A method for predicting changes in the canopy aging rate of swamp vegetation

Abstract

A method for predicting changes in the aging rate of swamp vegetation canopies. The present invention relates to a method for predicting swamp vegetation canopies. The present invention solves the problem that existing swamp vegetation cannot accurately predict changes in the aging rate of vegetation canopies at a regional scale through field observations. Methods: Acquire and process remote sensing and meteorological data; extract the swamp vegetation distribution that has not changed during the study period as the study area; interpolate and resample the meteorological element dataset; calculate the difference between the November NDVI and the October NDVI corresponding to all pixels in the study area and extract the values of each meteorological element; construct a pixel-by-pixel prediction model for changes in the aging rate of swamp vegetation canopies under the influence of climate change; and combine future meteorological element datasets to predict future changes in the aging rate of swamp vegetation canopies within the study area. The present invention utilizes the characteristics of remote sensing data, which have a wide spatial range and strong continuity, to provide a new method for predicting changes in the aging rate of swamp vegetation canopies.

Description

Technical Field

[0001] The invention relates to a method for predicting a swamp vegetation canopy. Background Art

[0002] Wetlands are globally important ecosystems, playing a vital role in conserving biodiversity and promoting regional carbon cycling. Swamps, as a key wetland type, have a significant impact on ecosystem carbon sequestration. Vegetation, a crucial component of swamps, regulates regional climate and protects the ecosystem environment. Vegetation canopy senescence is a key factor influencing plant growth. Studying the rate of canopy senescence is crucial for understanding vegetation responses to climate change and predicting future vegetation growth.

[0003] Changes in vegetation structure and function are among the most significant impacts of global climate change. Previous studies have shown that climate warming may slow the aging of marsh vegetation canopies and extend the growing season, thereby affecting the carbon sequestration of marsh ecosystems. Understanding changes in the rate of marsh vegetation canopy aging is crucial for predicting carbon sequestration in marsh ecosystems. However, due to limited human, material, and financial resources, field observation methods cannot continuously monitor the aging of marsh vegetation canopies across an entire region, nor can they accurately predict changes in the rate of canopy aging in regional marshes. Summary of the Invention

[0004] The present invention aims to solve the problem that the existing swamp vegetation canopy aging rate change cannot be accurately predicted through field observation at a regional scale. A method for predicting the aging rate change of swamp vegetation canopy is provided.

[0005] The method for predicting changes in the aging rate of swamp vegetation canopy of the present invention is carried out according to the following steps:

[0006] Step 1: Obtain the normalized vegetation index dataset for each decade in October and November of each year in the study area, two-period swamp distribution data, and historical and future meteorological element datasets, and perform data preprocessing;

[0007] Step 2: Use the maximum value synthesis method to combine the NDVI data sets of October and November of each year into the NDVI data sets of October and November of each year;

[0008] Step 3: Use the ordinary kriging interpolation method to perform spatial interpolation on the historical and future meteorological element datasets, and resample the long-term meteorological element dataset to the same resolution and projection as the NDVI dataset;

[0009] Step 4: Based on the two-period swamp distribution data, extract the swamp distribution range that has not changed during the study period;

[0010] Step 5: Extract the pixel distribution of NDVI ≥ 0.1 in October of each year during the study period within the unchanged swamp distribution range to obtain the unchanged swamp vegetation distribution, and use this as the study area;

[0011] Step 6: Using the NDVI dataset of October and November obtained in step 2 of the study area, extract the NDVI values of October and November for each pixel within the study area, and calculate the difference V between the NDVI of November and the NDVI of October for each pixel. NDVI差 , get V per pixel per year NDVI差 value;

[0012] Step 7: Using the historical meteorological element dataset obtained in step 3 of the study area, extract the meteorological element values of each pixel within the study area;

[0013] Step 8: V NDVI差 The values of the meteorological factors were analyzed by multiple stepwise regression to construct a pixel-by-pixel prediction model of the aging rate of swamp vegetation canopy under the influence of climate change.

[0014] Step 9. Based on the pixel-by-pixel prediction model for changes in the aging rate of swamp vegetation canopy, combined with the future meteorological element dataset, calculate the future trend of changes in the aging rate of swamp vegetation canopy in the study area and predict future changes in the aging rate of swamp vegetation canopy.

