A method for monitoring the operating status of urban building complexes based on night-time remote sensing

Abstract

The present invention discloses a method for monitoring the operating status of urban building complexes based on night-time remote sensing, belonging to the field of night-time remote sensing image processing. Specifically, first, according to the location of the urban building complexes to be monitored and the monitoring time range, monthly night-time data is downloaded and cropped, and then the pixel values of the image are checked for positive and negative and updated. Then, a reference month and the brightest central area are selected as the reference area. Using the cropped night-time remote sensing raster data, the relative correction coefficients corresponding to each month are calculated respectively, the pixel values of the night-time remote sensing raster data for each month are corrected and converted into vector files, and the thin plate spline method is used for spatial interpolation processing to obtain the night-time intensity values at the positions of the specified building complexes. By tracking month by month, the operating status of the building is analyzed. The present invention solves the problem of differences in time-series monthly night-time data and improves the consistency of monthly night-time data.

Description

Technical Field

[0001] The present invention belongs to the field of night light remote sensing image processing, and particularly relates to a method for monitoring the operation state of urban building complexes based on night light remote sensing. Background Art

[0002] With the continuous development of the urbanization process, the connection between urban population and urban economic elements has become increasingly close. The distribution of urban buildings in space has become more and more intensive, and the functional positioning has become more and more clear. Therefore, the building complex that reflects the urban physical form has gradually become an important force in the urban space and is the main spatial carrier for urban residents to concentrate on political, economic, social activities and living behaviors.

[0003] At the same time, in order to realize the effective utilization of different living units and their spatial functions in the city, the functional layout and spatial layout of cities in China are gradually developing in the direction of optimization and coordination. According to the definition in the "Encyclopedia of China", an urban building complex refers to the sum of buildings composed of several adjacent buildings in the city and closely connected in spatial organization. The building complex includes both the building group composed of several interrelated building monomers and the environment around the building group in the region.

[0004] The urban building complex is not a simple aggregation of multiple single buildings in the physical space, but has functional complementarity and agglomeration, jointly constituting a relatively independent living and working unit with complete functions. The layout of different types of urban building complexes in space determines the urban landscape. Taking the commercial building complex as an example, it often presents an aggregated shape, consisting of several shopping centers and other buildings, forming a commercial aggregate integrating shopping, dining, entertainment and sightseeing activities, attracting a large number of people and resources, and to a certain extent reflecting the active situation of the local economy and population. For the industrial park building complex, according to the park development plan and industry aggregation, it is composed of several industrial buildings with similar architectural styles, gathering relevant facilities, supporting industries and employees together. The operation situation of the building complex objectively reflects the industrial development and the local economy.

[0005] With the rapid development of satellite remote sensing technology and the continuous enrichment of image resolution and payload means, the dynamic monitoring of the design, construction, operation and transformation stages of urban building complexes using high-resolution optical satellite and radar satellite remote sensing data has become a reality, and has played an important role in urban optimal design, construction progress monitoring, urban management, disaster warning, ecological protection and illegal building investigation.

[0006] Nighttime remote sensing (i.e., nighttime light data) is a new and active branch in the field of remote sensing applications. Compared with traditional optical and radar remote sensing satellites, nighttime remote sensing uses remote sensing satellites to obtain visible light-near infrared electromagnetic wave information emitted by the earth's surface under cloudless conditions at night. Most of this information is emitted by human activities on the earth's surface, the most important of which is human nighttime lighting, and also includes sources such as oil and gas combustion, fishing boats at sea, forest fires, and volcanic eruptions. Due to the objectivity of nighttime light data, all nighttime lights on the ground can be obtained from space, including residential light sources, community street lights, lighting lights on urban roads, as well as lights in commercial and industrial areas.

[0007] Compared with ordinary remote sensing satellite images, nighttime remote sensing images can more directly reflect human activities. Therefore, they are widely used in the fields of socio-economic parameter estimation, urban monitoring, major event changes, ecological environment assessment, and public health.

