CN116596100B - Carbon sink monitoring and early warning method based on land utilization change simulation - Google Patents
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Abstract
The invention provides a carbon sink monitoring and early warning method based on land utilization change simulation, which comprises the following steps: step 1, constructing a database, step 2, adopting a LUCC classification system to obtain land utilization data of different time nodes in a research range, step 3, analyzing land utilization change characteristics and rules based on GIS space analysis to obtain a driving factor index system, step 4, adopting a SLUTH model, presetting different planning development scenes by setting different exclusion layers, performing future multi-year land utilization change simulation, step 5, calculating each land utilization type area of different time nodes, calculating a carbon sink metering result, and step 6, determining monitoring and early warning indexes in time. The invention constructs a complete carbon sink simulation prediction and monitoring early warning process.
Description
Technical Field
The invention relates to the technical fields of remote sensing and geographic information, ecology and urban and rural planning, in particular to a carbon sink monitoring and early warning method based on land utilization change simulation.
Background
The research investment is increased in the low-carbon field in China, and a data analysis technology is introduced, so that preliminary progress is made in the measuring and calculating directions of carbon sources and carbon sinks. Existing research forms a certain research basis in the carbon sink field, but has some defects.
Firstly, a GLO-PEM model, a simple biosphere model SIB2, a CASA model, a forest ecosystem carbon balance model and other relatively mature models and methods are formed for carbon sink calculation of remote sensing data, a certain research basis is provided, and a system flow for carrying out carbon sink simulation prediction according to scene conditions cannot be constructed;
secondly, as the land utilization type changes, the carbon sink changes continuously, and most of existing researches are static researches aiming at the current situation of land utilization, and the change situation of the carbon sink cannot be monitored in time and early warning is carried out.
Disclosure of Invention
The invention aims to provide a carbon sink monitoring and early warning method based on land utilization change simulation, aiming at the technical defects of carbon sink simulation prediction flow and real-time monitoring and early warning in the prior art.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a carbon sink monitoring and early warning method based on land utilization change simulation comprises the following steps:
step 1, constructing a database:
step 1-1, setting a time span, respectively acquiring high-resolution remote sensing image data of a plurality of years in a research area range, preprocessing the remote sensing image data, integrating a DEM (digital elevation model), and acquiring elevation and gradient grid data;
step 1-2, analyzing historical data to obtain vector data of traffic facilities, river water systems, basic farmlands, general farmlands, ecological protection red lines and general ecological protection areas corresponding to the year of the remote sensing image;
step 1-3, inquiring a statistical yearbook, obtaining population and GDP statistical data, and converting the population and GDP statistical data into vector data based on ArcGIS;
step 1-4, acquiring homeland space planning results corresponding to a research area range, and extracting planning development areas and traffic facility planning vector data;
step 1-5, based on an ArcGIS platform, performing visualization processing on the data obtained in the steps 1-1, 1-2, 1-3 and 1-4, and converting the data obtained in the steps 1-2, 1-3 and 1-4 into corresponding grid data to construct a basic research database;
step 2, according to the feature of the ground reflected by the spectrum curve of the remote sensing image, the high-resolution remote sensing image data obtained in the step 1-1 are interpreted based on an ENVI platform, and a LUCC classification system is adopted to obtain land utilization data of different time nodes in a research range, wherein the land utilization data specifically comprises vector data of cultivated land, woodland, grasslands, water areas, construction land and unused land, and the vector data is converted into corresponding grid data;
step 3, integrating the elevation and gradient grid data obtained in the step 1-1, the traffic facility, basic farmland, general farmland, ecological protection red line and general ecological protection area vector data obtained in the step 1-2, the population and GDP vector data obtained in the step 1-3, the planning development area and traffic facility planning vector data obtained in the step 1-4, and the woodland, grassland, water area and construction land vector data obtained in the step 2, analyzing land utilization change characteristics and rules based on GIS space analysis, and obtaining a driving factor index system;
step 4, combining the basic research database obtained in the step 1 and land utilization grid data of different time nodes obtained in the step 2, introducing the driving factor index system obtained in the step 3, adopting a SLUTH model, and carrying out future multi-year land utilization change simulation by setting three planning development scenes of different exclusion layers, namely preset existing trend, reasonable development and ecological low carbon;
step 5, calculating the area of each land utilization type of different time nodes, and calculating the carbon absorption of the land in a certain time period in the past and in the future by utilizing the carbon absorption coefficient to be used as a carbon sink measurement result;
and 6, monitoring the carbon sink change condition in real time, comparing the carbon sink simulation result with monitoring and early warning indexes, and early warning in time under the condition that the carbon sink simulation result exceeds or is lower than an expected interval.