[0015] The advantages of the present invention are that it uses remote sensing data and meteorological data to effectively predict the future changes in the rate of aging of swamp vegetation canopies within the study area, which is conducive to understanding the growth changes of swamp vegetation and the carbon sequestration capacity of swamp ecosystems, and solves the problem that field observation methods cannot continuously observe the aging status of swamp vegetation canopies in the entire region due to limitations of manpower, material resources and financial resources, and cannot accurately predict the changes in the aging rate of regional swamp vegetation canopies. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of Example 1;

[0017] Figure 2 The distribution range of the swamps in the Sanjiang Plain remained unchanged from 2000 to 2015 in Example 1;

[0018] Figure 3 This is the NDVI of the marsh vegetation in Sanjiang Plain in Example 1 in October 2010;

[0019] Figure 4 is the swamp vegetation V in Sanjiang Plain under the RCP4.5 scenario from 2021 to 2050 in Example 1 NDVI差 Changing trends. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0022] Specific implementation method 1: The method for predicting the change in the aging rate of swamp vegetation canopy in this implementation method is carried out according to the following steps:

[0023] Step 1: Obtain the normalized vegetation index dataset for each decade in October and November of each year in the study area, two-period swamp distribution data, and historical and future meteorological element datasets, and perform data preprocessing;

[0024] Step 2: Use the maximum value synthesis method to combine the NDVI data sets of October and November of each year into the NDVI data sets of October and November of each year;

[0025] Step 3: Use the ordinary kriging interpolation method to perform spatial interpolation on the historical and future meteorological element datasets, and resample the long-term meteorological element dataset to the same resolution and projection as the NDVI dataset;

[0026] Step 4: Based on the two-period swamp distribution data, extract the swamp distribution range that has not changed during the study period;

[0027] Step 5: Extract the pixel distribution of NDVI ≥ 0.1 in October of each year during the study period within the unchanged swamp distribution range to obtain the unchanged swamp vegetation distribution, and use this as the study area;

[0028] Step 6: Using the NDVI dataset of October and November obtained in step 2 of the study area, extract the NDVI values of October and November for each pixel within the study area, and calculate the difference between the NDVI of November and the NDVI of October for each pixel (V NDVI差 ), and get V per pixel per year NDVI差 value;

[0029] Step 7: Using the historical meteorological element dataset obtained in step 3 of the study area, extract the meteorological element values of each pixel within the study area;

[0030] Step 8: V NDVI差The values of the meteorological factors were analyzed by multiple stepwise regression to construct a pixel-by-pixel prediction model of the aging rate of swamp vegetation canopy under the influence of climate change.

[0031] Step 9. Based on the pixel-by-pixel prediction model for changes in the aging rate of swamp vegetation canopy, combined with the future meteorological element dataset, calculate the future trend of changes in the aging rate of swamp vegetation canopy in the study area and predict future changes in the aging rate of swamp vegetation canopy.

[0032] In step 4 of this embodiment, the swamp distribution refers to the swamp distribution range, and the research time period is the year, such as 2001-2020, and does not include other months.

[0033] Specific embodiment 2: This embodiment differs from Specific embodiment 1 in that the data preprocessing method in step 1 involves converting and reprojecting the October and November 10-day NDVI datasets and the two-period wetland distribution data to the same projection and coordinate system. Other steps and parameters are the same as those in Specific embodiment 1.

[0034] Specific embodiment 3: This embodiment differs from specific embodiment 1 in that the two phases of swamp wetland distribution data in step 4 are both swamp pixel distributions, that is, the swamp distribution range remains unchanged. Other steps and parameters are the same as those in specific embodiment 1.

[0035] Specific embodiment 4: This embodiment differs from the specific embodiment 1 in that: in step 6, V NDVI差 The value is calculated as follows:

[0036] V NDVI差 (t) = NDVI 11 (t) - NDVI 10 (t) Formula (1)

[0037] Among them, t is the year, V NDVI差 (t) refers to the V in year t NDVI差 Value, NDVI 11 (t) is the NDVI value in November of year t, NDVI 10 (t) refers to the NDVI value in October of year t. Other steps and parameters are the same as those in the first embodiment.

[0038] Specific embodiment 5: This embodiment differs from specific embodiment 1 in that: in step 8, a multivariate stepwise regression analysis is used to construct a pixel-by-pixel prediction model of the aging rate of swamp vegetation canopy under the influence of climate change. The formula is as follows: V NDVI差 =a+a1X1+a2X2+a3X3+...+a k X k Formula (2)

[0039] Among them, a is a constant term, a1, a2, ...a k are regression coefficients, X1, X2, …X K The other steps and parameters are the same as those in the first embodiment.

