Method, medium and device for extracting carbon sequestration potential of urban difficult sites

Abstract

The present application relates to the technical field of remote sensing, and particularly relates to a method, medium and equipment for extracting carbon fixation potential of urban difficult sites, comprising the following steps: determining and identifying the range of urban difficult sites; splicing and re-projecting GLASS NPP data to WBG1984 coordinate system; taking GLASS NPP data of consecutive years as basic data, calculating the optimal value of NPP that can be reached under the restriction of climate conditions and human activity conditions in the consecutive years, and performing difference calculation on the result and the annual interannual cumulative value of the benchmark year NPP to obtain the vegetation carbon fixation potential data of the benchmark year urban difficult sites; performing mask extraction operation on the spliced data according to the determined urban difficult site range as the bottom map range to determine the vegetation carbon fixation potential in the urban difficult site range. The present application provides a method for obtaining carbon fixation potential of urban difficult sites by using remote sensing data, and provides a reference for spatial level carbon fixation research.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing, and in particular to a method, medium and device for extracting carbon sequestration potential of urban difficult sites. BACKGROUND

[0002] Wang Yaching (2020) et al. found that urbanization increases energy consumption by 1% for every 0.095% increase in carbon emissions. Therefore, in addition to controlling carbon emissions from human activities, increasing urban green space and improving vegetation coverage are important steps for carbon sequestration. However, existing urban planning makes it difficult to plan new spaces for urban green space, so the importance of reusing "urban difficult sites" such as unused land and relocation sites is increasingly prominent. Studying the carbon sequestration capacity of urban difficult sites can help urban low-carbon transformation and create an environment where humans and nature coexist in harmony.

[0003] Current carbon emission reduction methods include fossil energy, renewable energy, and vegetation carbon sequestration. China has abundant vegetation resources, and studying the carbon sequestration effect of vegetation is important for green development. Through this research, vegetation management can be optimized, the ecological environment can be improved, and sustainable development policy references can be provided.

[0004] Current research on vegetation carbon sequestration effect evaluation mainly focuses on carbon sequestration capacity evaluation, biodiversity and carbon sequestration relationship, climate change evaluation, and land use change impact analysis. The above research covers current hot topics related to vegetation carbon sequestration, and mainly analyzes the impact of existing vegetation carbon sequestration capacity. There are few predictions of vegetation carbon sequestration, and vegetation carbon sequestration potential evaluates the size and saturation of carbon sequestration volume, providing prediction and indication for subsequent carbon sequestration research.

[0005] Current research on carbon sequestration mainly uses the CASA model and the Miami model, both of which are more suitable for large-scale NPP (net primary productivity of vegetation) research. They refer to the influence of natural factors such as precipitation, temperature, and light, and may lack differences in data for urban built-up areas where natural factors have little difference.

[0006] Therefore, this paper selects a method based on remote sensing observation of NPP to extract the carbon sequestration potential of urban difficult site vegetation. SUMMARY

[0007] Net primary productivity (NPP) of vegetation refers to the remaining part after deducting autotrophic respiration from the total amount of organic matter produced by plants through photosynthesis, and is the most direct and significant parameter representing the carbon sequestration capacity of vegetation. As an important source of urban greening, the increase of vegetation coverage after ecological restoration can enhance the urban carbon sink, and the potential possibility of improving the carbon sequestration capacity of vegetation is large, which is helpful for predicting the low-carbon transformation path of the city. Therefore, the present application provides a method for extracting the carbon sequestration potential of vegetation of urban difficult sites based on GLASS NPP data.

[0008] In one aspect, the present application provides a method for extracting the carbon sequestration potential of urban difficult sites, which extracts the carbon sequestration potential of vegetation of urban difficult sites based on GLASS NPP data, comprising the following steps:

[0009] determining and identifying the range of urban difficult sites;

[0010] splicing and reprojecting the GLASS NPP data to the WBG1984 coordinate system;

[0011] taking the GLASS NPP data of consecutive years as the basic data, calculating the optimal value of NPP that can be reached under the restriction of climate conditions and human activity conditions in the consecutive years, and calculating the difference between the result and the annual cumulative value of NPP in the base year to obtain the vegetation carbon sequestration potential data of the base year urban difficult sites;

[0012] performing mask extraction operation on the spliced GLASS NPP data according to the determined range of urban difficult sites as the bottom map range to determine the vegetation carbon sequestration potential in the urban difficult sites.

