CN108020211B - Method for estimating biomass of invasive plants through aerial photography by unmanned aerial vehicle - Google Patents
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Abstract
The invention discloses a method for estimating invasive plant biomass by unmanned aerial vehicle aerial photography, which comprises the steps of continuously shooting images of an area to be estimated by an unmanned aerial vehicle, and simultaneously carrying out field acquisition on invasive plant biomass samples of the area to be estimated and calculating biomass; processing the image of the area to be estimated to obtain a high-resolution ground ortho-image of the area to be estimated, and further establishing a height change model image of a ground plant community of the area to be estimated and a visible light vegetation index model image; classifying the ground ortho-images of the area to be estimated, and confirming the spatial distribution of the invasive plants; establishing a regression model of plant biomass, plant community height and visible vegetation index, and estimating invasive plant biomass with confirmed spatial distribution through the regression model; the method can quickly estimate the biomass and the spatial distribution of the plants in and out of the invader, greatly improves the working efficiency and quality, and is more accurate and lower in cost.
Description
Technical Field
The invention relates to an invasive plant biomass estimation technology in natural resource investigation, in particular to a method for estimating invasive plant biomass by unmanned aerial vehicle aerial photography.
Background
Biological invasion is considered to be the second leading cause of global biodiversity loss, and poses serious hazards to local ecological environment, economy, and human health. How to effectively prevent and control the invasive species is a major problem which is currently concerned. Population expansion of the invasive species at a new place can be expressed as exponential expansion, and according to the principle of 'early discovery and early eradication', early warning of the invasive species is a key step of prevention and control work. The concrete content comprises: the method is characterized in that potential invasive species or invasive species which are invasive but locally distributed are rapidly identified, and the possibility of damage, the range and the degree of the damage are evaluated by combining the ecological and biological characteristics and the environmental characteristics, so that feasible preventive and control measures are made. Therefore, there is an urgent need to develop a technology for rapidly identifying the population of the expanding foreign species and rapidly evaluating the distribution area, the degree of damage, the occurrence ratio, and the like.
At present, the traditional methods for monitoring the distribution pattern of invasive plants mainly comprise two methods: (1) a manual on-site sample monitoring method; (2) satellite image monitoring method. The two methods have advantages and disadvantages, the first method can clearly distinguish the invasive plants from other plants, the investigation result is accurate, but the method is time-consuming and labor-consuming and has high labor cost; the second method is simple and convenient to investigate, short in required time and low in labor cost, but has poor image resolution for monitoring the invasive plants and large error of investigation results.
The UAV aerial survey technology belongs to the low-altitude remote sensing technology, is not interfered by atmospheric factors in the process of obtaining images, and can obtain aerial photography data in different seasons. The method has the advantages of low use cost, simple operation, high image acquisition speed, high ground resolution, capability of acquiring the height of the surface plant community and the like which are incomparable with the traditional remote sensing technology. The method can obtain aerial images with abundant textural features and high spatial resolution. And obtaining the height of the surface plant community after software processing such as an image processing platform, a remote sensing image processing platform and the like is applied. In the visible light channel, the green vegetation has high reflectivity in the green light channel and low reflectivity in the red light channel and the blue light channel, and the difference between the vegetation and surrounding ground objects can be enhanced through the operation between the green light channel and the red light channel and the blue light channel, so that the visible vegetation index can be established based on the visible light channel. The technology has higher accuracy and feasibility for estimating the biomass of the invasive plants.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for estimating biomass of invasive plants by using unmanned aerial vehicle aerial photography; the method adopts an unmanned aerial vehicle as a remote sensing photography platform, continuously shoots images of an area to be estimated through the unmanned aerial vehicle, simultaneously collects biomass samples of invasive plants in the area to be estimated on site, calculates biomass of the invasive plants at sampling points, and measures GPS positions of the sampling points;
processing the image of the area to be estimated by using a digital photogrammetry technology to obtain a high-resolution ground orthoimage of the area to be estimated, and further establishing a model image of the height change of the plant community on the ground surface of the area to be estimated and a model image of the visible light vegetation index; classifying the ground ortho-images of the area to be estimated, confirming the spatial distribution of the invasive plants, and obtaining an invasive plant classification image model;
utilizing the image processing function of the geographic information system software to enable the ground plant community height change model image and the visible vegetation index model image to have the same resolution, corresponding the sampling point positions of the invasive plant biomass samples to the ground plant community height change model image and the visible vegetation index model image of the area to be estimated, and extracting the ground plant community falling height value and the visible vegetation index value of each sampling point in the model images; and establishing a regression model of the biomass of the invasive plant, the height of the plant community and the visible vegetation index according to the biomass of the invasive plant at the sampling point, the height value of the ground plant community and the visible vegetation index value, and estimating the biomass of the invasive plant with confirmed spatial distribution through the regression model.