[0008] Currently, the nighttime remote sensing data with global coverage and long time series in orbit is the first satellite, Suomi NPP, of the National Polar-orbiting Operational Environmental Satellite System Preparatory Project (NPP) of the United States. The Visible Infrared Imaging Radiometer Suite (VIIRS) carried by this satellite can obtain nighttime light remote sensing images (Day / Night Band, DNB band) of most regions of the world every day. The monthly fusion data provided by the official filters out the light effects from aurora, fire, ships, and other temporary light sources during synthesis, and has undergone global mosaicking processing.

[0009] Currently, the official nighttime light data product range is from 180°W - 180°E and 75°N - 65°S. Divided by 120° longitude, the world is divided into 6 image regions and can be downloaded as needed. Among them, the data in the area of 75°N and 60°E includes the Chinese region. The resolution of the monthly nighttime remote sensing product is 15 arcseconds geographic grid, equivalent to a ground resolution of 500 meters. According to the usage instructions of the VIIRS DBN data released by the official, the original daily nighttime light data has undergone on-board radiometric calibration. Therefore, the monthly and annual data products synthesized in time series have a certain comparability. However, due to the fact that nighttime imaging is still affected by factors such as the atmosphere and moonlight, there are still fluctuations in the nighttime light brightness values of the monthly nighttime remote sensing data synthesized from daily data at different times.

[0010] At the same time, due to the resolution limitation of the night light remote sensing images, this data cannot be directly used for the monitoring and analysis of the fine scale of urban building complexes, which to a certain extent limits the application ability of the night light remote sensing data in the monitoring of typical industries and industry status. Summary of the Invention

[0011] Aiming at the problem that the existing night light remote sensing data has low spatial resolution and cannot monitor local areas, the present invention proposes a method for monitoring the operation status of urban building complexes based on night light remote sensing. By means of relative correction and spatial interpolation processing, the time-series night light remote sensing results of the building positions are obtained, so as to effectively monitor the operation status of the building groups.

[0012] The method includes the following steps:

[0013] Step 1: According to the location of the urban building complexes to be monitored and the monitoring time range, download all NPP / VIIRS monthly night light data products of the corresponding image area through the website;

[0014] The selected monthly night light data is the vcmsl version.

[0015] Step 2: Crop the monthly night light data according to the administrative region to obtain the night light remote sensing raster data of the area to be monitored. Check the pixel values of the raster data for positive and negative, and update the negative pixel values to 0.

[0016] Step 3: Select the middle month of the monitoring time range as the reference month, and select the brightest central area of the city to be measured as the reference area;

[0017] The reference area is the central range with the largest pixel brightness value in the night light remote sensing raster data. Usually, 2*2 or 3*3 is selected according to the size of the city;

[0018] Step 4: Use the pixel values in the cropped night light remote sensing raster data to calculate the relative correction coefficients corresponding to each month respectively, and correct the pixel values of the night light remote sensing raster data for each month.

[0019] The specific steps are as follows:

[0020] Step 401: Calculate the mean value C_Mean of all pixel brightness values in the reference area under the reference month;

[0021] Step 402: Similarly, calculate the mean value C_Mean of the corresponding pixel brightness values in the reference area for each month respectively k ;

[0022] k = 1, 2,...K; K represents the number of months within the monitoring time range.

[0023] Step 403: Use the brightness mean value C_Mean of each monthk Calculate the relative correction coefficient for each month with reference to the reference mean C_Mean;

[0024] Calculate the relative correction coefficient a for the k-th month k The formula is as follows:

[0025] a k = C_Mean / C_Mean k

[0026] Step 404: Use the relative correction coefficient of each month to perform relative correction processing on the night light remote sensing raster data of different months, and unify the night light remote sensing data values of all months to the intensity value of the reference month;

[0027] The correction means: for the night light intensity DN value of each pixel in the cropped night light remote sensing raster data, update it respectively using the correction coefficient. The calculation formula is as follows:

[0028] DN new = DN · a k

[0029] Step Five: Convert the corrected monthly night light remote sensing raster data into a vector file, and perform spatial interpolation processing using the thin plate spline method to obtain the night light value at the location of the specified building complex.

