Vegetation condition monitoring method and device based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of oil drilling and production measurement, in particular to a vegetation condition monitoring method and device based on an unmanned aerial vehicle, and the method comprises the steps: obtaining an image data set of a target region based on the unmanned aerial vehicle; constructing a digital grid image of the target area according to the image data set; and extracting vegetation information in the digital grid image. A large number of pre-drilling engineering land images shot by an unmanned aerial vehicle are collected and matched and spliced to generate a digital grid image of the pre-drilling engineering land, then vegetation information in the digital grid image is extracted through an object-oriented classification method, and a vegetation classification thematic map of the pre-drilling engineering land is generated. According to the method, all-dimensional and multi-scale basic geographic space data are provided, all-dimensional data support is provided for vegetation condition monitoring of the pre-drilling engineering land, the accuracy and timeliness of vegetation condition monitoring are guaranteed, and estimation of the vegetation carbon sequestration loss amount of the pre-drilling engineering is met.

Description

Technical Field

[0001] This application relates to the technical field of oil drilling and production measurement, and particularly relates to a method and device for monitoring vegetation conditions based on unmanned aerial vehicles (UAVs). Background Art

[0002] Pre-drilling engineering is the various preparatory work carried out to provide the necessary conditions for the drilling of oil and gas wells. The land used for pre-drilling engineering mainly includes permanent land occupation and temporary land occupation. After the land occupation, projects such as site vegetation removal, topsoil stripping, and earthwork projects are mainly carried out.

[0003] During the process of vegetation removal and topsoil stripping, carbon losses will occur. The carbon losses caused by permanent land occupation will not be able to be compensated, while the carbon losses of temporary land occupation can be repaired to a certain extent by covering the soil and restoring farming after well completion. The amount of carbon loss is mainly composed of vegetation carbon sequestration loss and soil carbon sequestration loss, and is a quantity related to time, space, and geographical conditions.

[0004] Currently, there is no effective method for monitoring the vegetation conditions of the land used for pre-drilling engineering. Moreover, the vegetation conditions of the land used for pre-drilling engineering directly affect the estimation results of vegetation carbon sequestration losses. Therefore, there is an urgent need for a vegetation condition monitoring means to monitor the vegetation conditions of the land used for pre-drilling engineering and meet the estimation of the vegetation carbon sequestration loss amount of pre-drilling engineering. Summary of the Invention

[0005] This application aims to at least partly solve one of the technical problems in the above technologies, and for this purpose, a method for monitoring vegetation conditions based on UAVs is proposed, including:

[0006] Obtaining an image data set of a target area based on a UAV;

[0007] Constructing a digital raster image of the target area according to the image data set;

[0008] Extracting vegetation information from the digital raster image.

[0009] Preferably, extracting the vegetation information from the digital raster image includes:

[0010] Extracting the remote sensing characteristic vegetation index from the digital raster image;

[0011] Obtaining the vegetation information according to the remote sensing characteristic vegetation index.

[0012] Preferably, the remote sensing characteristic vegetation index includes: normalized difference vegetation index, chlorophyll index, green normalized difference vegetation index, and normalized red-green difference index.

[0013] Preferably, the calculation formula corresponding to the normalized difference vegetation index includes:

[0014]

[0015] Among them, ρ NIR represents the reflectance in the near-infrared band; ρ RED represents the reflectance in the red band; NDVI represents the normalized difference vegetation index.

[0016] Preferably, the calculation formula corresponding to the chlorophyll index includes:

[0017]

[0018] Among them, ρ NIR represents the reflectance in the near-infrared band; ρ GREEN represents the reflectance in the green band; GCI represents the chlorophyll index.

[0019] Preferably, the calculation formula corresponding to the green normalized difference vegetation index includes:

[0020]

[0021] Among them, ρ NIR represents the reflectance in the near-infrared band; ρ GREEN represents the reflectance in the green band; GNDVI represents the green normalized difference vegetation index.

[0022] Preferably, the calculation formula corresponding to the normalized green-red difference index includes:

[0023]

[0024] Among them, ρ GREEN represents the reflectance in the green band; ρ RED represents the reflectance in the red band; NGRDI represents the normalized green-red difference index.

