CN111767865A - Method for inverting mangrove forest biomass by using aerial image and laser data - Google Patents
Method for inverting mangrove forest biomass by using aerial image and laser data Download PDFInfo
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Abstract
The invention belongs to the technical field of mangrove forest biomass inversion, and particularly relates to a method for inverting mangrove forest biomass by utilizing aerial images and laser data, which comprises the following steps of: and selecting a mangrove biomass inversion area in the coastal zone, and carrying out low-altitude control flight test and data acquisition on the mangrove test area in the inversion area. And setting an actual measurement sample plot in the mangrove forest sample plot, and calculating the actual measurement biomass of the mangrove forest sample plot. Preprocessing the obtained original mangrove forest laser point cloud data, acquiring an orthophoto map of a region by using Pix4Dmap, extracting the orthophoto data, spectral data, texture data and laser point cloud data of the mangrove forest, screening variables of a research region by means of a random forest algorithm in combination with sample plot survey data, and inverting the biomass and the spatial distribution of the biomass on the scale of the region by constructing a random forest model. The invention solves the technical problems of high field work difficulty, low result precision, time and labor waste and the like of mangrove biomass manual investigation.
Description
Technical Field
The invention relates to the technical field of mangrove forest biomass inversion, in particular to a method for inverting mangrove forest biomass by utilizing aerial images and laser data.
Background
Mangrove is an important plant type of protection forest of tropical and subtropical coastal zones, is one of the ecosystems with the highest productivity on earth, and is an important object for protecting biodiversity and wetland ecology internationally. The mangrove wetland has high carbon sink potential and plays an important role in slowing down climate change and global carbon cycle, and previous researches show that although the mangrove only covers 0.1% of the surface area of the earth, the mangrove wetland fixes 5% of carbon in the atmosphere. The biomass is used as an important component of the carbon storage and carbon sink potential of mangrove forest, and the isolated 'blue carbon' is an important component of the carbon cycle of the coastal ecosystem. How to estimate the biomass of the mangrove rapidly and accurately becomes a hotspot of the research on the mangrove ecosystem in recent years, which has important significance for coping with global climate change and reducing greenhouse gas emission.
Hitherto, most international researches on mangrove forest biomass mainly comprise regular sample prescription, sample plot or sample zone investigation, most international researches comprise field actual measurement or drawing, and the inherent influence of mangrove forest growing in a muddy intertidal zone is added, so that the working situation that field practice is difficult and result accuracy is low is caused. Currently, few scholars estimate the aboveground biomass of mangrove forest by combining the measured data of the sample plot, the low-altitude unmanned aerial photography influence and the laser LiDAR data by a machine learning method. Therefore, the biomass problem on the mangrove forest land is estimated by adopting low-altitude unmanned remote sensing, and the method is an efficient innovative research method.
