Urban forest cooling function baseline construction and attenuation recovery evaluation method and system

Abstract

The invention relates to an urban forest cooling function baseline construction and attenuation recovery evaluation method and system. The method comprises the following steps: acquiring day-by-day meteorological data of a research area to identify starting and ending dates of an extreme high temperature event; acquiring urban forest coverage distribution information in the research area and near-surface air temperature data or surface temperature data in a first date range, and calculating urban forest cooling efficiency data of each date in the first date range; calculating statistical characteristics of the urban forest cooling efficiency data of all dates of the beforehand reference period; determining the maximum attenuation value and date of the cooling efficiency during the extreme high-temperature event; and calculating the statistical characteristics of the urban forest cooling efficiency data of each sliding window by using the sliding windows, and calculating the recovery time and rate of urban forest cooling efficiency attenuation according to the baseline and a preset discrimination condition. It is ensured that attenuation is caused by the extreme high-temperature event, and the attenuation degree and the recovery process of the cooling function under the influence of the extreme high-temperature event are quantified.

Description

Technical Field

[0001] This disclosure relates to the field of urban ecology and climate adaptation assessment technology, and in particular to assessment methods and systems for establishing baselines for the cooling function of urban forests and assessing their decline and recovery. Background Technology

[0002] Extreme heat events (also known as heat waves) are characterized by temperatures significantly exceeding local climate norms for several consecutive days. They are easily superimposed on the urban heat island effect, significantly impacting urban heat exposure levels and ecological regulation functions. Extreme heat events can disrupt the structure and function of urban ecosystems, thereby weakening the city's ability to adapt and regulate under extreme climatic conditions.

[0003] Urban forests encompass green space units primarily composed of tree communities, including street trees, protective forests, and parkland woodlands within built-up areas. Through processes such as canopy shading, transpiration, and altering the distribution of surface radiation energy, urban forests effectively reduce near-surface air temperature and surface temperature, resulting in a significant cooling effect. As an important component of urban ecosystem functions, the cooling function of urban forests plays a crucial role in regulating the urban thermal environment and constructing climate-adaptive cities.

[0004] Under the influence of extreme heat events, the cooling function of urban forests exhibits nonlinear response characteristics. Under short-term high-temperature conditions, enhanced transpiration can improve the cooling effect, while sustained high temperatures and water stress can lead to stomatal closure and restricted physiological activity, thereby causing a decline in cooling function. These changes typically have obvious time-series characteristics, involving the functional state before the extreme heat event, the functional deviation during the event, and the recovery process after the event.

[0005] Existing methods for assessing the resilience of urban forest ecological functions are mostly based on simple comparisons of long-term characteristics or states before and after external disturbances. However, these methods have limitations in distinguishing between functional changes caused by natural fluctuations and extreme events, as well as in accurately identifying the actual recovery process. Therefore, a baseline for the cooling function of urban forests and an assessment method and system for its attenuation and recovery are needed to achieve a more scientific and practically valuable assessment. Summary of the Invention

[0006] Therefore, it is necessary to provide a baseline construction and assessment method and system for the cooling function of urban forests to address the problems of difficulty in distinguishing between functional changes caused by natural fluctuations and extreme high-temperature events, and difficulty in quantifying the degree of attenuation and recovery process of cooling function under the influence of extreme high-temperature events.

[0007] To solve the above problems, the present disclosure adopts the following technical solution:

[0008] Firstly, this disclosure provides a method for establishing a baseline for the cooling function of urban forests and assessing its decline and recovery, including the following steps:

[0009] Step 1: Obtain daily meteorological data for the study area and identify the start and end dates of extreme high-temperature events;

[0010] Step 2: Obtain urban forest coverage distribution information within the study area, obtain near-surface air temperature data or surface temperature data for the first date range, and calculate urban forest cooling efficiency data for each date within the first date range. The first date range includes the date range of the extreme high temperature event, the date range prior to the extreme high temperature event, and the date range after the extreme high temperature event. The date range prior to the event and the date range after the event are both adjacent to the date range of the extreme high temperature event.

[0011] Step 3: Calculate the statistical characteristics of urban forest cooling efficiency data for all dates in the prior baseline period as the baseline, where the prior date range of the extreme high temperature event includes the prior baseline period;

[0012] Step 4: Determine the maximum attenuation value of cooling efficiency and the date t of the maximum attenuation value during the extreme high temperature event based on the statistical characteristics. d ;

[0013] Step 5: Based on the urban forest cooling efficiency dataset within the second date range, calculate the statistical characteristics of the urban forest cooling efficiency data for each sliding window using a sliding window method. Calculate the recovery time and recovery rate of the urban forest cooling efficiency decay based on the baseline and preset discrimination conditions. The second date range includes the time range within the first date range up to date t. d For all subsequent dates, the urban forest cooling efficiency dataset for the second date range is a dataset sorted by time series.

