CN111460003A - Method for detecting coupling relation between land utilization and earth surface temperature based on urban grouping scale - Google Patents
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Abstract
The invention discloses a method for detecting a coupling relation between land utilization and earth surface temperature based on urban mass scale, which comprises the following steps of (1) obtaining land utilization data of a region to be researched, analyzing landscape pattern characteristics, (2) obtaining remote sensing image data of the region to be researched and carrying out earth surface temperature inversion to obtain earth surface temperature, (3) carrying out global Morland index calculation and local Morland index calculation based on the earth surface temperature, and then discussing spatial clustering characteristics of the average earth surface temperature of the region to be researched, and (4) obtaining average earth surface temperature in each grid unit and various land utilization ratios in each grid unit, and analyzing the coupling relation and spatial instability between the land utilization and the earth surface temperature of the region by adopting a geographical weighted regression model.
Description
Technical Field
The invention belongs to the research on the coupling relationship between land utilization and earth surface temperature, and particularly relates to a method for detecting the coupling relationship between land utilization and earth surface temperature based on urban grouping scale.
Background
Since the 90 s of the 20 th century, research related to land use/coverage changes has become the leading edge and hot spot of current global change research. Land use/coverage changes can have an impact on regional climate and global energy balance. With the development of social economy, the population is rapidly increased, the original land utilization pattern is changed by large-scale and rapid urbanization, and the ecological safety of the area is seriously threatened. Therefore, the research on the coupling relation between the land utilization and the earth surface temperature can provide theoretical reference for effectively revealing the health condition of the urban area and guaranteeing the ecological safety of the area.
The method is characterized in that the natural landscape of the suburb, which originally mainly comprises a permeable surface and transpiration vegetation, is gradually replaced by a man-made landscape which mainly comprises an impermeable surface due to the expansion of a city, the property of an underlying surface of the city is changed, and conditions are created for the formation of a heat island of the city.
Disclosure of Invention
The method combines a landscape pattern index, a global Moran index, an L ISA cluster map and a GWR model, effectively reveals local coupling relations between land utilization and earth surface temperature in different areas in the urbanization process, improves the accuracy of coupling relation analysis results, and provides reference for future urban health development, reasonable regulation and control of urban heat island effect and regional ecological safety guarantee.
In order to achieve the purpose, the invention adopts the following technical scheme:
the method for detecting the coupling relation between land utilization and earth surface temperature based on the urban grouping scale comprises the following steps:
(1) the method comprises the steps of obtaining land utilization data of a to-be-researched area, analyzing various land utilization ratios and landscape indexes in the to-be-researched area based on the land utilization data, and analyzing landscape pattern characteristics based on the landscape indexes;
(2) acquiring remote sensing image data of a region to be researched, performing data preprocessing on the remote sensing image data, and performing surface temperature inversion on the basis of the preprocessed remote sensing image data to obtain surface temperature;
(3) performing global Moran index calculation and local Moran index calculation based on the surface temperature to obtain a spatial correlation local index cluster map, and discussing the spatial clustering characteristics of the average surface temperature of the area to be researched based on the spatial correlation local index cluster map;
(4) and acquiring the average earth surface temperature in each grid unit and the utilization ratio of various types of land in each grid unit, and analyzing the coupling relation and the spatial instability between the regional land utilization and the earth surface temperature by adopting a geographical weighted regression model.
Specifically, the step of analyzing the landscape pattern characteristics in the step (1) is as follows:
and respectively calculating 7 landscape indexes, namely a maximum plaque area index, a plaque density, an average area, a plaque shape index, an average nearest distance, a plaque combination degree and an aggregation degree, by taking the grid unit with a set size as an evaluation unit, and analyzing the landscape fragmentation degree and connectivity of the region to be researched based on the calculated landscape indexes.
