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

Abstract

The invention relates to the technical field of remote sensing, in particular to a method, medium and equipment for extracting the carbon sequestration potential of an urban difficult site, and the method comprises the following steps: determining and recognizing the range of the urban difficult site; splicing and re-projecting the GLASS NPP data, and projecting the GLASS NPP data to a WBG1984 coordinate system; gLASS NPP data of continuous years are used as basic data, an NPP optimal value which can be achieved under the limitation of climate conditions and human activity conditions in the time period of the continuous years is calculated, difference value calculation is carried out on the result and an NPP inter-year accumulated value of the reference year, and vegetation carbon sequestration potential data of the urban difficult site of the reference year are obtained; and performing mask extraction operation on the spliced data by taking the determined urban difficult site range as a base map range, and determining the vegetation carbon sequestration potential in the urban difficult site range. The invention provides the method for acquiring the carbon sequestration potential of the urban difficult site by using remote sensing data, and reference is provided for spatial-level carbon sequestration research.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing, and particularly to a method, medium and device for extracting the carbon sequestration potential of difficult urban sites. Background Art

[0002] Wang Yaqing (2020) et al. found that for every 0.095% increase in urbanization, energy consumption increases by 1%, and carbon emissions also increase accordingly. Therefore, in addition to controlling the carbon emissions from human activities, increasing the urban green space area and improving the vegetation coverage rate are important steps for carbon sequestration. However, due to the existing urban planning, it is difficult to plan new spaces for urban green areas. Therefore, the importance of reusing "difficult urban sites" such as unused land and relocated sites has become increasingly prominent. Studying the carbon sequestration capacity of difficult urban sites helps the low-carbon transformation of cities and creates an environment for harmonious coexistence between humans and nature.

[0003] Currently, carbon emission reduction measures include aspects such as fossil energy, renewable energy, and vegetation carbon sequestration. China has rich vegetation resources, and studying its carbon sequestration effect plays an important role in promoting green development. Through this research, vegetation management can be optimized, the ecological environment can be improved, and references for sustainable development policies can be provided.

[0004] Currently, research on the evaluation of vegetation carbon sequestration effects mainly focuses on aspects such as carbon sequestration capacity evaluation, the relationship between biodiversity and carbon sequestration, climate change assessment, and analysis of the impact of land use change. The above research covers the current hot topics regarding vegetation carbon sequestration, and the above mainly analyzes the corresponding impacts of the existing vegetation carbon sequestration capacity. There is relatively little research on the prediction of vegetation carbon sequestration. Vegetation carbon sequestration potential evaluates the size and saturation degree of the carbon sequestration volume, providing prediction and indication functions for subsequent carbon sequestration research.

[0005] Existing research on carbon sequestration mainly uses the CASA model and the Miami model. Both are more suitable for large-scale spatial NPP (Net Primary Productivity of Vegetation) research, referring to the influence of natural factors such as precipitation, temperature, and sunlight. For the situation where the natural factors within the urban built-up area have small differences, the data may lack variability.

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

[0007] Vegetation net primary productivity (NPP) refers to the remaining part after subtracting autotrophic respiration from the total amount of organic matter produced by plants through photosynthesis, and it is the most direct and significant parameter characterizing the carbon sequestration ability of vegetation. As an important source of urban greening, the increase in vegetation coverage after ecological restoration in urban difficult sites can enhance the urban carbon sink. The potential for improving the vegetation carbon sequestration ability is high, which helps in predicting the path of urban low-carbon transformation. Therefore, the present invention proposes a method for extracting the vegetation carbon sequestration potential of urban difficult sites based on GLASS NPP data.