The time for collecting the images of the selected invasive plants is the flowering phase, and the plants in the flowering phase have obvious characteristics in image classification and are easy to distinguish from other ground types on the same image.
The method for acquiring the high-resolution ground orthographic image comprises the following steps: the method comprises the steps of performing quality optimization and GPS track information importing processing on an unmanned aerial vehicle aerial image, splicing the image by utilizing a motion recovery structure algorithm and a multi-view three-dimensional reconstruction method to generate sparse point cloud data, inputting longitude and latitude coordinates and elevations of ground control points, introducing the control points, determining a coordinate system of an orthoimage to be generated, encrypting the sparse point cloud to form dense point cloud, generating a triangulation network model based on the dense point cloud data, and enabling all point cloud data on the image to be in the same plane by taking the triangulation network model as a reference to generate the ground orthoimage.
Because the ground orthoimage with high resolution is the basic data generated by the model image of the height change of the plant community on the earth and the model image of the index of the visible light vegetation, the generation of the ground orthoimage with high resolution is the crucial step of modeling analysis, firstly, the aerial image data is led into image processing software Lightroom to adjust the parameters of the image, improve the quality of the image data, then, the aerial GPS track information in a flight path track recorder carried by an unmanned aerial vehicle is extracted, the image after quality processing is led in, the GPS information of the image is matched at the same time correspondingly, corresponding GPS information is given to all the images, the processed image data is spliced by utilizing a self-contained motion recovery structure algorithm and a multi-view angle stereo reconstruction algorithm in software Agisostatic photo, sparse point cloud data is generated, the longitude and latitude coordinates and the elevation of a ground control point are input, introducing control points, determining a coordinate system of an orthoimage to be generated, enabling all generated point cloud data to be in the same coordinate system, then encrypting the sparse point cloud to form dense point cloud, then generating a triangulation network model based on the dense point cloud data, enabling all the point cloud data on the image to be in the same plane by taking the triangulation network model as reference, and finally generating a high-resolution ground orthoimage.
The method for acquiring the image of the model of the height change of the surface plant community of the area to be estimated comprises the following steps: and generating a digital surface model DSM from the encrypted dense point cloud data, classifying a basic ground change model by using the dense point cloud, generating a ground change model DEM from the digital surface model DSM and the basic ground change model by using an interpolation method, and superposing and subtracting the digital surface model DSM and the ground change model DEM to obtain a ground plant community height change model CHM, namely obtaining a ground plant community height change model image of the area to be estimated.
Specifically, a Digital Surface Model (DSM) is generated by using dense point cloud data obtained by software Agisoft Photoscan encryption, then a basic ground change Model is classified by using the dense point cloud, then a Surface change Model DEM is generated by using the DSM and the basic ground change Model through an interpolation method, and then the Digital Surface Model DSM and the Surface change Model DEM are superposed and subtracted by using an image data operation function of software SAGA-GIS to obtain a Surface plant community Height change Model (Canopy Height Model, CHM), namely a Surface plant community Height change Model image of a region to be estimated is obtained.
