Evaluation method for global forest-cultivated land transformation topographic change and carbon loss of global forest-cultivated land transformation topographic change

Abstract

The invention relates to a method for evaluating global forest-cultivated land transformation topographic change and carbon loss thereof. The method comprises the following steps: acquiring global high-resolution remote sensing data; performing data conversion and space matching on the obtained remote sensing data; tracking and recording pixels of all cultivated land expansion occurring in forest coverage, and analyzing a space-time relationship between forest loss and cultivated land expansion; evaluating forest-cultivated land conversion topographic features and carbon loss thereof in combination with topographic and carbon density data; and evaluating the topographic change trend of forest-cultivated land conversion by using a decentralized regression model. The beneficial effects of the invention are that the method can dynamically evaluate the variation of global forest-cultivated land conversion in time and space and hot spots, and provides a quantitative basis for the topographic change trend and the distribution of forest carbon loss.

Description

Technical Field

[0001] The present invention relates to the technical field of agroforestry remote sensing, and more specifically, it relates to a method for assessing global forest-cropland conversion terrain changes and their carbon losses. Background Art

[0002] The rapid increase in the global population and the improvement of living standards have greatly increased the demand for commodities grown on farmland, necessitating the expansion of agricultural land globally. In most regions, the land available for new cultivation is increasingly limited and reduced, leading to the encroachment of cropland on sensitive land cover types. The United Nations 2030 Sustainable Development Goals (SDGs) advocate achieving a balance between increasing agricultural production and protecting ecosystem services. Within the framework of the Sustainable Development Goals, the conversion of forests to cropland, especially in tropical regions, is considered largely unsustainable because of the severe damage to vegetation, which can lead to associated carbon losses and threats to biodiversity, as well as other far-reaching impacts. In addition, local studies have shown that forest-converted farmland in Southeast Asia is encroaching on high-altitude and steep-slope areas; similar findings have also been reported in China. This terrain change phenomenon is concerning because mountain forests are crucial for biodiversity and have high carbon storage.

[0003] Globally, the interaction between cropland expansion and forest loss and the associated impact on the carbon cycle remain unclear. With the accelerating global forest carbon loss, partly due to the expansion of agricultural activities, there is an urgent need to quantify the forest-to-farmland conversion related to terrain and its concomitant impact on the biomass carbon pool at high spatio-temporal resolutions. Previous studies on this issue have been limited to specific regions, often using inconsistent methods to produce uncertain estimates, or being restricted by the temporal and spatial resolutions of their analyses. Summary of the Invention

[0004] The object of the present invention is to address the deficiencies of the prior art and propose a method for assessing global forest-cropland conversion terrain changes and their carbon losses.

[0005] In a first aspect, a method for assessing global forest-cropland conversion terrain changes and their carbon losses is provided, including:

[0006] Step 1, obtaining global high-resolution remote sensing data, where the remote sensing data includes forest cover, cropland expansion, terrain, and carbon density distribution data;

[0007] Step 2, performing data conversion and spatial matching on the obtained remote sensing data;

[0008] Step 3, using pixel-level discrimination technology to track all cropland expansion occurring in forest-covered pixels at the spatial scale and record them, and analyzing the spatio-temporal relationship between forest loss and cropland expansion;

[0009] Step 4: Evaluate the topographic characteristics and carbon losses of forest-cropland conversion by combining topographic and carbon density data;

[0010] Step 5: Use the decentralized regression model to evaluate the topographic change trend of forest-cropland conversion.

[0011] Preferably, in Step 1, the topographic data includes global mountain distribution vector data, elevation data, and slope data.

[0012] Preferably, Step 4 includes:

[0013] Step 4.1: Extract the topographic and carbon density data corresponding to the forest-cropland conversion pixels traced and recorded in Step 3;

[0014] Step 4.2: Statistically calculate the forest-cropland conversion area, the average elevation slope of forest-cropland conversion, and the carbon losses caused by forest-cropland conversion for all recorded pixels at a preset scale;

[0015] Step 4.3: Use visualization tools to perform spatio-temporal visualization on the statistical results.