[0014] Secondly, this disclosure provides an assessment system for establishing and restoring the baseline of urban forest cooling function, including:

[0015] The acquisition module is used to acquire daily meteorological data of the study area and identify the start and end dates of extreme high-temperature events;

[0016] The acquisition and calculation module is used to acquire urban forest coverage distribution information within the study area, acquire near-surface air temperature data or surface temperature data for a first date range, and calculate urban forest cooling efficiency data for each date within the first date range. The first date range includes the date range of the extreme high temperature event, the date range prior to the extreme high temperature event, and the date range after the extreme high temperature event. The date range prior to the event and the date range after the event are both adjacent to the date range of the extreme high temperature event.

[0017] The first calculation module is used to calculate the statistical characteristics of urban forest cooling efficiency data for all dates in the prior baseline period as the baseline, and the prior date range of the extreme high temperature event includes the prior baseline period;

[0018] The attenuation determination module is used to determine the maximum attenuation value of cooling efficiency and the date t of the maximum attenuation value during an extreme high-temperature event based on the statistical characteristics. d ;

[0019] The second calculation module is used to calculate the statistical characteristics of the urban forest cooling efficiency data for each sliding window based on the urban forest cooling efficiency dataset within a second date range, and to calculate the recovery time and recovery rate of the urban forest cooling efficiency decay according to the baseline and preset discrimination conditions; the second date range includes the time in the first date range on date t. d For all subsequent dates, the urban forest cooling efficiency dataset for the second date range is a dataset sorted by time series.

[0020] The effectiveness of the aforementioned method and system for constructing a baseline for the cooling function of urban forests and evaluating its attenuation recovery is as follows: Using specific extreme high-temperature events as time anchors, the system performs steps such as calculating urban forest cooling efficiency data, determining the baseline, calculating attenuation values, and determining attenuation dates, ensuring that the attenuation is caused by extreme high-temperature events rather than natural fluctuations. By calculating the baseline, the maximum attenuation value and the date of the maximum attenuation value are determined. Then, based on the baseline and preset discrimination conditions, the recovery time and rate of urban forest cooling efficiency attenuation are calculated, thus quantifying the degree of attenuation and recovery process of the cooling function under the influence of extreme high-temperature events. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a method in one embodiment of the present disclosure;

[0022] Figure 2 This is a schematic diagram illustrating the attenuation and recovery process of urban forest cooling efficiency under the influence of extreme high-temperature events in the embodiments of this disclosure;

[0023] Figure 3 This is a schematic diagram illustrating the conditions for determining the recovery state of urban forest cooling efficiency in this embodiment of the present disclosure.

[0024] Figure 4 This is a schematic diagram of the system structure in one embodiment of the present disclosure. Detailed Implementation

[0025] The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and preferred embodiments.

[0026] See Figure 1This embodiment provides a method for establishing a baseline for the cooling function of urban forests and evaluating its attenuation and recovery, including:

[0027] Step 1: Obtain daily meteorological data for the study area and identify the start and end dates of extreme high-temperature events;

[0028] Step 2: Obtain urban forest coverage distribution information within the study area, obtain near-surface air temperature data or surface temperature data for the first date range, and calculate urban forest cooling efficiency data for each date within the first date range. The first date range includes the date range of the extreme high temperature event, the date range prior to the extreme high temperature event, and the date range after the extreme high temperature event. The date range prior to the event and the date range after the event are both adjacent to the date range of the extreme high temperature event.

[0029] Step 3: Calculate the statistical characteristics of urban forest cooling efficiency data for all dates in the prior baseline period as the baseline, where the prior date range of the extreme high temperature event includes the prior baseline period;

[0030] Step 4: Determine the maximum attenuation value of cooling efficiency and the date t of the maximum attenuation value during the extreme high temperature event based on the statistical characteristics. d ;

[0031] Step 5: Based on the urban forest cooling efficiency dataset within the second date range, calculate the statistical characteristics of the urban forest cooling efficiency data for each sliding window using a sliding window method. Calculate the recovery time and recovery rate of the urban forest cooling efficiency decay based on the baseline and preset discrimination conditions. The second date range includes the time range within the first date range up to date t. d For all subsequent dates, the urban forest cooling efficiency dataset for the second date range is a dataset sorted by time series.