[0013] Base year: the base year for comparison is any year in history that can obtain quantitative data or the average value of several years, which can be used for comparative analysis and prediction of historical data. In the present application, the base point refers to the end year of consecutive years. After determining the research base year, the required research period is selected, and the length can be changed according to the research. (For example, to study the vegetation carbon sequestration potential from 2000 to 2020, taking 2020 as the base year, the vegetation carbon sequestration potential in 2020 can be calculated by using the cumulative data from 2000 to 2020 and the annual cumulative data in 2020.)

[0014] Annual cumulative value of NPP: the GLASS NPP data of each period in the base year is calculated by period-by-period accumulation, and the result is the annual cumulative value of that year.

[0015] Preferably, the specific method for determining and identifying the range of urban difficult sites is: according to different cause types and site types, the urban difficult sites are divided into two categories of natural type urban difficult sites and artificial type urban difficult sites;

[0016] The natural type urban difficult site and the artificial type urban difficult site type are integrated with the existing land use classification data respectively, the natural type urban difficult site is identified by using the remote sensing image of the existing land use classification data, and the artificial type urban difficult site is identified according to whether the construction land needs to be transferred as a standard combined with the remote sensing image;

[0017] And further through the remote sensing image data inversion NDVI, the low coverage grassland with NDVI less than 0.2 is supplemented as the newly divided natural type urban difficult site;

[0018] The identified natural type urban difficult site and artificial type urban difficult site range are combined to obtain the complete urban difficult site range.

[0019] The natural type urban difficult site is an ecological land that the site condition is dominated by natural factors such as climate and geology, and that causes obstacles to plant growth. Reference is made to Zhang Lang's edited 'Urban Difficult Site Ecological Landscape Construction Method and Practice'.

[0020] The artificial type urban difficult site is an ecological land that the site condition is dominated by human interference factors such as engineering construction, land type change and pollutant discharge, and that causes obstacles to plant growth or seriously damages the ecological system function.

[0021] The construction land transfer is that whether the land type is affected by human construction activities has become construction land, and the repair is based on the construction land, and in the present application, it particularly refers to the current identified green land but the previous construction land.

[0022] Preferably, the natural type urban difficult site type includes saline-alkali soil, grassland, sand land, bare land and bare rock gravel land, glacier and permanent snow, damaged wetland or water area; the artificial type urban difficult site type includes industrial relocation land, idle land under control, garbage landfill site and building green space.

[0023] Preferably, the existing land use classification data comes from the national glacier permafrost desert scientific data center, the land use is classified as arable land, forest land, grassland, shrub land, construction land, unused land, water body, desert land, snow mountain glacier land and wetland, the unused land, desert land, snow mountain glacier land and wetland in the city range are considered as the natural type urban difficult site, the natural type urban difficult site type is identified and superimposed by using the existing land use classification data, the low coverage grassland with NDVI less than 0.2 is supplemented as the natural type urban difficult site by further inverting NDVI through Landsat 8 remote sensing image data, and the two parts are superimposed to form the natural type urban difficult site range.

[0024] Preferably, the NDVI less than 0.2 low coverage grassland data is obtained by inverting NDVI according to the Landsat 8 remote sensing image data.

[0025] In the present application, NDVI is retrieved by Landsat 8 remote sensing image data of 30m extracted by the United States Geological Survey.

[0026] Preferably, the identification is carried out by using land use transfer data of two consecutive times, and the land currently identified as green land but once as construction land is screened, that is, it is identified as artificial type urban difficult site.

[0027] Specifically, the land use data used in the identification method of artificial type urban difficult site is from the National Glacier Permafrost and Desert Scientific Data Center, which classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water body, desert land, snow mountain glacier land and wetland. The cultivated land, forest land, grassland and shrub land in the data classification are reclassified as green land, the construction land and water body in the data classification are still identified as construction land and water body, and the remaining categories are classified as natural type urban difficult site. Finally, the nine types of land use in the original data are reclassified into four categories of green land, construction land, natural type urban difficult site and water body. The transfer trend and transfer amount of construction land and green land are calculated by land use transfer matrix, and the part transferred from construction land to green land is identified as artificial type difficult site.