[0016] Preferably, in Step 4.2, the calculation of the forest-cropland conversion area is based on the area of the pixel and the number of pixels that meet the requirements and are recorded; the calculation of the average elevation slope of forest-cropland conversion is based on the elevation and slope of the pixels that meet the requirements and are recorded and the number of pixels that meet the requirements and are recorded; the calculation of the carbon losses caused by forest-cropland conversion is based on the area of the pixel and the carbon density.

[0017] Preferably, Step 5 includes:

[0018] Step 5.1: Calculate the topographic contribution of each pixel in the time series;

[0019] Step 5.2: Quantify the topographic change trend through a linear regression model.

[0020] In a second aspect, an evaluation system for global forest-cropland conversion topographic changes and their carbon losses is provided, which is used to execute any of the methods in the first aspect, and includes:

[0021] An acquisition module, configured to acquire global high-resolution remote sensing data, where the remote sensing data includes forest cover, cropland expansion, topographic, and carbon density distribution data;

[0022] A conversion module, configured to perform data conversion and spatial matching on the acquired remote sensing data;

[0023] A tracking module, configured to use pixel-level discrimination technology to track and record all pixels where cropland expansion occurs in forest cover at the spatial scale, and analyze the spatio-temporal relationship between forest loss and cropland expansion;

[0024] A first evaluation module for evaluating the topographic characteristics of forest-cropland conversion and its carbon loss by combining topographic and carbon density data;

[0025] A second evaluation module for evaluating the topographic change trend of forest-cropland conversion using a decentralized regression model.

[0026] In a third aspect, a computer storage medium is provided, in which a computer program is stored; when the computer program runs on a computer, the computer is caused to execute any of the methods described in the first aspect.

[0027] In a fourth aspect, an electronic device is provided, including:

[0028] A memory for storing a computer program;

[0029] A processor for executing the computer program to implement any of the methods described in the first aspect.

[0030] The beneficial effects of the present invention are as follows: Based on global high-resolution remote sensing data (such as forest cover data, cropland expansion data, topographic data, and carbon density distribution data, etc.) and a decentralized regression model, the present invention has invented a method capable of quantifying the topographic changes and carbon losses of global forest-cropland conversion with high precision and high resolution. This method can dynamically evaluate the variations and hotspots of global forest-cropland conversion in time and space, and provide a quantitative basis for the topographic change trend and the distribution of forest carbon losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flowchart of a method for evaluating the topographic changes and carbon losses of global forest-cropland conversion provided by an embodiment of the present invention;

[0032] FIG. 2 is a schematic diagram of the time series and spatial pattern of the forest-cropland conversion area and related topography around the world during the period from 2004 to 2019 visualized using MATLAB provided by an embodiment of the present invention;

[0033] Figure 3 It is a schematic structural diagram of an evaluation system for the topographic changes and carbon losses of global forest-cropland conversion provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The present invention will be further described below in conjunction with embodiments. The description of the following embodiments is only for helping to understand the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0035] Embodiment 1:

[0036] Example 1 of this application provides a quantification method and a decentralized regression model. Combining the advantages of high spatio-temporal resolution of the remote sensing data used, it can more accurately and effectively evaluate the global forest-cropland conversion terrain change and its carbon loss, overcoming the defects existing in the existing methods.

[0037] Specifically, Example 1 of this application provides an evaluation method for the global forest-cropland conversion terrain change and its carbon loss, as Figure 1 shown, including:

[0038] Step 1, obtain global high-resolution remote sensing data, where the remote sensing data includes forest cover, cropland expansion, terrain, and carbon density distribution data.

[0039] Specifically, the remote sensing data includes: global forest cover data in 2000 (GFCC and GLCLUC), with a spatial resolution of 30m, where 1 represents forest and 0 represents non-forest; global cropland expansion data from 2000 to 2019 (GLULUC), with a total of five cropland distribution maps for each four-year scene, a spatial resolution of 30m, where 1 represents cropland and 0 represents non-cropland; global mountain distribution vector data (GMBA), where 1 represents mountain and 0 represents lowland; NASA global elevation data and the calculated slope data, with a spatial resolution of 30m; global carbon density distribution data in 2000 (Zarin), with a spatial resolution of 30m.

[0040] Step 2, perform data conversion and spatial matching on the obtained remote sensing data.