[0032] The following section details the baseline construction and assessment methods for the degradation and recovery of the cooling function of urban forests.

[0033] Preferably, all datasets described in the embodiments of this article are time-series sorted datasets, that is, sorted by date from earliest to latest.

[0034] Step 1: Obtain daily meteorological data for the study area, and identify the start and end dates of extreme high-temperature events based on the daily meteorological data for the study area;

[0035] The daily meteorological data for the study area refers to the observation or forecast information of daily meteorological elements within one or more geographical areas under study. It is typically designed to cover a continuous date series, but is not limited to this. Meteorological elements mainly refer to temperature, but may also include precipitation, wind speed, etc. Here, as a preferred option, the temperature is near-surface temperature.

[0036] In one specific embodiment, the start and end dates of extreme high-temperature events are identified based on daily near-surface air temperature data of the study area. The method for determining extreme high-temperature events is as follows: the highest near-surface air temperature over several consecutive days is greater than or equal to a preset extreme high-temperature discrimination threshold. It is understood that the near-surface air temperature typically refers to the air temperature at a height of 1.5 meters above the ground.

[0037] If the highest near-surface temperature is greater than or equal to the preset extreme high temperature threshold for several consecutive days, these days are recorded as one extreme high temperature event. For example, if the near-surface temperature from August 1 to August 8 is greater than or equal to the extreme high temperature threshold, and the near-surface temperature on July 31 and August 9 is less than the extreme high temperature threshold, then the consecutive days are defined as three consecutive days. Therefore, August 1 to August 3 is defined as one extreme high temperature event, and August 4 to August 6 is defined as another extreme high temperature event, for a total of two extreme high temperature events.

[0038] In implementation, extreme high-temperature events are identified based on daily near-surface temperature data; let T(t) be the highest temperature in the study area on date t, where t is the date, and the range of values ​​for t depends on the specific circumstances; let T be the threshold for extreme high-temperature discrimination. thr The extreme high temperature discrimination threshold can be determined in any of the following ways:

[0039] ① The 90th or 95th percentile of the highest daily temperature (e.g., near-surface temperature) in summer over many years (e.g., the last 3 years);

[0040] ②Refer to the fixed threshold value of existing implementation standards, such as 35℃;

[0041] These are merely two preferred examples, not limitations.

[0042] When T(t) ≥ T for d consecutive days thr When the specified time period (date range) is considered to constitute an extreme high-temperature event, the value of d is an integer greater than or equal to 3, and the start date of the extreme high-temperature event is denoted as t. s The end date is denoted as t. e d is usually taken as 3-5 days.

[0043] Step 2: Obtain information on the distribution of urban forest cover within the study area, and calculate the urban forest cover x of each spatial unit within the study area. i ; Obtain near-surface air temperature data or surface temperature data for each date within the first date range. i(t). The first date range includes the date range of the extreme high temperature event, the date range prior to the extreme high temperature event, and the date range after the extreme high temperature event; the date range prior to the extreme high temperature event and the date range after the extreme high temperature event are both adjacent to the date range of the extreme high temperature event, that is, the day before the date range of the extreme high temperature event belongs to the date range prior to the event, and the day after the date range of the extreme high temperature event belongs to the date range after the event. For each date in the first date range, the urban forest cooling efficiency data CE(t) for that date is calculated, and the urban forest cooling efficiency dataset {CE(t)} for the first date range is constructed. Here, the data in {CE(t)} is sorted according to time series.

[0044] Step 2 includes the following specific steps:

[0045] Step 2.1, Data Collection and Processing: Based on remote sensing imagery or land cover data, quantitatively characterize the urban forest cover within the study area to obtain the urban forest cover x for each spatial unit. i Simultaneously, it obtains the near-surface air temperature or surface temperature y of the corresponding spatial unit for each of multiple dates t (usually consecutive dates). i (t); i serves as the number of the spatial unit. The plurality of dates constitute the first date range.

[0046] Understandably, both remote sensing imagery and land cover data record the distribution of natural and man-made covers such as forests, farmland, and water bodies on the land surface. The study area comprises multiple spatial units, and this disclosure does not limit the method of dividing these spatial units.

[0047] The quantitative characterization of urban forest coverage within the study area is expressed as a percentage of forest coverage.