The method for acquiring the visible light vegetation index model image of the area to be estimated comprises the following steps: by utilizing a vegetation index model building function in a remote sensing image processing platform (software ENVI), visible light band operation is carried out on the ground orthoimage of the area to be estimated by adopting a visible light vegetation index formula, so as to generate a visible light vegetation index (VDVI) model image.
The method for classifying the ground orthographic images of the area to be estimated and confirming the spatial distribution of the invasive plants comprises the following steps: importing the ground ortho-image of the area to be estimated into software eCoginization Developer, performing multi-scale segmentation on the image based on the pixel layer of the ortho-image by using a multi-scale segmentation algorithm, wherein red light, green light and blue light wave bands of visible light of the ortho-image participate in the segmentation, and the segmentation scale parameter is set as 150-class 200; then setting two land type standards of invasive plants and non-invasive plants based on the segmented images, writing land type description, selecting different land type characteristic samples on the segmented images, generating classified images by using algorithm classification, obtaining the distribution condition of the invasive plants in the area to be estimated, and finally deriving an estimation region classified image model.
The field collection method of the biomass sample of the invasive plant to be estimated comprises the steps of firstly determining a plurality of representative sample plots in a selected area according to the standards of the height, the middle height and the short height of the invasive plant, wherein the area of the sample plots is 1 × 1m2(Total area 1 m)2) Collecting biomass of invasive plants into aboveground biomass, and then recording GPS position information of sample plot centers to ensure that selected sample points correspond to spliced images of the unmanned aerial vehicle; and calculating the biomass of the invasive plant by using a drying method, and using the biomass to establish and analyze a regression model.
The method for establishing the regression model of the biomass of the invasive plants, the height of the plant community and the visible light vegetation index and estimating the biomass comprises the following steps: establishing a univariate function relationship between the visible light vegetation index and the height of the surface plant community through software SPSS according to the height value of the surface plant community and the visible light vegetation index value, which are extracted from the image of the surface plant community height change model and the image of the visible light vegetation index model and correspond to the position of the sampling point; then establishing a binary linear model of the biomass of the sampling point, the visible light vegetation index and the height of the surface plant community by using the SPSS; substituting the unitary function relationship into a binary linear model to obtain a total function relationship of the biomass and the visible vegetation index; and finally, a total function relation is input into the software ENVI, and the visible light vegetation index model image is processed to obtain a biomass space model, so that the biomass estimation of the invasive plant is realized.
Wherein the relationship y of a unitary function1=A1x1+C1(y1Is the height of the surface plant community, x1VDVI value), biomass and vegetation index, and surface plant community height2=A2y1+B2x1+C2(wherein y is2To biomass, y1Is the height of the surface plant community, x1Is a VDVI value). The height y of the land surface plant community of the binary linear model1Vegetation index y in unitary function relationship1And carrying out replacement, wherein the replacement formula is as follows: y is2=A2（A1x1+C1）+B2x1+C2Obtaining a total function relation of the biomass and the vegetation index after integration; and (3) after a general function relational expression is input into the software ENVI, processing the vegetation index model image to obtain a biomass space model, and finally overlapping the estimation region classification image model and the biomass space model to realize the estimation of the biomass distribution of the invasive plant.
The invention has the characteristics and advantages that: the biomass is fitted with the height of the plant community and the vegetation index bivariate, so that the precision is higher and the applicability is wider; the unmanned aerial vehicle aerial photography can be well suitable for biomass estimation in a mesoscale area range, and is easy to popularize;
the space distribution of invasive plants is confirmed through an unmanned aerial vehicle image product, and biomass on the space distribution of the invasive plants is estimated by utilizing the correlation between a vegetation index VDVI and biomass and a height mean value of a corresponding sample; the obtained estimation result has strong correlation with the actual biomass.
