Regional temperature effect evaluation method for forest landscape mode change

Abstract

The invention discloses a method for evaluating the regional temperature effect of forest landscape mode change, and belongs to the technical field of the regional temperature effect of forest landscape mode change, and the method comprises the steps: data acquisition and processing; evaluating the influence of the forest landscape mode change and the biophysical process on the surface temperature based on a space-to-time method and an area weighting method; evaluating the carbon deficit and accumulation characteristics of the forest landscape mode change based on a space-to-time method and the carbon density data; evaluating the influence of the forest landscape mode change and the biogeochemical process on the surface temperature based on a space-to-time method, carbon density data and a climate model; evaluating the influence of the forest landscape mode change on the regional temperature in cooperation with biophysical and biogeochemical processes; by integrating the high-resolution remote sensing data, space-time dynamic simulation of the forest landscape recovery and degradation process is realized, and the temperature effect of the forest landscape is accurately evaluated.

Description

Technical Field

[0001] The present invention belongs to the technical field of regional temperature effects of changes in forest landscape patterns, and in particular relates to a method for evaluating regional temperature effects of changes in forest landscape patterns. Background Art

[0002] Quantifying the impact of forest landscape changes on temperature through biophysical and biogeochemical mechanisms can comprehensively assess their role in climate regulation. Biogeochemical mechanisms are influenced by multiple external factors at large scales, and their temperature-regulating effects are easily offset. Biophysical mechanisms, on the other hand, are primarily regulated by forest spatial activity at regional and local scales, and their temperature impacts are more significant and ecologically significant. Forest ecosystems produce a cooling effect by absorbing greenhouse gases (such as CO2) from the atmosphere, a process primarily regulated by biogeochemical mechanisms. However, existing technologies do not adequately assess the temperature effects of forest surface energy feedback and its interaction with the atmosphere. Specifically, forests have unique albedo and evapotranspiration characteristics: higher evapotranspiration can lead to cooling, while lower albedo can cause energy accumulation and a warming effect. These processes are primarily governed by biophysical mechanisms. It is worth noting that dynamic changes in forest landscapes (including degradation and restoration) and changes in land use patterns (such as returning farmland to forests and deforestation caused by urbanization) will have differential impacts on temperature by changing the surface energy balance. However, there is currently a lack of a targeted assessment system to combine it with the temperature effects of biogeochemical cycles to achieve systematic quantification of forest restoration and degradation and their temperature impacts.

[0003] The degradation and restoration of forest landscapes alter their spatial pattern, influencing land-atmosphere interactions and ultimately leading to temperature changes. This process plays an irreplaceable role in climate regulation. However, existing technologies lack an efficient and reliable assessment framework that synergistically integrates biophysical and biogeochemical mechanisms to comprehensively assess the impact of forest landscape change on temperature. Currently, the main assessment methods include site observations, flux tower observations, satellite remote sensing, and climate model simulations. Site observations and climate models, however, struggle to achieve comprehensive assessments due to spatiotemporal discontinuities and scale limitations. In contrast, satellite remote sensing technology offers the possibility of continuous spatiotemporal observations, and its integration with climate models provides strong support for overcoming the limitations of existing methods. Existing theoretical studies have confirmed a significant positive correlation between cumulative carbon emissions and climate warming, providing a theoretical basis for linking biophysical and biogeochemical mechanisms through climate model simulations. However, relying solely on climate models fails to effectively integrate information on forest landscape pattern changes and fails to synergize these two mechanisms to accurately characterize the temperature effects of forest landscape change, resulting in a lack of rapid and effective integrated assessment methods. These challenges have prompted technological research and development to pay more attention to the biophysical and biogeochemical mechanisms of changes in forest landscape patterns and their regional temperature effects. At the same time, they have also promoted the integrated application of multiple observation methods to make up for the shortcomings of the existing assessment system.

[0004] Currently, the main monitoring methods for the impact of forest landscape pattern on temperature include:

[0005] Traditional statistical methods: Although traditional statistical methods are easy to operate, they fail to take into account the interference effect of the atmospheric background field and ignore the mechanism of action of biophysical mechanisms.

[0006] Site observation method: Due to the insufficient density of the observation network, the monitoring data of the site observation method often cannot accurately reflect the real impact of forest changes, and may even result in negligible impacts.

[0007] Satellite observation technology: Satellite observation is often combined with spatiotemporal change analysis and the space-for-time method. By comparing real forest change scenarios with hypothetical ones and incorporating biophysical mechanisms, these methods are used to assess the temperature effects of forest change and its interaction with the ground and atmosphere. These two methods have become mainstream assessment techniques thanks to the wide coverage and multi-temporal nature of satellite observations. The space-for-time method is more practical due to its ease of use.

