CN108256015A - A kind of Chinese population spatial grid method based on nighttime light data - Google Patents
A kind of Chinese population spatial grid method based on nighttime light data Download PDFInfo
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Abstract
The invention discloses a kind of Chinese population spatial grid methods based on nighttime light data, and this method comprises the following steps：Step 1) is based on U.S. national defense meteorological satellite plan line scan sensor (Defense Meteorological Satellite Program ' s Operational Linescan System, DMSP_OLS) nighttime light data (the Nighttime Light Time series obtained, NLT), a set of nighttime light data process flow is researched and developed, obtaining has compatibility, successional nighttime light data collection for 1,992 2013 years；Step 2) using every sq-km as grid, is obtained Chinese population spatial distribution map, is expressed the size of population of each grid based on the NLT data after census data and correction, the structure population spatial distribution model of 2000 and 2010.
Description
Technical field
The present invention relates to nighttime light data processing and population spatial grid field more particularly to a kind of night lights numbers
According to mutual calibration, geometric correction, incompatibility and discontinuity correction, to weaken based on vegetation information light saturation existing with overflowing
The method of elephant.
Background technology
China is the world's largest developing country, and urbanization rate remains faster growth rate, meanwhile, population expansions
Series of negative is brought to influence, such as：Cultivated Land Area Decrease, consumed resource increase, safety environment is serious etc..Using distant
Sense means grasp the dynamic-change information of Chinese population spatial distribution, can be that relevant departments carry out China's city planning, population
Regulation and control, resources and ecological environmental protection etc. provide the theoretical foundation of science.
At present, China authority, reliable population spatial distribution data are mainly 1953,1964,1990,2000 and 2010
Six national censuses carried out.According to this kind of data, the average population density of each administrative unit and whole sky can only be obtained
Between distribution dynamic information, the special heterogeneity of population distribution inside administrative unit can not be reacted.Population spatial grid is a kind of
The method for calculating the size of population of the smaller carrying of area on the spot can usually calculate the size of population of every sq-km, energy
The special heterogeneity of population distribution enough inside reaction administration cell.Compared with census data, the population letter of spatial grid
Breath is in further detail, really.
Nighttime light data has the characteristics that acquisition is convenient, area coverage is wide, and proves its luminance information by most experiments
It is related to the extent heights such as human living, activity, it has been widely used in the research of population spatial gridization.But night lights
Data are due to lacking onboard process, and there are geographical deviation, light saturation, light overflows, pixel value is incompatible and discontinuous etc. asks
Topic, serious error can be led to by being directly used in progress population spatial gridization.It is existing to carry out population using nighttime light data
In the research of spatial grid, majority does not carry out the correction work of nighttime light data, and the corrected work in part also only solves
It has determined a or two item problem of nighttime light data.Systematic correction is carried out to nighttime light data, and utilizes systematic correction
Nighttime light data afterwards carries out the opposite shortage of work of population spatial gridization research.
In view of problem above, the present invention has developed a kind of systematic night lights correction using nighttime light data
With population spatial grid method, corrected, based on vegetation information including mutual calibration, geometric correction, incompatibility and discontinuity
Weaken light saturation and spilling, the method for population spatial grid.
Invention content
The present invention provides a kind of nighttime light data correction and population spatial grid method, can be compatible with, is continuous
Night lights information, based on after correction nighttime light data carry out population spatial grid, improve population spatial distribution
The accuracy and authenticity of information.
