Methods, devices, and equipment for predicting the impact of marsh vegetation change on surface temperature.

Abstract

This application provides a method, apparatus, and equipment for predicting the impact of marsh vegetation change on land surface temperature, belonging to the field of ecological remote sensing and climate effect assessment technology. Based on long-term series leaf area index, land surface temperature, digital elevation model, and marsh wetland distribution data, it first determines the vegetation pixel distribution as the study area within the unchanged marsh wetland distribution range; it then constructs annual and multi-year average growing season datasets using the maximum value synthesis and arithmetic mean methods; within the grid cells, pixels are divided into high and low value groups based on the multi-year average growing season leaf area index, and the difference between the multi-year average growing season leaf area index and the average land surface temperature of all pixels in the high and low value groups is calculated to quantify the degree of land surface temperature change caused by marsh wetland vegetation cover change; finally, by combining the vegetation change trend at the pixel scale, it achieves accurate prediction of the future impact on land surface temperature.

Description

Technical Field

[0001] This application relates to the field of ecological remote sensing and climate effect assessment technology, specifically to methods, devices and equipment for predicting the impact of swamp vegetation changes on surface temperature. Background Technology

[0002] As an important wetland type in China, marsh wetlands play a vital role in regulating regional climate, protecting biodiversity, and maintaining the global hydrological cycle. Vegetation is an essential component of marsh wetland ecosystems, playing a crucial role in water conservation, maintaining ecosystem functions, and regulating surface energy balance. Marsh wetlands possess unique hydrothermal conditions, and changes in vegetation cover exert complex biophysical effects on surface temperature by altering surface radiation and evapotranspiration processes. Therefore, marsh wetlands are considered one of the potential natural solutions for regulating surface temperature and mitigating climate change. Clarifying the biophysical impacts of marsh wetland vegetation cover change on climate is crucial for revealing the interaction mechanisms between marsh wetlands and climate, and for accurately assessing the climate-regulating function of wetlands. However, traditional research methods struggle to separate the extent of the biophysical impacts of wetland vegetation change on surface temperature from the context of global climate change. This makes it impossible to accurately quantify the biophysical impacts of marsh wetland vegetation cover change on surface temperature and to predict the extent of future surface temperature changes caused by marsh wetland vegetation cover changes. Summary of the Invention

[0003] In view of this, the embodiments of this application are committed to providing a method, apparatus and equipment for predicting the impact of swamp vegetation change on surface temperature, so as to solve the technical problems that it is impossible to accurately and quantitatively assess the biophysical impact of swamp wetland vegetation cover change on surface temperature, and that it is impossible to predict the degree of surface temperature change caused by future swamp wetland vegetation cover change.

[0004] In a first aspect, the present invention provides a method for predicting the impact of swamp vegetation change on surface temperature, comprising:

[0005] Acquire LAI dataset, LST dataset, DEM data, and swamp distribution dataset for the study area during the study period, and perform preprocessing.

[0006] The LAI dataset and LST dataset are processed to obtain monthly LAI and monthly LST respectively; the monthly LAI and monthly LST are then combined to form annual average LAI and multi-year average LAI, as well as annual average LST and multi-year average LST respectively.

[0007] Based on the swamp distribution dataset, the unchanged swamp distribution range is extracted, and all pixels with the multi-year average LAI > 0 are extracted within the unchanged swamp distribution range as the swamp vegetation distribution and study area;

[0008] The annual average LAI, multi-year average LAI, annual average LST, and multi-year average LST are cut out;

[0009] Calculate the average LAI trend value per pixel within the study area, Slope LAI ;

[0010] Within the study area, grid cells are divided. Within each grid cell, based on the pixel-by-pixel DEM value and the multi-year average LAI, pixels are divided into a high LAI value group and a low LAI value group, and the ΔLAI value of the grid cell is calculated.

[0011] Extract the multi-year average LST of the corresponding pixels in the high LAI value group and the low LAI value group to obtain the ΔLST value;

[0012] Calculate ΔLST / ΔLAI;

[0013] Using the Slope LAI Calculate the average LAI trend value of all pixels within each grid cell, M-Slope LAI Based on the aforementioned ΔLST / ΔLAI, the biophysical impact of future changes in marsh vegetation cover on surface temperature in the study area is predicted, ΔLST future .