[0028] Preferably, the identification method of artificial type urban difficult site is as follows: the identification is carried out by using land use transfer data of two consecutive times, and the land currently identified as green land but once as construction land is screened, that is, it is identified as artificial type urban difficult site. The land use data used in the identification method of artificial type urban difficult site is from the National Glacier Permafrost and Desert Scientific Data Center, which classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water body, desert land, snow mountain glacier land and wetland. The cultivated land, forest land, grassland and shrub land in the data classification are reclassified as green land, the construction land and water body in the data classification are still identified as construction land and water body, and the remaining categories are classified as natural type urban difficult site. Finally, the nine types of land use in the original data are reclassified into four categories of green land, construction land, natural type urban difficult site and water body. The transfer trend and transfer amount of construction land and green land are calculated by land use transfer matrix, and the part transferred from construction land to green land is identified as artificial type difficult site.

[0029] Land use transfer data: data of the land currently identified as green land but previously as construction land.

[0030] Preferably, ENVI is used for data splicing, and ArcGIS software is used for data re-projection.

[0031] Preferably, the specific method for calculating the optimal value of NPP that can be achieved under the restriction of climate conditions and human activity conditions in a continuous year period is as follows: taking GLASS NPP data of continuous years as basic data, performing maximum synthesis on each period of NPP data corresponding to the continuous years by maximum synthesis method, and then performing accumulation calculation on the synthesized data, the result obtained is the optimal value of NPP that can be achieved under the restriction of climate conditions and human activity conditions in a continuous year period, the difference between the result and the annual accumulation value of NPP of the base year is calculated, and the result obtained is the value of NPP promotion space of the base year, that is, the vegetation carbon sequestration potential data of urban difficult sites.

[0032] The second aspect of the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for extracting the carbon sequestration potential of urban difficult sites when executing the program.

[0033] The third aspect of the present application provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the method for extracting the carbon sequestration potential of urban difficult sites.

[0034] The fourth aspect of the present application provides a computer program product comprising a computer program, wherein the computer program is executable by a processor to implement the method for extracting the carbon sequestration potential of urban difficult sites.

[0035] The present application has the following advantages and beneficial effects:

[0036] The method of the present application fills the gap in the research on the carbon sequestration potential of urban difficult sites, confirms the promoting effect of urban difficult sites on urban carbon sequestration, and provides an optimization direction for later urban ecological construction and urban low-carbon transformation.

[0037] The present application provides a method for obtaining the carbon sequestration potential of urban difficult sites by using remote sensing data, and provides a reference for spatial level carbon sequestration research. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The 2020 land use data of a certain city in Example 2 extracted in the range of the urban built-up area of the certain city in 2020 is reclassified into four categories (left) of green land, unused land, construction land and water body by using ArcGIS, and the NDVI data (right) obtained by vegetation inversion of Landsat8 remote sensing image in the range;

[0039] Figure 2 The range map of natural type urban difficult sites in the urban built-up area of the certain city in 2020 in Example 2;

[0040] Figure 3Figure 2 shows a schematic diagram of the transfer of 2020-2021 calculated using the land use transfer matrix method in the intersection module of ArcGIS for the reclassified data in Example 2 for 2020 and 2021;

[0041] Figure 4 Figure 4 shows a schematic diagram of the final urban difficult site range in 2020 in a certain city in Example 2;

[0042] Figure 5 Figure 5 shows the maximum synthesis results of NPP data in a certain city in Example 2 over 20 years (left) and the cumulative calculation results of all NPP data in 2020 (right);

[0043] Figure 6 Figure 6 shows the vegetation carbon sequestration potential data results of the urban built-up area in a certain city in 2020 in Example 2;

[0044] Figure 7 Figure 7 shows the vegetation carbon sequestration potential distribution of urban difficult sites in a certain city in 2020 in Example 2. DETAILED DESCRIPTION

[0045] To better understand the present application, the following examples are further illustrations of the present application, but the scope of the present application is not limited to the following examples.

