CN110705449A - Land utilization change remote sensing monitoring analysis method - Google Patents
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Abstract
The invention discloses a remote sensing monitoring and analyzing method for land utilization change, and relates to the technical field of remote sensing. The method comprises the steps of pre-collecting data, collecting remote sensing images of a region to be researched before and after N years, and simultaneously collecting vector boundary data and auxiliary materials of land utilization statistical data of the region to be researched; data image radiometric calibration processing: utilizing various standard radiation sources and establishing a quantitative relation between the corresponding radiation brightness value and the digital quantization value by a radiometric calibration method of a sensor, thereby obtaining a radiometric calibration graph of a remote sensing image of the to-be-researched area before N years and a radiometric calibration graph of a remote sensing image of the to-be-researched area after N years; atmospheric correction of the radiation calibration map. According to the method, the error rate in the land classification error can be effectively reduced by monitoring the creation of the classification chart, the classification precision evaluation and the data analysis process, and the precision of land utilization change data analysis can be effectively improved.
Description
Technical Field
The invention belongs to the technical field of remote sensing, and particularly relates to a remote sensing monitoring and analyzing method for land utilization change.
Background
With the continuous development of high and new technologies such as a remote sensing technology, a geographic information system technology department, a global positioning system technology and the like, the most advanced technology such as 3S and the like is used for detecting land use change, compared with the traditional method, the method has the characteristics of short period, high efficiency, timeliness, macroscopicity and the like, is widely applied to various fields of national economy and social development, and can provide scientific data support for the government to make land use plans and urban layouts.
The land utilization remote sensing dynamic monitoring covers land utilization and the number and the spatial position of change of the land utilization, the land utilization remote sensing dynamic monitoring is used as an important component of national land resource investigation, the red line of cultivated land and the occupation and supplement of the cultivated land in China are ensured to have important significance, and scholars generally use two methods, namely an image preprocessing method and an information extraction method, to carry out the remote sensing dynamic monitoring, wherein the image preprocessing method covers methods of wave band combination, image enhancement, image fusion and the like, and the information extraction method has a plurality of types; the image enhancement method can be used for highlighting according to the feature of the ground feature type which is interested by people; if the remote sensing images of different time phases are directly compared without image classification, an image direct comparison method can be selected for processing, and the commonly used methods respectively comprise a vegetation index method, a difference method/ratio method, a principal component analysis method and a change vector analysis method, although the direct comparison method can avoid errors caused by multiple classification, the direct comparison method only extracts a change pixel, and meanwhile, the method needs to carry out geometric fine correction on the images to eliminate the influence of various factors; in order to obtain land use change information and visually see the state of land use change, a post-classification comparison method is generally used to compare and analyze remote sensing images in different time phases, but due to accumulation of classification errors, final precision is often reduced.
Disclosure of Invention
The invention aims to provide a remote sensing monitoring and analyzing method for land utilization change, which solves the problem of low precision of the existing remote sensing monitoring and analyzing method for land utilization change through optimization of the analyzing method.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a remote sensing monitoring and analyzing method for land utilization change, which comprises the following steps:
SS001, data pre-collection: collecting remote sensing images of the area to be researched before and after N years, and simultaneously collecting vector boundary data and land utilization statistical data of the area to be researched;
SS002, data image radiometric calibration processing: utilizing various standard radiation sources and establishing a quantitative relation between the corresponding radiation brightness value and the digital quantization value by a radiometric calibration method of a sensor, thereby obtaining a radiometric calibration graph of a remote sensing image of the to-be-researched area before N years and a radiometric calibration graph of a remote sensing image of the to-be-researched area after N years;
SS003, atmospheric correction of radiometric calibration plots: carrying out atmospheric correction on the radiometric calibration graph of the remote sensing image of the area to be researched before N years and the radiometric calibration graph of the remote sensing image of the area to be researched after N years obtained in the SS002 step by a radiometric transmission model method, and obtaining physical models of surface temperature, reflectivity, radiance and the like of the area to be researched before N years and the area to be researched after N years after atmospheric correction, and obtaining an atmospheric correction image of the area to be researched before N years and an atmospheric correction image of the area to be researched after N years;