[0014] Furthermore, the data preprocessing involves uniformly resampling the LST dataset, the DEM data, and the swamp distribution dataset to the same projection, coordinate system, and spatial resolution as the LAI dataset.

[0015] Furthermore, the average LAI change trend value per pixel, Slope LAI The calculation formula is:

[0016]

[0017] Where n is the total number of years within the research period, i is the year number, and LAI i For the first The average LAI value for the year.

[0018] Furthermore, based on the pixel-by-pixel DEM value and the multi-year average LAI, pixels are divided into a high LAI value group and a low LAI value group, specifically including:

[0019] Within each grid cell, pixels with an elevation difference greater than 100 meters are removed;

[0020] The remaining pixels are sorted from largest to smallest according to the multi-year average LAI.

[0021] If the number of remaining pixels is even, the remaining pixels are divided into the high LAI value group and the low LAI value group.

[0022] If the number of remaining pixels is odd, then after discarding the median of the multi-year average LAI, the remaining pixels are divided into the high LAI value group and the low LAI value group.

[0023] Furthermore, the M-Slope LAI The calculation formula is:

[0024]

[0025] Where m is the total number of cells in each of the grid cells, and Slope LAI j The average LAI trend value of the j-th pixel within the grid cell.

[0026] Furthermore, the predicted biophysical impacts of future changes in marsh vegetation cover on surface temperature, ΔLST, are also considered. future The formula is:

[0027]

[0028] Where y represents the difference between the future study year and the end year of the study period; ΔLST / ΔLAI represents the biophysical impact of the change in marsh vegetation cover corresponding to each grid cell in the study area on surface temperature; M-Slope LAI The average value of the average LAI change trend value of all pixels within each grid cell.

[0029] Secondly, the present invention also provides a device for predicting the impact of swamp vegetation change on surface temperature, comprising:

[0030] Acquisition module: Acquires LAI dataset, LST dataset, DEM data, and swamp distribution dataset for the study area during the study period, and performs preprocessing.

[0031] First calculation module: Processes the LAI dataset and LST dataset to obtain monthly LAI and monthly LST respectively; Combines the monthly LAI and monthly LST to form annual average LAI and multi-year average LAI, annual average LST and multi-year average LST respectively;

[0032] Extraction module: Based on the swamp distribution dataset, extract the unchanged swamp distribution range, and extract all pixels with the multi-year average LAI > 0 within the unchanged swamp distribution range as the swamp vegetation distribution and study area;

[0033] The cropping module: crops out the annual average LAI, multi-year average LAI, annual average LST, and multi-year average LST;

[0034] The second calculation module calculates the average LAI trend value per pixel within the study area, and the slope. LAI

[0035] The third calculation module divides the study area into grid cells. Within each grid cell, based on the pixel-by-pixel DEM value and the multi-year average LAI, the pixels are divided into a high LAI value group and a low LAI value group, and the ΔLAI value of the grid cell is calculated.

[0036] Fourth calculation module: Extract the multi-year average LST of the corresponding pixels in the high LAI value group and the low LAI value group to obtain the ΔLST value;

[0037] Fifth calculation module: Calculate ΔLST / ΔLAI;

[0038] Prediction module: Using the SlopeLAI, calculate the average value of the average LAI change trend value of all pixels in each grid cell, M-SlopeLAI, and combine it with ΔLST / ΔLAI to predict the degree of biophysical impact of future changes in marsh vegetation cover on surface temperature in the study area, ΔLSTfuture.

[0039] Thirdly, the present invention also provides an electronic device, characterized in that it includes a memory and a processor, the memory being used to store a computer program, and the processor, when executing the computer program, implementing the method for predicting the impact of swamp vegetation change on surface temperature as described in any of the preceding claims.

