CN115218947A - Three gorges reservoir area hydro-fluctuation belt typical vegetation biomass space-air ground integrated monitoring method - Google Patents
Three gorges reservoir area hydro-fluctuation belt typical vegetation biomass space-air ground integrated monitoring method Download PDFInfo
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Abstract
The invention discloses a three gorges reservoir area hydro-fluctuation belt typical vegetation biomass sky-ground integrated monitoring method, which utilizes a means of combining satellite remote sensing (sky basis), unmanned aerial vehicle aerial photography (sky basis) and ground actual measurement (ground) and takes normalized vegetation index NDVI as a core to carry out sky-ground vegetation index extraction and observation on a research area in 4 time periods of 5 months, 6 months, 7 months and 8 months respectively, and finally establishes a low-medium-high resolution hydro-fluctuation belt typical vegetation biomass inversion model based on the satellite NDVI, the unmanned aerial vehicle NDVI and the ground actual measurement data, thereby realizing the on-ground fresh weight of xanthium, bermuda grass and mixed type vegetation biomass and the air-based unmanned aerial vehicle NDVI data in the hydro-fluctuation belt and the conversion between the sky-based sentinel NDVI data and the air-based unmanned aerial vehicle NDVI remote sensing data, improving the biomass inversion accuracy and providing a reference basis for the evaluation of the recovery effect of the tri gorges reservoir area hydro-fluctuation belt vegetation.
Description
Technical Field
The invention relates to the technical field of ecological environment monitoring and evaluation, in particular to a three gorges reservoir area hydro-fluctuation belt typical vegetation biomass space-sky integrated monitoring method.
Background
The hydro-fluctuation belt is also called a hydro-fluctuation area, and is a phenomenon specific to rivers, lakes and reservoirs. It is mainly formed by two reasons, namely seasonal water level fluctuation, which mainly refers to the area where the submerged land periodically emerges on the water surface. In addition, the water level of the cave lake is reduced due to special climates (such as drought). The other is periodic water storage, mainly in large reservoirs (such as three gorges dam), and the formation of the hydro-fluctuation belt is mainly caused by the rise and fall of water level caused by periodic flood storage or flood discharge. The latter is the subject of this project.
After the three gorges project is completely built, the water level for water storage and power generation in winter is 175 meters, the flood prevention water level in summer is lowered to 145 meters, and the land exposed by the water level difference of 30 meters in the water level difference is the hydro-fluctuation belt. The Yangtze river dry flow is in a flood season from 6 months to 9 months every year, and a dry season from the bottom of 9 months to 5 months in the next year. The water discharge period of the three gorges reservoir is about 5 months from the bottom to the beginning of 6 months, and before the flood season comes, the water level of the reservoir area is guaranteed to be reduced to 145 meters. At the bottom of 9 months and at the beginning of 10 months when the flood season is about to end, the three gorges reservoir starts to store water until the water level reaches the design normal water storage level of 175 meters. The water level rising and falling seasons of the water-falling areas are against the natural flood drying rule, and the water-falling areas are most hot and humid in the period of emerging into the land, and heavy rain are frequent. Therefore, the water storage capacity of the three gorges reservoir in summer is the minimum, the exposed area of the hydro-fluctuation belt is the maximum, the vegetation is the most abundant, and 4-9 months are selected as the research period in the research.
The space-air-ground integrated ecological environment monitoring system provides powerful technical support for ecological environment monitoring work and becomes a new direction for future development of environmental monitoring.
The space-air-ground integrated ecological environment monitoring is a three-dimensional ecological environment monitoring perception system which obtains more accurate data support by comprehensively utilizing means such as satellite remote sensing monitoring, aviation remote sensing monitoring, ground fixed monitoring and the like and based on key technologies such as data mining, data fusion, data cooperation, data assimilation and the like. The space-ground integrated ecological environment monitoring system can more comprehensively reflect the current situation and the development trend of the environmental quality, comprehensively and finely evaluate the ecological environment conditions before and after the implementation of the major ecological restoration project, and provide scientific basis for macroscopic environment management, environment planning and the like.
At present, china is still in a starting stage in the construction of a space-ground integrated ecological environment monitoring system, and the problems of high construction cost, non-uniform data fusion standards, urgent need for establishing a data sharing mechanism and the like exist. The integrated construction of space, air and ground needs to coordinate monitoring data acquired by satellite remote sensing, ground stations and aviation, realize data fusion and data sharing, and really construct a three-dimensional monitoring system integrating satellite, aviation and ground monitoring. In recent years, the technology of acquiring near-earth (50-100 m) remote sensing information by developing an unmanned aerial vehicle as a remote sensing platform becomes an ideal connection point for constructing a star-sky integrated remote sensing system, meanwhile, with the popularization of digital cameras, the photography technology becomes a good-hand digital image information acquisition means, and the cameras based on the digital image technology, such as plant biomass, leaf Area Index (LAI) and normalized vegetation index zone, are specially designed for measurement purposes. Therefore, a skyline integrated monitoring method for dynamically monitoring the biomass of vegetation typical of a hydro-fluctuation belt in a three gorges reservoir area is needed.
Disclosure of Invention
In view of the above, the present invention aims to provide an integrated monitoring method for biomass space and ground of typical vegetation in a hydro-fluctuation belt in a three gorges reservoir area.
In order to achieve the purpose, the invention provides the following technical scheme:
1. a three gorges reservoir area hydro-fluctuation belt typical vegetation biomass space-air ground integrated monitoring method comprises the following steps:
according to the principle of uniform distribution, consideration of landform and landform, good flight condition and various land cover types, the landform of a hydro-fluctuation belt, the type of vegetation in the hydro-fluctuation belt, the external condition of the hydro-fluctuation belt and position factors are comprehensively considered, a representative, universal, operable and typical hydro-fluctuation belt area of the three gorges reservoir area is selected as a research area, the research area is subjected to space-ground vegetation index extraction and observation in 4 time periods of 5 months, 6 months, 7 months and 8 months by using a means of combining satellite remote sensing, unmanned aerial vehicle aerial photography and ground actual measurement and taking a normalized vegetation index NDVI as a core, finally a low-medium-high resolution ratio typical vegetation biomass inversion model of the hydro-fluctuation belt based on the satellite NDVI, the unmanned aerial vehicle NDVI and the ground actual measurement data is established, and the biomass space-time dynamic change characteristic of the vegetation in the 5-8 months of the hydro-fluctuation belt is analyzed;
the satellite remote sensing adopts a European Sentinel 2 (Sentinel-2) satellite; the unmanned aerial vehicle adopts a Dajiang genius 4 multi-spectral version unmanned aerial vehicle for aerial photography; the ground actual measurement adopts ground sample prescription or sample line investigation, the investigation content is the name of ground vegetation, the land utilization type, the height, the biomass and the distribution position, and meanwhile, a field crop NDVI measuring instrument is adopted to carry out ground NDVI measurement;
when a ground sample prescription or a sample line is investigated, in order to improve the inversion accuracy of the typical vegetation biomass of the hydro-fluctuation belt, the ground actual measurement data is divided into three groups of bermudagrass, xanthium and a mixture which are respectively analyzed, wherein the mixture is the type of vegetation inside the hydro-fluctuation belt except the bermudagrass and the xanthium;
the biomass is above ground fresh weight, underground dry weight, underground fresh weight, underground dry weight, and total fresh weight and total dry weight.
