A method, system and device for extracting a high-warming strip on a complex mountain surface

Abstract

The application discloses a complex mountainous area surface high-warming strip extraction method, system and equipment, and relates to the field of mountain temperature monitoring. Surface temperature field data and digital elevation model data are acquired; the surface temperature field data and the digital elevation model data are superimposed and preprocessed to obtain first surface temperature field data and first digital elevation model data with the same boundary, the same pixel size and the same projection coordinates; the height of adjacent pixels in a preset window of the first digital elevation model data is judged to obtain a height result; the average change rate of the first surface temperature field data with the height result is calculated to obtain surface temperature vertical straight reduction rate field data; high-warming pixels in the surface temperature vertical straight reduction rate field data are selected according to a preset threshold to obtain high-warming grid data; and the high-warming grid data is converted into polygon vector data to obtain a complex mountainous area surface high-warming strip. The application realizes rapid extraction of a mountainous area surface warming strip with a grid geographic point as a unit.

Description

Technical Field

[0001] This invention relates to the field of mountain temperature monitoring technology, and in particular to a method, system and equipment for extracting high-temperature bands on the surface of complex mountainous areas. Background Technology

[0002] Local temperature abrupt changes, such as temperature inversions or significant warming, are unique meteorological features of complex mountainous regions. Identifying the distribution areas of temperature abrupt changes and analyzing their geographical distribution characteristics are of great significance for carrying out agricultural and forestry crop planting planning, species migration prediction and analysis, ecological simulation and prediction, biodiversity conservation, and mountain ecological disaster monitoring and early warning.

[0003] It is generally believed that atmospheric temperature decreases by approximately 0.6℃ with an elevation increase of 100m. In mountainous areas with significant and complex microclimate characteristics, the lapse rate of surface temperature exhibits significant variability depending on the sampling point. However, classic mountain studies primarily rely on observations from meteorological stations or temporary field stations. This discrete, point-like observation leads to different conclusions from different explorations of the same study area, resulting in drawbacks such as sparse samples, over-reliance on experience, and poor universality. With the maturity and improvement of remote sensing and GIS technologies, we have been provided with technical means to conduct large-scale explorations of small-scale thermal patterns. Based on the theory of temperature lapse rate calculation, we used surface temperature fields retrieved from MODIS remote sensing images as experimental data to calculate a continuous vertical temperature lapse rate field for the entire region. This study shows that the vertical temperature lapse rate exhibits significant spatial distribution characteristics with the elevation changes of the mountain, especially in high mountains where there are often two strip-shaped areas of dense lapse rate variation.

[0004] However, this study did not fully consider the impact of the elevation difference between two adjacent pixels on the slope on the central pixel's reduction rate. For example, when two adjacent pixels are located on opposite slopes in a "two slopes and one gully" landform, it is necessary to first determine their elevation differences. Furthermore, the reduction rate calculation did not consider whether the value was positive or negative, and it was not possible to further determine the type of temperature change phenomenon (whether it is temperature inversion or warming). Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and device for extracting high-temperature warming bands on the surface of complex mountainous areas, enabling rapid extraction of mountain warming bands using grid geographic points as units.

[0006] To achieve the above objectives, embodiments of the present invention provide the following solutions:

[0007] A method for extracting high-temperature-increasing bands on the surface of complex mountainous areas includes:

[0008] Acquire surface temperature field data and digital elevation model data;

[0009] The surface temperature field data and the digital elevation model data are superimposed and preprocessed to obtain first surface temperature field data and first digital elevation model data with the same boundary, the same pixel size and the same projection coordinates.

[0010] Determine the elevation of adjacent pixels within the preset window of the first digital elevation model data to obtain the elevation result;

[0011] Calculate the average rate of change of the first surface temperature field data corresponding to adjacent pixels within the preset window with the result of the terrain elevation, and obtain the surface temperature vertical lapse rate field data.

[0012] High-warming pixels are selected from the surface temperature vertical lapse rate field data according to a preset threshold to obtain high-warming raster data;

[0013] The high-temperature-increasing raster data is converted into polygonal vector data to obtain the high-temperature-increasing strips on the surface of the complex mountainous area.

[0014] Optionally, acquiring surface temperature field data and digital elevation model data specifically includes:

[0015] Images that meet preset meteorological conditions are obtained from thermal infrared images on a geospatial data cloud website; the preset meteorological conditions include: cloud cover less than 10%;

[0016] The MODIS land surface temperature inversion algorithm was used to invert the image to obtain the land surface temperature field data;

[0017] The digital elevation model data is obtained by acquiring ASTR DEM data from a geospatial data cloud website.

