Forest ecological data monitoring system based on unmanned aerial vehicle

Abstract

The invention discloses a forest ecological data monitoring system based on an unmanned aerial vehicle, which comprises an ecological monitoring object selection module, a monitoring system establishment module and a characteristic parameter extraction module, and is characterized in that the ecological monitoring object selection module selects the following objects: a natural landscape, and when the natural landscape of a forest is monitored, the characteristic parameter extraction module extracts the characteristic parameter of the forest; the monitored items comprise the extraction of the overall features of the natural landscape, the composition of plant diversity in the natural landscape of the forest during extraction, the topographic topography fluctuation of the forest and the health condition of vegetation growth. According to the invention, by arranging the ecological monitoring object selection module, more comprehensive selection of monitoring objects can be ensured in the process of carrying out data monitoring on forest ecology, and the monitoring objects are classified and then subdivided, so that diversified data monitoring on a forest system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of forest ecological data monitoring, and in particular to a forest ecological data monitoring system based on unmanned aerial vehicle (UAV). Background Art

[0002] In the prior art, forests play an important role as an important part of the earth's environment. The forest ecological environment is of great significance to species diversity and maintaining the global carbon balance. However, since the forest ecological environment is relatively complex and diverse, how to achieve systematic monitoring of various data and indicators of the forest ecological environment has become a difficult problem. Since the area of ​​forests is relatively vast, the commonly used method is to use drones in combination with monitoring devices to carry out ecological monitoring of forests. For this reason, this application proposes a forest ecological data monitoring system based on drones;

[0003] After searching, the Chinese patent application number 202110604221.X discloses a forest ecological data monitoring system and monitoring method based on drones, the system including several ecological data collection nodes deployed on the ground, drones for collecting data collected by the ecological data collection nodes, and a control center for processing data and controlling the flight of the drones; the ecological data collection nodes include a mainboard and multiple sensors mounted on the mainboard, as well as a LoRa module, a storage module, a GPS module and a power module 1; the drone is equipped with a LoRa gateway 1 and a gimbal camera, a power module 2 for powering the LoRa gateway 1, and a power module 3 for powering the drone flight and the gimbal camera.

[0004] The UAV-based forest ecological data monitoring system and monitoring method in the above patent has the following shortcomings: in the process of monitoring forest ecological data, there is no detailed division of the monitoring items, resulting in uneven priorities during monitoring, and it is impossible to efficiently monitor important monitoring objects. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a forest ecological data monitoring system based on unmanned aerial vehicles.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A forest ecological data monitoring system based on unmanned aerial vehicles includes an ecological monitoring object selection module, a monitoring system establishment module and a characteristic parameter extraction module. The ecological monitoring object selection module selects the following objects:

[0008] 1: Natural landscape. When monitoring the natural landscape of a forest, the monitoring items include the extraction of the overall characteristics of the natural landscape, the composition of plant diversity in the natural landscape of the forest, the undulations of the forest terrain, and the health of vegetation growth;

[0009] 2: Forest level. When monitoring the forest level, it is necessary to select sample plots within the monitored forest area to monitor the structure and composition of the ecology within the sample plots. During the monitoring, various monitoring indicators are set, including forest type diversity, forest age group structure, forest naturalness, and forest area;

[0010] 3: Species diversity monitoring. Species diversity monitoring objects include mammals, birds, amphibians, reptiles, and insects.

[0011] Preferably: the monitoring system establishment module is established using various scientific technologies. The technologies used in the establishment process include drone-based remote sensing monitoring technology and infrared camera technology. In the process of monitoring forest ecological data using remote sensing technology, remote sensing equipment or infrared cameras are installed on drones.

[0012] Further: The monitoring system establishment module uses the Internet of Things monitoring module to establish connections between drones, remote sensing detection, infrared cameras and forest ecological data monitoring terminals. The Internet of Things monitoring module uses various digital intelligent sensors, lidar, microwave and mobile communication technologies to build a nature reserve positioning monitoring network to conduct sustainable and large-scale monitoring of forest humidity, temperature and smoke concentration.