Specifically, the step (2) of performing an earth surface temperature inversion method based on the preprocessed remote sensing image data includes:
(201) calculating a normalized difference vegetation index based on the preprocessed remote sensing image data, and then calculating the coverage of surface vegetation;
(202) estimating the specific radiance of the natural surface pixels and the specific radiance of the town pixels based on the coverage of the surface vegetation;
(203) calculating a radiance value based on the radiance ratio of the natural surface pixel and the radiance ratio of the town pixel;
(204) and calculating the earth surface temperature based on the amplitude brightness value.
Specifically, the step (3) includes:
(301) according to the earth surface temperature, counting the average temperature in the grid units with set sizes, and calculating the global Moran index and the local Moran index of each grid unit based on a spatial autocorrelation tool;
(302) performing autocorrelation analysis based on the calculation result of the global Moran index;
(302) and performing spatial clustering on the calculation result of the local Molan index to obtain a spatial correlation local index cluster diagram, and analyzing according to the spatial correlation local index cluster diagram to obtain surface temperature aggregation characteristics, wherein the surface temperature aggregation characteristics comprise high aggregation, high and low aggregation, low and high aggregation, low aggregation and non-significance.
Specifically, the step (4) includes:
(401) calculating the relevance of various land utilization types by a geographical weighted regression analysis tool based on the average earth surface temperature of each grid unit and the proportion of different land utilization types by taking the grid units with set sizes as standards;
(402) and analyzing different influence degrees of the land use types on the earth surface temperature in space from the aspects of model goodness of fit and correlation coefficient.
Compared with the prior art, the method has the advantages that the landscape pattern index, the global Moran' sI index, the L ISA cluster map and the geographical weighted regression model GWR are combined to analyze the relationship between the landscape pattern index and the ISA cluster map, the fragmentation degree of the land type of the area to be researched can be reflected to serve as the basis of analysis, the global Moran index and the L ISA cluster map are used for analyzing the spatial correlation and the aggregation of the ground surface temperature, the relationship between the land type and the temperature can be simply analyzed through the L cluster map and the land utilization spatial distribution map, the GWR model can reflect the difference of the temperature increasing or reducing capacity in space, the relationship between the land type and the temperature can be deeply analyzed, the local coupling relationship between the land utilization and the ground surface temperature in the urbanization process is effectively disclosed, the accuracy of the coupling relationship analysis result is improved, and reference is provided for future urban health development, reasonable regulation and control of urban heat island effect and regional ecological safety guarantee.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a method for sensing land use and earth surface temperature coupling based on the urban mass scale of the present invention;
FIG. 2 is a spatial distribution map of land use type for a research area;
FIG. 3 is a plot of the surface temperature profile of the area of interest;
FIG. 4 is a plot of the mean surface temperature L ISA clusters for a study area;
figure 5 is a plot of the spatial variation of the fitted land use ratio versus surface temperature for a GWR model in a study area.
Detailed Description
The invention is further elucidated with reference to the drawings and the detailed description. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the method for detecting a coupling relationship between land utilization and earth surface temperature based on an urban mass scale provided in this embodiment includes the following steps:
step 1, land use data of the area to be researched is obtained, for example, downloaded from an administrative unit server where the area to be researched is located, and the land use data may be simple tabular data, such as cultivated land: the area is 200 square kilometers, and according to the data mapping, the space distribution map can also be directly downloaded, as shown in figure 2, various land utilization proportion statistics and landscape index calculation are carried out, and landscape pattern characteristics are analyzed. The landscape pattern characteristics comprise landscape fragmentation degree and connectivity.
Specifically, the processing procedure of this step is as follows:
step 101, using 2km × 2km (or other set size) grid units as evaluation units, selecting landscape indexes (meaning and ecological meaning are shown in table 1 below) of a maximum patch AREA index (L PI), a Patch Density (PD), an average AREA (AREA _ MN), a patch SHAPE index (shield _ MN), an average nearest distance (ENN _ MN), a patch combination degree (COHESION), and an aggregation degree (AI) 7.