[0008] However, satellite observation methods are only suitable for assessing temperature impacts from the perspective of biophysical mechanisms, and it is difficult to quantify the role of biogeochemical mechanisms. This shows that relying solely on satellite data cannot achieve a coupled assessment of the two mechanisms. Although climate model simulation methods cover global and regional scales, due to significant differences in parameter scales, their assessment accuracy, especially at small regional and local scales, is still insufficient. Considering that refined quantification at regional and local scales is of great significance to forest management and climate response strategy formulation, it can provide scientific decision-making support for the sustainable development of typical regions. Although climate models have accuracy limitations in regional and local scale assessments, the linear positive correlation between carbon emissions and temperature established by them provides the possibility of achieving a coordinated assessment of biophysical mechanisms and biogeochemical mechanisms through climate models. Summary of the Invention

[0009] In response to the problems mentioned in the background technology, the present invention proposes a method for evaluating the regional temperature effects of changes in forest landscape patterns. The present invention aims to integrate satellite observation technology and climate model indicators to construct a high-precision collaborative evaluation technology system suitable for regional and local scales; the system organically combines biophysical mechanisms with biogeochemical mechanisms to achieve a comprehensive evaluation of forest landscape patterns and their temperature effects, providing a scientific basis for forest resource management and climate adaptation strategy formulation.

[0010] Technical solution: In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0011] A method for evaluating regional temperature effects of forest landscape pattern changes includes the following steps:

[0012] S1: Data acquisition and processing;

[0013] S2: Evaluate the impact of forest landscape pattern changes and their biophysical processes on land surface temperature based on the space-for-time method and area-weighted method;

[0014] S3: Evaluate the carbon deficit and accumulation characteristics of forest landscape pattern changes based on the space-for-time method and carbon density data;

[0015] S4: Assess the impact of changes in forest landscape patterns and their biogeochemical processes on land surface temperature using the space-for-time method, carbon density data, and climate models;

[0016] S5: Synergistically assess the impact of changes in forest landscape patterns on regional temperature using biophysical and biogeochemical processes.

[0017] Preferably, in S1, national land cover data of past years are obtained, and morphological spatial pattern analysis is performed based on the obtained national land cover data; monthly Landsat surface temperature data are obtained; forest aboveground biomass data are obtained and multiplied with the carbon conversion coefficient to obtain carbon density data.

[0018] Preferably, in S2, the specific process is:

[0019] S21: Using satellite observations and the space-for-time method, select effective grids to assess the surface temperature impact of potential forest landscape restoration and degradation;

[0020] S22: Use the area-weighted method to calculate and statistically evaluate the temperature impact of forest landscape restoration and degradation and its biophysical mechanism processes at the study area scale.

[0021] As a preferred method, in S21, satellite observation and space-for-time method are used to select effective grids to evaluate the surface temperature impact value of potential forest landscape restoration and degradation. The specific contents are as follows:

[0022] The temporal and spatial differences in regional surface temperature at the grid scale under the influence of potential forest restoration and degradation were quantified from the perspective of biophysical mechanisms. The specific calculation process is as follows:

[0023] ΔLST restortaion_i =LST core_i -LST others_i (1)

[0024] ΔLST degradation_i =LST others_i -LST core_i (2)

[0025] Where ΔLST degradation_i The difference in the mean local surface temperature indicating forest landscape degradation; ΔLST restortaion_i The difference in the mean local surface temperature of the forest landscape indicates the restoration of the forest landscape; LST core_i represents the mean surface temperature of the core forest in the i-th grid after forest landscape restoration or before forest landscape degradation; LST others_i It represents the mean surface temperature of other forest landscape types in the i-th grid before forest landscape restoration or after forest landscape degradation.

[0026] As a preferred method, in S22, the area-weighted method is used to calculate and statistically evaluate the temperature impact of forest landscape restoration and degradation and its biophysical mechanism processes at the study area scale, specifically:

[0027]

[0028]

[0029] Where ΔLST restortaion_BGP ΔLST represents the average land surface temperature change in the study area caused by the biophysical impact of forest landscape restoration; degradation_BGP The average surface temperature change in the study area caused by the biophysical impact of forest landscape degradation; Area core_i Area represents the area of the core forest landscape type after forest landscape restoration in the i-th grid; others_i represents the area of other forest landscape types after forest landscape degradation in the i-th grid, Area represents the area of the study area; n represents the number of grids; ΔLST degradation_i The difference in the mean local surface temperature indicating forest landscape degradation; ΔLST restortaion_i The difference in the mean local surface temperature indicates the recovery of the forest landscape.