In the above technical solution, the step 4 specifically includes the following steps:
step 4-1, data preparation: manufacturing an urban range layer, a traffic layer, a gradient layer, a land utilization layer, a mountain shadow layer, an exclusion layer and other information required by SLEETH model simulation based on a GIS platform, and constructing a model database by unifying information such as format, size, range and coordinates;
step 4-2, model calibration: inputting a plurality of layers in the model database obtained in the step 4-1 into a SLEEUTH model for parameter calibration to obtain optimal parameters simulated by the SLEEUTH model;
step 4-3, scene presetting: according to land utilization change characteristics, selecting driving factor indexes of land utilization change, presetting three planning development scenes of existing trend, reasonable development and ecological low carbon, and performing layer element exclusion setting in a SLUTH model;
step 4-4, simulation prediction: setting the initial year and the final year of SLEETH model simulation, inputting different exclusion layers, and respectively simulating and predicting the land utilization change condition of a research area by adopting the optimal parameters under the three planning development scenes of the existing trend, reasonable development and ecological low carbon.
In the above technical scheme, the step 4-1 specifically includes the following steps:
step 4-1-1, manufacturing raster data of construction lands of a plurality of time nodes obtained in the step 2 into an urban area map layer;
step 4-1-2, manufacturing the raster data of the traffic facilities with the plurality of time nodes obtained in the step 1-5 into a traffic layer;
step 4-1-3, manufacturing gradient raster data in the range of the research area obtained in the step 1-1 into a gradient map layer and manufacturing DEM data into a mountain shadow map layer;
step 4-1-4, manufacturing the raster data of the land utilization data of the plurality of time nodes obtained in the step 2 into a land utilization layer;
step 4-1-5, manufacturing the river system grid data obtained in the step 1-5 as a limiting condition into an exclusion layer for calibrating the model in the step 4-2; three different development scenarios can be preset for step 4-3 by excluding the layer settings.
In the above technical scheme, the model calibration process in step 4-2 adopts a Monte Carlo iterative calculation method, four links are obtained through coarse calibration, fine calibration, final calibration and simulation parameters, and an OSM index is used as a best fitting goodness index for determining the model;
OSM＝Compare×Pop×Edges×Clusters×Slope×X-mean×Y-mean×Fmatch (1)
finally, obtaining the value ranges and the step sizes of 5 simulation parameters, namely a diffusion coefficient, a propagation coefficient, a gradient coefficient and a road gravitation coefficient, which are used for the model prediction process under different scenes in the step 4-3.
In the above technical scheme, the method for excluding the layer calculation in the SLEUTH model in the step 4-3 is as follows:
4-3-3-1, original index layer:
wherein y is mi An original index layer, x, which is the ith index mij Index value lambda of the jth element level of the mth scene which is the ith index mij The exclusion coefficient of the jth element level of the ith index for the mth scenario, where m=i, ii or iii represents the existing trend, rational development or ecological low carbon, i=a, respectively 1 、A 2 、B 11 、B 12 、B 13 、B 21 、B 22 、B 31 、B 32 、C 11 、C 12 、C 2 、D 1 Or D 2 Respectively representing elevation, gradient, water area, woodland, grassland, ecological protection red line, general ecological protection area, basic farmland, general farmland, construction land, transportation facilities, population, GDP, j=a, b, c or d, representing different element levels;
4-3-3-2, carrying out normalization processing on the original index layer:
expressing the screened index values in a digital mode, and respectively carrying out quantitative evaluation, wherein the higher the forward index expression value is, the higher the land utilization change possibility is; the higher the negative index expression value is, the lower the possibility of land utilization change is, the index normalization processing is carried out by adopting an extremum standardization method, and the range of all index value intervals is controlled to be 0, 1;
the forward index processing method comprises the following steps:
the negative index processing method comprises the following steps:
wherein L is mi For the index layer after the i-th index normalization processing, max (y mi ) And min (y) mi ) Respectively the maximum value and the minimum value in all the original index layers;
4-3-3-3, excluding layer fabrication:
wherein E is m An exclusion layer, k, for the mth scene mi A superposition coefficient of an ith index of the mth scene;
y referred to above mi 、L mi And E is m The layers are all stored in raster data form.