Drawings
FIG. 1 is a schematic view of a process-generated high-resolution ground orthographic image model;
FIG. 2 is a schematic view of a visible vegetation index (VDVI) image model generated by the process;
FIG. 3 is a schematic view of a polyline difference analysis of an estimation model test;
FIG. 4 is a schematic diagram of an image model for classifying an estimated region;
FIG. 5 is a schematic diagram of an estimated region biomass model.
Detailed Description
In order to more clearly understand the technical features, objects and effects of the present invention, the present invention will be further described with reference to the accompanying drawings and examples, but the present invention is not limited to the above description, and the methods in the examples are conventional methods unless otherwise specified.
Example 1:
the aerial photography remote sensing platform of the unmanned aerial vehicle and the parameters adopted in the embodiment are as follows:
the maximum pixel of a camera sensor carried by the unmanned aerial vehicle is 7360 × 4912, the size of a fixed aperture is f/4.0, the ISO value is 100, the shutter speed is 1/1000S, camera parameters are called to compensate geometric distortion, the lens parameters are fixed focal length 35mm, the unmanned aerial vehicle uses a Dajiang M600 Pro six-rotor flight platform and is calibrated by using parameter adjusting software DJI Assistan2, and a sky-end GPS uses a HOLUX M241-A track recorder.
In order to acquire effective data, the flight mission is integrally planned and designed before data acquisition, so that the effectiveness of data acquisition and the safety of flight are ensured.
Considering that the specified transverse overlapping rate of flight mapping should be higher than 50%, the side overlapping rate should be higher than 20%, and after the acquired image data are processed, the acquired height data, visible vegetation index (VDVI) data and sample point data of the measured biomass are in one-to-one correspondence, and the corresponding areas are as close as possible, therefore, in this embodiment, the acquired image data keep the flight speed of 4m/S and the flight altitude of 100m, and the ground resolution is calculated by the relation between the ground resolution GSD and the flight altitude, and the relation is as follows:
wherein a is the pixel size, GSD is the ground resolution, f is the focal length of the lens, and h is the altitude.
Through calculation, the pixel size is 4.8256 μm, the flight altitude is 100m, and the ground resolution is 1.4cm according to a formula.
The area to be estimated is located in the province of the Ministry of China, the West and the south of the Yanshan county of Yuxi city, Yunnan, the position of the area to be estimated is between 102 degrees to 102 degrees 08 'of east longitude and 24 degrees to 24 degrees 15' of north latitude, the climate type of the area belongs to subtropical monsoon climate, invasive plants in the estimation area mainly comprise tithonia diversifolia, the distribution range of the invasive plants is wide, the biomass is large, the surrounding terrain is open, and artificial shelters are few, so that the area is suitable for monitoring the invasive plants; the image collection is carried out in the florescence of tithonia diversifolia. The method comprises the following steps of obtaining biomass data, height data and visible light vegetation index (VDVI) data of invasive plants as modeling data of an estimation model, and specifically operating as follows:
1. the method for acquiring biomass of the invasive plant comprises the steps of continuously shooting images of a region to be estimated by an unmanned aerial vehicle, simultaneously carrying out sampling work on the biomass of the invasive plant during the flight process of the unmanned aerial vehicle, wherein measurement parameters mainly comprise the biomass of the invasive plant and GPS (global positioning system) positions of sampling points, and the method for acquiring the invasive plant comprises the step of determining 30 representative sample plots with the area of 1 × 1m according to the high growth vigor, the medium growth vigor and the low growth vigor of the invasive plant tithonia diversifolia in a selected area2(ii) a Collecting biomass of the invasive plants into aboveground biomass, and then recording GPS position information of a sample plot center to ensure that sampling points of biomass samples of the invasive plants can correspond to the height change model image of the wetland surface plant community to be estimated and the visible light vegetation index model image; and finally, calculating the biomass of the invasive plant by using a drying method, placing the collected overground part of the invasive plant in an oven, drying each sample at a constant temperature of 85 ℃ until the sample reaches a constant weight, weighing, recording a numerical value (shown in table 1), and using the numerical value for modeling analysis.
