence, and fitting the reflectivity of pixels in continuous monthly synthesized remote sensing images into a continuous harmonic model; S2, calculating reflectivity of pixel fitting by using a harmonic model, screening out a mutation pixel, obtaining a mutation month synthesized remote sensing image, and further identifying a mutation point of disturbance of human activity on a target area; S3, calculating a normalized disturbance index of the pixel by using the average reflectivity of the pixel in the month synthesized remote sensing image of the year in which the mutation point is located, and further calculating the disturbance intensity of disturbance of human activity to the target area to obtain a habitat suitability index of the pixel; S4, merging month synthesized remote sensing images of the year in which the mutation point is located, calculating a habitat suitability index of the pixels, screening suitable habitat pixels, merging the suitable habitat pixels into suitable habitat plaques, and calculating the maximum activity radius and ecological area of organisms in the suitable habitat plaques; S5, calculating a survival suitability index of the animal by using the maximum activity radius, calculating an ecological diversity influence coefficient by combining the ecological area of the suitable habitat plaque, and evaluating the ecological diversity influence condition of human activity on a target area; the step S2 includes: Step S21, calculating the reflectivity of the moon time t fitting by using a harmonic model According to the actual reflectivity of the pixel at the month time t Calculating reflectivity residual ; Step S22, setting a reflectivity residual error threshold If (if) Judging that the pixel has mutation, otherwise, judging that the pixel has no mutation; calculating the pixel mutation rate according to the number m of pixels generating mutation in the month synthesized remote sensing image corresponding to the month time t M is the number of pixels in the monthly synthesized remote sensing image; step S23, setting a pixel mutation rate threshold If (if) Determining that mutation is generated in the month synthesized remote sensing image corresponding to the month time t, and taking the month time t as a mutation point of disturbance of human activity on a target area; The step S3 includes: Step S31, calculating the average reflectivity of the pixels in the month synthesized remote sensing image of the year in which the mutation point is located Average reflectivity of pixels in a month synthesized remote sensing image three years before a point of mutation Reuse of average reflectivity And average reflectivity Calculating normalized disturbance index of year pixel where mutation point is ; Step S32, according to the normalized disturbance index Calculating disturbance intensity of disturbance of human activity on target area ; Step S33, utilizing disturbance intensity Calculating habitat suitability index of pixels in month synthesized remote sensing image of year in which mutation point is located ; The step S4 includes: Step S41, merging the month synthesized remote sensing images of the year of the mutation point to obtain the year synthesized remote sensing image of the year of the mutation point, and calculating the habitat suitability index of the pixels in the year synthesized remote sensing image ; Setting a threshold for a habitat suitability index If (if) Judging the pixel i as a suitable habitat pixel, otherwise, judging the pixel i as an unsuitable habitat pixel; Step S42, merging continuous suitable habitat pixels in the annual synthetic remote sensing image of the year in which the mutation point is located to obtain suitable habitat plaques, and calculating the ecological area of each suitable habitat plaque P is the number of the suitable habitat plaque; Step S43, according to the coordinates of the suitable habitat pixels Calculating the center coordinates of the suitable habitat patches ; Step S44, calculating the distance between the suitable habitat pixels and the central coordinates in the suitable habitat plaque to obtain the maximum activity radius of organisms in the suitable habitat plaque ; The step S5 includes: Step S51, according to the ideal radius of motion of the animal in the target area Calculating a survival suitability index for animals in suitable habitat plaques ; ; Step S52, according to the index of suitability for survival And ecological area Calculating the ecological diversity influence coefficient of human activities on target areas ; ; S is the ecological total area of the target area, and P is the number of suitable habitat patches; step S53, setting a threshold value of the ecological diversity influence coefficient If (if) And if the influence of the human activities on the ecological diversity of the target area is smaller, judging that the influence of the human activities on the ecological diversity of the target area is larger.
