CN115909080A - Land utilization data-based multi-source noctilucent remote sensing image integration method - Google Patents
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Abstract
The invention discloses a land utilization data-based multi-source luminous remote sensing image integration method, which comprises the following steps: selecting DMSP/OLS and NPP/VIIRS luminous remote sensing images and land utilization status grid data covering a research area in 2013; carrying out coordinate transformation, spatial resampling and projection transformation to ensure that a coordinate system, spatial resolution and a projection mode are consistent; registering the DMSP/OLS and the NPP/VIIRS noctilucent remote sensing images; cutting the land utilization status grid data, DMSP/OLS and NPP/VIIRS luminous remote sensing images; classifying the two types of noctilucent remote sensing images according to land categories to respectively obtain noctilucent remote sensing images of different land utilization categories; respectively establishing regression models for the DMSP/OLS and NPP/VIIRS luminous remote sensing images of each land use category to combine into a regression model group; and simulating and generating the NPP/VIIRS image of the year by using the current data based on the regression model group. The invention effectively integrates the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images, and well weakens the light intensity difference between the two night light remote sensing images.
Description
Technical Field
The invention belongs to the technical field of remote sensing image processing, and particularly relates to a multisource noctilucent remote sensing image integration method based on land utilization data.
Background
The night light (NTL) is closely related to human activities, and the intensity and intensity of lighting facilities can reflect the intensity of human activities and economic prosperity of the area to some extent. The luminous remote sensing image can reflect night town light, and can capture forest fire, natural gas combustion, light emission of a fishing boat at night and the like; and the method is widely applied to a plurality of research fields such as social and economic parameter estimation, population analysis, regional development research, urban spatial distribution pattern research, energy consumption, fishery monitoring, major event evaluation and the like.
At present, the main data sources for performing space-time analysis by noctilucent remote sensing are DMSP/OLS remote sensing images and NPP/VIIRS remote sensing images, the time coverage of the DMSP/OLS remote sensing images is 1992-2013, and the time coverage of the NPP/VIIRS remote sensing images is 2013 till now (2022). Both sufficient data volume and 30 years time coverage provide a data basis for spatio-temporal analysis. Unfortunately, there is a significant difference between DMSP/OLS and NPP/VIIRS data, and the two sets of data cannot be used simultaneously, thereby limiting the length of the available time series of night light data. The difference of the two sets of data is mainly reflected in the following aspects that 1) the remote sensing platforms are inconsistent; 2) The sensors acquiring the data are not consistent; 3) The spatial resolution of the images is inconsistent; 4) The imaging times are inconsistent; 5) The pixel value of DMSP is a dimensionless DN value, and NPP is the radiation intensity value actually detected; 6) In the same pixel (or region) for a period of time, the change trends and the change modes of the luminous intensity presented by DMSP-OLS and VIIRS are different; 7) Applications based on DMSP-OLS and VIIRS (e.g., city extraction, GDP estimation) have different results.
Meanwhile, due to the fact that the application of noctilucent remote sensing still lacks of a continuous and consistent global data set, integration of night light data of the 20 th century 90 s to date provides possibility for monitoring human activities in global and regional scales for a long time. Several studies have begun to attempt time series analysis using DMSP/OLS and NPP/VIIRS, such as Shao's attempt to calibrate the daily DMSP/OLS image over the south Pole C to data similar to NPP/VIIRS based on the DNB band of NPP/VIIRS, but this method is only applicable to a limited range of DN values for DMSP/OLS. Li proposes a mutual calibration model based on a power function and Gaussian low-pass filtering, ordered monthly DMSP/OLS data and NPP/VIIRS data are integrated, changes of light brightness of a residential district of Syrian people in 2011-2017 are explored, and the method does not have universal data acquirability. Dong Hesong and the like construct a long-time sequence night light data set of three major urban groups in China in 1992-2017 based on an optimal power function model, and explore space expansion and space-time dynamics of the three major urban groups in development. In recent years, ma, zhao and the like propose new integrated models, NPP/VIIRS data are used for simulating DMSP/OLS data after 2013 based on a logic function model, and the accuracy of the models can reach more than 95%. Yu et al integrates the nighttime light data sets of the long triangular urban groups in 2001-2019 based on the model, and explores the space-time heterogeneity of the built-up area expansion and the light brightness change in the urbanization process of the long triangular urban groups. However, these studies are based on the DMSP/OLS image at an earlier time, and the NPP/VIIRS with better data quality is changed to simulate the DMSP/OLS image, which reduces the quality of the noctilucent image, and often only modeling analysis can be performed on some overall levels, and only on a large-area, large-range and overall evaluation scale, the accuracy is better, and the accuracy is very low when the image is refined to a city or even the pixel scale, and the model does not have much practical significance. And the integration of the DMSP/OLS remote sensing image and the NPP/VIIRS remote sensing image is rarely carried out by combining the land utilization current grid data.