[0030] The specific steps are as follows:

[0031] Step 501: Convert the corrected monthly night light remote sensing raster data into a shp format vector file using the raster to point function of GIS software, and assign the night light intensity value of each pixel to the corresponding point vector value in the vector file.

[0032] Step 502: According to the location values (x0, y0) of the specified building complex to be monitored, obtain the locations and night light intensity values of N known vector points around it, which are respectively expressed as (x i , y i , z i ); i = 1, 2,... N;

[0033] Step 503: Calculate the distances between the N known vector points and between each vector point and the point to be interpolated (x0, y0) respectively;

[0034] For the distance r i , y i ) and the vector point (x j , y j ) between, the calculation formula is as follows: ij The formula is as follows:

[0035]

[0036] Step 504: Using the known distances between the N vector points and the night light intensity values, calculate the parameters a and coefficient λ of the thin plate tension spline method for spatial interpolation through the equation group. j ;

[0037] The system of equations is expressed as follows:

[0038]

[0039] Among them, a is the local trend function, λ j is the coefficient of the solution of the linear equations, R represents the basis function of the thin plate tension spline, and the expression is:

[0040]

[0041] in is the weight parameter, K0 is the modified Bessel function, and c is a constant.

[0042] Step 505: Using the local trend function a and coefficient λ j , calculate the interpolation result z0 of the specified building complex position (x0, y0) to be monitored, and obtain the night light intensity value of the building complex.

[0043]

[0044] Step 6: By tracking the night light intensity values at the designated building complex location on a monthly basis, the time series night light intensity values of the designated building complex are obtained, and the operating status of the building is analyzed based on the changes in the night light intensity at the location.

[0045] Generally, if the light intensity at the building complex is close to 0, it indicates that human activities in the area are limited; if the light intensity at the building complex is higher than the average night light intensity of the entire area, it indicates that human or social activities in the area are frequent; if the light intensity gradually increases over time, it indicates that the building complex is in good operating condition, indicating that economic or social activities continue to increase.

[0046] The advantages of the present invention are:

[0047] 1) A method for monitoring the operating status of urban building complexes based on night light remote sensing. By selecting data from local areas with strong night light, relative correction processing of night light data of different time series is carried out, which solves the problem of differences in time series monthly night light data and improves the consistency of monthly night light data.

[0048] 2) A method for monitoring the operating status of urban building complexes based on night light remote sensing. Based on the gradually changing characteristics of night light data in the building complex area, night light data interpolation based on thin plate tension splines is used to achieve the optimal fusion of smoothness and accuracy of the interpolation model, effectively obtaining the light intensity values of the building complex points. Through long-term time series analysis, the operating status of the building complex can be obtained, effectively expanding the application capabilities of the NPP / VIIRS large-scale night light data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a flow chart of a method for monitoring the operating status of urban building complexes based on night light remote sensing.

[0050] Figure 2 This is a monthly night light intensity diagram of a typical building complex in the embodiment adopted by the present invention. DETAILED DESCRIPTION

[0051] In order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail and in depth below with reference to the accompanying drawings.

[0052] The present invention provides a method for monitoring the operation status of urban buildings based on night light remote sensing. Figure 1 The specific steps are as follows:

[0053] Step 1: Download all NPP / VIIRS monthly night light data products for the corresponding image area by visiting the website according to the location and monitoring time range of the urban building complex to be monitored;

[0054] The data downloaded by directly accessing the website includes three types of products: daily, monthly and annual. This embodiment uses the vcmsl version of monthly night light data.

[0055] Step 2: Crop the monthly night light data according to the county / city administrative area where the city is located to obtain the night light remote sensing raster data of the area to be monitored, check the positive and negative pixel values of the raster data, and update the negative pixels to 0.

[0056] Monthly night light data is raster data and is generally clipped according to administrative divisions;

[0057] Step 3: Select the middle month of the monitoring time range as the reference month, and select the brightest area in the center of the city to be tested as the reference area;

[0058] The reference area is the central range with the largest pixel brightness value in the night light remote sensing raster data, usually 2*2 or 3*3 according to the size of the city;

[0059] Step 4: Calculate the relative correction coefficients corresponding to each month using the pixel values in the cropped nightlight remote sensing raster data, and correct the pixel values of the nightlight remote sensing raster data for each month to be unified to the reference month.