[0041] Specifically, in Step 2, ARCGIS and MATLAB are used to perform data conversion and spatial matching on the remote sensing data, convert the global mountain distribution vector data into a unified tif format with a spatial resolution of 0.5°, and perform spatial matching on the pixels of the remaining remote sensing data with a spatial resolution of 30m, with a range of 80°N to 60°S.

[0042] Step 3, use pixel-level discrimination technology to track and record all cropland expansions that occur in forest-covered pixels at the spatial scale, and analyze the spatio-temporal relationship between forest loss and cropland expansion.

[0043] It should be noted that the pixel-level discrimination technology described in Step 3 discriminates pixel by pixel and must meet the following two requirements to be tracked and recorded: being forest-covered in 2000 and having had cropland expansion after 2000.

[0044] Step 4, evaluate the forest-cropland conversion terrain characteristics and its carbon loss in combination with terrain and carbon density data.

[0045] Step 4 includes:

[0046] Step 4.1: Extract the topographic and carbon density data corresponding to the forest-cropland conversion pixels traced and recorded in Step 3.

[0047] Step 4.2: Statistically calculate the forest-cropland conversion area, the average elevation slope of the forest-cropland conversion, and the carbon loss caused by the forest-cropland conversion for all recorded pixels at a preset scale (such as 0.5°).

[0048] In Step 4.2, the calculation of the forest-cropland conversion area is based on the area of the pixel and the number of pixels that meet the requirements and are recorded; the calculation of the average elevation slope of the forest-cropland conversion is based on the elevation and slope of the pixels that meet the requirements and are recorded and the number of pixels that meet the requirements and are recorded; the calculation of the carbon loss caused by the forest-cropland conversion is based on the area of the pixel and the carbon density.

[0049] Specifically, the calculation formula for the forest-cropland conversion area is:

[0050] S = s·p

[0051] where S is the forest-cropland conversion area at 0.5°, s is the area of a 30m resolution pixel, and p is the number of pixels that meet the requirements and are recorded in 0.5°.

[0052] The calculation formula for the average elevation slope of the forest-cropland conversion is:

[0053]

[0054] where H and I are the average elevation and slope of the forest-cropland conversion at 0.5° respectively, h and i are the elevation and slope of the pixels that meet the requirements and are recorded respectively, and p is the number of pixels that meet the requirements and are recorded in 0.5°.

[0055] The calculation formula for the carbon loss caused by the forest-cropland conversion is:

[0056] C = Σcs

[0057] where C is the carbon loss caused by the forest-cropland conversion at 0.5°, s is the area of a 30m resolution pixel, and c is the carbon density of a 30m resolution pixel.

[0058] Step 4.3: Use a visualization tool (such as MATLAB) to perform spatio-temporal visualization on the statistical results.

[0059] Step 5: Use a decentralized regression model to evaluate the topographic change trend of the forest-cropland conversion.

[0060] Step 5 includes:

[0061] Step 5.1: Calculate the topographic contribution of each pixel in the time series.

[0062] Step 5.2. Quantify the trend of terrain change through a linear regression model.

[0063] Specifically, the weighted regression model is expressed as:

[0064]

[0065] where H t,k and I t,k are respectively the average elevation and slope of forest - cropland conversion considering the conversion area and terrain (elevation / slope) effects for the k - th 0.5° pixel (or country) during period t; and are respectively the average elevation and slope of global forest - cropland conversion from 2004 to 2019; s t,k and s t are respectively the forest = cropland conversion areas of the k - th 0.5° pixel (or country) and other pixels (or countries) during period t. We first assume that the elevation and slope data of other pixels (or countries) are the global averages (and) for the entire period from 2004 to 2019, while those of the k - th 0.5° pixel (or country) are the actual values. Therefore, the global time - series trend is only affected by the change of the k - th 0.5° pixel (or country). In other words, the global average elevation and slope trends are decomposed into each 0.5° pixel (or country), so that the different contributions of each pixel (or country) to global terrain change can be determined. After using area weighting, a simple linear regression model is applied to quantify the terrain contribution (trend) in the time - series of each 0.5° pixel (or country) globally.