Through the analysis, the incompatibility problem of the multi-source luminous remote sensing image seriously restricts the application range and the application effect of the multi-source luminous remote sensing image, the existing integration technology adopts a method that all pixels share one model to integrate, the precision is good only in the large-area and large-range and general evaluation scale, the image is refined to the city, even the precision of the pixel scale is very low, and the method does not have much practical significance.
Disclosure of Invention
Aiming at the problems in the prior art, the method carries out saturation analysis on the DMSP/OLS image, and carries out continuous correction and average on the monthly NPP/VIIRS image to obtain an annual image; then, various conventional fitting models are established according to various DMSP/OLS data and NPP/VIIRS data of different land utilization areas. And different night illumination data preprocessing methods, various land utilization areas and fitting models are contrastively analyzed.
The invention is realized in such a way, and provides a multisource noctilucent remote sensing image integration method based on land utilization data, which is specifically shown in figure 1 and comprises the following steps:
step a, selecting a DMSP/OLS luminous remote sensing image, an NPP/VIIRS luminous remote sensing image and land use status grid data covering a research area in 2013;
step b, carrying out projection transformation on the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land utilization status grid data, and converting the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land utilization status grid data into a WGS _ 1984' u albers projection coordinate system;
step c, carrying out spatial resampling on the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land use current state grid data to ensure that the spatial resolution of the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land use current state grid data is 500 meters;
step d, carrying out geographic registration on the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image respectively by taking the land utilization status grid data as a reference;
step e, using boundary vector data of the research area, inlaying and cutting the land use current situation raster data, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image to obtain the land use current situation raster data, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image of the research area;
step f, extracting DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images of various categories according to land utilization category information of the land utilization current situation grid data;
step g, performing linear and nonlinear regression analysis on the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images of the current land utilization state types of each type, and combining models of each type to obtain a regression model group;
and h, simulating and generating an NPP/VIIRS image of the year by using the DMSP/OLS image between 1992 and 2012 and the land utilization status grid data of the corresponding year based on the obtained regression model group, and completing integration of the multi-source luminous remote sensing image.
Further, in step a, 2013 DMSP/OLS year stable light images of version 4 are selected.
Further, in step a, a lamp image of the annual average value of NPP/VIIRS of 2013, V2 edition, is selected.
Further, the V2 version NPP/VIIRS annual average value light image is obtained by masking 2013 version NPP/VIIRS annual average value data by 2013 version light mask grid data, and the extraction process calculation formula is as follows:
wherein,
represents the pixel value of the ith row and the jth column of the V2 NPP/VIIRS year image>
Represents the pixel value of the ith row and the jth column of the nth lamp light mask grid data>
Represents the year of the nth NPP/VIIRSThe value is like the pixel value of the ith row and the jth column.
Further, in the step a of the embodiment of the present invention, the method for selecting the land use status grid data includes:
if the grid data is the land utilization current grid data, selecting the land utilization current grid data with the spatial resolution close to NPP/VIIRS, classifying as detailed as possible and having higher precision as possible;
in the case of vector data, specialized software, such as Arcgis, is required to convert the vector data into raster data.
