CN116309670A - Bush coverage measuring method based on unmanned aerial vehicle - Google Patents
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Abstract
The invention provides a shrub coverage measurement method based on an unmanned aerial vehicle, and belongs to the field of vegetation remote sensing measurement. The measuring method comprises the steps of obtaining multispectral data of a shrub grassland through unmanned aerial vehicle remote sensing, obtaining DOM, NDVI and DSM of a shrub distribution area through preprocessing the data, dividing the NDVI data based on an LSMS algorithm, extracting an NDVI mean value and a relative height difference in the range of each divided object, identifying a shrub coverage divided object through setting a first NDVI threshold value and a relative height difference threshold value, extracting an NDVI value of pixels in the shrub coverage divided object based on the DOM, and distinguishing vegetation coverage pixels and non-vegetation coverage pixels through setting a second NDVI threshold value; and finally, calculating the proportion of the total pixel number in the vegetation coverage pixel number occupying area to obtain the bush coverage. The invention has the advantages of no need of manual participation, accurate calculation result and high measurement efficiency, and realizes the real-time monitoring of the shrub grassland.
Description
Technical Field
The invention belongs to the field of vegetation remote sensing measurement, and particularly relates to a shrub coverage measurement method based on an unmanned aerial vehicle.
Background
In arid/semiarid grassland areas, which account for about 41% of the total land area, approximately 24 hundred million people live, mostly in animal husbandry, wherein 10% -20% of arid/semiarid grasslands are undergoing shrubs. Shrubrication, which refers to the phenomenon of increased density, coverage and biomass of native woody plants, is one of the main manifestations of grassland vegetation change in arid/semiarid regions for more than a century. The shrub coverage (Fraction of Shrubs Coverage, FSC) refers to the proportion of the vertical projected area of the shrub to the total area of the grass, which directly reflects the severity of the grass shrub. The accurate measurement of FSC is of great importance for grasping the spatial distribution of the shrub grasslands and for understanding the shrub progress of the grasslands in arid/semiarid regions.
In the prior art, unmanned aerial vehicles are generally adopted for identifying bush vegetation. The unmanned aerial vehicle can rapidly acquire high-resolution and high-precision multispectral data, accurately identify the bush vegetation, and can acquire an orthographic image, and accords with the concept of vertical projection in the definition of the bush coverage, so that the measurement of the bush coverage based on the unmanned aerial vehicle multispectral data is an accurate and reliable method. For example, chinese patent publication No. CN115187875a discloses an unmanned aerial vehicle remote sensing monitoring method for a bush and forest biomass carbon library, by acquiring unmanned aerial vehicle remote sensing images of an area to be monitored, splicing to form real color remote sensing images and digital elevation data of the area, extracting bush and forest plaques according to the real color remote sensing images, and acquiring bush and forest coverage; according to the bushes and the forest gap, combining the digital elevation data to obtain bushes and forest heights; and (5) solving a biomass carbon library according to the acquired coverage and height data of the bushes and the forest lands. However, the method still needs to manually outline the plaque of each plant of shrubs and trees in the remote sensing image, the advantages of the remote sensing image can not be fully exerted, and the monitoring efficiency and accuracy are low.