[0040] This application achieves at least the following beneficial effects: A prediction method for the biophysical impacts of marsh vegetation cover change on land surface temperature, based on a combination of grid cell grouping within an unchanged marsh distribution area and pixel subtraction, can effectively isolate background information on global climate change and quantitatively assess the biophysical impacts of marsh wetland vegetation cover change on land surface temperature. Simultaneously, by introducing pixel-by-pixel LAI change trends, it quantitatively predicts the degree of future land surface temperature change caused by marsh vegetation cover change. It possesses advantages such as convenient data acquisition, flexible applicable scales, and fine spatial representation, achieving accurate and efficient prediction of the biophysical impacts of marsh vegetation cover change on land surface temperature at a regional scale. This solves the technical problems of being unable to accurately and quantitatively assess the biophysical impacts of marsh vegetation cover change on land surface temperature and the inability to predict the degree of future land surface temperature change caused by marsh vegetation cover change. Attached Figure Description

[0041] Figure 1The diagram shown is a flowchart illustrating the method for predicting the impact of swamp vegetation change on surface temperature, as provided in Example 1 of this specification. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] As an important wetland type in China, marsh wetlands play a vital role in regulating regional climate, protecting biodiversity, and maintaining the global hydrological cycle. Vegetation is an essential component of marsh wetland ecosystems, playing a crucial role in water conservation, maintaining ecosystem functions, and regulating surface energy balance. Marsh wetlands possess unique hydrothermal conditions, and changes in vegetation cover exert complex biophysical effects on surface temperature by altering surface radiation and evapotranspiration processes. Therefore, marsh wetlands are considered one of the potential natural solutions for regulating surface temperature and mitigating climate change. Clarifying the biophysical impacts of marsh vegetation cover change on climate is crucial for revealing the interaction mechanisms between marsh wetlands and climate, and for accurately assessing the climate-regulating function of wetlands. However, traditional research methods struggle to separate the extent of the biophysical impacts of wetland vegetation change on surface temperature from the context of global climate change. This makes it impossible to accurately quantify the biophysical impacts of marsh wetland vegetation cover change on surface temperature and to predict the extent of future surface temperature changes caused by marsh wetland vegetation cover changes.

[0044] To address the shortcomings of the existing technology, this solution provides the following embodiments:

[0045] The following describes the embodiments in conjunction with the appendix to the instruction manual. Figure 1 Specific details:

[0046] Example 1 of this specification provides a method for predicting the impact of swamp vegetation change on surface temperature, including:

[0047] S1: Obtain the LAI dataset, LST dataset, DEM data, and swamp distribution dataset for the study area during the study period, and perform preprocessing.

[0048] S1 specifically includes: acquiring LAI dataset, LST dataset, DEM data, and swamp distribution dataset covering the study area during the study period, and performing preprocessing. Here, LAI is leaf area index; LST is land surface temperature; DEM is digital elevation model; and the study period is the time range corresponding to the study.

[0049] S2: Process the LAI dataset and LST dataset to obtain monthly LAI and monthly LST respectively; synthesize the monthly LAI and monthly LST into annual average LAI and multi-year average LAI, as well as annual average LST and multi-year average LST respectively.

[0050] S2 specifically includes: processing the LAI dataset using the maximum value synthesis method and the arithmetic mean method to obtain a monthly LAI dataset; processing the LST dataset using the maximum value synthesis method and the arithmetic mean method to obtain a monthly LST dataset; and based on the arithmetic mean method, assembling the monthly LAI dataset into an annual average LAI and a multi-year average LAI corresponding to the research period; and based on the arithmetic mean method, assembling the monthly LST dataset into an annual average LST and a multi-year average LST corresponding to the research period; the preferred research period in step two is May to September.

[0051] S3: Based on the swamp distribution dataset, extract the unchanged swamp distribution range, and extract all pixels with the multi-year average LAI > 0 within the unchanged swamp distribution range as the swamp vegetation distribution and study area.

[0052] S3 specifically includes: based on the swamp distribution dataset, extracting the unchanged swamp distribution range, extracting all pixels with a multi-year average LAI > 0 within the unchanged swamp distribution range, and using the distribution of all pixels with a multi-year average LAI > 0 as the swamp vegetation distribution and study area.

[0053] S4: Cut out the annual average LAI, multi-year average LAI, annual average LST and multi-year average LST.

[0054] S4 specifically includes: based on the annual average LAI and the multi-year average LAI, using the study area, cropping out the annual average LAI and the multi-year average LAI for each pixel within the study area; based on the annual average LST and the multi-year average LST, using the study area range, cropping out the annual average LST and the multi-year average LST for each pixel within the study area.

[0055] S5: Calculate the average LAI trend value per pixel within the study area, Slope LAI .