Further, in step e of the embodiment of the present invention, the land use status grid data of the research area needs to judge whether multiple scenes of land use status need to be embedded according to the size of the research area and the actual situation of the selected land use status grid data to obtain an image covering the research area, and then the image is cut by using the boundary vector data of the research area to obtain the land use status grid data of the research area.
Further, in the step f, extracting DMSP/OLS and NPP/VIIRS luminous remote sensing images of each category according to land use category information of the land use present situation grid data, according to the following formula:
in the formula (Landuse) ij ＝＝Value n ) Is a logic statement, landuse ij Equal to Value n If so, the part is 1, otherwise, the part is 0; where n is the number of categories of land use presence grid data,
represents the pixel value of the ith row and the jth column of the light data of the Nth type of DMSP/OLS (digital multiplex/optical storage system), and is combined with the pixel value of the jth column in the ith row and the jth column in the light data of the Nth type of DMSP/OLS>
Representing the nth row of NPP/VIIRS light dataPixel value of jth column, landuse ij A Value representing a pixel Value of the ith row and the jth column of the land use status grid data n Representing the pixel value of the nth type of land use status grid data, DMSP _ NTL ij Representing the pixel value, NPP _ NTL, of the ith row and jth column of DMSP/OLS lighting data ij And the pixel value of the ith row and the jth column of the NPP/VIIRS light data is represented.
Further, in the step g, linear and nonlinear regression analysis is performed on the DMSP/OLS noctilucent remote sensing images and the NPP/VIIRS noctilucent remote sensing images of the current land utilization status types of each type, and the method for combining models of each type to obtain a regression model group specifically comprises the following steps:
establishing a regression model for each category of DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images, wherein the model is established in a mode that the pixel value of the DMSP/OLS image at the same spatial position of each category is used as an independent variable X, the pixel value of the NPP/VIIRS image is used as a dependent variable Y, and regression analysis is respectively carried out through a linear function model and a nonlinear function model;
by a correlation coefficient R 2 Based on the size of R 2 The maximum fitting function is taken as the regression model function, f is calculated i (x) A regression model function of the ith category;
combining regression model functions of all categories into a regression model group, and visually expressing the regression model group in a function group form as follows:
f i (x) Is a regression model of the i-th class.
Further, the method for simulating the NPP/VIIRS image of the corresponding year by using the DMSP/OLS light image and the land use data in the step h comprises the following steps: firstly, obtaining a DMSP/OLS light image of 1992 to 2012 and land utilization status grid data of the same year which are consistent in coordinate system, spatial resolution and projection mode of a research area and are subjected to geographic registration through steps b, c, d and e, taking a DMSP/OLS annual stable light image of each year and the land utilization status grid data of the corresponding year as input data in 1992 to 2013, and calculating pixel values of simulated NPP/VIIRS image pixels of the corresponding year pixel one by one according to a regression model group F (x) in step g.
By combining all the technical schemes, the invention has the advantages and positive effects that:
the invention uses the data of the current land utilization situation to assign the land attributes to DMSP/OLS and NPP/VIIRS. And establishing a regression model for the lamplight of each land attribute, and finally forming an integrated model for converting the DMSP/OLS into the NPP/VIIRS-like light. Experiments show that the invention effectively integrates DMSP/OLS and NPP/VIIRS luminous remote sensing images, and better weakens the light intensity difference between the two kinds of luminous remote sensing images at night.
The method fully analyzes the relationship between the light intensity of DMSP/OLS and NPP/VIIRS of various land use categories, obtains the light conversion models of DMSP/OLS and NPP/VIIRS of different land types, and establishes the multi-source noctilucent remote sensing image integration method based on land use data. The method solves the problem of incompatibility of multi-source luminous remote sensing image data, and lays a foundation for constructing a high-quality and long-time luminous remote sensing time sequence data set with wide application value.