[0025] Preferably, the method further includes: calculating the impact of the environment of the target area on the naturally growing vegetation according to the remote sensing characteristic vegetation index corresponding to the target area and the remote sensing characteristic vegetation index corresponding to the reference area. The corresponding calculation formula includes:

[0026]

[0027] Among them, D AB represents the impact coefficient. If the value of D AB is positive, it means that the vegetation condition in the reference area is better than that in the target area; on the contrary, it means that the vegetation condition in the reference area is worse than that in the target area; I A represents the remote sensing characteristic vegetation index of the reference area; I B represents the remote sensing characteristic vegetation index of the target area.

[0028] The present application also provides a vegetation condition monitoring system based on an unmanned aerial vehicle (UAV), including:

[0029] An image acquisition module, configured to acquire an image dataset of a target area based on the UAV;

[0030] An image processing module, configured to construct a digital raster image of the target area according to the image dataset;

[0031] An information extraction module, configured to extract vegetation information from the digital raster image.

[0032] The present application also provides an electronic device, including a memory and a processor. A computer program or instruction is stored in the memory. When the computer program or instruction is executed by the processor, it is at least used to implement the above method.

[0033] The present application also provides a computer-readable storage medium. A computer program or instruction is stored in the computer-readable storage medium. When the computer program or instruction is executed by a processor, it is at least used to implement the above method.

[0034] Compared with the prior art, the beneficial effects of the present application are:

[0035] The present application collects a large number of images of the pre-drilling engineering land taken by the UAV, matches and stitches them, generates a digital raster image of the pre-drilling engineering land, and then extracts the vegetation information in the digital raster image through an object-oriented classification method to generate a vegetation classification thematic map of the pre-drilling engineering land. The present application uses the images collected by the UAV to generate a digital raster image, provides all-round and multi-scale basic geospatial data, provides all-round data support for the vegetation condition monitoring of the pre-drilling engineering land, ensures the accuracy and timeliness of the vegetation condition monitoring, and meets the estimation of the vegetation carbon sequestration loss amount of the pre-drilling engineering.

[0036] Other features and advantages of the present application will be described in the following specification. And, part of them will become obvious from the specification, or be understood by implementing the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures specifically pointed out in the written specification and the drawings.

[0037] The technical solutions of the present application will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0038] The drawings are used to provide further understanding of the present application, and constitute a part of the specification. They are used to explain the present application together with the embodiments of the present application, and do not constitute a limitation to the present application. In the drawings:

[0039] Figure 1Schematic diagram of the UAV-based vegetation condition monitoring method given in the embodiment;

[0040] Figure 2 Schematic diagram of the UAV-based vegetation condition monitoring system given in the embodiment;

[0041] Figure 3 Schematic diagram of the electronic device given in the embodiment;

[0042] Figure 4 Schematic diagram of the computer-readable storage medium given in the embodiment. Detailed implementation manners

[0043] The following describes the present application with reference to the accompanying drawings. The preferred embodiments described herein are only for illustrating and explaining the present application and are not used to limit the present application.

[0044] Figure 1 The UAV-based vegetation condition monitoring method provided by the present application includes:

[0045] S11. Obtaining an image dataset of a target area based on a UAV;

[0046] S12. Constructing a digital raster image of the target area according to the image dataset;

[0047] S13. Extracting vegetation information from the digital raster image.

[0048] According to some embodiments of the present application, extracting vegetation information from a digital raster image includes: extracting a remote sensing characteristic vegetation index from the digital raster image; obtaining vegetation information according to the remote sensing characteristic vegetation index, and the remote sensing characteristic vegetation index includes: normalized difference vegetation index, chlorophyll index, green normalized difference vegetation index, and normalized red-green difference index.

[0049] According to some embodiments of the present application, extracting vegetation information from a digital raster image through an object-oriented classification method specifically means using the ENVI (The Environment for Visualizing Images, a remote sensing processing platform) platform to perform remote sensing characteristic vegetation index extraction on the generated digital raster image through an object-oriented classification method, and comprehensively reflecting the growth status of vegetation according to the vegetation index.