[0018] Optionally, the surface temperature field data and the digital elevation model data are overlaid and preprocessed to obtain first surface temperature field data and first digital elevation model data with the same boundaries, the same pixel size, and the same projected coordinates, specifically including:

[0019] The ASTR DEM data is preprocessed to obtain first surface temperature field data and first digital elevation model data with the same boundary, same pixel size and same projection coordinates; the preprocessing includes: stitching the surface temperature field data and the ASTR DEM data, mask extraction based on boundary vector data, projection and resampling.

[0020] Optionally, the elevation of adjacent pixels within a preset window of the first digital elevation model data is determined to obtain the elevation result, specifically including:

[0021] The first digital elevation model data is subjected to a preset window of pixels traversal loop to obtain the elevation of the pixels within the preset window;

[0022] The elevation of adjacent pixels within the preset window is compared numerically to obtain the elevation result of adjacent pixels within the preset window.

[0023] Optionally, the average rate of change of the first surface temperature field data corresponding to adjacent pixels within the preset window with respect to the terrain elevation is calculated to obtain the surface temperature vertical lapse rate field data, specifically including:

[0024] Based on the elevation results of adjacent pixels within the preset window, calculate the elevation difference between adjacent high-elevation pixels and low-elevation pixels; calculate the surface temperature difference of the first surface temperature field data corresponding to adjacent high-elevation pixels and low-elevation pixels.

[0025] Dividing the elevation difference by the surface temperature difference yields the vertical descent rate of surface temperature for adjacent pixels within the preset window.

[0026] Then, the vertical lapse rate of the land surface temperature of the center pixel of the preset window is calculated by averaging the vertical lapse rate of the land surface temperature of all adjacent pixels within the window.

[0027] The first surface temperature field data and terrain elevation results corresponding to all pixels within the preset window are traversed to obtain the surface temperature vertical lapse rate field data.

[0028] Optionally, the step of selecting high-warming pixels from the surface temperature vertical lapse rate field data according to a preset threshold to obtain high-warming raster data specifically includes:

[0029] High-temperature-increase pixels with pixel values ​​less than -0.6 are selected from the surface temperature vertical lapse rate field data to obtain the high-temperature-increase raster data.

[0030] Optionally, converting the high-warming raster data into polygonal vector data to obtain the high-warming stripes on the surface of the complex mountainous area specifically includes:

[0031] The high-temperature-increasing raster data is converted into polygonal vector data to obtain the high-temperature-increasing strips on the surface of the complex mountainous area.

[0032] To achieve the above objectives, embodiments of the present invention also provide the following solutions:

[0033] A system for extracting high-temperature bands on the surface of complex mountainous areas includes:

[0034] The data acquisition module is used to acquire surface temperature field data and digital elevation model data;

[0035] A data preprocessing module, connected to the data acquisition module, is used to overlay and preprocess the surface temperature field data and the digital elevation model data to obtain first surface temperature field data and first digital elevation model data with the same boundary, the same pixel size and the same projection coordinates.

[0036] The surface temperature vertical lapse rate field calculation module is connected to the data preprocessing module. It is used to determine the terrain elevation of adjacent pixels within the preset window of the first digital elevation model data and obtain the terrain elevation result; and to calculate the average rate of change of the first surface temperature field data corresponding to adjacent pixels within the preset window with the terrain elevation result, thereby obtaining the surface temperature vertical lapse rate field data.

[0037] The high-warming raster extraction module is connected to the surface temperature vertical lapse rate field calculation module and is used to select high-warming pixels in the surface temperature vertical lapse rate field data according to a preset threshold to obtain high-warming raster data.

[0038] A high-temperature-increasing raster vectorization module, connected to the high-temperature-increasing raster extraction module, is used to vectorize the high-temperature-increasing raster data into polygonal vector data to obtain the high-temperature-increasing stripe on the surface of the complex mountainous area.

[0039] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for extracting high-temperature bands on the surface of complex mountainous areas.

[0040] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method for extracting high-temperature bands on the surface of complex mountainous areas.

