CN109903234B - Quantitative description and multi-scale feature analysis method for urban thermal landscape - Google Patents
Quantitative description and multi-scale feature analysis method for urban thermal landscape Download PDFInfo
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Abstract
The invention discloses a method for quantitatively describing urban thermodynamic landscape and analyzing multi-scale characteristics, which comprises the following steps: the Landsat-8OLI/TIRS remote sensing image is preprocessed through the existing methods of radiometric calibration, atmospheric correction, geometric correction and the like, and the preprocessed image is inverted into an earth surface temperature image through the existing method. Dividing the earth surface temperature image into thermal landscapes by using an improved 'mean-standard deviation' method aiming at a research area, calculating landscape indexes at three levels of patches, types and landscapes, and analyzing the urban thermal landscape spatial pattern according to index results. And carrying out scale change on two or more research areas, including space granularity change and space amplitude change, calculating the selected landscape index on the space granularity and the space amplitude, analyzing the multi-scale characteristics of the landscape index, and finally carrying out comparative analysis on the change trends of the multi-scale characteristics in different research areas. The method can be used for analyzing the urban thermodynamic landscape of a single or a plurality of research areas, and the analysis is more comprehensive and specific.
Description
Technical Field
The invention belongs to the field of urban heat island and space structure analysis. In particular to a quantitative description method for analyzing spatial distribution characteristics and multi-scale characteristics of urban heat islands in different research areas based on landscape indexes.
Background
The Urban Heat Island Effect (UHI) is a climate phenomenon in which the Urban temperature is higher than the suburban temperature due to the reduction of high-Heat-storage bodies, greenbelts and water bodies, and the like, caused by human activities, buildings, roads and the like. The urban heat island effect aggravates the frequency of urban high temperature and the possibility of high temperature disasters, so that the environmental quality of urban areas is reduced, and the life of urban residents and the sustainable development of cities are seriously influenced. However, it is generally believed that the ecological process determines the landscape pattern, which in turn affects the ecological process, and therefore, analyzing the interaction between the landscape pattern of a city and the urban heat island process is of great significance in understanding the urban heat island effect, alleviating the urban heat island, and the like.
Since the 50 s in the 20 th century, the urban heat island effect has gradually become one of the hot problems in the research of urban climate science, and scholars at home and abroad use meteorological observation, ground remote sensing monitoring, numerical simulation and other research methods to widely research the aspects of urban heat island formation mechanism, urban heat island effect strength and spatial-temporal variation characteristics, urban heat island effect hazard and urban heat island effect mitigation strategies. Landscape ecology is a relatively young and widely-applied branch of ecology, and mainly studies the types, the number, the spatial distribution and the pattern of landscape constituent units. The coupling of the landscape pattern and the ecological process is the core research field of landscape ecology, the landscape pattern not only reflects the interaction results of various ecological processes on different space scales, but also determines the distribution and combination of various natural environment factors in the landscape space, thereby restricting various ecological processes. The heat island effect is one of the most direct consequences of the evolution of the urban landscape pattern as a representative urban ecological environment effect. At present, the research on urban heat islands based on landscape patterns is mainly carried out from three aspects of distribution characteristics, granularity effect and space-time evolution, wherein the clear Land use/Land cover (LULC) division on the earth surface is the most common practice in landscape and urban heat island analysis and other researches on landscape ecology. Scholars such as Chen L, xu S, weng Q and the like use traditional landscape indexes to carry out related quantitative description on the spatial distribution characteristics of the thermal landscape; scholars such as Shenweijun, xujianhua and Mengchen utilize relevant landscape pattern software to carry out relevant research on the change of landscape indexes along with the granularity and draw a plurality of conclusions; students like Yueyang, bai Y, denui and the like perform relevant analysis on the space-time evolution of the urban heat island landscape pattern from the time perspective, and draw a plurality of conclusions which are helpful for urban planning and relieving the urban heat island effect. Besides, there are some researchers who have also made relevant studies on the spatial amplitude effect of the urban thermal landscape pattern, in order to analyze the intrinsic formation mechanism and its spatial distribution characteristics of the urban thermal island landscape pattern from different perspectives and more comprehensive aspects.