In the above technical solution, in the step 5, the calculation formula of the amount of carbon absorption used by the land is:
E d ＝∑e i ＝∑(S i ×K i ) (6)
wherein E is d E is the total carbon absorption i Carbon absorption produced for the ith land use type, S i For the area of the ith land use type, K i Carbon absorption coefficient for the i-th land use type;
the final calculation formula of the carbon absorption capacity of the land is as follows:
E d ＝S Woodlands ×K Woodlands +S grassland ×K Grassland +S Water area ×K Water area +S Unused land ×K Unused land (7)。
In the above technical solution, the step 6 includes the following steps:
step 6-1, determining early warning indexes:
determining that the carbon sink calculation result under the reasonable development situation is the lowest value of the early warning and the carbon sink calculation result under the ecological low-carbon situation is the highest value of the early warning according to the carbon sink calculation results corresponding to the three planning development situations of the existing trend, reasonable development and ecological low-carbon set in the step 4;
step 6-2, carbon sink simulation:
repeating the steps 1-5, acquiring real-time remote sensing data and interpreting, simulating land utilization change conditions under the existing trend situation or under the assumption of development conditions, and calculating carbon sink according to carbon absorption coefficients;
step 6-3, real-time early warning:
and (3) monitoring the carbon sink change condition in real time, and comparing the simulation result with an early warning index, if the carbon sink monitoring or predicting value of a certain land block is found to exceed or be lower than an expected interval or the preset scene conflicts with the current development, timely sending out an early warning prompt.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, carbon sink measurement is carried out through land utilization change simulation based on remote sensing, GIS, SLEEUTH model and other technical means, and a complete carbon sink simulation prediction and monitoring early warning process is constructed;
2. according to the invention, various alternative planning schemes can be provided by setting different simulation scenes, the carbon sink is subjected to simulation prediction and real-time quantitative monitoring based on land utilization change, and early warning service is provided in time, so that the method has the characteristics of science and high efficiency.
3. The invention is beneficial to forming a targeted land carbon control planning strategy, providing scientific data support for land planning management and decision making, and realizing the power-assisted double-carbon target.
Drawings
FIG. 1 is a schematic overall flow chart of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
A carbon sink monitoring and early warning method based on land utilization change simulation comprises the following steps:
step 1, constructing a database:
step 1-1, setting a time span, respectively acquiring high-resolution remote sensing image data of a plurality of years in a research area range, preprocessing the remote sensing image data, integrating a DEM (digital elevation model), and acquiring elevation and gradient grid data;
step 1-2, analyzing historical data to obtain vector data of traffic facilities, river water systems, basic farmlands, general farmlands, ecological protection red lines and general ecological protection areas corresponding to the year of the remote sensing image;
step 1-3, inquiring a statistical yearbook, obtaining population and GDP statistical data, and converting the population and GDP statistical data into vector data based on ArcGIS;
step 1-4, acquiring homeland space planning results corresponding to a research area range, and extracting planning development areas and traffic facility planning vector data;
step 1-5, based on an ArcGIS platform, performing visualization processing on the data obtained in the steps 1-1, 1-2, 1-3 and 1-4, and converting the data obtained in the steps 1-2, 1-3 and 1-4 into corresponding grid data to construct a basic research database;
step 2, according to the feature of the ground reflected by the spectrum curve of the remote sensing image, the high-resolution remote sensing image data obtained in the step 1-1 are interpreted based on an ENVI platform, and a LUCC classification system is adopted to obtain land utilization data of different time nodes in a research range, wherein the land utilization data specifically comprises vector data of cultivated land, woodland, grasslands, water areas, construction land and unused land, and the vector data is converted into corresponding grid data;
step 3, integrating the elevation and gradient grid data obtained in the step 1-1, the traffic facility, basic farmland, general farmland, ecological protection red line and general ecological protection area vector data obtained in the step 1-2, the population and GDP vector data obtained in the step 1-3, the planning development area and traffic facility planning vector data obtained in the step 1-4, and the woodland, grassland, water area and construction land vector data obtained in the step 2, analyzing land utilization change characteristics and rules based on GIS space analysis, and obtaining a driving factor index system (table 1);
table 1 shows a driving factor index system for land use variation
( And (3) injection: the index type represents the impact of the index on land use variation. "negative" means limiting the development of land utilization and "positive" means promoting the development of land utilization. )
And carrying out further integration processing on the data. Wherein A is 1 、A 2 Obtained in step 1-1, B 11 、B 12 、B 13 Obtained from step 2, B 21 、B 22 、B 31 、B 32 、C 2 (traffic facility vector data of different time nodes) is obtained by step 1-2, C 11 Obtained from step 2, C 12 And C 2 (traffic facility planning vector data) is obtained by steps 1-4, D 1 、D 2 Obtained by the steps 1-3.
And 4, introducing the driving factor index system obtained in the step 3 by combining the basic research database obtained in the step 1 and the grid data of the land utilization data of different time nodes obtained in the step 2, and carrying out future multi-year land utilization change simulation by setting three planning development scenes of different exclusion layers to preset the existing trend (I), reasonably develop (II) and ecologically lower carbon (III) by adopting a SLUTH model.