Drawings
FIG. 1 is a flow chart of a multisource noctilucent remote sensing image integration method based on land use data according to an embodiment of the invention;
FIG. 2 is a flow chart of an image integration experiment of a multisource noctilucent remote sensing image integration method based on land utilization data provided by the embodiment of the invention;
fig. 3 is a schematic view of a land use image of the Jiangxi province area in 2013 according to an embodiment of the present invention;
fig. 4 is a schematic view of a DMSP/OLS annual stable night light remote sensing image in the west and Jiangxi province area of 2013 according to an embodiment of the present invention;
fig. 5 is a schematic view of a V2 nd annual night light remote sensing image of NPP/VIIRS in the Jiangxi province area of 2013 according to an embodiment of the present invention;
fig. 6 is a schematic view of an integrated year-old stable night light remote sensing image of DMSP/OLS in jiangxi province in 2010 provided in the embodiment of the present invention;
fig. 7 is a schematic diagram of a land use image of a 2010 region in Jiangxi province according to an embodiment of the present invention;
fig. 8 is a schematic view of an integrated simulated NPP/VIIRS noctilucent remote sensing image in jiang west 2010 provided by the embodiment of the present invention;
FIG. 9 is a schematic view of a simulated NPP/VIIRS luminous remote sensing image integrated in Jiangxi province in 2010 by using the method Chen Zuoqi for comparison with the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problem that the difference between the NPP/VIIRS light image and the DMSP/OLS light image in the prior art is caused by compounding of various factors, and the difference is huge. The DMSP/OLS light images and the NPP/VIIRS light images of different land utilization types have different interrelations. According to the different light distribution rules of the DMSP/OLS and the NPP/VIIRS, the intensity characteristics of the DMSP/OLS images with different types of attributes and the NPP/VIIRS are different through experiments. The invention provides a multisource noctilucent remote sensing image integration method based on land use data, which is described in detail with reference to the attached drawings. As shown in fig. 1, the method includes:
step a, selecting a DMSP/OLS luminous remote sensing image, an NPP/VIIRS luminous remote sensing image and land use current situation grid data of a 2013 covered research area;
step b, carrying out projection transformation on the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land utilization status grid data, and converting the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land utilization status grid data into a WGS _ 1984' u albers projection coordinate system;
step c, carrying out spatial resampling on the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land use current state grid data to ensure that the spatial resolution of the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land use current state grid data is 500 meters;
step d, respectively carrying out geographical registration on the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image by taking the land utilization current situation grid data as a reference;
step e, inlaying and cutting the land utilization current state grid data, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image to obtain the land utilization current state grid data, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image of the research area by using the boundary vector data of the research area;
step f, extracting DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images of various categories according to land utilization category information of the land utilization current situation grid data;
step g, performing linear and nonlinear regression analysis on the DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images of each type of land utilization current situation type, and combining all types of models to obtain a regression model group;
and h, simulating and generating an NPP/VIIRS image of the year by using the DMSP/OLS image between 1992 and 2012 and the land utilization status grid data of the corresponding year based on the obtained regression model group, and completing integration of the multi-source luminous remote sensing image.
In the step a of the embodiment of the invention, the 2013 DMSP/OLS annual stable light image of version 4 is selected.
In step a of the embodiment of the invention, a V2 version of a 2013 annual average NPP/VIIRS lamp image is selected.
Further, the V2 version NPP/VIIRS annual average value light image needs to mask the 2013 version NPP/VIIRS annual average value data by 2013 year light mask grid data, so as to obtain the 2013 version NPP/VIIRS annual average value light image, and the calculation formula of the extraction process is as follows:
wherein
Represents the pixel value of the ith row and the jth column of the V2 NPP/VIIRS year image>
Represents the pixel value of the ith row and the jth column of the nth lighting mask grid data, and is combined with the pixel value of the jth row and the jth column of the nth lighting mask grid data>
And the pixel value of the average value of the NPP/VIIRS year in the nth year like the jth column and the ith row is represented.
The method for selecting the land use status grid data in the step a in the embodiment of the invention comprises the following steps:
if the grid data is the land utilization current grid data, selecting the land utilization current grid data with the spatial resolution close to NPP/VIIRS, classifying as detailed as possible and having higher precision as possible;
in the case of vector data, specialized software, such as Arcgis, is required to convert the vector data into raster data.