[0008] On the one hand, the present invention provides a method for extracting the carbon sequestration potential of urban difficult sites, which extracts the vegetation carbon sequestration potential of urban difficult sites based on GLASS NPP data, and includes the following steps:

[0009] Determine and identify the scope of urban difficult sites;

[0010] Mosaic and reproject the GLASS NPP data and project it onto the WBG1984 coordinate system;

[0011] Using the GLASS NPP data of consecutive years as the basic data, calculate the optimal value of NPP that can be achieved under the limitations of climatic conditions and human activities during the period of consecutive years, and calculate the difference between this result and the annual cumulative value of NPP in the reference year to obtain the vegetation carbon sequestration potential data of urban difficult sites in the reference year;

[0012] Perform extraction by mask operation on the mosaicked GLASS NPP data with the determined scope of urban difficult sites as the base map scope to determine the vegetation carbon sequestration potential within the scope of urban difficult sites.

[0013] Reference year: The reference year for comparison, which is any year in history that can obtain quantitative data or the historical average of several years, and can be used for comparative analysis and prediction of historical data. In the present invention, the reference point refers to the end year of consecutive years. After determining the research reference year, select the required research period, and the duration can be changed according to the research. (For example, to study the vegetation carbon sequestration potential from 2000 to 2020, taking 2020 as the reference 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 of 2020).

[0014] Annual cumulative value of NPP: Accumulate each period of GLASS NPP data in the reference year one by one, and the obtained result is the annual cumulative value of that year.

[0015] Preferably, the specific method for determining and identifying the scope of urban difficult sites is: According to different cause types and site types, divide urban difficult sites into two major categories: natural urban difficult sites and artificial urban difficult sites;

[0016] Integrate the natural and artificial types of difficult urban sites with the existing land use classification data respectively. Use the remote sensing images of the existing land use classification data to identify natural difficult urban sites, and identify artificial difficult urban sites in combination with remote sensing images according to whether construction land transfer is required;

[0017] Furthermore, invert NDVI through remote sensing image data, and supplement low-coverage grasslands with NDVI < 0.2 as newly classified natural difficult urban sites;

[0018] Merge the identified ranges of natural and artificial difficult urban sites to obtain the complete range of difficult urban sites.

[0019] Natural difficult urban sites: Ecological land where the site conditions are dominated by natural factors such as climate and geology, causing obstacles to plant growth. Refer to "Methods and Practices for the Construction of Ecological Gardens in Difficult Urban Sites" edited by Zhang Lang.

[0020] Artificial difficult urban sites: Ecological land where the site conditions are dominated by human interference factors such as engineering construction, land use type conversion, and pollutant emissions, causing obstacles to plant growth or severely damaging the ecosystem function.

[0021] Construction land transfer: Whether the land use type has been affected by human construction activities and has become construction land, and it is repaired on the basis of construction land. In the present invention, it specifically refers to the situation where it is currently identified as green land but was previously construction land.

[0022] Preferably, the types of natural difficult urban sites include saline-alkali land, waste grassland, sandy land, bare land and bare rock gravel land, glaciers and permanent snow, damaged wetlands or water areas; the types of artificial difficult urban sites include industrial relocation sites, controlled idle land, landfill sites, and building greening spaces.

[0023] Preferably, the existing land use classification data comes from the National Science Data Center for Glaciers, Permafrost and Deserts. This data 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 unused land, desert land, snow mountain glacier land and wetland within the urban area are considered as natural difficult urban sites. Use the existing land use classification data to identify and overlay the types of natural difficult urban sites. Furthermore, invert NDVI through Landsat 8 remote sensing image data, and supplement low-coverage grasslands with NDVI < 0.2 as natural difficult urban sites. The two parts are overlaid to form the range of natural difficult urban sites.

[0024] Preferably, invert NDVI according to Landsat 8 remote sensing image data to obtain low-coverage grassland data with NDVI < 0.2.

[0025] In the present invention, 30m Landsat 8 remote sensing image data extracted by the United States Geological Survey is used to invert NDVI.

[0026] Preferably, the delineation is carried out by using the land use transfer data of two consecutive time periods, and the plots that are currently identified as green land by remote sensing but were formerly construction land are screened, that is, they are identified as artificial urban difficult sites.