[0056] S5 specifically includes: using the annual average LAI value per pixel within the study area during the growing season, and employing a linear regression method to calculate the trend value of the average LAI value per pixel during the growing season within the study area, and the slope. LAI .

[0057] S6 includes: dividing the study area into grid cells, and within each grid cell, dividing the pixels into a high LAI value group and a low LAI value group based on the pixel-by-pixel DEM value and the multi-year average LAI, and calculating the ΔLAI value of the grid cell.

[0058] S6 specifically includes: dividing the study area into multiple grid cells. The size of the grid cells can be determined based on the size of the study area, preferably a square grid with a length and width of 50km or 40km. However, the specific size of the grid cells can be set according to actual conditions and is not limited here. Within each grid cell, based on the pixel-by-pixel DEM value and the multi-year average LAI, the pixels are divided into a high LAI value group and a low LAI value group. The multi-year average growing season LAI value of all pixels corresponding to the high LAI value group is subtracted from the multi-year average growing season LAI value of all pixels corresponding to the low LAI value group to obtain the ΔLAI value of the grid cell.

[0059] S7 includes: extracting the multi-year average LST of the corresponding pixels of the high LAI value group and the low LAI value group to obtain the ΔLST value.

[0060] S7 specifically includes: extracting the multi-year average LST of the corresponding pixels in the high LAI value group and the low LAI value group, and subtracting the multi-year average LST of the corresponding pixels in the low LAI value group from the multi-year average LST of all pixels in the high LAI value group to obtain the ΔLST value of the grid cell.

[0061] S8 includes: calculating ΔLST / ΔLAI.

[0062] S8 specifically includes: calculating the ratio of ΔLST to ΔLAI to obtain the biophysical impact of swamp vegetation cover change on surface temperature for each grid cell within the study area, ΔLST / ΔLAI; in step eight, ΔLST / ΔLAI represents the degree of LST change caused by each unit increase in LAI. If ΔLST / ΔLAI is positive, it indicates that increased swamp vegetation cover will cause surface warming; if it is negative, it indicates that increased swamp vegetation cover will cause surface cooling; the larger the absolute value of ΔLST / ΔLAI, the greater the degree of surface warming or cooling caused by increased swamp vegetation cover; the smaller the absolute value of ΔLST / ΔLAI, the smaller the degree of surface warming or cooling caused by increased swamp vegetation cover.

[0063] S9 includes: utilizing the Slope LAI Calculate the average LAI trend value of all pixels within each grid cell, M-Slope LAI Based on the aforementioned ΔLST / ΔLAI, the biophysical impact of future changes in marsh vegetation cover on surface temperature in the study area is predicted, ΔLST future .

[0064] S9 specifically includes: utilizing the average LAI change trend value per pixel, Slope LAI Calculate the average LAI trend value of all pixels within each grid cell, M-Slope LAI By combining ΔLST / ΔLAI, the biophysical impact of future changes in marsh vegetation cover on surface temperature within the study area is predicted. ΔLST future In step nine, ΔLST future A positive value indicates that increased vegetation cover in future marshlands will cause surface temperature to rise; a negative value indicates that increased vegetation cover in future marshlands will cause surface temperature to drop. ΔLST future The larger the absolute value, the greater the effect of increased vegetation cover in marshlands on surface warming or cooling; ΔLST future The smaller the absolute value, the less the increase in vegetation cover in marsh wetlands will affect the surface temperature.

[0065] Furthermore, data preprocessing involves resampling the LST dataset, DEM data, and swamp distribution dataset to the same projection, coordinate system, and spatial resolution as the LAI dataset.

[0066] Furthermore, the average LAI change trend value per pixel, Slope LAI The calculation formula is:

[0067]

[0068] Where n is the total number of years within the research period, i is the year number, and LAI i For the first Average LAI value during the growing season of the year.

[0069] Furthermore, based on the pixel-by-pixel DEM value and the multi-year average LAI, pixels are divided into a high LAI value group and a low LAI value group, specifically including:

[0070] Within each grid cell, pixels with an elevation difference greater than 100 meters are removed;

[0071] The remaining pixels are sorted from largest to smallest according to the multi-year average LAI.

[0072] If the number of remaining pixels is even, the remaining pixels are divided into a high LAI value group and a low LAI value group.