Although significant progress has been made in the research on the urban heat island effect, the current understanding of the heat island effect is not complete. The study of most scholars on landscape patterns and urban heat islands is finally converted into the analysis of the relationship between LULC and LST or the relationship between LULC and UHI landscape, and the landscape patterns of UHI landscape divided by LST are not directly analyzed. The study of scale effects is also directed to a single city or a single area, lacking scale effect analysis based on two or more regions of study. And most scholars select a rectangular research area, but generally consider that the information carried by a circular research area is more extensive and continuous, and whether scale effect has a shifting feature in two research areas with completely different terrain structures, namely whether the multi-scale feature has similar variation trends in different research areas. The invention aims to provide a method for quantitatively analyzing urban thermodynamic landscape space distribution characteristics and multi-scale characteristics based on landscape indexes.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A multi-scale feature analysis method for the urban heat island landscape based on the landscape index is provided, wherein the analysis is more comprehensive and specific. The technical scheme of the invention is as follows:
a quantitative description and multi-scale feature analysis method for urban thermal landscape comprises the following steps:
s1, acquiring Landsat-8OLI/TIRS remote sensing satellite image data of a research area;
s2, remote sensing image data preprocessing: preprocessing Landsat-8OLI/TIRS remote sensing image data acquired in the step S1, including radiometric calibration, atmospheric correction, geometric correction and cutting;
s3, inverting the earth surface temperature: inverting the surface temperature of the remote sensing image data preprocessed in the step S2 by adopting a classical radiation transmission equation method to obtain surface temperature data of the research area;
step S4, dividing the thermal landscape level: obtaining the highest value of the earth surface temperature of the vegetation area by random sampling, and establishing a thermodynamic landscape grading standard by adopting a mean-standard deviation method;
step S5, selecting the landscape pattern index: according to the geographic environment and the research requirement of the research area, required indexes are selected from three levels of plaques, categories and landscapes, the thermodynamic landscape data obtained in the step S4 are substituted into the calculation to obtain a result, and the spatial distribution characteristics of the urban heat island are quantitatively analyzed according to the result;
step S6, scale change: the thermal landscape data obtained in the step S4) is subjected to granularity conversion in terms of spatial granularity, and is subjected to amplitude variation in terms of spatial amplitude;
and S7, performing multi-scale feature analysis, calculating index results of data with different granularities and amplitudes at three levels of patches, categories and landscapes from the spatial granularity and the spatial amplitude according to the landscape pattern index selected in the step S5 and the scale change performed in the step S6, and obtaining the multi-scale features of the urban heat island landscape according to the response relation of the urban heat island landscape analysis index represented by the landscape index along with the change of the spatial scale.
Further, the remote sensing image data preprocessing in the step S2 specifically includes: by means of existing software including ENVI5.3, other high-resolution satellite images are taken as reference images, 30-40 same-name feature points at road intersections, river turns and intersections are taken as control points, all wave bands including thermal infrared wave bands in the Landsat 8OLI/TIRS images are subjected to quadratic polynomial geometric fine correction, then radiation calibration and atmospheric correction are carried out on all the wave bands by using a radiation calibration tool and a FLAASH tool which are carried in the ENVI5.3 software, and preprocessed data are obtained and cut according to requirements so as to obtain final research area data.