[0048] Step 2.2: Calculate the urban forest cooling efficiency data for each date within the first date range, and construct the urban forest cooling efficiency dataset for the first date range:

[0049] The cooling function of urban forests is quantitatively characterized by the urban forest cooling efficiency index, denoted as urban forest cooling efficiency data; on date t, multiple (usually all) spatial units (x) within the study area are used. i , y i Using (t) as the sample, construct a linear regression model:

[0050]

[0051] in, The intercept term is represented by β(t), which represents the regression coefficient. Under normal climatic conditions, the regression coefficient β(t) is mostly negative, representing the amount of temperature reduction caused by an increase in urban forest coverage per unit area. x represents the random error term; i As the independent variable, it represents the urban forest coverage of spatial unit i; As the dependent variable, This represents the near-surface air temperature or surface temperature of date t and spatial unit i.

[0052] To uniformly characterize the strength of urban forest cooling efficiency, the absolute value of the regression coefficient β(t) is defined as the urban forest cooling efficiency data CE(t). A larger CE(t) value indicates a stronger cooling efficiency per unit urban forest coverage within the study area. Therefore, a first-date-range urban forest cooling efficiency dataset {CE(t)} is constructed, preferably corresponding to the date range of extreme high-temperature events [t]. s , t e Expand forward and backward, including at least the date range [t] s - p, t e + m], where p is the preset pre-event baseline period length and m is the preset post-event extension number of days. The values ​​of both are determined according to the actual needs during the implementation process.

[0053] Step 3: Calculate the statistical characteristics of the urban forest cooling efficiency dataset for the pre-base period. It is understood that the urban forest cooling efficiency dataset for the pre-base period includes urban forest cooling efficiency data for all dates within the pre-base period. Clearly, the date range of the pre-base period is adjacent to the start date of the extreme high-temperature event.

[0054] Using a preset time window before an extreme high-temperature event as a benchmark, the statistical characteristics of urban forest cooling efficiency data are quantified as a pre-event baseline. In this embodiment, step 3 includes the following specific steps:

[0055] Step 3.1, Set the pre-event baseline period: Based on the start date t of the extreme high temperature event obtained in Step 1. s Select the length before the event occurs as The time window is used as the pre-existing baseline period W pre :

[0056]

[0057] The date range of the extreme heat event is from the day before to the day before. This date range is a pre-event date range. It is an integer greater than 1. Less than or equal to p, meaning the prior reference period falls within the prior date range.

[0058] Step 3.2, Calculate the ex-ante baseline: based on the ex-ante baseline period W preThe data on urban forest cooling efficiency (i.e., the dataset) is used to calculate statistical characteristics (called pre-emptive baseline statistical characteristics). These statistical characteristics serve as the baseline, including the mean μ0 (baseline mean) and, further, the standard deviation σ0 (baseline standard deviation).

[0059] The formula for calculating the baseline mean μ0 is:

[0060]

[0061] The formula for calculating the baseline standard deviation σ0 is:

[0062]

[0063] Among them, the baseline mean μ0 represents the reference level of urban forest cooling efficiency data before the occurrence of extreme high temperature events, and the baseline standard deviation σ0 represents its fluctuation range under natural conditions.

[0064] Step 4: Determine the maximum attenuation value of cooling efficiency during the extreme heat event and its corresponding date based on the statistical characteristics. This involves calculating the deviation of urban forest cooling efficiency data from the baseline during the extreme heat event to determine the functional attenuation characteristics. Specifically, calculate the deviation of urban forest cooling efficiency data for each date during the extreme heat event from the mean; this deviation is used as the urban forest cooling efficiency attenuation value. The maximum value of this attenuation value is taken as the maximum attenuation value, and the date corresponding to the maximum attenuation value is determined. This includes the following steps:

[0065] Step 4.1, Baseline Deviation Analysis: Based on the baseline mean μ0 obtained in Step 3, deviation analysis is performed on the urban forest cooling efficiency data CE(t) during the extreme high-temperature event. The deviation amount D(t) is defined and calculated using the following formula:

[0066]

[0067] Among them, if A value less than 0 indicates that the cooling efficiency of the urban forest has decreased relative to the pre-existing baseline, representing a deviation of [value missing]. This represents the attenuation value of the cooling efficiency of urban forests, which can be understood as the degree of attenuation.

[0068] Step 4.2, Maximum Attenuation Analysis: The maximum value of the urban forest cooling efficiency attenuation is taken as the maximum attenuation value. The maximum attenuation value of the urban forest cooling efficiency during an extreme heat event (the date range of the extreme heat event) is defined as ΔCE. max :

[0069]

[0070] Step 4.3: Determine the date of the maximum attenuation value, and define the date (occurrence date) of the maximum attenuation value of the urban forest cooling function as t. d :

[0071]

[0072] Among them, t d During extreme heat events [t s , t e ], argmax represents the input value that makes a function reach its maximum value.