Disclosure of Invention
In view of the above-mentioned defects or shortcomings in the prior art, the invention aims to provide a shrub coverage measurement method based on an unmanned aerial vehicle, which is based on remote sensing data and image data acquired by the unmanned aerial vehicle and adopts an LSMS algorithm to perform data processing, so as to calculate the shrub coverage, improve the measurement efficiency and realize real-time monitoring of the shrubs on the premise of ensuring accurate shrub coverage calculation.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
a bush coverage measuring method based on unmanned aerial vehicle comprises the following steps:
step S1, defining a shrub grassland area and a route, and determining a growth time interval of the area; collecting a plurality of groups of multispectral data of a defined region in a time continuous mode through the unmanned aerial vehicle in a growth time interval;
step S2, photo automatic matching, three-dimensional reconstruction, dense point cloud generation and orthographic product manufacturing are carried out on the multispectral data, an orthographic image map DOM and a normalized difference vegetation index NDVI image of a shrub grassland area are obtained, and a digital surface model DSM is constructed;
s3, performing image segmentation on the NDVI images to obtain vector boundaries of segmented objects, and extracting an NDVI mean value and a relative height difference of each segmented object based on the vector boundaries;
step S4, setting a first NDVI threshold to remove the segmented objects without vegetation coverage, setting a relative height difference threshold to remove the segmented objects covered by the low grass plants, and only keeping the segmented objects covered by the bushes;
step S5, setting a second NDVI threshold value, and distinguishing vegetation coverage pixels and non-vegetation coverage pixels in the bush coverage segmentation objects;
and S6, counting the number of vegetation coverage pixels in all the shrub coverage segmentation objects, and calculating the proportion of the number of vegetation coverage pixels to the total number of pixels in the unmanned aerial vehicle flight area, wherein the proportion is used as the shrub coverage of the area at the time.
As a preferred embodiment of the invention, the demarcation of the bushed grass area and the course, the selection of the open area away from roads, towns and obvious obstacles; heading and side overlap are set to be 80% and 70% respectively, and flying height is 50 m.
As a preferred embodiment of the invention, the unmanned aerial vehicle adopts a Dajiang genius 4 multi-spectral version unmanned aerial vehicle.
As a preferred embodiment of the present invention, the NDVI has a value in the range of [ -1,1]; the digital orthophoto map DOM is an RGB true color image.
As a preferred embodiment of the present invention, the automatic matching of the photos, three-dimensional reconstruction, dense point cloud generation and orthographic product production are completed by using the DJI Terra software.
As a preferred embodiment of the present invention, the image segmentation of the NDVI image adopts LSMS algorithm, which includes 3 parameters: a space radius defining a maximum Euclidean space distance between pixels in the same segmented object; the range radius is the maximum Euclidean distance of the spectrum characteristic value between pixels in the same segmentation object; the minimum division size, the minimum number of pixels that the division object contains.
As a preferred embodiment of the present invention, step S4 specifically includes:
step S41, setting a first NDVI threshold, taking a segmented object higher than the first NDVI threshold as a vegetation coverage area, taking a segmented object lower than the first NDVI threshold as a non-vegetation coverage area, distinguishing whether the segmented object has vegetation coverage, and reserving the segmented object with the NDVI average value higher than the first NDVI threshold;
step S42, setting a relative height difference threshold for the segmented objects with vegetation coverage reserved, taking segmented objects higher than the relative height difference threshold as shrub coverage segmented objects, taking segmented objects lower than the relative height difference threshold as sparse grass vegetation coverage, and reserving shrub coverage segmented objects finally.
As a preferred embodiment of the present invention, the first NDVI threshold is 0.1.
As a preferred embodiment of the invention, the relative height difference threshold is 0.15m.
As a preferred embodiment of the invention, the bush coverage FSC is calculated as follows:
wherein ,P shrub in order to have the number of vegetation covering pixels,
is the total number of pixels in the flight area of the unmanned aerial vehicle.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the method for measuring the shrub coverage based on the unmanned aerial vehicle, multispectral data of a shrub grassland is obtained through remote sensing of the unmanned aerial vehicle, DOM, NDVI and DSM of a shrub distribution area are obtained through preprocessing of the data, then NDVI data are segmented based on an LSMS algorithm, an NDVI mean value and a relative height difference in the range of each segmented object are extracted, a shrub coverage segmented object is obtained through setting a first NDVI threshold value and a relative height difference threshold value, then the NDVI value of pixels in the shrub coverage segmented object is extracted based on the DOM, and vegetation coverage pixels and non-vegetation coverage pixels are distinguished through setting of a second NDVI threshold value; and finally, calculating the proportion of the vegetation coverage pixel number to the total pixel number in the flight area of the unmanned aerial vehicle, thereby obtaining the shrub coverage. The measuring method disclosed by the invention does not need to be manually participated, the coverage of the shrubs to the grasslands is measured based on the image segmentation algorithm, the calculation result is accurate, the measuring efficiency is high, and the real-time monitoring of the shrubs to the grasslands can be realized.
