Urban forest cooling intensity and stability evaluation method and temperature monitoring system

Abstract

This invention discloses a method and temperature monitoring system for assessing the intensity and stability of cooling in urban forests. The method includes: collecting basic forest data in various local climate types; calibrating the spatial distribution of temperature data across multiple time periods based on parameter calibration data and temperature monitoring data; calculating the intensity and stability of the cooling effect of urban forests based on urban forest attribute data and calibrated temperature monitoring data; standardizing the intensity and stability of the cooling effect of urban forests; analyzing the driving mechanisms that cause the intensity and stability of the forest cooling effect; and constructing a spatial distribution map of urban vulnerable forest patches. Based on the spatial distribution map of urban vulnerable forest patches, optimization schemes for urban forests in different local climate zones are formulated. This invention proposes differentiated tree species configuration, structural optimization, and spatial transformation schemes for different local climate zones and synergistic types, achieving synergistic maximization of cooling benefits with limited resources.

Description

Technical Field

[0001] This invention belongs to the field of ecological benefit assessment of urban forest cooling, specifically involving an assessment method and temperature monitoring system for the intensity and stability of urban forest cooling. Background Technology

[0002] To meet the development requirements of "improving the quality and efficiency of existing urban green spaces," it is urgent to scientifically reveal the intrinsic driving mechanism of the synergistic enhancement of the cooling effect of urban forests, address the issues of urban planning becoming merely a "hotspot" and lacking functionality, and effectively improve the actual ecological benefits of the green space system. By accurately quantifying the intensity and stability of cooling and systematically analyzing its key influencing mechanisms, a scientific basis can be provided for the precise construction of local-scale green spaces and the efficient transformation of ecological product value, thereby maximizing the cooling service function of limited urban green space resources.

[0003] To address the challenges posed by my country's urbanization rate exceeding 65%, such as the intensified urban heat island effect and extreme high temperatures, the cooling function of urban forests, based on nature-based solutions, is becoming increasingly crucial. However, their cooling effectiveness exhibits significant spatiotemporal variability. Traditional assessment methods are largely limited to measuring instantaneous cooling intensity, lacking a systematic consideration of forest temperature stability under temperature gradients; they simplify the complex interactions between environmental factors and tree species and community structure, failing to reveal the deep-seated mechanisms regulating cooling intensity and stability; and at the data level, they rely on limited point observations and coarse-resolution remote sensing, making it difficult to finely characterize the temperature dynamics within the forest, resulting in a lack of precise basis for planning decisions. Summary of the Invention

[0004] In view of the above-mentioned shortcomings in the prior art, the present invention provides an assessment method and temperature monitoring system for the cooling intensity and stability of urban forests, which solves the problem that the existing urban green space cooling efficiency assessment methods are too simple and static, and lack a comprehensive and systematic consideration of the dynamic stability of the cooling effect.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is: a method for assessing the cooling intensity and stability of urban forests, comprising the following steps: S1. Deploy temperature monitoring systems in urban forests to collect basic data on forests in various local climate types, including urban forest attribute data, temperature monitoring data, and parameter calibration data. S2. Based on the parameter calibration data and temperature monitoring data, perform spatial distribution calibration of temperature data over multiple time periods to generate calibrated temperature monitoring data; S3. Based on urban forest attribute data and calibrated temperature monitoring data, calculate the intensity and stability of the urban forest cooling effect; S4. Standardize the intensity and stability of the cooling effect of urban forests, analyze the driving mechanisms that cause the intensity and stability of the forest cooling effect, and construct a spatial distribution map of urban fragile forest patches. S5. Based on the spatial distribution map of urban fragile forest patches, and with the driving mechanism as scientific support, formulate optimization schemes for urban forests in different local climate zones.