Disclosure of Invention
The invention aims to solve the problems, provides a method for inverting the biomass of the mangrove forest by utilizing aerial images and laser data, can quickly improve the efficiency of estimating the biomass of the mangrove forest in a large range, can provide technical support for local forestry departments and marine environment departments, and solves the technical problems of high difficulty in field work, low result precision, time and labor waste, difficulty in scale popularization and the like in manual survey of the biomass of the mangrove forest.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a method for inverting mangrove forest biomass by using aerial images and laser data comprises the following steps:
(1) selecting a mangrove biomass inversion area in the coastal zone, and avoiding selecting a tidal ditch area when selecting the sample plot;
(2) carrying out low-altitude control flight test and data acquisition on a mangrove forest test area in an inversion area by using a common digital camera and a laser sensor carried by an unmanned aerial vehicle;
(3) selecting a time period with a low tide level less than 0.5m, clear weather, no continuous wind direction on the ground and wind power less than 2 levels for unmanned aerial vehicle data aerial photography;
(4) setting actual measurement sample plot survey in mangrove sample plots, mainly surveying the breast height, phenology and branch number of different types of mangroves, and simultaneously recording X and Y coordinate values of each tree; then put it into database with ArcGISI 10.5;
(5) calculating the actually measured biomass of different mangrove forest species sample plots by adopting a different-speed growth equation;
(6) preprocessing original mangrove forest laser point cloud data obtained by unmanned aerial vehicle aerial photography, wherein the preprocessing mainly comprises point cloud denoising, point cloud classification and point cloud normalization operation;
(7) acquiring an orthophotomap of the area by using Pix4Dmap, and storing the orthophotomap in a TIFF format;
(8) extracting height percentile variables and intensity percentile variables of the mangrove forest based on the data obtained by preprocessing in the step (6);
(9) extracting the peak value of the laser point cloud height of the mangrove forest, the skewness of the height, the average value of the height, the maximum value of the height, the standard deviation of the height, the variation coefficient of the height and the fluctuation rate of the canopy of the height based on the data obtained by preprocessing in the step (6); in the same way, the peak value of the mangrove laser point cloud intensity, the skewness of the intensity, the average value of the intensity, the maximum value of the intensity, the standard deviation of the intensity, the variation coefficient of the intensity and the crown layer undulation rate of the intensity can be extracted;
(10) generating an RGB image based on the orthophoto map obtained in the step (7), an unsupervised classified image and a maximum raster image of the laser point cloud height in the step (9) to be combined, classifying mangrove forests and non-mangrove forest landscapes in the research area under the support of a classification and regression tree CART method, and extracting mangrove forest land types;
(11) extracting various spectral indexes of mangrove forest plants based on the orthoimages in the step (7);
(12) based on the orthoimage in the step (7), extracting various texture information of the mangrove forest by means of a gray level and symbiotic matrix method;
(13) extracting biomass data estimated by the different growth equation of the mangrove forest species in the step (5), extracting variables in the steps 8, 9, 11 and 12 at the same coordinate position (the coordinate position is consistent with the position in the step 4), taking the measured biomass data in the step 4 as a dependent variable, extracting the variables in the steps 8, 9, 11 and 12 at the same measured sample coordinate position, taking the extracted numerical values as independent variables, screening the mangrove forest variables in the steps 8, 9, 11 and 12 under the support of an RF algorithm, and simultaneously calculating the importance degree of the independent variables relative to the dependent variables, wherein the obtained variable can enter a regression equation;
(14) putting variables obtained after the importance degree is screened and calculated in the step 13 into a random forest algorithm, training a model, and obtaining a mangrove forest biomass regression equation;
(15) and (3) taking the whole set of grid pixels of the variables screened in the step (13) as independent variables to be substituted into the mangrove forest biomass regression equation established in the step (14), so that spatial distribution of the mangrove forest biomass in the whole region of the research area can be obtained.
Preferably, the main steps of performing the low-altitude control flight test and acquiring the data in the step (2) include erection of a ground GPS base station, mounting and installation of an unmanned aerial vehicle carrying a laser sensor, and air route planning and uploading.
Preferably, the step (4) is specifically to set an actual measurement sample plot in a mangrove sample plot, and two mangrove forest communities, namely the sonneratia apetala and the tung tree, exist in the region, wherein the sonneratia apetala in the northern region of the research region is artificially planted, and the tung tree community in the southern region is a local tree species; two samples are arranged in the valveless sea community, the size of the two samples is 20 multiplied by 20m, and two samples are also arranged in the tung tree community with a better growth condition, the size of the two samples is10 multiplied by 10 m; measuring the characteristics of all individuals with the diameter at breast height of more than or equal to 6cm in the sample prescription, including relative coordinates, plant height, diameter at breast height, coverage, phenology and branch number by adopting a per-tree investigation method; the X and Y coordinate positions of each tree are measured using a high precision GPS receiver that accesses the carrier phase differential RTK base station signal and then put into a database using the geographic information management software arcgis 10.5.