TABLE 1 statistical tables of biomass
| Sample plot number | All-grass of Ottelia | Biomass (kg/m)2） | Sample plot number | All-grass of Ottelia | Biomass (kg/m)2） |
| 1 | Is low in | 1.0910 | 16 | In | 2.2867 |
| 2 | Is low in | 1.2164 | 17 | In | 2.2432 |
| 3 | Is low in | 1.2480 | 18 | In | 2.4287 |
| 4 | Is low in | 1.1594 | 19 | In | 2.7413 |
| 5 | Is low in | 1.5007 | 20 | In | 2.4892 |
| 6 | Is low in | 1.4295 | 21 | Height of | 2.5394 |
| 7 | Is low in | 1.7135 | 22 | Height of | 2.7513 |
| 8 | Is low in | 1.7012 | 23 | Height of | 2.9843 |
| 9 | Is low in | 1.8580 | 24 | Height of | 3.3229 |
| 10 | Is low in | 1.6893 | 25 | Height of | 3.2150 |
| 11 | In | 2.0148 | 26 | Height of | 3.4824 |
| 12 | In | 1.9342 | 27 | Height of | 3.5975 |
| 13 | In | 2.1081 | 28 | Height of | 3.2694 |
| 14 | In | 2.0490 | 29 | Height of | 4.0985 |
| 15 | In | 2.1924 | 30 | Height of | 4.3452 |
2. High resolution ground orthographic image acquisition
The generation of the surface plant community height model and the visible light vegetation index (VDVI) model is based on high-resolution ground orthographic images, so that the modeled height data and VDVI data can be obtained. The method for processing the orthoimage of the research area in the embodiment is as follows:
the image data acquired by the unmanned aerial vehicle is influenced by environmental factors, and in the acquisition process, the situation that accumulated clouds shield sunlight can occur, so that part of the image is under-exposed, therefore, the image is subjected to parameter adjustment processing by using image processing software Lightroom, so that the image reaches proper exposure, and all the images are under the same exposure, and the quality optimization of all the images is ensured;
next, in this embodiment, aerial survey GPS track information in a HOLUX M241-a track recorder mounted on an unmanned aerial vehicle is extracted by using an aerial terminal GPS self-contained software eztourour, a quality-processed image is imported, GPS information of a photograph is matched corresponding to the same time, and an aerial photograph track and a machine position are directly formed in photo import software given to the GPS information.
Then importing the image matched with the GPS information into software Agisoft Photoscan, splicing the image by utilizing a motion recovery structure algorithm and a multi-view three-dimensional reconstruction algorithm built in the software, firstly, generating sparse point cloud data, setting an accuracy parameter as Highest, and selecting other parameters as default; then inputting longitude and latitude coordinates and elevations of the ground control points (as shown in table 2), introducing the control points, wherein the control points are generally selected to be four corners of a research area, and the purpose is to determine a coordinate system of an orthoimage to be generated; then, encrypting the sparse point cloud to form dense point cloud, setting the quality parameter as High, and selecting other parameters as default; generating a triangulation network model based on the dense point cloud data, setting a surface type parameter as Height field, selecting the dense point cloud by a data source, and setting an accuracy parameter as High; and finally, taking the triangulation model as a reference, enabling all point cloud data on the image to be in the same plane, and generating an orthoimage (shown in figure 1), wherein the processing is realized in software Agisoft Photoscan.