[0060] The specific steps are as follows:

[0061] Step 401: Calculate the mean value C_Mean of the brightness values of all pixels in the reference area in the reference month;

[0062] Selecting the middle month as the reference month reduces errors. For the time-series nightlight remote sensing data, select the middle month as the reference month. Determine the central urban area range of the nightlight data for this month according to the administrative region, and select the local area with the largest image brightness value within this range as the reference matrix, and calculate the mean value C_Mean of the brightness values of all pixels within the reference matrix;

[0063] Step 402: Similarly, calculate the mean value C_Mean of the pixel brightness values corresponding to the reference area for each month k ;

[0064] k = 1, 2,... K; K represents the number of months within the monitoring time range.

[0065] Step 403: Use the brightness mean value C_Mean of each month k and the reference mean value C_Mean to calculate the relative correction coefficients for each month;

[0066] Calculate the relative correction coefficient a for the k-th month k The formula is as follows:

[0067] a k = C_Mean / C_Mean k

[0068] Step 404: Use the relative correction coefficients of each month to perform relative correction processing on the nightlight remote sensing raster data of different months, and unify the nightlight remote sensing data values of all months to the intensity values of the reference month;

[0069] The correction means: For the nightlight intensity DN value of each pixel in the cropped nightlight remote sensing raster data, update it using the correction coefficient respectively, and the calculation formula is as follows:

[0070] DN new = DN · a k

[0071] Theoretically, the mean value of the brightest reference area should be the same every month.

[0072] Step 5. Convert the corrected and updated monthly night light remote sensing raster data into vector files, and perform spatial interpolation using the thin plate spline method to obtain the night light values at the locations of the specified building complexes.

[0073] The specific steps are as follows:

[0074] Step 501. Use the raster to point function of GIS software to convert the corrected monthly night light remote sensing raster data into a vector file in shp format, and assign the night light intensity values of each pixel to the corresponding point vectors in the vector file.

[0075] Step 502. According to the position values (x0, y0) of the specified building complex to be monitored, obtain the positions and night light intensity values of N known vector points around it, which are respectively expressed as (x i , y i , z i ); i = 1, 2,... N;

[0076] Step 503. Calculate the distances between the N known vector points and between each vector point and the point to be interpolated (x0, y0) respectively;

[0077] For the distance r i , y i ) and the vector point (x j , y j ) between, the calculation formula is as follows: ij The formula is as follows:

[0078]

[0079] Step 504. Use the distances and night light intensity values between the known N vector points to calculate the parameters a and coefficient λ of the thin plate spline method for spatial interpolation through a system of equations j ;

[0080] The system of equations is expressed as follows:

[0081]

[0082] Among them, a is the local trend function, λ j is the coefficient of the solution of the linear system of equations, R represents the basis function of the thin plate spline, and the expression is:

[0083]

[0084] Among them is the weight parameter, K0 represents the modified Bessel function, and c is the constant 0.577215.

[0085] Step 505. Use the local trend function a and coefficient λ j, calculate the interpolation result z0 of the specified building complex position (x0, y0) to be monitored, and obtain the night light intensity value of the building complex.

[0086]

[0087] Step 6: By tracking the night light intensity values at the designated building complex location on a monthly basis, the time series night light intensity values of the designated building complex are obtained, and the operating status of the building is analyzed based on the changes in the night light intensity at the location.

[0088] Generally, if the light intensity at the building complex is close to 0, it indicates that human activities in the area are limited; if the light intensity at the building complex is higher than the average night light intensity of the entire area, it indicates that human or social activities in the area are frequent; if the light intensity gradually increases over time, it indicates that the building complex is in good operating condition, indicating that economic or social activities continue to increase.

[0089] Example

[0090] Taking the status of typical building complexes in Xiongan Rongcheng area as an example, the operating status monitoring method of urban building complexes based on night light remote sensing is explained.

