Ecological system service quantitative evaluation method based on remote sensing data

Abstract

The invention discloses an ecological system service quantitative evaluation method based on remote sensing data. The method comprises the following steps: determining an ecological system service quantitative evaluation region and collecting remote sensing image data; preprocessing the remote sensing influence data; defining a typical sample area according to a local climate zone (LCZ), and obtaining a training data set; training based on the training data set and a random forest classification algorithm; related temperature data, hydrological data and carbon reserve data are obtained, and a thermal mitigation service distribution result, rainwater regulation and storage benefits and carbon offset service contribution are output in combination with an InVEST model; and fusing the data to the preliminary local climate zone map, and analyzing the change of the fused LCZ map along with time. According to the invention, comprehensive evaluation and monitoring of the urban ecosystem service are realized. The method can be widely applied to the field of service monitoring and evaluation.

Description

Technical Field

[0001] The present invention relates to the field of service monitoring and evaluation, and in particular to a method for quantitatively evaluating ecosystem services based on remote sensing data. Background Art

[0002] Ecosystem services (ES) connect ecosystem structure, function and processes. Ecosystem services not only meet the most basic human needs such as food, energy and medicine, but also maintain agricultural production, maintain water quality and quantity, regulate climate, conserve soil, nutrient cycles and protect biodiversity, and provide environmental conditions for life. Ecosystem services have many aspects, including soil and water conservation services, creating habitable space for life through plants and crops.

[0003] At present, the monitoring and assessment of ecosystem services mainly rely on traditional field surveys and manual data collection. The application of smart monitoring is relatively lagging behind, and there are still deficiencies in the comprehensive quantification of ecosystem services. Summary of the invention

[0004] In view of this, in order to solve the technical problem that most of the existing ecosystem service quantitative assessments cannot be done by manual data collection, which leads to insufficient comprehensive quantification, the present invention proposes a method for quantitative assessment of ecosystem services based on remote sensing data, which includes the following steps:

[0005] Identify regional assessment areas for quantitative assessment of ecosystem services and collect remote sensing image data;

[0006] Preprocessing of remote sensing impact data, including denoising and resampling to appropriate resolution;

[0007] Delineate typical sample areas according to local climate zones (LCZs) and obtain training data sets;

[0008] Based on the training data set and the random forest classification algorithm, a preliminary LCZ map was generated;

[0009] The confusion matrix was used to evaluate the accuracy of the generated LCZ map;

[0010] Obtain relevant temperature data, hydrological data and carbon storage data, and combine them with the InVEST model to output the distribution results of heat mitigation services, rainwater storage benefits and carbon offset service contributions;

[0011] The heat mitigation service distribution results, the rainwater storage benefits and the carbon offset service contribution are integrated into the preliminary local climate zone map, and the changes of the integrated LCZ map over time are analyzed to obtain the changes in different local climate zones.

[0012] In some embodiments, in the step of obtaining relevant temperature data, hydrological data and carbon storage data, combining the InVEST model, and outputting the heat mitigation service distribution results, rainwater storage benefits and carbon offset service contributions, it specifically includes:

[0013] Collect temperature and land use data of the assessment area and input them into the urban cooling module of the InVEST model;

[0014] Run the InVEST model to obtain the heat mitigation service distribution results;

[0015] Collect rainfall raster data, soil hydrology grouping maps, stormwater runoff coefficients, and stormwater infiltration coefficients, and input them into the stormwater retention module of the InVEST model;

[0016] Run the InVEST model to calculate the amount of rainwater retention in the region and evaluate the benefits of rainwater storage;

[0017] The carbon storage module of the InVEST model was used to assess total carbon storage, and the NPP remote sensing data from the MODIS satellite was used to assess the average annual carbon sequestration in the region.

[0018] Calculate carbon offset factors to convert ecosystem services into contributions to offset carbon emissions.

[0019] In some embodiments, it also includes:

[0020] Analyze the changing trends of rainwater storage, heat mitigation and carbon offset services, and generate recommendations for regional coordinated development.