[0073] Step 5: Based on the urban forest cooling efficiency dataset for the second date range, calculate the statistical characteristics of the urban forest cooling efficiency data for each sliding window using a sliding window, and calculate the recovery time and recovery rate of the urban forest cooling efficiency decay according to the baseline and preset discrimination conditions.

[0074] In some embodiments, the step of using a second date range urban forest cooling efficiency dataset may also be included. However, since the second date range urban forest cooling efficiency dataset is essentially part of the first date range urban forest cooling efficiency dataset, it is not necessary to construct the second date range urban forest cooling efficiency dataset; the first date range urban forest cooling efficiency dataset can be used directly.

[0075] Understandably, the sliding window in step 5 typically does not require a prior date range of urban forest cooling efficiency datasets.

[0076] A sliding time window was used to continuously compare the differences in statistical characteristics with the pre-existing baseline. The statistical characteristics of the urban forest cooling efficiency data for each sliding window included the mean. and standard deviation .

[0077] Specifically, step 5 includes: using the date of the maximum attenuation value as the starting point for sliding the sliding window, which is time-driven and called a sliding time window, and the sliding direction is forward (moving in the direction of increasing time), and calculating the mean value of the urban forest cooling efficiency data for each sliding window. and standard deviation Based on the statistical characteristics and the mean and the standard deviation Determine whether the preset discrimination conditions are met; on date t d The date on which the preset discrimination condition is first met is taken as the recovery date t of the urban forest cooling efficiency. r ; Calculate the recovery time and recovery rate of the cooling efficiency degradation of urban forests.

[0078] In one embodiment, step 5 includes the following specific steps:

[0079] Step 5.1, define the sliding window: from the maximum decay time point (date) t d Starting from q, for any point in time (date) Construct a dataset of urban forest cooling efficiency based on... Analysis window for time series datasets centered on :

[0080]

[0081] Understandably, the center point of the first sliding window is not required to be t. d Preferably, it is slightly later than t d The date. To improve accuracy, preferably, the first sliding window is defined to include t. d Clearly, the corresponding second date range includes date t. d .

[0082] Step 5.2, Sliding calculation of statistical characteristics: For each analysis window Calculate the mean of urban forest cooling efficiency data and standard deviation The calculation formula is as follows:

[0083]

[0084]

[0085] Where q is the length of the sliding window. For date A sliding window centered on the center. express The date in It is the date The cooling efficiency of urban forests. Understandably, when the sliding time window exceeds the valid date range of the urban forest cooling efficiency data, the time window is truncated or extended.

[0086] Step 5.3, Determine the Functional Recovery Status: The urban forest cooling efficiency is determined to have reached the recovery status when the following conditions are met simultaneously:

[0087] Condition 1, Mean Condition:

[0088]

[0089] Where k is the threshold coefficient;

[0090] Condition 2, stability condition:

[0091]

[0092] Among them, on date t d Subsequently, when conditions 1 and 2 are simultaneously met for the first time, the preset discrimination condition is satisfied, and the corresponding time point is defined as the recovery time point t of the urban forest cooling efficiency. r That is, the recovery date;

[0093] Understandably, the date that satisfies the preset discrimination condition is the center time of the sliding window that satisfies the preset discrimination condition, which means that the average value satisfies the preset discrimination condition. and standard deviation This means it has a corresponding sliding window, with the center time (date) of that sliding window. The date that satisfies the preset discrimination condition is the recovery date.

[0094] Step 5.4, calculate the recovery time and rate based on the following formula:

[0095]

[0096]

[0097] Among them, T rec R represents the recovery time for the decline in the cooling efficiency of urban forests. rec The recovery rate of the reduced cooling efficiency of urban forests. Indicates date Data on the cooling efficiency of urban forests.

[0098] The following is a specific application example of the method:

[0099] In a practical application, relevant data for a certain region was obtained: daily surface temperature data for July of a certain year was obtained from Google Earth Engine; annual vegetation cover index was obtained from Google Earth Engine.

[0100] Data preprocessing: Using Google Earth Engine and Javascript, daily land surface temperature data were standardized to the same projection. Quality control was performed using QC bands. The two data products were merged into daily composite data using the averaging method on the corresponding dates. Missing pixels were reconstructed using 5-day moving average interpolation to generate daily daytime land surface temperature data in °C. Using Google Earth Engine and Javascript, the annual vegetation cover index was standardized to the same projection and spatial resolution as the land surface temperature data to ensure pixel and boundary alignment in the dimension %.