TABLE 1
And 102, calculating each landscape index by using Fragstatss 4.2 software, and analyzing the fragmentation degree and connectivity of the regional landscape based on the landscape indexes. Based on the land use data of the area studied in the test example, the results of each landscape index calculated are shown in table 2. Based on the land use data of the area to be researched in the test example, various land use ratios of the area to be researched are obtained through calculation, and various land use ratios in each grid unit can be calculated based on the various land use ratios of the area to be researched.
TABLE 2
TABLE 3
| Type of land use | Ratio (%) |
| Cultivation of land | 8.13 |
| Garden ground | 18.73 |
| Forest land | 53.15 |
| Grass land | 3.76 |
| Land for construction | 12.12 |
| Water area | 3.24 |
Note: 0.87 percent of unused land.
As shown in Table 2, the 7 landscape index calculation results show that grass land has the highest patch density, L PI, AREA _ MN and SHAPE _ MN indexes are all the smallest, which shows that the grass land has high fragmentation degree but regular SHAPE, forest land and water AREA have relatively low patch density, but the ENN _ MN calculation result has large difference, the distance of the nearest patch of the water AREA is the largest, the distance of the adjacent patch of the forest land is the smallest, which can show that the water AREA is discrete distribution, the fragmentation degree of the forest land is low, and the forest land has sheet distribution, meanwhile, from the physical Connectivity (COHESION) and polymerization degree index (AI), the forest land is 99.86% and 79.61%, and the grass land is only 36.07% and 15.45%, which further shows that in 6 land utilization types, the forest land has low fragmentation degree of landscape, high polymerization degree, and relatively broken grass land connectivity is low.
And 2, acquiring remote sensing image data of the area to be researched, for example, downloading the remote sensing image data from an administrative unit server where the area to be researched is located, performing data preprocessing of geometric correction, radiometric calibration, atmospheric correction and registration cutting on the remote sensing image data by utilizing ENVI software, and performing surface temperature inversion by adopting a radiative transfer equation method to obtain the surface temperature.
Specifically, the steps of performing a surface temperature inversion method based on the remote sensing image data comprise:
step 201, calculating the coverage degree Fv of the surface vegetation according to a formula (1);
Fv＝(NDVI-NDVIs)/(NDVIv-NDVIs) (1)
in the formula, NDVI is a normalized difference vegetation index, and NDVIv is 0.70 and NDVIs is 0.05.
Step 202, estimating the emissivity of the natural surface and the town pixel by formulas (2) and (3);
surface＝0.9625+0.0614Fv-0.0461Fv2(2)
building＝0.9589+0.086Fv-0.0671Fv2(3)
in the formula (I), the compound is shown in the specification,surfaceandbuildingrespectively representing the emissivity of natural surface pixels and the emissivity of town pixels.
Step 203, calculating the radiation brightness value B (T) at the temperature Ts according to the formula (4)s)；
B(Ts)＝[Lλ-L↑-τ·(1-)L↓]/τ· (4)
In the formula, the atmospheric transmittance tau, the atmospheric uplink radiation L ↓andthe atmospheric downlink radiation L ↓canbe input into imaging time and central longitude and latitude query and calculated through an NASA (http:// atmcorr.gsfc.nasa.gov /), and are surface ratio radiation rate values, LλThe radiation calibration values for the thermal infrared band are determined. The surface emissivity is the sum of the emissivity of the water body, the natural surface and the town pixels, and generally, the emissivity of the water body pixels is assigned 0995, and the emissivity of the natural surface and the town pixels is calculated according to the formulas (2) and (3). The radiation Calibration value of the thermal infrared band is calculated according to deviation parameters in a header file of an image, an on-satellite radiation brightness value and the like, and is calculated by using a radio Calibration tool in ENVI5.0 software.