[0040] Specific embodiment 6: The difference between this embodiment and specific embodiment 1 is that the calculation formula for the aging rate change trend of the swamp vegetation canopy in step 9 is as follows:

[0041]

[0042] M i Refers to V in the future i year NDVI差 The value is: n represents the cycle length; i represents the year; and V represents the changing trend of the aging rate of the marsh vegetation canopy. If V < 0, it indicates that the aging rate of the marsh vegetation canopy is increasing; if V > 0, it indicates that the aging rate of the marsh vegetation canopy is decreasing; and if V = 0, it indicates that the aging rate of the marsh vegetation canopy is unchanged. Other steps and parameters are the same as those in the first embodiment.

[0043] Example 1: The Sanjiang Plain was selected as the implementation area, and the method of the present invention was used to predict the changes in the aging rate of the canopy of swamp vegetation in the Sanjiang Plain. Figure 1 This is a flow chart of this embodiment, and the specific steps are as follows:

[0044] (1) Obtain the October and November ten-day normalized difference vegetation index (NDVI) dataset, two-period swamp distribution data, and historical and future meteorological element datasets covering the study area during the study period, and perform data preprocessing;

[0045] We obtained the MODIS NDVI dataset covering the Sanjiang Plain region from October and November 2000 to 2015, two periods of marsh wetland distribution data from 2000 and 2015, a meteorological element dataset from 2000 to 2015, and a meteorological element dataset from 2021 to 2050 under the RCP4.5 scenario. We converted the October and November NDVI dataset and the two periods of marsh wetland distribution data into the same projection and coordinate system through format conversion and reprojection.

[0046] (2) Use the maximum value synthesis method to combine the ten-day NDVI data sets of October and November into the October and November NDVI data sets;

[0047] (3) Use the ordinary kriging interpolation method to spatially interpolate the historical and future meteorological element datasets, and resample the long-term meteorological element dataset to the same resolution and projection as the NDVI dataset;

[0048] (IV) Based on the two-period swamp distribution data, the swamp distribution range that did not change during the study period was extracted; the pixels that were swamps in the swamp wetland distribution data of Sanjiang Plain in 2000 and 2015 were extracted, which was the swamp distribution range that did not change in Sanjiang Plain during the study period; the swamp distribution range that did not change in Sanjiang Plain from 2000 to 2015 was as follows: Figure 2 As shown;

[0049] (V) The unchanged swamp distribution range in Sanjiang Plain from 2000 to 2015 was extracted. The pixel distribution with NDVI ≥ 0.1 in each year from 2000 to 2015 was obtained to obtain the unchanged swamp vegetation distribution. This was used as the study area. The NDVI of swamp vegetation in Sanjiang Plain in October 2010 was as follows: Figure 3 As shown;

[0050] (VI) Using the NDVI dataset for October and November from 2000 to 2015 obtained in step 2 of the study area clipping, extract the NDVI values for October and November for each pixel within the study area, and calculate the difference between the NDVI for November and the NDVI for October for each pixel (V NDVI差 );V NDVI差 The value is calculated as follows:

[0051] V NDVI差 (t) = NDVI 11 (t)-NDVI 10 (t) Formula (1)

[0052] Among them, t is the year, V NDVI差 (t) refers to the V in year t NDVI差 Value, NDVI 11 (t) is the NDVI value in November of year t, NDVI 10 (t) refers to the NDVI value in October of year t;

[0053] (7) Using the historical meteorological element dataset of the study area clipping step 3, extract the meteorological element values of swamp vegetation pixel by pixel within the study area;

[0054] (8) Variance of swamp vegetation per pixel and per year from 2000 to 2015 NDVI差 The values of the meteorological elements were analyzed by multiple stepwise regression to construct a pixel-by-pixel prediction model of the change in the aging rate of swamp vegetation canopy under the influence of climate change. The multiple stepwise regression analysis was used to construct a pixel-by-pixel prediction model of the change in the aging rate of swamp vegetation canopy under the influence of climate change. The formula is as follows:

[0055] V NDVI差 =a+a1X1+a2X2+a3X3+…+a k X k Formula (2)

[0056] Among them, a is a constant term, a1, a2, ...a k are regression coefficients, X1, X2, …X K For each meteorological element within a certain period of time;

[0057] (IX) Based on the pixel-by-pixel prediction model for changes in the aging rate of swamp vegetation canopy, combined with the 2021-2050 meteorological element dataset under the RCP4.5 scenario, the future trend of changes in the aging rate of swamp vegetation canopy in the study area was calculated, and the future changes in the aging rate of swamp vegetation canopy were predicted. The calculation formula for the trend of changes in the aging rate of swamp vegetation canopy is as follows:

[0058]

[0059] M i Refers to V in the future i year NDVI差 value; n represents the cycle length; i represents the year; V represents the changing trend of the aging rate of the swamp vegetation canopy. If V<0, it means that the aging rate of the swamp vegetation canopy increases; V>0, it means that the aging rate of the swamp vegetation canopy decreases; V=0, it means that the aging rate of the swamp vegetation canopy does not change.