[0030] Preferably, in S3, the specific implementation process is:

[0031] Using satellite observations and the space-for-time method, we selected effective grids to assess the aboveground carbon density deficit and carbon deficit of forest landscape restoration and degradation. The specific calculation process is as follows:

[0032] AGCDD restortaion_BGC =AGC density others_i -AGC density core_i (5)

[0033] AGCD restortaion_BGC =AGCDD restortaion_BGC ×Area core_i (6)

[0034] AGCDD degradation_BGC =AGC density core_i -AGC density others_i (7)

[0035] AGCD degradation_BGC =AGCDD degradation_BGC ×Area others_i (8)

[0036] Among them, AGCDD restortaion_BGC Represents the carbon density deficit of forest landscape restoration; AGCD restortaion_BGC AGC density represents the carbon deficit of forest landscape restoration; others_i represents the aboveground carbon density of other forest landscape types before forest landscape restoration in the i-th grid; AGC density core_irepresents the AGC density of the core forest landscape type after forest landscape restoration in the i-th grid; Area core_i represents the area of core forest after forest landscape restoration in the i-th grid; AGCDD degradation_BGC Carbon density deficit representing forest landscape degradation; AGCD degradation_BGC Represents the carbon deficit of forest landscape degradation; Area others_i It represents the area of other forest landscape types after forest landscape degradation in the i-th grid.

[0037] Preferably, in S4, the specific implementation content is:

[0038] S41: Estimating the transient climate response to cumulative carbon emissions;

[0039] S42: Use AGC deficit combined with TCRE to derive equivalent surface temperature changes in forest landscape restoration and degradation and their biogeochemical mechanisms.

[0040] Preferably, in S41, the specific content of estimating the transient climate response value to the cumulative carbon emissions is:

[0041] The transient climate response indicator to cumulative carbon emissions is the ratio of temperature change to cumulative carbon emissions. The specific calculation process is:

[0042]

[0043] Where TCRE represents the transient climate response to cumulative carbon emissions, ΔT ESM_i Represents 21 Earth system models based on the Coupled Model Intercomparison Project Phase 5, Area i Indicates the area of the i-th grid; Carbon emission represents the carbon emission parameter; n represents the number of grids.

[0044] Preferably, in S42, the equivalent surface temperature changes of forest landscape restoration and degradation and their biogeochemical mechanism processes are derived using the AGC deficit combined with TCRE, specifically:

[0045]

[0046] Where ΔLST BGC represents the average surface temperature change in the study area caused by the biogeochemical impacts of forest landscape restoration and degradation; TCRE represents the transient climate response to cumulative carbon emissions; AGCD represents the average surface temperature change in the study area caused by the biogeochemical impacts of forest landscape restoration and degradation; BGC represents the AGC deficit; Area represents the area of the study area; Area GL Represents the global land surface area.

[0047] As a preferred method, in S5, the sum and ratio of the two mechanisms and their temperature effects are calculated to achieve the synergy between biophysical and biogeochemical mechanisms, thereby completing the multi-faceted impact measurement of forest landscape restoration and degradation on regional temperature. The specific calculation process is as follows:

[0048] ΔLST sum =ΔLST BGP +ΔLST BGC (11)

[0049]

[0050] Where ΔLST sum is the cumulative impact of forest landscape restoration or degradation and its two mechanisms on land surface temperature, ΔLST BGP / BGC is the ratio of biophysical and biogeochemical effects, ΔLST BGC represents the average land surface temperature change in the study area caused by the biogeochemical effects of forest landscape restoration and degradation; ΔLST BGP Represents the average surface temperature changes in the study area caused by the biophysical impacts of forest landscape restoration and degradation.

[0051] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0052] (1) By integrating high-resolution remote sensing data, this paper simulates the spatiotemporal dynamics of forest landscape restoration and degradation processes and accurately assesses their temperature effects. Unlike previous assessments that primarily focused on the impacts of afforestation and deforestation on climate, this paper innovatively focuses on the restoration and degradation of existing forests. Given the limited space available for afforestation, this strategy of focusing on the restoration of existing forests offers a new solution to climate change.

[0053] (2) This invention innovatively constructs a high-resolution model for assessing forest landscape changes and their regional temperature effects by synergistically integrating satellite observation data with climate models. This model leverages the temporal and spatial multi-scale characteristics of satellite observations and the systematic advantages of climate simulation to accurately assess the temperature effects of forest landscape changes from the perspective of both biophysical and biogeochemical mechanisms. This invention provides an important theoretical basis and data support for improving regional climate by optimizing forest landscape patterns. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of a method for evaluating regional temperature effects of forest landscape pattern changes according to the present invention;

[0055] Figure 2It is a statistical diagram of the change in average surface temperature in the study area caused by the individual and cumulative effects of biophysical and biogeochemical changes in the forest landscape pattern of the present invention;

[0056] Figure 3 is a spatial distribution map of carbon density deficit of forest landscape pattern changes of the present invention;

[0057] Figure 4 It is a statistical graph of carbon density deficit, carbon deficit and cumulative carbon deficit of forest landscape pattern changes of the present invention;