[0050] According to some embodiments of the present application, the corresponding calculation formula of the normalized difference vegetation index includes:

[0051]

[0052] where ρ NIR represents the reflectance of the near-infrared band; ρ REDρRed represents the red band reflectance; NDVI represents the Normalized Difference Vegetation Index. According to the embodiments of the present application, the Normalized Difference Vegetation Index has a strong linear relationship with the vegetation coverage, and is used to monitor the seasonal and inter-annual changes of vegetation growth activities; its value ranges from -1 to 1.0, where the Normalized Difference Vegetation Index in the green vegetation area > 0.1. When the Normalized Difference Vegetation Index is positive, the larger the value of the Normalized Difference Vegetation Index, the higher the vegetation coverage and the more vegetation. Negative values represent snow and water bodies.

[0053] According to some embodiments of the present application, the corresponding calculation formula of the Chlorophyll Index includes:

[0054]

[0055] where ρ NIR represents the near-infrared band reflectance; ρ GREEN represents the green band reflectance; GCI represents the Chlorophyll Index. According to some embodiments of the present application, the Chlorophyll Index is used to characterize the chlorophyll content of plant leaves and reflect the health status and photosynthesis level of green vegetation; the larger its value, the higher the chlorophyll content in the area.

[0056] According to some embodiments of the present application, the corresponding formula of the Green Normalized Difference Vegetation Index includes:

[0057]

[0058] where ρ NIR represents the near-infrared band reflectance; ρ GREEN represents the green band reflectance; GNDVI represents the Green Normalized Difference Vegetation Index. According to some embodiments of the present application, the Green Normalized Difference Vegetation Index is related to the chlorophyll content, and the value range of the Green Normalized Difference Vegetation Index is -1.0 to 1.0. The larger the value, the higher the greenness.

[0059] According to some embodiments of the present application, the corresponding calculation formula of the Normalized Green-Red Difference Index includes:

[0060]

[0061] where ρ GREEN represents the green band reflectance; ρ RED represents the red band reflectance; NGRDI represents the Normalized Green-Red Difference Index. According to some embodiments of the present application, the Normalized Green-Red Difference Index is used to reflect the vegetation coverage, is sensitive to green vegetation, and its value range is -1.0 to 1.0. The larger the value, the better the growth condition.

[0062] According to some embodiments of the present application, constructing a digital raster image of a target area based on the image dataset includes: matching and stitching the obtained image dataset, specifically, calibrating using ground control points, calculating through the UAV flight route and offset angle to obtain the entire image.

[0063] According to some embodiments of the present application, geometric correction is performed on the entire image obtained by calculating through the UAV flight route and offset angle, specifically, using aerial triangulation encryption points for correction, and after correction, a digital raster image is obtained.

[0064] According to some embodiments of the present application, the method for monitoring vegetation conditions based on UAVs proposed in the present application further includes: combining Worldview-2 images, comparing the differences in the growth conditions between the artificially managed vegetation area and the natural growth vegetation area, and avoiding the influence of different sensors. Obtaining from the Worldview-2 image, to avoid the influence of vegetation greening index changes caused by sensor band settings and resolution differences on the comparative analysis, an area with stable growth conditions through artificial management and little influence from climate is selected as area A, and an area close to natural growth and capable of reflecting the natural growth conditions of vegetation is called area B. The difference in the average values of four vegetation indices of the two areas can be calculated to reflect whether the meteorological conditions cause stress to the vegetation.

[0065] According to some embodiments of the present application, based on the remote sensing characteristic vegetation index corresponding to the target area and the remote sensing characteristic vegetation index corresponding to the reference area, calculate the impact of the environment of the target area on the naturally growing vegetation. The corresponding calculation formula includes:

[0066]

[0067] Among them, D AB represents the influence coefficient. If the value of D AB is positive, it indicates that the vegetation condition in the reference area is better than that in the target area; conversely, it indicates that the vegetation condition in the reference area is inferior to that in the target area; I A represents the remote sensing characteristic vegetation index of the reference area; IB represents the remote sensing characteristic vegetation index of the target area. That is, if the value of D AB is positive, it means that the vegetation growth under artificial management is better than that of natural growth, indicating that there is environmental stress in natural growth. If the value of D AB is negative, it means that the vegetation growth under artificial management is worse than that of natural growth, indicating that the climate conditions for natural vegetation growth are more suitable.