The purpose of the present invention is realized by following technical step：
1. a kind of nighttime light data correction and population spatial grid method, include the following steps：
Step 1) selects immutable object area and with reference to image, establishes other images subject to registration and the quadratic regression with reference to image
Equation selects the regression equation of coefficient of determination maximum and root-mean-square error minimum as optimal mutual calibration model, based on institute
Optimal mutual calibration model is stated mutually to correct image subject to registration；
Step 2) carries out 25 kinds of shifting processings based on the image subject to registration after mutual correction, direction of displacement for it is upper and lower, left,
The eight neighborhoods directions such as right, upper left, upper right, bottom left, bottom right, translocation distance is between 0-2 pixel, based on the shadow subject to registration after displacement
As with establishing quadratic regression equation with reference to image, selecting the regression equation of coefficient of determination maximum and root-mean-square error minimum as most
Excellent geometric correction model carries out geometric correction based on the optimal geometric correction model to image subject to registration；
Step 3) is based on the image subject to registration after geometric correction, if the same time, there are the nights that two sensors obtain
Light data calculates its average value, obtains compatible nighttime light data；
Step 4), if the value of same pixel is more than latter year in the previous time, is enabled based on compatible nighttime light data
The value of the latter year pixel is equal with the previous year, obtains the nighttime light data of positive adjustment；
Step 5), if the value of same pixel is less than the previous year in the latter time, is enabled based on compatible nighttime light data
The pixel value of the previous year pixel is equal with latter year, obtains the nighttime light data of reverse adjustment；
The nighttime light data of nighttime light data and reverse adjustment of the step 6) based on forward direction adjustment, to the same time
Data are averaged, and obtain continuous nighttime light data collection；
Step 7) is based on Moderate Imaging Spectroradiomete (Moderate Resolution Imaging
Spectroradiometer, MODIS) data normalized difference vegetation index (Normalized Difference
Vegetation Index, NDVI) product, continuous nighttime light data collection is adjusted, weakens its saturability and light
Spillover obtains the nighttime light data after systematic correction；
Step 8) is counted total pixel value of each administrative unit at county level, is built based on the nighttime light data after systematic correction
Total pixel value and the cubic regression equation of corresponding county's census sum are found, obtains population spatial grid model；
Step 9) obtains the corresponding original demographic's quantity of each pixel, i.e. original demographic based on population spatial grid model
Spatial distribution map, counts original demographic's sum of each administrative unit at county level, and establishes proportionality coefficient using census data, will
The proportionality coefficient in each county is multiplied with original demographic's spatial distribution map, obtains population spatial distribution map.
Description of the drawings
Fig. 1 is the mutual correcting process figure of nighttime light data；
Fig. 2 is nighttime light data geometric correction flow chart；
Fig. 3 is nighttime light data compatibility correcting process figure；
Fig. 4 is nighttime light data correction for continuity flow chart；
Fig. 5 is adjustment flow chart of the nighttime light data based on vegetation information；
Fig. 6 is population spatial grid flow chart；
Fig. 7 is the forward and backward night lights striograph of correction (by taking Wuhan, Xi'an, Urumchi as an example)；
Fig. 8 is Chinese population spatial distribution map in 2000；
Fig. 9 is Chinese population spatial distribution map in 2010.
Specific embodiment
" a kind of Chinese population spatial grid method based on nighttime light data " of the invention is made below in conjunction with the accompanying drawings
Explanation is expanded on further.Invention involved " nighttime light data correcting process " is important innovations, which is based on ArcGIS
Software is realized, detailed displaying has been carried out in Fig. 1-Fig. 5.Involved " population spatial grid " flow of invention is carried out in Fig. 6
Displaying.
Fig. 1 is the mutual correcting process figure of nighttime light data provided by the invention.Utilize the Extract of ArcGIS softwares
By Mask tools cut out the Sicanian tif images of Italy of immutable object area from image subject to registration and reference image.
It is the night lights image of 2003 that F15 sensors obtain with reference to image, other night lights images are image subject to registration.
The corresponding image subject to registration of each pixel and the pixel value with reference to image are extracted successively with C++ programmings, are formed a multirow two and are arranged
Array.Two columns values of the array are then based on, the One- place 2-th Order established respectively between image subject to registration and reference image is more
Formula, DN represents the pixel value with reference to image, DN_ in formulaint=α0+α1*DN+α2*DN2, DN_intRepresent the picture of image subject to registration
Element value.The One- place 2-th Order multinomial correlation of coefficient of determination maximum and root-mean-square error minimum is most strong, as optimal mutual correction
Model.It brings image subject to registration into optimal mutual calibration model, obtains the nighttime light data after mutually correction.Mutual straightening die
The factor alpha of type0、α1And α2It is shown in Table 1 respectively.
1 mutual correction coefficient table of table
Fig. 2 is nighttime light data geometric correction flow chart provided by the invention.It, will with reference to image without any processing
Image subject to registration carries out the displacement of 8 neighborhood directions, and translocation distance is between 0-2 pixel.Wherein, p represents line number, and q represents row
Number, formula DN(p,q)=DN(p,q+1), (p >=0, q >=0) can realize the distance for moving up a pixel, DN(p,q)=DN(p,q-1),(p
>=0, q >=1) it can realize the distance for moving down a pixel, DN(p,q)=DN(p+1,q), (p >=0, q >=0), which can be realized, moves to left one
The distance of a pixel, DN(p,q)=DN(p-1,q), (p >=1, q >=0) can realize the distance for moving to right a pixel, based on this four
Formula arbitrarily combines, and can realize other move modes, these formula collectively form geometric correction model.By image subject to registration point
It does not bring various geometric correction models into, cuts out the tif images of regional, each image subject to registration can obtain 25 geometry
Tif images after correction.The pixel value of 25 each pixels of tif images and its corresponding with reference to shadow is extracted successively with C++ programmings
The pixel value of picture forms the array that 25 pairs of multirows two arrange.It is then based on 25 pairs of arrays and carries out correlation analysis, the coefficient of determination is maximum
Most strong, as optimal array, corresponding geometric correction model are with the One- place 2-th Order multinomial correlation of root-mean-square error minimum
Optimal geometric correction model.It brings the nighttime light data after mutual correction into optimal geometric correction model, obtains geographical deviation
The nighttime light data being weakened.The corresponding displacement mode of optimal geometric correction model is shown in Table 2.