[0041] In this embodiment of the invention, the average temperature in mountainous areas generally decreases with altitude, and the rate of temperature decrease with altitude varies in different mountainous areas. Remote sensing image pattern recognition technology is used to identify and extract high-temperature warming bands using a spatially continuously distributed vertical lapse rate field. This embodiment of the invention uses thermal infrared remote sensing images as input and extracts warming bands via computer, achieving rapid extraction of mountain warming zones using grid geographic points as units. Supported by the vast amount of remote sensing imagery available today, computers can quickly extract regional abrupt change zones. This allows for targeted guidance on the utilization of mountain climate resources in specific mountainous areas, which is of great significance for the full development and protection of mountain resources, the development of the regional economy, and the in-depth study of the formation mechanism of mountain climate. For example, it enables the scientific planting of agricultural and forestry crops in mountainous areas (such as rubber and cloud tea), the formulation of real-time forest fire fighting measures (such as the deployment of troops and the arrangement of firefighting routes), the protection of biodiversity resources in temperature abrupt change zones (such as the exploration and protection of the origin of mid-mountain wet broad-leaved forests), the detailed spatial simulation of mountain temperatures, and the study of the distribution characteristics of mountain humidity within abrupt change zones. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A schematic flowchart illustrating the method for extracting high-temperature bands on the surface of complex mountainous areas provided in this embodiment of the invention;

[0044] Figure 2 This is a schematic diagram of the structure of the complex mountainous surface high-temperature stripe extraction system provided in an embodiment of the present invention;

[0045] Figure 3 This is an image showing the result of warming pixel recognition provided in an embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram of a high-temperature increasing strip provided in an embodiment of the present invention.

[0047] Symbol explanation:

[0048] Data acquisition module - 100, data preprocessing module - 200, surface temperature vertical lapse rate field calculation module - 300, high-temperature-increasing grid extraction module - 400, high-temperature-increasing grid vectorization module - 500. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] In mountainous areas with significant and complex microclimate characteristics, the temperature lapse rate exhibits significant variability depending on the sampling point. Fu Baopu et al. pointed out that the annual average temperature lapse rate in the Taibai Mountains is 0.5℃ on the south slope and 0.44℃ on the north slope. However, Tang Zhiyao, using a temperature and humidity meter, obtained an annual average temperature lapse rate of 0.37℃ on the south slope and 0.49℃ on the north slope. The book "Yunnan Forest Site Classification and Its Application" indicates that the annual average temperature lapse rate in Yunnan is between 0.6 and 0.7℃. However, Tao Guoxiang, based on data from meteorological stations in the western Yunnan forest distribution area, obtained an annual average temperature lapse rate of 0.52℃. Wang Yanxia et al., using MODIS surface temperature products, statistically determined that the lapse rate is 0.47℃ in the high-altitude areas of northwestern Yunnan, 0.51℃ in the mid-mountain areas of central and eastern Yunnan, 0.57℃ in the wide valleys and low mountains of southern Yunnan, and 0.54℃ in the high mountain valleys of northwestern Yunnan and the deep valleys of southern Yunnan. Wason et al. pointed out that within a 20km range in the mountainous region of the northeastern United States, the terrain undulation exceeds 1500m, and the temperature difference between the lowest and highest elevations reaches as high as -7.5℃. From this, they inferred that the maximum lapse rate in this area is 0.5℃, which differs from the earlier conclusion by Richardson et al. that the average temperature lapse rate in the region was 0.5℃. Due to the limitations of classical research methods, local temperature variation trends in mountainous areas often suffer from poor universality due to sparse samples and limited representativeness (Li Guoping, 2016).

[0051] The maturity and refinement of remote sensing and GIS technologies have provided us with effective technical means and insights to overcome the limitations of classical research. Qin Zhihao et al. improved upon the split-window algorithm proposed by AVHRR and established a land surface temperature inversion algorithm applicable to MODIS data. Fan Qi et al. and Wang Xing et al. used numerical simulation and GIS spatial simulation techniques to study the average distribution patterns of temperature inversions. Studies by Clancy et al., Leblanc et al., and Leblanc & Hauchecorne et al. used remote sensing to reveal the regional expansion and duration of inversion layers over several hours. Many scholars have explored the patterns of temperature abrupt changes in studies on the relationship between warming and ecosystem change (Dai et al., 2015; Eastaugh et al., 2011; Kaplan et al., 2012; Olesen et al., 2007; Dong et al., 2019; Hui et al., 2020), strategies for protecting species migration due to climate warming, and the characteristics of warming in permafrost regions (Bonnaventure & Lewkowicz, 2011; Hoelzle & Haeberli, 2011; Salzmann et al., 2007). (&Leszkiewicz, 2012). However, none of these methods address the simultaneous extraction of high-temperature bands over large areas in complex mountainous regions.

[0052] Traditional observations using discretely distributed sampling points are insufficient to obtain spatially continuous mountain climate patterns.

[0053] Classical mountain studies primarily rely on observations from meteorological stations or temporary field stations. This discrete, point-like approach leads to different researchers arriving at different conclusions for the same study area.