Preferably, the step (7) is specifically to automatically correct the photos obtained from the research area by using the Pix4Dmapper optimization technology and the area network adjustment technology, then identify the original POS data, obtain POS information of valid photos, wherein the POS information includes the numbers, longitude, latitude, altitude, roll angle, course angle, and pitch angle of the photos, and finally output the empty three calculation result, the image stitching result, the digital ground model, and the orthophoto map to be stored in the TIFF format.
Preferably, the height percentile variable in step (8) is generated by sorting all normalized laser radar point clouds of mangrove forest according to height, and then calculating the height of X% of points in each statistical unit, namely the height percentile of the statistical unit.
Preferably, the spectral indexes in step (11) include a green-red ratio index, a green-blue ratio index, a red-green ratio index, a red-blue ratio index, a normalized green-red difference index, a normalized green-blue difference index, a visible light atmospheric impedance index, a visible differential vegetation index, and a super-green index, and the specific indexes are calculated as follows: green-red ratio index GRRI-DNG/DNR, green-blue ratio index GBRI-DNG/DNB, red-green ratio index RGRI-DNR/DNG, red-blue ratio index RBRI-DNR/DNB, normalized green-red difference index NGRDI- (DNG-DNR)/(DNG + DNR), normalized green-blue difference index NGBDI- (DNG-DNB)/(DNG + DNB), visible atmosphere resistance index VARI- (DNG-DNR)/(DNG + DNR-DNB), visible difference vegetation index VDVI- (2DNG-DNR-DNB)/(2DNG + DNR + DNB), ultragreen index EXG-2 DNG-DNR-DNB, where g represents the value of green light in the ortho-image, DNB represents the value of blue light band in the ortho-image, and r represents the value of red light wave in the segment of the ortho-image.
Preferably, the texture information of step (12) includes mean, variance, cooperativity, contrast, dissimilarity, entropy and correlation.
Preferably, the step (14) is specifically to put the mangrove forest variables in the sample area in the step 13 into ArcGIS10.5, convert the values into point data, obtain the values of each grid point, the values are used as independent variables of the random forest algorithm, the mangrove forest biomass in the sample area is used as dependent variables, and a random forest model is established under IBM SPSS Modeler 18.0, so that a model of a regression equation of the mangrove forest biomass can be obtained.
By adopting the technical scheme, the invention has the beneficial effects that:
the method is based on the orthographic data of the aerial photography data of the unmanned aerial vehicle, the spectral data of the unmanned aerial vehicle, the texture data and the laser LiDAR point cloud data, meanwhile, a small amount of sample plot survey data is combined, firstly, the variables of a research area are screened by means of a random forest algorithm, and the biomass and the spatial distribution of the biomass on the area scale are inverted by constructing an equation, so that the problems of large field work difficulty and low result precision of the traditional method are solved.
Drawings
FIG. 1 is a mangrove forest sample map;
FIG. 2 is an orthographic view of mangrove aerial photography
FIG. 3 is a chart showing the classification result of mangrove forest species;
FIG. 4 is a graph of the result of the variable importance screening of the RF random forest algorithm;
FIG. 5 is a flow chart of establishing a random forest model under IBM SPSS Modeller 18.0;
FIG. 6 is a flow chart of mangrove forest biomass prediction in the research area under IBM SPSS Modeller 18.0;
FIG. 7 is a spatial distribution diagram of mangrove forest biomass based on RF random forest algorithm inversion.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Examples
A method for inverting mangrove forest biomass by using aerial images and laser data comprises the following steps:
step 1, selecting a mangrove biomass inversion area in the coastal zone, and avoiding selecting a tidal ditch area during sample selection.
And 2, carrying out low-altitude control flight test and data acquisition on the mangrove forest test area of the inversion area by using a common digital camera and a laser sensor carried by a DJI M600 PRO unmanned aerial vehicle, wherein the main steps comprise erection of a ground GPS base station, mounting and installation of the laser sensor carried by the unmanned aerial vehicle, air route planning and uploading air routes.