[0046] Example 1

[0047] A method for extracting carbon sequestration potential of urban difficult sites

[0048] Step 1: Understand the definition of vegetation carbon sequestration potential. For urban difficult sites, vegetation carbon sequestration potential can be considered as the carbon sequestration space that can be improved after restoration.

[0049] Step 2: Select NPP data for calculating vegetation carbon sequestration potential. Use the current more advanced GLASS NPP data, which is sourced from the University of Maryland (http: / / glass.umd.edu / ). The data has a spatial resolution of 500m and a temporal resolution of 8 days. The product model fully considers the effects of atmospheric radiation transmission and surface evaporation during calculation, and the data is less affected by environmental errors. Use python to batch download the data, combine it with ENVI for splicing, and use ArcGIS software for re-projection to the WBG1984 coordinate system.

[0050] Third, determine the scope of urban difficult sites. According to the definition and causes of urban difficult sites, the large categories of urban difficult sites are determined, which are natural and artificial types. Secondly, according to the description of land categories in "Urban Land Classification and Planning and Construction Land Standards" (GB50137-2011) and "Land Management Law of the People's Republic of China (2019 Revision)", and reference to existing research on urban ecological site types, the types included in the secondary categories of urban difficult sites are determined. According to the cause type and site type, urban difficult sites are divided into two categories: artificial and natural, as shown in Table 1:

[0051] Table 1 Classification of optimized urban difficult sites

[0052]

[0053] Fourth, according to the different causes of natural and artificial types, different identification methods are selected:

[0054] Natural type urban difficult sites are distributed in each category of existing land use classification standards, and are identified and superimposed using existing land use classification data. The land use data used in this example comes from the National Glacier Permafrost Desert Science Data Center (http: / / www.ncdc.ac.cn). This dataset uses Landsat remote sensing images to provide 37 years of land cover type data in China. Through stable sample extraction and visual interpretation sample collection, training samples are constructed, and temporal indicators are obtained through a random forest classifier. The data quality is good. The data classifies land use into 9 categories, namely cultivated land, forest land, grassland, shrub land, construction land, unused land, water body, desert land, snow mountain glacier land and wetland. The unused land and desert land, snow mountain glacier land and wetland within the city are considered as natural type urban difficult sites.

[0055] Specifically, the type definition contained in the natural type urban difficult site is compared with the definition of nine types of land use classification in the data set, wherein the unused land refers to the land other than the agricultural land and the construction land, mainly including grassland, saline-alkali land, marsh land, sandy land, bare land, bare rock, etc. The unused land includes two secondary land classes, i.e., the unused land and other unused land. The unused land is divided into: 1) grassland. The tree coverage is less than 10%, the surface is soil, and weeds grow, excluding saline-alkali land, marsh land and bare land. 2) saline-alkali land. The surface is saline-alkali accumulation, and only natural salt-tolerant plants grow. 3) sandy land. The surface is covered with sand, and there is basically no vegetation, including desert, but excluding sand beach in water system. 4) bare land. The surface is soil, and there is basically no vegetation coverage. 5) bare rock land. The surface is rock or gravel, and the coverage area of the land is more than 50%. The other unused land is divided into: 1) other land. Other water area land not included in the agricultural land and the construction land. 2) river water surface. The land below the natural or artificial river water level shoreline. 3) lake water surface. The land below the natural water accumulation area water level shoreline. 4) reed land. The land where reeds grow, including reed land on the beach. 5) glacier and permanent snow. The land with the surface covered by ice and snow all year round. The land type in the natural type urban difficult site classification has a high degree of coincidence, the sandy land in the unused land is similar to the desert land type, but the desert land is separately divided in the land use data used in the application, and the desert land also meets the classification of the natural type urban difficult site, therefore, the unused land and the desert land, the snow mountain glacier land and the wetland in the urban built-up area are considered as the natural type urban difficult site. If the desert land, the snow mountain glacier land and the wetland cannot be identified in the urban built-up area, the part of the unused land is considered as the natural type urban difficult site. If all of them can be identified, all of them are considered as the natural type urban difficult site, which depends on the difference of the land use types in different urban built-up areas. Through practice test, the urban built-up area almost does not contain the desert land, the snow mountain glacier land and the wetland, therefore, the range of the unused land is mainly extracted.