Preferably, the landform of the hydro-fluctuation belt is one or more of a steep slope type, a flat dam type, a gentle slope type and a storehouse tail type; the vegetation type in the hydro-fluctuation belt is one or more of cocklebur, bermuda grass, bidens tripartita, verbena compressa, cyperus rotundus, shortleaf kyllinga herb and ragweed; the external conditions of the hydro-fluctuation belt are land utilization types, including one or more of cultivated land, forest land, shrub forest land, orchard, garden land, grassland, bare land, water area and residential area.
Preferably, the research areas are 5 areas including Wushan Tang bay, kaizhou Dalang dam, yunyang flagstone ditch, zhongxian Hongyozi, fuling Murray and village, and cover 2 tributary point locations and 3 Yangtze river main flow point locations of the three gorge area.
Preferably, in ground monitoring, in order to ensure data quality, the number of ground NDVI monitoring points and sampling points in each research area is not less than 5, the number of sampling lines is not less than 1, and 1km of each research area is ensured 2 Not less than 10 points, each point is measured in the range of 1M multiplied by 1M and 5 groups of values are averaged.
Preferably, the inversion model of the biomass of the typical vegetation in the hydro-fluctuation belt comprises an inversion model between biomass data obtained by ground actual measurement and unmanned aerial vehicle NDVI data and an inversion model between sentinel NDVI and unmanned aerial vehicle NDVI.
Preferably, the method for constructing the inversion model between the biomass data obtained by ground actual measurement and the unmanned aerial vehicle NDVI data comprises the following steps: obtaining unmanned aerial vehicle NDVI data on corresponding time and space according to the ground actual measurement point, performing related linear analysis on the above-ground fresh weight, the above-ground dry weight, the underground fresh weight, the underground dry weight, the total fresh weight and the total dry weight of the xanthium sibiricum, the bermuda grass and the mixed sample prescription respectively to obtain the relation between biomass of different vegetation types and NDVI, and finally constructing an inversion model between the biomass data obtained by the ground actual measurement and the unmanned aerial vehicle NDVI data by comprehensively considering the rationality of biological significance and relevance.
Further preferably, in the inversion model between the biomass data obtained by the ground actual measurement and the unmanned aerial vehicle NDVI data, the fitting equation of the overground fresh weight (Y) of the xanthium biomass and the unmanned aerial vehicle NDVI (X) is as follows:
Y＝224062X 2 -331611X+124488(R 2 ＝0.7368)；
the equation fitted to overground dry weight of xanthium biomass (Y) and unmanned NDVI (X) is:
Y＝166267X 2 -244406X+90193(R 2 ＝0.7161)；
the equation fitted to total xanthium biomass dry weight (Y) and unmanned NDVI (X) is:
Y＝174560X 2 -256952X+95166(R 2 ＝0.7246)；
the above-ground fresh weight (Y) of the bermuda grass biomass and the unmanned plane NDVI (X) are fitted by the following equation:
Y＝30940X 2 -36279X+11316(R 2 ＝0.5515)；
the above-ground dry weight of bermuda grass biomass (Y) is fitted to the unmanned NDVI (X) equation as:
Y＝24728X 2 -28505X+8454.4(R 2 ＝0.6548)；
the equation fitted to total bermudagrass biomass dry weight (Y) and unmanned NDVI (X) is:
Y＝20800X 2 -23020X+6893.1(R 2 ＝0.6264)；
the above-ground biomass fresh weight (Y) and unmanned aerial vehicle NDVI (X) fitting equation of the mixed plant is as follows:
Y＝13694X 2 -15468X+5525(R 2 ＝0.5254)。
preferably, the construction method of the inversion model between the sentinel NDVI and the unmanned aerial vehicle NDVI comprises the following steps: with sentinel data of different months in a research area as reference, respectively selecting unmanned aerial vehicle images of corresponding months and the same places to compare with the sentinel data, and synchronously selecting homonymous points under different land utilization types such as vegetation full coverage, bare land, water areas, houses and the like; because the spatial resolution of the sentinel data is 10m and the spatial resolution of the unmanned aerial vehicle is 0.1m, the unmanned aerial vehicle image is matched with the sentinel data by calculating the average value of NDVI in the pixel range of 100 multiplied by 100.
