Remote sensing-based method for monitoring temperature of canopy, air under canopy and soil near root system in three dimensions

Abstract

The application provides a stereoscopic temperature monitoring method for forest canopy and soil near roots under the forest canopy based on remote sensing, and specifically provides a plant community biomass monitoring method for urban green land, and the method comprises the following steps: acquiring three-dimensional information of trees in a target area by a UAV carrying a laser radar to construct a forest canopy structure model of the target area; acquiring a temperature distribution image of a forest canopy surface of the target area by an infrared camera carried by the UAV, and combining the forest canopy structure model to acquire monitoring data of the temperature of the forest canopy and the temperature under the forest canopy; acquiring monitoring data of soil temperature near roots of the trees in the target area by a soil temperature sensor; fusing the monitoring data of the temperature of the forest canopy and the temperature under the forest canopy, the monitoring data of the soil temperature, different tree species and a growth amount model to establish an estimation model of temperature difference and tree species biomass, so as to acquire plant community biomass of the target area. The method has a wide application prospect in the field of ecological benefit evaluation of urban green land, and provides a basis for detecting plant community biomass of urban green land.

Description

Technical Field

[0001] This invention belongs to the field of urban green space ecological benefit assessment technology, specifically involving a three-dimensional temperature monitoring method based on remote sensing of the canopy-under-canopy air-soil near the roots, and more specifically, a method for monitoring plant community biomass in urban green spaces. Background Technology

[0002] The species composition and quantity of trees in green space plant communities have a significant impact on microclimate and temperature, hence forests are often referred to as natural air conditioners, and urban green space plant communities are no exception. Trees in urban green spaces play a crucial role in regulating temperature, altering the microclimate, improving the ecological environment, and enhancing ecosystem services. Biomass is a key indicator of ecosystem productivity, referring to the total output of an ecosystem over a period of time. Steady growth in biomass indicates a healthy ecosystem, while a decline suggests potential stress or damage. Monitoring tree biomass not only reveals the health of an ecosystem but also helps us adjust and improve the ecological environment. For example, if tree biomass is declining in a region, actions such as afforestation may be necessary to restore the area's ecosystem. Furthermore, trees are the largest biomass reservoir on Earth, absorbing carbon dioxide from the atmosphere through photosynthesis, which is essential for understanding and predicting climate change. Therefore, accurate assessment of the species composition and biomass of urban green space plant communities is crucial for configuration patterns. Currently, the monitoring of biomass in urban green space plant communities mainly involves manually measuring the growth of different trees at sampling sites, establishing growth models, and estimating community biomass. This method is not only time-consuming and labor-intensive, but also cannot continuously obtain information on tree growth. Furthermore, it is susceptible to external interference, making it difficult to guarantee the accuracy of the monitoring data. Moreover, it is difficult to conduct comprehensive monitoring and analysis of the entire forest area, thus failing to meet the needs of modern urban landscaping. Summary of the Invention

[0003] Based on the technical problems existing in the prior art, the present invention provides a three-dimensional temperature monitoring method based on remote sensing of the canopy-under-canopy air-soil near the roots, specifically a method for monitoring the biomass of plant communities in urban green spaces, in order to solve the technical problems of large workload, low efficiency and difficulty in ensuring data accuracy in the monitoring of plant community biomass in urban green spaces.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a three-dimensional temperature monitoring method based on remote sensing of the canopy, under-canopy air, and soil near the roots, comprising the following steps: acquiring three-dimensional information of trees in the target area using a drone equipped with a lidar to construct a canopy structure model of the target area; acquiring temperature distribution images of the canopy surface in the target area using a drone equipped with an infrared camera, and combining them with the canopy structure model to obtain monitoring data of canopy temperature and under-canopy temperature; acquiring monitoring data of soil temperature near the roots of trees in the target area using a soil temperature sensor; and fusing the monitoring data of canopy temperature and under-canopy temperature, soil temperature monitoring data, different tree species, and their growth models to establish an estimation model of temperature difference and tree species biomass to obtain the plant community biomass of the target area.

[0005] Optionally, the three-dimensional information includes height, density, and structure.

[0006] Optionally, an infrared camera mounted on a drone can be used to acquire temperature distribution images of the canopy surface in the target area. Combined with a canopy structure model, monitoring data of canopy temperature and temperature under the canopy can be obtained. This includes the following steps: processing and analyzing the infrared images captured by the infrared camera to convert them into temperature distribution images; and using convolutional neural network deep learning classification and feature extraction algorithms, combined with a canopy structure model, to analyze and identify the temperature distribution images to obtain monitoring data of canopy temperature and temperature under the canopy can.