[0073] If the number of remaining pixels is odd, then after discarding the median of the multi-year average LAI, the remaining pixels are divided into a high LAI value group and a low LAI value group.

[0074] Furthermore, the average of the average LAI trend values ​​of all pixels within each grid cell, M-Slope LAI， The calculation formula is:

[0075]

[0076] Where m is the total number of cells in each grid cell, and Slope LAI j Let be the growing season average LAI trend value of the j-th pixel within the grid cell.

[0077] Furthermore, the biophysical impacts of future changes in marsh vegetation cover on surface temperature (ΔLST) in the study area are predicted. future The formula is:

[0078]

[0079] Where y represents the difference between the future study year and the end year of the study period; ΔLST / ΔLAI represents the biophysical impact of swamp vegetation cover change on surface temperature for each grid cell within the study area; M-Slope LAI The average value of the average LAI trend value of all cells within each grid cell.

[0080] Based on the same idea, Embodiment 2 of the present invention provides a device for predicting the impact of swamp vegetation change on surface temperature, comprising:

[0081] Acquisition module: Acquires LAI dataset, LST dataset, DEM data, and swamp distribution dataset for the study area during the study period, and performs preprocessing.

[0082] First calculation module: Processes the LAI dataset and LST dataset to obtain monthly LAI and monthly LST respectively; Combines the monthly LAI and monthly LST to form annual average LAI and multi-year average LAI, annual average LST and multi-year average LST respectively;

[0083] Extraction module: Based on the swamp distribution dataset, extract the unchanged swamp distribution range, and extract all pixels with the multi-year average LAI > 0 within the unchanged swamp distribution range as the swamp vegetation distribution and study area;

[0084] The cropping module: crops out the annual average LAI, multi-year average LAI, annual average LST, and multi-year average LST;

[0085] The second calculation module calculates the average LAI trend value per pixel within the study area, and the slope. LAI

[0086] The third calculation module divides the study area into grid cells. Within each grid cell, based on the pixel-by-pixel DEM value and the multi-year average LAI, the pixels are divided into a high LAI value group and a low LAI value group, and the ΔLAI value of the grid cell is calculated.

[0087] Fourth calculation module: Extract the multi-year average LST of the corresponding pixels in the high LAI value group and the low LAI value group to obtain the ΔLST value;

[0088] Fifth calculation module: Calculate ΔLST / ΔLAI;

[0089] Prediction Module: Using the SlopeLAI, the average value of the average LAI change trend value of all pixels in each grid cell is calculated, M-SlopeLAI. Combined with ΔLST / ΔLAI, the biophysical impact of future changes in marsh vegetation cover on surface temperature in the study area is predicted, ΔLSTfuture. Based on the same idea, Embodiment 3 of the present invention provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor, when executing the computer program, implements the above-described method for predicting the impact of marsh vegetation change on surface temperature.

[0090] Example 4 of this specification provides a method for selecting the middle and lower reaches of the Yangtze River as the study area. The specific implementation of the technical solution of this invention will take the period from 2003 to 2022 as an example to predict the biophysical impacts of changes in marsh vegetation cover in the middle and lower reaches of the Yangtze River on surface temperature in 2050, and will be combined with... Figure 1 The prediction method shown is used for prediction, and the specific steps are as follows:

[0091] (i) Obtain the 8-day MOD15A2H LAI dataset, the daily MYD11A1 LST dataset, the DEM data of the middle and lower reaches of the Yangtze River from 2003 to 2022, and the two-period swamp distribution datasets of the middle and lower reaches of the Yangtze River in 2003 and 2022. Then, resample the LST, DEM, and swamp distribution datasets to the same projection, coordinate system, and spatial resolution as the LAI dataset.

[0092] (ii) The 8-day MOD15A2H LAI dataset and the daily MYD11A1 LST dataset were processed using the maximum value synthesis method and the arithmetic mean method respectively to obtain the monthly LAI and LST datasets. Based on the arithmetic mean method, the annual growing season average LAI and LST datasets and the multi-year average growing season LAI and LST values ​​for 2003 to 2022 were obtained.