Further preferably, in the inversion model between the sentinel NDVI and the unmanned NDVI, the fitting equation of the sentinel NDVI (X) and the unmanned NDVI (Y) is as follows:
Y＝1.3695X-0.3007(R 2 ＝0.954)。
the invention has the beneficial effects that: the invention discloses a space-sky integrated monitoring method for biomass of typical vegetation in a hydro-fluctuation belt of a three gorges reservoir area, which utilizes a means of combining satellite remote sensing (space basis), unmanned aerial vehicle aerial photography (space basis) and ground actual measurement (foundation), takes a normalized vegetation index NDVI as a core, carries out space-sky-ground vegetation index extraction and observation on a research area in 4 time periods of 5 months, 6 months, 7 months and 8 months respectively, finally establishes a low-medium-high resolution inversion model for the biomass of the typical vegetation in the hydro-fluctuation belt based on the satellite NDVI, the unmanned aerial vehicle NDVI and ground actual measurement data, analyzes the biomass space-time dynamic change characteristics of the vegetation in the hydro-fluctuation belt in the growing season of 5-8 months, realizes canger, berdon and mixed vegetation biomass ground fresh weight of the hydro-fluctuation belt and space unmanned aerial vehicle NDVI data, and conversion between space-sky-base NDVI data and space-sky-whistle-sky-unmanned aerial vehicle NDVI data, thereby improving the biomass inversion accuracy and providing reference basis for the vegetation recovery effect of the hydro-fluctuation belt of the three gorges reservoir area.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a diagram of a distribution of unmanned aerial vehicles and ground survey points;
FIG. 2 is a technical roadmap for the present invention;
FIG. 3 is remote sensing image data of European Sentinel No. 2 (Sentinel-2);
FIG. 4 is a diagram of the appearance of a Dajiang eidolon 4 multi-spectral version unmanned aerial vehicle;
FIG. 5 is a data production (part) of aerial photography by unmanned aerial vehicle;
FIG. 6 is a croPSENSE measuring instrument for field crop NDVI;
FIG. 7 is NDVI actual measurement point location collated data (section);
FIG. 8 is a curve fitted by the fresh weight on the cocklebur ground and the unmanned aerial vehicle NDVI;
FIG. 9 is a dry weight rain unmanned aerial vehicle NDVI fitting relation on the Xanthium sibiricum ground;
FIG. 10 is a curve fitted by the underground fresh weight of Xanthium sibiricum and the NDVI of the unmanned aerial vehicle;
FIG. 11 is a fitting relationship of xanthium underground dry weight and unmanned aerial vehicle NDVI;
FIG. 12 is a curve fitted by total fresh weight of Xanthium sibiricum and unmanned aerial vehicle NDVI;
FIG. 13 is a curve fit of total dry weight of Xanthium sibiricum to the unmanned aerial vehicle NDVI;
FIG. 14 is a plot of the above-ground fresh weight of Bermuda grass fitted to the unmanned plane NDVI;
FIG. 15 is a plot of the overground dry weight of Bermuda grass fitted to the unmanned plane NDVI;
FIG. 16 is a curve fitted by the underground fresh weight of Bermuda grass and the NDVI of the unmanned aerial vehicle;
FIG. 17 is a plot of Bermuda grass subsurface dry weight fitted to unmanned aerial vehicle NDVI;
FIG. 18 is a curve fitted by total fresh weight of Bermuda grass and unmanned plane NDVI;
figure 19 is a curve fitted to total bermuda grass dry weight and unmanned plane NDVI;
fig. 20 is a curve fitted between the mixed above-ground fresh weight and the unmanned aerial vehicle NDVI;
FIG. 21 is a mixed aboveground dry weight versus unmanned aerial vehicle NDVI fit curve;
FIG. 22 is a mixed underground fresh weight and unmanned aerial vehicle NDVI fit curve;
FIG. 23 is a curve fitted to mixed class subsurface dry weight and unmanned aerial vehicle NDVI;
fig. 24 is a mixed class total fresh weight and unmanned aerial vehicle NDVI fitted curve;
FIG. 25 is a curve fitting mixed class total dry weight to unmanned aerial vehicle NDVI;
FIG. 26 is a fitting relation diagram of Fuling August sentinel NDVI and unmanned aerial vehicle NDVI;
FIG. 27 is a chart of a fitting relationship between the Zyguengqiyue sentinel NDVI and the unmanned aerial vehicle NDVI in loyalty county;
fig. 28 is a fitting relationship diagram of wushanense sentinel NDVI and unmanned aerial vehicle NDVI;
FIG. 29 is a fitting relationship diagram of the comprehensive three-place sentinel NDVI and the unmanned aerial vehicle NDVI;
fig. 30 is a schematic diagram of the unmanned aerial vehicle image and the actual positioning error.
Detailed Description
The present invention is further described with reference to the following drawings and specific examples so that those skilled in the art can better understand the present invention and can practice the present invention, but the examples are not intended to limit the present invention.
Example 1 determination of regions of investigation, content of investigation and method of investigation
1. Principle of study area selection
(1) Is uniformly distributed
The research area selection should cover the main flow and the branch flow of the Yangtze river, and the south of the Yangtze river and the north of the Yangtze river, and be uniformly distributed along the hydro-fluctuation belt in Chongqing to ensure that the research area selection is representative.
(2) Giving consideration to both topography and landform
The hydro-fluctuation belt is divided into a steep slope type, a flat dam type, an island type, a garage tail type and a canyon type, and all types are covered as far as possible when a research area is selected, so that the universality of the research area selection is ensured.
(3) The flight condition is better
The unmanned aerial vehicle has the advantages that a relatively flat and open area is selected as much as possible in flying, no flying-forbidden areas such as airports and military facilities, sensitive areas such as railways and high-voltage line towers are avoided, no barriers which can easily block signals such as high-rise buildings and mountains exist in the flying range, and operability in selection of a research area is ensured.
(4) The type of the ground cover is various
Under the influence of climatic conditions and landforms, herbaceous and shrub vegetations such as bermuda grass, cocklebur and the like are mainly used in the hydro-fluctuation belt, and cultivated land, forest land, garden land and a small number of residents exist at the periphery of the hydro-fluctuation belt. The research area selection should comprehensively consider the internal and external conditions of the hydro-fluctuation belt to ensure that the research area selection has typicality.
In summary, the study selects 5 areas of wushantang bay, kaizhou billian dam, yunyang flagstone ditch, zhongxian redrock, fuling and village, including 2 tributary point locations and 3 Yangtze river mainstream point locations. The unmanned aerial vehicle and ground survey point distribution diagram is shown in fig. 1, and the study area condition statistics are shown in table 1.
TABLE 1 statistical Table of study area conditions
2. Content and objectives of research
The research mainly aims at seasonal change characteristics and overall distribution conditions of nature/artificial recovery of plants in the hydro-fluctuation zone when the water level of the three gorges reservoir area is reduced to 145m in 5-8 months, and by means of satellite remote sensing, unmanned aerial vehicle aerial photography, ground monitoring and the like, a hydro-fluctuation zone sky-space-ground vegetation condition monitoring system is established, and monitoring contents cover the aspects of microbial biomass, vegetation indexes and the like.
The selection principle is mainly used for researching the ground cover from the aspects of ground land utilization type, patch number, area, diversity, space distribution rule and the like, and the vegetation index mainly comprises an NDVI index.
3. Research methods and technical lines
The research mainly takes NDVI vegetation coverage index as a core, and carries out the extraction and observation of the sky-space-ground vegetation index in 4 time periods of 5 months, 6 months, 7 months and 8 months in 2021 to construct a space-ground integrated monitoring system for vegetation in the water-level-fluctuating zone, and the technical route of the invention is shown in figure 2.
1. Satellite remote sensing monitoring
"sky" refers to the large-scale environmental monitoring of the ecological environment condition and the variation trend by using the satellite remote sensing technology. The satellite remote sensing environment monitoring is implemented by taking artificial satellites such as high-grade first, high-grade second and high-grade sixth satellites, resource third and environment first satellites and the like as platforms, and utilizing detecting instruments such as visible light, infrared and microwave to perform remote sensing monitoring on large-scale macroscopic environment quality and ecological conditions through photographing or scanning, information induction, transmission and processing. The precision can reach the meter level, and the multi-time-sequence and large-range monitoring can be carried out on a research area.