Further, the step S3 is to perform inversion of the earth surface temperature by using a classical radiation transport equation method, and includes the steps of: according to the data acquired in the step S2, the ground object categories of the research area are firstly divided into three categories of natural surface, town and water body according to the normalized vegetation index (Normalized Difference vector orientation Index, NDVI), the water body pixel is often a single water body, so the radiation ratio epsilon of the natural surface and cities and towns is mainly considered, and the epsilon of the satellite pixel scale can be calculated as the formula: ε = P V R V ε V +(1-P V )R X ε X + d ε formula wherein P V Indicating vegetation coverage, R V Is the temperature ratio of vegetation, ε V Specific radiance of vegetation, R X Is the temperature ratio of bare soil or building surface, epsilon X The specific radiance of bare soil or the surface of a building is adopted, d epsilon is the thermal radiation interaction, and the temperature ratio of the ground and the living things, the temperature ratio of vegetation, bare soil and the surface of the building are estimated according to the vegetation coverage; secondly, estimating the influence of the atmosphere on the earth surface thermal radiation, and calculating the thermal infrared radiation brightness value received by the satellite sensor: l is λ ＝[εB(T surface )+(1-ε)L down ]τ+L upper In the formula, L λ Indicating the brightness of the thermal infrared radiation received by the satellite sensor; epsilon represents the surface emissivity; τ represents the transmittance of the atmosphere in the thermal infrared band; t is a unit of surface Representing the true surface temperature (K); b (T) surface ) Denotes the temperature T s Black body radiation brightness of (d); l is upper And L down Respectively representing the upward and downward radiation brightness of the atmosphere, and finally obtaining the surface temperature T by utilizing the Planck formula 1.3 sruface ：T surface ＝K 2 /[ln(1+K 1 /B(T surface ))]In the formula, K 1 ，K 2 All of which are constants and can be obtained from the header file of the corresponding data.
Further, in the step S4, the highest value of the earth surface temperature of the vegetation area is obtained by using random sampling, and then a mean-standard deviation method is adopted to establish a thermodynamic landscape grading standard, which specifically includes:
for the classification of the single-band gray level image, selecting a segmentation interval as a maximum value and a minimum value of the earth surface temperature, classifying the earth surface temperature image, and reflecting the type of the thermal landscape according to the classification formula as follows:
wherein R is LST For a calculated temperature threshold range, ->
The method comprises the following steps of selecting a vegetation sample area for a research area, wherein the highest temperature of the vegetation sample area is the selected vegetation sample area, SD is the standard deviation of the earth surface, n is the multiple of the standard deviation, the research area is divided into 4 thermal landscape types according to a classification formula, and the thermal landscape types are sequentially a Non Heat island, a Weak Heat island, a Heat island and a Strong Heat island, and the threshold value is expressed by the following formula:
further, the step S5 of selecting the landscape pattern index specifically includes: fragstats 4.2 is used for calculating landscape pattern indexes, required indexes are selected from three levels of patches, categories and landscapes to calculate by combining landscape characteristics of a research area, and index calculation formulas and meanings are shown in Fragstats 4.2 software description and related documents. And analyzing the spatial distribution characteristics of the urban thermal landscape from the aspects of area, density, shape, clustering property and diversity according to the index result.
Further, the maximum plaque index LPI, the percentage of plaque area occupied by landscape PLAND, the number of plaque NP, the plaque density PD, the landscape shape index LSI, the aggregation index AI, the vintage index CONTAG and the shannon diversity index SHDI are selected from five aspects of area, density, shape, clustering property and diversity.
Further, step S6, according to the thermodynamic landscape data obtained in step S4, performs granularity conversion on the thermodynamic landscape data from a spatial granularity, and performs amplitude change on the thermodynamic landscape data from a spatial amplitude, specifically including:
determining the spatial amplitude of the research by the total area of the research area, sequentially resampling the image to 300m by adopting a landscape pattern multi-scale analysis method for changing the spatial scale with the original resolution of 30m as a starting point in the research area and the step length of 30m, then resampling to 600m by adopting the step length of 60m, and finally resampling to 960m by adopting the step length of 120m to complete the transformation of spatial granularity; and (3) sequentially expanding the research range to 24km by taking a circle with the radius of 6km as an initial interval according to the step length of 3km to finish the change of the space amplitude.
Further, the step S7 of multi-scale feature analysis specifically includes: and (3) following the landscape pattern index selected in the step (S5), acquiring data for completing space granularity transformation and space amplitude change from the step (S6), calculating the selected index from three levels of patches, categories and landscapes in Fragstats 4.2 software, and performing multi-scale feature analysis on the urban heat island landscape pattern by calculating the landscape pattern indexes under different scales.