2 geometric correction pixel shift table of table
Fig. 3 is nighttime light data compatibility correcting process figure provided by the invention.By the night lights after geometric correction
Data are divided into two classes, if the same time only has the data that sensor obtains, are classified as the first kind, this kind of tif images not into
Any processing of row；If the same time, there are two the data that sensor obtains, the second class is classified as, two tif of same year are schemed
Calculated as carrying out (T1+T2)/2 under Raster Calculator tools in ArcGIS softwares, by the use of obtained average as
The year unique tif images.This step can obtain 1992-2013 totally 22 scape night lights tif images, i.e. compatible night
Light data.
Fig. 4 is nighttime light data correction for continuity flow chart provided by the invention.First, the night after being corrected to compatibility
Between light data be compared, if the pixel value in same pixel corresponding latter year be greater than or equal to the previous year, without place
Reason；If the pixel value in same pixel corresponding latter year is less than the previous year, formula DN_ is utilizedlatter=DN_formerIt is replaced
It is changed to the pixel value of the previous year.It is hereby achieved that the nighttime light data after positive correction.Then, after being corrected to compatibility
Nighttime light data is compared, if the pixel value of same pixel corresponding the previous year is less than or equal to latter year, without
Processing；If the pixel value of same pixel corresponding the previous year is more than latter year, formula DN_ is utilizedformer=DN_latterBy its
Replace with the pixel value in latter year.It is hereby achieved that the nighttime light data after reverse correction.The same time is corresponding just
It brings the Raster Calculator tools in ArcGIS softwares into correction tif images and reverse correction tif images, carries out (T1
+ T2)/2 calculate average, obtain continuous nighttime light data.
Fig. 5 is adjustment flow chart of the nighttime light data provided by the invention based on vegetation information.Utilize MOD13A3 data
NDVI products, less, principle that exclusion area's vegetation growth is more vigorous is distributed based on human settlement vegetation, will be continuous
Nighttime light data (i.e. NLT images) and NDVI products bring NLT* (1-NDVI) formula into, can obtain more objective night lamp
Nighttime light data after light data, i.e. systematic correction.
Fig. 6 is population spatial grid flow chart provided by the invention.Based on the nighttime light data after correction, statistics is each
The sum of pixel value of administrative unit at county level, if the size of population of certain administrative unit at county level is less than 10000 times of the sum of pixel value,
Part1 classes are classified as, otherwise are classified as Part2 classes.To the total pixel value and population of Part1 and Part2 classes administrative unit at county level
Census data establishes unitary cubic polynomial f=a*DN3+b*DN2+ c*DN, as population spatial grid model, DN values represent
Total pixel value of each administrative unit at county level, f are the census number of the administrative unit at county level, and the value of coefficient a, b, c are shown in Table 3.It will
Each pixel value brings the model into, obtains the corresponding initialization size of population of each pixel.Count the first of each administrative unit at county level
The beginningization size of population, as divisor, census data brings formula K into as dividendi=Population_census/
Population_initial, obtained quotient KiProportionality coefficient for each administrative unit at county level.The proportionality coefficient is turned in ArcGIS
Tif images are turned to, bring its initialization population corresponding with each pixel into formula Pop=Ki* it is empty to obtain final population by DN
Layout networking figure.