Further, in step e of the embodiment of the present invention, the land use status grid data of the research area needs to judge whether multiple scenes of land use status need to be embedded according to the size of the research area and the actual situation of the selected land use status grid data to obtain an image covering the research area, and then the image is cut by using the boundary vector data of the research area to obtain the land use status grid data of the research area.
Further, in the step f, extracting DMSP/OLS and NPP/VIIRS luminous remote sensing images of each category according to land use category information of the land use present situation grid data, according to the following formula:
wherein, (Landuse) ij ＝＝Value n ) Is a logical statement, landuse ij Equal to Value n If so, the part is 1, otherwise, the part is 0; where n is the number of categories of land use status grid data,
represents the pixel value of the ith row and the jth column of the light data of the nth type of DMSP/OLS (digital multiplex/optical storage system), and is/are selected>
Represents the pixel value of the ith row and the jth column of the NPP/VIIRS lamplight data, landuse ij The Value of the pixel Value representing the ith row and the jth column of the land use status grid data n Representing the pixel value of the nth type of land use status grid data, DMSP _ NTL ij Representing the pixel value, NPP _ NTL, of the ith row and jth column of DMSP/OLS lighting data ij And the pixel value of the ith row and the jth column of the NPP/VIIRS light data is represented.
Further, in the step g, linear and nonlinear regression analysis is performed on the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images of each type of land use status type, and the method for combining models of each type to obtain a regression model group specifically includes:
establishing a regression model for each category of DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images, wherein the model is established in a mode that the pixel value of the DMSP/OLS image at the same spatial position of each category is used as an independent variable X, the pixel value of the NPP/VIIRS image is used as a dependent variable Y, and regression analysis is respectively carried out through a linear function model and a nonlinear function model;
by a correlation coefficient R 2 Based on the size of R 2 The maximum fitting function is taken as the regression model function, f is calculated i (x) A regression model function of the ith category;
combining regression model functions of various categories into a regression model group, and visually expressing the regression model group in a function group form as follows:
f i (x) Is a regression model of the i-th class.
The method for simulating the NPP/VIIRS image of the corresponding year by using the DMSP/OLS light image and the land utilization data in the step h comprises the following steps: firstly, obtaining a DMSP/OLS light image of 1992 to 2012 and land utilization status grid data of the same year which are consistent in coordinate system, spatial resolution and projection mode of a research area and are subjected to geographic registration through steps b, c, d and e, taking a DMSP/OLS annual stable light image of each year and the land utilization status grid data of the corresponding year as input data in 1992 to 2013, and calculating pixel values of simulated NPP/VIIRS image pixels of the corresponding year pixel one by one according to a regression model group F (x) in step g.
The technical solution of the present invention is further described below with reference to experiments.
In order to better understand the technical scheme of the invention, a specific embodiment is described below by referring to fig. 2, which is shown in the following, through an experiment of integrating the DMSP/OLS year-stable night-luminous remote sensing image and the NPP/VIIRS year 2013 in the west and Jiangxi province.
Step a, selecting a DMSP/OLS luminous remote sensing image, an NPP/VIIRS luminous remote sensing image and land use status grid data of Jiangxi province in 2013, and implementing the following steps:
1. it should be noted that, for the research area selection, for example, in Jiangxi province of China, the NPP/VIIRS year image of V2 edition 2013 and the DMSP/OLS year stable lighting image are downloaded from an EOG data website (https:// eogdata. Mines. Edu/products/vnl/# annular _ V2), wherein the NPP/VIIRS image includes an annual average value image and a lighting area mask file;
2. synthesizing a V2 version NPP/VIIRS annual average value lamp image of 2013 by using MATLAB software through the lamp mask grid data of 2013 and the NPP/VIIRS annual average value data of 2013, wherein the calculation formula is as follows:
wherein
Represents the pixel value of the ith row and the jth column of the V2 NPP/VIIRS year image>
Represents the pixel value of the ith row and the jth column of the nth lamp light mask grid data>
And the pixel value of the NPP/VIIRS year mean value of the nth year like the ith row and the jth column.