[0054] Existing articles do not fully consider the impact of the elevation difference between two adjacent pixels on the central pixel's lapse rate. For example, when two adjacent pixels are located on opposite slopes in a "two slopes and one gully" landform, it is necessary to first determine their elevation differences. Furthermore, the lapse rate calculation does not consider whether the value is positive or negative, and cannot further determine the type of temperature change phenomenon (whether it is temperature inversion or warming).

[0055] The purpose of this invention is to provide a method, system, and device for extracting high-temperature bands on the surface of complex mountainous areas, in order to solve the problem that existing methods do not fully consider the influence of the elevation difference between two adjacent pixels on the slope on the direct reduction rate of the central pixel.

[0056] To address the aforementioned problems, this invention proposes a method, system, and equipment for extracting high-temperature warming bands on the surface of complex mountainous areas, based on the principle that the rate of temperature change is the first derivative of altitude and the theory of remote sensing image pattern recognition. Utilizing remote sensing and GIS technologies, this method overcomes the limitations of sample point observations, saves the high costs associated with establishing field observation stations, and is a widely applicable method that does not overly rely on experience and can quickly obtain warming zones at the regional scale.

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] Figure 1 An exemplary workflow for the above-mentioned method of extracting high-temperature bands on the surface of complex mountainous areas is shown. The steps are described in detail below.

[0059] Step S1: Acquire surface temperature field data and digital elevation model data; specifically including:

[0060] Step S11: Obtain an image that meets preset meteorological conditions from the thermal infrared imagery on the geospatial data cloud website; the preset meteorological conditions include: cloud cover less than 10%;

[0061] Step S12: The image is inverted using the MODIS surface temperature inversion algorithm to obtain the surface temperature field data;

[0062] Step S13: Obtain ASTR DEM data from the geospatial data cloud website to obtain the digital elevation model data.

[0063] In one example, remote sensing imagery is downloaded and surface temperature field data is retrieved. The remote sensing imagery is a multispectral satellite imagery containing the thermal infrared band. This imagery must meet the conditions of cloud cover less than 10% and good quality. The retrieved surface temperature field data uses industry-standard temperature retrieval algorithms or software. Let the averaged temperature field at a certain time point or time period be A(i,j). In a map coordinate system, it is stored in the computer as a digital matrix. i and j represent the row and column sequence numbers, respectively, i = 1, 2, ..., m; j = 1, 2, ..., n; m and n are the total number of rows and columns of the digital matrix, respectively.

[0064] Step S2: Overlay and preprocess the surface temperature field data with the digital elevation model data to obtain first surface temperature field data and first digital elevation model data with the same boundaries, the same pixel size, and the same projection coordinates; specifically including:

[0065] Step S21: Preprocess the ASTR DEM data to obtain first surface temperature field data and first digital elevation model data with the same boundary, same pixel size and same projection coordinates; the preprocessing includes: stitching the surface temperature field data and the ASTR DEM data, mask extraction based on boundary vector data, projection and resampling.

[0066] In one example, ArcMap software is used to fuse the surface temperature field data with the digital elevation model (DEM) of the same area. The data is denoted as DEM(i,j). In the map coordinate system, it is stored in the computer as a digital matrix, where i and j represent the row and column sequence numbers, i = 1, 2, ..., m; j = 1, 2, ..., n; and m and n are the total number of rows and columns of the digital matrix, respectively.

[0067] Step S3: Determine the elevation of adjacent pixels within the preset window of the first digital elevation model data to obtain the elevation result;

[0068] Calculate the average rate of change of the first surface temperature field data corresponding to adjacent pixels within the preset window with respect to the terrain elevation, to obtain the surface temperature vertical lapse rate field data; specifically including:

[0069] Step S31: Perform a preset window pixel traversal loop on the first digital elevation model data to obtain the elevation of the pixels within the preset window;

[0070] Step S32: Compare the elevation values ​​of the pixels within the preset window to obtain the elevation results of adjacent pixels within the preset window;

[0071] Step S33: Calculate the elevation difference between adjacent high-altitude pixels and low-altitude pixels based on the elevation results of adjacent pixels within the preset window; calculate the surface temperature difference of the first surface temperature field data corresponding to adjacent high-altitude pixels and low-altitude pixels.

[0072] Step S34: Divide the elevation difference by the surface temperature difference to obtain the vertical descent rate of surface temperature of adjacent pixels within the preset window;

[0073] Then, the vertical lapse rate of the land surface temperature of the center pixel of the preset window is calculated by averaging the vertical lapse rate of the land surface temperature of all adjacent pixels within the window.