The invention has the following advantages and beneficial effects:
the invention is based on a landscape pattern index analysis method, and various characteristics of the urban thermal landscape are analyzed by representing the urban thermal landscape with landscape indexes, including spatial pattern distribution and multi-scale characteristics. Referring to the traditional landscape pattern research method of landscape ecology scholars, a landscape pattern index analysis method is used for carrying out quantitative analysis on the urban thermodynamic landscape represented by the landscape index. When a thermodynamic landscape grading method is formulated, the comparative analysis among a plurality of research areas is considered, the highest temperature of a vegetation area is randomly selected on the traditional grading standard of mean-standard deviation to serve as a threshold value for defining the heat island effect, the part below the threshold value is regarded as a heat island-free area, and the part above the threshold value is subdivided into three or more thermodynamic landscapes by the multiple of the standard deviation of the surface temperature. Secondly, when multi-scale feature analysis of the thermal landscape pattern is carried out, the circle is used for replacing the rectangle to carry out analysis on the space amplitude of the research area, the defects of the existing space amplitude analysis method are overcome, and the analysis is more comprehensive. Finally, the invention can further compare and analyze two or more research areas by calculating landscape indexes of three levels of plaques, types and landscapes through granularity conversion and amplitude change so as to analyze the scientific problem that whether the change trends of the multi-scale features of the thermal landscape among different types of cities are the same or not.
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FIG. 1 is a flow chart of a method for quantitative description and multi-scale feature analysis of urban thermal landscape according to a preferred embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the invention aims at the relevant research of landscape patterns of urban heat islands, and analyzes the spatial patterns and multi-scale characteristics of the thermodynamic landscape by means of landscape pattern indexes and scale changes. The Landsat-8OLI/TIRS remote sensing image is used as a data source, and the original image is preprocessed by means of an existing remote sensing image processing tool including ENVI5.3, wherein the preprocessing comprises cutting, radiometric calibration, atmospheric correction and geometric correction, and the surface temperature of the research area is further inverted. On the basis of the work, the highest value of the temperature of the vegetation sample area is used as a threshold value to distinguish whether the urban heat island effect exists or not, and the classification of the thermal landscape is further completed according to an improved 'mean-standard deviation' method. In the test, relevant landscape indexes are selected from three levels of plaques, types and landscapes according to geographic elements in a research area, calculation is carried out, and the spatial pattern of the urban thermodynamic landscape is quantitatively analyzed according to the values of the landscape indexes. Finally, the multi-scale characteristics of the urban thermal landscape in the research area are analyzed through the scale change and the index selected in the previous step, the change trend of the multi-scale characteristics in two or more different types of cities is further discussed, and the more comprehensive analysis of the urban thermal landscape space structure and the scale effect is completed.
Fig. 1 of the present invention shows a flow chart of the method of the present invention, and the following research cases using main cities of Chongqing and main cities of Chengdu as target research areas comprise the following specific steps:
(1) Data acquisition, selected from the national geological survey official (A) network based on the study protocol and image qualityhttps://earthexplorer.usgs.gov/) And two landscapes of Landsat 8OLI _TIRSimages, namely Chongqing city (track number 128/39) on day 16 in year 2013 and Chengdat city (track number 129/39) on day 1 in month 5 in year 2017 are acquired. Landsat 8 satellite towerAn Operating Land Imager (OLI) providing multispectral data and a Thermal Infrared Sensor (TIRS) providing Thermal Infrared data are provided, wherein the spatial resolution of the multispectral band in the OLI Land Imager is 30m and the spatial resolution of the Thermal Infrared band in the TIRS Sensor is 100m.
(2) And data preprocessing, wherein the preprocessing mainly uses the existing software including ENVI5.3, takes other satellite images with the resolution higher than that of the acquired data as reference images, selects 30-40 equivalent ground object points at road intersections, river turns and intersections as control points, performs quadratic polynomial geometric fine correction on all bands including thermal infrared bands in the Landsat 8OLI/TIRS images, and then performs atmospheric correction on all bands by using a FLAASH tool carried in the ENVI5.3 software to acquire research area data after geometric correction and atmospheric correction.