[0021] Based on the above scheme, the present invention provides a quantitative assessment method of ecosystem services based on remote sensing data. By acquiring high-resolution remote sensing images, combined with local climate zone (LCZ) classification, rainwater retention and carbon storage calculation of the InVEST model, etc., it is possible to achieve a comprehensive assessment and monitoring of urban ecosystem services, thereby accurately quantifying the functions and benefits of various ecosystem services. This technical means not only improves the efficiency and accuracy of monitoring, but also provides scientific and objective data support for urban management, and realizes the intelligent assessment and decision-making of ecosystem services. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the steps of a method for quantitative assessment of ecosystem services based on remote sensing data of the present invention;

[0023] Figure 2 It is a structural block diagram of an ecosystem service quantitative assessment method system based on remote sensing data of the present invention. DETAILED DESCRIPTION

[0024] This paper proposes a quantitative assessment technology for ecosystem services based on remote sensing data and the InVEST model. By processing high-resolution remote sensing images and combining local climate zone (LCZ) classification, the multiple service functions of urban ecosystems are converted into carbon offsets, thereby achieving quantitative analysis, including rainwater storage, carbon storage, and heat mitigation. This technology is scientific and systematic, and provides an efficient and accurate data analysis tool for the sustainable management of ecosystems.

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] It should be noted that, for the convenience of description, only the parts related to the relevant invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0027] It should be understood that the "system", "device", "unit" and / or "module" used in this application is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the word can be replaced by other expressions.

[0028] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "a kind" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements. The elements defined by the sentence "includes a..." do not exclude the existence of other identical elements in the process, method, commodity or device that includes the elements.

[0029] In the description of the embodiments of the present application, "plurality" means two or more than two. The following terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0030] In addition, flow charts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, the various steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0031] Reference Figure 1 , which is a flow chart of an optional example of a method for quantitatively evaluating ecosystem services based on remote sensing data proposed in the present invention. The method can be applied to computer devices. The quantitative evaluation method proposed in this embodiment may include but is not limited to the following steps:

[0032] Step S1, determining an assessment area and collecting remote sensing image data of the assessment area;

[0033] Step S2, defining a sample area according to the local climate zone and obtaining a training data set;

[0034] Specifically, based on remote sensing image data, the model is trained to divide geographical regions, generate local climate zone maps, and delineate at least 20 typical sample areas.

[0035] Step S3, using SAGA-GIS software, training based on the training data set and random forest classification algorithm to generate a preliminary local climate zone map;

[0036] Step S4, obtaining relevant temperature data, hydrological data and carbon storage data, combining with the InVEST model, outputting the heat mitigation service distribution results, rainwater storage benefits and carbon offset service contributions;

[0037] Step S5: integrating the heat mitigation service distribution results, the rainwater storage benefits and the carbon offset service contribution into the preliminary local climate zone map, and analyzing them to obtain the expansion and contraction of different LCZ types.

[0038] In some feasible embodiments, it also includes:

[0039] Preprocess the collected remote sensing image data.

[0040] 1) Mosaic multiple remote sensing images to cover the boundaries of the study area.

[0041] 2) Detect the image to ensure that the image has low cloud content, high atmospheric transparency and high ground visibility.

[0042] 3) Resample the remote sensing satellite images to a resolution of about 300m.

[0043] In some feasible embodiments, the step S4 specifically includes:

[0044] S4.1. Obtain temperature data and land use data of the assessment area, input them into the urban cooling module of the InVEST model, and calculate the distribution results of heat mitigation services;

[0045] S4.1.1. Input the tree canopy cover, evapotranspiration and albedo for each local climate zone;

[0046] S4.1.2. Calculate the thermal mitigation index for each pixel and evaluate its impact on the urban heat island effect.

[0047] S4.1.3. Accumulate the thermal mitigation service outputs of all pixels to obtain the total energy savings for the corresponding area.

