A CSET approach to future scenarios of mountain ecosystems

Abstract

The present invention provides a CSET method for future scenarios of mountain ecosystems, comprising the following steps: S1. identification of mountain ecosystem landscape patterns; S2. determination and collection of carbon pool data; S3. prediction of future landscape patterns of mountain ecosystems. Effective prediction of future mountain ecosystem carbon reserves based on historical parameters is also of great theoretical value for exploring the influencing factors of mountain ecosystem carbon storage, and improving a series of technical systems for the evolution and quality evaluation of the ecological environment of mountain ecosystems, such as CSET, ecosystem service value estimation technology, and ecosystem habitat quality evaluation technology.

Description

Technical Field

[0001] The invention belongs to the technical field of quantitative ecological assessment, and in particular relates to a CSET method for future scenarios of mountain ecosystems. Background Art

[0002] In the context of global change, the status and role of mountain ecosystems in global change have also received much attention. Temperature greatly affects soil organic carbon storage in mid- and high-latitude regions, and hydroclimate in tropical regions may be the main driving factor for soil carbon retention. The global forest ecosystem carbon pool is extremely complex, and forest water use efficiency is closely related to atmospheric carbon dioxide. Therefore, model simulation methods play an important role in estimating ecosystem parameters such as NPP. At present, people have explored many methods around the assessment of ecosystem service value and the estimation of ecosystem carbon storage. Value assessment method, physical quantity assessment method, and energy value analysis method are important types of assessment methods; and ecological model method is a hot topic that has been actively developed in recent years. With the development of ecological information science, environmental information science, and geographic information science, the assessment methods of ecosystem services and the estimation methods of carbon storage, carbon budget, and carbon source and sink are gradually associated with ecological models and further deepened. At present, common ecosystem models include InVEST, ARIES, SoLVES and other models, among which InVEST model is widely used. The many modules of the InVEST model are sorted into different types. One type is the modules that support ecosystem service functions, such as habitat quality, habitat risk assessment and other modules; the other type is the final ecosystem service module, including carbon storage, water conservation, water purification and soil conservation and other service functions.

[0003] In recent years, affected by climate change, especially intensive human activities such as overgrazing, illegal construction, mineral mining, and tourism, some areas have experienced problems such as vegetation degradation such as natural forests and grasslands, soil erosion, shrinking ice and snow coverage areas, and reduced biodiversity. Therefore, studying the dynamic changes in the ecological service functions of mountain ecosystems under climate change scenarios and estimating changes in carbon storage at different temporal and spatial scales are of great theoretical value for comprehensively revealing the stability laws of mountain ecosystems and scientifically evaluating the comprehensive environmental effects of mountain ecosystems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a CSET method for future scenarios of mountain ecosystems.

[0005] The present invention adopts the following technical solution:

[0006] A CSET (Carbon Stock Estimation Technique) method for future scenarios of mountain ecosystems, comprising the following steps:

[0007] S1. Identification of landscape patterns of mountain ecosystems: According to the area where the mountain ecosystem is located, obtain medium-resolution and / or high-resolution remote sensing data of the area, conduct supervised and unsupervised classification, realize the identification of landscape patterns of mountain ecosystems in different periods, and obtain a landscape pattern spatial distribution atlas;

[0008] S2. Carbon pool data measurement and collection: obtain the carbon density of aboveground materials, underground materials, soil carbon density and dead organic matter in mountain ecosystems;

[0009] S3. Prediction of future landscape pattern of mountain ecosystems, including the following steps:

[0010] S31. extracting the DEM digital elevation data of the mountain ecosystem and the slope and aspect data extracted based on the DEM data, and extracting the vector data of town points, main roads, highways and main railways from the basic geographic information database, using the distance analysis tool to calculate the Euclidean distance grid map of each element, and then clipping to obtain the raster data of the mountain ecosystem;

[0011] S32. The raster data obtained in step S31 is normalized using the fuzzy membership tool in ArcGIS to obtain data of each factor with a value between 0 and 1, and define the LUCC driving factor;

[0012] S33. Based on the spatial distribution atlas of the landscape pattern of the mountain ecosystem obtained in step S1, the GeoSOS-FLUS model is used to simulate the landscape pattern situation, and compared and verified with the actual land use status map to determine the model accuracy, and the latest landscape pattern data and natural and social LUCC driving factors are further used as input data of the GeoSOS-FLUS model. At the same time, the predicted quantity is input into the future pixel number of the cellular machine to obtain the predicted data of the future landscape pattern of the mountain ecosystem; S4. Estimation of future carbon storage of mountain ecosystem: Combined with the predicted data of the future landscape pattern of the mountain ecosystem obtained in step S3 and the carbon pool data of aboveground carbon density, underground carbon density, soil carbon density and dead organic matter carbon density obtained in step S2, the carbon storage of the mountain ecosystem is estimated to obtain the future carbon storage of the mountain ecosystem.

[0013] Furthermore, after acquiring the remote sensing data in step S1, supervised and unsupervised classification is performed in combination with ENVI software to achieve the identification of the landscape pattern of the mountain ecosystem in different periods.