[0066] It should be noted that when the conversion area ratio remains unchanged, the change of H t,k is proportional to h t,k ; when h t,k remains unchanged, if h t,k is less than then the change of H t,k is inversely proportional to . However, if h t,k is greater than then the change of H t,k is proportional to . When and h t,k are both changing, the change in H t,k is driven by the factor with the largest change, or if both are nearly equal, both will drive. Therefore, these two equations can be used to analyze the driving factors for the change of H t,k in each 0.5° pixel (or country), including region - driven pixels (or countries), that is, the change in H t,k is caused by st,k driven by the changes of, terrain-driven pixels (or countries), i.e., H t,k The changes in are driven by both h t,k driven by the changes in, or jointly driven by both.

[0067] Example 2:

[0068] Based on Example 1, Example 2 of the present application provides a more specific method for evaluating the global forest-cropland conversion terrain changes and their carbon losses, including:

[0069] Step 1, obtain global high-resolution remote sensing data, where the remote sensing data includes forest cover, cropland expansion, terrain, and carbon density distribution data.

[0070] Step 2, perform data conversion and spatial matching on the obtained remote sensing data.

[0071] Step 3, use pixel-level discrimination technology to track all cropland expansions occurring in forest-covered pixels at the spatial scale and record them, and analyze the spatio-temporal relationship between forest loss and cropland expansion.

[0072] Exemplarily, after completing data collection and data preprocessing, the GLCLUC forest cover map in 2000 is used to identify forest-covered pixels (i.e., pixels with a value of 1), and then the GLCLUC cropland cover maps from two adjacent time periods are used to determine whether this pixel has experienced a conversion from non-cropland to cropland during this period (i.e., the pixel value was 0 before and became 1 later). The present application defines this change as forest-cropland conversion. To analyze the spatio-temporal changes of forest-cropland conversion, the data for the entire analysis period from 2004 to 2019 in the present application is divided into four 4-year periods: 2004 - 2007, 2008 - 2011, 2012 - 2015, and 2016 - 2019, and the determination is made pixel by pixel.

[0073] Step 4, evaluate the forest-cropland conversion terrain characteristics and their carbon losses by combining terrain and carbon density data.

[0074] Exemplarily, to analyze the topographic patterns of forest-to-farmland conversion, a combination of mountain range polygons and elevation datasets was used. Specifically, the mountain range polygons were obtained from the Global Mountain Biodiversity Assessment (GMBA) inventory. The GMBA inventory uses specific criteria to classify 2.5 arc-minute pixels with a vertical difference of more than 200 meters between the highest and lowest elevations as mountainous areas. Areas not covered by the GMBA mountain polygons are considered lowlands. An elevation dataset with a spatial resolution of 30 meters was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model. Subsequently, the slope values were derived from the elevation dataset using the average maximum method. The topographic characteristics of global forest-cropland conversion were evaluated in combination with the topographic data, and the results are shown in Figure 2.

[0075] From 2004 to 2019, forest-to-cropland conversion showed a non-linear increase, growing rapidly in the second half (see Figure 2a ). During the period from 2004 to 2007, the conversion rate was approximately 0.9 Mha yr -1 , and it increased slightly to 1 million hectares-year in the subsequent period (2008 - 2011). From 2012 to 2015, this rate increased by more than 50% to 1.6 Mha yr -1 . By 2016 - 2019, the conversion rate reached 2 Mha yr -1 , more than twice that of the first stage. Due to cropland expansion, the largest forest losses occurred in Africa (41.4%), South America (33.8%), and Asia (18.6%) (see Figure 2c ). Regional hotspots of forest conversion to farmland include central South America, the African region between 12°N and 20°S, rural areas of the Southeast Asian mainland, and certain areas of the Indonesian archipelago. Other small, scattered areas that have experienced significant changes include the southeastern United States, the Yucatan Peninsula, the Sichuan Basin in China, Sri Lanka, the Philippines, and eastern India.

[0076] Most of the conversion occurred in lowlands, but the proportion of mountain forest-to-cropland conversion increased from 6.5% in 2004 - 2007 to more than 9% in 2016 - 2019 (see Figure 2a ). Substantial conversion was observed in the mountains of East Africa, East and Southeast Asia, and South America. During the four study periods, the average elevation of forest-to-cropland conversion increased from 460 meters in 2004 - 2007 to approximately 520 meters in 2012 - 2015, and then decreased slightly to approximately 500 meters in the last period. The average slope of forest-to-cropland conversion increased slightly from 5.5° to 6.0° over the four periods, and like elevation, the change in slope mainly occurred in the latter two periods (see Figure 2b ). The rate of change in elevation of global conversion during the entire study period was 15.4 ± 7.9 m period -1, the slope change rate is 0.24 ± 0.08° period -1 (See Figure 2b ).