[0056] To prevent the identification of part of the natural type urban difficult site and other areas from being missed, the NDVI is derived from the 30m Landsat 8 remote sensing image data extracted by the United States Geological Survey (https: / / earthexplorer.usgs.gov / ), and the low coverage grassland with NDVI less than 0.2 is supplemented as the natural type urban difficult site. The two parts are superimposed to form the range of the natural type urban difficult site.

[0057]

[0058] In the formula, NIR is the near-infrared band reflectance value, and R is the infrared band reflectance value

[0059] In the fifth step, the artificial urban difficult site is determined based on the artificial urban difficult site description, and the idle or negative land is repaired or controlled based on the construction land. After the ecological restoration, the land is mainly used for greening or surface remote sensing identification of green space. Therefore, the remote sensing identification of such land can be determined by using the land use data transfer situation of two consecutive times, and the land that is currently identified as green land is screened out. The land use data is still from the National Glacier Permafrost and Desert Science Data Center (http: / / www.ncdc.ac.cn). The cultivated land, forest land, grassland and shrub land are reclassified as green land. The transfer trend and transfer amount of construction land and green land are calculated by the land use transfer matrix. The part of the construction land transferred to the green land is identified as the artificial difficult site.

[0060] Specifically, the urban green space refers to various green spaces in the urban planning area, which is a general term for the land, open space and water body covered by vegetation in the urban planning area. In the present application, the artificial urban difficult site is identified as the green land that was once construction land, and does not need to be further analyzed for each type of green land. Therefore, considering the difference in image interpretation, the overall spatial pattern analysis is facilitated, the four types of cultivated land, forest land, grassland and shrub land in the data classification are reclassified as green land, the construction land and water body in the data classification are still identified as construction land and water body, and the remaining categories are classified as natural urban difficult sites as described above. Finally, the nine types of land use in the original data are reclassified as four categories of green land, construction land, natural urban difficult site and water body, and the overall transfer trend is analyzed. The transfer trend and transfer amount of construction land and green land are calculated by the land use transfer matrix, and the land use transfer matrix is based on the land use change information mining method of the transfer matrix.

[0061] Table 2 Land use transfer matrix

[0062]

[0063] In the table, the row represents the land use type at T1, and the column represents the land use type at T2. ij P represents the percentage of the area of land type i converted to land type j during T1-T2 to the total land area. ii P represents the percentage of the area of i land use type remaining unchanged during T1-T2. i+ P represents the total area percentage of land type i at T1. +j P represents the total area percentage of i land use type at T2. i+ P iiis the percentage of the area of land class i that decreased during T1-T2; P +j is the percentage of the area of land class i that decreased during T1-T2; P jj is the percentage of the area of land class j that increased during T1-T2.

[0064] The land use data obtained from the National Glacier Permafrost Desert Science Data Center in 2020 and 2021 was imported into Arc GIS 10.7 software, and the data was cropped according to the study area using the "crop" function. The land use classification was reclassified into four categories: green space, construction land, unused land, and water body using the "reclassify" tool. The raster data was converted to vector data using the "raster to face" tool. The 2020 reclassified data and the 2021 land use transfer were overlaid and analyzed using the "intersection" tool. The part of the construction land that was transferred to green space was identified as artificial difficult site in 2020.

[0065] After the preliminary steps were completed, the attribute table of the data was opened in Arc GIS 10.7, and the area calculation of "construction land-green space" was added to the category. The area of construction land transferred to green space from 2020 to 2021 was calculated, which was the area of artificial urban difficult site in 2020.

[0066] In the sixth step, the identified natural and artificial urban difficult site ranges were merged to obtain the complete urban difficult site range.

[0067] In the seventh step, NPP data was used to calculate the vegetation carbon sequestration potential. The GLASS NPP data of consecutive years was used as the basis data. The maximum value synthesis method was used to synthesize the NPP data of each period of 8 days in consecutive years. The synthesized data was then accumulated to obtain the optimal NPP value that could be achieved under the constraints of climate conditions and human activity conditions in the continuous year period. The result was then subtracted from the NPP annual accumulation value of the reference year (usually the end year of the consecutive years) to obtain the NPP improvement space value, which was the vegetation carbon sequestration potential data of the urban difficult site. The calculation formula is as follows:

[0068] CSP CUS = NPP max -NPP t (2)

[0069] In the formula, CSP CUS is the urban difficult site vegetation carbon sequestration potential; NPP max is the maximum value synthesis accumulation value of NPP calculated from consecutive years data; NPP t is the NPP annual accumulation value of the reference year t.

