CN-121211353-B - Caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring

CN121211353BCN 121211353 BCN121211353 BCN 121211353BCN-121211353-B

Abstract

The invention relates to the technical field of ecological restoration, in particular to a caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring. The method comprises the steps of obtaining multispectral remote sensing images of target grasslands and ground survey data, extracting vegetation coverage, soil wind erosion strength and surface roughness, evaluating degradation degree of the target grasslands according to the vegetation coverage, the soil wind erosion strength and the surface roughness, determining a grassland degradation partition map, determining caragana microphylla paving parameters based on the grassland degradation partition map, leveling and cleaning the target grasslands, paving the caragana microphylla on the preprocessed target grassland surface according to the caragana microphylla paving parameters, and collecting monitoring images of paving areas in the target grasslands according to preset periods by using an unmanned aerial vehicle. The method can accurately identify the degradation level and the space difference of the grasslands, thereby providing scientific and reasonable parameter setting for caragana microphylla laying, remarkably improving the pertinence and the effectiveness of the repairing measures and optimizing the resource utilization efficiency.

Inventors

ZHAO TIANQI
Lu Naijing
GUO JIANYING
GUAN JIAN
WANG YONG
Suo Rongzhen
ZHENG YING
ZHANG TIEGANG
LIU JING
CHEN YUXIN

Assignees

水利部牧区水利科学研究所

Dates

Publication Date: 20260508
Application Date: 20251027

Claims (8)