[0058] Figure 5 It is a spatial distribution map of the average surface temperature changes in the study area caused by the biogeochemical impact of the forest landscape pattern changes in the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further illustrated below with reference to specific examples. The examples are implemented based on the technical solutions of the present invention. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0060] The method for evaluating the regional temperature effects of changes in forest landscape patterns provided in this embodiment is a method system for collaboratively evaluating the forest landscape pattern and its temperature effects of biophysical and biogeochemical mechanisms. The method is first based on satellite remote sensing data, using the space-for-time method and the area-weighted method to quantitatively evaluate the forest landscape restoration (i.e., the transformation of other landscape types into core forest types) and degradation (i.e., the transformation of core forest types into other landscape types) processes, and calculates its surface temperature changes (dominated by the biophysical mechanism BGP) and the corresponding carbon deficit. Subsequently, by integrating carbon deficit data, a collection of earth system models, and local transient climate response indicators to cumulative carbon emissions, the surface temperature effects of changes in forest landscape pattern and its biogeochemical mechanism BGC are simulated. Ultimately, a comprehensive method that can collaboratively evaluate biophysical and biogeochemical mechanisms is constructed, which achieves a quantitative evaluation of the temperature effects of forest landscape patterns and can analyze the synergistic effects and independent contributions of the two mechanisms.

[0061] The specific implementation steps are as follows:

[0062] S1: Data acquisition and processing;

[0063] First, we collected consistent and high-resolution land cover data, surface temperature data, and forest aboveground biomass data. We then reclassified the land cover data into forest and non-forest distributions. We used morphological spatial pattern analysis methods to identify the distribution of core forest landscape types and other forest landscape types. We defined the spatial conversion between the core forest landscape type (core) and other landscape types (others) as forest landscape restoration and degradation.

[0064] Taking the Yangtze River Delta urban agglomeration as an example, we collected 2020 30m-resolution national land cover data (CLCD) from Professor Yang Xin's team at Wuhan University (the data is from the team led by Professor Yang Xin of Wuhan University and can be downloaded at https: / / zenodo.org / record / 5816591). This dataset was used to generate potential spatial patterns of forest landscape restoration and degradation. The overall accuracy of this dataset reached 80%, with an accuracy of 85% for forest types and 90.6% for the Yangtze River Delta region.

[0065] We obtained 30m-resolution Hi-GLASS monthly land surface temperature data from Wuhan University (developed collaboratively by Wuhan University and other institutions; downloadable at http: / / higlass.whu.edu.cn / ). We used a filtering and filling method to improve the quality of the available land surface temperature data, combined with surface temperature data from adjacent months, to calculate the annual mean land surface temperature. We optimized and integrated the data to obtain consecutive monthly data and extracted the annual mean LST data.

[0066] Obtain the ESA Climate Change Initiative (CCI) biomass data (from the ESA Climate Change Initiative, official website: https: / / climate.esa.int / en / projects / biomass / data / ). This data has a resolution of 100 meters and is currently the highest spatial and temporal resolution available for forest aboveground biomass data. Multiply this data by a biomass conversion factor of 0.5 to generate carbon density data. All data used must be standardized to a 30-meter resolution.

[0067] Based on the above CLCD data and morphological spatial pattern analysis method, the core types of forest landscape patterns and other landscape types were extracted, and the conversion of core forest types into other forest landscape types was defined as forest degradation, and vice versa as forest restoration.

[0068] S2: Evaluate the impact of forest landscape pattern changes and their biophysical processes on land surface temperature based on the space-for-time method and area-weighted method;

[0069] S21: Using satellite observations and the space-for-time method, select effective grids to assess the surface temperature impact of potential forest landscape restoration and degradation;

[0070] A 4 km × 4 km effective grid was selected to evaluate the surface temperature impact of potential forest restoration and degradation, that is, the difference in the mean surface temperature (ΔLST) corresponding to the conversion of core forests within the grid to other forest landscape types (forest degradation) was evaluated. degradation_i ), and conversely, the difference in mean land surface temperature (ΔLST) corresponding to the conversion of other forest landscape types to core forest (forest restoration) was evaluated. restortaion_i), quantifying the spatiotemporal differences in regional surface temperature at the grid scale under the influence of potential forest restoration and degradation from the perspective of biophysical mechanisms, and assuming the background climate to be consistent. The specific equations are shown in Equations (1) and (2).

[0071] ΔLST restortaion_i =LST core_i -LST others_i (1)

[0072] ΔLST degradation_i =LST others_i -LST core_i (2)

[0073] Where ΔLST degradation_i The difference in the mean local surface temperature indicating forest landscape degradation; ΔLST restortaion_i The difference in the mean local surface temperature of the forest landscape indicates the restoration of the forest landscape; LST core_i represents the mean surface temperature of the core forest in the i-th grid after forest landscape restoration or before forest landscape degradation; LST others_i It represents the mean surface temperature of other forest landscape types in the i-th grid before forest landscape restoration or after forest landscape degradation.