[0070] Step 8, extract the urban difficult site vegetation carbon sequestration potential range using the urban difficult site range. Use ArcGIS software to perform mask extraction operation on the urban difficult site range and the calculated vegetation carbon sequestration potential data, and extract the vegetation carbon sequestration potential in the range.

[0071] Example 2

[0072] As Figure 1 shown, Figure 1 The left figure is the land use data of a certain city in 2020 extracted from the urban built-up area range of a certain city in 2020. After extraction using the urban built-up area range, the land use types in the built-up area range of a certain city in 2020 are seven types of cultivated land, forest land, grassland, shrub land, unused land, construction land and water body. Desert and snow-capped mountain and wetland are not included. Therefore, ArcGIS is used for reclassification into four types of green land, unused land (after superimposing low coverage grassland, natural urban difficult site), construction land and water body.

[0073] Figure 1 The right is the NDVI data obtained by vegetation inversion of Landsat8 remote sensing image in this range, with a value range of-1-1.

[0074] Use the attribute extraction module in ArcGIS to extract unused land and grassland. Extract the part with NDVI value less than 0.2 and overlap with grassland. Use the mask extraction method to screen out low coverage grassland with NDVI<0.2, and perform inlay operation with unused land to a new grid to synthesize the overall urban built-up area natural urban difficult site range of a certain city in 2020, as shown in Figure 2 .

[0075] For the extraction of artificial urban difficult site of a certain city in 2020, 2020 and 2021 land use classification data of a certain city are needed. First, reclassify the 2020-2021 land use data into four types of green land, unused land, construction land and water body. The part transferred from construction land to green land is artificial urban difficult site. Then, use the land use transfer matrix method to calculate the transfer of 2020-2021 in the intersection module of ArcGIS, as shown in Figure 3 .

[0076] Use the attribute extraction in ArcGIS to extract the part of construction land converted to green land, and merge it with the natural urban difficult site layer to finally obtain the urban difficult site range of a certain city in 2020, as shown in Figure 4 .

[0077] After calculating the extent of urban hardship sites in 2020, the carbon sequestration potential of vegetation on urban hardship sites in a certain city was calculated using MODIS NPP data from 2000 to 2020. Data for each of the eight days from 2000 to 2020 was extracted in batches, and batch preprocessing and projection were performed using ENVI. First, a mask was extracted based on the urban built-up area of ​​the city. Then, the maximum values ​​of the NPP data over the 20 years were synthesized. The results are as follows. Figure 5 As shown on the left. The result of summing up the NPP data for all periods over 20 years is as follows. Figure 5 As shown on the right, the figure shows that the maximum NPP value and the cumulative NPP value over 20 years are mainly distributed on the edge of the urban built-up area.

[0078] Using calculation formulas to Figure 5 The difference between the two results was calculated to obtain the data on the carbon sequestration potential of vegetation in the urban built-up area of ​​a certain city in 2020, such as... Figure 6 As shown:

[0079] The results were used as a mask to extract data on the carbon sequestration potential of vegetation in urban difficult sites in a certain city in 2020. For example... Figure 7 The figure shows the distribution of carbon sequestration potential of vegetation in challenging urban sites in a certain city in 2020.

[0080] Example 3

[0081] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for extracting carbon sequestration potential from challenging urban sites.

[0082] Example 4

[0083] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for extracting carbon sequestration potential from challenging urban sites.

[0084] Example 5

[0085] A computer program product includes a computer program that, when executed by a processor, implements the method for extracting the carbon sequestration potential of urban challenging sites.