3. Downloading a 2013 land utilization status image covering Jiangxi province from a resource environment science and data center of Chinese academy of sciences, wherein the land utilization type information is shown in table 1 as shown in FIG. 3;
TABLE 1 integration model and model precision of land utilization image land utilization type information table and corresponding type of Jiangxi province region in 2013
According to the results, the correlation coefficient s of (1) the power function and the polynomial function model is the highest in various fitting models. (2) The D saturated DMSP/OLS data is less accurate in a power function model than the original DMSP/OLS data, but is opposite in a polynomial function model. (3) The fitting precision can be effectively improved by the logarithm transformation processing of the NPP/VIIRS data. (4) The fitting precision of each land utilization area of the artificial elements is higher than that of the whole area, and the fitting precision of the non-artificial elements is the lowest.
Step b, carrying out projection transformation on land utilization present grid data, DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images, and converting the transformed images into WGS _1984 u albers projection coordinate systems, wherein the specific implementation mode comprises the following steps:
the land utilization current grid data, the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images are subjected to projection transformation by utilizing a projection transformation function in ENVI software, so that the geographical coordinate system of the land utilization current grid data, the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images is consistent with projection, and the geographical coordinate system is converted into an Albers _ WGS84 projection coordinate system;
step c, carrying out spatial resampling on the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land utilization current situation grid data, wherein the specific implementation mode that the spatial resolution is 500 meters is as follows:
by utilizing a grid resampling function in ENVI software, the spatial resolution of land utilization status grid data, DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images is consistent and is 500 meters;
step d, carrying out geographic registration on the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image respectively by taking the land utilization data as a reference:
in the embodiment, the selected remote sensing images have no obvious geographical position difference, so that geographical registration is not carried out;
step e, using the boundary vector data of the research area, inlaying and cutting the land utilization current state grid data, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image to obtain the land utilization current state grid data, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image of the research area, wherein the specific implementation mode of the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image is as follows:
utilizing a raster image cutting function in ENVI software, and cutting the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image by combining boundary vector data of a research area to obtain the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image of Jiangxi province in China, wherein the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image are specifically shown in the figures 4 and 5; it should be noted that, the present land use raster data used in the present embodiment can be directly downloaded to the present land use raster data in the province of Jiangxi, and thus further cutting and embedding are not required.
Step f, extracting DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images of various categories according to land utilization category information of the land utilization present situation grid data in a specific implementation mode that:
extracting DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images of various types according to land utilization type information of land utilization current state grid data by using MATLAB software according to the following formula:
in the formula (Landuse) ij ＝＝Value n ) Is a logical statement, landuse ij Equal to Value n If so, the part is 1, otherwise, the part is 0; where n is the number of categories of land use presence grid data,
represents the pixel value of the ith row and the jth column of the light data of the nth type of DMSP/OLS (digital multiplex/optical storage system), and is/are selected>
Represents the pixel value of the ith row and the jth column of the NPP/VIIRS lamplight data, landuse ij A Value representing a pixel Value of the ith row and the jth column of the land use status grid data n Representing the size of the pixel value of the nth type of land use grid data, DMSP-NTL ij Representing pixel value of ith row and jth column of DMSP/OLS lighting data, NPP _ NTL ij And the pixel value of the ith row and the jth column of the NPP/VIIRS light data is represented.
Step g, performing linear and nonlinear regression analysis on the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images of the current land utilization state types of each type, and combining models of each type to obtain a regression model group in the following specific implementation mode:
respectively carrying out linear and nonlinear regression analysis on the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images of each category by using MATLAB software, and carrying out linear and nonlinear regression analysis by taking the pixel values of the DMSP/OLS images at the same spatial position as an abscissa and the pixel values of the NPP/VIIRS images as an ordinate;
and by a correlation coefficient R 2 Based on the size of R, find R 2 The maximum fitting curve function f (x) is an integrated model of the category, and each function model and the precision thereof are shown in table 1; the regression model functions of each category are combined into a regression model group, and the specific reference table 2 shows the visual expression in the form of the function group.