[0101] Weather events with daily maximum temperatures exceeding 35°C for three consecutive days or more are classified as extreme high-temperature events. Specifically, only one extreme high-temperature event was identified, from July 8th to July 14th, lasting for seven days. For the first date range, p is set to 3 and m to 10, with the first date range being from July 5th to July 24th.

[0102] In step 3, a time window of 3 days prior to the event is selected as the pre-event baseline period W. pre The period from July 5th to July 7th is used to characterize the background level of urban forest cooling efficiency before extreme high-temperature events occur. This time length can ensure the basic sample size for calculating baseline statistical characteristics and avoid interference from other weather processes due to an excessively long base period.

[0103] In step 4, μ0 is 0.1866℃ / %, and σ0 is 0.014℃ / %.

[0104] In this embodiment, D(t) < 0 on July 9 and from July 11 to July 14, indicating that the cooling efficiency of the urban forest decreased relative to the prior baseline during the above time period.

[0105] In this embodiment, ΔCE max The value is -0.0823℃ / %, and the negative value indicates that the cooling efficiency of the urban forest at that time point has decreased relative to the prior baseline.

[0106] In this embodiment, the cooling efficiency of the urban forest reaches its maximum attenuation level ΔCE. max The corresponding time point t d For July 12th, the corresponding urban forest cooling efficiency CE(t) d The value is 0.1043℃ / %, such as Figure 2 As shown.

[0107] In step 5 Choosing option 3, we constructed an analysis window. This setting, while suppressing daily random fluctuations, maintains time sensitivity to changes in the cooling efficiency of urban forests, thus forming a time-point-based analysis starting from July 12th. Continuous analysis window centered on ;

[0108] In this embodiment, k is set to 1, and one standard deviation is used as the recovery judgment threshold. This can ensure the judgment sensitivity while avoiding the interference of short-term random fluctuations on the recovery identification. In other embodiments, the threshold coefficient k can be adjusted according to the climate characteristics of the study area and the application requirements.

[0109] In this embodiment, July 19th is the time point t at the maximum decay. dThe point in time when conditions 1 and 2 are simultaneously met for the first time is determined as the recovery point t of the urban forest's cooling efficiency. r ,like Figure 3 As shown.

[0110] In this embodiment, the recovery time T rec The duration is 7 days; μ0 is 0.1866℃ / %, CE(t) d The recovery rate R of the cooling efficiency of urban forests was calculated based on the value of 0.1043℃ / %. rec The recovery rate was 0.012℃ / (%·d);

[0111] See Figure 4 This disclosure provides an assessment system for establishing and restoring the baseline of urban forest cooling function, including:

[0112] The acquisition module is used to acquire daily meteorological data of the study area and identify the start and end dates of extreme high-temperature events;

[0113] The acquisition and calculation module is used to acquire urban forest coverage distribution information within the study area, acquire near-surface air temperature data or surface temperature data for a first date range, and calculate urban forest cooling efficiency data for each date within the first date range. The first date range includes the date range of the extreme high temperature event, the date range prior to the extreme high temperature event, and the date range after the extreme high temperature event. The date range prior to the event and the date range after the event are both adjacent to the date range of the extreme high temperature event.

[0114] The first calculation module is used to calculate the statistical characteristics of urban forest cooling efficiency data for all dates in the prior baseline period as the baseline, and the prior date range of the extreme high temperature event includes the prior baseline period;

[0115] The attenuation determination module is used to determine the maximum attenuation value of cooling efficiency and the date t of the maximum attenuation value during an extreme high-temperature event based on the statistical characteristics. d ;

[0116] The second calculation module is used to calculate the statistical characteristics of the urban forest cooling efficiency data for each sliding window based on the urban forest cooling efficiency dataset within a second date range, and to calculate the recovery time and recovery rate of the urban forest cooling efficiency decay according to the baseline and preset discrimination conditions; the second date range includes the time in the first date range on date t. d For all subsequent dates, the urban forest cooling efficiency dataset for the second date range is a dataset sorted by time series.

[0117] In this embodiment, the acquisition module is specifically used to acquire daily meteorological data of the study area to identify extreme high temperature events. An extreme high temperature event is defined as follows: if the highest near-surface temperature is greater than or equal to the extreme high temperature discrimination threshold for d consecutive days, then it is an extreme high temperature event. The acquisition module is also specifically used to acquire the start and end dates of the extreme high temperature event, where d is an integer greater than or equal to 3.