(3) The surface temperature inversion is carried out by using a classical radiation transmission equation method, and the steps are as follows: according to the data acquired in the step S2, the ground object types of the research area are firstly divided into three categories of natural surface, town and water body. And further inverting the earth surface emissivity according to the NDVI on the basis of classification. The water body pixels are often single water bodies, so the specific radiance epsilon of a natural surface and towns is mainly considered. The epsilon of the satellite pixel scale can be calculated as the formula: ε = P V R V ε V +(1-P V )R X ε X + d ε formula wherein P V Indicating vegetation coverage, R V Is the temperature ratio of vegetation, ε V Specific radiance of vegetation, R X Is the temperature ratio of bare soil or building surface, epsilon X Is the specific radiance of bare soil or building surfaces, and d epsilon is the thermal radiation interaction. According to the vegetation coverage, the temperature ratio of the ground features, the temperature ratio of vegetation, bare soil and the surface temperature ratio of buildings can be further estimated. Secondly, estimating the influence of the atmosphere on the earth surface thermal radiation, and calculating the thermal infrared radiation brightness value received by the satellite sensor: l is λ ＝[εB(T surface )+(1-ε)L d o wn ]τ+L upper In the formula, L λ Indicating the brightness of the thermal infrared radiation received by the satellite sensor; epsilonRepresenting the emissivity of the earth surface, and directly bringing the result of the previous step into the earth surface; τ represents the transmittance of the atmosphere in the thermal infrared band; t is surface Representing the true surface temperature (K); b (T) surface ) Denotes the temperature T s Black body radiant brightness of (a); l is upper And L down Respectively representing the upward and downward radiation brightness of the atmosphere, and finally obtaining the surface temperature T by using the Planck formula 1.3 sruface ：T surface ＝K 2 /[ln(1+K 1 /B(T surface ))]In the formula, K 1 ，K 2 All of which are constants, and can be obtained from the header file of the corresponding data. For the 10 th band, K, of a TIRS thermal infrared sensor 1 ＝774.89Wm-2·sr-1·um-1，K 2 ＝1321.08K。
(4) Selecting the segmentation interval as the maximum value and the minimum value of the earth surface temperature, classifying the earth surface temperature image, and reflecting the type of the thermal landscape according to the classification formula as follows:
wherein R is LST In order to calculate the range of the temperature threshold,
the highest temperature of the selected vegetation sample area for the study area, SD is the surface temperature standard deviation, and n is a multiple of the standard deviation. The study area was further divided into 4 types of thermal landscape according to the above formula, in order Non-Heat island (Non Heat island), weak Heat island (Weak Heat island), heat island (Heat island) and Strong Heat island (Strong Heat island). The threshold interval is expressed by the following formula:
(5) And calculating the landscape pattern index by using Fragstats 4.2. According to relevant research results of scholars at home and abroad and in combination with landscape characteristics of a research area, a maximum plaque index (LPI), a landscape occupied plaque area Percentage (PLAND), a plaque Number (NP), a Plaque Density (PD), a Landscape Shape Index (LSI), an Aggregation Index (AI), a vintage index (CONTAG) and a Shannon diversity index (SHDI) are selected from five aspects of area, density, shape, clustering and diversity, 6 indexes are selected respectively at a type level and a landscape level for calculation, and the spatial pattern of the scholars is analyzed according to the result of the indexes.
(6) Adopting a landscape pattern multi-scale analysis method for changing the spatial scale, sequentially resampling the image to 300m according to the step length of 30m by taking the original resolution of 30m as a starting point in a research area, then resampling to 600m according to the step length of 60m, and finally resampling to 960m according to the step length of 120m to finish the transformation of the spatial granularity; and (3) sequentially expanding the research range to 24km by taking a circle with the radius of 6km as an initial interval according to the step length of 3km to finish the change of the space amplitude.