Table 2 combining regression model functions of each category into regression model group, and visually expressing the regression model functions in the form of function group
Step h, based on the regression model group obtained in step g, simulating the NPP/VIIRS image of the corresponding year by using the DMSP/OLS image and the land use data in 1992-2012, wherein the specific implementation mode is as follows:
1. downloading a DMSP/OLS image in 1992-2012 and land use present grid data in a corresponding year according to the steps a and b, obtaining a projection coordinate system of a research area in 1992-2012, a DMSP/OLS image and land use present grid data which are consistent in spatial resolution and are subjected to geographic registration according to the steps c, d, e and f, and respectively displaying a DMSP/OLS year stable luminous remote sensing image and land use present grid data in a research area in 2010 as shown in FIGS. 6 and 7;
2. using MATLAB software, the DMSP/OLS image of each year and the land use status grid data of the corresponding year are used as input data, and the NPP/VIIRS image of each year is simulated through a function group F (x), as shown in fig. 8, the simulated NPP/VIIRS image of 2010 is displayed.
In order to verify the effectiveness of the method, the method is compared with another method for simulating an NPP/VIIRS image of a corresponding year by using a DMSP/OLS image, and the method is a cross-sensor correction method which is provided by Chen and the like and utilizes an enhanced vegetation index and an automatic encoder model;
FIG. 9 is a 2010 simulation NPP/VIIRS result image of Chen et al in the province; from the comparison between fig. 8 and fig. 9, the invention establishes the complex relationship between the DMSP/OLS lights and the NPP/VIIRS lights on all the land types, relatively speaking, more effectively integrates the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images, and better weakens the light intensity difference between the two night light remote sensing images.
In the invention, NPP/VIIRS year images synthesized by other reasonable methods can be used for replacing the V2 version NPP/VIIRS year images as long as the NPP/VIIRS light images eliminate background noise and abnormal value noise.
In conclusion, the method and the device alleviate the incompatibility problem of DMSP/OLS and NPP/VIIRS multi-source luminous remote sensing images, embody the consistency of multi-source luminous remote sensing data, and lay a foundation for constructing a high-quality and long-time luminous remote sensing time sequence data set with wide application value. The method has certain practical value and application prospect in the field of long-time sequence image-based luminous remote sensing application.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A multi-source noctilucent remote sensing image integration method based on land utilization data is characterized by comprising the following steps:
step a, selecting a DMSP/OLS luminous remote sensing image, an NPP/VIIRS luminous remote sensing image and land use current situation grid data of a 2013 covered research area;
step b, carrying out projection transformation on the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land utilization status grid data, and converting the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land utilization status grid data into a WGS _ 1984' u albers projection coordinate system;
step c, carrying out spatial resampling on the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land use current state grid data to ensure that the spatial resolution of the DMSP/OLS luminous remote sensing image, the NPP/VIIRS luminous remote sensing image and the land use current state grid data is 500 meters;
step d, carrying out geographic registration on the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image respectively by taking the land utilization status grid data as a reference;
step e, using boundary vector data of the research area, inlaying and cutting the land use current situation raster data, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image to obtain the land use current situation raster data, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image of the research area;
step f, extracting DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images of various categories according to land utilization category information of the land utilization current situation grid data;
step g, performing linear and nonlinear regression analysis on the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images of the current land utilization state types of each type, and combining models of each type to obtain a regression model group;
and h, simulating and generating an NPP/VIIRS image of the year by using the DMSP/OLS image between 1992 and 2012 and the land utilization status grid data of the corresponding year based on the obtained regression model group, and completing integration of the multi-source luminous remote sensing image.
2. The land use data-based multi-source luminous remote sensing image integration method according to claim 1, wherein in the step a, 2013 DMSP/OLS annual stable lighting images of version 4 are selected, and NPP/VIIRS annual average lighting images of version V2 and 2013 are selected.