[0006] Furthermore: In S1, urban forest attribute data includes forest structure attribute data, spatial configuration attribute data, and habitat soil attribute data; temperature monitoring data includes temperature monitoring network data, air temperature meteorological station data, and thermal infrared UAV remote sensing data; parameter calibration data includes ground real temperature data used for model construction over multiple time periods and measured temperature data not involved in model construction used for calibration and verification. Among them, forest structure attribute data include leaf area index, tree species composition, tree height, canopy height, diameter at breast height, crown width, and vegetation hierarchy; Spatial configuration attribute data include patch area and shape, vegetation connectivity, aggregation, diversity, and local climate type; Habitat soil attribute data includes soil type, soil temperature, soil moisture, and underlying surface type.

[0007] Furthermore: In S2, the specific method for calibrating the thermal infrared UAV remote sensing data is as follows: (1) Temperature inversion is performed on the thermal infrared UAV remote sensing data, and spatial data is matched to obtain the UAV pixel value; (2) The uncertainty of ground measurement values ​​and UAV pixel values ​​is quantified by using an error propagation model; Among them, the uncertainty of ground measurement values The specific expression is:

[0008]

[0009] In the formula, Due to instrument error, For spatial representativeness error, To use the standard deviation of measurements taken at multiple points within the sample plot, N This represents the number of measurement points within the sample plot; Uncertainty in drone pixel values The specific expression is:

[0010] In the formula, To use the temperature standard deviation of corresponding pixels in the sample plot, M The number of pixels covered by the sample plot; (3) Establish a pixel-by-pixel temperature calibration model based on uncertainty and ground true temperature data; Based on the relationships in the scatter plot, construct a linear model:

[0011] In the formula, These are ground-measured values. For drone pixel values, It is the minimum value. and The parameter to be determined in the least squares estimation formula is specifically expressed as follows:

[0012]

[0013] In the formula, This represents the average pixel value of the drone. This is the average of the ground measurements. and The parameters for the least squares estimation formula are calculated. For a moment j Ground measurements, For a moment j The pixel value of the drone , k For time number, For a moment j The weights;

[0014] In the formula, For a moment j The uncertainty of ground measurement values, For a moment j The uncertainty of drone pixel values; (4) The pixel-by-pixel temperature calibration model is corrected and verified using measured temperature data to generate high-precision thermal infrared UAV remote sensing data.

[0015] Furthermore: In S3, the method for calculating the intensity of the cooling effect of urban forests is as follows: the difference between the temperature of different areas inside the forest and the impermeable surface measured every 2 hours is calculated to obtain the intensity of the area during that time period. The specific method for calculating cooling stability is as follows: under a temperature gradient, calculate the change in surface temperature caused by the increase in temperature in different areas within the forest, and obtain the stability of the region.

[0016] Furthermore: S4 specifically refers to: The intensity and stability of the cooling effect of urban forests are standardized, and the key driving factors causing the intensity and stability of urban forests are analyzed using structural equation modeling. The intensity and stability of urban cooling in each local climate zone are calculated, thereby constructing a spatial distribution map of urban fragile forest patches. Among them, key driving factors include community structure and morphological characteristics and tree species physiological and process characteristics. Community structure and morphological characteristics include tree species diversity, canopy coverage, tree density, average tree height and leaf area index. Tree species physiological and process characteristics include dominant tree species, leaf thickness, leaf water content, net photosynthetic rate and transpiration rate.

[0017] Furthermore: S5 specifically refers to: Based on the spatial distribution map of urban fragile forest patches, the intensity and stability of urban cooling in each local climate zone are obtained. By balancing the synergistic relationship, tree species and layout suggestions are generated, resulting in a written report and a visualization map.

[0018] A temperature monitoring system includes a ground temperature monitoring station, a handheld temperature monitor, and a thermal infrared monitoring drone.

[0019] Furthermore: The ground temperature monitoring station is equipped with a data box, with a magnetic chuck at the bottom. Inside the data box are a data acquisition unit, a 5G data transmission module, a GPS positioning module, and a battery. A support rod and a solar photovoltaic panel are fixedly installed on the top of the data box. An air temperature and humidity sensor, a black ball temperature sensor, and a wind speed and direction instrument are installed on the top of the support rod.