[0074] The first surface temperature field data and terrain elevation results corresponding to all pixels within the preset window are traversed to obtain the surface temperature vertical lapse rate field data.

[0075] In one example, the default window is a 3x3 window. ArcInfo software is used to determine the elevation of adjacent pixels within the 3x3 window. Let f(i,j) be the center pixel in the temperature field A(i,j). The 3x3 neighborhood window of f(i,j) is shown in Table 1 below. Let the pixel value of the center pixel f(i,j) be T(i,j), and its pixel value in the digital elevation model DEM(i,j) be E(i,j). The eight adjacent pixels of the center pixel are T(i+1,j), T(i+1,j+1), T(i,j+1), T(i-1,j), T(i-1,j-1), T(i,j-1), T(i+1,j-1), T(i-1,j+1) in A(i,j), and E(i+1,j+1), E(i,j+1), E(i-1,j), E(i-1,j-1), E(i,j-1), E(i+1,j-1), E(i-1,j+1) in DEM(i,j). For the central pixel E(i,j) of DEM(i,j), determine the size of E(i-1,j-1) and E(i+1,j+1), E(i-1,j) and E(i+1,j), E(i-1,j+1) and E(i+1,j-1), and E(i,j-1) and E(i,j+1). If the former is smaller than the latter, the calculation positions of the two adjacent pixels in the following calculation formula (1) are interchanged.

[0076] Table 1

[0077] f(i,j-1) f(i,j) f(i,j+1) f(i+1,j-1) f(i+1,j) f(i+1,j+1)

[0078] Depending on the spatial scale of the remote sensing image, the 3*3 window can be set to other sizes, such as 5*5, 7*7, etc., which will not be elaborated here.

[0079] The determination of the elevation of adjacent pixels within a 3x3 window is necessary to meet the requirements of calculating the vertical lapse rate for high-elevation subtraction. The rationale is as follows: In real-world geographical scenarios, pixels within the window may be grid points on opposite slopes forming valleys or on adjacent slopes forming mountain peaks, and the elevations of these grid point pairs are randomly distributed. Therefore, before calculating the vertical lapse rate, it is necessary to first determine the elevation of the point pairs and then calculate the lapse rate according to the rule of high-elevation subtraction.

[0080] Step S4: Select high-warming pixels from the surface temperature vertical lapse rate field data according to a preset threshold to obtain high-warming raster data; specifically including:

[0081] High-temperature-increase pixels with pixel values ​​less than -0.6 are selected from the surface temperature vertical lapse rate field data to obtain the high-temperature-increase raster data.

[0082] In one example, ArcInfo software is used to calculate the vertical lapse rate field of land surface temperature. Using the land surface temperature field and DEM as input, and based on the fact that the rate of temperature change is the first derivative of altitude, the vertical lapse rate field of land surface temperature is constructed using a pixel window as the calculation unit. According to formula (1), all pixels in the temperature field A(i,j) are traversed, and a window sliding operation is performed to calculate the vertical lapse rate I(i,j) of the central pixel f(i,j), thus obtaining the vertical lapse rate field B(i,j).

[0083]

[0084] In one example, a preset threshold method is used to identify high-heating pixels from the vertical lapse rate field B(i,j). A conditional selection operation is performed in the attribute table of B(i,j) based on a pixel value less than -0.6. The result is exported as new raster data, namely the high-heating pixel raster data, denoted as C(i,j), where the grayscale value of non-heating pixels is defined as NoData.

[0085] A positive vertical lapse rate indicates that the temperature decreases with decreasing altitude, indicating a temperature inversion. A negative vertical lapse rate indicates that the temperature increases with decreasing altitude. Based on the common knowledge that the average temperature in the free atmosphere decreases by approximately 0.6°C for every 100m increase in altitude, this embodiment of the invention considers a high-temperature increase phenomenon in pixels when the temperature increase is greater than 0.6°C for every 100m decrease in altitude. Therefore, the threshold for high-temperature increase pixels is -0.6. The condition for high-temperature increase raster pixels is: a vertical lapse rate less than -0.6.

[0086] Step S5: Convert the high-warming raster data into polygon vector data to obtain the high-warming stripes on the surface of the complex mountainous area. Specifically, this includes:

[0087] The high-temperature-increasing raster data is converted into polygonal vector data to obtain the high-temperature-increasing strips on the surface of the complex mountainous area.

[0088] In one example, the raster-to-vector GIS method of ArcInfo software is used to vectorize the area where high-temperature-increasing pixels are concentrated in C(i,j) to obtain high-temperature-increasing strip vector data.