[0068] According to some embodiments of the present application, the method for monitoring vegetation conditions based on unmanned aerial vehicles proposed in the present application further includes: analyzing the changes in the vegetation growth status among the monitoring months, and analyzing the potential climate driving forces for the poor growth status of the vegetation during the monitoring months through meteorological data. To further test the monitoring effect of the unmanned aerial vehicle on the vegetation growth status, the monitoring months with better vegetation growth are selected as the control, with May of that year as the reference, and Worldview-2 images are used for comparison. The four typical vegetation characteristic indices of NDVI, GCI, GNDVI, and NGRDI are also extracted. To avoid the change in the vegetation greening index caused by sensor differences, the present study conducts a vegetation growth dynamics analysis based on the differences in the vegetation growth status between the artificially managed vegetation area (A) and the naturally growing vegetation area (B). Box plots of each vegetation index are drawn for the selected areas A and B, and the calculation result is the difference between the vegetation indices of the artificially managed vegetation and the naturally growing vegetation. Conclusions are drawn therefrom.

[0069] According to the NDVI and NGRDI indices that are more sensitive to coverage, the difference in the values of the vegetation area under artificial management compared to the natural growth area is not very large. The vegetation growth in the artificially managed vegetation area is slightly worse than that in the natural growth area. It is speculated that this may be due to human trampling on the vegetation, resulting in a lower vegetation coverage density than in the natural growth area. For the GCI and GNDVI that are more sensitive to chlorophyll, the values of the vegetation area under artificial management are slightly larger than those in the natural growth area, indicating that artificial management has improved the physiological health status of the vegetation. Looking at the comparison values, the difference in the vegetation indices between the two areas is large, and all the indices show that the values in the natural growth area are smaller than those in the vegetation area under artificial management. This shows that the vegetation growth in the natural growth area is significantly worse than that in the vegetation area under artificial management. The reason may be that the vegetation withers due to strong environmental stress, while the vegetation in the artificially managed vegetation area grows normally because human watering offsets a certain amount of stress. This further proves that the poor health status of the vegetation has been effectively monitored, verifying the monitoring ability of the unmanned aerial vehicle.

[0070] Vegetation growth is vulnerable to human activities and climate change. Considering that there are few human activities during the monitoring months, the influence of human factors on vegetation growth is small. It is speculated that climate change may be the main reason for the vegetation change. To further analyze the specific driving mechanism, the changes in climate elements during the monitoring months were analyzed. Judging from the average temperature of the months, it is higher than the same period and the historical same period. Especially around June of each year, the overall increase reaches the peak. The increase in temperature can bring strong evapotranspiration, thus increasing the water demand of the vegetation. According to the average wind speed during the monitoring months, especially in the middle and late ten days of April and May, the daily average wind speed increases significantly compared with other months in the same period. The increase in wind speed will increase the evapotranspiration of the ground surface, resulting in soil water deficit and being unfavorable to vegetation growth. It can be concluded that the precipitation in the two monitoring months is equal, and the precipitation is significantly reduced compared with the same period. The reduction in precipitation will aggravate the water deficit and have an adverse impact on vegetation growth.

[0071] Based on the same concept as the above monitoring method, as Figure 3 shown, this application also proposes a drone-based vegetation condition monitoring system, including: an image acquisition module 201, which is used to acquire an image dataset of a target area based on a drone; an image processing module 202, which is used to construct a digital raster image of the target area according to the image dataset; and an information extraction module 203, which is used to extract vegetation information from the digital raster image.

[0072] Figure 2 For the drone-based vegetation condition monitoring system given in the embodiments of this application, the monitoring system includes a drone, several ground control points arranged in the area of the pre-drilling engineering area, and a computer device. The drone is equipped with a digital camera for image acquisition of the pre-drilling engineering area; the computer device is used to receive the image dataset taken by the drone and process the received image dataset according to some or all of the steps in the above embodiments; the ground control points are used to calibrate the spliced images when the computer device processes the matching and splicing of the image dataset taken by the drone.