[0093] (III) Based on the swamp distribution datasets of the middle and lower reaches of the Yangtze River in 2003 and 2022, the unchanged swamp wetland distribution range in the middle and lower reaches of the Yangtze River was extracted. Within the unchanged swamp wetland distribution range, all pixels with a multi-year average LAI > 0 were extracted. The distribution of these pixels was used as the distribution of swamp wetland vegetation in the middle and lower reaches of the Yangtze River and the final study area.

[0094] (iv) Based on the annual average LAI and LST and the multi-year average LAI and LST values ​​obtained in step two, the annual average and multi-year average LAI and LST values ​​per pixel in the study area are cropped out.

[0095] (V) Using the annual average LAI value per pixel, the linear regression method is employed to calculate the average LAI trend value SlopeLAI within the study area. The calculation formula is as follows:

[0096]

[0097] Where n is the total number of years in the study period, in this embodiment n = 20, i is the year number, and LAIi is the year number. The average LAI value during the growing season in the middle and lower reaches of the Yangtze River.

[0098] (vi) Within the study area, a 40km grid cell is divided. Within each grid cell, the pixels are divided into a high LAI value group and a low LAI value group based on the pixel-by-pixel DEM value and the multi-year average LAI. The multi-year average LAI value of all pixels corresponding to the high LAI value group is subtracted from the multi-year average LAI value of all pixels corresponding to the low LAI value group to obtain the ΔLAI value of the grid cell.

[0099] (vii) Extract the multi-year average LST value of the corresponding pixels of the high LAI value group and the low LAI value group, and subtract the multi-year average LST value of the corresponding pixels of the low LAI value group from the multi-year average LST value of all pixels corresponding to the high LAI value group to obtain the ΔLST value of the grid cell.

[0100] (viii) Calculate the ratio of ΔLST value to ΔLAI value obtained in step six to obtain the biophysical impact of swamp vegetation cover change on surface temperature in each grid cell of the middle and lower reaches of the Yangtze River, ΔLST / ΔLAI.

[0101] (ix) Using the average LAI trend value SlopeLAI obtained in step five for each pixel in the middle and lower reaches of the Yangtze River, calculate the average value M-SlopeLAI of the average LAI trend value of all pixels in each grid cell. Combined with ΔLST / ΔLAI obtained in step eight, predict the degree of biophysical impact of future changes in marsh vegetation cover in the middle and lower reaches of the Yangtze River on surface temperature. The formulas for calculating ΔLSTfuture, M-SlopeLAI, and predicting ΔLSTfuture are as follows:

[0102]

[0103] Where m is the total number of pixels in each grid cell, and SlopeLAI j is the average LAI trend value of the j-th pixel in the grid cell.

[0104]

[0105] Where y represents the difference between the future research year and the end year of the research period, and in this embodiment, y = 28.

[0106] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0107] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0108] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0109] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0110] It should be understood that the qualifying terms "first", "second", "third", "fourth", "fifth" and "sixth" used in the description of the embodiments of the present invention are only used to more clearly illustrate the technical solutions and are not intended to limit the scope of protection of the present invention.

[0111] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for predicting the impact of swamp vegetation change on surface temperature, characterized in that, include: Acquire LAI dataset, LST dataset, DEM data, and swamp distribution dataset for the study area during the study period, and perform preprocessing. The LAI dataset and the LST dataset are processed to obtain monthly LAI and monthly LST respectively; the monthly LAI and monthly LST are then combined to form annual average LAI and multi-year average LAI, as well as annual average LST and multi-year average LST respectively. Based on the swamp distribution dataset, the unchanged swamp distribution range is extracted, and all pixels with the multi-year average LAI > 0 are extracted within the unchanged swamp distribution range as the swamp vegetation distribution and study area; The annual average LAI, multi-year average LAI, annual average LST, and multi-year average LST are cut out; Calculate the average LAI trend value per pixel within the study area, Slope LAI ; Within the study area, grid cells are divided. Within each grid cell, based on the pixel-by-pixel DEM value and the multi-year average LAI, pixels are divided into a high LAI value group and a low LAI value group. The ΔLAI value of the grid cell is calculated, where the ΔLAI value is the multi-year average growing season LAI value of all pixels corresponding to the high LAI value group minus the multi-year average growing season LAI value of all pixels corresponding to the low LAI value group. Extract the multi-year average LST of the corresponding pixels of the high LAI value group and the low LAI value group to obtain the ΔLST value, wherein the ΔLST is the multi-year average LST of all pixels corresponding to the high LAI value group minus the multi-year average LST of all pixels corresponding to the low LAI value group. Calculate ΔLST / ΔLAI; Using the Slope LAI Calculate the average LAI trend value of all pixels within each grid cell, M-Slope LAI Based on the aforementioned ΔLST / ΔLAI, the biophysical impact of future changes in marsh vegetation cover on surface temperature in the study area is predicted, ΔLST future .