[0060] V of marsh vegetation in Sanjiang Plain from 2021 to 2050 under RCP4.5 scenario NDVI差 The changing trend is Figure 4 shown.

[0061] In this embodiment, remote sensing data and meteorological data are used to effectively predict the future changes in the rate of aging of swamp vegetation canopies between 2021 and 2050, so as to understand the growth changes of swamp vegetation and the carbon sequestration capacity of swamp ecosystems. The present invention solves the problem that field observation methods cannot continuously observe the aging status of swamp vegetation canopies in the entire region due to limitations of manpower, material and financial resources, and cannot accurately predict the changes in the aging rate of regional swamp vegetation canopies.

Claims

1. A method for predicting changes in the aging rate of swamp vegetation canopy, characterized in that The prediction method for changes in the aging rate of swamp vegetation canopy is carried out in the following steps: Step 1: Obtain the normalized vegetation index dataset for each decade in October and November of each year in the study area, two-period swamp distribution data, and historical and future meteorological element datasets, and perform data preprocessing; Step 2: Use the maximum value synthesis method to combine the NDVI data sets of October and November of each year into the NDVI data sets of October and November of each year; Step 3: Use the ordinary kriging interpolation method to perform spatial interpolation on the historical and future meteorological element datasets, and resample the long-term meteorological element dataset to the same resolution and projection as the NDVI dataset; Step 4: Based on the two-period swamp distribution data, extract the swamp distribution range that has not changed during the study period; Step 5: Extract the NDVI values in October of each year within the study period within the unchanged swamp distribution range. The pixel distribution of 0.1 obtained the unchanged swamp vegetation distribution, which was used as the study area; Step 6: Using the NDVI dataset of October and November obtained in step 2 of the study area, extract the NDVI values of October and November for each pixel within the study area, and calculate the difference V between the NDVI of November and the NDVI of October for each pixel. NDVI差 , get V per pixel per year NDVI差 value; Step 7: Using the historical meteorological element dataset obtained in step 3 of the study area, extract the meteorological element values of each pixel within the study area; Step 8: V NDVI差 The values of the meteorological elements were analyzed by multiple stepwise regression to construct a pixel-by-pixel prediction model of the aging rate of swamp vegetation canopy under the influence of climate change. The multiple stepwise regression analysis was used to construct a pixel-by-pixel prediction model of the aging rate of swamp vegetation canopy under the influence of climate change. The formula is as follows: ， in, are constant terms, a1, a2,…a k are regression coefficients, X1, X2, …X K For each meteorological element within a certain period of time; Step 9: Based on the pixel-by-pixel swamp vegetation canopy aging rate change prediction model and combined with the future meteorological element dataset, calculate the future swamp vegetation canopy aging rate change trend within the study area and predict the future swamp vegetation canopy aging rate change; the calculation formula for the swamp vegetation canopy aging rate change trend is as follows: ， M i Refers to V in the future i year NDVI差 value; n represents the cycle length; i represents the year; V represents the changing trend of the aging rate of the swamp vegetation canopy. If V < 0, it means that the aging rate of the swamp vegetation canopy increases; V > 0, it means that the aging rate of the swamp vegetation canopy decreases; V = 0, it means that the aging rate of the swamp vegetation canopy does not change.

2. The method for predicting changes in the aging rate of swamp vegetation canopy according to claim 1, characterized in that The data preprocessing method in step 1 is: the NDVI datasets for each decade in October and November and the two periods of swamp wetland distribution data are unified into the same projection and coordinate system through format conversion and reprojection.

3. The method for predicting changes in the aging rate of swamp vegetation canopy according to claim 1, characterized in that In step 4, the two phases of swamp wetland distribution data are all swamp pixel distributions, that is, the unchanged swamp distribution range.

4. The method for predicting changes in the aging rate of swamp vegetation canopy according to claim 1, characterized in that Step 6 V NDVI差 The value is calculated as follows: V NDVI差 (t) = NDVI 11 (t) - NDVI 10 (t) Among them, t is the year, V NDVI差 (t) refers to the V in year t NDVI差 Value, NDVI 11 (t) is the NDVI value in November of year t, NDVI 10 (t) refers to the NDVI value in October of year t.

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