[0013] Further: After the monitoring system establishes a module to collect various data on forest ecology, the characteristic parameter extraction module is used to extract and analyze the collected data. The monitoring system establishes a module to collect forest data types based on drones and remote sensing technology, and the collected items are dominant forest species.

[0014] As a preferred solution of the present invention: the monitoring system establishes a module that converts the collected original impact into point cloud data when collecting data, and segment the point cloud data after the collection is completed. The segmentation process is based on GIS technology to extract GIS factors.

[0015] As a further solution of the present invention: the GIS factors include the height of the tree, the luxuriance of branches and leaves, and the diameter of the tree. After the GIS factors are collected, a dominant tree species monitoring model is established.

[0016] As a further solution of the present invention: the feature parameter extraction module extracts feature parameters of GIS factors. During the extraction process, the dominant tree species are classified using the watershed algorithm. The classification standard uses the height, luxuriance of branches and leaves, and diameter of trees under the same growth environment and the same lighting conditions as the judgment criteria.

[0017] Based on the above scheme: the data collection method of tree height and diameter includes the following steps:

[0018] 1: Use LIDAR360 tools to denoise the GIS factors collected by the monitoring system establishment module to generate a DEM (digital elevation model) and DSM (digital surface model) with a resolution of 0.6m;

[0019] 2: Subtract DEM from DSM to obtain CHM (canopy height model). The pixel value of each point in CHM is the elevation value of the point. Through MATLAB software programming, read the average elevation of the CHM model of each el. class sample, and retain the points with elevation values ​​not equal to 0 to calculate the average elevation and average diameter;

[0020] 3: Combined with the read CHM average elevation, the average tree height model and average diameter model were established using the univariate linear regression method.

[0021] On the basis of the above-mentioned scheme: the dominant tree species monitoring model was established based on the partial least squares regression method.

[0022] The beneficial effects of the present invention are:

[0023] 1. A forest ecological data monitoring system based on drones can ensure that the selection of monitoring objects is more comprehensive during the data monitoring of forest ecology by setting up an ecological monitoring object selection module, and the monitoring objects are classified and then subdivided, thereby ensuring diverse data monitoring of the forest system.

[0024] 2. A forest ecological data monitoring system based on drones, which monitors forest ecological data by combining drones with remote sensing technology and infrared cameras. The advantages of drones for fast acquisition speed and remote sensing technology and infrared cameras for efficient monitoring of large areas of forests are combined to improve the monitoring rate of forest ecological data.

[0025] 3. A forest ecological data monitoring system based on drones, which uses the Internet of Things monitoring module to achieve the ability to quickly collect and analyze data, and conduct evaluation after analysis, so as to facilitate the identification and judgment of key objects of concern in the forest ecological environment, thereby promoting the healthy and sustainable development of forest ecology.

[0026] 4. A forest ecological data monitoring system based on drones. The feature parameter extraction module effectively improves the accuracy and systematicness of data collection by establishing a dominant tree species monitoring model. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a monitoring flow chart of a forest ecological data monitoring system based on unmanned aerial vehicles proposed by the present invention;

[0028] Figure 2 The present invention provides a system structure block diagram of a forest ecological data monitoring system based on unmanned aerial vehicles. DETAILED DESCRIPTION

[0029] The technical solution of this patent is further described in detail below in conjunction with specific implementation methods.

[0030] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0031] Embodiment 1:

[0032] A forest ecological data monitoring system based on unmanned aerial vehicles includes an ecological monitoring object selection module, a monitoring system establishment module and a characteristic parameter extraction module. The ecological monitoring object selection module selects the following objects:

[0033] 1: Natural landscape. When monitoring the natural landscape of a forest, the monitoring items include the extraction of the overall characteristics of the natural landscape, the composition of plant diversity in the natural landscape of the forest, the undulations of the forest terrain, and the health of vegetation growth;

[0034] 2: Forest level. When monitoring the forest level, it is necessary to select sample plots within the forest area to monitor the structure and composition of the ecology within the sample plots. During the monitoring, various monitoring indicators should be set. The monitoring indicators include forest type diversity, forest age group structure, forest naturalness, and forest area.