TABLE 2 longitude and latitude coordinates and elevation table of control points
| # Label | X/East | Y/North | Z/Altitude |
| Point1 | 102.205638 | 24.073308 | 1109.951 |
| Point2 | 102.206218 | 24.073640 | 1119.528 |
| Point3 | 102.206078 | 24.073120 | 1118.545 |
| Point4 | 102.205515 | 24.072895 | 1113.263 |
| # Total error |
3. Classifying the ground orthographic images of the area to be estimated and confirming the spatial distribution of the invasive plants
Importing an orthoscopic image into software eCG (imaging development device), carrying out multi-scale segmentation on the image based on a pixel layer of the orthoscopic image by using an image segmentation tool built in the software, wherein red light, green light and blue light wave bands of visible light of the orthoscopic image participate in the segmentation, and a segmentation scale parameter is set to be 150; then, based on the segmented images, 5 kinds of ground standards of tithonia diversifolia, artificial objects, water bodies, bare soil and other plants are set, ground descriptions are written for all the ground standards according to the classification rules of mean values and standard deviations, different ground feature samples are selected from the segmented images, and finally, classification images are generated by using algorithm classification, so that the distribution condition of the invasive plants in the area to be estimated is obtained, and finally, an estimation area classification image model is derived in an Erdas Imagiinemages format (as shown in figure 4).
4. Obtaining the height data of the invasive plant community: the method comprises the steps of generating a Digital Surface Model (DSM) by using dense point cloud data obtained by software Agisoft Photoscan encryption, classifying a basic ground change Model by using the dense point cloud, generating a ground change Model DEM by using the DSM and the basic ground change Model through an interpolation method, superposing and subtracting the DSM and the DEM by using an image data operation function of software SAGA-GIS to obtain a ground plant community Height change Model (chop), namely obtaining a Surface Height change Model image of a research area, acquiring Height data (shown in a table 3) in sample points corresponding to biomass on the Surface Height change Model image, and using the Height data in the sample points to perform modeling analysis on an estimation Model.
TABLE 3 table of height data of invasive plant communities
5. Acquisition of visible vegetation index (VDVI) data: the generated high-resolution ground ortho-image is led into a remote sensing image processing platform ENVI, Band Math is selected from a Basic Tools menu, and a Band operation expression of a corresponding vegetation index VDVI is input into an input expression dialog box: (2 × float (b2) - (float (b1) + float (b 3)))/(2 × float (b2) + (float (b1) + float (b3))), to avoid data overflow, the data type of the input band is selected as floating point. And then selecting the wave bands of the remote sensing image, wherein the red light wave band is designated as b1, the green light wave band is designated as b2, and the blue light wave band is designated as b3, and outputting a vegetation index model. Then changing the brightness value of the image pixel by using Enhance to change the contrast of the image pixel, selecting Linear 2%, linearly stretching the image with DN value distributed between 2% and 98%, and discarding most abnormal values during stretching, thereby improving the image quality, generating a final visible light vegetation index (VDVI) model image (shown in figure 2), and then applying the image to the visible light vegetation (shown in table 4) for modeling analysis of an estimation model;
TABLE 4 visible Vegetation index (VDVI) data sheet
| Numbering | VDVI extraction value | Numbering | |
| 1 | 0.0690 | 16 | 0.1538 |
| 2 | 0.0745 | 17 | 0.2385 |
| 3 | 0.0383 | 18 | 0.2463 |
| 4 | 0.1071 | 19 | 0.2359 |
| 5 | 0.0926 | 20 | 0.2186 |
| 6 | 0.1563 | 21 | 0.2264 |
| 7 | 0.1156 | 22 | 0.1927 |
| 8 | 0.1454 | 23 | 0.1865 |
| 9 | 0.1682 | 24 | 0.2746 |
| 10 | 0.1517 | 25 | 0.2155 |
| 11 | 0.1158 | 26 | 0.2359 |
| 12 | 0.1765 | 27 | 0.2438 |
| 13 | 0.1235 | 28 | 0.2864 |
| 14 | 0.1236 | 29 | 0.2986 |
| 15 | 0.1859 | 30 | 0.3129 |
The estimation model establishment (including model verification) of the present embodiment specifically operates as follows:
(1) establishing a regression model of plant biomass, plant community height and visible light vegetation index (VDVI): importing the obtained height data of the invasive plant community and the obtained visible light vegetation index (VDVI) data into mathematical statistics software IBMSPSS, and obtaining a VDVI and surface plant community height model y after processing by using the curve estimation function of the software IBM SPSS1=2.2952+6.3621x1（R2=0.7503, P < 0.05, wherein y1Is the height of the surface plant community, x1Is VDVI); and obtaining biomass data, invasive plant community height data and visible light vegetation index (VDVI) data of the invasive plants by processing the same with IBM SPSS software to obtain a binary linear model y about biomass, vegetation index and surface plant community height2=0.6476y1+6.7000x1-1.0811(R2=0.8264, P < 0.05, wherein y2To biomass, y1Is the height of the surface plant community, x1Is VDVI). Performing variable replacement on the height of the VDVI and the height of the surface plant community, and changing the height y of the surface plant community of the binary linear model1Vegetation index y in unitary function relationship1After replacement, the replacement formula is:
y2=0.6476×（2.2952+6.3621x1）+6.7000x11.0811, obtaining a total function y of biomass and vegetation index after integration2=10.8201x1+0.4053。
(2) Then, the generated visible light vegetation index (VDVI) model is imported into a remote sensing image processing platform ENVI, Band Math is selected from a Basic Tools menu, and then a total function relation y =10.8201x of vegetation index and biomass obtained after input variables are replaced in an input expression dialog box1+0.4053, obtaining a biomass model map (as shown in fig. 5) visually reflecting the biomass of the current region to be estimated through ArcGis analysis, and overlapping the image model for classifying the estimation region in step 3 with the biomass model map, thereby finally achieving the purpose of estimating the biomass distribution of the invasive plant in the region to be estimated in the embodiment；
(3) And (3) biomass estimation model verification: randomly sampling 30 tithonia diversifolia biomass samples from all the samples for model review; the results are shown in Table 5. In the biomass estimated by using the model and the actually measured biomass, the maximum relative error is 14.09%, the minimum relative error is 3.23%, and the average error is 8.19%, and the data show that the precision of the whole experiment is higher, and the method is scientific and feasible; to visually express the biomass estimation model versus the observed differences we generated a polyline difference graph, as shown in fig. 3.
TABLE 5 model review results
The above description is only an exemplary embodiment of the present invention, and is not intended to limit the scope of the present invention. Any equivalent alterations, modifications and combinations can be made by those skilled in the art without departing from the spirit and principles of the invention.
Claims (3)
1. A method for estimating biomass of invasive plants by unmanned aerial vehicle aerial photography is characterized in that: an unmanned aerial vehicle is used as a remote sensing photography platform, images of an area to be estimated are continuously shot through the unmanned aerial vehicle, meanwhile, invasive plant biomass samples of the area to be estimated are collected on site, invasive plant biomass of sampling points is calculated, and GPS positions of the sampling points are measured;
processing the image of the area to be estimated by using a digital photogrammetry technology to obtain a high-resolution ground orthoimage of the area to be estimated, and further establishing a model image of the height change of the plant community on the ground surface of the area to be estimated and a model image of the visible light vegetation index; classifying the ground ortho-images of the area to be estimated, confirming the spatial distribution of the invasive plants, and obtaining an invasive plant classification image model;
utilizing the image processing function of the geographic information system software to enable the ground plant community height change model image and the visible vegetation index model image to have the same resolution, corresponding the sampling point positions of the invasive plant biomass samples to the ground plant community height change model image and the visible vegetation index model image of the area to be estimated, and extracting the ground plant community falling height value and the visible vegetation index value of each sampling point in the model images; establishing a regression model of the biomass of the invasive plant, the height of the plant community and the visible vegetation index according to the biomass of the invasive plant at the sampling point, the height value of the plant community at the ground surface and the visible vegetation index value, and estimating the biomass of the invasive plant with confirmed spatial distribution through the regression model;