[0048] When evaluating the overall thermal mitigation service performance of the study area, the model calculates the total energy savings by accumulating the output results of all pixels. The calculation formula for thermal mitigation service is as follows:

[0049] T air,i =T ref +(1-HMI)·UHI max

[0050]

[0051] Among them, T air,i represents the temperature of the air at pixel i after complete mixing (℃); T ref represents the baseline temperature, i.e., the suburban temperature not affected by the urban heat island effect (℃); HMI is the heat mitigation index of pixel i calculated by the model, which is determined by the pixel's tree canopy coverage, evapotranspiration index, albedo, building intensity, and its distance from the surrounding green space; UHI max Indicates the maximum intensity of urban heat island effect in the assessment area (℃); ES b represents the energy saving of building b (kW·h); Q c Indicates the energy consumption per unit temperature rise (kW·h / ℃·m 2 ); It represents the average temperature difference (℃) in building b.

[0052] S4.2. Obtain rainfall raster data, soil hydrology grouping map, rainwater runoff coefficient and rainwater infiltration coefficient, input them into the rainwater retention module of the InVEST model, calculate the rainwater retention volume and evaluate the rainwater storage benefits;

[0053] The calculation formula of rainwater storage benefits is as follows:

[0054]

[0055] in, Indicates rainwater retention; Pi represents the average annual rainfall in pixel i (mm / yr); RC x represents the rainwater runoff coefficient, which is defined by the LCZ type and soil hydrology group corresponding to pixel i; x represents the soil hydrology group type at pixel i, and S represents the area of ​​pixel i (m 2 ).

[0056] S4.3, assessing the total carbon storage based on the carbon storage module of the InVEST model, and predicting the average annual carbon sequestration in the assessment area;

[0057] S4.4. Calculate the carbon offset factor and convert ecosystem services into carbon emission offset contributions.

[0058] S4.4.1. Determine the basic value of the carbon offset factor based on the regional carbon emission factor and carbon stock data.

[0059] S4.4.2. Adjust the calculation results of the carbon offset factor based on the actual contribution of ecosystem services.

[0060] S4.4.3. Comprehensively evaluate the results of the carbon offset factor with other ecosystem services, convert heat mitigation services into energy conservation and carbon reduction, convert rainwater storage services into water conservation and carbon reduction, spatially superimpose carbon sequestration, energy conservation and carbon reduction, and water conservation and carbon reduction, and add them up with equal weights to quantify their contribution to human well-being.

[0061] The calculation formula of carbon fixation rate is as follows:

[0062]

[0063] in, Indicates the CO2 absorbed by vegetation per unit area (g / m 2 ); NPP is the net primary productivity per unit area (g·C / m 2 ); NEP is the net ecosystem productivity per unit area (g·C / m 2 );R h represents the carbon released by soil microbial heterotrophic respiration (g·C / m 2 );S c It represents the carbon fixation rate per unit area (g·C / m 2 ·yr); T represents the study duration (yr).

[0064] In some feasible embodiments, it also includes:

[0065] Accuracy verification was performed and the confusion matrix was used to evaluate the accuracy of the generated LCZ map.

[0066] like Figure 2As shown in the figure, a quantitative assessment system of ecosystem services based on remote sensing data includes:

[0067] An area setting unit, which determines an assessment area and collects remote sensing image data of the assessment area;

[0068] Regional division unit, demarcates sample areas according to local climate zones and obtains training data sets;

[0069] A training unit, which performs training based on the training data set and the random forest classification algorithm to generate a preliminary local climate zone map;

[0070] The calculation unit obtains relevant temperature data, hydrological data and carbon storage data, and combines them with the InVEST model to output the distribution results of heat mitigation services, rainwater storage benefits and carbon offset service contributions;

[0071] The analysis unit integrates the heat mitigation service distribution results, the rainwater storage benefits and the carbon offset service contributions into the preliminary local climate zone map, and performs analysis to obtain the changes in different local climate zones.

[0072] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0073] A device for quantitative assessment of ecosystem services based on remote sensing data:

[0074] at least one processor;

[0075] at least one memory for storing at least one program;

[0076] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for quantitative assessment of ecosystem services based on remote sensing data.

[0077] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0078] A storage medium stores processor-executable instructions, which are used to implement a method for quantitatively evaluating ecosystem services based on remote sensing data when executed by a processor.