[0014] Furthermore, in step S2, the carbon density of aboveground materials, the carbon density of underground materials, the carbon density of soil and the carbon density of dead organic matter are obtained according to the practice of mountain ecosystems, combined with field testing and literature collection.

[0015] Furthermore, the conversion matrix in the GeoSOS-FLUS model in step S33 uses the conversion matrix of the natural development scenario, the economic growth scenario and the ecological protection scenario to predict the future landscape pattern distribution of the mountain ecosystem under different scenarios.

[0016] Furthermore, step S4 uses the InVEST model to estimate the carbon storage of mountain ecosystems.

[0017] Beneficial effects of the invention: The invention can effectively predict the future carbon storage of mountain ecosystems based on historical parameters, and has important theoretical value for exploring the influencing factors of carbon storage in mountain ecosystems, and improving a series of technical systems for ecological environment evolution and quality evaluation of mountain ecosystems, such as CSET, ecosystem service value estimation technology, and ecosystem habitat quality evaluation technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the future carbon stock estimation technology roadmap of the present invention;

[0019] Figure 2 is a satellite image diagram according to an embodiment of the present invention;

[0020] Figure 3 is a spatial distribution map of the QLMES landscape pattern of an embodiment of the present invention;

[0021] Figure 4 2015 QLMES landscape pattern simulation (a) and actual spatial distribution map (b) of an embodiment of the present invention;

[0022] Figure 5 is a simulated spatial distribution map of the landscape pattern of QLMES in 2050 according to an embodiment of the present invention;

[0023] Figure 6 is the spatial distribution characteristics of carbon storage in QLMES in 2050 based on CSET according to an embodiment of the present invention;

[0024] Figure 7 It is the average carbon storage and total amount change law of QLMES 1985-2050 based on CSET in the embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with examples. The examples are only used to illustrate the present invention and do not constitute a limitation on the scope of the claims. Other alternative means that can be thought of by those skilled in the art are all within the scope of the claims of the present invention.

[0026] Example 1

[0027] A CSET approach to future scenarios for mountain ecosystems, such as Figure 1 As shown, the following steps are included:

[0028] S1. Identification of mountain ecosystem landscape pattern: According to the area where the mountain ecosystem is located, obtain medium-resolution and / or high-resolution remote sensing data for the region in 2020. The results are as follows: Figure 2 As shown, the supervised and unsupervised classification was then performed with ENVI software to identify the landscape patterns of mountain ecosystems at different periods and obtain a landscape pattern spatial distribution atlas, as shown in Figure 3 shown.

[0029] S2. Determination and collection of carbon pool data: Combine field testing and literature collection to obtain the aboveground carbon density, underground carbon density, soil carbon density and dead organic matter carbon density of mountain ecosystems in 2020, which are C_above, C_below, C_soil and C_dead in Table 1 below, respectively.

[0030] Table 1 Carbon pool density of carbon storage model

[0031]

[0032] S3. Prediction of future landscape pattern of mountain ecosystems, including the following steps:

[0033] S31. Extract the DEM digital elevation data of the mountain ecosystem and the slope and aspect data extracted based on the DEM data, and extract the vector data of town points, main roads, highways and main railways from the basic geographic information database. Use the distance analysis tool to calculate the Euclidean distance grid map of each element, and then clip it to obtain the raster data of the mountain ecosystem. The calculation formula is as follows:

[0034]

[0035] In the formula, ρ is the point (x 2 ,y 2 ) and point (x 1 ,y 1 );|X| is the Euclidean distance of the point (x 2 ,y 2 ) to the origin.

[0036] S32. The raster data obtained in step S31 is normalized using the fuzzy membership tool in ArcGIS to obtain data of each factor with a value between 0 and 1, and define the LUCC driving factor;

[0037] S33. Based on the spatial distribution atlas of the landscape pattern of the mountain ecosystem obtained in step S1, the GeoSOS-FLUS model is used to simulate the landscape pattern, and compared and verified with the actual land use status map to determine the model accuracy, and the latest landscape pattern data and natural and social LUCC driving factors are further used as input data of the GeoSOS-FLUS model, and the predicted number is input into the future pixel number of the cellular machine to obtain the future landscape pattern prediction data of the mountain ecosystem;

[0038] S4. Estimation of future carbon storage of mountain ecosystems: Combine the predicted data on the future landscape pattern of mountain ecosystems obtained in step S3 with the carbon pool data on aboveground carbon density, underground carbon density, soil carbon density and dead organic matter carbon density obtained in step S2 to estimate the carbon storage of mountain ecosystems and obtain the future carbon storage of mountain ecosystems.

[0039] (1) Dynamic changes in land use in the future

[0040] Based on the principles and methods of the GeoSOS-FLUS model, the land use situation in 2000 was used to simulate the land use and coverage situation in 2010, and the GeoSOS-FLUS model was further calibrated to obtain its suitability probability, and then the land use situation in 2015 was simulated, and the actual land use data was combined for comparative verification. The Kappa coefficient of the verification result was 0.72, and the overall accuracy of the simulation was 0.81. The results show that the model can simulate the changes in land use types in the study area well, and can be used to estimate the future land use situation in the study area, such as Figure 4 (a) and 4(b) are the simulated and actual space diagrams, respectively.