[0073] The embodiments of this application also propose an electronic device, the structure of which is as Figure 3 shown, including a memory 1002 and a processor 1001. A computer program or instruction is stored in the memory 1002. When the computer program or instruction is executed by the processor 1001, it is at least used to implement the above-mentioned drone-based vegetation condition monitoring method. The embodiments of this application also propose a computer-readable storage medium 1100, the structure of which is as Figure 4 shown. A computer program or instruction is stored in the computer-readable storage medium 1100. When the computer program or instruction is executed by the processor, it is at least used to implement the above-mentioned drone-based vegetation condition monitoring method.

[0074] It is obvious that those of ordinary skill in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application also intends to include these changes and modifications.

Claims

1. A method for monitoring vegetation conditions based on an unmanned aerial vehicle, characterized in that, Comprising: Obtaining an image dataset of a target area based on a drone; Constructing a digital raster image of the target area according to the image dataset; Extracting vegetation information from the digital raster image.

2. The method for monitoring vegetation conditions based on an unmanned aerial vehicle according to claim 1, characterized in that, Extracting the vegetation information from the digital raster image includes: Extracting a remote sensing characteristic vegetation index from the digital raster image; Obtaining the vegetation information according to the remote sensing characteristic vegetation index.

3. The method for monitoring vegetation conditions based on an unmanned aerial vehicle according to claim 2, characterized in that, The remote sensing characteristic vegetation index includes: normalized difference vegetation index, chlorophyll index, green normalized difference vegetation index, and normalized red-green difference index.

4. The method for monitoring vegetation conditions based on an unmanned aerial vehicle according to claim 3, characterized in that, The calculation formula corresponding to the normalized difference vegetation index includes: Among them, ρ NIR represents the reflectance in the near-infrared band; ρ RED represents the reflectance in the red band; NDVI represents the normalized difference vegetation index.

5. The method for monitoring vegetation conditions based on an unmanned aerial vehicle according to claim 3, characterized in that, The calculation formula corresponding to the chlorophyll index includes: Among them, ρ NIR represents the reflectance in the near-infrared band; ρ GREEN represents the reflectance in the green band; GCI represents the chlorophyll index.

6. The method for monitoring vegetation conditions based on an unmanned aerial vehicle according to claim 3, characterized in that, The calculation formula corresponding to the green normalized difference vegetation index includes: Among them, ρ NIR represents the reflectance in the near-infrared band; ρ GREEN represents the reflectance in the green band; GNDVI represents the green normalized difference vegetation index.

7. The method for monitoring vegetation conditions based on an unmanned aerial vehicle according to claim 3, characterized in that, The calculation formula corresponding to the normalized red-green difference index includes: Among them, ρ GREEN represents the green band reflectance; ρ RED represents the red band reflectance; NGRDI represents the normalized green-red difference index.

8. The method for monitoring vegetation conditions based on an unmanned aerial vehicle according to any one of claims 1-7, characterized in that, Further comprising: Calculating the impact of the environment of the target area on naturally growing vegetation according to the remote sensing characteristic vegetation index corresponding to the target area and the remote sensing characteristic vegetation index corresponding to a reference area, and the corresponding calculation formula includes: Among them, D AB represents the influence coefficient. If D AB is positive, it indicates that the vegetation condition in the reference area is better than that in the target area; conversely, it indicates that the vegetation condition in the reference area is inferior to that in the target area; I A represents the remote sensing characteristic vegetation index of the reference area; I B represents the remote sensing characteristic vegetation index of the target area.

9. A system for monitoring vegetation conditions based on an unmanned aerial vehicle, characterized in that, Including: An image acquisition module for obtaining an image dataset of a target area based on a drone; An image processing module for constructing a digital raster image of the target area according to the image dataset; An information extraction module for extracting vegetation information from the digital raster image.

10. An electronic device, comprising a memory and a processor, characterized in that, A computer program or instruction is stored in the memory, and when the computer program or instruction is executed by the processor, it is at least used to implement the method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, A computer program or instruction is stored in the computer-readable storage medium, and when the computer program or instruction is executed by the processor, it is at least used to implement the method according to any one of claims 1-8.

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