3. The land use data-based multi-source luminous remote sensing image integration method as claimed in claim 2, wherein the V2 version of the annual average value NPP/VIIRS light image of 2013 requires masking the annual average value NPP/VIIRS data of 2013 through 2013 light mask grid data, so as to obtain the V2 version of the annual average value NPP/VIIRS light image of 2013, and the extraction process calculation formula is as follows:
wherein,
represents the pixel value of the ith row and the jth column of the V2 NPP/VIIRS year-th image>
Represents the pixel value of the ith row and the jth column of the nth lamp light mask grid data>
And the pixel value of the NPP/VIIRS year mean value of the nth year like the ith row and the jth column.
4. The land use data-based multi-source luminous remote sensing image integration method according to claim 1, wherein in the step a, the land use status grid data is selected by the following method:
when the land use current situation data is the grid data, directly selecting the land use current situation grid data; and if the current land use data is vector data, converting the vector data into raster data.
5. The land use data-based multi-source luminous remote sensing image integration method according to claim 1, wherein in the step e, the land use current situation raster data of the research area is obtained by judging whether multi-scene land use current situations need to be embedded according to the size of the research area and the actual situation of the selected land use current situation raster data to obtain an image covering the research area, and then the image is cut by using boundary vector data of the research area to obtain the land use current situation raster data of the research area.
6. The land use data-based multi-source luminous remote sensing image integration method according to claim 1, wherein in the step f, the DMSP/OLS luminous remote sensing image and the NPP/VIIRS luminous remote sensing image of each category are extracted according to land use category information of land use present situation grid data, and are extracted according to the following formula:
wherein, (Landuse) ij ＝＝Value n ) Is a logical statement, landuse ij Equal to Value n If so, the part is 1, otherwise, the part is 0; where n is the number of categories of land use presence grid data,
represents the pixel value of the ith row and the jth column of the light data of the nth type of DMSP/OLS (digital multiplex/optical storage system), and is/are selected>
Represents the pixel value of the ith row and the jth column of the NPP/VIIRS lamplight data, landuse ij A Value representing a pixel Value of the ith row and the jth column of the land use status grid data n Representing the pixel value of the nth type of land use status grid data, DMSP _ NTL ij Representing the pixel value, NPP _ NTL, of the ith row and jth column of DMSP/OLS lighting data ij And the pixel value of the ith row and the jth column of the NPP/VIIRS light data is represented.
7. The land use data-based multi-source noctilucent remote sensing image integration method according to claim 1, characterized in that in the step g, linear and nonlinear regression analysis is performed on each type of land use current situation type noctilucent remote sensing image, and the method for combining models of each type to obtain a regression model group specifically comprises:
establishing a regression model for each category of DMSP/OLS luminous remote sensing images and NPP/VIIRS luminous remote sensing images, wherein the model is established in a mode that pixel values of the DMSP/OLS images at the same spatial position of each category are used as independent variables X, pixel values of the NPP/VIIRS images are used as dependent variables Y, and regression analysis is respectively carried out through linear and nonlinear function models;
by a correlation coefficient R 2 Based on the size of R 2 The maximum fitting function is taken as the regression model function, f is calculated 1 (x) A regression model function for the ith category;
and combining the regression model functions of each category into a regression model group.
8. The land use data-based multi-source luminous remote sensing image integration method according to claim 7, wherein the regression model set of functions is represented as follows:
wherein f is 1 (x) Is a regression model of the i-th class.
9. The land use data-based multi-source luminous remote sensing image integration method according to claim 1, wherein in the step h, the method for simulating the NPP/VIIRS image of the corresponding year by using the DMSP/OLS light image and the land use data comprises the following steps:
firstly, obtaining DMSP/OLS light images of 1992 to 2012 of a research area and land utilization status grid data of the same year, wherein the DMSP/OLS light images are consistent in a coordinate system, a spatial resolution and a projection mode and are subjected to geographic registration through steps b, c, d and e; and secondly, taking the DMSP/OLS annual stable light images of each year in 1992-2013 and land utilization status grid data of the corresponding year as input data, and calculating pixel values of simulated NPP/VIIRS image pixels of the corresponding year one by one according to the regression model group F (x) in the step g to complete integration of the DMSP/OLS luminous remote sensing images and the NPP/VIIRS luminous remote sensing images.
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