[0118] In this embodiment, the acquisition and calculation module specifically includes:

[0119] The first acquisition unit is used to acquire information on the distribution of urban forest coverage within the study area and to calculate the urban forest coverage of each spatial unit within the study area.

[0120] The second acquisition unit is used to acquire near-surface air temperature data or surface temperature data for each spatial unit for each date in the first date range;

[0121] The first calculation unit is used to calculate the urban forest cooling efficiency data CE(t) for each date in the first date range.

[0122] In this embodiment, the attenuation determination module is specifically used to calculate the deviation of the urban forest cooling efficiency data for each date in an extreme high-temperature event from the mean. The deviation is used as the urban forest cooling efficiency attenuation value. The maximum value of the urban forest cooling efficiency attenuation value is taken as the maximum attenuation value, and the date corresponding to the maximum attenuation value is determined.

[0123] In this embodiment, the second calculation module includes:

[0124] The first sliding calculation unit is used to calculate the mean value of urban forest cooling efficiency data in each sliding window, using the date of the maximum attenuation value as the starting point of the sliding window. and standard deviation The sliding window is time-driven, and the sliding direction is forward.

[0125] The discrimination unit is used to determine the statistical features and the mean. and the standard deviation Determine whether the preset discrimination conditions are met; on date t d The date on which the preset discrimination condition is first met is taken as the recovery date t of the urban forest cooling efficiency. r ;

[0126] The second calculation unit is used to calculate the recovery time and recovery rate of the cooling efficiency decay of urban forests.

[0127] An electronic device can be implemented according to the method of this disclosure, the electronic device comprising: a memory; one or more processors; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing an evaluation method for establishing and restoring the cooling function baseline of urban forests according to any of the above embodiments.

[0128] This disclosure also provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the steps of the assessment method for establishing and restoring the urban forest cooling function baseline as described in any of the above embodiments.

[0129] The advantages of the disclosed method and system for baseline construction and attenuation recovery assessment of urban forest cooling function are as follows: This disclosure uses specific extreme high-temperature events as time anchors for steps such as calculating urban forest cooling efficiency data, determining the baseline, calculating attenuation values, and determining attenuation dates, ensuring that attenuation is caused by extreme high-temperature events rather than natural fluctuations. By calculating the baseline, the maximum attenuation value and the date td of the maximum attenuation value are determined. Then, based on the baseline and preset discrimination conditions, the recovery time and recovery rate of urban forest cooling efficiency attenuation are calculated, realizing the quantification of the degree of attenuation and recovery process (recovery time and efficiency) of cooling function under the influence of extreme high-temperature events. This disclosure achieves a more scientific and more practically valuable assessment.

[0130] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] The embodiments described above are merely illustrative of several implementations of this disclosure, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this disclosure, and these all fall within the protection scope of this disclosure. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for establishing a baseline for the cooling function of urban forests and assessing its attenuation and recovery, characterized in that... Includes the following steps: Step 1: Obtain daily meteorological data for the study area and identify the start and end dates of extreme high-temperature events; Step 2: Obtain urban forest coverage distribution information within the study area, obtain near-surface air temperature data or surface temperature data for the first date range, and calculate urban forest cooling efficiency data for each date within the first date range. The first date range includes the date range of the extreme high temperature event, the date range prior to the extreme high temperature event, and the date range after the extreme high temperature event. The date range prior to the event and the date range after the event are both adjacent to the date range of the extreme high temperature event. Step 3: Calculate the statistical characteristics of urban forest cooling efficiency data for all dates in the prior baseline period as the baseline, where the prior date range of the extreme high temperature event includes the prior baseline period; Step 4: Determine the maximum attenuation value of cooling efficiency and the date t of the maximum attenuation value during the extreme high temperature event based on the statistical characteristics. d ; Step 5: Based on the urban forest cooling efficiency dataset within the second date range, calculate the statistical characteristics of the urban forest cooling efficiency data for each sliding window using a sliding window method. Calculate the recovery time and recovery rate of the urban forest cooling efficiency decay based on the baseline and preset discrimination conditions. The second date range includes the time range within the first date range up to date t. d For all subsequent dates, the urban forest cooling efficiency dataset for the second date range is a dataset sorted by time series.