[0077] In addition, in Step 4, to quantify forest carbon loss, a high-resolution aboveground live woody biomass (AGB) density map was integrated into the forest-cropland conversion analysis. This map shows the AGB density (Mg ha -1 ) at a spatial resolution of 30 m around 2000. This map has been widely used for forest AGB quantification. Given the lack of available data, an anisotropic growth model was used to estimate the belowground root biomass (BGB) density at the 30-m pixel level:

[0078] BGB = 0.489 × AGB 0.89

[0079] The forest carbon stock density was calculated using AGB and BGB:

[0080] AGC = AGB·α

[0081] BGC = BGB·α

[0082] CS = AGC + BGC

[0083] where AGB is the aboveground biomass density, AGC is the belowground biomass density, α is the conversion coefficient, which is 0.5, and CS is the forest carbon stock density.

[0084] The global forest-cropland carbon loss was evaluated by combining the calculated forest carbon stock density data.

[0085] The total global forest carbon loss due to forest-cropland conversion was 1546 TgC, equivalent to 97 TgC per year from 2004 to 2019. Almost all regions with carbon losses exceeding the average of 0.01 TgC showed an acceleration of carbon loss. In cases where local cropland expansion gradually encroached on forests with higher carbon density, the related carbon loss trends were inconsistent. For example, although the conversion area in southern Thailand and the southern coastal areas of China decreased over time, the carbon loss increased. The forest-cropland conversion in these areas has been expanding to higher altitudes and slopes with higher forest carbon stocks (see Figure 1 ). Therefore, despite the overall decrease in the total area of forest converted to cropland, a large carbon stock loss still occurred. From a policy perspective, this apparent contradiction is disturbing because reducing deforestation and degradation is usually regarded as synonymous with reducing carbon loss at a finer national scale.

[0086] Step 5: Use the decentralized regression model to evaluate the topographic change trend of forest-cropland conversion.

[0087] Specifically, in step 5, a decentralized regression model is used to evaluate the topographic change trend of forest-cropland conversion, and the impact degree of the conversion from forest to farmland in different regions on the global upward conversion trend is evaluated, which is defined as topographic contribution.

[0088] To analyze the global spatial pattern, the contribution of topography to the global elevation and slope changes at the grid scale (0.5°) was calculated. The spatial pattern shows a large heterogeneity in the global topographic contribution, and positive and negative impacts often coexist. East Asia and Southeast Asia play a major positive role in the global conversion of forests to steep slopes, while the central and southern regions of Africa are converted to higher elevations. Globally, the decline in topographic contribution at different locations balances to some extent the overall trend of forest-to-farmland conversion towards higher elevations and steeper slopes.

[0089] It should be noted that the same or similar parts in this embodiment and Embodiment 1 can be referred to each other and will not be elaborated in this application.

[0090] Embodiment 3:

[0091] Based on Embodiments 1 and 2, Embodiment 3 of this application provides an evaluation system for the topographic change and its carbon loss of global forest-cropland conversion, including:

[0092] An acquisition module for acquiring global high-resolution remote sensing data, where the remote sensing data includes forest cover, cropland expansion, topography, and carbon density distribution data;

[0093] A conversion module for performing data conversion and spatial matching on the acquired remote sensing data;

[0094] A tracking module for tracking all cropland expansions occurring in forest-covered pixels at the spatial scale using pixel-level discrimination technology and recording, and analyzing the spatio-temporal relationship between forest loss and cropland expansion;

[0095] A first evaluation module for evaluating the topographic characteristics and its carbon loss of forest-cropland conversion by combining topography and carbon density data;

[0096] A second evaluation module for evaluating the topographic change trend of forest-cropland conversion using a decentralized regression model

[0097] Specifically, the system provided in this embodiment is the system corresponding to the methods provided in Embodiments 1 and 2. Therefore, the same or similar parts in this embodiment and Embodiments 1 and 2 can be referred to each other and will not be elaborated in this application.