Table 3 2000 and population spatial grid model coefficient table in 2010
Claims (4)
1. a kind of Chinese population spatial grid method based on nighttime light data, includes the following steps：
Nighttime light data of the step 1) based on acquisition, selection mutually correct image subject to registration with reference to image；
Step 2) is carried out shifting processing, geometric correction is carried out to image subject to registration based on the image subject to registration after mutual correction；
Step 3) is based on the image subject to registration after geometric correction, if the same time, there are the night lights that multiple sensors obtain
Data calculate its average value, obtain compatible nighttime light data collection；
Step 4), if the pixel value in same pixel previous time is more than latter year, is enabled based on compatible nighttime light data collection
The pixel value of the latter year pixel is equal with the previous year, obtains the nighttime light data collection of positive adjustment；
Step 5), if the pixel value in same pixel latter time is less than the previous year, is enabled based on compatible nighttime light data collection
The pixel value of the previous year pixel is equal with latter year, obtains the nighttime light data collection of reverse adjustment；
The nighttime light data collection of nighttime light data collection and reverse adjustment of the step 6) based on forward direction adjustment, to the same time
Data are averaged, and obtain continuous nighttime light data collection；
Step 7) is based on normalized difference vegetation index (Normalized Difference Vegetation Index, NDVI)
Product is adjusted continuous nighttime light data collection, weakens its saturability and light spillover, obtains systematic correction
Nighttime light data afterwards；
Step 8) based on the nighttime light data after systematic correction, establish total pixel value of each administrative unit at county level with it is corresponding
The cubic regression equation of census sum obtains population spatial grid model；
Step 9) obtains the corresponding original demographic's quantity of each pixel, i.e. original demographic space based on population spatial grid model
Distribution map, counts original demographic's sum of each administrative unit at county level, and establishes proportionality coefficient, Jiang Ge counties using census data
Proportionality coefficient be multiplied with original demographic's spatial distribution map, obtain population spatial distribution map.
2. according to the method described in claims 1, which is characterized in that the step 1) includes：Select immutable object area and ginseng
Image is examined, establishes other images subject to registration and the quadratic regression equation formula with reference to image, selects coefficient of determination maximum and root mean square
The regression equation of error minimum as optimal mutual calibration model, based on the optimal mutual calibration model to image subject to registration into
Row mutually correction.
3. according to the method described in claims 1, which is characterized in that the step 2) includes：Based on treating after mutual correction
Image is registrated, carries out shifting processing, direction of displacement is upper and lower, left and right, upper left, upper right, bottom left, bottom right eight neighborhood direction, is moved
Position distance is between 0-2 pixel, and based on the image subject to registration after displacement with establishing quadratic regression equation with reference to image, selection determines
The regression equation of coefficient maximum and root-mean-square error minimum is as optimal geometric correction model, based on the optimal geometric correction mould
Type carries out geometric correction to image subject to registration.
4. according to the method described in claims 1, which is characterized in that the step 8) includes：After systematic correction
Nighttime light data counts total pixel value TDN (i) of each administrative unit's light data at county level, is counted based on census data
The population Pop (i) of corresponding administrative unit at county level, if TDN (i) * 1000>Pop (i) is then classified as Part1 classes, instead
Be classified as Part2 classes, cubic regression side of total pixel value with corresponding county's census sum is established to Part1 and Part2 classes
Journey respectively obtains population spatial grid model.
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| CN110276797A (en) * | 2019-07-01 | 2019-09-24 | 河海大学 | A kind of area of lake extracting method |
| CN111192298A (en) * | 2019-12-27 | 2020-05-22 | 武汉大学 | Relative radiation correction method for luminous remote sensing image |
| CN111896680A (en) * | 2020-07-08 | 2020-11-06 | 天津师范大学 | Greenhouse gas emission analysis method and system based on satellite remote sensing data |
| CN114138926A (en) * | 2022-01-27 | 2022-03-04 | 中国测绘科学研究院 | Method and system for determining size of population distribution grid |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109509154A (en) * | 2018-10-23 | 2019-03-22 | 东华理工大学 | A kind of stable noctilucence remote sensing image desaturation bearing calibration of DMSP/OLS |
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| CN110276797A (en) * | 2019-07-01 | 2019-09-24 | 河海大学 | A kind of area of lake extracting method |
| CN110276797B (en) * | 2019-07-01 | 2022-02-11 | 河海大学 | Lake area extraction method |
| CN111192298A (en) * | 2019-12-27 | 2020-05-22 | 武汉大学 | Relative radiation correction method for luminous remote sensing image |
| CN111192298B (en) * | 2019-12-27 | 2023-02-03 | 武汉大学 | Relative radiation correction method for luminous remote sensing image |
| CN111896680A (en) * | 2020-07-08 | 2020-11-06 | 天津师范大学 | Greenhouse gas emission analysis method and system based on satellite remote sensing data |
| CN111896680B (en) * | 2020-07-08 | 2022-07-05 | 天津师范大学 | Greenhouse gas emission analysis method and system based on satellite remote sensing data |
| CN114138926A (en) * | 2022-01-27 | 2022-03-04 | 中国测绘科学研究院 | Method and system for determining size of population distribution grid |
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