2. The method for constructing and assessing the attenuation and recovery of the urban forest cooling function baseline according to claim 1, characterized in that, Step 1 specifically involves: acquiring daily meteorological data for the study area; if the highest near-surface temperature is greater than or equal to the extreme high temperature discrimination threshold for d consecutive days, it is identified as an extreme high temperature event; and obtaining the start and end dates of the extreme high temperature event, where d is an integer greater than or equal to 3.

3. The method for constructing and assessing the attenuation and recovery of the urban forest cooling function baseline according to claim 1, characterized in that, Step 2 includes: Obtain information on the distribution of urban forest cover within the study area, and calculate the urban forest cover of each spatial unit within the study area; Obtain near-surface air temperature data or surface temperature data for each spatial unit within the first date range; For each date within the first date range, calculate the urban forest cooling efficiency data CE(t) for that date.

4. The method for constructing and assessing the attenuation and recovery of the urban forest cooling function baseline according to claim 3, characterized in that, The calculation method for CE(t) is as follows: construct a linear regression model, wherein x i As the independent variable, y i (t) is the dependent variable, x i Indicates the urban forest coverage of spatial unit i. Let t represent the near-surface air temperature or surface temperature of spatial unit i, and let CE(t) be the absolute value of the regression coefficients of the linear regression model.

5. The method for constructing and assessing the attenuation and recovery of the urban forest cooling function baseline according to claim 1, characterized in that, The statistical characteristics include the mean μ0.

6. The method for constructing and assessing the attenuation and recovery of the urban forest cooling function baseline according to claim 5, characterized in that, Step 4 specifically includes: calculating the deviation of the urban forest cooling efficiency data for each date during the extreme high-temperature event from the mean, using the deviation as the urban forest cooling efficiency attenuation value, taking the maximum value of the urban forest cooling efficiency attenuation value as the maximum attenuation value, and determining the date corresponding to the maximum attenuation value.

7. The method for constructing and assessing the attenuation and recovery of the urban forest cooling function baseline according to claim 5, characterized in that, Step 5 includes: The date of maximum attenuation is used as the starting point for the sliding window, which is time-driven and slides forward. The mean value of urban forest cooling efficiency data is calculated for each sliding window. and standard deviation ; Based on the statistical characteristics and the mean and the standard deviation Determine whether the preset discrimination conditions are met; on date t d The date on which the preset discrimination condition is first met is taken as the recovery date t of the urban forest cooling efficiency. r ; Calculate the recovery time and recovery rate of the cooling efficiency degradation of urban forests.

8. The method for constructing and assessing the attenuation and recovery of the urban forest cooling function baseline according to claim 7, characterized in that, The statistical characteristics also include the standard deviation σ0; the preset discrimination condition is... and , where k is a threshold coefficient; the date that satisfies the preset discrimination condition is the center time of the sliding window that satisfies the preset discrimination condition.

9. The method for constructing and assessing the attenuation and recovery of the urban forest cooling function baseline according to claim 7, characterized in that, The formula for calculating the recovery rate is: Among them, T rec R represents the recovery time for the decline in the cooling efficiency of urban forests. rec The recovery rate of the reduced cooling efficiency of urban forests. Indicates date Data on the cooling efficiency of urban forests.

10. An assessment system for the baseline construction and attenuation recovery of the cooling function of urban forests, characterized in that, include: The acquisition module is used to acquire daily meteorological data of the study area and identify the start and end dates of extreme high-temperature events; The acquisition and calculation module is used to acquire urban forest coverage distribution information within the study area, acquire near-surface air temperature data or surface temperature data for a first date range, and calculate urban forest cooling efficiency data for each date within the first date range. The first date range includes the date range of the extreme high temperature event, the date range prior to the extreme high temperature event, and the date range after the extreme high temperature event. The date range prior to the event and the date range after the event are both adjacent to the date range of the extreme high temperature event. The first calculation module is used to calculate the statistical characteristics of urban forest cooling efficiency data for all dates in the prior baseline period as the baseline, and the prior date range of the extreme high temperature event includes the prior baseline period; The attenuation determination module is used to determine the maximum attenuation value of cooling efficiency and the date t of the maximum attenuation value during an extreme high-temperature event based on the statistical characteristics. d ; The second calculation module is used to calculate the statistical characteristics of the urban forest cooling efficiency data for each sliding window based on the urban forest cooling efficiency dataset within a second date range, and to calculate the recovery time and recovery rate of the urban forest cooling efficiency decay according to the baseline and preset discrimination conditions; the second date range includes the time in the first date range on date t. d For all subsequent dates, the urban forest cooling efficiency dataset for the second date range is a dataset sorted by time series.

Images