[0074] The area proportions of the two landscape pattern categories were used to select valid grids, and these proportions had to meet the following criteria: when selecting a grid consistent with forest landscape restoration, core forests accounted for at least 5% of the grid and other forest landscape types accounted for at least 15%. When selecting a grid consistent with forest landscape degradation, other forest landscape types accounted for at least 5% of the grid and core forests accounted for at least 80%. Annual temperature changes associated with forest landscape restoration and degradation were then calculated and statistically quantified for each valid grid.

[0075] S22: Use area-weighted method to calculate and statistically evaluate the temperature impact of forest landscape restoration and degradation and its biophysical mechanism processes at the study area scale;

[0076] In order to further consider the differences in grid pixel areas at different latitudes, the area-weighted method was used to achieve forest landscape restoration at the study area scale (ΔLST) by combining the grid-scale temperature change values obtained above with the ratio of the core forest or other forest landscape types in the grid to the study area. restortain_BGP ) and degradation (ΔLST degradation_BGP ) and its biophysical mechanism process temperature impact calculation and statistical evaluation, the specific equations are shown in Equation (3) and Equation (4).

[0077]

[0078]

[0079] Where ΔLST restortaion_BGP ΔLST represents the average land surface temperature change in the study area caused by the biophysical impact of forest landscape restoration; degradation_BGP The average surface temperature change in the study area caused by the biophysical impact of forest landscape degradation; Area core_i 、Are others_i They represent the area of core forest after forest landscape restoration and the area of other forest landscape types after forest landscape degradation in the i-th grid, respectively. Area represents the area of the Yangtze River Delta urban agglomeration in the study area. ΔLST degradation_i The difference in the mean local surface temperature indicating forest landscape degradation; ΔLST restortaion_i The difference in the mean local surface temperature indicates the recovery of the forest landscape.

[0080] Positive values indicate a warming effect, negative values indicate a cooling effect, and other values indicate no change. Student's t-test estimates are used as an important indicator of uncertainty in the results.

[0081] S3: Evaluate the carbon deficit and accumulation characteristics of forest landscape pattern changes based on the space-for-time method and carbon density data;

[0082] Using satellite observations and the space-for-time method, we selected effective grids to assess aboveground carbon density deficits and carbon deficits in forest landscape restoration and degradation.

[0083] By constructing an effective grid and combining the space-for-time method and the area-weighted method, the surface temperature impact evaluation of forest landscape restoration and degradation is quantified from the satellite observation process and the biophysical mechanism process (BGP), and the temporal and spatial regularity of the carbon density deficit and carbon deficit of forest landscape restoration and degradation are evaluated.

[0084] The above-ground carbon density deficit and carbon deficit of forest landscape restoration and degradation were evaluated in the selected effective grids using the consistent space-for-time method. That is, the above-ground carbon density (AGC density) of other forest landscape types before forest landscape restoration in the i-th grid was calculated. others_i ) and the AGC density of the core forest after forest landscape restoration (AGC density core_i ) represents the carbon density deficit of forest landscape restoration (AGCDD restortaion_BGC ), and compared it with the area of core forest after forest landscape restoration (Area core_i ) multiplied by the former is the carbon deficit (AGCD restortaion_BGC ), on the contrary, the AGC density of the core forest before the forest landscape degradation in the i-th grid is calculated. core_i ) and the AGC density of other forest landscape types after forest landscape degradation (AGC densityothers_i ) represents the carbon density deficit of forest landscape degradation (AGCDD degradation_BGC ), and compared it with other forest landscape types after forest landscape degradation (Area others_i ) is the carbon deficit (AGCD) of the latter. degradation_BGC The specific calculation formula is:

[0085] AGCDD restortaion_BGC =AGC density others_i -AGC density core_i (5)

[0086] AGCD restortaion_BGC =AGCDD restortaion_BGC ×Area core_i (6)

[0087] AGCDD degradation_BGC =AGC density core_i -AGC density others_i (7)

[0088] AGCD degradation_BGC =AGCDD degradation_BGC ×Area others_i (8)

[0089] In the above results, positive values indicate carbon density deficit and carbon deficit, while negative values indicate carbon density accumulation and carbon accumulation. The Student's t-test estimate was used as an important indicator to verify the uncertainty of the results.

[0090] S4: Assess the impact of changes in forest landscape patterns and their biogeochemical processes on land surface temperature using the space-for-time method, carbon density data, and climate models;

[0091] The surface temperature data simulated by the Earth system model ensemble are used to derive the response indicators of transient climate to cumulative carbon emissions, and the surface temperature changes of forest landscape restoration and degradation and their biogeochemical mechanisms (BGC) are analyzed.

