CN116796123A - Land ecological system carbon sink distribution calculation method and system based on multi-source data - Google Patents
Land ecological system carbon sink distribution calculation method and system based on multi-source data Download PDFInfo
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Abstract
The invention discloses a land ecological system carbon sink distribution calculation method and system based on multi-source data, and relates to the field of ecological system research. The invention comprises the following steps: acquiring and preprocessing remote sensing images and meteorological data of an area to be calculated; calculating vegetation net primary productivity data NPP based on the preprocessed remote sensing image; calculating relative humidity RH based on the preprocessed meteorological data, and deriving an annual average precipitation amount and an annual average air temperature map; the land cover data, vegetation net primary productivity data NPP and relative humidity RH are respectively imported into ArcGIs, and vegetation net ecosystem productivity NEP is calculated. The invention calculates the carbon sink distribution of the land ecological system of the area to be calculated around various dimensions such as vegetation index change, NPP change trend, NEP change trend, land utilization classification change, air temperature change, precipitation change, population and economic growth.
Description
Technical Field
The invention relates to the field of ecological system research, in particular to a land ecological system carbon sink distribution calculation method and system based on multi-source data.
Background
Since the 80 s of the 20 th century, extensive researches on regional carbon sources and carbon sinks have been carried out at home and abroad, and the research contents are mainly focused on the following three aspects: first, measurement studies of carbon sources and sinks include measurement studies of total primary productivity-GPP, net primary productivity-NPP, and vegetation net ecosystem productivity-NEP. Second, the response of the major influencing factors or various factors, such as climate change, human activity, natural environment, etc., to the change in the carbon source and sink of the region was analyzed. Third, future forms are predicted by modeling, adjusting parameters, or selecting parameters. For example, combining the machine learning technology with the remote sensing technology, a data set is established, then a random forest is adopted as an estimation model, after modeling, the model is adjusted according to the data characteristics, and the carbon emission is estimated by using the model, so that a new observation method is formed. By analyzing the long-term emission characteristics of industries with larger carbon emissions in a certain area, the future carbon emission change situation of the area is predicted. In addition, these studies require a variety of high-precision data, complex calculations and a large number of model parameters, and the scope of the studies is mostly focused on the original ecological area, with less study on the temporal variation of the spatial distribution pattern of the carbon sink of the terrestrial ecosystem in urban clusters, which are economically fast developing, in particular the variation of the land type under different land cover types (such as woodland, grassland, etc.).
Therefore, how to solve the above problems is a urgent need for research by those skilled in the art.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for computing carbon sink distribution of a land ecological system based on multi-source data, so as to solve the problems in the background art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a land ecological system carbon sink distribution calculating method based on multi-source data comprises the following steps:
acquiring and preprocessing remote sensing images and meteorological data of an area to be calculated;
calculating vegetation net primary productivity data NPP based on the preprocessed remote sensing image;
calculating relative humidity RH based on the preprocessed meteorological data, and deriving an annual average precipitation amount and an annual average air temperature map;
the land cover data, vegetation net primary productivity data NPP and relative humidity RH are respectively imported into ArcGIs, and vegetation net ecosystem productivity NEP is calculated.
Optionally, the NEP change trend of each pixel of the area to be calculated is calculated by adopting a unitary linear regression analysis method, and the trend line of the single pixel is simulated by using unitary linear regression through the pixel values.
Optionally, the calculation formula of vegetation net primary productivity data NPP is as follows:
where RDI represents the radiation dryness, rn represents the annual net radiation, and R represents the annual precipitation.
Alternatively, the relative humidity RH is calculated as follows:
RH＝0.22×(e(0.0913T)+ln(0.3145R+1))×30×46.5％
wherein T represents air temperature and R represents precipitation.
Optionally, the specific formula for calculating the net vegetation ecosystem productivity NEP is as follows:
NEP＝NPP–RH。
a land ecological system carbon sink distribution computing system based on multi-source data comprises a data layer, an intermediate layer and a client;
the data layer is used for acquiring and preprocessing remote sensing images and meteorological data of the area to be calculated; calculating vegetation net primary productivity data NPP based on the preprocessed remote sensing image; calculating relative humidity RH based on the preprocessed meteorological data, and deriving an annual average precipitation amount and an annual average air temperature map;
the middle layer is used for respectively importing land coverage data, vegetation net primary productivity data NPP and relative humidity RH into ArcGIs to calculate vegetation net ecosystem productivity NEP;
the client is used for realizing map presentation and space analysis of the data.