[0020] The beneficial effects of this invention are as follows: (1) This invention provides a method for assessing the cooling intensity and stability of urban forests. Through a GIS platform, multi-dimensional spatial data is integrated to divide local climate units with climate response characteristics. The system integrates three core data types: urban forest attribute data, multi-source temperature monitoring data, and parameter calibration data. This invention designs a "ground-air collaborative, dynamic-static combination" temperature monitoring system, including a ground temperature monitoring station, a handheld temperature monitoring instrument, and a thermal infrared monitoring drone, to acquire high-resolution drone impact data in both time and space. A structural equation model is used to systematically analyze the driving paths and interactions of multiple factors on cooling intensity and stability. Differentiated tree species configurations, structural optimization, and spatial transformation schemes are proposed for different local climate zones and collaborative types to maximize the synergistic cooling benefits of limited resources. This solves the problem that existing urban green space cooling efficiency assessment methods are too singular and static, lacking a comprehensive and systematic consideration of the dynamic stability of cooling effects.

[0021] (2) This invention provides a temperature monitoring system that establishes a multi-dimensional temperature monitoring network covering "points, lines, and surfaces," enabling data collaboration from static fixed-point to dynamic spatial scanning, and significantly improving the spatiotemporal resolution and completeness of urban forest thermal environment data. It integrates multi-source heterogeneous temperature data to construct standardized spatiotemporal measurement products, supporting multi-scale analysis from micro to macro levels, and enhancing data comparability and system scientific rigor. Combining local climate zoning and structural equation modeling, the system reveals the key environmental and structural driving mechanisms affecting forest cooling intensity and stability, forming a data- and mechanism-based forest configuration optimization strategy. This strategy can propose differentiated and operable forest structure optimization schemes for different climate zones and urban environments, contributing to the precise improvement of urban green space quality and ecological function. Attached Figure Description

[0022] Figure 1 This is a flowchart of a method for assessing the cooling intensity and stability of urban forests according to the present invention.

[0023] Figure 2 This is a four-quadrant diagram of an embodiment of the present invention.

[0024] Figure 3 This is a model path for the direct and indirect effects of a local climate to be constructed in an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of a temperature monitoring system according to the present invention.

[0026] The components include: 1. Ground temperature monitoring station; 11. Wind speed and direction indicator; 12. Sensor assembly; 13. Weather station data receiver; 14. Solar photovoltaic panel; 15. Data box; 16. Connection interface; 2. Handheld temperature monitor; 3. Thermal infrared monitoring drone; 31. Data acquisition device. Detailed Implementation

[0027] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0028] like Figure 1 As shown, in one embodiment of the present invention, a method for assessing the cooling intensity and stability of urban forests includes the following steps: S1. Deploy temperature monitoring systems in urban forests to collect basic data on forests in various local climate types, including urban forest attribute data, temperature monitoring data, and parameter calibration data. S2. Based on the parameter calibration data and temperature monitoring data, perform spatial distribution calibration of temperature data over multiple time periods to generate calibrated temperature monitoring data; S3. Based on urban forest attribute data and calibrated temperature monitoring data, calculate the intensity and stability of the urban forest cooling effect; S4. Standardize the intensity and stability of the cooling effect of urban forests, analyze the driving mechanisms that cause the intensity and stability of the forest cooling effect, and construct a spatial distribution map of urban fragile forest patches. S5. Based on the spatial distribution map of urban fragile forest patches, and with the driving mechanism as scientific support, formulate optimization schemes for urban forests in different local climate zones.

[0029] In this embodiment, the local climate classification process can be based on GIS spatial analysis functions, comprehensively integrating multi-dimensional spatial data layers including land use type, building height and density, forest canopy height, and green coverage. Through spatial coupling and weighted superposition of the above multi-source data, different spatial combinations can influence the local climate of the region, such as temperature, humidity, and wind speed. This local climate classification can provide targeted and adaptive forest structure optimization and spatial configuration strategies.