[0092] S41: Estimating the transient climate response to cumulative carbon emissions;

[0093] The transient climate response to cumulative carbon emissions (TCRE) indicator is the ratio of temperature change to cumulative carbon emissions. Existing technologies have proven that there is a significant linear relationship between temperature change and cumulative carbon emissions.

[0094] The TCRE (1,000 PgC K) is estimated by analyzing the difference in LST for each grid cell i between the experiment after doubling the atmospheric CO2 concentration and the pre-industrial CO2 emission level (which leads to a doubling of the atmospheric CO2 concentration). The TCRE calculation equation for the Yangtze River Delta urban agglomeration is shown in (9):

[0095]

[0096] Where, ΔT ESM_i It represents the surface temperature response to a doubling of atmospheric CO2 concentration based on 21 Earth system models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under the condition that CO2 concentration increases at a rate of 1% per year until the atmospheric CO2 concentration doubles. i Represents the area of the ith grid. emission ) parameter is calculated by doubling the difference in ppm between 286 and 572 ppm in the Transient Climate Response to Cumulative Carbon Emissions (TCRE) experiment and then multiplying by 2.13 to convert ppm to gigatonnes of carbon (GtC).

[0097] S42: Using AGC deficit (AGCD BGC ) Combined with TCRE, the equivalent surface temperature changes of forest landscape restoration and degradation and their biogeochemical mechanisms were derived;

[0098] In this embodiment, the CO2 in the atmosphere is expressed in parts per million (ppm). GL , excluding the ocean), and scaled the AGC deficit in the Yangtze River Delta to global emissions. Finally, the mean land surface temperature change (ΔLST) in the Yangtze River Delta caused by the biogeochemical (BGC) impacts of potential forest landscape restoration and degradation was calculated. BGC , see equation (10)), and the Student's t-test estimate is used as an important indicator to verify the uncertainty of the results.

[0099]

[0100] Where ΔLST BGC represents the average surface temperature change in the study area (Yangtze River Delta) caused by the biogeochemical effects of forest landscape restoration and degradation (BGC); TCRE represents the response of transient climate to cumulative carbon emissions; AGCD BGC represents the AGC deficit; Area represents the area of the study area; Area GL Represents the global land surface area.

[0101] S5: Coordinate biophysical and biogeochemical processes to assess the impact of forest landscape pattern changes on regional temperature;

[0102] The sum and ratio of the two mechanisms and their temperature impacts are obtained to achieve the coordination of biophysical and biogeochemical mechanisms, thereby completing the multi-faceted impact measurement of forest landscape restoration and degradation on regional temperature.

[0103] Based on the above-constructed model, in order to further improve the evaluation of the temperature effect of the two mechanisms and evaluate the synergistic effect and individual contribution of the two, the following calculation formula is established, as shown in Equations 11 and 12:

[0104] ΔLST sum =ΔLST BGP +ΔLST BGC (11)

[0105]

[0106] Where ΔLST sum is the cumulative impact of forest landscape restoration or degradation and its two mechanisms on land surface temperature, in K, ΔLST BGP / BGC is the ratio of biophysical and biogeochemical effects, in %. If ΔLST sum >0, it means that forest landscape restoration or degradation has an overall warming effect on the surface temperature; if ΔLST sum <0, it means that the restoration or degradation of forest landscape has an overall cooling effect on the surface temperature; if ΔLST sum = 0, it means that the two mechanisms of forest landscape restoration or degradation have a mutually offsetting effect on the surface temperature. BGP / BGC , if ΔLST BGP / BGC >1, it means that the biophysical effect is dominant in the process of forest landscape restoration or degradation affecting land surface temperature; if ΔLST BGP / BGC <1, it means that the biogeochemical effect is dominant in the process of forest landscape restoration or degradation affecting land surface temperature; if ΔLST BGP / BGC =1, it means that in the process of forest landscape restoration or degradation affecting the surface temperature, the biophysical effect and the biogeochemical effect have equivalent effects on the surface temperature.

[0107] ΔLST BGP represents the average land surface temperature change in the study area caused by the biogeochemical effects of forest landscape restoration and degradation; ΔLST BGP represents the average land surface temperature change in the study area caused by the biophysical impacts of forest landscape restoration and degradation. In this example, ΔLST BGP The calculation process is ΔLST restortaion_BGP , ΔLST degradation_BGP These two variables.

[0108] Results and Analysis:

[0109] 1. Analysis of the impact of forest landscape restoration and degradation and their biophysical BGP and biogeochemical BGC mechanisms on land surface temperature;

[0110] Figure 2 The results show that the temperature effects of forest landscape restoration and degradation in the Yangtze River Delta urban agglomeration were evaluated from the perspective of biophysical mechanism processes. Specifically, forest landscape restoration brought about a cooling effect (-0.0172K to -0.0168K), while forest landscape degradation brought about a warming effect (0.0059K to 0.0061K). The two offset each other, indicating that the cooling effect of forest landscape restoration was greater.