2. A method for assessing the effect of human activity on the ecological diversity of forestry as recited in claim 1, wherein said step S1 comprises: Step S11, collecting a historical remote sensing image sequence of a target area, performing radiation calibration, atmosphere correction and shadow masking on the historical remote sensing image, and synthesizing the remote sensing image sequence within one month by taking the month as a unit to obtain a monthly synthesized remote sensing image sequence; Step S12, aligning the month synthesized remote sensing images in the month synthesized remote sensing image sequence, and fitting the reflectivity of pixels in continuous month synthesized remote sensing images into a continuous harmonic model; ; Wherein, the The reflectivity fitted for the month time t, For trend terms and trend coefficients, k is the harmonic order number, Respectively sine harmonic coefficients and cosine harmonic coefficients, wherein T is a year period, and n is a harmonic order number.

Description

Method for evaluating influence of human activities on ecological diversity of forestry Technical Field The invention relates to the field of forestry management, in particular to a method for evaluating influence of human activities on ecological diversity of forestry. Background Forestry ecosystems are important carriers of biodiversity. Changes to forest structures from human activities (e.g., praise, meta-cut, road construction, travel development, etc.) can directly or indirectly affect species habitat quality and connectivity, leading to loss of biodiversity. The current influence evaluation method is mostly dependent on field investigation with long period, high cost and limited coverage range, and is difficult to realize dynamic monitoring with large range and high frequency. In the prior art, the remote sensing data is used for biological diversity assessment, and single index (such as NDVI) or simple habitat classification is mostly adopted, so that the following defects mainly exist: 1. the time sequence characteristics of the remote sensing data cannot be fully mined, and the capturing of the dynamic process of the forest structure change is insufficient; 2. The habitat quality assessment is too static, the subjectivity of the parameters is strong, and the consideration of the survival adaptability of the species is lacking. Disclosure of Invention Aiming at the defects of the prior art, the invention 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. In order to achieve the aim of the invention, the invention adopts the following technical scheme: there is provided a method of assessing the effect of human activity on ecological diversity in forestry, comprising: Step S1, acquiring a historical remote sensing image sequence of a target area, preprocessing and merging to obtain a monthly synthesized remote sensing image sequence, and fitting the reflectivity of pixels in continuous monthly synthesized remote sensing images into a continuous harmonic model; S2, calculating reflectivity of pixel fitting by using a harmonic model, screening out a mutation pixel, obtaining a mutation month synthesized remote sensing image, and further identifying a mutation point of disturbance of human activity on a target area; S3, calculating a normalized disturbance index of the pixel by using the average reflectivity of the pixel in the month synthesized remote sensing image of the year in which the mutation point is located, and further calculating the disturbance intensity of disturbance of human activity to the target area to obtain a habitat suitability index of the pixel; S4, merging month synthesized remote sensing images of the year in which the mutation point is located, calculating a habitat suitability index of the pixels, screening suitable habitat pixels, merging the suitable habitat pixels into suitable habitat plaques, and calculating the maximum activity radius and ecological area of organisms in the suitable habitat plaques; and S5, calculating the survival suitability index of the animal by using the maximum activity radius, calculating the ecological diversity influence coefficient by combining the ecological area of the suitable habitat plaque, and evaluating the ecological diversity influence condition of human activity on the target area. Further, step S1 includes: Step S11, collecting a historical remote sensing image sequence of a target area, performing radiation calibration, atmosphere correction and shadow masking on the historical remote sensing image, and synthesizing the remote sensing image sequence within one month by taking the month as a unit to obtain a monthly synthesized remote sensing image sequence; Step S12, aligning the month synthesized remote sensing images in the month synthesized remote sensing image sequence, and fitting the reflectivity of pixels in continuous month synthesized remote sensing images into a continuous harmonic model; ; Wherein, the The reflectivity fitted for the month time t,For trend terms and trend coefficients, k is the harmonic order number,Respectively sine harmonic coefficients and cosine harmonic coefficients, wherein T is a year period, and n is a harmonic order number. Further, step S2 includes: Step S21, calculating the reflectivity of the moon time t fitting by using a harmonic model According to the actual reflectivity of the pixel at the month time tCalculating reflectivity residual; Step S22, setting a reflectivity residual error thresholdIf (if)Judging that the pixel has mutation, otherwise, judging that the pixel has no mutation; calculating the pixel mutation rate according to the number m of pixels generating mutation in the month synthesized remote sensing image corresponding to the month time t M is the number of pixels in the monthly synth