The remote sensing image data of European Sentinel 2 (Sentinel-2) is mainly used in the research, as shown in FIG. 3. Sentinel 2 is a high resolution multispectral imaging satellite, carries a multispectral imager (MSI), is used for land monitoring, can provide images such as vegetation, soil and water coverage, inland waterway and coastal area, can also be used for emergency rescue service, divide into two satellites of 2A and 2B. The satellite carries a multispectral imager (MSI), the height is 786km, 13 spectral bands can be covered, and the breadth reaches 290 km. The ground resolution is respectively 10m, 20m and 60m, the revisit period of one satellite is 10 days, the two are complementary, and the revisit period is 5 days. The data of the sentinel-2 is the only satellite data containing three wave bands in the red range, which is very effective for monitoring the vegetation health information.
The hydro-fluctuation belt is narrow, so that the sentinel image becomes ideal data for remote sensing monitoring of the hydro-fluctuation belt in the three gorges reservoir area due to high time and space resolution.
2. Aerial remote sensing monitoring
The 'empty' refers to the situation that the environment quality of a key concern area is monitored in a medium and small scale environment by using an aerial remote sensing technology. The aerial remote sensing environmental monitoring is implemented by using various airplanes, airships, high-altitude balloons and the like as sensor carriers and carrying specific sensors to carry out remote sensing monitoring on single environmental events, local layers and local areas. Satellite remote sensing is compared in unmanned aerial vehicle monitoring, and the cost is lower, easy operation, precision can reach centimetre level, can carry out low-altitude flight monitoring under adverse conditions, and data acquisition cycle is short, ageing height, is applicable to the monitoring of view aspect such as small-range falling zone view type, patch number, area, density, variety, view spatial distribution law.
In the research, a small unmanned aerial vehicle (a Dajiang fairy 4 multispectral version unmanned aerial vehicle, 6 1/2.9-inch CMOS (complementary metal oxide semiconductor) comprises 1 color sensor for visible light imaging and 5 monochromatic sensors for multispectral imaging, wherein a single sensor comprises 208 thousands of effective pixels, 212 thousands of total pixels and 1600 multiplied by 1300 ℃ of the maximum resolution of a picture) is used for acquiring aerial photographing data such as a low-altitude (height: 600FT/182.88m, precision: 9.7 cm) image, NDVI and the like. The Xinjiang spirit 4 multispectral version unmanned aerial vehicle is shown in fig. 4, and part of aerial data production of the unmanned aerial vehicle is shown in fig. 5. And (4) interpreting the aerial image data by utilizing the ENVI to obtain centimeter-level land utilization types of the 5 research areas. Meanwhile, combining high-time-resolution Sentinel 2 (Sentinel-2) data, establishing a vegetation coverage and biomass inversion model of the hydro-fluctuation belt based on sample actual measurement data and vegetation index NDVI data, and analyzing the time-space dynamic change characteristics of the hydro-fluctuation zone during the vegetation growth season of 2021 years (5-8 months).
3. Ground monitoring
The 'ground' refers to that specific monitoring items such as vegetation, soil, water quality, air quality, enterprise pollution discharge conditions and the like in a set area are accurately and dynamically monitored by utilizing laboratory manual monitoring or online automatic monitoring. Species levels such as chlorophyll, NDVI (normalized vegetation index), LAI (leaf area index), biomass, name, type, height, coverage and distribution position of the vegetation in the elimination space are monitored by combining with handheld instrument equipment and ground sample survey.
Ground-based dailies/dailies were conducted in 5 areas of study, and the contents of the survey included the name, type, height, biomass, location of distribution, etc. of the ground vegetation. The ground vegetation index monitoring equipment adopts a novel field crop NDVI measuring instrument CropSense (shown in figure 6) developed by Beijing agricultural information technology research center, and can obtain the NDVI of vegetation, leaf Area Index (LAI), estimated yield, recommended nitrogen, coverage, chlorophyll content and other main growth indexes in real time. In order to ensure the data quality, the number of the ground NDVI monitoring points and the sampling points in each area is not less than 5 (the number of sampling lines is not less than 1).
The ground survey of the hydro-fluctuation belt is carried out when the vegetation grows out completely, and the specific survey indexes are shown in a table 2.
TABLE 2 land survey form (sample table) for vegetation in water-level-fluctuating zone
Note: the land types include cultivated land, forest land, garden land, grassland, bare land, water area, and construction land.
Ground NDVI measurements ensure 1km per study area 2 Not less than 10 point locations, each point location is in 1M within the range of 1M 1M 5 groups of values and the average value is taken, the measured data quality is ensured, and the vegetation NDVI ground measurement table (sample table) is shown in table 3.
TABLE 3 vegetation NDVI ground survey table (sample table)
NDVI site location alignment data (in part) is shown in fig. 7.
And finally, establishing a low-medium-high resolution inversion model of the vegetation biomass of the hydro-fluctuation belt based on the sentinel NDVI, the unmanned aerial vehicle NDVI and the measured data, and analyzing the biomass space-time dynamic change characteristics of the hydro-fluctuation belt in the 2021 year vegetation growing season (5-8 months) of the hydro-fluctuation area.
Example 2 sky-ground integrated monitoring System construction
1. Measured data on the ground
The data acquisition time of the research is 2021, 5 months and 13 days to 2021, 8 months and 25 days, unmanned aerial vehicle aerial photography and ground actual measurement are carried out on the relevant research areas in 5 counties, and total 4-period data of 5 months, 6 months, 7 months and 8 months are formed. Influenced by weather elements and instruments, acquire unmanned aerial vehicle aerial photography image 16 times, reap 73 plant prescriptions.
Table 4 statistical table of data acquisition situation of falling zone sample
The field investigation result shows that the vegetation types of the hydro-fluctuation belt are mainly bermudagrass and xanthium sibiricum, so that ground actual measurement data are divided into three groups of bermudagrass, xanthium sibiricum and mixed vegetation types (the types of the rest vegetation except the bermudagrass and the xanthium sibiricum) to be respectively analyzed in the construction process of the sky-ground integrated monitoring system.
The measured data of the biomass of the cynodon dactylon of the hydro-fluctuation belt are 60. The overground fresh weight, the overground dry weight, the underground fresh weight, the underground dry weight, the total fresh weight and the total dry weight of the bermuda grass are counted, and the statistical result is specifically shown in table 5.
TABLE 5 statistics of biomass data of Cynodon dactylon in hydro-fluctuation belt
Table 5 shows that the average value of the biomass of Bermuda grass is 3044.49g as the total fresh weight at the maximum, and 362.62g as the underground dry weight at the minimum; the largest standard deviation is the total fresh weight 1992.67g, and the smallest is the underground dry weight 265.6g.
The measured data of the biomass of the cocklebur in the hydro-fluctuation belt is totally 48, the overground fresh weight, the underground dry weight, the underground fresh weight and the underground dry weight of the cocklebur are counted, and the statistical result is shown in a table 6.