Of course, it is not necessary for any one product or method of practicing the invention to achieve all of the advantages set forth above at the same time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow chart of a method for measuring bush coverage based on an unmanned aerial vehicle according to an embodiment of the invention;
FIG. 2 is a regional diagram of an embodiment of the invention applied to the inner Mongolian tin Lin Guole allied white flag (army Darcy southern area);
FIG. 3 is a partial digital orthophoto map DOM acquired when an embodiment of the present invention is applied to the region of FIG. 2;
FIG. 4 is a view of a local NDVI image obtained when an embodiment of the present invention is applied to the region shown in FIG. 2;
FIG. 5 is a partial DSM view taken when an embodiment of the present invention is applied to the region of FIG. 2;
FIG. 6 is a map of localized vegetation cover pixels obtained when an embodiment of the invention is applied to the area of FIG. 2.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. It should be noted that, in the case of no conflict, the embodiments of the present invention and features in the embodiments may also be combined with each other.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. In the description of the present invention, the terms "first," "second," "third," "fourth," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
The embodiment of the invention provides a shrub coverage measurement method based on an unmanned aerial vehicle, which comprises the steps of obtaining multispectral data of shrub grasslands through remote sensing of the unmanned aerial vehicle, obtaining a high-spatial-resolution unmanned aerial vehicle digital orthophoto map (Digital Orthophoto Map, DOM) of a shrub distribution area through preprocessing of the data, normalizing difference vegetation index (NormalizedDifference Vegetation Index, NDVI) images and a digital surface model (Digital Surface Model, DSM), segmenting the NDVI images based on an LSMS algorithm, extracting an NDVI mean value and a relative height difference in the range of each segmented object, obtaining segmented objects covered by the shrubs through threshold setting, setting a second NDVI threshold according to the DOM, and judging whether pixels in the segmented objects covered by the shrubs have vegetation coverage. And finally, calculating the proportion of the vegetation coverage pixel number to the total pixel number in the flight area of the unmanned aerial vehicle, thereby obtaining the shrub coverage. The calculation process does not need to be manually participated, the coverage of the shrubs to the grasslands is measured based on an image segmentation algorithm, the calculation result is accurate, the measurement efficiency is high, and the real-time monitoring of the shrubs to the grasslands can be realized. The DOM, the NDVI image and the DSM are grid images, wherein each pel value of the DSM is an elevation value of the pel position and is used for describing the height change of an area or a measured object.
Referring to fig. 1, the method for measuring the bush coverage based on the unmanned aerial vehicle according to the embodiment of the invention comprises the following steps:
step S1, defining a shrub grassland area and a route, and determining a growth time interval of the area; and collecting a plurality of groups of multispectral data of the delimited area in a time continuous mode through the unmanned aerial vehicle in the growth time interval.
In this step, the shrub grassland area and the course are delimited, and an open area far from the road, town, and obvious obstacles (high-voltage electric wires, electric towers, etc.) is selected. Preferably, a rectangular region is selected, and the course direction is parallel to the region boundary to improve data acquisition efficiency. Preferably, when the route is defined, the heading and the side overlapping degree are respectively set to be 80 percent and 70 percent and the flying height is 50 meters according to the low-altitude aerial photography data acquisition standard of the national standard (GB/T39612-2020), so that the high-precision and high-spatial resolution image data can be acquired.
Preferably, the unmanned aerial vehicle adopts a Dajiang eidolon 4 multispectral version unmanned aerial vehicle.
And S2, performing photo automatic matching, three-dimensional reconstruction, dense point cloud generation and orthographic product manufacturing on the multispectral data, acquiring DOM and NDVI images of the bush distribution area, and constructing a digital surface model DSM.
In the step, the NDVI value range is [ -1,1], and the higher the vegetation coverage, the larger the NDVI value. Based on NDVI, it is possible to effectively distinguish whether there is vegetation coverage. The digital orthophoto map DOM is an RGB true color image, and the high-resolution unmanned plane data can clearly depict the spatial distribution condition of the bushes. Each pixel value of the DSM represents an absolute elevation of the location, which can represent a vertical structural distribution of the bushy grassland.