Step 204, obtaining the surface temperature T by using the formula (5)s。
Ts＝K2/ln(K1/B(Ts)+1)-273.15 (5)
In the formula, K1And K2Respectively, the scaling constant, K in L andsat81＝480.89，K2＝1201.14。
In the experimental example, temperature inversion is performed by using a corresponding tool in the ENVI5.0 software, and the result is shown in FIG. 3.
And 3, calculating a global Moran index and a local Moran index on the basis of the inversion data of the earth surface temperature, and discussing the spatial clustering characteristic of the average temperature of the earth surface of the region on the basis of an L ISA cluster map.
Specifically, first, the average temperature within the grid cells of set size is counted according to the surface temperature calculated in step 2, and the global Moran's I (Moran index) and local Moran's I for each grid cell are calculated based on the spatial autocorrelation tool.
The global Moran index is calculated as:
the local Moire index is calculated as:
in the formula, I represents the Moire index, xi、xjRespectively represent the average value of the vulnerability indexes in the ith grid unit and the jth grid unit,
means of vulnerability, w, of all evaluation unitsijThe method refers to a space weight matrix, s represents the sum of elements of the space weight matrix, and n is the number of grid units.
Then, Spatial clustering is carried out on the calculation result of the local Molan index, so as to obtain a Spatial correlation local index (L external Indicators of Spatial Association, L ISA) cluster map, wherein the cluster map presents the distribution situation of cluster modes, and the cluster map mainly comprises 5 different Spatial aggregation modes, namely high-high aggregation (H-H), high-low aggregation (H-L), low-high aggregation (L-H), low-low aggregation (L-L) and non-significance (No significan). definition of various L ISA aggregation modes is shown in Table 4.
TABLE 4
And judging the spatial correlation of the earth surface temperature according to the global Moran index. Through calculation, the global Moran's I index of the ground surface temperature of the study area in the experiment is 0.7809, the Z score is 188.9284 (under p <0.05, when Z >1.96 or Z < -1.96, the study object has obvious spatial autocorrelation in space, and the Moran's I index >0 is positive correlation, and vice versa, the ground surface temperature of the urban population has obvious spatial autocorrelation and is obvious positive correlation).
It can be seen from the various clustering pattern ratios and distributions in the L ISA cluster map (shown in fig. 4) that the surface temperature of the metropolitan area exhibits significant spatial clustering and is dominated by high (H-H) and low (L-L) clustering.
And 4, acquiring the average earth surface temperature and the proportion of different land use types in each grid unit, and analyzing the coupling relation and the spatial instability between the regional land use and the earth surface temperature by adopting a Geographical Weighted Regression (GWR) model.
Specifically, in this step, firstly, the grid units with set sizes are used as a standard, and correlation of various land use types is calculated by a geographical weighted regression analysis tool based on the average surface temperature of each grid unit and the proportion of different land use types.
Then, the different degrees of influence of the land use types on the earth surface temperature in space are analyzed from the aspects of model goodness of fit and correlation coefficient (shown in table 5). Based on the results obtained from the analysis, a corresponding display diagram can be created, as shown in fig. 5, or a corresponding analysis report can be created.
The local coupling relation between the land utilization types of cultivated land, forest land, construction land and water areas 4 and the earth surface temperature is mainly analyzed, and the space difference of the relation between the land utilization ratio and the earth surface temperature based on GWR model fitting is shown in figure 5.
From the results plot, it can be seen that: the temperature of the cultivated land, the construction land and the water area has the function of increasing the temperature, and the temperature increasing degrees of the cultivated land and the construction land have larger difference in space. The temperature rise degree is far greater than that of the southeast plain area due to the increase of unit area of cultivated land or construction land in mountainous and hilly areas in the middle and west. Probably because cultivated land or construction land invade the original garden area, not only the underlying surface condition of the area is changed, but also the environmental disturbance caused by the change of the land utilization type influences the growth and cooling capacity of peripheral garden lands and forest lands. The water area normally has the function of cooling, but the result reveals that the water area in the research area has the function of heating. The possible reasons are that the coverage proportion of the water area is small, the distribution is scattered, and the influence of artificial disturbance action is strong, such as excavation and drainage, but the areas of construction land, cultivated land, garden land and the like around the water area are also enlarged, and the surface temperature does not fall or rise reversely due to environmental disturbance.