(7) Selecting a maximum plaque index (LPI), a landscape plaque area Percentage (PLAND), a plaque Number (NP), a Plaque Density (PD), a Landscape Shape Index (LSI), an Aggregation Index (AI), a tendentiousness index (CONTAG) and a Shannon diversity index (SHDI) from five aspects of area, density, shape, clustering property and diversity, respectively selecting 6 indexes at a type level and a landscape level for calculation, simultaneously calculating the selected indexes at 18 granularities and 7 amplitudes, and analyzing the multi-scale characteristics and the change trend between two cities according to the results.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (7)
1. A quantitative description and multi-scale feature analysis method for urban thermodynamic landscapes is characterized by comprising the following steps:
s1, acquiring Landsat-8OLI/TIRS remote sensing satellite image data of a research area;
s2, remote sensing image data preprocessing: preprocessing the Landsat-8OLI/TIRS remote sensing image data acquired in the step S1, including radiometric calibration, atmospheric correction, geometric correction and cutting;
s3, inverting the earth surface temperature: inverting the surface temperature of the remote sensing image data preprocessed in the step S2 by adopting a classical radiation transmission equation method to obtain surface temperature data of the research area;
step S4, thermal landscape level division: obtaining the highest value of the earth surface temperature of the vegetation area by random sampling, and establishing a thermodynamic landscape grading standard by adopting a mean-standard deviation method;
step S5, selecting the landscape pattern index: according to the geographic environment and the research requirement of the research area, required indexes are selected from three levels of plaques, categories and landscapes, the thermodynamic landscape data obtained in the step S4 are substituted into the calculation to obtain a result, and the spatial distribution characteristics of the urban heat island are quantitatively analyzed according to the result;
step S6, scale change: performing granularity conversion on the thermal landscape data acquired in the step S4 in terms of spatial granularity, and performing amplitude change on the thermal landscape data acquired in the step S4 in terms of spatial amplitude;
s7, performing multi-scale feature analysis, namely calculating index results of data with different granularities and amplitudes at three levels of patches, categories and landscapes from the spatial granularity and the spatial amplitude according to the landscape pattern index selected in the step S5 and the scale change performed in the step S6, and obtaining the multi-scale features of the urban heat island landscape according to the response relation of the urban heat island landscape analysis index represented by the landscape index along with the change of the spatial scale;
the step S4 of obtaining the highest value of the earth surface temperature of the vegetation area by utilizing random sampling, and then formulating the thermodynamic landscape grading standard by adopting a mean-standard deviation method specifically comprises the following steps:
for the classification of the single-band gray level image, selecting a segmentation interval as a maximum value and a minimum value of the earth surface temperature, classifying the earth surface temperature image, and reflecting the type of the thermal landscape according to the classification formula as follows:
wherein R is LST For a calculated temperature threshold range, ->
The method comprises the following steps of (1) dividing a research area into 4 thermal landscape types according to a classification formula, wherein the maximum temperature of a vegetation sample area selected for the research area is SD (standard deviation of earth surface temperature), n is a multiple of the standard deviation, the thermal landscape types are sequentially a Non Heat island, a Weak Heat island, a week Heat island, a Heat island and a Strong Heat island, and the threshold value is expressed by the following formula:
2. the method for quantitative description and multi-scale feature analysis of urban thermal landscape according to claim 1, wherein the step S2 of preprocessing the remote sensing image data specifically comprises: by means of existing software including ENVI5.3, other high-resolution satellite images are taken as reference images, 30-40 same-name feature points at road intersections, river turns and intersections are taken as control points, all wave bands including thermal infrared wave bands in the Landsat 8OLI/TIRS images are subjected to quadratic polynomial geometric fine correction, then radiation calibration and atmospheric correction are carried out on all the wave bands by using a radiation calibration tool and a FLAASH tool which are carried in the ENVI5.3 software, and preprocessed data are obtained and cut according to requirements so as to obtain final research area data.