[0035] 3: Species diversity monitoring. In addition to plants, forests also include various animals and insects, which together constitute the diversity of the forest ecosystem and ensure the integrity of the forest ecosystem. Various biological populations are interconnected and restricted. Species diversity monitoring objects include mammals, birds, amphibians, reptiles, and insects.

[0036] When further subdivided, for the monitoring of species diversity, the objects that need to be monitored include key species, alien species, indicator species, and key tree species. The monitoring content includes population size and density, and population structure;

[0037] The monitoring system establishment module is established using various scientific and technological means. The technologies used in the establishment process include remote sensing monitoring technology based on drones and infrared camera technology. Remote sensing monitoring technology, as a mature and efficient monitoring technology, is widely used in the field of forest ecological monitoring. Remote sensing monitoring technology provides new means for large-scale real-time monitoring of forest resources, early warning and decision-making analysis. The application of remote sensing technology in forest resource monitoring has brought revolutionary improvements to traditional ground monitoring, greatly improved the efficiency and level of forest resource monitoring, and provided a favorable supplement to traditional artificial ground survey methods. Currently, remote sensing monitoring technology has become an important means of biodiversity monitoring and is widely used in biodiversity monitoring;

[0038] Infrared cameras combine infrared camera survey technology with traditional monitoring methods to greatly improve work efficiency, cost and data quality.

[0039] In the process of using remote sensing technology to monitor forest ecological data, remote sensing equipment or infrared cameras are installed on drones. Drones have the advantages of fast image acquisition speed, short application cycle, high image clarity, less constraints from the natural environment, low cost and easy operation;

[0040] The monitoring system establishment module uses the Internet of Things monitoring module to establish connections between drones, remote sensing detection, infrared cameras and forest ecological data monitoring terminals. The Internet of Things monitoring module effectively improves the real-time and rapid analysis capabilities of monitoring data collection, promotes the sharing and exchange of information resources, gives full play to the role of monitoring data, and effectively improves the efficiency of biodiversity monitoring in nature reserves. The Internet of Things monitoring module uses various digital intelligent sensors, laser radars, microwaves and mobile communication technologies to build a positioning monitoring network for nature reserves, and conducts sustainable and large-scale monitoring of forest humidity, temperature and smoke concentration;

[0041] After the monitoring system establishment module collects various data on forest ecology, the feature parameter extraction module is used to extract and analyze the collected data. The monitoring system establishment module collects forest data types based on drones and remote sensing technology. The collected items are dominant forest species. During processing, the original impacts collected by the monitoring system establishment module are converted into point cloud data. After the point cloud data is collected, it is segmented. The segmentation process is based on GIS technology to extract GIS factors. GIS factors include tree height, lushness of branches and leaves, and tree diameter. After the above GIS factors are collected, a dominant tree species monitoring model is established to effectively improve the accuracy and systematicness of data collection;

[0042] The feature parameter extraction module extracts feature parameters from GIS factors. During the extraction process, the dominant tree species are classified using the watershed algorithm. The classification standard is based on the height, luxuriant degree of branches and leaves, and diameter of trees under the same growth environment and the same lighting conditions. Tree species with superior height, luxuriant degree of branches and leaves, and diameter can better adapt to the forest environment of the monitoring site, and can be cultivated as dominant tree species. The luxuriant degree of leaves is judged by the distribution density and shape of leaves.

[0043] The data collection method for the height and diameter of trees includes the following steps:

[0044] 1: Use LIDAR360 tools to denoise the GIS factors collected by the monitoring system establishment module to generate a DEM (digital elevation model) and DSM (digital surface model) with a resolution of 0.6m;

[0045] 2: Subtract DEM from DSM to obtain CHM (canopy height model). The pixel value of each point in CHM is the elevation value of the point. Through MATLAB software programming, read the average elevation of the CHM model of each el. class sample, and retain the points with elevation values ​​not equal to 0 to calculate the average elevation and average diameter;

[0046] 3: Combined with the read CHM average elevation, the average tree height model and average diameter model were established using the univariate linear regression method;

[0047] The dominant tree species monitoring model is established based on the partial least squares regression method. It has the advantages of canonical correlation analysis and principal component analysis, and has multivariate linear analysis functions. Its advantages are that when selecting GIS factors, the quality selection of factors does not require high precision, and more variables are beneficial to the cumulative interpretation analysis of the extracted principal components.