Preferably, this step is accomplished using DJI Terra software.
And S3, performing image segmentation on the NDVI images to obtain vector boundaries of the segmented objects, and extracting an NDVI mean value and a relative height difference of each segmented object based on the vector boundaries.
In this step, the high-resolution NDVI image is rapidly and accurately segmented based on a Large Scale-Mean-Shift (LSMS) algorithm. The algorithm combines the thought of block processing and the Mean shift Mean-shift clustering algorithm, and effectively improves the image segmentation efficiency. LSMS includes 3 main parameters: a space radius defining a maximum Euclidean space distance between pixels in the same segmented object; the range radius is the maximum Euclidean distance of the spectrum characteristic value between pixels in the same segmentation object; the minimum division size, the minimum number of pixels that the division object contains. And adjusting the three parameters to obtain an optimal segmentation result. Based on the vector boundaries of the segmented objects, an area statistics tool is adopted to calculate the NDVI mean value and the relative height difference of all pixels in each segmented object.
And S4, setting a first NDVI threshold to remove the segmented objects without vegetation coverage, setting a relative height difference threshold to remove the segmented objects covered by the short grass plants, and only keeping the segmented objects covered by the bushes.
In this step, the segmented image is screened based on the extracted NDVI mean value and relative height difference in each segmented object range. In the segmentation result, the segmentation objects with vegetation coverage and the segmentation objects without vegetation coverage exist; among vegetation covered segmented subjects, there are both shrub covered subjects and non-shrub covered subjects, so that the subject selection is first performed by the first NDVI threshold and then by the relative height difference threshold. The method specifically comprises the following steps:
in step S41, a first NDVI threshold is set, so that it is generally considered that there is no vegetation coverage when the first NDVI threshold is lower than 0.1, and therefore, the first NDVI threshold is set to 0.1, and the segmented object above the first NDVI threshold is used as the vegetation coverage area, and the segmented object below the first NDVI threshold is used as the vegetation-free coverage area, so as to distinguish whether the segmented object has vegetation coverage, and the segmented object with the NDVI average value higher than the first NDVI threshold is reserved.
Step S42, setting the relative height difference threshold value to be 0.15m for the reserved vegetation covered segmented objects, taking segmented objects higher than the relative height difference threshold value as shrub covered segmented objects, taking segmented objects lower than the relative height difference threshold value as sparse grass vegetation covered areas, and finally reserving shrub covered segmented objects.
And S5, setting a second NDVI threshold value, and distinguishing vegetation coverage pixels and non-vegetation coverage pixels in the bush coverage segmentation objects.
In this step, the shrub coverage segmentation object includes a plurality of pixels, the coverage condition of each pixel is different, some pixels belong to a vegetation coverage area, other pixels belong to a gap area without vegetation coverage, and whether the pixels are covered by vegetation in the scope of the shrub coverage segmentation object is judged by setting a second NDVI threshold. Specifically, the step of setting the second NDVI threshold is as follows:
firstly, selecting a bush coverage segmentation object, interpreting each pixel in the object range on a DOM, dividing all pixels into two types of vegetation coverage and non-vegetation coverage, counting NDVI distribution histograms of the two types of pixels, and determining an NDVI value capable of distinguishing the two types of pixels as a second NDVI threshold. And taking the pixels higher than the second NDVI threshold value as vegetation coverage pixels, and taking the pixels lower than the second NDVI threshold value as non-vegetation coverage pixels. And applying a second NDVI threshold to all the bush coverage segmentation objects, and judging whether all pixels are covered by vegetation.
And S6, counting the number of vegetation coverage pixels in all the shrub coverage segmentation images, and calculating the proportion of the number of vegetation coverage pixels to the total number of pixels in the unmanned aerial vehicle flight area as the shrub coverage of the area at the time.
In this step, the calculation formula of the bush coverage FSC is as follows:
wherein ,P shrub in order to have the number of vegetation covering pixels,
is the total number of pixels in the flight area of the unmanned aerial vehicle.