TABLE 5
The forest land has a cooling effect, and as shown in table 5, the spatial instability of the forest land on the surface temperature is also significant. In fig. 5, (a), (B), (C), and (D) show cultivated land, forest land, construction land, and water area, respectively, and (a), (B), and (C) show the occupation ratio, correlation coefficient, and model goodness of fit, respectively. It can be found from fig. 5 that the cooling capacity of the forest land in the forest area is higher than that of the eastern coastal area, the main reason may be that in plain areas, especially in urban gathering areas, the forest land proportion is small and the fragmentation degree is high, the urban environmental effect makes the growth situation of the forest land vegetation poor, and further the transpiration of the vegetation is weak.
Through the analysis of the steps, it can be seen that: the surface temperatures of different land utilization types in the research area are obviously different, the temperature of the construction land is the highest, and the temperatures of the forest land and the garden land are lower; the earth surface temperature has obvious space positive autocorrelation and space aggregation, and the overall view shows the space distribution pattern of low temperature in the northwest and high temperature in the southeast. In eastern coastal areas, mainly construction land, cultivated land and garden land, the surface temperature is highly concentrated; and the western mountain areas mainly including forest lands represent low-value gathering areas. The cultivated land, the garden land, the grassland, the construction land and the water area have the effect of temperature increase, the forest land has the function of temperature reduction, but the capabilities of temperature increase and temperature reduction at different spatial positions have larger difference; the spatial instability of the surface temperature is closely related to the land utilization type and the surrounding environment thereof, the expansion of the construction land, the increase of cultivated land or garden land can change the underlying surface condition, and environmental disturbance is caused to influence the regulation and control capability of the original land utilization type on the temperature; the cooling effect of the forest land can generate difference in space, and the difference has great relation with the distribution aggregation degree, the internal vegetation growth environment, the artificial influence and the like.
The method for analyzing land utilization and land surface temperature comprises the steps of combining a landscape pattern index, a global Moran's I index, a L ISA cluster map and a geographical weighted regression model GWR to analyze the relationship between the landscape pattern index and the ground surface temperature, wherein the landscape pattern index can reflect the breakage degree of the land type of a region to be researched and is used as the basis of analysis, the global Moran index and the L ISA cluster map are used for analyzing the spatial correlation and the aggregation of the land surface temperature, the relationship between the land type and the temperature can be simply analyzed through the L ISA cluster map and a land utilization spatial distribution map, for example, the land type and the temperature are shown in the east coastal region mainly comprising construction land, cultivated land and garden, the land surface temperature shows high aggregation, and the land type and the temperature show in the west region mainly comprising forest land show low-value aggregation region "
A global regression model such as a least square method O L S is adopted in many researches, but only certain land types can be obtained through analysis and have the warming or cooling effect, but certain space non-stationarity exists in the influence of land use change on the earth surface temperature, a GWR model can reflect the difference of warming or cooling capacity in space, the relationship between the land types and the temperature can be analyzed deeply, the local coupling relationship between the land use and the earth surface temperature in different areas in the urbanization process is effectively disclosed, the accuracy of the coupling relationship analysis result is improved, and reference is provided for future urban health development, reasonable regulation and control of urban heat island effect and regional ecological safety guarantee.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. The method for detecting the coupling relation between land utilization and earth surface temperature based on the urban mass scale is characterized by comprising the following steps of:
(1) the method comprises the steps of obtaining land utilization data of a to-be-researched area, analyzing various land utilization ratios and landscape indexes in the to-be-researched area based on the land utilization data, and analyzing landscape pattern characteristics based on the landscape indexes;
(2) acquiring remote sensing image data of a region to be researched, performing data preprocessing on the remote sensing image data, and performing surface temperature inversion on the basis of the preprocessed remote sensing image data to obtain surface temperature;
(3) performing global Moran index calculation and local Moran index calculation based on the surface temperature to obtain a spatial correlation local index cluster map, and discussing the spatial clustering characteristics of the average surface temperature of the area to be researched based on the spatial correlation local index cluster map;
(4) and acquiring the average earth surface temperature in each grid unit and the utilization ratio of various types of land in each grid unit, and analyzing the coupling relation and the spatial instability between the regional land utilization and the earth surface temperature by adopting a geographical weighted regression model.