4-1, data preparation:
and (3) manufacturing information such as an Urban range layer (Urman), a traffic layer (Transportation), a Slope layer (Slope), a land utilization layer (Landsuse), a mountain shadow layer (Hillshadow) and an exclusion layer (Extruded) required by SLUTH model simulation based on a GIS platform, and constructing a model database according to the information such as unified format, size, range and coordinates.
4-1-1, the construction land grid data of the plurality of time nodes obtained in the step 2 is manufactured into a city range layer (Urban).
4-1-2, making the traffic facility grid data of the plurality of time nodes obtained in the step 1-2 into a traffic layer (Transportation).
4-1-3, making gradient raster data in the range of the research area obtained in the step 1-1 into a gradient map layer (Slope) and DEM data into a mountain shadow map layer (Hillshade).
4-1-4, creating land utilization layer (Landsuse) by using the raster data of the land utilization data of the plurality of time nodes interpreted in the step 2.
4-1-5, the river system raster data obtained in the step 1-5 is used as a limiting condition to be manufactured into an exclusion layer (Extruded) for the step 4-2 model calibration. Three different development scenarios can be preset for step 4-3 by excluding the layer (Extruded) settings, see 4-3 for specific operation of the scenario presets.
4-2, model calibration:
and inputting a plurality of layers in the model database into the SLUTH model for parameter calibration to obtain optimal parameters simulated by the SLUTH model.
The model calibration process adopts a Monte Carlo iterative calculation method, and comprises four links of coarse calibration, fine calibration, final calibration and analog parameter acquisition. After repeated simulation, the finally obtained 5 simulation parameters are respectively Diffusion coefficient (Diffusion), propagation coefficient (seed), propagation coefficient (Spread), gradient coefficient (Slope) and Road gravitation coefficient (Road gradient), and are calculated and generated by a SLUTH model.
In the calibration stage, OSM is adopted as a best-fit goodness index for determining a model, and the calculation formula is as follows:
OSM＝Compare×Pop×Edges×Clusters×Slope×X-mean×Y-mean×Fmatch (1)
wherein Compare is the ratio of the simulated value of the last year to the actual simulated value, pop is the least squares regression value of the range of the simulated and actual cities of different years, edge is the least squares regression value of the boundary of the simulated and actual cities of different years, clusters is the least squares regression value of the plaque number of the simulated and actual cities of different years, slope is the least squares regression value of the simulated and actual average gradient value of different years, X-mean is the least squares regression value of the average X coordinate value of the pixels of the simulated and actual village and villages of different years, Y-mean is the least squares regression value of the average Y coordinate value of the pixels of the simulated and actual village and Fmatch is the space matching degree of the land utilization type and actual conditions of different years obtained through simulation. The above indexes can be calculated by SLUTH model.
The specific operation steps of the model calibration are as follows: in the three links of coarse calibration, fine calibration and final calibration which are sequentially carried out in the calibration process, 5 simulation parameters of Diffusion coefficient (Diffusion), propagation coefficient (seed), propagation coefficient (Spread), gradient coefficient (Slope) and Road gravitation coefficient (Road gradient) corresponding to the simulation result of the 5 previous bits of OSM index row are respectively selected, the value range and the step length of the simulation parameters obtained by calibration in the link are determined, and a SLUTH model is input to simulate the next link. And in the calibration process, the iteration times are continuously adjusted, the parameter value range is narrowed, and finally, the optimal simulation parameter combination is obtained through a simulation parameter acquisition link and is used for the model prediction process under different scenes in the step 4-3.
4-3, scene presetting:
4-3-1, selecting a driving factor index of land utilization change according to the land utilization change characteristics, and presetting three planning development scenes (table 2) of the existing trend (I), reasonable development (II) and ecological low carbon (III).
4-3-1-1, existing trend (i) scenario: it is assumed that urban development is not interfered by external factors and proceeds according to historical trends.
4-3-1-2, reasonably developing the (II) scene: and the urban balance is assumed to develop steadily, and moderate development is carried out on the premise of reasonable and compliance.
4-3-1-3, ecological low carbon (iii) scenario: the urban development is assumed to be limited by ecological factors, so that the environment is protected, the urban development is strictly limited, and the aims of reducing carbon emission and increasing carbon sink are fulfilled.
4-3-2, layer element exclusion setting principle:
4-3-2-1, topography factors: under the circumstance of the existing trend (I), comprehensively considering the current terrain conditions, and grading, removing and setting; extracting a region suitable for development and construction under the situation of reasonable development (II); in the ecological low-carbon (III) scene, the area unsuitable for development and construction is excluded.
4-3-2-2, ecological factors: under the conditions of the existing trend (I) and reasonable development (II), only the development-restricted area is excluded; under the ecological low-carbon (III) scene, the area which is unfavorable for ecological environment protection is comprehensively eliminated.