the method for acquiring the high-resolution ground orthographic image of the area to be estimated comprises the following steps: performing quality optimization and GPS track information import processing on an unmanned aerial vehicle aerial image, performing splicing processing on the image by using a motion recovery structure algorithm and a multi-view three-dimensional reconstruction method to generate sparse point cloud data, inputting longitude and latitude coordinates and elevations of ground control points, introducing the control points, determining a coordinate system of an orthoimage to be generated, encrypting the sparse point cloud to form dense point cloud, generating a triangulation network model based on the dense point cloud data, and enabling all point cloud data on the image to be in the same plane by taking the triangulation network model as a reference to generate the ground orthoimage;
the method for acquiring the image of the model for the height change of the surface plant community of the area to be estimated comprises the following steps: generating a digital surface model DSM from the encrypted dense point cloud data, classifying a basic ground change model by using the dense point cloud, generating a ground change model DEM from the digital surface model DSM and the basic ground change model by using an interpolation method, and superposing and subtracting the digital surface model DSM and the ground change model DEM to obtain a ground plant community height change model CHM, namely obtaining a ground plant community height change model image of the area to be estimated;
the method for acquiring the visible light vegetation index model image of the area to be estimated comprises the following steps: performing visible light band operation on a ground orthoimage of a region to be estimated by using a visible light vegetation index formula by utilizing a vegetation index model building function in a remote sensing image processing platform to generate a visible light vegetation index model image;
the method for classifying the ground orthographic images of the area to be estimated and confirming the spatial distribution of the invasive plants comprises the following steps: importing the ground ortho-image of the area to be estimated into software eCoginization Developer, performing multi-scale segmentation on the image based on the pixel layer of the ortho-image by using a multi-scale segmentation algorithm, wherein red light, green light and blue light wave bands of visible light in the ortho-image participate in the segmentation, and the segmentation scale parameter is set as 150-plus-200;
then, based on the segmented images, setting land standards of invasive plants and non-invasive plants, writing land descriptions, selecting different land feature samples on the segmented images, generating classified images by using algorithm classification, obtaining the distribution condition of the invasive plants in the area to be estimated, and finally exporting the classified images.
2. The method for estimating biomass of invasive plants by unmanned aerial vehicle aerial photography according to claim 1, wherein the method for establishing the regression model of biomass of invasive plants, height of plant communities and visible vegetation indexes and estimating biomass comprises the following steps: establishing a univariate function relationship between the visible light vegetation index and the height of the surface plant community through software SPSS according to the height value of the surface plant community and the visible light vegetation index value, which are extracted from the image of the surface plant community height change model and the image of the visible light vegetation index model and correspond to the position of the sampling point; then establishing a binary linear model of the biomass of the sampling point, the visible light vegetation index and the height of the surface plant community by using the SPSS; substituting the unitary function relationship into a binary linear model to obtain a total function relationship of the biomass and the visible vegetation index; and finally, a total function relation is input into the software ENVI, the visible light vegetation index model image is processed to obtain a biomass space model, and the biomass space model and the invasive plant classification image model are overlapped to realize the estimation of the biomass of the invasive plants in the area to be estimated.
3. The method for estimating biomass of invasive plants by UAV aerial photography according to claim 1, wherein the method for collecting biomass samples of invasive plants in the area to be estimated in field is as follows: determining plants based on invasive plant growthThe standards of height, middle and short are determined, a plurality of sample plots are determined in the selected wetland area according to the standards of height, middle and short of the invasive plants, and the area of the sample plots is 1 × 1m2(ii) a The biomass of the invasive plant is collected as aboveground biomass.
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