[0098] In summary, compared with the existing methods, the present invention has achieved the following innovations:

[0099] 1. The present invention proposes a method based on global high-resolution remote sensing data (such as forest cover data, cultivated land expansion data, terrain data, carbon density distribution data, etc.) and a decentralized regression model, which can quantitatively analyze the global forest-cultivated land conversion, terrain changes, and their carbon losses with high precision and high resolution.

[0100] 2. The minimum spatial resolution of the method proposed by the present invention is 30m, and the time resolution is from 2004 to 2019, without being restricted by time and spatial resolutions.

[0101] 3. The present invention can dynamically evaluate the variations and hotspots of global forest-cultivated land conversion in terms of time and space, providing a quantitative basis for the trends of terrain changes and the distribution of forest carbon losses, rather than being limited to specific regions.

[0102] 4. The present invention is not limited to the spatio-temporal analysis of forest-cultivated land conversion, and can also be used in forest remote sensing monitoring applications such as the destruction of forests caused by urbanization, agricultural expansion, fires, etc.

Claims

1. A method for assessing global forest-to-cultivated land conversion topographic changes and carbon losses, characterized in that: include: Step 1: Acquire global high-resolution remote sensing data, including forest cover, cultivated land expansion, topography and carbon density distribution data; Step 2: Perform data conversion and spatial matching on the acquired remote sensing data; Step 3: Use pixel-level discrimination technology to track and record all pixels where farmland expansion occurs in forest cover at a spatial scale, and analyze the spatiotemporal relationship between forest loss and farmland expansion; Step 4: Combine topography and carbon density data to assess the topographic characteristics of forest-to-cultivated land conversion and its carbon loss; Step 5: Use the decentralized regression model to evaluate the topographic change trend of forest-to-cultivated land conversion.

2. The method for assessing global forest-to-cultivated land conversion topographic changes and their carbon losses according to claim 1, characterized in that: In step 1, the terrain data includes global mountain distribution vector data, elevation data and slope data.

3. The method for assessing global forest-to-cultivated land conversion topographic changes and their carbon losses according to claim 2, characterized in that: Step 4 includes: Step 4.1, extract the terrain and carbon density data corresponding to the forest-cultivated land conversion pixels tracked and recorded in step 3; Step 4.2: Count the forest-to-cultivated land conversion area, average elevation slope of forest-to-cultivated land conversion, and carbon loss caused by forest-to-cultivated land conversion for all recorded pixels at a preset scale; Step 4.3: Use visualization tools to visualize the statistical results in time and space.

4. The method for assessing global forest-to-cultivated land conversion topographic changes and carbon losses according to claim 3, characterized in that: In step 4.2, the calculation of the forest-cultivated land conversion area is based on the area of ​​the pixel and the number of pixels that meet the requirements and are recorded; the calculation of the average elevation slope of the forest-cultivated land conversion is based on the elevation and slope of the pixels that meet the requirements and the number of pixels that meet the requirements and are recorded; the calculation of the carbon loss caused by the forest-cultivated land conversion is based on the area and carbon density of the pixel.

5. The method for assessing global forest-to-cultivated land conversion topographic changes and carbon losses according to claim 4, characterized in that: Step 5 includes: Step 5.1, calculate the terrain contribution of each pixel in the time series; Step 5.2: Quantify the terrain change trend through linear regression model.

6. A global forest-to-cultivated land conversion terrain change and carbon loss assessment system, characterized in that: The method for executing any one of claims 1 to 5 comprises: An acquisition module, used to acquire global high-resolution remote sensing data, wherein the remote sensing data includes forest cover, cultivated land expansion, topography and carbon density distribution data; A conversion module is used to perform data conversion and spatial matching on the acquired remote sensing data; The tracking module is used to track and record all pixels where farmland expansion occurs in forest cover at a spatial scale using pixel-level discrimination technology, and analyze the spatiotemporal relationship between forest loss and farmland expansion; The first assessment module is used to evaluate the terrain characteristics and carbon loss of forest-to-cultivated land conversion by combining topography and carbon density data; The second assessment module is used to evaluate the topographic change trend of forest-to-cultivated land conversion using a decentralized regression model.

7. A computer storage medium, characterized in that: The computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any one of the methods described in claims 1 to 5.

8. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 5.

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