The total number of pixels in the unmanned aerial vehicle flight area is stored in attribute information of the unmanned aerial vehicle image, and is the number of rows and columns of the grid image, for example, in a 30m×30m area, the number of rows is 1500, the number of columns is 1500, and the total number of pixels is 1500×1500= 2250000.
The shrub coverage measuring method based on the unmanned aerial vehicle is applied to the measurement of shrub coverage of the southern area of the Mongolian tin Lin Guole allied white flag-inlaid and army dakesha.
As shown in fig. 2, the area belongs to temperate continental arid climate, the main vegetation types are sandy grasslands, typical grasslands, and the grassland shrubs are serious. The bushes distributed therein include Caragana parvifolia, caragana petunia, artemisia sphaerocephala, and Sasa veitchii. The caragana microphylla is of the genus caragana of the family Leguminosae, has the growth characteristics of light preference, high temperature resistance and cold resistance, and has strong adaptability to soil. It is compatible with the habitat conditions of arid/semiarid regions and therefore becomes the dominant shrub species for that region.
Taking the white flag-inlaid small leaf caragana shrub grassland as an example, the shrub coverage high-precision measurement work based on unmanned aerial vehicles is carried out. Multispectral data were acquired 7 months in 2022 using a calix fairy 4 multispectral version unmanned aerial vehicle with a spatial resolution of 0.02m and data in the 30m x 30m range were cut out for example display.
And executing steps S1 to S6 on multispectral data acquired by the unmanned aerial vehicle, wherein step S2 adopts DJI Terra software to perform data preprocessing. Fig. 3 shows the DOM map obtained after photo-automatic matching, three-dimensional reconstruction, dense point cloud generation and orthographic production of multispectral data, fig. 4 shows the NDVI data obtained, and fig. 5 shows the DSM results obtained. And (3) obtaining all pixels covered by vegetation in the range of all the bush coverage segmentation objects through the processing of the steps S3 to S5, as shown in fig. 6.
According to the vegetation coverage pixels illustrated in fig. 6, step S6 is performed, the number of pixels is counted, and the result of calculating the brush coverage is as follows:
from this, it can be derived that in the small leaf caragana shrubricated grassland area (example area) in the south of the armyworm, shrubricated coverage is 13.94%, a relatively serious shrubricated level has been reached, and corresponding ecological improvement is required, and real-time continuous monitoring is performed.
According to the technical scheme, according to the method for measuring the bush coverage based on the unmanned aerial vehicle, which is provided by the embodiment of the invention, multispectral data of a bush-made grassland is obtained through remote sensing of the unmanned aerial vehicle, DOM, NDVI and DSM of a bush distribution area are obtained through preprocessing of the data, then the NDVI data is segmented based on an LSMS algorithm, an NDVI mean value and a relative height difference in the range of each segmented object are extracted, segmented objects of bush coverage are obtained through threshold setting, then the NDVI value of pixels in the segmented objects of bush coverage is extracted based on the DOM, and vegetation coverage pixels and vegetation-free coverage pixels are distinguished through setting of a second NDVI threshold; and finally, calculating the proportion of the vegetation coverage pixel number to the total pixel number in the flight area of the unmanned aerial vehicle, thereby obtaining the shrub coverage. The measuring method disclosed by the invention does not need to be manually participated, the coverage of the shrubs to the grasslands is measured based on the image segmentation algorithm, the calculation result is accurate, the measuring efficiency is high, and the real-time monitoring of the shrubs to the grasslands can be realized.