[0027] Specifically, the land use data used in the identification method of artificial urban difficult sites comes from the National Data Center for Glaciology, Cryopedology and Desert Science. This data 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 urban difficult sites. Finally, the original nine land use types are reclassified into four categories: green land, construction land, natural urban difficult sites and water body. The transfer trend and transfer volume of construction land and green land are calculated through the land use transfer matrix, and the part transferred from construction land to green land is identified as artificial difficult sites.

[0028] Preferably, the identification method of artificial urban difficult sites is as follows: the delineation is carried out by using the land use transfer data of two consecutive time periods, and the plots that are currently identified as green land by remote sensing but were formerly construction land are screened, that is, they are identified as artificial urban difficult sites. The land use data used in the identification method of artificial urban difficult sites comes from the National Data Center for Glaciology, Cryopedology and Desert Science. This data 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 urban difficult sites. Finally, the original nine land use types are reclassified into four categories: green land, construction land, natural urban difficult sites and water body. The transfer trend and transfer volume of construction land and green land are calculated through the land use transfer matrix, and the part transferred from construction land to green land is identified as artificial difficult sites.

[0029] Land use transfer data: refers to the data situation that is currently identified as green land but was formerly construction land.

[0030] Preferably, ENVI is used to splice the data, and ArcGIS software is used to reproject the data.

[0031] Preferably, the specific method for calculating the optimal NPP value that can be achieved under the constraints of climate conditions and human activities within a continuous year time period is as follows: Using the GLASS NPP data of continuous years as the basic data, the maximum value synthesis method is used to synthesize the maximum value of each period of NPP data corresponding to continuous years, and then the synthesized data is accumulated and calculated. The result obtained is the optimal NPP value that can be achieved under the constraints of climate conditions and human activities within a continuous year time period. The difference between this result and the annual cumulative value of NPP in the base year is calculated, and the result obtained is the value of the NPP improvement space in the base year, which is the vegetation carbon sequestration potential data of urban difficult sites.

[0032] The second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method for extracting the carbon sequestration potential of urban difficult sites as described above is implemented.

[0033] The third aspect of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for extracting the carbon sequestration potential of urban difficult sites as described above is implemented.

[0034] The fourth aspect of the present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for extracting the carbon sequestration potential of urban difficult sites as described above is implemented.

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

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

[0037] The present invention provides a method for obtaining the carbon sequestration potential of urban difficult sites by using remote sensing data, providing a reference for carbon sequestration research at the spatial level. Description of the Drawings

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

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

[0040] Figure 3Schematic diagram of calculating the transfer situation from 2020 to 2021 using the land use transfer matrix method for the reclassified data of 2020 and 2021 in Example 2 in the Intersect module of ArcGIS;

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

[0042] Figure 5 For the NPP data of a certain city in 20 years in Example 2, the maximum value synthesis result (left) and the cumulative calculation result of all period NPP data in 2020 (right);

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

[0044] Figure 7 Distribution of the vegetation carbon sequestration potential in the urban difficult sites of a certain city in 2020 in Example 2. Detailed implementation method

[0045] For a better understanding of the present invention, the following examples are further descriptions of the present invention, but the content of the present invention is not limited to the following examples only.

[0046] Example 1

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

[0048] The first step is to understand the definition of vegetation carbon sequestration potential. For urban difficult sites, the vegetation carbon sequestration potential can be considered as the carbon sequestration space that can be improved after the restoration of this area.

[0049] The second step is to select the NPP data for calculating the vegetation carbon sequestration potential. Use the currently relatively advanced GLASS NPP data, which is sourced from the University of Maryland, USA (http: / / glass.umd.edu / ). The spatial resolution of this data is 500m, and the temporal resolution is an 8-day synthesis. The product model fully considers the influence 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, splice the data in combination with ENVI, and reproject the data using ArcGIS software to the WBG1984 coordinate system.