1. The caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring is characterized by comprising the following steps of: s1, acquiring multispectral remote sensing images of target grasslands and ground investigation data, and extracting vegetation coverage, soil wind erosion strength and surface roughness; Step S2, evaluating the degradation degree of the target grassland according to the vegetation coverage, the soil wind erosion strength and the surface roughness, and determining a grassland degradation partition map; Step S3, determining caragana microphylla paving parameters based on the grassland degradation partition map, leveling and cleaning the target grassland, wherein the caragana microphylla paving parameters are determined based on the grassland degradation partition map, and the method comprises the following steps: Setting a caragana microphylla density parameter according to the degradation level in the grassland degradation partition map; Determining the coverage type of the earth surface sundries of the target grassland based on the multispectral remote sensing image, wherein the coverage type of the earth surface sundries comprises shrub residues, construction waste and plastic garbage; calculating coverage area occupation ratios of different types of sundries based on multispectral remote sensing images; When the coverage area occupation ratio of the shrub residues is larger than a preset first occupation ratio threshold value, reducing the density parameter of the caragana microphylla according to a preset adjustment range; when the coverage area occupation ratio of the construction waste or the plastic waste is larger than a preset second occupation ratio threshold value, the density parameter of the caragana microphylla is improved according to a preset adjustment range; wherein the preset first duty cycle threshold is greater than the preset second duty cycle threshold; step S4, paving the caragana microphylla on the pretreated target grassland surface according to the caragana microphylla paving parameters, and acquiring monitoring images of a paving area in the target grassland according to a preset period by using an unmanned aerial vehicle, wherein the monitoring images comprise monitoring multispectral images and oblique photographic images; Step S5, extracting vegetation indexes and wind erosion moduli of the paved area based on the monitoring images, evaluating the repair effect, generating a repair effect evaluation report, and adjusting the caragana microphylla paving parameters or paved areas according to the repair effect evaluation report, wherein the vegetation indexes and the wind erosion moduli of the paved areas are extracted based on the monitoring images, and evaluating the repair effect comprises the following steps: performing radiation correction and geometric correction on the monitored multispectral image, outputting a corrected multispectral image, performing orthographic projection and mosaic, and outputting an orthographic image; determining the vegetation coverage after restoration according to the orthophoto map; constructing a three-dimensional model of a paved area based on the oblique photographic image, and determining the volume and the density of the cavitation pits based on the three-dimensional model of the paved area; Calculating the wind erosion modulus after repair according to the wind erosion pit volume and the wind erosion pit density; Acquiring an initial vegetation index and an initial wind erosion modulus of a target grassland; Determining vegetation coverage change rate according to the restored vegetation coverage and the initial vegetation index, and determining wind erosion module change rate according to the restored wind erosion module and the initial wind erosion module; And evaluating the repair effect according to the vegetation coverage change rate and the wind erosion modulus change rate, and evaluating and reporting the repair effect.
2. The caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring as set forth in claim 1, wherein the extracting vegetation coverage in step S1 includes: Extracting reflectance values of a red light wave band and a near infrared wave band from the multispectral remote sensing image; vegetation coverage is calculated based on reflectance values in the red and near infrared bands.
3. The caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring according to claim 1, wherein the extracting of the soil wind erosion intensity in the step S1 comprises the following steps: Extracting the average reflectivity of the visible light wave band and the average brightness temperature of the thermal infrared wave band of the multispectral remote sensing image; and calculating a wind erosion sensitivity index based on the average reflectivity and the average brightness temperature, wherein the wind erosion sensitivity index has a calculation formula as follows: ; Wherein, the In order to be an index of susceptibility to wind erosion, For the average reflectivity in the visible band, For the average brightness temperature in the thermal infrared band, Is the included angle between the local annual average wind direction and the ground surface slope direction; Extracting the density of the cavitation pits and the annual average wind speed from ground investigation data; and calculating the soil wind erosion intensity based on the wind erosion sensitivity index and the wind erosion pit density and the annual average wind speed.
4. The caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring according to claim 1, wherein the step S1 of extracting the surface roughness comprises the following steps: S131, in the multispectral remote sensing image, a sliding window of a pixel in a preset range is adopted to calculate a gray level co-occurrence matrix, and contrast parameters and homogeneity parameters are extracted; step S132, normalizing the contrast parameter and the homogeneity parameter to generate a standard contrast parameter and a standard homogeneity parameter; S133, taking the standard comparability parameter and the standard homogeneity parameter as input, analyzing and reducing the dimension through the main component, and extracting a first main component as a texture roughness index; And S134, correcting the texture roughness index through a preset linear regression model in combination with the measured value of the surface protrusion height in the ground investigation data to determine the surface roughness.
5. The caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring according to claim 1, wherein the determining of the grassland degradation partition map in step S2 comprises: Inputting vegetation coverage, soil wind erosion strength and surface roughness into a preset classification model, and outputting a preliminary classification result of grassland degradation level; The pixels in the preliminary classification result grid are taken as basic units, and continuous level degradation areas are identified according to a preset clustering density threshold value and a preset clustering radius; merging degradation levels of adjacent areas, and dividing the adjacent areas into the same partition when the degradation level difference of the adjacent areas does not exceed 1 level; distributing a unique partition identifier for each merged partition, and recording the geographic boundary coordinates of the partition; carrying out spatial registration on the partition identifier and the spatial resolution of the multispectral remote sensing image to generate a partition boundary layer; And correcting the partition boundary in the partition boundary layer by combining with the sampling point distribution in the ground survey data, and outputting a grassland degradation partition map, wherein the grassland degradation partition map comprises degradation grades, areas, geographic coordinates and partition identifiers of each partition.
6. The caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring according to claim 5, wherein in step S3, the target grassland is cleaned flatly, and further comprising determination of a cleaning mode, wherein the determination of the cleaning mode comprises: Determining surface debris distribution data based on the multispectral remote sensing image, and determining debris coverage area occupation ratio according to the multispectral remote sensing image and the surface debris distribution data; when the coverage area occupation ratio of sundries is larger than a preset area occupation ratio threshold value, cleaning the surface layer of the target grassland by adopting a rotary cultivator; And when the coverage area occupation ratio of the sundries is smaller than or equal to a preset area occupation ratio threshold value, flattening the surface layer of the target grassland by adopting a laser land leveler.
7. The caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring according to claim 6, wherein the restoration effect is estimated according to the vegetation coverage change rate and the wind erosion modulus change rate, and the method comprises the following steps: When the vegetation coverage change rate is greater than a preset first change rate threshold value and the wind erosion modulus change rate is greater than a preset second change rate threshold value, judging that the restoration effect accords with the expectation, and no further adjustment of restoration measures is needed; when the vegetation coverage change rate is smaller than or equal to a preset first change rate threshold value or the wind erosion modulus change rate is smaller than or equal to a preset second change rate threshold value, the restoration effect is judged to be not in line with the expectation, and further adjustment of restoration measures is needed.
8. The caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring according to claim 1, wherein in step S4, monitoring images of a laying area are collected by using an unmanned aerial vehicle according to a preset period, and the method comprises the following steps: Configuring an unmanned aerial vehicle carrying a multispectral camera and an oblique photography camera, wherein the multispectral camera covers a visible light wave band and a near infrared wave band, and the oblique photography camera collects high-resolution images in K directions, wherein K is a positive integer; setting unmanned aerial vehicle flight parameters, wherein the unmanned aerial vehicle flight parameters comprise flight altitude, flight speed and image overlapping degree; and planning a flight route according to the flight parameters and a preset period of the unmanned aerial vehicle, generating a flight path covering a paved area, collecting a monitoring multispectral image and an oblique photographic image according to the flight path in each flight, and recording the monitoring multispectral image and the oblique photographic image as monitoring images.