[0030] In S1, urban forest attribute data includes forest structure attribute data, spatial configuration attribute data, and habitat soil attribute data; temperature monitoring data includes temperature monitoring network data, air temperature meteorological station data, and thermal infrared UAV remote sensing data; parameter calibration data includes ground real temperature data used for model construction at multiple time periods and measured temperature data not involved in model construction used for calibration and verification. Among them, forest structure attribute data include leaf area index, tree species composition, tree height, canopy height, diameter at breast height, crown width, and vegetation hierarchy; Spatial configuration attribute data include patch area and shape, vegetation connectivity, aggregation, diversity, and local climate type; Habitat soil attribute data includes soil type, soil temperature, soil moisture, and underlying surface type.

[0031] In S2, the specific method for calibrating thermal infrared UAV remote sensing data is as follows: (1) Temperature inversion is performed on the thermal infrared UAV remote sensing data, and spatial data is matched to obtain the UAV pixel value; In this embodiment, the temperature inversion method specifically involves performing atmospheric correction and emissivity correction on the original radiance values ​​to obtain the land surface temperature. Furthermore, in GIS software, the polygonal extent of each ground sample plot is precisely spatially overlaid with the corresponding pixels of the UAV temperature map. The average and standard deviation of the temperature of all pixels within the sample plot polygon are extracted for subsequent analysis.

[0032] (2) The uncertainty of ground measurement values ​​and UAV pixel values ​​is quantified by using an error propagation model; Uncertainty in ground measurements stems from instrument error and spatial sampling error. Instrument error... This is given by the calibration certificate of the handheld thermometer, typically ±0.5℃. Spatial representativeness error. The standard deviation is measured by multiple points within the sample plot. To characterize the uncertainty of ground measurements The specific expression is:

[0033]

[0034] In the formula, N This represents the number of measurement points within the sample plot.

[0035] Uncertainty in drone pixel values: by using the temperature standard deviation of corresponding pixels in the sample plot To characterize it. The higher the heterogeneity, the greater the uncertainty of the pixel's average value, and the greater the uncertainty of the UAV pixel value. The specific expression is:

[0036] In the formula, M This represents the number of pixels covered by the sample plot.

[0037] (3) Establish a pixel-by-pixel temperature calibration model based on uncertainty and ground true temperature data; Based on the relationships in the scatter plot, construct a linear model:

[0038] In the formula, These are ground-measured values. For drone pixel values, It is the minimum value. and The parameters for the least squares estimation formula to be determined are calculated as follows: For each sample plot, data pairing is performed on the corresponding pixels. k Ground measurements at each time point were paired to form a calibration dataset. , For a moment j Ground measurements, For a moment j The pixel value of the drone Weighted regression requires assigning different weights to each data point pair due to varying levels of uncertainty. Since the weights are inversely proportional to the overall variance, data points with lower uncertainty tend to have greater weight in the regression calculation. j weight The specific expression is:

[0039] In the formula, For a moment j The uncertainty of ground measurement values, For a moment j The uncertainty of drone pixel values.

[0040] The formula for calculating the parameters of the weighted least squares estimation formula is as follows:

[0041]

[0042] In the formula, This represents the average pixel value of the drone. This is the average of the ground measurements. and The parameters are used to calculate the least squares estimation formula.

[0043] (4) The pixel-by-pixel temperature calibration model is corrected and verified using measured temperature data to generate high-precision thermal infrared UAV remote sensing data.

[0044] Validation was performed using data observed simultaneously outside the sample points, and the results were verified using the root mean square error (RMSE) and the coefficient of determination (R²). 2 The accuracy between the corrected temperature and the ground truth value on the validation set is calculated. The final result is a high-precision urban forest temperature distribution with controllable errors across multiple time periods.