[0050] Step 3: Determine the scope of urban difficult sites. According to the definition and causes of urban difficult sites, the major categories of urban difficult sites are clarified, which are natural type and artificial type. Secondly, a comparison is made based on the descriptions of land types in the "Standard for Classification of Urban Land Use and Planning Construction Land" (GB50137-2011) and the "Land Administration Law of the People's Republic of China (Revised in 2019)", and the types included in the secondary categories of urban difficult sites are determined by referring to the urban ecological site types in existing research. According to the cause types and site types, urban difficult sites are divided into two major categories: artificial type and natural type, as shown in Table 1:

[0051] Table 1 Classification of Optimized Urban Difficult Sites

[0052]

[0053] Step 4: Select different identification methods according to the different causes of natural type and artificial type:

[0054] The distribution of natural type urban difficult site types is in each major category of the existing land use classification standard. The existing land use classification data is used for identification and overlay. In this embodiment, the land use data selected comes from the National Data Center for Glaciology, Cryopedology and Desertology (http: / / www.ncdc.ac.cn). This dataset uses Landsat remote sensing images to provide data on China's land cover types over 37 years. Training samples are constructed by extracting stable samples and collecting visual interpretation samples, and temporal indicators are constructed to obtain results through a random forest classifier. The data quality is good. This 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, desert land, snow mountain glacier land and wetland within the urban scope are considered natural type urban difficult sites.

[0055] Specifically, the type definitions included in the natural urban difficult sites are compared with the definitions of the nine land use classifications in the dataset. Unused land refers to land other than agricultural land and construction land, mainly including waste grassland, saline-alkali land, marshland, sandy land, bare land, bare rock, etc. It includes two secondary land types: unused land and other unused land. Unused land is further divided into: ① Waste grassland. The tree canopy density is <10%, the surface layer is soil, and weeds grow, excluding saline-alkali land, marshland, and bare land. ② Saline-alkali land. The surface layer has saline-alkali accumulation and only grows natural salt-tolerant plants. ③ Sandy land. The surface layer is covered with sand and has basically no vegetation, including deserts, excluding the sandy beaches in water systems. ④ Bare land. The surface layer is soil and has basically no vegetation cover. ⑤ Bare rock and gravel land. The surface layer is rock or gravel, and the covered area >50%. Other unused land is further divided into: ① Other land. Other water areas not included in agricultural land and construction land. ② River water surface. The land below the normal water level shoreline of a naturally formed or artificially excavated river. ③ Lake water surface. The land below the normal water level shoreline of a naturally formed water accumulation area. ④ Reed land. Land where reeds grow, including reed land on the beach. ⑤ Glaciers and permanent snow cover. Land whose surface is covered by ice and snow all year round. It has a relatively high overlap with the site types in the classification of natural urban difficult sites. The sandy land in unused land is similar to the desert land type, but the desert land is separately classified in the land use data used in the present invention, and the desert land also conforms to the classification of natural urban difficult sites. Therefore, the unused land, desert land, snow-capped mountain glacier land, and wetland within the city are considered as natural urban difficult sites. If no desert land, snow-capped mountain glacier land, and wetland are identified within the urban built-up area, the unused land part is considered as natural urban difficult sites. If all three can be identified, they are all considered as natural urban difficult sites, depending on the differences in land use types in different urban built-up areas. Through practical tests, the urban built-up area hardly contains desert land, snow-capped mountain glacier land, and wetland. Therefore, the scope of unused land is mainly extracted.

[0056] To prevent the omission of the identification of some areas such as natural urban difficult sites, the 30m Landsat 8 remote sensing image data extracted by the United States Geological Survey (https: / / earthexplorer.usgs.gov / ) is used to invert NDVI, and the low-coverage grassland with NDVI < 0.2 is supplemented as natural urban difficult sites. The two parts are superimposed to form the scope of natural urban difficult sites.