Compared with the prior art, the method and the system for calculating the carbon sink distribution of the land ecological system based on the multi-source data are provided, and the carbon source, the time change of the carbon sink quantity, the spatial distribution pattern and the influence factors of the area to be calculated are analyzed by acquiring MODIS data products, meteorological data, land coverage data and the like, so that the land ecological system distribution of the area to be calculated is calculated around various dimensions such as vegetation index change, NPP change trend, NEP change trend, land utilization classification change, air temperature change, precipitation change, population and economic growth.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a land ecological system carbon sink distribution calculation method based on multi-source data, which is shown in figure 1 and comprises the following steps:
s1: acquiring and preprocessing remote sensing images and meteorological data of an area to be calculated;
s2: calculating vegetation net primary productivity data NPP based on the preprocessed remote sensing image;
s3: calculating relative humidity RH based on the preprocessed meteorological data, and deriving an annual average precipitation amount and an annual average air temperature map;
s4: the land cover data, vegetation net primary productivity data NPP and relative humidity RH are respectively imported into ArcGIs, and vegetation net ecosystem productivity NEP is calculated.
Further, in S1, remote sensing images are processed by MRT software through the environment installation MRT software of MODIS image configuration JDK-9.0.4. (1) Selecting an image to be processed; (2) selecting a wave band to be processed; (3) selecting an output folder, setting a file name and an output file type); (4) selecting a resampling type, a type of output projection and an output pixel; (5) clicking run converts the image and opens it in ArcMap.
Further, in S1, meteorological data processing (calculation of Rh) is further included: (1) data sources: the national weather center screens data and products in the ground data according to month and space resolution, and downloads a China ground weather standard month value data set. (2) And (3) data processing: since the data downloaded on the website is text data, the text data is imported into an Excel table to obtain a month average weather information table of 208 sites in the long triangular region, and the weather data of 12 months are added and divided by 12 to obtain specific results of month average air temperature and precipitation. (3) Calculating Rh formula: rh=0.22× (e (0.0913T) +ln (0.1345r+1)) ×30×46.5% [9], T is air temperature, and R is precipitation. (4) And (3) processing. Shp data and interpolation data are imported, and a kriging interpolation method is used: the input point elements, the Z value fields and the output surface grids, and the size of the output pixels can be according to actual requirements; selecting a study area range from a treatment range option in the environment, and finally, plotting to derive an annual average precipitation amount and an annual average air temperature map; raster data is derived.
Further, in S2, the following steps are specifically included: (1) long triangle TIF image data is obtained by clipping and mask extraction, then a valid value range is obtained by calculating a value larger than 32767 in a grid calculator by using a SETNULL function, and grid data is derived. (2) Then, a grid calculator is applied to multiply the TIF data derived in the previous step by a conversion factor of 0.0001 to derive grid data. (3) And opening partition statistics, wherein input raster data is long triangle shp data, a region field is NAME, the input raster data is raster data exported in the last step, and then the raster data is exported. (4) And opening to display partition statistics by a table, inputting raster data into the table to be long triangle shp data, inputting raster data derived in the last step into the table to be regional field to be NAME, and then deriving annual average NPP data.
The calculation formula of NPP is as follows:
wherein RDI is the radiant dryness; rn is annual net radiation; r is annual precipitation; NPP is the net primary productivity of vegetation. The above formula can be further categorized as follows:
further, in S3, the calculation NEP is specifically: net ecosystem productivity—nep, although NEP is not equal to carbon sink on a regional scale, is often a measure of the size of carbon sink. (1) The calculation formula is as follows: nep=npp-RH, where NEP is net ecological system productivity of vegetation, NPP is net primary productivity of vegetation, RH is respiration of soil microorganisms, and if NEP >0, it indicates that the carbon immobilized on vegetation is higher than carbon discharged from soil, and represents carbon sink; conversely, NEP <0 represents a carbon source. (2) The processing steps are as follows: NPP and RH data were imported into ArcGIs, and NEP data were obtained by subtracting RH from NPP by opening the grid calculator.