[0091] In this example, the Xiong'an Citizen Service Center and Aowei Building in Rong County were selected as the building complex for analysis. The Xiong'an Citizen Service Center includes a planning and exhibition center, a conference and training center, a government service center, office space, turnover housing, and living services. It is located in the eastern part of Rongcheng, within the boundaries of Xiaobai Tower and Mazhuang Village, in an area formerly occupied by farmland. The Aowei Building is located in the commercial center of Rongcheng County, adjacent to the county party committee and government and Huiyou Commercial Building.

[0092] According to relevant news reports, the Hebei Xiong'an New Area Preparatory Committee was established on April 1, 2017, with its temporary office located in the Aowei Building in Rong County. In November 2017, the Xiong'an Citizen Service Center construction project completed its bidding and public notice process. Multiple construction companies completed the over 100,000 square meters of construction over 120 days, and the center was essentially completed on March 29, 2018. On April 16, 2018, the Xiong'an New Area Party Working Committee and Management Committee officially opened in the Xiong'an Citizen Service Center office building, and most business departments also gradually relocated there.

[0093] First, download all monthly night light data from December 2016 to December 2018 from the official website of the National Oceanic and Atmospheric Administration of the United States, and select the vcmsl version of the monthly product.

[0094] Then, the monthly night light data is cropped according to the administrative division data of Rong County to obtain night light remote sensing raster data, and the pixels with negative values are updated to 0. The monthly night light data is subjected to relative calibration processing. Taking the night light data of December 2017 as a reference and selecting the brightest area in the urban center of Rong County as a benchmark, the relative calibration coefficients of each monthly night light data are calculated, and the values of night light remote sensing data for all months are unified to the reference month.

[0095] Again, the updated monthly night light data is converted into a vector file, and then spatial interpolation processing is carried out using the thin plate spline method, where the weight parameter is set to 0.1, and the night light values of 12 surrounding pixels are selected for processing. The night light values for 25 months at the locations of Aowei Building and the office building of Xiongan Citizen Service Center are extracted, as shown respectively in Figure 2 shown.

[0096] According to the 25 - period night light intensity results of the office building of Xiongan Citizen Service Center, it can be clearly found that the night - time light brightness at the location of the office building of Xiongan Citizen Service Center where the Xiongan New Area Management Committee is located suddenly increased starting from November 2017, which is completely consistent with the relevant report that the ground construction of the office building of Xiongan Citizen Service Center started after the bidding and publicity were completed in November 2017. Especially in December 2017 and January 2018, the night light intensity at this location was at its peak. Combining with the 120 - day construction period, it indicates that in order to meet the construction schedule, the entire construction site was in continuous day - and - night operation, completing the foundation construction and the main body construction. With the opening of the office building of Xiongan Citizen Service Center in April 2018, the night light intensity gradually decreased and remained stable, and this intensity value is basically the same as the average night light intensity in the urban area of Rong County, indicating that the office building of Xiongan Citizen Service Center has been normally transferred to the operating state.

[0097] Similarly, for the analysis of the processing results of the monthly night light intensity of Aowei Building, the night light intensity gradually increased starting from March 2017, which is consistent with the time when the Preparatory Committee of Hebei Xiongan New Area started to operate. Starting from May 2018, the night light intensity gradually decreased and gradually returned to the average night light intensity in the urban area of Rong County, which is also consistent with the time node when the office building of Xiongan Citizen Service Center was opened in April 2018.

[0098] Therefore, based on the analysis of typical building groups, the monthly moonlight data processing method proposed by the present invention can obtain the light intensity values of building group points in a long time series, and the changes in the intensity values are highly consistent with the operating state of the building group, which can meet the requirements of local refined analysis of the building group.