[0057]

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

[0059] Step 5: Referring to the description of artificial urban difficult sites, determine the plots that are based on construction land and have become idle or have a negative impact on the surrounding environment after restoration or control, and are mainly used for greening or areas identified as green spaces by surface remote sensing after ecological restoration. Therefore, for the remote sensing identification of such land, the transfer of land use data at two consecutive times can be used to delimit it. Screen the plots that were construction land but are currently identified as green spaces by remote sensing, that is, identify them as artificial urban difficult sites. The land use data used still comes from the National Science Data Center for Glaciers, Permafrost and Deserts (http: / / www.ncdc.ac.cn). Reclassify cultivated land, forest land, grassland, and shrub land as green spaces, calculate the transfer trend and transfer volume between construction land and green spaces through the land use transfer matrix, and the part transferred from construction land to green spaces is identified as artificial difficult sites.

[0060] Specifically, in a broad sense, urban green spaces refer to various green spaces within the urban planning area, which are the general term for the land covered by vegetation, open spaces, and water bodies within the urban planning area. In the present invention, the identification method for artificial urban difficult sites is that the areas currently identified as green spaces were once construction land, and there is no need for further targeted analysis of various types within the green spaces. Therefore, considering the differences in image interpretation and facilitating the analysis of the overall spatial pattern, the four types of cultivated land, forest land, grassland, and shrub land in the data classification are reclassified as green spaces, and the construction land and water bodies in the data classification are still identified as construction land and water bodies. The remaining categories are classified as natural urban difficult sites as described above. Finally, the nine land use types in the original data are reclassified into four categories: green spaces, construction land, natural urban difficult sites, and water bodies, and the overall transfer trend is tested and analyzed. Calculate the transfer trend and transfer volume between construction land and green spaces through the land use transfer matrix, and explore the method of mining land use change information based on the transfer matrix. Its calculation method is shown in Table 2 below.

[0061] Table 2 Land Use Transfer Matrix

[0062]

[0063] In the table: The rows represent the land use types at time point T1, and the columns represent the land use types at time point T2. P ij represents the percentage of the area where land type i is converted to land type j during the period from T1 to T2 in the total land area; P ii represents the percentage of the area where the i-th land use type remains unchanged during the period from T1 to T2. P i+ represents the total percentage of land type i at time point T1. P +j represents the total percentage of the i-th land use type at time point T2. P i+ -P iiis the percentage of the reduction in the area of land use type i during the period from T1 to T2; P +j -P jj is the percentage of the increase in the area of land use type j during the period from T1 to T2.

[0064] Import the land use data of 2020 and 2021 obtained from the National Data Center for Glaciology, Cryopedology and Desert Research into ArcGIS 10.7 software, and clip the data according to the study area using the "Clip" function; reclassify the land use classification into four categories: green land, construction land, unused land, and water bodies using the "Reclassify" tool; convert the raster data into vector data using the "Raster to Polygon" tool; perform a land use transfer overlay analysis on the reclassified data in 2020 and 2021 using the "Intersect" tool. The part transferred from construction land to green land is identified as the artificial type of difficult-to-plant sites in 2020.

[0065] After the previous steps are completed, open the attribute table of the data in ArcGIS 10.7, add area calculation to the category of "construction land - green land", and the area transferred from construction land to green land from 2020 to 2021 can be obtained. This area is the area of artificial type of urban difficult-to-plant sites in 2020.

[0066] Step 6: Merge the identified natural and artificial types of urban difficult-to-plant site ranges to obtain the complete urban difficult-to-plant site range.

[0067] Seventh, calculate the vegetation carbon sequestration potential using NPP data. Using the GLASS NPP data of consecutive years as the basic data, the maximum value synthesis method is used to synthesize the maximum value of each 8-day period of NPP data corresponding to consecutive years, and then the synthesized data is accumulated. The result obtained is the optimal value of NPP that can be achieved under the constraints of climate conditions and human activities during the consecutive year time period. Calculate the difference between this result and the annual cumulative value of NPP in the reference year (usually the end year of consecutive years). The result obtained is the value of the NPP improvement space in the reference year, which is the vegetation carbon sequestration potential data of urban difficult-to-plant sites. The calculation formula is as follows:

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

[0069] In the formula: CSP CUS is the vegetation carbon sequestration potential of urban difficult-to-plant sites; NPP max is the cumulative value of the maximum value synthesis of NPP calculated from consecutive year data; NPP t is the annual cumulative value of NPP in the reference year t.