[0045] In S3, the method for calculating the intensity of the cooling effect of urban forests is as follows: the difference between the temperature of different areas inside the forest and the impermeable surface measured every 2 hours is calculated to obtain the intensity of the area during that time period. The impermeable surface temperature refers to the temperature within a 10-meter buffer zone around the green space. The specific method for calculating cooling stability is as follows: under a temperature gradient, calculate the change in surface temperature caused by the increase in temperature in different areas within the forest, and obtain the stability of the region.

[0046] S4 specifically refers to: The intensity and stability of the urban forest cooling effect are standardized, and the trade-offs and synergies between intensity and stability in different areas within the forest are analyzed to identify vulnerable forest patches for urban cooling. In this embodiment, the following method is used: Figure 2The four-quadrant diagram shown divides each sample region into four types: high intensity-high stability (synergistic region), high intensity-low stability (trade-off region), low intensity-high stability (trade-off region), and low intensity-low stability (synergistic region).

[0047] Structural equation modeling was used to analyze the key driving factors causing the intensity and stability of urban forests, calculate the intensity and stability of urban cooling in each local climate zone, and analyze the trade-off and synergistic relationship between intensity and stability in different areas within the forest, identify urban cooling vulnerable forest patches, and construct a spatial distribution map of urban vulnerable forest patches. Among them, key driving factors include community structure and morphological characteristics and tree species physiological and process characteristics. Community structure and morphological characteristics include tree species diversity, canopy coverage, tree density, average tree height and leaf area index. Tree species physiological and process characteristics include dominant tree species, leaf thickness, leaf water content, net photosynthetic rate and transpiration rate.

[0048] Constructing a structural equation model first requires pre-assuming the model path. Figure 3 This study aims to construct a model to explore the direct and indirect impacts of a local climate, revealing the direct and indirect effects of driving factors on intensity and stability, as well as the interaction between them. Next, SPSS Amos software is used to fit the structural equation model, continuously refining the model. Model fit metrics are used to assess the quality of the model construction; the following are the model construction metrics: χ² 2 / df: <3 indicates a good fit. CFI and TLI: >0.90 indicates a good fit, >0.95 indicates an excellent fit. RMSEA: <0.05 indicates a good fit, <0.08 indicates a reasonable fit. SRMR: <0.08 indicates a good fit. And path coefficient significance (p<0.05). The interaction between urban forest stability and intensity was explored by varying the values.

[0049] S5 specifically refers to: Based on the spatial distribution map of urban fragile forest patches, the intensity and stability of urban cooling in each local climate zone are obtained. By balancing the synergistic relationship, tree species and layout suggestions are generated, resulting in a written report and a visualization map.

[0050] In this embodiment, by calculating the urban cooling intensity and stability in each local climate zone, the green space in each local climate zone is simulated using a structural equation model to obtain core data such as the direct and indirect impacts on different regions. Based on the research results, targeted suggestions are made regarding tree species and layout, such as "forests in areas with high building density are suitable for planting 40% canopy coverage and the most suitable tree species is Sophora japonica" and "for forests in areas with low building density are suitable for planting 30% canopy coverage and the most suitable tree species is poplar". These suggestions are then presented in a written report and a visual map.

[0051] like Figure 4 As shown, a temperature monitoring system includes a ground temperature monitoring station 1, a handheld temperature monitor 2, and a thermal infrared monitoring drone 3.