[0007] Optionally, the remote sensing-based three-dimensional temperature monitoring method for the canopy-under-canopy air-soil near the roots further includes the following steps: acquiring monitoring data of the air temperature under the canopy in the target area using an infrared thermometer, and comparing and correcting it with the monitoring data of the temperature under the canopy acquired by an infrared camera.

[0008] Optionally, monitoring data on soil temperature near the roots of trees in the target area can be obtained through a soil temperature sensor, including the following steps: selecting a soil temperature sensor; selecting a location to install the soil temperature sensor; collecting and processing data from the soil temperature sensor to obtain information on soil temperature change trends and temperature distribution; and visualizing the soil temperature change trends and temperature distribution information.

[0009] Optionally, in acquiring monitoring data on soil temperature near the roots of trees in the target area through a soil temperature sensor, the soil temperature sensor may include a thermocouple, a resistance temperature detector (RTD), or a semiconductor sensor.

[0010] Optionally, data fusion can be performed by using a weighted average method or a maximum value method, which integrates monitoring data of canopy temperature and temperature under the canopy, soil temperature, different tree species, and their growth models.

[0011] As can be seen from the technical solutions provided by the embodiments of the present invention above, the three-dimensional temperature monitoring method based on remote sensing of the canopy-under-canopy air-soil near the roots provided by the present invention has the following beneficial effects:

[0012] (1) High timeliness of monitoring data: The remote sensing-based monitoring method can obtain temperature data of the target area in real time, quickly grasp the temperature changes of urban green space, and take corresponding measures for maintenance in a timely manner.

[0013] (2) High accuracy of monitoring data: Remote sensing-based monitoring methods can accurately obtain temperature data of urban green space plant communities in the canopy, under the canopy and near the root system through infrared thermometry, which can improve the accuracy and reliability of monitoring data.

[0014] (3) Wide monitoring scope: Remote sensing-based monitoring methods can achieve rapid monitoring and analysis of large areas of green space, and can comprehensively grasp the distribution of urban green space, providing strong support for urban green space resource management and regulation.

[0015] (4) Low monitoring cost: Remote sensing-based monitoring methods can be implemented using equipment such as drones, which is much cheaper than traditional manual observation and sensor deployment methods.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a three-dimensional temperature monitoring method based on remote sensing for the canopy, air beneath the canopy, and soil near the roots, provided in an embodiment of the present invention.

[0019] Figure 2 A flowchart illustrating the processing of monitoring data for obtaining canopy temperature and temperature beneath the canopy, provided in an embodiment of the present invention.

[0020] Figure 3 A flowchart illustrating another remote sensing-based three-dimensional temperature monitoring method for the canopy, air beneath the canopy, and soil near the roots, provided in an embodiment of the present invention.

[0021] Figure 4This is a flowchart illustrating the process of acquiring monitoring data on soil temperature near the roots of trees, as provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0023] It should be noted that when an element is referred to as being "fixed to" or "set on" another element, it can be directly or indirectly on that other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to that other element. Unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] It should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0025] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] See Figure 1 As shown, this embodiment of the invention provides a three-dimensional temperature monitoring method based on remote sensing, encompassing the canopy, air beneath the canopy, and soil near the roots, including the following steps:

[0027] Step S10. Obtain three-dimensional information of trees in the target area using a drone equipped with a lidar system to construct a canopy structure model of the target area;

[0028] The lidar carried by the drone can quickly and accurately collect information such as the height, density, and structure of trees. In practice, a lidar device with suitable parameters (such as wavelength, scanning frequency, and laser power) is selected according to the actual situation to obtain high-quality three-dimensional information of trees (including height, density, and structure), thereby obtaining an accurate canopy structure model. At the same time, automatic flight path planning and control technology can be used during drone flight to ensure the integrity and consistency of lidar data.

[0029] Step S20. Acquire temperature distribution images of the canopy surface in the target area using an infrared camera mounted on a drone, and combine these images with a canopy structure model to obtain monitoring data on canopy temperature and temperature beneath the canopy. See [link to relevant documentation]. Figure 2 As shown, it includes the following steps:

[0030] Step S201. Process and analyze the infrared images captured by the infrared camera, and convert the infrared images into temperature distribution images.

[0031] Understandably, infrared cameras on drones can quickly and efficiently acquire temperature distribution data over a large area of ​​the forest canopy surface. Before shooting, an infrared camera with suitable parameters (such as wavelength, resolution, and frame rate) can be selected according to actual needs to obtain high-quality infrared images.

[0032] Step S202. Using convolutional neural network deep learning classification and feature extraction algorithms, combined with the canopy structure model, the temperature distribution image is analyzed and identified to obtain monitoring data of canopy temperature and temperature under the canopy.

[0033] In image processing and analysis, combining machine learning and other algorithms enables automated processing and intelligent recognition, thereby improving efficiency and accuracy.