And 3, vertically shooting at the shooting height of about 70-100m, setting the course and the lateral overlapping degree to be 80%, and selecting a time period with a low sea level less than 0.5m, clear weather, no continuous wind direction on the ground and wind power less than 2 levels to ensure that the obtained image is not influenced by atmospheric conditions and factors that the tide ditch has water.
And 4, setting actual measurement sample plots (figure 1) in the mangrove sample plots, wherein two mangrove colonies in the areas are respectively a Sonneratia apetala (Sonneratia apetala) and a tung tree (aegeceras), wherein the Sonneratia apetala in the northern part of the research area is artificially planted, and the tung tree colony in the southern part is a local tree species. The invention sets two sample parties (c) with the size of 20 x 20m in the valveless sea community, and sets two sample parties (c) with the size of 10 x 10m in the tung tree community with a better growth condition. The method of per-tree investigation is adopted to measure the relative coordinates, plant heights, breast diameters (1.3 m is the breast diameter measuring height of the sonneratia apetala or 0.3 m is the breast diameter measuring height of the tung tree), coverage, phenological features, branches and other numerical characteristics of all the individuals with the breast diameters of not less than 6cm in the sample prescription. The X and Y coordinate positions of each tree are measured using a high precision GPS receiver that accesses the carrier phase differential RTK base station signal and then put into the database using arcgis 10.5.
And 5, calculating the actually measured biomass of the mangrove forest sample plot by adopting a different-speed growth equation, wherein the different-speed growth equation mainly considers parameters of the breast height and the tree height of different mangrove forest tree species when calculating the biomass, and the research area mainly comprises two tree species which are respectively a nonpetal mulberry and a tung tree.
And 6, preprocessing the obtained original mangrove laser point cloud data, wherein the preprocessing mainly comprises point cloud denoising, point cloud filtering and ground point cloud and non-ground point cloud separation.
And 7, automatically correcting the photos obtained in the research area by using the Pix4Dmapper optimization technology and the block adjustment technology, then identifying the original POS data, obtaining POS information (including the numbers, longitudes, latitudes, altitudes, roll angles, course angles and pitch angles of the photos) of effective photos, and finally outputting the air-to-three calculation results, the image splicing results, the digital ground model and the orthophoto map to be stored in a TIFF format.
And 8, extracting height percentile variables and intensity percentile variables of the mangrove forest based on the data obtained by preprocessing in the step 6, wherein the height percentile variables are generated by sequencing all normalized laser radar point clouds of the mangrove forest according to heights, and then calculating the height of X% of points in each statistical unit, namely the height percentile of the statistical unit. The intensity percentile of the intensity Y% can be calculated by the same method. Here, the heights H _ P50 (percentile of 50% height), H _ P75 (percentile of 75% height), H _ P95 (percentile of 95% height), H _ P99 (percentile of 99% height), and H _ IQ (difference of H _ P95 and H _ P75) are selected, and the intensities I _ P50, I _ P75, I _ P95, I _ P99, and I _ IQ (difference of I _ P95 and I _ P75) are extracted, and then all converted into a grid image, with the resolution being kept consistent with that in step 7.
And step 10, generating an RGB image and an unsupervised classified image based on the orthophoto map (figure 2) obtained in the step 7 under the support of ENVI software. Then combining the RGB image, the unsupervised classification image and the maximum laser Point cloud height (H _ Point _ max) grid image in the step 9, segmenting the mangrove forest and the non-mangrove forest (mainly comprising ground mudflats and other land categories such as dunaliella salina or cyperus malaccensis) in the research area based on a classification and regression tree CART method, and extracting the land categories of the mangrove forest (figure 3).