Claims

1. A method for monitoring the operating status of urban buildings based on night light remote sensing, characterized by: First, based on the location and monitoring timeframe of the urban buildings to be monitored, all NPP / VIIRS monthly night light data products for the corresponding image area were downloaded from the website. After cropping the data according to administrative regions, the pixel values were checked for positive and negative values, and negative pixels were updated to 0. Then, the middle month of the monitoring time range is selected as the reference month, and the brightest area in the center of the city to be measured is selected as the reference area; the pixel values in the cropped night light remote sensing raster data are used to calculate the relative correction coefficients corresponding to each month, and the pixel values of the night light remote sensing raster data of each month are corrected; Finally, the updated monthly night light remote sensing raster data was converted into a vector file and spatially interpolated using the thin plate tension spline method to obtain the night light intensity value at the specified building complex location. The night light intensity value of the specified building complex was tracked monthly, and the operating status of the building complex was analyzed based on the changes in the night light intensity at that location. Use spatial interpolation to obtain the night light intensity value of the specified building complex location. The specific steps are as follows: Step 501: Convert the corrected monthly night light remote sensing raster data into a shp format vector file using the raster-to-point function of the GIS software, and assign the night light intensity value of each pixel to the value of the corresponding point vector in the vector file; Step 502: According to the position values (x0, y0) of the specified building complex to be monitored, obtain the positions and night light intensity values of N known vector points around it, which are respectively represented as (x i , y i , z i ); i = 1, 2,... N; Step 503: Calculate the distances between the N known vector points and between each vector point and the point to be interpolated (x0, y0); For the distance r between the known vector point (x i , y i ) and the vector point (x j , y j ), the calculation formula is as follows: ij ​ Step 504: Calculate the parameters a and coefficient λ of the thin plate spline method for spatial interpolation through a system of equations using the distances between N known vector points and the night light intensity values j ; The system of equations is expressed as follows: where a is the local trend function, and λ j is the coefficient of the solution of the linear equations, and R represents the basis function of the thin plate tension spline, and the expression is: wherein is a weight parameter, K0 represents a modified Bessel function, and c is a constant; Step 505: Use the local trend function a and the coefficient λ j , and calculate the interpolation result z0 at the position (x0, y0) of the specified building complex to be monitored; The calculation formula is as follows: The interpolation result z0 is the night light intensity value of the building complex.

2. The method for monitoring the operation status of urban building complexes based on nocturnal remote sensing according to claim 1, wherein: The monthly night light data is the vcmsl version of raster data.

3. The method for monitoring the operation status of an urban building complex based on nocturnal remote sensing according to claim 1, wherein: The reference area is the central range of the pixel with the largest brightness value in the night light remote sensing raster data, and is usually selected as 2*2 or 3*3 according to the size of the city.

4. The method for monitoring the operation status of urban building complexes based on night light remote sensing according to claim 1, wherein: The pixel values of the night light remote sensing raster data of each month are corrected using the relative correction coefficient. The specific steps are as follows: Step 401: Calculate the mean C_Mean of the brightness values of all pixels in the reference area in the reference month; Step 402. Similarly, calculate the average pixel brightness value C_Mean corresponding to the reference area for each month k ; k=1,2,...K; K represents the number of months in the monitoring time range; Step 403: Calculate the relative correction coefficient for each month by using the brightness mean C_Mean of each month k and the reference mean C_Mean Calculation of the relative correction coefficient for the k-th month a k The formula is as follows: a k = C_Mean / C_Mean k Step 404: performing relative correction processing on the night light remote sensing raster data of different months using the relative correction coefficient of each month, and unifying the night light remote sensing data values of all months to the intensity value of the reference month; Correction means updating the night light intensity DN value of each pixel in the cropped night light remote sensing raster data using the correction coefficient. The calculation formula is as follows: DN new = DN·a k .

5. The method for monitoring the operation status of urban building complexes based on night light remote sensing according to claim 1, wherein: The above method tracks the changes in the night light intensity of a designated building complex on a monthly basis and analyzes the operating status of the building complex. The specific process is as follows: If the light intensity at the building complex is close to 0, it indicates that human activities in the building complex area are limited; if the light intensity at the building complex is higher than the average night light intensity of the entire area, it indicates that human or social activities in the area are frequent; if the light intensity gradually increases with time, it indicates that the building complex is in good operating condition, indicating that economic or social activities continue to increase.

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