4-3-2-3, construction factors: under the conditions of the existing trend (I) and the ecological low-carbon (III), developing according to the current trend; in the context of rational development (II), more areas with development potential are considered in addition to the current situation.
4-3-2-4, social factors: under the circumstance of the existing trend (I), comprehensively considering the influence of population and GDP on development; extracting a region favorable for development under the condition of reasonable development (II); in the context of ecologically low carbon (III), areas of unfavorable development are excluded.
4-3-3, excluding the layer calculation method:
4-3-3-1, original index layer:
wherein y is mi An original index layer, x, which is the ith index mij Index value lambda of the jth element level of the mth scene which is the ith index mij The exclusion coefficient of the jth element level of the ith index for the mth scenario, where m=i, ii or iii represents the existing trend, rational development or ecological low carbon, i=a, respectively 1 、A 2 、B 11 、B 12 、B 13 、B 21 、B 22 、B 31 、B 32 、C 11 、C 12 、C 2 、D 1 Or D 2 Respectively, elevation, slope, water area, woodland, grassland, ecological protection red line, general ecological protection area, basic farmland, general farmland, construction land (town, village), traffic facilities, population, GDP, j=a, b, c, or d, and represent different element levels.
4-3-3-2, carrying out normalization processing on the original index layer:
and expressing the screened index values in a digital mode, and respectively carrying out quantitative evaluation. Because each index unit, calculation mode and judgment standard are different and cannot be directly calculated, the original data is converted into a standardized index value, and the positive index and the negative index are separately processed. The higher the forward index representation value, the higher the probability of land use change; the higher the negative indicator representation value, the lower the likelihood of land use change. And (3) carrying out index normalization processing by adopting an extremum standardization method, and controlling the range of all index value intervals to be 0 and 1.
The forward index processing method comprises the following steps:
the negative index processing method comprises the following steps:
wherein L is mi For the index layer after the i-th index normalization processing, max (y mi ) And min (y) mi ) The maximum and minimum values in all original index layers, respectively.
4-3-3-3, excluding layer fabrication:
wherein E is m Is the mthExclusion layer, k of scene mi The superposition coefficient of the ith index for the mth scene.
Y referred to above mi 、L mi And E is m The layers are all stored in raster data form.
Table 2 sets up conditions for scene presettings and excludes layer elements
Wherein,
(1) The important river water area, the important forest land and the important grassland are water areas, forest lands and grasslands which are defined by the government of people at all levels of province, city and county and are required to be specially protected for bearing specific ecological functions, or are positioned in ecological protection red lines.
(2) Population density was used as a judgment population (D 1 ) Is an indicator of (2). The region with higher population density is a region with population density higher than the average level of the region, and the region with lower population density is a region with population density lower than the average level of the region.
(3) With the ground-average GDP as a judgment GDP (D) 2 ) Is an indicator of (2). The region with higher ground average GDP is a region with higher ground average GDP than the average region level, and the region with lower ground average GDP is a region with lower ground average GDP than the average region level.
4-4, simulation prediction:
setting the initial year and the final year of SLEETH model simulation, inputting different exclusion layers, and respectively adopting the optimal parameters to simulate and predict land utilization change conditions of a research area under three planning development scenes of the existing trend (I), reasonable development (II) and ecological low carbon (III) to obtain land utilization distribution conditions of different time nodes.
And 5, calculating the area of each land utilization type of different time nodes, and calculating the carbon absorption of the land in the past and future in a certain time period by using the carbon absorption coefficient (table 3) as a carbon sink measurement result.
The calculation formula of the land utilization carbon absorption amount is as follows:
E d ＝∑e i ＝∑(S i ×K i ) (6)
wherein E is d For total carbon absorption (kg/a), e i Carbon absorption (kg/a), S for the ith land use type i For the i-th land use type area (m 2 )，K i Carbon absorption coefficient (kg/(m) 2 ·a))。
Table 3 shows the carbon absorption coefficient (unit: kg/(m) 2 ·a))
| Land use type | Woodlands | Grassland | Water area | Unused land |
| Carbon absorption coefficient | 0.0644 | 0.021 | 0.0253 | 0.005 |
The 6 land use types under the LUCC classification system, only 4 land types of cultivated land, grassland, water area, and unused land, produce carbon absorption. Therefore, carbon sequestration across the area of investigation can be obtained by calculating only the carbon pick-up of these 4 land use types.