Step 11, based on the orthoimage in step 7, extracting 9 mangrove forest spectral indexes including a green-red ratio index, a green-blue ratio index, a red-green ratio index, a red-blue ratio index, a normalized green-red difference index, a normalized green-blue difference index, a visible light atmosphere impedance index, a visible difference vegetation index and a super-green index by means of a waveband calculator in ENVI5.3 software, wherein the specific indexes are calculated as follows: Green-Red ratio index (GRRI) DNG/DNR, Green-blue ratio index (GBRI) DNG/DNB, Red-Green ratio index (RGRI) DNR/DNG, Red-blue ratio index (RBRI) DNR/DNB, Normalized Green-Red difference index (NGRDI) (DNG-DNR)/(DNG + DNR), Normalized Green-blue difference index (NGBDI) (DNG-DNB)/(G + DNB), Visible light large air resistance index (Visilated)/(reactive index (VARI) DNG + DNB), Visible light large air resistance index (VDVI + VDG + DNB), the ultragreen index (EXG) is 2DNG-DNR-DNB, and the resolution is consistent with that in step 7. Wherein DNG represents the value of the green band in the ortho image, DNB represents the value of the blue band in the ortho image, and DNR represents the value of the red band in the ortho image.
And step 12, extracting 7 texture information of mangrove forest by means of a gray level co-occurrence matrix method (GLCM) in ENVI5.3 based on the orthoimage in the step 7, wherein the information comprises Mean (Mean), Variance (Variance), cooperativity (Hom), Contrast (Con), Dissimilarity (Contrast), Entropy (Entropy) and Correlation (Correlation), and the resolution is consistent with the resolution in the step 7.
Step 13, extracting biomass data of mangrove Forest in step 4, extracting variables in step 8, step 9, step 11 and step 12 at the same coordinate position, extracting variables in step 8, step 9, step 11 and step 12 at the same measured sample coordinate position by taking measured biomass data in step 4 as a dependent variable, taking the extracted values as independent variables, screening 30 original mangrove Forest variables by means of a Boruta feature selection algorithm under the Random Forest program package of the R language with the support of an RF algorithm, and simultaneously calculating the importance degree of the independent variables relative to the dependent variables (figure 4), wherein the obtained variable can enter a regression equation.
Step 14, putting mangrove forest variables (NGBDI, VARI, GBRI, H _ Point _ mean, Con, Hom, I _ Skewness, H _ IQ, I _ Kurtosis and H _ P99) in the sample area in the step 13 into ArcGIS10.5, converting the values into Point data to obtain a value of a grid Point, wherein the value is used as an independent variable of a random forest algorithm, the mangrove forest biomass in the sample area is a dependent variable, and establishing a random forest model under an IBM SPSS Modeler 18.0 to obtain a model of a mangrove forest biomass regression equation (figure 5).
Step 15, converting the whole set of grid pixels of the variables screened in the step 13 into grid point data, then taking the data as an independent variable to be brought into the mangrove forest biomass regression model established in the step 14, predicting the mangrove forest biomass grid point data of the whole region of the research area according to the model (figure 6), and then converting the grid point data into spatial distribution of the mangrove forest biomass of the whole region of the research area under the support of ArcGIS10.5 according to spatial positions (figure 7).