TABLE 6 statistics of Xanthium sibiricum biomass data in hydro-fluctuation belt
Table 6 shows that the average value of the xanthium biomass is 4223.05g of the total fresh weight at the maximum, and the underground dry weight is 249.81g at the minimum; the maximum standard deviation is 3400.45g total fresh weight and the minimum is 101.94g underground dry weight.
The actual measurement data of the mixed biomass of the hydro-fluctuation belt are 168, the overground fresh weight, the underground dry weight, the underground fresh weight, the underground dry weight, the total fresh weight and the total dry weight of the mixed biomass are counted, and the statistical condition is specifically shown in table 7.
TABLE 7 Mixed class Biomass data statistics
Table 7 shows that the average mixed biomass amount is at most 3480.67g in total fresh weight and at least 311.82g in underground dry weight; the maximum standard deviation is 2485.07g of total fresh weight, and the minimum is 270.46g of underground dry weight.
2. Air-based (unmanned aerial vehicle) data modeling
In the research, 44 pieces of unmanned aerial vehicle NDVI data are obtained from the hydro-fluctuation belt, the spatial resolution is mainly 0.1m, and the data volume is 11.7G. 4 Wushan among them, data volume 0.92G; 10 Yunyang, data volume 2.07G; fuling 12 pieces, data volume 4G; 12 Kaihua, data volume 3.23G; 6 faithful county, data volume 1.48G.
On the basis, a biomass space-air-ground integrated monitoring system is constructed by utilizing series of biomass data obtained by ground actual measurement and unmanned aerial vehicle NDVI data to perform corresponding analysis.
The method comprises the specific steps of obtaining unmanned aerial vehicle NDVI data on corresponding time and space according to ground actual measurement point positions, carrying out related linear analysis on overground fresh weight, overground dry weight, underground fresh weight, underground dry weight, total fresh weight and total dry weight of xanthium sibiricum, bermuda grass and a mixed sample, exploring the relation between biomass of different vegetation types and NDVI, and finally reasonably constructing a hydro-fluctuation belt biomass sky and land integrated monitoring system by comprehensively considering biological significance and correlation.
1. Xanthium sibiricum biomass and unmanned aerial vehicle NDVI analysis
The fitting relation of the overground fresh weight of the xanthium biomass and the unmanned aerial vehicle NDVI is shown in a figure 8. The above-ground fresh weight (Y) of the xanthium sibiricum biomass and the unmanned aerial vehicle NDVI (X) are fitted by the following equation:
Y＝224062X 2 -331611X+124488(R 2 ＝0.7368)
the fitting result shows that the overground fresh weight of the xanthium biomass and the NDVI of the unmanned aerial vehicle have a good fitting relationship, and the correlation coefficient R 2 Reaches over 0.7, and the two are in obvious positive correlation.
The fitted relationship of overground dry weight of xanthium biomass and unmanned aerial vehicle NDVI is shown in FIG. 9. The equation of the overground dry weight (Y) of the xanthium biomass and the unmanned plane NDVI (X) is fitted as follows:
Y＝166267X 2 -244406X+90193(R 2 ＝0.7161)
the fitting result shows that the overground dry weight of the xanthium biomass has a good fitting relation with the NDVI of the unmanned aerial vehicle, and the correlation coefficient R 2 Reaches over 0.7, and the two are in obvious positive correlation.
The fitting relation of the underground fresh weight of the xanthium biomass and the unmanned plane NDVI is shown in a figure 10. The equation for fitting the underground fresh weight (Y) of the xanthium sibiricum biomass with the unmanned plane NDVI (X) is as follows:
Y＝120917X 2 +186801X-71160(R 2 ＝0.2648)
the fitting result shows that the fitting relation between the underground fresh weight of the xanthium biomass and the NDVI of the unmanned aerial vehicle is poor, and the correlation coefficient R 2 Below 0.3.
The fitted relationship of xanthium biomass subsurface dry weight to drone NDVI is shown in figure 11. The equation fitted to xanthium biomass underground dry weight (Y) and unmanned plane NDVI (X) is:
Y＝8293X 2 -12546X+4973.1(R 2 ＝0.0272)
the fitting result shows that the fitting relation between the dry weight of the xanthium underground biomass and the NDVI of the unmanned aerial vehicle is poor, and the two have almost no correlation.
The fitting relation of the total fresh weight of the xanthium sibiricum biomass and the unmanned plane NDVI is shown in figure 12. The fitting equation of the total fresh weight (Y) of the xanthium sibiricum biomass and the unmanned plane NDVI (X) is as follows:
Y＝103145X 2 -144810X+53327(R 2 ＝0.4648)
the fitting result shows that the correlation coefficient R of the total fresh weight of the xanthium sibiricum biomass and the NDVI of the unmanned aerial vehicle 2 Above 0.4, the correlation is general.
The relationship between the total dry weight of xanthium biomass and the NDVI of the drone is shown in fig. 13. The equation fitted to total xanthium biomass dry weight (Y) and unmanned NDVI (X) is:
Y＝174560X 2 -256952X+95166(R 2 ＝0.7246)
the fitting result shows that the fitting relation between the total dry weight of the xanthium biomass and the NDVI of the unmanned aerial vehicle is good, the correlation coefficient reaches more than 0.7, and the correlation is strong.
The analysis results show that the aboveground fresh weight, the aboveground dry weight and the total dry weight of the xanthium biomass have good fitting relationship with the unmanned aerial vehicle NDVI, and the correlation coefficient reaches over 0.7, so that the xanthium biomass remote sensing inversion method can be used for constructing a remote sensing inversion model of the biomass in the falling zone.
2. Bermuda grass biomass and unmanned plane NDVI analysis
The fitted relationship between the overground fresh weight of bermuda grass and the unmanned plane NDVI is shown in figure 14. The above-ground fresh weight (Y) of the bermuda grass biomass and the unmanned plane NDVI (X) are fitted by the following equation:
Y＝30940X 2 -36279X+11316(R 2 ＝0.5515)
the fitting result shows that the overground fresh weight of the cynodon dactylon and the NDVI fitting correlation coefficient R of the unmanned aerial vehicle 2 Above 0.5, the correlation is strong.
The above ground dry weight of bermuda grass is fitted to the unmanned NDVI as shown in figure 15. The above-ground dry weight (Y) of bermuda grass biomass and the unmanned plane NDVI (X) are fitted by the following equation:
Y＝24728X 2 -28505X+8454.4(R 2 ＝0.6548)
the fitting result shows that the overground dry weight of the biomass of the cynodon dactylon is fitted with the NDVI of the unmanned aerial vehicle to obtain a correlation coefficient R 2 Reaches more than 0.6, and has stronger correlation.