[0086] The above description is merely a preferred embodiment of the present invention, and should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for extracting carbon sequestration potential in urban difficult sites, characterized by, The method for extracting the carbon sequestration potential of vegetation in urban difficult sites based on GLASS NPP data comprises the following steps: determining and identifying the range of urban difficult sites; splicing and re-projecting the GLASS NPP data to the WBG1984 coordinate system; taking the GLASS NPP data of consecutive years as the basic data, calculating the optimal NPP value that can be achieved under the restriction of climate conditions and human activity conditions in the consecutive years, and then calculating the difference between the optimal NPP value and the annual cumulative value of NPP in the base year to obtain the vegetation carbon sequestration potential data of the urban difficult sites in the base year; performing the mask extraction operation on the spliced GLASS NPP data according to the determined range of urban difficult sites to determine the vegetation carbon sequestration potential in the urban difficult sites; the specific method for determining and identifying the range of urban difficult sites is as follows: according to different cause types and site types, the urban difficult sites are divided into two categories, i.e. natural type urban difficult sites and artificial type urban difficult sites; integrating the natural type urban difficult sites and the artificial type urban difficult sites with the existing land use classification data, identifying the natural type urban difficult sites by using the remote sensing image of the existing land use classification data, and identifying the artificial type urban difficult sites according to whether the construction land needs to be transferred as the standard combined with the remote sensing image; further supplementing the low-coverage grassland with NDVI<0.2 as the newly divided natural type urban difficult sites by means of the remote sensing image data inversion NDVI; combining the identified range of the natural type urban difficult sites and the artificial type urban difficult sites to obtain the complete range of urban difficult sites.

2. The method of urban difficult site carbon sequestration potential extraction according to claim 1, characterized in that, The natural type urban difficult sites include saline-alkali land, grassland, sand land, bare land, bare rock and gravel land, glacier and permanent snow, damaged wetland or water area; the artificial type urban difficult sites include industrial relocation land, idle land under control, landfill, and building green space.

3. The method of urban difficult site carbon sequestration potential extraction according to claim 1, characterized in that, The existing land use classification data comes from the National Glacier and Permafrost Desert Scientific Data Center, which classifies the land use into arable land, forest land, grassland, shrub land, construction land, unused land, water body, desert land, snow-capped mountain and glacier land, and wetland. The unused land, desert land, snow-capped mountain and glacier land, and wetland in the urban area are considered as the natural type urban difficult sites. The natural type urban difficult sites are identified and superimposed by using the existing land use classification data, and the low-coverage grassland with NDVI<0.2 is supplemented as the natural type urban difficult sites by means of the Landsat 8 remote sensing image data inversion NDVI, so as to form the range of the natural type urban difficult sites.

4. The method of urban difficult site carbon sequestration potential extraction according to claim 1, characterized in that, The land use transfer data of two consecutive years are used to determine and screen the land blocks that are currently identified as green land but were construction land, i.e. the artificial type urban difficult sites. Specifically, the land use data used in the artificial type urban difficult site identification method comes from the National Glacier Permafrost and Desert Science Data Center, which classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water body, desert land, snow-capped mountain and glacier land, and wetland. The cultivated land, forest land, grassland and shrub land in the data classification are reclassified as green land, the construction land and water body in the data classification are still identified as construction land and water body, and the remaining categories are classified as natural type urban difficult site. Finally, the nine types of land use in the original data are reclassified into four categories: green land, construction land, natural type urban difficult site and water body. The transfer trend and transfer amount of construction land and green land are calculated through the land use transfer matrix. The part transferred from construction land to green land is identified as artificial type difficult site.

5. The method of urban difficult site carbon sequestration potential extraction of claim 1, wherein, ENVI is used for data splicing, and ArcGIS software is used for data re-projection.

6. The method of urban difficult site carbon sequestration potential extraction according to claim 1, characterized in that, The specific method for calculating the optimal value of NPP that can be achieved under the restriction of climate conditions and human activity conditions in a continuous year period is as follows: taking the GLASS NPP data of continuous years as the basic data, the maximum value synthesis method is used to synthesize the NPP data of each period corresponding to the continuous years, and then the synthesized data is accumulated to obtain the optimal value of NPP that can be achieved under the restriction of climate conditions and human activity conditions in a continuous year period. The result is the value of the NPP promotion space of the base year, which is the vegetation carbon sequestration potential data of urban difficult site.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that: The processor executes the program to realize the method for extracting the carbon sequestration potential of urban difficult site according to any one of claims 1-6.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the method for extracting the carbon sequestration potential of urban difficult site according to any one of claims 1-6.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the method for extracting the carbon sequestration potential of urban difficult site according to any one of claims 1-6.

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