[0070] Step 8: Use the urban difficult site range to extract the urban difficult site vegetation carbon sequestration potential range. Use ArcGIS software to perform a masking extraction operation on the urban difficult site range and the calculated vegetation carbon sequestration potential data to extract the vegetation carbon sequestration potential within 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 the city in 2020. After extracting using the urban built-up area range, the land use types within the built-up area of the city in 2020 are seven types: cultivated land, forest land, grassland, shrub land, unused land, construction land, and water body, excluding desert land, snow-capped glacier land, and wetlands. Therefore, using ArcGIS reclassification, it is divided into four types: green land, unused land (natural-type urban difficult sites after overlaying low-coverage grassland), construction land, and water body.

[0073] Figure 1 The right figure is the NDVI data obtained after vegetation inversion of the Landsat8 remote sensing image within this range, with a value range between -1 and 1.

[0074] Use the extraction by attribute module in ArcGIS to extract unused land and grassland; extract the part where the NDVI value is less than 0.2 and overlaps with the grassland, and use the masking extraction method to screen out the low-coverage grassland with NDVI < 0.2, and perform a mosaic to new raster operation with the unused land to synthesize the overall natural-type urban difficult site range of the urban built-up area of a certain city in 2020, as Figure 2 shown

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

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

[0077] After calculating the scope of urban difficult sites in 2020, the carbon sequestration potential of vegetation in urban difficult sites of a certain city in 20 years was calculated using MODIS NPP data from 2000 to 2020. Batch extract the data of each 8-day period from 2000 to 2020, and use ENVI for batch preprocessing and projection. First, perform mask extraction according to the scope of the urban built-up area of the city, and perform maximum value synthesis on the NPP data in 20 years. The result is as Figure 5 shown on the left. Accumulate and calculate the NPP data of all periods in 20 years, and the result is as Figure 5 shown on the right. It can be seen from the figure that both the maximum value of NPP and the accumulated value of NPP in 20 years are mainly distributed at the edge of the urban built-up area.

[0078] Use the calculation formula to calculate the difference between the two results of Figure 5 , and obtain the carbon sequestration potential data of the vegetation in the urban built-up area of a certain city in 2020, as Figure 6 shown:

[0079] Extract the carbon sequestration potential data of the vegetation in the urban difficult sites with the scope of the urban difficult sites in 2020 of a certain city as the mask. As Figure 7 shown, it is the distribution of the carbon sequestration potential of the vegetation in the urban difficult sites of a certain city in 2020.

[0080] Example 3

[0081] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method for extracting the carbon sequestration potential of urban difficult sites is implemented.

[0082] Example 4

[0083] A non-transitory computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method for extracting the carbon sequestration potential of urban difficult sites is implemented.

[0084] Example 5

[0085] A computer program product includes a computer program. When the computer program is executed by a processor, the method for extracting the carbon sequestration potential of urban difficult sites is implemented.

[0086] The above is the preferred implementation manner of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. It should be pointed out that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and changes can be made, and these improvements and changes are also regarded as the protection scope of the present invention.

Claims

1. A method for extracting carbon sequestration potential in difficult urban sites, characterized in that: Extracting the carbon sequestration potential of vegetation in difficult urban sites based on GLASS NPP data includes the following steps: Determine and identify the areas of difficult urban sites; The GLASS NPP data were spliced ​​and reprojected to the WBG1984 coordinate system; Using the GLASS NPP data of consecutive years as the basic data, the optimal NPP value that can be achieved under the constraints of climate conditions and human activity conditions in the consecutive years is calculated, and the difference between the result and the interannual cumulative value of NPP in the base year is calculated to obtain the vegetation carbon sequestration potential data of urban difficult sites in the base year; The mask extraction operation was performed on the spliced ​​GLASS NPP data using the determined urban difficult site range as the base map range to determine the carbon sequestration potential of vegetation within the urban difficult site range.