The fitting relation of the underground fresh weight of the bermuda grass and the NDVI of the unmanned aerial vehicle is shown in figure 16. The underground fresh weight (Y) of the bermuda grass biomass and the unmanned plane NDVI (X) are fitted by the following equation:
Y＝6464.2X 2 +8728.8X-2402.9(R 2 ＝0.325)
the fitting result shows that the underground fresh weight of the biomass of the cynodon dactylon and the NDVI fitting correlation coefficient R of the unmanned aerial vehicle 2 Above 0.3, the correlation is weak.
The fit of bermuda grass subsurface dry weight to drone NDVI is shown in figure 17. The equation fitted to the underground dry weight of bermuda grass biomass (Y) and unmanned NDVI (X) is:
Y＝-3928.4X 2 +5485X-1561.2(R 2 ＝0.3074)
the fitting result shows that the underground dry weight of the biomass of the cynodon dactylon and the NDVI of the unmanned aerial vehicle are fitted to form a correlation coefficient R 2 The correlation is weak at about 0.3.
The fitting relation of the total fresh weight of the bermuda grass and the unmanned plane NDVI is shown in figure 18. The fitting equation of the total fresh weight (Y) of the cynodon dactylon biomass and the unmanned plane NDVI (X) is as follows:
Y＝24476X 2 -27550X+8913.2(R 2 ＝0.4964)
the fitting result shows that the total fresh weight of the biomass of the cynodon dactylon and the NDVI fitting correlation coefficient R of the unmanned aerial vehicle 2 The correlation is strong when the value is about 0.5.
The total dry weight of bermuda grass is fitted to the NDVI of the drone in figure 19. The equation fitted to total bermudagrass biomass dry weight (Y) and unmanned NDVI (X) is:
Y＝20800X 2 -23020X+6893.1(R 2 ＝0.6264)
the fitting result shows that the total dry weight of the biomass of the cynodon dactylon and the NDVI fitting correlation coefficient R of the unmanned aerial vehicle 2 Reaches more than 0.6, and has stronger correlation.
The analysis results show that similar to xanthium sibiricum, the aboveground fresh weight, the aboveground dry weight and the total dry weight of the biomass of the bermudagrass have the best fitting relationship with the unmanned aerial vehicle NDVI, and the correlation coefficients all reach about 0.6, so that the method can be used for constructing the remote sensing inversion model of the biomass in the hydro-fluctuation belt.
3. Mixed biomass-like mass and unmanned aerial vehicle NDVI analysis
The fitting relation of the overground fresh weight of the mixed plant and the unmanned aerial vehicle NDVI is shown in a figure 20. The above-ground biomass fresh weight (Y) and unmanned aerial vehicle NDVI (X) fitting equation of the mixed plant is as follows:
Y＝13694X 2 -15468X+5525(R 2 ＝0.5254)
the fitting result shows that the mixed plant overground biomass fresh weight and the unmanned aerial vehicle NDVI fitting correlation coefficient R 2 Reaches more than 0.5, and has stronger correlation.
The above-ground dry weight of mixed plants is fitted to the NDVI of the drone as shown in figure 21. The above-ground biomass dry weight (Y) of the mixed plant species is fitted to the unmanned NDVI (X) equation as:
Y＝977.77X 2 +198.11X+415.15(R 2 ＝0.3616)
the fitting result shows that the mixed plant overground biomass dry weight and the unmanned aerial vehicle NDVI fit correlation coefficient R 2 Only 0.36, the correlation is weak.
The fit relation of the underground fresh weight of the mixed plant and the NDVI of the unmanned aerial vehicle is shown in figure 22. The fit equation of the underground biomass fresh weight (Y) of the mixed plant and the unmanned aerial vehicle NDVI (X) is as follows:
Y＝2019.4X 2 -4428.9X+2632.6(R 2 ＝0.8049)
the fitting result shows that the underground fresh weight of the mixed plant and the NDVI fitting correlation coefficient R of the unmanned aerial vehicle 2 Reaches more than 0.8, and has strongest correlation.
The mixed plant underground dry weight is fitted to the unmanned plane NDVI as shown in fig. 23. The mixed-species underground biomass dry weight (Y) is fitted to the drone NDVI (X) equation:
Y＝624.92X 2 -1910.3X+1281.6(R 2 ＝0.7676)
the fitting result shows that the underground dry weight of the mixed plant is fitted with the NDVI of the unmanned aerial vehicle to obtain a correlation coefficient R 2 Reaches more than 0.7, and has obvious correlation.
The fitting relationship between the total fresh weight of the mixed plants and the unmanned plane NDVI is shown in figure 24. The fitting equation of the total fresh weight (Y) of the mixed plant biomass and the unmanned plane NDVI (X) is as follows:
Y＝10695X 2 -14960X+7887.4(R 2 ＝0.4258)
the fitting result shows that the biomass of the mixed plant is total fresh weight and noneMan-machine NDVI fitting correlation coefficient R 2 The correlation is weak at about 0.4.
The fit relationship of total mixed plant dry weight to drone NDVI is shown in figure 25. The equation fitted to the mixed plant biomass total dry weight (Y) and unmanned plane NDVI (X) is:
Y＝7079.5X 2 -7925.9X+3294(R 2 ＝0.4818)
the fitting result shows that the total dry weight of biomass of the mixed plant and the NDVI of the unmanned aerial vehicle fit a correlation coefficient R 2 The correlation is strong around 0.5.
The above analysis results show that the mixed plants exhibit a different law from that of Xanthium strumarium and Cynodon dactylon. The aboveground fresh weight, the underground fresh weight and the underground dry weight of the mixed plant biomass have the best fitting relationship with the unmanned aerial vehicle NDVI, the correlation coefficients all reach more than 0.5, and the correlation between the aboveground fresh weight, the underground fresh weight and the underground dry weight is strongest with the unmanned aerial vehicle NDVI, so that the mixed plant biomass remote sensing inversion method can be theoretically used for building a remote sensing inversion model of the biomass in a falling zone. However, the mixed plant-like biomass underground fresh weight and underground dry weight have no realistic correlation with the forming mechanism of NDVI data, so that the mixed plant-like biomass underground fresh weight and underground dry weight are not suitable for constructing a biomass remote sensing inversion model.
The results of the relevant analysis contents of the xanthium sibiricum, the bermuda grass and the mixed plants show that the index of the biomass overground fresh weight has stronger relevance with NDVI of three vegetation types. Wherein the correlation coefficient R of the overground fresh weight of the cocklebur and the NDVI of the unmanned aerial vehicle 2 =0.7368, correlation coefficient R of overground fresh weight of Bermuda grass and unmanned aerial vehicle NDVI 2 =0.5515, correlation coefficient R of overground fresh weight of mixed plant and NDVI of unmanned aerial vehicle 2 =0.5254, therefore, overground fresh weight of xanthium sibiricum, bermuda grass, mixed biomass and unmanned plane NDVI are selected for modeling.