[0052] Ground temperature monitoring stations 1 are deployed within urban forests with varying local climates, arranged according to monitoring network data. Each station is equipped with a data box 15, with a magnetic chuck at the bottom. Inside the data box 15 are a data acquisition unit 31, a 5G data transmission module, a GPS positioning module, and a battery. A support rod and a solar photovoltaic panel 14 are fixedly mounted on the top of the data box 15. A sensor assembly 12 and an anemometer 11 are mounted on the top of the support rod. The sensor assembly 12 includes an air temperature and humidity sensor and a black sphere temperature sensor. Preferably, ground temperature monitoring stations 1 complete a full monitoring cycle every 2 hours, with data transmitted back to the data center in real time via the 5G network. Additionally, ground temperature monitoring stations 1 are used as mobile measurement units, attached to a patrol vehicle's mobile platform via the magnetic chuck, and used for mobile monitoring along a planned route. During movement, surface temperature and air temperature are collected simultaneously, and the location temperature data is uploaded in real time via a wireless network. Mobile measurement completes area coverage every 2 hours along a preset route.

[0053] The handheld temperature monitor 2 requires manual measurement by the human body, and is used to collect data in areas where mobile devices are inconvenient to reach, as well as to verify data collection. This unit integrates infrared and contact dual-mode temperature probes, and has a built-in positioning and wireless communication module to collect air temperature and upload the positioning temperature data in real time via wireless network.

[0054] The thermal infrared monitoring UAV 3 performs gridded flight missions every two hours. Before takeoff, the flight control system presets a temperature measurement route to ensure over 80% lateral overlap coverage of the target area. During flight, the onboard thermal infrared imager collects surface radiation data at second-level intervals, while the RTK positioning module embeds centimeter-level geographic coordinates into each image frame. After completing a single flight mission, the thermal infrared monitoring UAV 3 autonomously returns to the ground temperature monitoring station 1.

[0055] The critical calibration process begins after the UAV returns to base. The UAV docks with the connection interface 16 on the side of ground temperature monitoring station 1 via its bottom data export interface. Through the synergy of the spiral guide groove and magnetic adsorption components, a precise physical connection of the data interface is achieved. After docking, the system automatically triggers the temperature difference sensor to detect the temperature gradient at the interface, and simultaneously uses continuous measured temperature data from ground temperature monitoring station 1 for the same period as a benchmark. The temperature error correction unit, based on an error propagation model, performs pixel-by-pixel dynamic compensation on the UAV's thermal infrared radiation data, generating a calibrated high-precision temperature distribution map. The calibrated data is transmitted to the data center via a 5G network, where it, together with the time-series data from ground temperature monitoring station 1, constructs a spatiotemporal database of the temperature field.

[0056] In the description of this invention, the above are merely preferred embodiments and are not intended to limit the scope of protection of this invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An urban forest cooling intensity and stability evaluation method, characterized in that, Includes the following steps: S1. Deploy temperature monitoring systems in urban forests to collect basic data on forests in various local climate types, including urban forest attribute data, temperature monitoring data, and parameter calibration data. S2. Based on the parameter calibration data and temperature monitoring data, perform spatial distribution calibration of temperature data over multiple time periods to generate calibrated temperature monitoring data; S3. Based on urban forest attribute data and calibrated temperature monitoring data, calculate the intensity and stability of the urban forest cooling effect; S4. Standardize the intensity and stability of the cooling effect of urban forests, analyze the driving mechanisms that cause the intensity and stability of the forest cooling effect, and construct a spatial distribution map of urban fragile forest patches. S5. Based on the spatial distribution map of urban fragile forest patches, and with the driving mechanism as scientific support, formulate optimization schemes for urban forests in different local climate zones.

2. The method for assessing the cooling intensity and stability of urban forests according to claim 1, characterized in that, In S1, urban forest attribute data includes forest structure attribute data, spatial configuration attribute data, and habitat soil attribute data; temperature monitoring data includes temperature monitoring network data, air temperature meteorological station data, and thermal infrared UAV remote sensing data; parameter calibration data includes ground real temperature data used for model construction at multiple time periods and measured temperature data not involved in model construction used for calibration and verification. Among them, forest structure attribute data include leaf area index, tree species composition, tree height, canopy height, diameter at breast height, crown width, and vegetation hierarchy; Spatial configuration attribute data include patch area and shape, vegetation connectivity, aggregation, diversity, and local climate type. Habitat soil attribute data includes soil type, soil temperature, soil moisture, and underlying surface type.