Claims (8)
1. A method for inverting mangrove forest biomass by using aerial images and laser data is characterized by comprising the following steps:
(1) selecting a mangrove biomass inversion area in the coastal zone, and avoiding selecting a tidal ditch area when selecting the sample plot;
(2) carrying out low-altitude control flight test and data acquisition on a mangrove forest test area in an inversion area by using a common digital camera and a laser sensor carried by an unmanned aerial vehicle;
(3) selecting a time period with a low tide level less than 0.5m, clear weather, no continuous wind direction on the ground and wind power less than 2 levels for unmanned aerial vehicle data aerial photography;
(4) setting actual measurement sample plot survey in mangrove sample plots, mainly surveying the breast height, phenology and branch number of different types of mangroves, and simultaneously recording X and Y coordinate values of each tree; then put it into database with ArcGISI 10.5;
(5) calculating the actually measured biomass of different mangrove forest species sample plots by adopting a different-speed growth equation;
(6) preprocessing original mangrove forest laser point cloud data obtained by unmanned aerial vehicle aerial photography, wherein the preprocessing mainly comprises point cloud denoising, point cloud classification and point cloud normalization operation;
(7) acquiring an orthophotomap of the area by using Pix4Dmap, and storing the orthophotomap in a TIFF format;
(8) extracting height percentile variables and intensity percentile variables of the mangrove forest based on the data obtained by preprocessing in the step (6);
(9) extracting the peak value of the laser point cloud height of the mangrove forest, the skewness of the height, the average value of the height, the maximum value of the height, the standard deviation of the height, the variation coefficient of the height and the fluctuation rate of the canopy of the height based on the data obtained by preprocessing in the step (6); in the same way, the peak value of the mangrove laser point cloud intensity, the skewness of the intensity, the average value of the intensity, the maximum value of the intensity, the standard deviation of the intensity, the variation coefficient of the intensity and the crown layer undulation rate of the intensity can be extracted;
(10) generating an RGB image based on the orthophoto map obtained in the step (7), an unsupervised classified image and a maximum raster image of the laser point cloud height in the step (9) to be combined, classifying mangrove forests and non-mangrove forest landscapes in the research area under the support of a classification and regression tree CART method, and extracting mangrove forest land types;
(11) extracting various spectral indexes of mangrove forest plants based on the orthoimages in the step (7);
(12) based on the orthoimage in the step (7), extracting various texture information of the mangrove forest by means of a gray level and symbiotic matrix method;
(13) extracting biomass data estimated by the different growth equation of the mangrove forest species in the step (5), extracting variables in the steps 8, 9, 11 and 12 at the same coordinate position (the coordinate position is consistent with the position in the step 4), taking the measured biomass data in the step 4 as a dependent variable, extracting the variables in the steps 8, 9, 11 and 12 at the same measured sample coordinate position, taking the extracted numerical values as independent variables, screening the mangrove forest variables in the steps 8, 9, 11 and 12 under the support of an RF algorithm, and simultaneously calculating the importance degree of the independent variables relative to the dependent variables, wherein the obtained variable can enter a regression equation;
(14) putting variables obtained after the importance degree is screened and calculated in the step 13 into a random forest algorithm, training a model, and obtaining a mangrove forest biomass regression equation;
(15) and (3) taking the whole set of grid pixels of the variables screened in the step (13) as independent variables to be substituted into the mangrove forest biomass regression equation established in the step (14), so that spatial distribution of the mangrove forest biomass in the whole region of the research area can be obtained.
2. The method for inverting mangrove forest biomass by using aerial images and laser data according to claim 1, wherein the main steps of performing the low-altitude control flight test and data acquisition in the step (2) comprise the erection of a ground GPS base station, the mounting and installation of an unmanned aerial vehicle carrying a laser sensor, air route planning and uploading air route.
3. The method for inverting mangrove forest biomass using aerial image and laser data according to claim 2, wherein the step (4) is specifically to set an actual measurement sample plot in the mangrove forest sample plot, there are two mangrove forest communities in the area, respectively, sonneratia apetala and paulownia, wherein the sonnera apetala in the northern part of the study area is planted manually, and the paulownia community in the southern part is a local tree species; two samples are arranged in the valveless sea community, the size of the two samples is 20 multiplied by 20m, and two samples are also arranged in the tung tree community with a better growth condition, the size of the two samples is10 multiplied by 10 m; measuring the characteristics of all individuals with the diameter at breast height of more than or equal to 6cm in the sample prescription, including relative coordinates, plant height, diameter at breast height, coverage, phenology and branch number by adopting a per-tree investigation method; the X and Y coordinate positions of each tree are measured using a high precision GPS receiver that accesses the carrier phase differential RTK base station signal and then put into a database using the geographic information management software arcgis 10.5.
4. The method for inverting mangrove forest biomass by using aerial images and laser data as claimed in claim 3, wherein the step (7) is specifically to automatically correct the photos obtained from the research area by using the Pix4Dmapper optimization technique and the block adjustment technique, then identify the original POS data, obtain the POS information of the effective photos, wherein the POS information comprises the numbers, the longitude, the latitude, the altitude, the roll angle, the course angle and the pitch angle of the photos, and finally output the aerial three calculation results, the image stitching results, the digital ground model and the orthophoto image to be stored in the TIFF format.