The final calculation formula of the carbon absorption capacity of the land is as follows:
E d ＝S Woodlands ×K Woodlands +S grassland ×K Grassland +S Water area ×K Water area +S Unused land ×K Unused land (7)
Step 6, determining monitoring and early warning indexes according to carbon sink prediction results of different scenes; acquiring and interpreting remote sensing image data in real time by combining a computer technology, simulating future land utilization change conditions according to current trend or set development conditions, and calculating carbon sink; and (3) monitoring the carbon sink change condition in real time, comparing the carbon sink simulation result with monitoring and early warning indexes, and early warning in time under the condition that the carbon sink simulation result exceeds or is lower than an expected interval, so as to finally determine an optimal planning development plan.
6-1, determining early warning indexes:
and (3) determining that the carbon sink calculation result in the situation of reasonable development (II) is the lowest value of the early warning and the carbon sink calculation result in the situation of ecological low carbon (III) is the highest value of the early warning according to the carbon sink calculation results corresponding to the three planning development situations of the existing trend (I), the reasonable development (II) and the ecological low carbon (III) set in the step (4).
6-2, carbon sink simulation:
and (3) repeating the steps 1-5, acquiring real-time remote sensing data and interpreting, simulating land utilization change conditions under the situation of the existing trend (I) or under the assumption of development conditions, and calculating carbon sink according to the carbon absorption coefficient.
6-3, real-time early warning:
and monitoring the carbon sink change condition in real time, and comparing the simulation result with the early warning index. If the monitoring or predicting value of the carbon sink of a certain land block exceeds or is lower than the expected interval or the preset scene conflicts with the current development, an early warning prompt is sent out in time.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (7)
1. A carbon sink monitoring and early warning method based on land utilization change simulation is characterized by comprising the following steps:
step 1, constructing a database:
step 1-1, setting a time span, respectively acquiring high-resolution remote sensing image data of a plurality of years in a research area range, preprocessing the remote sensing image data, integrating a DEM (digital elevation model), and acquiring elevation and gradient grid data;
step 1-2, analyzing historical data to obtain vector data of traffic facilities, river water systems, basic farmlands, general farmlands, ecological protection red lines and general ecological protection areas corresponding to the year of the remote sensing image;
step 1-3, inquiring a statistical yearbook, obtaining population and GDP statistical data, and converting the population and GDP statistical data into vector data based on ArcGIS;
step 1-4, acquiring homeland space planning results corresponding to a research area range, and extracting planning development areas and traffic facility planning vector data;
step 1-5, based on an ArcGIS platform, performing visualization processing on the data obtained in the steps 1-1, 1-2, 1-3 and 1-4, and converting vector data obtained in the steps 1-2, 1-3 and 1-4 into corresponding grid data to construct a basic research database;
step 2, according to the feature of the ground reflected by the spectrum curve of the remote sensing image, the high-resolution remote sensing image data obtained in the step 1-1 are interpreted based on an ENVI platform, and a LUCC classification system is adopted to obtain land utilization data of different time nodes in a research range, wherein the land utilization data specifically comprises vector data of cultivated land, woodland, grasslands, water areas, construction land and unused land, and the vector data is converted into corresponding grid data;
step 3, integrating the elevation and gradient grid data obtained in the step 1-1, the traffic facility, basic farmland, general farmland, ecological protection red line and general ecological protection area vector data obtained in the step 1-2, the population and GDP vector data obtained in the step 1-3, the planning development area and traffic facility planning vector data obtained in the step 1-4, and the woodland, grassland, water area and construction land vector data obtained in the step 2, analyzing land utilization change characteristics and rules based on GIS space analysis, and obtaining a driving factor index system;
step 4, combining the basic research database obtained in the step 1 and land utilization grid data of different time nodes obtained in the step 2, introducing the driving factor index system obtained in the step 3, adopting a SLUTH model, and carrying out future multi-year land utilization change simulation by setting three planning development scenes of different exclusion layers, namely preset existing trend, reasonable development and ecological low carbon;
step 5, calculating the area of each land utilization type of different time nodes, and calculating the carbon absorption of the land in a certain time period in the past and in the future by utilizing the carbon absorption coefficient to be used as a carbon sink measurement result;
and 6, monitoring the carbon sink change condition in real time, comparing the carbon sink simulation result with monitoring and early warning indexes, and early warning in time under the condition that the carbon sink simulation result exceeds or is lower than an expected interval.