[0089] In summary, the average temperature in mountainous areas generally decreases with altitude, and the rate of temperature decrease varies in different mountainous regions. This invention utilizes remote sensing image pattern recognition technology, employing a spatially continuous vertical lapse rate field to identify and extract high-temperature warming bands. This embodiment of the invention uses thermal infrared remote sensing imagery as input and extracts warming bands via computer, achieving rapid extraction of mountain warming zones at grid geographic points. Supported by the vast amount of remote sensing imagery available today, computers can quickly extract regional abrupt change zones. This allows for targeted guidance on the utilization of mountain climate resources in specific mountainous areas, which is of great significance for the full development and protection of mountain resources, the development of the regional economy, and the in-depth study of the formation mechanism of mountain climate. For example, it enables the scientific planting of agricultural and forestry crops in mountainous areas (such as rubber and cloud tea), the formulation of real-time forest fire fighting measures (such as the deployment of troops and the arrangement of firefighting routes), the protection of biodiversity resources in temperature abrupt change zones (such as the exploration and protection of the origin of mid-mountain wet broad-leaved forests), the detailed spatial simulation of mountain temperatures, and the study of the distribution characteristics of mountain humidity within abrupt change zones.

[0090] Example 1:

[0091] Take Yunnan Province in southwestern China as an example. Yunnan Province is located in the southwestern border region of China, in the southeastern part of the Eurasian continent. It has a special geographical location. To the northwest is the Qinghai-Tibet Plateau, known as the "Roof of the World," and to the south is the vast ocean. It lies between 97°31′39″-106°11′47″ east longitude and 21°8′32″-29°15′8″ north latitude.

[0092] Step S1: Download remote sensing imagery and retrieve surface temperature field data. From numerous MODIS thermal infrared images collected over the years on the geospatial data cloud website, a scene passing through the area at 10:30 AM on October 25, 2005, was selected. This scene had a cloud cover of less than 10% and was of good quality. The instantaneous surface temperature field data was retrieved using the MODIS surface temperature retrieval algorithm proposed by Qin Zhihao et al.

[0093] Step S2: Download ASTR DEM data for the entire Yunnan Province from the Geospatial Data Cloud website. In ArcMap software, through processes such as stitching, mask extraction based on Yunnan Province's administrative boundary vector data, projection, and resampling, the DEM and surface temperature field data are unified to have the same boundaries, the same pixel size, and the same projected coordinate system. In this example, the pixel size is 1 km, and the coordinate system is WGS1984_UTM_Zone_48N.

[0094] Step S3: Determine the elevation of adjacent pixels within a 3x3 window and calculate the vertical lapse rate field of land surface temperature. Using ArcInfo software's AML macro language, a 3x3 window pixel traversal loop is performed to determine the size of pixels within the 3x3 window and calculate the vertical lapse rate field data of land surface temperature. The computer-implemented algorithm is as follows:

[0095]

[0096]

[0097] Step S4: Using the Con() function in ArcInfo software, perform pixel filtering based on the condition that the zlw pixel value is less than -0.6. The result is the High Temperature Rise (HTI) raster data. The computer implementation code is as follows:

[0098] HTI = Con(zlw < -0.6, zlw)

[0099] HTI results are as follows Figure 3 As shown.

[0100] Step S5: Use the gridpoly() function in ArcInfo software to convert the high-temperature grid data HTI into polygon vector data HTI_poly. The computer implementation code is as follows:

[0101] HTI_poly = gridpoly(HTI)

[0102] The HTI_poly result is as follows: Figure 4 As shown.

[0103] Across the entire region, warming is widespread and consistent with the distribution of mountainous terrain. From north to south, warming occurs in all mountain ranges, from low to medium and then to high mountains. Warming is concentrated in the Hengduan Mountains of northwestern and southern Yunnan and the plateau mountains of northeastern Yunnan, while its distribution is sparser in relatively flat plains and hilly areas. Besides mountains, ring-shaped warming zones are visible along the edges of large bodies of water. Randomly selected warming zones in seven regions—northwestern, northeastern, western, north-central, southern, southeastern, and the southern edge of Yunnan—are magnified as shown below. Figure 4 As shown in figures a, b, c, d, e, f, and g, the warming zone extends north-south in a strip-like pattern due to the influence of mountain and valley topography. The more concentrated the mountainous terrain, the denser the warming zone. The larger the mountain, the more pronounced and elongated the warming zone distribution. Figure 4 In the first image of the Yunnan-Northwest sample area, the warming zone is generally distributed in a north-south direction along the main mountain or valley, and locally it extends along the east and west slopes. There are often three warming zones on the same mountain slope, located at the bottom, middle and top of the slope respectively. Figure 4 The b-th map in the picture shows the sample area in northeastern Yunnan. Figure 4In the d-th image of the sample area in north-central Yunnan, there is a distinct warming zone on the transition surface from the basin to the mountain. Figure 4 In the c-th image of the western Yunnan sample area, there are two warming zones distributed along the upper and lower parts of the mountain on the east and west sides. As the mountain narrows, the warming zone in the upper part of the mountain disappears, leaving only the warming zone in the lower part.