5. The method for inverting mangrove forest biomass using aerial images and laser data as claimed in claim 1, wherein the height percentile variable of step (8) is generated by sorting all normalized lidar point clouds of mangrove forest according to height, and then calculating the height of X% of points in each statistical unit, which is the height percentile of the statistical unit.
6. The method for inverting mangrove forest biomass using aerial images and laser data according to claim 1, wherein the spectral indices in step (11) comprise green-to-red ratio index, green-to-blue ratio index, red-to-green ratio index, red-to-blue ratio index, normalized green-to-red difference index, normalized green-to-blue difference index, visible light atmospheric impedance index, visible differential vegetation index and ultragreen index, and the specific indices are calculated as follows: green-red ratio index GRRI-DNG/DNR, green-blue ratio index GBRI-DNG/DNB, red-green ratio index RGRI-DNR/DNG, red-blue ratio index RBRI-DNR/DNB, normalized green-red difference index NGRDI- (DNG-DNR)/(DNG + DNR), normalized green-blue difference index NGBDI- (DNG-DNB)/(DNG + DNB), visible atmosphere resistance index VARI- (DNG-DNR)/(DNG + DNR-DNB), visible difference vegetation index VDVI- (2DNG-DNR-DNB)/(2DNG + DNR + DNB), ultragreen index EXG-2 DNG-DNR-DNB, where g represents the value of green light in the ortho-image, DNB represents the value of blue light band in the ortho-image, and r represents the value of red light wave in the segment of the ortho-image.
7. The method for inverting mangrove forest biomass using aerial imagery and laser data according to claim 1, wherein the texture information of step (12) comprises mean, variance, cooperativity, contrast, dissimilarity, entropy and correlation.
8. The method for inverting mangrove forest biomass by using aerial image and laser data as claimed in claim 1, wherein the step (14) is specifically to put the mangrove forest variables in the sample area of step 13 into ArcGIS10.5, convert them into point data, obtain the values of each grid point, the values are used as independent variables of the random forest algorithm, while the mangrove forest biomass in the sample area is used as dependent variables, and build a random forest model under IBM SPSS Modeler 18.0, so as to obtain a model of the regression equation of mangrove forest biomass.
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| CN113160302A (en) * | 2021-04-25 | 2021-07-23 | 国家***南海环境监测中心（中国海监南海区检验鉴定中心） | Coral community analysis method and device |
| CN113552079A (en) * | 2021-06-17 | 2021-10-26 | 海南省林业科学研究院(海南省红树林研究院) | Mangrove forest quantitative inversion system based on aviation hyperspectral data |
| CN113970320A (en) * | 2021-09-18 | 2022-01-25 | 河南省远志林业规划设计有限公司 | Measuring method for forest biodiversity monitoring fixed observation sample plot |
| CN113970320B (en) * | 2021-09-18 | 2023-11-14 | 河南省远志林业规划设计有限公司 | Measuring method for forest biodiversity monitoring fixed observation sample plot |
| CN115223062A (en) * | 2022-06-30 | 2022-10-21 | 桂林理工大学 | UAV data-based method for correcting forest stand accumulation amount time difference of eucalyptus artificial forest region |
| CN115223062B (en) * | 2022-06-30 | 2023-10-20 | 桂林理工大学 | Eucalyptus artificial forest area stand accumulation amount time difference correction method based on UAV data |
| CN117218531A (en) * | 2023-09-08 | 2023-12-12 | 国家***南海规划与环境研究院 | Sea-land ecological staggered zone mangrove plant overground carbon reserve estimation method |
| CN117607070A (en) * | 2023-11-22 | 2024-02-27 | 广州双木林业有限公司 | Mangrove detection method, system, equipment and medium based on unmanned aerial vehicle |
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