3. The method for assessing the cooling intensity and stability of urban forests according to claim 2, characterized in that, In S2, the specific method for calibrating thermal infrared UAV remote sensing data is as follows: (1) Temperature inversion is performed on the thermal infrared UAV remote sensing data, and spatial data is matched to obtain the UAV pixel value; (2) The uncertainty of ground measurement values ​​and UAV pixel values ​​is quantified by using an error propagation model; Among them, the uncertainty of ground measurement values The specific expression is: ； ； In the formula, Due to instrument error, For spatial representativeness error, To use the standard deviation of measurements taken at multiple points within the sample plot, N This represents the number of measurement points within the sample plot; Uncertainty in drone pixel values The specific expression is: ； In the formula, To use the temperature standard deviation of corresponding pixels in the sample plot, M The number of pixels covered by the sample plot; (3) Establish a pixel-by-pixel temperature calibration model based on uncertainty and ground true temperature data; Based on the relationships in the scatter plot, construct a linear model: ； In the formula, These are ground-measured values. For drone pixel values, It is the minimum value. and The parameter to be determined in the least squares estimation formula is specifically expressed as follows: ； ； In the formula, This represents the average pixel value of the drone. This is the average of the ground measurements. and The parameters for the least squares estimation formula are calculated. For a moment j Ground measurements, For a moment j The pixel value of the drone , k For time number, For a moment j The weights; ； In the formula, For a moment j The uncertainty of ground measurement values, For a moment j The uncertainty of drone pixel values; (4) The pixel-by-pixel temperature calibration model is corrected and verified using measured temperature data to generate high-precision thermal infrared UAV remote sensing data.

4. The method for assessing the cooling intensity and stability of urban forests according to claim 3, characterized in that, In S3, the method for calculating the intensity of the cooling effect of urban forests is as follows: the difference between the temperature of different areas inside the forest and the impermeable surface measured every 2 hours is calculated to obtain the intensity of the area during that time period. The specific method for calculating cooling stability is as follows: under a temperature gradient, calculate the change in surface temperature caused by the increase in temperature in different areas within the forest, and obtain the stability of the region.

5. The method for assessing the cooling intensity and stability of urban forests according to claim 1, characterized in that, S4 specifically refers to: The intensity and stability of the cooling effect of urban forests are standardized, and the key driving factors causing the intensity and stability of urban forests are analyzed using structural equation modeling. The intensity and stability of urban cooling in each local climate zone are calculated, thereby constructing a spatial distribution map of urban fragile forest patches. Among them, key driving factors include community structure and morphological characteristics and tree species physiological and process characteristics. Community structure and morphological characteristics include tree species diversity, canopy coverage, tree density, average tree height and leaf area index. Tree species physiological and process characteristics include dominant tree species, leaf thickness, leaf water content, net photosynthetic rate and transpiration rate.

6. The method for assessing the cooling intensity and stability of urban forests according to claim 1, characterized in that, S5 specifically refers to: Based on the spatial distribution map of urban fragile forest patches, the intensity and stability of urban cooling in each local climate zone are obtained. By balancing the synergistic relationship, tree species and layout suggestions are generated, resulting in a written report and a visualization map.

7. A temperature monitoring system, applied to the assessment method for the cooling intensity and stability of urban forests as described in claims 1-6, characterized in that, The system includes ground temperature monitoring stations, handheld temperature monitors, and thermal infrared monitoring drones.

8. The temperature monitoring system according to claim 7, characterized in that, The ground temperature monitoring station is equipped with a data box. The bottom of the data box is equipped with a magnetic chuck. Inside the data box are a data acquisition device, a 5G data transmission module, a GPS positioning module, and a battery. The top of the data box is fixedly installed with a support rod and a solar photovoltaic panel. The top of the support rod is equipped with an air temperature and humidity sensor, a black ball temperature sensor, and an anemometer.

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