2. The method for detecting the coupling relation between land utilization and earth surface temperature based on the urban grouping scale as claimed in claim 1, wherein the step of analyzing the landscape pattern characteristics in the step (1) is as follows:
and respectively calculating 7 landscape indexes, namely a maximum plaque area index, a plaque density, an average area, a plaque shape index, an average nearest distance, a plaque combination degree and an aggregation degree, by taking the grid unit with a set size as an evaluation unit, and analyzing the landscape fragmentation degree and connectivity of the region to be researched based on the calculated landscape indexes.
3. The method for detecting the coupling relation between land utilization and earth surface temperature based on the urban mass scale as claimed in claim 1, wherein the step of performing an earth surface temperature inversion method based on the preprocessed remote sensing image data in the step (2) comprises the following steps:
(201) calculating a normalized difference vegetation index based on the preprocessed remote sensing image data, and then calculating the coverage of surface vegetation;
(202) estimating the specific radiance of the natural surface pixels and the specific radiance of the town pixels based on the coverage of the surface vegetation;
(203) calculating a radiance value based on the radiance ratio of the natural surface pixel and the radiance ratio of the town pixel;
(204) and calculating the earth surface temperature based on the amplitude brightness value.
4. The method for detecting land use and earth surface temperature coupling relation based on urban grouping dimension as claimed in claim 1, wherein step (3) comprises:
(301) according to the earth surface temperature, counting the average temperature in the grid units with set sizes, and calculating the global Moran index and the local Moran index of each grid unit based on a spatial autocorrelation tool;
(302) performing autocorrelation analysis based on the calculation result of the global Moran index;
(303) and performing spatial clustering on the calculation result of the local Molan index to obtain a spatial correlation local index cluster diagram, and analyzing according to the spatial correlation local index cluster diagram to obtain surface temperature aggregation characteristics, wherein the surface temperature aggregation characteristics comprise high aggregation, high and low aggregation, low and high aggregation, low aggregation and non-significance.
5. The method for detecting land use and earth surface temperature coupling relation based on urban grouping dimension as claimed in claim 1, wherein step (4) comprises:
(401) calculating the relevance of various land utilization types by a geographical weighted regression analysis tool based on the average earth surface temperature of each grid unit and the proportion of different land utilization types by taking the grid units with set sizes as standards;
(402) and analyzing different influence degrees of the land use types on the earth surface temperature in space from the aspects of model goodness of fit and correlation coefficient.
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| WO2022161105A1 (en) * | 2021-01-27 | 2022-08-04 | 河南理工大学 | Multi-temporal and multi-level urban temperature remote sensing data analysis method |
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| CN113553551A (en) * | 2021-07-28 | 2021-10-26 | 生态环境部华南环境科学研究所 | Ozone concentration prediction model of coupling view pattern |
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| CN114493346A (en) * | 2022-02-16 | 2022-05-13 | 重庆大学 | Rural industry centralized layout method, system, device and storage medium |
| CN114494377A (en) * | 2022-02-16 | 2022-05-13 | 中国科学院空天信息创新研究院 | Construction method of pixel scale directional emissivity model |
| CN115129799A (en) * | 2022-06-24 | 2022-09-30 | 绍兴市勘察测绘院 | Network space-time big data processing and analyzing method based on geographical weight matrix |
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