[0034] Before using CNN (Convolutional Neural Network) to classify and extract features from canopy infrared images, a series of data preprocessing steps are required, including:

[0035] First, the infrared images of the canopy captured by the drone need to be denoised, cropped, and scaled to facilitate subsequent processing and analysis.

[0036] Secondly, the canopy infrared images need to be converted into digital form, typically using grayscale or RGB images.

[0037] Finally, the image needs to be normalized to ensure that the values ​​of each pixel are within an appropriate range.

[0038] It is understood that a CNN comprises convolutional layers, pooling layers, and fully connected layers arranged sequentially. Convolutional and pooling layers are the two core components of a CNN, used to extract features from images. Convolutional layers perform convolution operations on the image using kernels to extract features. Pooling layers reduce the dimensionality and size of features by pooling the convolution results, thereby reducing the computational load of subsequent layers. In some embodiments of this invention, multiple convolutional and pooling layers can be used for feature extraction and dimensionality reduction of canopy infrared images. The fully connected layer is the last layer in the CNN, used to classify the extracted features. In some embodiments of this invention, multiple fully connected layers are used to classify canopy infrared images, identify temperature information in different parts, unfold the features extracted by the convolutional and pooling layers, and then classify the features using multiple fully connected layers. After the CNN network is constructed, it is trained and optimized. The training process uses a large dataset and GPUs for accelerated computation. The purpose of training is to continuously optimize the CNN parameters through backpropagation, enabling it to more accurately classify and extract features from canopy infrared images. During the optimization process, appropriate loss functions and optimization algorithms need to be selected based on specific circumstances to improve the classification accuracy and generalization ability of the CNN. Finally, deep learning technology is used for image classification and feature extraction to obtain the image results of the canopy surface temperature distribution. Subsequently, the canopy structure model obtained from the LiDAR method in step S10 is combined with the image analysis and recognition to further improve the precision and accuracy of canopy temperature monitoring.

[0039] See Figure 3 As shown, in some optional embodiments, the remote sensing-based three-dimensional temperature monitoring method for the canopy-under-canopy air-soil near the roots further includes the following steps:

[0040] Step S21. Obtain monitoring data of air temperature under the canopy in the target area using an infrared thermometer, and compare and correct it with the monitoring data of temperature under the canopy obtained by the infrared camera in step S20.

[0041] In practical implementation, the first step is to select a suitable infrared thermometer. When selecting one, factors such as measurement range, accuracy, response time, and reliability must be considered. A suitable location should be chosen for air temperature measurement. Generally, a location close to the ground and far from tree trunks should be selected to avoid measurement errors caused by close proximity. Simultaneously, it is important to choose a location that adequately represents the temperature conditions of the area to ensure the representativeness of the measurement results. Randomly sampled plots are selected, and air temperature data from different locations within the lowest layer of the canopy is acquired using a handheld infrared thermometer at a specific sampling density and interval. The infrared thermometer is then aimed at the measurement location, and the measurement button is pressed to initiate the measurement. During measurement, it is crucial to maintain a stable measurement distance and angle, and to avoid interference from reflected and scattered light from the surface of the object being measured. Finally, the data is compared and corrected with the acquired infrared data to improve the reliability and consistency of the measurement.

[0042] Step S30: Obtain monitoring data of soil temperature near the roots of trees in the target area using a soil temperature sensor. (See [link]) Figure 4 As shown, it includes the following steps:

[0043] Step S301. Select a soil temperature sensor. Commonly used soil temperature sensors include thermocouples, resistance temperature detectors (RTDs), and semiconductor sensors. When selecting a soil temperature sensor, factors such as accuracy, response speed, reliability, and cost need to be considered.

[0044] Step S302. Select a location for installing the soil temperature sensor. Typically, the soil temperature sensor needs to be buried in the soil, close to the tree roots, to ensure accurate measurement of the soil temperature near the roots. During installation, attention should be paid to the depth and location of the soil temperature sensor, as well as its contact with the surrounding soil.

[0045] Step S303. Data acquisition and processing of soil temperature sensors. Data processing includes data cleaning, preprocessing, and analysis to obtain information on soil temperature change trends and distribution. Based on the results of data acquisition and processing, data analysis and applications are performed. Soil temperature change trends and distribution information can be presented using data visualization technology. Simultaneously, the data can also be applied to related fields, such as forest ecology, land use planning, and agricultural production. It is important to note that in practical applications, corresponding adjustments and optimizations are needed to meet specific monitoring requirements. For example, soil temperature change trends and distribution may differ under different soil types and seasons, requiring adjustments and optimizations based on actual conditions to ensure the accuracy and reliability of monitoring results.

[0046] Step S40: Integrate the monitoring data of canopy temperature and temperature under the canopy, the monitoring data of soil temperature, and the growth models of different tree species to establish an estimation model of temperature difference and tree species biomass in order to obtain the plant community biomass of the target area.