[0111] Figure 3 、 Figure 4 The study found that forest restoration and degradation in the Yangtze River Delta urban agglomeration exhibited carbon density deficits. While restoration promoted carbon density accumulation (-15.59 MgC / ha to -14.41 MgC / ha), degradation led to a carbon density deficit (20.15 MgC / ha to 21.61 MgC / ha). Overall, the latter had a greater impact on carbon deficits in the Yangtze River Delta urban agglomeration. The cumulative total carbon deficits for restored and degraded forest landscapes ranged from -5.70 TgC to -5.08 TgC and from 1.54 TgC to 1.66 TgC, respectively. The carbon accumulation of the former exceeded that of the latter, indicating that forest restoration significantly contributes to carbon accumulation.

[0112] 2. Analysis of the impact of forest landscape restoration and degradation on land surface temperature through the synergistic effect of biophysical BGP and biogeochemical BGC mechanisms;

[0113] Figure 2 、 Figure 5 The results show that the surface temperature impact values of forest landscape restoration and degradation and their biogeochemical mechanisms derived from the TCRE indicator, a transient climate response to cumulative carbon emissions, and carbon deficit are equivalent. The increased carbon accumulation caused by forest landscape restoration is equivalent to a cooling effect, i.e., -0.0127K to -0.0113K, and the carbon deficit caused by forest landscape degradation is equivalent to a warming effect, i.e., 0.0029K to 0.0031K. This further indicates that the cooling effect equivalent to forest landscape restoration and its biogeochemical mechanism generally offsets the warming effect compared to the temperature impact of forest landscape degradation.

[0114] In summary, the influence of the two mechanisms on the surface temperature shows a consistent trend. In particular, the influence of the BGP mechanism exceeds that of the BGC mechanism. The sum of the surface temperature influence values brought by the two mechanisms indicates that the cooling and warming effects of the BGP mechanism make the temperature influence of the BGC mechanism play a synergistic role, that is, the synergistic cooling enhancement (-0.0299K to -0.0281K) and warming enhancement (0.0088K to 0.0092K) (see Figure 2 This further demonstrates the importance of building a BGP and BGC mechanism to collaboratively assess the impact of forest landscape pattern changes on surface temperature.

[0115] The present invention utilizes the space-for-time method of satellite observations, carbon density data, and climate models to collaboratively evaluate changes in forest landscape patterns and their regional surface temperature impacts, thereby developing an assessment framework for quantitatively improving the estimation of temperature effects that takes into account the BGP and BGC mechanisms. The technical object of this method shifts from focusing only on activities such as afforestation that change land use patterns to changes in forest landscape patterns. It starts from the level of the main objects of climate response, embodies the expansion strategy of technology research and development to serve climate response, and further realizes the accurate quantification of regional temperature from high-resolution satellite observations and climate model simulations in collaboration with biophysical and biogeochemical mechanisms, providing important scientific support for emphasizing the importance of forest restoration and reducing forest degradation to achieve long-term climate change mitigation.

[0116] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for evaluating regional temperature effects of forest landscape pattern changes, characterized by: The following steps are involved: S1: Data acquisition and processing; S2: Evaluate the impact of forest landscape pattern changes and their biophysical processes on land surface temperature based on the space-for-time method and area-weighted method; S3: Evaluate the carbon deficit and accumulation characteristics of forest landscape pattern changes based on the space-for-time method and carbon density data; S4: Assess the impact of changes in forest landscape patterns and their biogeochemical processes on land surface temperature using the space-for-time method, carbon density data, and climate models; S5: Synergistically assess the impact of changes in forest landscape patterns on regional temperature using biophysical and biogeochemical processes.

2. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 1, characterized in that: In S1, national land cover data for past years were obtained, and morphological spatial pattern analysis was performed based on the obtained national land cover data; monthly Landsat surface temperature data were obtained; forest aboveground biomass data were obtained and multiplied with the carbon conversion coefficient to obtain carbon density data.

3. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 1, characterized in that: In S2, the specific process is: S21: Using satellite observations and the space-for-time method, select effective grids to assess the surface temperature impact of potential forest landscape restoration and degradation; S22: Use the area-weighted method to calculate and statistically evaluate the temperature impact of forest landscape restoration and degradation and its biophysical mechanism processes at the study area scale.

4. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 3, characterized in that: In S21, satellite observations and the space-for-time method were used to select effective grids to assess the surface temperature impact of potential forest landscape restoration and degradation. The specific contents are as follows: The temporal and spatial differences in regional surface temperature at the grid scale under the influence of potential forest restoration and degradation were quantified from the perspective of biophysical mechanisms. The specific calculation process is as follows: ΔLST restortaion_i =LST core_i -LST others_i (1) ΔLST degradation_i =LST others_i -LST core_i (2) Where ΔLST degradation_i The difference in the mean local surface temperature indicating forest landscape degradation; ΔLST restortaion_i The difference in the mean local surface temperature of the forest landscape indicates the restoration of the forest landscape; LST core_i represents the mean surface temperature of the core forest in the i-th grid after forest landscape restoration or before forest landscape degradation; LST others_i It represents the mean surface temperature of other forest landscape types in the i-th grid before forest landscape restoration or after forest landscape degradation.

5. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 3, characterized in that: In S22, the area-weighted method was used to calculate and statistically evaluate the temperature impact of forest landscape restoration and degradation and their biophysical mechanisms at the study area scale. Specifically: Where ΔLST restortaion_BGP ΔLST represents the average land surface temperature change in the study area caused by the biophysical impact of forest landscape restoration; degradation_BGP The average surface temperature change in the study area caused by the biophysical impact of forest landscape degradation; Area core_i Area represents the area of the core forest landscape type after forest landscape restoration in the i-th grid; others_i represents the area of other forest landscape types after forest landscape degradation in the i-th grid, Area represents the area of the study area; n represents the number of grids; ΔLST degradation_i The difference in the mean local surface temperature indicating forest landscape degradation; ΔLST restortaion_i The difference in the mean local surface temperature indicates the recovery of the forest landscape.

6. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 1, characterized in that: In S3, the specific implementation process is: Using satellite observations and the space-for-time method, we selected effective grids to assess the aboveground carbon density deficit and carbon deficit of forest landscape restoration and degradation. The specific calculation process is as follows: AGCDD restortaion_BGC =AGC density others_i -AGC density core_i (5) AGCD restortaion_BGC =AGCDD restortaion_BGC ×Area core_i (6) AGCDD degradation_BGC =AGC density core_i -AGC density others_i (7) AGCD degradation_BGC =AGCDD degradation_BGC ×Area others_i (8) Among them, AGCDD restortaion_BGC Represents the carbon density deficit of forest landscape restoration; AGCD restortaion_BGC AGC density represents the carbon deficit of forest landscape restoration; others_i represents the aboveground carbon density of other forest landscape types before forest landscape restoration in the i-th grid; AGC density core_i represents the AGC density of the core forest landscape type after forest landscape restoration in the i-th grid; Area core_i represents the area of core forest after forest landscape restoration in the i-th grid; AGCDD degradation_BGC Carbon density deficit representing forest landscape degradation; AGCD degradation_BGC Represents the carbon deficit of forest landscape degradation; Area others_i It represents the area of other forest landscape types after forest landscape degradation in the i-th grid.

7. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 1, characterized in that: In S4, the specific implementation content is: S41: Estimating the transient climate response to cumulative carbon emissions; S42: Use AGC deficit combined with TCRE to derive equivalent surface temperature changes in forest landscape restoration and degradation and their biogeochemical mechanisms.

8. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 7, characterized in that: In S41, the specific content of estimating the response value of transient climate to cumulative carbon emissions is as follows: The transient climate response indicator to cumulative carbon emissions is the ratio of temperature change to cumulative carbon emissions. The specific calculation process is: Where TCRE represents the transient climate response to cumulative carbon emissions, ΔT ESM_i Represents 21 Earth system models based on the Coupled Model Intercomparison Project Phase 5, Area i Indicates the area of the i-th grid; Carbon emission represents the carbon emission parameter; n represents the number of grids.

9. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 7, characterized in that: In S42, the AGC deficit is combined with TCRE to derive the equivalent surface temperature changes of forest landscape restoration and degradation and their biogeochemical mechanisms, specifically: Where ΔLST BGC represents the average surface temperature change in the study area caused by the biogeochemical impacts of forest landscape restoration and degradation; TCRE represents the transient climate response to cumulative carbon emissions; AGCD represents the average surface temperature change in the study area caused by the biogeochemical impacts of forest landscape restoration and degradation; BGC represents the AGC deficit; Area represents the area of the study area; Area GL Represents the global land surface area.

10. The method for evaluating regional temperature effects of forest landscape pattern changes according to claim 1, characterized in that: In S5, the sum and ratio of the two mechanisms and their temperature effects are calculated to achieve the synergy between biophysical and biogeochemical mechanisms, thereby completing the multi-faceted impact measurement of forest landscape restoration and degradation on regional temperature. The specific calculation process is as follows: ΔLST sum =ΔLST BGP +ΔLST BGC (11) Where ΔLST sum is the cumulative impact of forest landscape restoration or degradation and its two mechanisms on land surface temperature, ΔLST BGP / BGC is the ratio of biophysical and biogeochemical effects, ΔLST BGC represents the average land surface temperature change in the study area caused by the biogeochemical effects of forest landscape restoration and degradation; ΔLST BGP Represents the average surface temperature changes in the study area caused by the biophysical impacts of forest landscape restoration and degradation.

Images