2. The method for extracting carbon sequestration potential of difficult urban sites according to claim 1, characterized in that: The specific method to determine and identify the scope of urban difficult sites is as follows: according to different causes and site types, urban difficult sites are divided into two categories: natural urban difficult sites and artificial urban difficult sites; Integrate the natural urban difficult sites and artificial urban difficult sites with the existing land use classification data, use the remote sensing images of the existing land use classification data to identify the natural urban difficult sites, and use the remote sensing images to identify the artificial urban difficult sites based on whether the construction land needs to be transferred. Furthermore, NDVI was inverted through remote sensing image data, and low-coverage grasslands with NDVI < 0.2 were added as newly classified natural urban difficult sites; The identified natural urban difficult sites and artificial urban difficult sites are merged to obtain the complete urban difficult site range.

3. The method for extracting carbon sequestration potential of difficult urban sites according to claim 2, characterized in that: Natural urban difficult site types include saline-alkali land, wasteland, sandy land, bare land and bare rock and gravel, glaciers and permanent snow, damaged wetlands or waters; artificial urban difficult site types include industrial relocation sites, controlled idle land, landfills, and building green spaces.

4. The method for extracting carbon sequestration potential of difficult urban sites according to claim 2, characterized in that: The existing land use classification data comes from the National Glacier, Frozen Soil and Desert Science Data Center, which classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water bodies, desert land, snow-capped mountain and glacier land, and wetland. Unused land, desert land, snow-capped mountain and glacier land, and wetland within the urban area are considered to be natural urban difficult sites. The existing land use classification data are used to identify and superimpose the types of natural urban difficult sites, and the NDVI is further inverted through the Landsat 8 remote sensing image data, and low-coverage grassland with NDVI < 0.2 is supplemented as a natural urban difficult site. The two parts are superimposed to form the scope of natural urban difficult sites.

5. The method for extracting carbon sequestration potential of difficult urban sites according to claim 2, characterized in that: The land use transfer data of two consecutive periods are used for delineation, and the plots currently identified as green land by remote sensing but once used as construction land are selected, that is, they are identified as artificial urban difficult sites; Specifically, the land use data used in the identification method of artificial urban difficult sites comes from the National Glacier, Frozen Soil and Desert Science Data Center, which classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water bodies, 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 bodies in the data classification are still identified as construction land and water bodies, and the remaining categories are classified as natural urban difficult sites. Finally, the nine land use types in the original data are reclassified into four categories: green land, construction land, natural urban difficult sites, and water bodies. The transfer trend and transfer amount of construction land and green land are calculated through the land use transfer matrix, and the part transferred from construction land to green land is identified as artificial difficult site.

6. The method for extracting carbon sequestration potential of difficult urban sites according to claim 1, characterized in that: ENVI was used to stitch the data and ArcGIS software was used to reproject the data.

7. The method for extracting carbon sequestration potential of difficult urban sites according to claim 1, characterized in that: The specific method for calculating the optimal NPP value that can be achieved under the constraints of climate conditions and human activity conditions in a period of consecutive years is as follows: using the GLASS NPP data of consecutive years as the basic data, the maximum value synthesis method is used to synthesize the NPP data of each period corresponding to the consecutive years, and then the synthesized data are accumulated and calculated. The result is the optimal NPP value that can be achieved under the constraints of climate conditions and human activity conditions in the period of consecutive years. The result is calculated by difference with the interannual accumulated value of NPP in the base year, and the result is the value of the NPP improvement space in the base year, that is, the vegetation carbon sequestration potential data of difficult sites in cities.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for extracting carbon sequestration potential of difficult urban sites as described in any one of claims 1 to 7 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for extracting carbon sequestration potential of difficult urban sites as described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for extracting carbon sequestration potential of difficult urban sites as described in any one of claims 1 to 7 is implemented.

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