[0047]

[0048]

[0049] In the process of data fusion, it is necessary to consider the differences and advantages of different data sources and select appropriate fusion methods, such as weighted average and maximum value methods, to ensure the accuracy and reliability of the fused data. To improve the visualization and analysis of data, technologies such as virtual reality can be used to visualize the data as 3D models or dynamic images. This allows for further analysis and research on the biomass and growth patterns of trees in green spaces, providing a scientific basis for the construction and management of urban green spaces. It also meets the needs of modern urban landscaping and green space plant community spatial temperature monitoring, improves the efficiency and accuracy of urban green space management, and promotes the high-quality development of urban landscaping.

[0050] In summary, the three-dimensional temperature monitoring method based on remote sensing for the canopy, under-canopy air, and soil near roots provided in this embodiment has at least the following beneficial technical effects:

[0051] (1) Three-dimensional monitoring: This invention uses a variety of temperature monitoring methods to achieve comprehensive and three-dimensional monitoring of the temperature of the forest canopy, the air under the forest canopy and the soil near the roots, providing more comprehensive and accurate data on the temperature distribution of the forest environment.

[0052] (2) Application of remote sensing technology: This invention uses drones to deploy infrared cameras for canopy temperature monitoring, making full use of remote sensing technology to improve monitoring efficiency and accuracy.

[0053] (3) Multi-source data fusion: This invention uses a data fusion analysis method to integrate data from different temperature monitoring methods, which better shows the temperature distribution pattern in the forest area and improves the accuracy and reliability of data analysis.

[0054] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0055] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0056] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0057] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A three-dimensional temperature monitoring method based on remote sensing of the canopy, sub-canopy air, and soil near the roots, characterized in that... It includes the following steps: The three-dimensional information of trees in the target area is obtained by using a drone equipped with a lidar to construct a canopy structure model of the target area; The temperature distribution image of the canopy surface in the target area is obtained by using an infrared camera mounted on a drone, and combined with the canopy structure model to obtain monitoring data of canopy temperature and temperature under the canopy; The soil temperature near the roots of trees in the target area is obtained by using a soil temperature sensor. By integrating the monitoring data of the canopy temperature and the temperature under the canopy, the monitoring data of the soil temperature, and the growth models of different tree species, an estimation model for temperature difference and tree species biomass is established to obtain the plant community biomass of the target area.

2. The three-dimensional temperature monitoring method based on remote sensing for canopy-under-canopy air-soil near roots as described in claim 1, characterized in that, The three-dimensional information includes height, density, and structure.

3. The three-dimensional temperature monitoring method based on remote sensing for canopy-under-canopy air-soil near roots as described in claim 1, characterized in that, The method of acquiring temperature distribution images of the canopy surface in the target area using an infrared camera mounted on a drone, and combining these images with the canopy structure model to obtain monitoring data on the canopy temperature and the temperature beneath the canopy, includes the following steps: The infrared images captured by the infrared camera are processed and analyzed, and the infrared images are converted into temperature distribution images; The temperature distribution image is analyzed and identified using a convolutional neural network deep learning classification and feature extraction algorithm, combined with the aforementioned canopy structure model, to obtain monitoring data on canopy temperature and temperature beneath the canopy.

4. The three-dimensional temperature monitoring method based on remote sensing for canopy-under-canopy air-soil near roots as described in claim 1, characterized in that, It also includes the following steps: The air temperature under the canopy in the target area is monitored by an infrared thermometer and compared and corrected with the temperature monitoring data under the canopy obtained by the infrared camera.

5. The three-dimensional temperature monitoring method based on remote sensing for canopy-under-canopy air-soil near roots as described in claim 1, characterized in that, The process of acquiring soil temperature monitoring data near the roots of trees in the target area using a soil temperature sensor includes the following steps: Select the soil temperature sensor; Select a location to install the soil temperature sensor; Data is collected and processed from soil temperature sensors to obtain information on the changing trend and temperature distribution of the soil temperature; The trend of soil temperature change and temperature distribution information are visualized.

6. The three-dimensional temperature monitoring method based on remote sensing for canopy-under-canopy air-soil near roots as described in claim 1, characterized in that, In the process of acquiring monitoring data on soil temperature near the roots of trees in the target area through a soil temperature sensor, the soil temperature sensor includes a thermocouple, a resistance temperature detector (RTD), or a semiconductor sensor.

7. The three-dimensional temperature monitoring method based on remote sensing for canopy-under-canopy air-soil near roots as described in claim 1, characterized in that, In the process of fusing the monitoring data of the canopy temperature and the temperature under the canopy, the monitoring data of the soil temperature, and the different tree species and their growth models, the weighted average method or the maximum value method is used for data fusion.

Images