2. The carbon sink monitoring and early warning method based on land use change simulation as claimed in claim 1, wherein the step 4 specifically comprises the following steps:
step 4-1, data preparation: manufacturing an urban range layer, a traffic layer, a gradient layer, a land utilization layer, a mountain shadow layer, an exclusion layer and other information required by SLEETH model simulation based on a GIS platform, and constructing a model database by unifying information such as format, size, range and coordinates;
step 4-2, model calibration: inputting a plurality of layers in the model database obtained in the step 4-1 into a SLEEUTH model for parameter calibration to obtain optimal parameters simulated by the SLEEUTH model;
step 4-3, scene presetting: according to land utilization change characteristics, selecting driving factor indexes of land utilization change, presetting three planning development scenes of existing trend, reasonable development and ecological low carbon, and performing layer element exclusion setting in a SLUTH model;
step 4-4, simulation prediction: setting the initial year and the final year of SLEETH model simulation, inputting different exclusion layers, and respectively simulating and predicting the land utilization change condition of a research area by adopting the optimal parameters under the three planning development scenes of the existing trend, reasonable development and ecological low carbon.
3. The carbon sink monitoring and early warning method based on land use change simulation as claimed in claim 2, wherein the step 4-1 specifically comprises the following steps:
step 4-1-1, manufacturing raster data of construction lands of a plurality of time nodes obtained in the step 2 into an urban area map layer;
step 4-1-2, manufacturing the raster data of the traffic facilities with the plurality of time nodes obtained in the step 1-5 into a traffic layer;
step 4-1-3, manufacturing gradient raster data in the range of the research area obtained in the step 1-1 into a gradient map layer and manufacturing DEM data into a mountain shadow map layer;
step 4-1-4, manufacturing the raster data of the land utilization data of the plurality of time nodes obtained in the step 2 into a land utilization layer;
step 4-1-5, manufacturing the river system grid data obtained in the step 1-5 as a limiting condition into an exclusion layer for calibrating the model in the step 4-2; three different development scenarios can be preset for step 4-3 by excluding the layer settings.
4. The carbon sink monitoring and early warning method based on land utilization change simulation as claimed in claim 2, wherein the model calibration process in the step 4-2 adopts a Monte Carlo iterative calculation method, four links are obtained through coarse calibration, fine calibration, final calibration and simulation parameters, and an OSM index is adopted as a best-fit goodness index for determining a model;
OSM＝Compare×Pop×Edges×Clusters×Slope×X-mean×Y-mean×Fmatch (1)
wherein Compare is the ratio of the simulated value of the last year to the actual simulated value, pop is the least squares regression value comparing the simulated value of the different years with the actual city range, edge is the least squares regression value comparing the simulated value of the different years with the actual city boundary, clusters is the least squares regression value comparing the simulated value of the different years with the actual city construction land patch number, slope is the least squares regression value comparing the simulated value of the different years with the actual average gradient value, X-mean is the least squares regression value comparing the average X coordinate value of the simulated value of the different years with the actual village landing pixels, Y-mean is the least squares regression value comparing the average Y coordinate value of the simulated value of the different years with the actual village landing pixels, and Fatch is the space matching degree of the simulated land utilization types of the different years with the actual conditions;
finally, obtaining the value ranges and the step sizes of 5 simulation parameters, namely a diffusion coefficient, a propagation coefficient, a gradient coefficient and a road gravitation coefficient, which are used for the model prediction process under different scenes in the step 4-3.
5. The carbon sink monitoring and early warning method based on land use change simulation as claimed in claim 2, wherein the layer calculation method is excluded from the SLEUTH model in step 4-3, and is as follows:
4-3-3-1, original index layer:
wherein y is mi An original index layer, x, which is the ith index mij Index value lambda of the jth element level of the mth scene which is the ith index mij The exclusion coefficient of the jth element level of the ith index for the mth scenario, where m=i, ii or iii represents the existing trend, rational development or ecological low carbon, i=a, respectively 1 、A 2 、B 11 、B 12 、B 13 、B 21 、B 22 、B 31 、B 32 、C 11 、C 12 、C 2 、D 1 Or D 2 Respectively represent elevation, gradient, water area, forest land, grassland, ecological protection red line and general ecological protectionGuard zone, primary farmland, general farmland, construction land, transportation facilities, population, GDP, j=a, b, c or d, representing different element levels;
4-3-3-2, carrying out normalization processing on the original index layer:
expressing the screened index values in a digital mode, and respectively carrying out quantitative evaluation, wherein the higher the forward index expression value is, the higher the land utilization change possibility is; the higher the negative index expression value is, the lower the possibility of land utilization change is, the index normalization processing is carried out by adopting an extremum standardization method, and the range of all index value intervals is controlled to be 0, 1;
the forward index processing method comprises the following steps:
the negative index processing method comprises the following steps:
wherein L is mi For the index layer after the i-th index normalization processing, max (y mi ) And min (y) mi ) Respectively the maximum value and the minimum value in all the original index layers;
4-3-3-3, excluding layer fabrication:
wherein E is m An exclusion layer, k, for the mth scene mi A superposition coefficient of an ith index of the mth scene;
y referred to above mi 、L mi And E is m The layers are all stored in raster data form.