Description

Caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring Technical Field The invention relates to the technical field of ecological restoration, in particular to a caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring. Background In the field of grassland ecological restoration, the accuracy of the existing restoration scheme is obviously insufficient. When the traditional method is used for determining the repair area and the paving parameters, the traditional method mainly depends on manual field investigation and experience judgment, and lacks of fine and quantitative evaluation on multidimensional indexes such as vegetation coverage, soil wind erosion strength and the like. The rough decision mode causes that the restoration measures are difficult to match with the actual degradation grade and the space difference of the grasslands, thereby restricting the improvement of the restoration effect and the resource input efficiency. In addition, there is also a lack of dynamic monitoring and regulation capabilities for repair processes. Traditional effect evaluation is dependent on periodic manual inspection or single remote sensing data source, the monitoring period is long, the cost is high, and continuous and high-frequency monitoring of a large-scale repair area is difficult to realize. The vegetation restoration dynamic and wind erosion control effects cannot be mastered timely and accurately, dynamic strategy adjustment is difficult to carry out according to restoration progress, and closed-loop optimization and self-adaptive management of an ecological restoration process are limited. Disclosure of Invention Based on the above, the present invention is necessary to provide a caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring, so as to solve at least one of the above technical problems. In order to achieve the purpose, the caragana microphylla laying ecological restoration method based on remote sensing and intelligent monitoring comprises the following steps: s1, acquiring multispectral remote sensing images of target grasslands and ground investigation data, and extracting vegetation coverage, soil wind erosion strength and surface roughness; Step S2, evaluating the degradation degree of the target grassland according to the vegetation coverage, the soil wind erosion strength and the surface roughness, and determining a grassland degradation partition map; Step S3, determining caragana microphylla laying parameters based on the grassland degradation partition map; step S4, paving the caragana microphylla on the pretreated target grassland surface according to the caragana microphylla paving parameters, and acquiring monitoring images of a paving area in the target grassland according to a preset period by using an unmanned aerial vehicle, wherein the monitoring images comprise monitoring multispectral images and oblique photographic images; And S5, extracting vegetation indexes and wind erosion moduli of the paved area based on the monitoring images, evaluating the repair effect, generating a repair effect evaluation report, and adjusting the caragana microphylla paved parameters or paved areas according to the repair effect evaluation report. The beneficial effects of the invention are as follows: On one hand, the high-precision quantitative evaluation of the grassland degradation degree is realized by fusing multispectral remote sensing and ground investigation data. The method can accurately identify the degradation level and the space difference of the grasslands, so that scientific and reasonable parameter setting is provided for caragana microphylla laying, the pertinence and the effectiveness of repairing measures are obviously improved, and the resource utilization efficiency is optimized. On the other hand, the unmanned aerial vehicle collects multispectral images and oblique photographic images of the paved area according to a preset period, so that dynamic monitoring of the repairing process is realized. The dynamic change of vegetation restoration and wind erosion control can be mastered in real time, the restoration strategy can be adjusted in time, the problem of insufficient dynamic monitoring in the traditional method is effectively solved, and closed-loop optimization and self-adaptive management of ecological restoration are realized. On the other hand, the vegetation index and the wind erosion modulus are extracted based on the monitoring images, the restoration effect is evaluated, an evaluation report is generated, and then the caragana microphylla paving parameters or paving areas are dynamically adjusted according to the evaluation result. The feedback adjustment mechanism can ensure that the restoration measures are always adapted to the actual restoration state of the grassland, and further improve the overall eff