3. The method for quantitative description and multi-scale feature analysis of urban thermal landscape according to claim 2, wherein said step S3 is for inversion of surface temperature by using classical radiative transfer equation method, comprising the steps of: according to the preprocessing data of the research area obtained in the step S2, firstly, the ground object types of the research area are divided into three categories of a natural surface, a town and a water body, the ground surface emissivity is inverted according to the normalized vegetation index NDVI, the water body pixel is often a single water body, and therefore the ground surface emissivity epsilon of the natural surface and the town is considered, and the epsilon is calculated according to the formula: ε = P V R V ε V +(1-P V )R X ε X + d ε formula wherein P V Indicating vegetation coverage, R V Is the temperature ratio of vegetation, ε V Is the specific radiance of vegetation, R X Is the temperature ratio of bare soil or building surface, epsilon X The specific radiance of bare soil or the surface of a building is adopted, d epsilon is the thermal radiation interaction, and the temperature ratio of the ground and the living things, the temperature ratio of vegetation, bare soil and the surface of the building are estimated according to the vegetation coverage; secondly, estimating the influence of the atmosphere on the earth surface thermal radiation, and calculating the thermal infrared radiation brightness value received by the satellite sensor: l is λ ＝[εB(T surface )+(1-ε)L down ]τ+L upper In the formula, L λ Indicating the brightness of the thermal infrared radiation received by the satellite sensor; epsilon represents the surface emissivity; τ represents the transmittance of the atmosphere in the thermal infrared band; t is surface Representing the true surface temperature (K); b (T) surface ) Denotes a temperature of T s Black body radiation brightness of (d); l is upper And L down Respectively representing the upward and downward radiation brightness of the atmosphere, and finally obtaining the surface temperature T by utilizing the Planck formula 1.3 sruface ：T surface ＝K 2 /[ln(1+K 1 /B(T surface ))]In the formula K 1 ，K 2 All of which are constants, and can be obtained from the header file of the corresponding data.
4. The method for quantitative description and multi-scale feature analysis of urban thermodynamic landscape according to claim 3, wherein the step S5 of selecting a landscape pattern index specifically comprises: fragstats 4.2 is used for calculating a landscape pattern index, required indexes are selected from three levels of patches, categories and landscapes by combining the landscape characteristics of a research area for calculation, and the spatial distribution characteristics of the urban thermal landscape are analyzed from the aspects of area, density, shape, clustering property and diversity according to the index result.
5. The method of claim 4, wherein the maximum plaque index LPI, percentage of plaque area occupied by landscape PLAND, number of plaques NP, plaque density PD, landscape shape index LSI, aggregation index AI, vintage index CONTAG, and Shannon diversity index SHDI are selected from the five aspects of area, density, shape, clustering, and diversity.
6. The method for quantitative description and multi-scale feature analysis of urban thermodynamic landscape according to claim 4, wherein the step S6 is to perform granularity transformation on the thermodynamic landscape data obtained in the step S4 from a spatial granularity and perform amplitude change on the thermodynamic landscape data from a spatial amplitude, and specifically comprises:
determining the spatial amplitude of the research by the total area of the research area, sequentially resampling the image to 300m by adopting a landscape pattern multi-scale analysis method for changing the spatial scale with the original resolution of 30m as a starting point in the research area and the step length of 30m, then resampling to 600m by adopting the step length of 60m, and finally resampling to 960m by adopting the step length of 120m to complete the transformation of spatial granularity; and (3) sequentially expanding the research range to 24km by taking a circle with the radius of 6km as an initial interval according to the step length of 3km to finish the change of the space amplitude.
7. The method for quantitative description and multi-scale feature analysis of urban thermodynamic landscape according to claim 6, wherein the step S7 of multi-scale feature analysis specifically comprises: and (3) following the landscape pattern index selected in the step (S5), acquiring data for completing space granularity transformation and space amplitude change from the step (S6), calculating the selected index from three levels of patches, categories and landscapes in Fragstats 4.2 software, and performing multi-scale feature analysis on the urban heat island landscape pattern by calculating the landscape pattern indexes under different scales.
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