[0048] The above is a preferred specific implementation manner of the present invention, and the protection scope of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by any technician familiar with the field within the technical scope disclosed by the present invention in combination with the prior art or public common sense, within the spirit and principle of the present invention, shall be covered by the protection scope of the present invention.

Claims

1. A forest ecological data monitoring system based on drones, comprising an ecological monitoring object selection module, a monitoring system establishment module and a characteristic parameter extraction module, characterized in that: The ecological monitoring object selection module selects the following objects: 1: Natural landscape. When monitoring the natural landscape of a forest, the monitoring items include the extraction of the overall characteristics of the natural landscape, the composition of plant diversity in the natural landscape of the forest, the undulations of the forest terrain, and the health of vegetation growth; 2: Forest level. When monitoring the forest level, it is necessary to select sample plots within the monitored forest area to monitor the structure and composition of the ecology within the sample plots. During the monitoring, various monitoring indicators are set, including forest type diversity, forest age group structure, forest naturalness, and forest area; 3: Species diversity monitoring. Species diversity monitoring objects include mammals, birds, amphibians, reptiles, and insects.

2. The forest ecological data monitoring system based on unmanned aerial vehicle according to claim 1 is characterized in that: The monitoring system establishment module is established using various scientific technologies. The technologies used in the establishment process include drone-based remote sensing monitoring technology and infrared camera technology. In the process of monitoring forest ecological data using remote sensing technology, remote sensing equipment or infrared cameras are installed on drones.

3. The forest ecological data monitoring system based on unmanned aerial vehicle according to claim 2 is characterized in that: The monitoring system establishment module uses the Internet of Things monitoring module to establish connections between drones, remote sensing detection, infrared cameras and forest ecological data monitoring terminals. The Internet of Things monitoring module uses various digital intelligent sensors, lidar, microwave and mobile communication technologies to build a nature reserve positioning monitoring network to conduct sustainable and large-scale monitoring of forest humidity, temperature and smoke concentration.

4. The forest ecological data monitoring system based on unmanned aerial vehicle according to claim 3 is characterized in that: After the monitoring system establishment module collects various data on forest ecology, the feature parameter extraction module is used to extract and analyze the collected data. The monitoring system establishment module collects forest data types based on drones and remote sensing technology, and the collected items are dominant forest species.

5. The forest ecological data monitoring system based on unmanned aerial vehicle according to claim 4 is characterized in that: The monitoring system establishment module converts the collected original impact into point cloud data during data collection. After the point cloud data is collected, it is segmented. The segmentation process is based on GIS technology to extract GIS factors.

6. The forest ecological data monitoring system based on unmanned aerial vehicle according to claim 5 is characterized in that: The GIS factors include tree height, luxuriance of branches and leaves, and tree diameter. After collecting the GIS factors, a dominant tree species monitoring model is established.

7. The forest ecological data monitoring system based on unmanned aerial vehicle according to claim 6 is characterized in that: The feature parameter extraction module extracts feature parameters from GIS factors. During the extraction process, the dominant tree species are classified using the watershed algorithm. The classification standard uses the height, luxuriance of branches and leaves, and diameter of trees under the same growth environment and the same lighting conditions as the criteria for judgment.

8. The forest ecological data monitoring system based on unmanned aerial vehicle according to claim 7 is characterized in that: The data collection method for tree height and diameter includes the following steps: 1: Use LIDAR360 tools to denoise the GIS factors collected by the monitoring system establishment module to generate a DEM (digital elevation model) and DSM (digital surface model) with a resolution of 0.6m; 2: Subtract DEM from DSM to obtain CHM (canopy height model). The pixel value of each point in CHM is the elevation value of the point. Through MATLAB software programming, read the average elevation of the CHM model of each el. class sample, and retain the points with elevation values ​​not equal to 0 to calculate the average elevation and average diameter; 3: Combined with the read CHM average elevation, the average tree height model and average diameter model were established using the univariate linear regression method.

9. The forest ecological data monitoring system based on unmanned aerial vehicle according to claim 8 is characterized in that: The dominant tree species monitoring model was established based on the partial least squares regression method.

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