Wherein, each land cover type NEP value is calculated: (1) the original 17 types of land are classified into seven major categories of woodland, shrubs, grasslands, wetlands, cultivated lands, construction lands and unused lands by introducing the land coverage data into ArcGis for reclassifying. (2) And carrying out partition statistics on the reclassified images according to the table to obtain basic data of seven land types so as to obtain NEP values of the land types.
Further, the invention also comprises NEP data change trend analysis: in order to quantitatively describe the change rule of NEP in 21 years, a unitary linear regression analysis method is adopted to analyze the NEP change trend of each pixel in 2000-2020 in the long triangular region, and the trend line of a single pixel is simulated by the pixel value in 21 years of the pixel by using a unitary linear regression. (1) The operation steps are as follows: substituting n=21 into the formula and calculating the NEP change trend graph according to the formula in the grid calculator.
Further, NEP of the vegetation ecosystem is an important indicator for measuring carbon sources and sinks of vegetation in the area. NEP is equal to the difference between vegetation NPP and soil microorganism respiratory carbon consumption without consideration of other natural and human factors. In the rapid development process of urban groups with the strongest comprehensive strength in developing countries, it is necessary to quickly, comprehensively and accurately understand the long-sequence variation of land vegetation NEP and its response to climate. The net ecosystem productivity NEP is the difference between the Net Primary Productivity (NPP) of vegetation and the respiratory carbon emissions (RH) of soil microorganisms in an ecoregion, is the rate of change of the carbon reserves of the ecosystem over time, and is an important indicator for the estimation of the carbon balance over the region. Although NEP is not equal to carbon sink at the regional scale, it is often a measure of the size of carbon sink. The calculation formula of NEP is:
NEP＝NPP-RH
wherein NEP is the net ecosystem productivity of vegetation in gC ● m -2● a -1 NPP is the net primary productivity of vegetation in gC ● m -2● a -1 Rn is the respiration rate of soil microorganism, and the unit is gC ● m -2● a -1 . It exhibits a carbon source effect. Can be used for quantitatively analyzing the carbon sequestration condition and potential of the vegetation ecosystem in the area. The vegetation year NPP image is based on MODIS satellite remote sensing parameters, and the data is calculated by adopting a BIOMe-BGC model and a light energy utilization model. Currently, these data have been validated and widely used in carbon cycling studies in many areas. The annual total value is the total amount of all pixels for one year, the average value of the annual total value is the annual total value of all pixels divided by the number of pixels, and the maximum and minimum values of the annual total value are the maximum and minimum values of all pixels in the local area. RH refers to the amount of respiration of microorganisms in the soil and is related to the number and type of microorganisms in the soil and the secretions of the plant roots. Temperature and precipitation are the most important two of all factors affecting respiration of soil microorganisms. Thus, RH is calculated using regression equations for temperature, precipitation and carbon emissions. Based on field measured data, a regression equation between the carbon emission of the soil and conventional meteorological data (temperature T and precipitation R) is established, and the regression equation is easy to apply to woodland, grassland and farmland. As shown below.
RH＝0.22×(e(0.0913T)+ln(0.3145R+1))×30×46.5％
Wherein T is the air temperature (DEG C), and R is precipitation (mm). The system calculates the NPP value by using MOD17A3 data, and removes the pixel value of the land in advance when calculating the NPP because the construction land and the unused land are carbon sources. And calculating RH by using the temperature and precipitation data, thereby completing the calculation of NEP and obtaining NEP data.
The invention also discloses a land ecological system carbon sink distribution computing system based on multi-source data, which is used for realizing the method of FIG. 1, and the specific structure is shown in FIG. 2, and comprises a data layer, an intermediate layer and a client;
the data layer is used for acquiring and preprocessing remote sensing images and meteorological data of the area to be calculated; calculating vegetation net primary productivity data NPP based on the preprocessed remote sensing image; calculating relative humidity RH based on the preprocessed meteorological data, and deriving an annual average precipitation amount and an annual average air temperature map;
the middle layer is used for respectively importing land coverage data, vegetation net primary productivity data NPP and relative humidity RH into ArcGIs to calculate vegetation net ecosystem productivity NEP;
the client is used for realizing map presentation and space analysis of the data.