[0041] Taking the land use of QLMES 2000 and 2015 as the initial and end years, the Markov Chain model was used to calculate the transition probability of each land use type, and then the land use structure quantity of the study area in 2050 was predicted. On this basis, the 2018 land use data and driving factor data were used as input data of the GeoSOS-FLUS model. Combined with the current objective conditions of QLME, the conversion matrix selected the natural protection scenario, and the land use type status of the study area in 2050 was simulated, as shown in the following figure: Figure 5 shown.

[0042] (2) Changes in QLMES carbon storage under future scenarios

[0043] Based on the natural development scenario, according to the land use type data in 2050 estimated by FLUS simulation, the carbon storage distribution of the Qilian Mountain Ecosystem (QLMES) study area in 2050 under the natural protection scenario is obtained by coupling the InVEST model. Figure 6 shown.

[0044] Quantitative analysis shows that the average carbon storage of QLMES in 2050 is mainly concentrated in the vegetation-covered areas in the east and middle. The average carbon storage in the study area in 2050 is 3813.38t / km 2 , with a growth rate of 8.69% over 2018, and a total carbon storage of 189.4182 million tons, an increase of 1,629.69 tons over 2018.

[0045] The temporal changes of QLMES carbon storage are as follows Figure 2 As shown in the figure, the carbon storage in QLME is on the rise, especially after the ecological restoration and management after 2010, the carbon storage has increased significantly. In the case of the set natural protection scenario, the forest area in QLMES has increased significantly, so the carbon storage has further increased by 2050.

[0046] Based on multivariate data, remote sensing and GIS technology, combined with InVEST and GeoSOS-FLUS models, we explored CSET and methods to estimate QLME carbon storage. Quantitative estimation and analysis showed that QLMES carbon storage has obvious temporal and spatial differences under different landscape types. Based on future nature conservation scenarios, QLME carbon storage tends to increase, with an average carbon storage of 3813.38t / km in 2050. 2 , a growth rate of 8.69% compared to 2018.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A CSET method for future scenarios of mountain ecosystems, characterized in that: The following steps are involved: S1. Identification of landscape patterns of mountain ecosystems: According to the area where the mountain ecosystem is located, obtain medium-resolution and / or high-resolution remote sensing data of the area, conduct supervised and unsupervised classification, realize the identification of landscape patterns of mountain ecosystems in different periods, and obtain a landscape pattern spatial distribution atlas; S2. Carbon pool data measurement and collection: obtain the carbon density of aboveground materials, underground materials, soil carbon density and dead organic matter in mountain ecosystems; S3. Prediction of future landscape pattern of mountain ecosystems, including the following steps: S31. extracting the DEM digital elevation data of the mountain ecosystem and the slope and aspect data extracted based on the DEM data, and extracting the vector data of town points, main roads, highways and main railways from the basic geographic information database, using the distance analysis tool to calculate the Euclidean distance grid map of each element, and then clipping to obtain the raster data of the mountain ecosystem; S32. The raster data obtained in step S31 is normalized using the fuzzy membership tool in ArcGIS to obtain data of each factor with a value between 0 and 1, and define the LUCC driving factor; S33. Based on the spatial distribution atlas of the landscape pattern of the mountain ecosystem obtained in step S1, the GeoSOS-FLUS model is used to simulate the landscape pattern, and compared and verified with the actual land use status map to determine the model accuracy, and the latest landscape pattern data and natural and social LUCC driving factors are further used as input data of the GeoSOS-FLUS model, and the predicted number is input into the future pixel number of the cellular machine to obtain the future landscape pattern prediction data of the mountain ecosystem; S4. Estimation of future carbon storage of mountain ecosystems: Combine the predicted data on the future landscape pattern of mountain ecosystems obtained in step S3 with the carbon pool data on aboveground carbon density, underground carbon density, soil carbon density and dead organic matter carbon density obtained in step S2 to estimate the carbon storage of mountain ecosystems and obtain the future carbon storage of mountain ecosystems.

2. The CSET method for future scenarios of mountain ecosystems according to claim 1, characterized in that: In step S1, after acquiring remote sensing data, supervised and unsupervised classification is performed in combination with ENVI software to identify the landscape pattern of mountain ecosystems in different periods.

3. The CSET method for future scenarios of mountain ecosystems according to claim 1, characterized in that: In step S2, the carbon density of aboveground materials, the carbon density of underground materials, the carbon density of soil and the carbon density of dead organic matter are obtained according to the practice of mountain ecosystems, combined with field testing and literature collection.

4. The CSET method for future scenarios of mountain ecosystems according to claim 1, characterized in that: The conversion matrix in the GeoSOS-FLUS model described in step S33 uses the conversion matrix of the natural development scenario, the economic growth scenario and the ecological protection scenario to predict the future landscape pattern distribution of the mountain ecosystem under different scenarios.

5. The CSET method for future scenarios of mountain ecosystems according to claim 1, characterized in that: Step S4 uses the InVEST model to estimate the carbon storage of mountain ecosystems.

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