6. The carbon sink monitoring and early warning method based on land use change simulation according to claim 1, wherein in the step 5, the land use carbon absorption amount calculation formula is:
E d ＝∑e i ＝∑(S i ×K i ) (6)
wherein E is d E is the total carbon absorption i Carbon absorption produced for the ith land use type, S i For the area of the ith land use type, K i Carbon absorption coefficient for the i-th land use type;
the final calculation formula of the carbon absorption capacity of the land is as follows:
E d ＝S Woodlands ×K Woodlands +S grassland ×K Grassland +S Water area ×K Water area +S Unused land ×K Unused land (7)。
7. The carbon sink monitoring and early warning method based on land use change simulation as claimed in claim 1, wherein the step 6 comprises the following steps:
step 6-1, determining early warning indexes:
determining that the carbon sink calculation result under the reasonable development situation is the lowest value of the early warning and the carbon sink calculation result under the ecological low-carbon situation is the highest value of the early warning according to the carbon sink calculation results corresponding to the three planning development situations of the existing trend, reasonable development and ecological low-carbon set in the step 4;
step 6-2, carbon sink simulation:
repeating the steps 1-5, acquiring real-time remote sensing data and interpreting, simulating land utilization change conditions under the existing trend situation or under the assumption of development conditions, and calculating carbon sink according to carbon absorption coefficients;
step 6-3, real-time early warning:
and (3) monitoring the carbon sink change condition in real time, and comparing the simulation result with an early warning index, if the carbon sink monitoring or predicting value of a certain land block is found to exceed or be lower than an expected interval or the preset scene conflicts with the current development, timely sending out an early warning prompt.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140022552A (en) * | 2012-08-14 | 2014-02-25 | 한국건설기술연구원 | Apparatus for estimating quantities of carbon emissions |
| KR20140102095A (en) * | 2013-02-13 | 2014-08-21 | 청주대학교 산학협력단 | Land use/transportation/environment model simulation system of low carbon emission style in urban development zone |
| CN113487079A (en) * | 2021-07-02 | 2021-10-08 | 天津大学 | Method and device for low-carbon layout of urban land utilization scale structure |
| CN113642764A (en) * | 2021-06-30 | 2021-11-12 | 重庆大学 | Village and town settlement space evolution simulation prediction method and computer equipment |
| CN114493099A (en) * | 2021-12-21 | 2022-05-13 | 成都理工大学 | Urban and rural gradient construction method for carbon sink ecological importance |
| CN114997480A (en) * | 2022-05-24 | 2022-09-02 | 福州大学 | Low-carbon guide provincial territory space partition optimization simulation method based on MOP-PLUS model |
| CN115130832A (en) * | 2022-06-06 | 2022-09-30 | 北京工业大学 | Urban carbon metabolism evolution analysis method under soil utilization change visual angle |
-
2022
- 2022-11-08 CN CN202211392728.4A patent/CN116596100B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140022552A (en) * | 2012-08-14 | 2014-02-25 | 한국건설기술연구원 | Apparatus for estimating quantities of carbon emissions |
| KR20140102095A (en) * | 2013-02-13 | 2014-08-21 | 청주대학교 산학협력단 | Land use/transportation/environment model simulation system of low carbon emission style in urban development zone |
| CN113642764A (en) * | 2021-06-30 | 2021-11-12 | 重庆大学 | Village and town settlement space evolution simulation prediction method and computer equipment |
| CN113487079A (en) * | 2021-07-02 | 2021-10-08 | 天津大学 | Method and device for low-carbon layout of urban land utilization scale structure |
| CN114493099A (en) * | 2021-12-21 | 2022-05-13 | 成都理工大学 | Urban and rural gradient construction method for carbon sink ecological importance |
| CN114997480A (en) * | 2022-05-24 | 2022-09-02 | 福州大学 | Low-carbon guide provincial territory space partition optimization simulation method based on MOP-PLUS model |
| CN115130832A (en) * | 2022-06-06 | 2022-09-30 | 北京工业大学 | Urban carbon metabolism evolution analysis method under soil utilization change visual angle |
Non-Patent Citations (4)
| Title |
|---|
| Exploring the impacts of urban growth on carbon storage under integrated spatial regulation: A case study of Wuhan, China;Lu Li etc.;Ecological Indicators;全文 * |
| Smart carbon monitoring platform under IoT-Cloud architecture for small cities in B5G;He Zhang etc.;Wireless Networks;全文 * |
| 基于差异化控碳思路的县级国 土空间低碳规划方法探索;张赫等;城市规划学刊;全文 * |
| 武汉城市圈城镇建设用地扩张模拟及碳排放响应研究;魏旭华;中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑;全文 * |
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