3. Space-based (sentinel remote sensing) data conversion
The unmanned aerial vehicle data accuracy is higher, but obtains the cost huge. The space resolution of the space-based sentinel remote sensing data is 10m, the space-based sentinel remote sensing data is high in resolution and can be obtained in a large area, and the biomass inversion method based on the space-based sentinel remote sensing data is more economical, operable and the like. Therefore, a conversion model between the sentinel remote sensing data and the unmanned aerial vehicle NDVI is established, and the dynamic observation of the landbelt biomass is realized based on the space-based sentinel data, so that the method has stronger practical significance.
Taking sentinel data of different months in Wushan June, qiyue Zhongxian and August Fuling as references, respectively selecting unmanned aerial vehicle images of corresponding months and the same places to compare with the sentinel data, and synchronously selecting homonymous points under different land utilization types such as vegetation full coverage, bare land, water areas, houses and the like. Because the spatial resolution of the sentinel data is 10m, and the spatial resolution of the unmanned aerial vehicle is 0.1m, the unmanned aerial vehicle image is matched with the sentinel data by calculating the average value of the NDVI in the pixel range of 100 x 100.
10-15 point locations are selected according to the land utilization type, and 168 point locations are selected. Wherein, fuling selects 50 point locations, zhongxian selects 54 point locations, wushan selects 64 point locations.
The following are three locally different month fitting processes, respectively.
The fitting relation of Fuling August sentinel NDVI (X) and unmanned aerial vehicle NDVI (Y) is shown in figure 26. The result of the fitting of Fuling August sentinel NDVI (X) and unmanned aerial vehicle NDVI (Y) is as follows:
Y＝1.5324X-0.3392(R 2 ＝0.9483)
the fitting result shows that the Fuling August sentinel NDVI data has a very good fitting relation with the corresponding unmanned aerial vehicle NDVI, and the correlation coefficient R 2 =0.9483, the correlation is extremely significant.
The matching relationship between the qiyue sentinel NDVI (X) and the unmanned NDVI (Y) is shown in fig. 27. Fitting results of Qiyue sentinels NDVI (X) and unmanned aerial vehicles NDVI (Y) in Zhongxian county:
Y＝1.3248X-0.3(R 2 ＝0.954)
the fitting result shows that the NDVI of the sentry in July county is very good in fitting relation with the corresponding NDVI of the unmanned aerial vehicle, and the correlation coefficient R 2 =0.954, the correlation is extremely significant.
The fitting relation of wushanjune sentinel NDVI and unmanned plane NDVI is shown in figure 28. Fitting results of Wushan June sentinels NDVI (X) and unmanned aerial vehicle NDVI (Y):
Y＝1.3256X-0.2947(R 2 ＝0.9675)
the fitting result shows that the Wingshan Juniperus NDVI is fitted with the corresponding unmanned aerial vehicle NDVIThe relation is very good, the correlation coefficient R 2 =0.9675, the correlation is extremely significant.
The data of the sentinel NDVI and the unmanned aerial vehicle NDVI in three places of Fuling August, qiyue in Zhongcounty and June in Wushan are integrated, and the fitting relationship is shown in a figure 29. The fitting results of Fuling August, qiyue in Zhongxian county, wushan June Sandi sentinel NDVI (X) and unmanned aerial vehicle NDVI (Y) are integrated:
Y＝1.3695X-0.3007(R 2 ＝0.954)
the comprehensive fitting result shows that the fitting relation between the sentry NDVI and the unmanned aerial vehicle NDVI of three places, namely Fuling August, qiyue in Zhongcounty and June in Wushan is very good, and the correlation coefficient R 2 =0.954, the correlation is extremely significant. Therefore, the space-based sentinel NDVI data can be converted into space-based unmanned aerial vehicle NDVI remote sensing data by using the comprehensive fitting formula, so that the biomass inversion accuracy is improved.
4. Looking at
1. Developing hydro-fluctuation belt vegetation classification
Through experiments in the research process, if all samples are taken as one type, the correlation between biomass and the unmanned aerial vehicle NDVI is found to be too poor to construct an inversion model. Therefore, good effects can be achieved by respectively establishing inversion models after roughly dividing the vegetation types into three vegetation types of xanthium, bermudagrass and mixed vegetation types according to the actual conditions of the hydro-fluctuation belt. Therefore, the inversion accuracy of the biomass of the hydro-fluctuation belt can be greatly improved by developing the hyperspectral research of vegetation classification of the hydro-fluctuation belt.
2. Improving monitoring device performance
The multispectral unmanned aerial vehicle used in the research is limited by the functions of the device, and does not have the RTK accurate positioning function. As shown in fig. 30, square wire frame is the unmanned aerial vehicle location, and there is the sample side to reap the dark zone block of appearance in the image for actual positioning, can see that both have the deviation, and unmanned aerial vehicle image data has about 1 meter positioning error when matching with the actual measurement point location of sample side on the space, and this research has carried out the space correction through the difference that adopts the sample side to reap front and back earth's surface cover to guarantee space matching precision, but the not high and easy mistake of efficiency. Therefore, better unmanned aerial vehicle equipment such as a hyperspectral-radar all-in-one machine is configured, accurate positioning can be achieved, model parameters such as vegetation height and terrain can be obtained, and a more ideal simulation effect can be achieved for the remote sensing modeling of biomass measured in a sample.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (9)
1. A three gorges reservoir area typical vegetation biomass space-ground integrated monitoring method is characterized by comprising the following steps:
according to the principle of uniform distribution, consideration of landform and landform, good flight condition and various land cover types, the landform of a hydro-fluctuation belt, the type of vegetation in the hydro-fluctuation belt, the external condition of the hydro-fluctuation belt and position factors are comprehensively considered, a representative, universal, operable and typical hydro-fluctuation belt area of the three gorges reservoir area is selected as a research area, the research area is subjected to space-ground vegetation index extraction and observation in 4 time periods of 5 months, 6 months, 7 months and 8 months by using a means of combining satellite remote sensing, unmanned aerial vehicle aerial photography and ground actual measurement and taking the normalized vegetation index NDVI as a core, finally, a low-medium-high resolution ratio typical vegetation biomass inversion model of the hydro-fluctuation belt based on satellite NDVI, NDVI and ground actual measurement data is established, and the biomass dynamic change characteristic of the hydro-space during the growth season of the hydro-fluctuation belt in 5-8 months is analyzed;
the satellite remote sensing adopts a European Sentinel 2 (Sentinel-2) satellite; the unmanned aerial vehicle adopts a Dajiang genius 4 multi-spectral version unmanned aerial vehicle; the ground actual measurement adopts a ground sample prescription or sample line investigation, the investigation is carried out when the vegetation grows out completely, the investigation contents are the name, the land utilization type, the height, the biomass and the distribution position of the ground vegetation, and meanwhile, a field crop NDVI measuring instrument is adopted to carry out the ground NDVI measurement;
when a ground sample prescription or a sample line is investigated, in order to improve the inversion accuracy of the typical vegetation biomass of the hydro-fluctuation belt, the ground actual measurement data is divided into three groups of bermudagrass, xanthium and a mixture which are respectively analyzed, wherein the mixture is the type of vegetation inside the hydro-fluctuation belt except the bermudagrass and the xanthium;
the biomass is above ground fresh weight, underground dry weight, underground fresh weight, underground dry weight, and total fresh weight and total dry weight.