The carbon sink visualization system displays various carbon sink information, realizes access and display of NEP map information of a research area, and comprises layer switching and information display under map service. Besides, the functions of inquiring, measuring and calculating various carbon sink values and displaying statistical results are realized. Under the safety guarantee system, the standard specification system and the operation management system, the design and development of the system are integrated, a three-layer architecture hierarchical design mode is adopted, and the three-layer architecture hierarchical design mode is a client application layer and a background logic layer data layer respectively from top to bottom. The layers in the architecture are mutually independent, the dependency is reduced, the new implementation can be easily used for replacing the implementation of the original layer, the standardization is facilitated, and the multiplexing of the logic of each layer is also facilitated.
Based on computer hardware and network communication platform, a great amount of data resources including geographic basic data, business data, etc. are structured by SQL Server relational database technology and stored in data layer to design data table of system function. The background logic layer realizes various functional services of business work through interfaces, realizes release and application of maps and geographic data through GIS servers such as Map servers, super maps and the like, sends HTTP requests to clients, and timely responds to the client application layer after receiving feedback data information.
The client application layer uses public map data service and carbon sink business data as data support, and uses an Angular front end frame, the client initiates a request, under the support of network and software and hardware services, through logic processing of a background logic layer, the Web server analyzes the request and makes a pre-response, and simultaneously sends the response to the GIS server, the GIS server realizes access to a database and completes processing analysis of the response, finally, the request result is responded to the client in json file format, and a user can see the rendered data result through a browser to complete one operation period.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A land ecological system carbon sink distribution calculating method based on multi-source data is characterized by comprising the following steps:
acquiring and preprocessing remote sensing images and meteorological data of an area to be calculated;
calculating vegetation net primary productivity data NPP based on the preprocessed remote sensing image;
calculating relative humidity RH based on the preprocessed meteorological data, and deriving an annual average precipitation amount and an annual average air temperature map;
the land cover data, vegetation net primary productivity data NPP and relative humidity RH are respectively imported into ArcGIs, and vegetation net ecosystem productivity NEP is calculated.
2. The method for computing carbon sink distribution of a land ecological system based on multi-source data according to claim 1, further comprising the step of computing NEP variation trend of each pixel of the area to be computed by adopting a unitary linear regression analysis method, wherein trend lines of single pixels are simulated by using unitary linear regression of pixel values.
3. The method for computing carbon sink distribution of a land ecosystem based on multi-source data according to claim 1, wherein the calculation formula of vegetation net primary productivity data NPP is as follows:
where RDI represents the radiation dryness, rn represents the annual net radiation, and R represents the annual precipitation.
4. The method for computing carbon sink distribution of a land ecological system based on multi-source data according to claim 1, wherein the formula for computing the relative humidity RH is as follows:
RH＝0.22×(e(0.0913T)+ln(0.3145R+1))×30×46.5％
wherein T represents air temperature and R represents precipitation.
5. The method for calculating the carbon sink distribution of the land ecosystem based on the multi-source data according to claim 1, wherein the specific formula for calculating the net ecological system productivity NEP of vegetation is as follows:
NEP＝NPP–RH。
6. a land ecological system carbon sink distribution computing system based on multi-source data is characterized by comprising a data layer, an intermediate layer and a client;
the data layer is used for acquiring and preprocessing remote sensing images and meteorological data of the area to be calculated; calculating vegetation net primary productivity data NPP based on the preprocessed remote sensing image; calculating relative humidity RH based on the preprocessed meteorological data, and deriving an annual average precipitation amount and an annual average air temperature map;
the middle layer is used for respectively importing land coverage data, vegetation net primary productivity data NPP and relative humidity RH into ArcGIs to calculate vegetation net ecosystem productivity NEP;
the client is used for realizing map presentation and space analysis of the data.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104537222A (en) * | 2014-12-18 | 2015-04-22 | 中国科学院东北地理与农业生态研究所 | Remote-sensing-based method for estimating influences of area vegetation cover on earth surface air temperature |
| CN115565063A (en) * | 2022-03-24 | 2023-01-03 | 中国矿业大学(北京) | Mining area vegetation carbon sink contribution amount calculation and analysis method based on climate potential compensation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104537222A (en) * | 2014-12-18 | 2015-04-22 | 中国科学院东北地理与农业生态研究所 | Remote-sensing-based method for estimating influences of area vegetation cover on earth surface air temperature |
| CN115565063A (en) * | 2022-03-24 | 2023-01-03 | 中国矿业大学(北京) | Mining area vegetation carbon sink contribution amount calculation and analysis method based on climate potential compensation |
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