[0104] Figure 4 In the e-th map of the Yunnan sample area, there are three warming zones distributed from northwest to southeast along the transverse mountain range, which are concentrated on the southwest slope. One of them is located at the top of the main mountain, one at the bottom of the main mountain, and one on the slope of the adjacent small mountain. Figure 4 The fth map in the picture shows the southeastern Yunnan sample area. Figure 4 In the g-th map of the sample area on the southern edge of Yunnan, warming is widespread but its distribution is relatively fragmented.

[0105] To achieve the above objectives, embodiments of the present invention also provide the following solutions:

[0106] A system for extracting high-temperature bands on the surface of complex mountainous areas; please refer to [link / reference]. Figure 2 ,include:

[0107] The data acquisition module 100 is used to acquire surface temperature field data and digital elevation model data;

[0108] The data preprocessing module 200 is connected to the data acquisition module 100. The data preprocessing module 200 is used to overlay the surface temperature field data and the digital elevation model data to obtain the first surface temperature field data and the first digital elevation model data with the same boundary, the same pixel size and the same projection coordinates.

[0109] The surface temperature vertical lapse rate field calculation module 300 is connected to the data preprocessing module 200. The surface temperature vertical lapse rate field calculation module 300 is used to determine the terrain elevation of adjacent pixels within the preset window of the first digital elevation model data, and obtain the terrain elevation result; calculate the average rate of change of the first surface temperature field data corresponding to adjacent pixels within the preset window with the terrain elevation result, and obtain the surface temperature vertical lapse rate field data.

[0110] The high-temperature-increase grid extraction module 400 is connected to the surface temperature vertical lapse rate field calculation module 300. The high-temperature-increase grid extraction module 400 is used to select high-temperature-increase pixels in the surface temperature vertical lapse rate field data according to a preset threshold to obtain high-temperature-increase grid data.

[0111] The high-temperature-increasing grid vectorization module 500 is connected to the high-temperature-increasing grid extraction module 400. The high-temperature-increasing grid vectorization module 500 is used to vectorize the high-temperature-increasing grid data into polygonal vector data to obtain the high-temperature-increasing strips on the surface of the complex mountainous area.

[0112] Furthermore, the present invention also provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor can call a computer program stored in the memory to execute the method for extracting high-temperature bands on the surface of complex mountainous areas.

[0113] Furthermore, when the computer program in the aforementioned memory is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0114] Furthermore, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the method for extracting high-temperature bands on the surface of complex mountainous areas.

[0115] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0116] This document uses specific examples to illustrate the principles and implementation methods of the embodiments of the present invention. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of the embodiments of the present invention. At the same time, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of the embodiments of the present invention. In summary, the content of this specification should not be construed as a limitation on the embodiments of the present invention.

Claims

1. A method for extracting high-temperature bands on the surface of complex mountainous areas, characterized in that, include: Acquire surface temperature field data and digital elevation model data; The surface temperature field data and the digital elevation model data are superimposed and preprocessed to obtain first surface temperature field data and first digital elevation model data with the same boundary, the same pixel size and the same projection coordinates. Determine the elevation of adjacent pixels within the preset window of the first digital elevation model data to obtain the elevation result; Calculate the average rate of change of the first surface temperature field data corresponding to adjacent pixels within the preset window with respect to the terrain elevation, to obtain the surface temperature vertical lapse rate field data, specifically including: Based on the elevation results of adjacent pixels within the preset window, calculate the elevation difference between adjacent high-elevation pixels and low-elevation pixels; calculate the surface temperature difference of the first surface temperature field data corresponding to adjacent high-elevation pixels and low-elevation pixels. Dividing the elevation difference by the surface temperature difference yields the vertical descent rate of surface temperature for adjacent pixels within the preset window. Then, the vertical lapse rate of the land surface temperature of the center pixel of the preset window is calculated by averaging the vertical lapse rate of the land surface temperature of all adjacent pixels within the window. By iterating through all the first surface temperature field data and terrain elevation results corresponding to all pixels within the preset window, the surface temperature vertical lapse rate field data is obtained. High-warming pixels are selected from the surface temperature vertical lapse rate field data according to a preset threshold to obtain high-warming raster data, specifically including: High-warming pixels with pixel values ​​less than -0.6 are selected from the surface temperature vertical lapse rate field data to obtain the high-warming raster data; The high-temperature-increasing raster data is converted into polygonal vector data to obtain the high-temperature-increasing strips on the surface of the complex mountainous area.