The above description is only of the preferred embodiments of the present invention and the description of the technical principles applied is not intended to limit the scope of the invention as claimed, but merely represents the preferred embodiments of the present invention. It will be appreciated by persons skilled in the art that the scope of the invention referred to in the present invention is not limited to the specific combinations of the technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the inventive concept. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Claims (9)
1. The shrub coverage measuring method based on the unmanned aerial vehicle is characterized by comprising the following steps of:
step S1, defining a shrub grassland area and a route, and determining a growth time interval of the area; collecting a plurality of groups of multispectral data of a defined region in a time continuous mode through the unmanned aerial vehicle in a growth time interval;
step S2, photo automatic matching, three-dimensional reconstruction, dense point cloud generation and orthographic product manufacturing are carried out on the multispectral data, an orthographic image map DOM and a normalized difference vegetation index NDVI image of a shrub grassland area are obtained, and a digital surface model DSM is constructed;
s3, performing image segmentation on the NDVI images to obtain vector boundaries of segmented objects, and extracting an NDVI mean value and a relative height difference of each segmented object based on the vector boundaries;
step S4, setting a first NDVI threshold to remove the segmented objects without vegetation coverage, setting a relative height difference threshold to remove the segmented objects covered by the low grass plants, and only keeping the segmented objects covered by the bushes;
step S5, setting a second NDVI threshold value, and distinguishing vegetation coverage pixels and non-vegetation coverage pixels in the bush coverage segmentation objects;
and S6, counting the number of vegetation coverage pixels in all the shrub coverage segmentation objects, and calculating the proportion of the number of vegetation coverage pixels to the total number of pixels in the unmanned aerial vehicle flight area, wherein the proportion is used as the shrub coverage of the area at the time.
2. The unmanned aerial vehicle-based bush coverage measurement method of claim 1, wherein the demarcating bush grassland areas and routes selects open areas far from roads, towns, and obvious obstacles; heading and side overlap are set to be 80% and 70% respectively, and flying height is 50 m.
3. The unmanned aerial vehicle-based bush coverage measurement method according to claim 1, wherein the NDVI range of values is [ -1,1]; the digital orthophoto map DOM is an RGB true color image.
4. A method of measuring bush coverage based on unmanned aerial vehicle as claimed in claim 3, wherein the automatic matching of photographs, three-dimensional reconstruction, dense point cloud generation and orthographic production are performed using DJI Terra software.
5. The method for measuring the bush coverage based on the unmanned aerial vehicle according to claim 1, wherein the image segmentation is performed on the NDVI image, and an LSMS algorithm is adopted, and the method comprises 3 parameters: a space radius defining a maximum Euclidean space distance between pixels in the same segmented object; the range radius is the maximum Euclidean distance of the spectrum characteristic value between pixels in the same segmentation object; the minimum division size, the minimum number of pixels that the division object contains.
6. The method for measuring bush coverage based on unmanned aerial vehicle according to claim 1, wherein step S4 specifically comprises:
step S41, setting a first NDVI threshold, taking a segmented object higher than the first NDVI threshold as a vegetation coverage area, taking a segmented object lower than the first NDVI threshold as a non-vegetation coverage area, distinguishing whether the segmented object has vegetation coverage, and reserving the segmented object with the NDVI average value higher than the first NDVI threshold;
step S42, setting a relative height difference threshold for the segmented objects with vegetation coverage reserved, taking segmented objects higher than the relative height difference threshold as shrub coverage segmented objects, taking segmented objects lower than the relative height difference threshold as sparse grass vegetation coverage, and reserving shrub coverage segmented objects finally.
7. The unmanned aerial vehicle-based brush coverage measurement method of claim 6, wherein the first NDVI threshold is 0.1.
8. The unmanned aerial vehicle-based brush coverage measurement method of claim 6, wherein the relative altitude difference threshold is 0.15m.
9. The unmanned aerial vehicle-based bush coverage measurement method according to claim 1, wherein the bush coverage FSC is calculated as follows:
wherein ,P shrub in order to have the number of vegetation covering pixels,
is the total number of pixels in the flight area of the unmanned aerial vehicle.
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Cited By (2)
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| CN116778303A (en) * | 2023-08-25 | 2023-09-19 | 山东省国土测绘院 | Vegetation coverage measuring method based on unmanned aerial vehicle remote sensing |
| CN117372503A (en) * | 2023-12-08 | 2024-01-09 | 水利部交通运输部国家能源局南京水利科学研究院 | River and lake shore zone vegetation classification and coverage calculation method and system |
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