2. The integrated monitoring method for the vegetation biomass sky and land of the hydro-fluctuation belt of the three gorges reservoir area according to claim 1, wherein the topography of the hydro-fluctuation belt is one or more of a steep slope type, a flat dam type, a gentle slope type and a reservoir tail type; the vegetation type in the hydro-fluctuation belt is one or more of cocklebur, bermuda grass, bidens tripartita, verbena compressa, cyperus rotundus, shortleaf kyllinga herb and ragweed; the external conditions of the hydro-fluctuation belt are land utilization types, including one or more of cultivated land, forest land, shrub forest land, orchard, garden land, grassland, bare land, water area and residential area.
3. The method for integrally monitoring vegetation biomass space-ground in a three gorge area hydro-fluctuation zone according to claim 2, wherein the research area is 5 areas of Wushan school bay, kaizhou great wave dam, yunyang flagstone ditch, zhongxian Hongyan, fuling Murray and village, and 2 branch point locations and 3 Yangtze river stem flow point locations covering the three gorge area.
4. The integrated monitoring method for the vegetation biomass space-ground in the hydro-fluctuation belt of the three gorges reservoir area according to claim 1, wherein in ground monitoring, in order to ensure data quality, ground NDVI monitoring points and sampling points of each research area are not less than 5, sampling lines are not less than 1, and 1km of each research area is ensured 2 Not less than 10 points, each point is measured in the range of 1M multiplied by 1M and 5 groups of values are averaged.
5. The method of claim 1, wherein the model comprises an inversion model between biomass data and drone NDVI data obtained from ground measurements, and an inversion model between sentinel NDVI and drone NDVI.
6. The integrated monitoring method for biomass space-ground of typical vegetation in hydro-fluctuation belts in the three gorges reservoir area according to claim 5, wherein a construction method of an inversion model between biomass data obtained by ground actual measurement and unmanned aerial vehicle NDVI data is as follows: obtaining unmanned aerial vehicle NDVI data on corresponding time and space according to the ground actual measurement point, performing related linear analysis on the above-ground fresh weight, the above-ground dry weight, the underground fresh weight, the underground dry weight, the total fresh weight and the total dry weight of the xanthium sibiricum, the bermuda grass and the mixed sample prescription respectively to obtain the relation between biomass of different vegetation types and NDVI, and finally constructing an inversion model between the biomass data obtained by the ground actual measurement and the unmanned aerial vehicle NDVI data by comprehensively considering the rationality of biological significance and relevance.
7. The method of claim 6, wherein in an inverse model between the biomass data obtained by ground actual measurement and the unmanned aerial vehicle NDVI data, the fitted equation of the overground fresh weight (Y) of the xanthium biomass and the unmanned aerial vehicle NDVI (X) is as follows:
Y＝224062X 2 -331611X+124488(R 2 ＝0.7368)；
the equation fitted to overground dry weight of xanthium biomass (Y) and unmanned NDVI (X) is:
Y＝166267X 2 -244406X+90193(R 2 ＝0.7161)；
the equation fitted to total xanthium biomass dry weight (Y) and unmanned NDVI (X) is:
Y＝174560X 2 -256952X+95166(R 2 ＝0.7246)；
the above-ground fresh weight (Y) of the Bermuda grass biomass and the unmanned aerial vehicle NDVI (X) are fitted by the following equation:
Y＝30940X 2 -36279X+11316(R 2 ＝0.5515)；
the above-ground dry weight of bermuda grass biomass (Y) is fitted to the unmanned NDVI (X) equation as:
Y＝24728X 2 -28505X+8454.4(R 2 ＝0.6548)；
the equation fitted to total bermudagrass biomass dry weight (Y) and unmanned NDVI (X) is:
Y＝20800X 2 -23020X+6893.1(R 2 ＝0.6264)；
the above-ground biomass fresh weight (Y) of the mixed plant and the unmanned aerial vehicle NDVI (X) are fitted by the following equation:
Y＝13694X 2 -15468X+5525(R 2 ＝0.5254)。
8. the integrated monitoring method for the typical vegetation biomass space-ground in the hydro-fluctuation belt of the three gorges reservoir area according to claim 5, wherein the construction method of the inversion model between the sentinel NDVI and the unmanned aerial vehicle NDVI is as follows: with sentinel data of different months in a research area as reference, respectively selecting unmanned aerial vehicle images of corresponding months and the same places to compare with the sentinel data, and synchronously selecting homonymous points under different land utilization types such as vegetation full coverage, bare land, water areas, houses and the like; because the spatial resolution of the sentinel data is 10m and the spatial resolution of the unmanned aerial vehicle is 0.1m, the unmanned aerial vehicle image is matched with the sentinel data by calculating the average value of NDVI in the pixel range of 100 multiplied by 100.
9. The integrated monitoring method for the biomass space-ground of the vegetation typical of the hydro-fluctuation belt of the three gorges reservoir area of claim 8, wherein in the inversion model between the sentinel NDVI and the unmanned aerial vehicle NDVI, the fitting equation of the sentinel NDVI (X) and the unmanned aerial vehicle NDVI (Y) is as follows:
Y＝1.3695X-0.3007(R 2 ＝0.954)。
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| KR102533928B1 (en) * | 2022-11-01 | 2023-05-19 | (주) 지오씨엔아이 | Method and System for Automatically Detecting Reservoirs Using the 3D Spatial Imagery Datasets |
| CN116385883A (en) * | 2023-04-13 | 2023-07-04 | 珠江水利委员会珠江水利科学研究院 | Unmanned plane mountain shadow area vegetation coverage correction method, equipment and medium |
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| KR102533928B1 (en) * | 2022-11-01 | 2023-05-19 | (주) 지오씨엔아이 | Method and System for Automatically Detecting Reservoirs Using the 3D Spatial Imagery Datasets |
| CN116385883A (en) * | 2023-04-13 | 2023-07-04 | 珠江水利委员会珠江水利科学研究院 | Unmanned plane mountain shadow area vegetation coverage correction method, equipment and medium |
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