2. The method for extracting high-temperature bands on the surface of complex mountainous areas according to claim 1, characterized in that, The acquisition of surface temperature field data and digital elevation model data specifically includes: Images that meet preset meteorological conditions are obtained from thermal infrared images on a geospatial data cloud website; the preset meteorological conditions include: cloud cover less than 10%. The MODIS land surface temperature inversion algorithm is used to invert the image to obtain the land surface temperature field data; The digital elevation model data is obtained by acquiring ASTR DEM data from a geospatial data cloud website.

3. The method for extracting high-temperature bands on the surface of complex mountainous areas according to claim 2, characterized in that, The surface temperature field data and the digital elevation model data are overlaid and preprocessed to obtain first surface temperature field data and first digital elevation model data with the same boundaries, the same pixel size, and the same projected coordinates. Specifically, this includes: The ASTR DEM data is preprocessed to obtain first surface temperature field data and first digital elevation model data with the same boundary, same pixel size and same projection coordinates; the preprocessing includes: stitching the surface temperature field data and the ASTR DEM data, mask extraction based on boundary vector data, projection and resampling.

4. The method for extracting high-temperature bands on the surface of complex mountainous areas according to claim 1, characterized in that, Determine the elevation of adjacent pixels within the preset window of the first digital elevation model data to obtain the elevation result, specifically including: The first digital elevation model data is subjected to a preset window of pixels traversal loop to obtain the elevation of the pixels within the preset window; The elevation of adjacent pixels within the preset window is compared numerically to obtain the elevation result of adjacent pixels within the preset window.

5. The method for extracting high-temperature bands on the surface of complex mountainous areas according to claim 1, characterized in that, Converting the high-warming raster data into polygonal vector data yields the specific high-warming stripes on the surface of the complex mountainous area, which include: The high-temperature-increasing raster data is converted into polygonal vector data to obtain the high-temperature-increasing strips on the surface of the complex mountainous area.

6. A system for extracting high-temperature bands on the surface of complex mountainous areas, characterized in that, include: The data acquisition module is used to acquire surface temperature field data and digital elevation model data; A data preprocessing module, connected to the data acquisition module, is used to overlay and preprocess the surface temperature field data and the digital elevation model data to obtain first surface temperature field data and first digital elevation model data with the same boundary, the same pixel size and the same projection coordinates. The surface temperature vertical lapse rate field calculation module is connected to the data preprocessing module and is used to determine the terrain elevation of adjacent pixels within the preset window of the first digital elevation model data to obtain the terrain elevation result. Calculate the average rate of change of the first surface temperature field data corresponding to adjacent pixels within the preset window with respect to the terrain elevation, to obtain the surface temperature vertical lapse rate field data, specifically including: Based on the elevation results of adjacent pixels within the preset window, calculate the elevation difference between adjacent high-elevation pixels and low-elevation pixels; calculate the surface temperature difference of the first surface temperature field data corresponding to adjacent high-elevation pixels and low-elevation pixels. Dividing the elevation difference by the surface temperature difference yields the vertical descent rate of surface temperature for adjacent pixels within the preset window. Then, the vertical lapse rate of the land surface temperature of the center pixel of the preset window is calculated by averaging the vertical lapse rate of the land surface temperature of all adjacent pixels within the window. By iterating through all the first surface temperature field data and terrain elevation results corresponding to all pixels within the preset window, the surface temperature vertical lapse rate field data is obtained. A high-warming raster extraction module, connected to the land surface temperature vertical lapse rate field calculation module, is used to select high-warming pixels in the land surface temperature vertical lapse rate field data according to a preset threshold to obtain high-warming raster data, specifically including: High-warming pixels with pixel values ​​less than -0.6 are selected from the surface temperature vertical lapse rate field data to obtain the high-warming raster data; A high-temperature-increasing raster vectorization module, connected to the high-temperature-increasing raster extraction module, is used to vectorize the high-temperature-increasing raster data into polygonal vector data to obtain the high-temperature-increasing stripe on the surface of the complex mountainous area.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for extracting high-temperature bands on the surface of complex mountainous areas as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method for extracting high-temperature bands on the surface of complex mountainous areas as described in any one of claims 1-5.

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