2. The method according to claim 1, characterized in that, The data preprocessing involves resampling the LST dataset, the DEM data, and the swamp distribution dataset to the same projection, coordinate system, and spatial resolution as the LAI dataset.

3. The method according to claim 1, characterized in that, The average LAI change trend value per pixel, Slope LAI The calculation formula is: Where n is the total number of years within the research period, i is the year number, and LAI i For the first The average LAI value for the year.

4. The method according to claim 1, characterized in that, Based on the pixel-by-pixel DEM value and the multi-year average LAI, pixels are divided into a high LAI value group and a low LAI value group, specifically including: Within each grid cell, pixels with an elevation difference greater than 100 meters are removed; The remaining pixels are sorted from largest to smallest according to the multi-year average LAI. If the number of remaining pixels is even, the remaining pixels are divided into the high LAI value group and the low LAI value group. If the number of remaining pixels is odd, then after discarding the median of the multi-year average LAI, the remaining pixels are divided into the high LAI value group and the low LAI value group.

5. The method according to claim 1, characterized in that, The M-Slope LAI The calculation formula is: Where m is the total number of pixels in each of the grid cells, and Slope LAI j The average LAI trend value of the j-th pixel within the grid cell.

6. The method according to claim 1, characterized in that, The predicted biophysical impacts of future changes in marsh vegetation cover on surface temperature (ΔLST) within the study area are given. future The formula is: Where y represents the difference between the future study year and the end year of the study period; ΔLST / ΔLAI represents the biophysical impact of the change in marsh vegetation cover corresponding to each grid cell in the study area on surface temperature; M-Slope LAI The average value of the average LAI change trend value of all cells within each grid cell.

7. A device for predicting the impact of swamp vegetation change on surface temperature, characterized in that, include: Acquisition module: Acquires LAI dataset, LST dataset, DEM data, and swamp distribution dataset for the study area during the study period, and performs preprocessing. First calculation module: Processes the LAI dataset and the LST dataset to obtain monthly LAI and monthly LST respectively; The monthly LAI and the monthly LST are respectively synthesized into annual average LAI and multi-year average LAI, as well as annual average LST and multi-year average LST; Extraction module: Based on the swamp distribution dataset, extract the unchanged swamp distribution range, and extract all pixels with the multi-year average LAI > 0 within the unchanged swamp distribution range as the swamp vegetation distribution and study area; The cropping module: crops out the annual average LAI, multi-year average LAI, annual average LST, and multi-year average LST; The second calculation module calculates the average LAI trend value per pixel within the study area, and the slope. LAI The third calculation module divides the study area into grid cells. Within each grid cell, based on the pixel-by-pixel DEM value and the multi-year average LAI, the pixels are divided into a high LAI value group and a low LAI value group. The ΔLAI value of the grid cell is calculated, wherein the ΔLAI value is the multi-year average growing season LAI value of all pixels corresponding to the high LAI value group minus the multi-year average growing season LAI value of all pixels corresponding to the low LAI value group. Fourth calculation module: Extract the multi-year average LST of the corresponding pixels of the high LAI value group and the low LAI value group to obtain the ΔLST value, wherein the ΔLST is the multi-year average LST of all pixels corresponding to the high LAI value group minus the multi-year average LST of all pixels corresponding to the low LAI value group. Fifth calculation module: Calculate ΔLST / ΔLAI; Prediction module: utilizing the Slope LAI Calculate the average LAI trend value of all pixels within each grid cell, M-Slope LAI Based on the aforementioned ΔLST / ΔLAI, the biophysical impact of future changes in marsh vegetation cover on surface temperature in the study area is predicted, ΔLST future .

8. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor, when executing the computer program, implementing the method for predicting the impact of swamp vegetation change on surface temperature as described in any one of claims 1 to 6.

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