[0079] The contents of the above method embodiments are all applicable to the present storage medium embodiments. The functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0080] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for quantitative assessment of ecosystem services based on remote sensing data, characterized in that: The following steps are involved: Determine an assessment area and collect remote sensing image data of the assessment area; Delineating a sample area according to the remote sensing image data and obtaining a training data set; Perform training based on the training data set and the random forest classification algorithm to generate a preliminary local climate zone map; Obtain relevant temperature data, hydrological data and carbon storage data, and combine them with the InVEST model to output the distribution results of heat mitigation services, rainwater storage benefits and carbon offset service contributions; The heat mitigation service distribution results, the rainwater storage benefits and the carbon offset service contribution are integrated into the preliminary local climate zone map, and analyzed to obtain the changes in different local climate zones.

2. The method for quantitatively evaluating ecosystem services based on remote sensing data according to claim 1, characterized in that: Also includes: The remote sensing image data is subjected to denoising and resampling processing.

3. The method for quantitatively evaluating ecosystem services based on remote sensing data according to claim 1, characterized in that: Also includes: The accuracy of the preliminary local climate zone map was verified based on the confusion matrix.

4. The method for quantitatively evaluating ecosystem services based on remote sensing data according to claim 1, characterized in that: The step of obtaining relevant temperature data, hydrological data and carbon storage data, combining with the InVEST model, and outputting the heat mitigation service distribution results, rainwater storage benefits and carbon offset service contributions specifically includes: Obtain temperature data and land use data of the assessment area, input them into the urban cooling module of the InVEST model, and calculate the distribution results of heat mitigation services; Obtain rainfall raster data, soil hydrology grouping maps, rainwater runoff coefficients and rainwater infiltration coefficients, and input them into the rainwater retention module of the InVEST model to calculate the rainwater retention volume and evaluate the benefits of rainwater storage; Assessing the total carbon storage based on the carbon storage module of the InVEST model and predicting the average annual carbon sequestration in the assessment area; Calculate the carbon offset factor, convert ecosystem services into direct carbon sequestration and indirect carbon sequestration equivalents, and then calculate the carbon emission offset contribution.

5. The method for quantitatively evaluating ecosystem services based on remote sensing data according to claim 4, characterized in that: The processing process of the urban cooling module of the InVEST model specifically includes: Input tree canopy cover, evapotranspiration, and albedo for each local climate zone; Calculate the heat mitigation index for each pixel and assess its impact on the urban heat island effect. The heat mitigation index of all pixels is accumulated to obtain the total energy saving of the corresponding area.

6. The method for quantitatively evaluating ecosystem services based on remote sensing data according to claim 4, characterized in that: The calculation formula for heat mitigation services is expressed as follows: T air,i =T ref +(1-HMI)·UHI max Among them, T air,i represents the temperature of the air at pixel i after it is completely mixed, T ref represents the baseline temperature, HMI is the thermal mitigation index of pixel i calculated by the model; UHI max Indicates the maximum intensity of urban heat island effect in the assessment area; ES b represents the energy saving of building b; Q c Indicates the energy consumption per unit temperature rise, represents the average temperature difference within building b.

7. The method for quantitatively evaluating ecosystem services based on remote sensing data according to claim 4, characterized in that: The calculation formula of rainwater storage benefits is as follows: in, represents the amount of rainwater retention, P i represents the average annual rainfall in pixel i, RC x represents the rainwater runoff coefficient, x represents the soil hydrological group type at pixel i, and S represents the area of ​​pixel i.

8. The method for quantitatively evaluating ecosystem services based on remote sensing data according to claim 4, characterized in that: The calculation formula of carbon fixation rate is as follows: in, represents the CO2 absorbed by vegetation per unit area, NPP represents the net primary productivity per unit area, NEP represents the net ecosystem productivity per unit area, R h represents the carbon released by heterotrophic respiration of soil microorganisms, S c represents the carbon fixation rate per unit area, and T represents the research duration.

9. An ecosystem service quantitative assessment device based on remote sensing data, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method for quantitative assessment of ecosystem services based on remote sensing data as described in any one of claims 1 to 8.

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