SS004, image mosaic: inlaying the atmospheric correction image of the area to be researched before N years and the atmospheric correction image of the area to be researched after N years obtained in the step of SS003 through ENVI5.3 image processing software, and sequentially carrying out strip processing and splicing processing on the two images obtained in the step of SS003 in the inlaying process so as to finally obtain a remote sensing image mosaic of the area to be researched before N years and a remote sensing image mosaic of the area to be researched after N years;
SS005, image cropping: vector cutting is carried out on the remote sensing image mosaic image of the area to be researched before N years and the remote sensing image mosaic image of the area to be researched after N years obtained in the SS004 step by utilizing vector boundary data of the area to be researched in the SS001 step through ENVI5.3 image processing software, so that a vector remote sensing image map of the area to be researched before N years and a vector remote sensing image map of the area to be researched after N years are obtained;
SS006, image radiance enhancement: synthesizing a standard false color image of a vector remote sensing image map of an area to be researched before N years and a standard false color image of the vector remote sensing image map of the area to be researched after N years in the SS005 step according to the Landsat wave band corresponding to the wave band central wavelength to perform color enhancement processing on the images, so as to emphasize different ground objects;
SS007, land type classification: setting the types of land features such as paddy fields, dry lands and the like as agricultural lands, setting the types of forest lands, grasslands, shrubs, urban green belts and the like as vegetation, uniformly setting the water-impermeable lands as construction lands, setting the types of residential lands in districts, parks, rural areas and the like as construction lands, and setting the types of the residential lands in other types as other lands;
SS008, creation of supervision classification chart: carrying out supervision and classification on the vector remote sensing image map of the area to be researched before N years and the vector remote sensing image map of the area to be researched after N years after the color increasing treatment in the SS006 step by utilizing a maximum likelihood classification method and a land type splitting standard in the SS007 step, thereby obtaining a land utilization supervision classification map of the area to be researched before N years, a land utilization supervision classification map of the area to be researched after N years, a cake-shaped map of a supervision classification result of the area to be researched before N years and a cake-shaped map of a supervision classification result of the area to be researched after N years;
SS009, adjusting classification error of supervision classification chart: respectively opening the vector remote sensing image maps of the corresponding year obtained in the step of the land utilization supervision classification map SS005 obtained in the step of the supervision classification post-processing obtained in the step of SS008 in ENVI image processing software, and comparing classification results through connection operation to eliminate land type classification errors caused by light shadows;
SS010, classification accuracy evaluation: establishing an error matrix of the remote sensing data by methods such as a confusion matrix and a KAPPA coefficient, and calculating various precision indexes to assist in precision evaluation;
SS011, data analysis: performing precision evaluation on the land utilization supervision classification map of the area to be researched before N years and the supervision classification result of the land utilization supervision classification map of the area to be researched after N years, which are obtained in the step of SS008, by using a confusion matrix;
SS012, change information extraction: analyzing an area matrix and a probability rectangle converted among the land utilization types within N years by using a land transfer matrix method to obtain a land utilization transfer matrix within N years, thereby obtaining a land utilization dynamic change map of a region to be researched within N years;
SS013, land use change analysis: and carrying out data analysis statistics on the land utilization dynamic change diagram of the area to be researched in the period of N years obtained in the SS012, and carrying out area statistics and percentage statistics of various types of land before and after the N years so as to obtain an area comparison diagram of different land utilization types before and after the N years, and obtaining a land utilization change histogram of the area to be researched in the period of N years through statistical analysis of data.
The invention has the following beneficial effects:
according to the method, through atmospheric correction, image mosaic and image radiation enhancement processing of the land remote sensing image, on one hand, radiation errors caused by atmospheric scattering can be eliminated to a limited extent, on the other hand, a researcher can judge the image visually more conveniently through image enhancement processing, and meanwhile, through the processes of creation of a supervision classification chart, classification precision evaluation and data analysis, the error rate in the land classification errors can be effectively reduced, and further the precision of land utilization change data analysis can be effectively improved.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for remote sensing monitoring and analyzing changes in land utilization;
FIG. 2 is a comparison graph of different land utilization type areas of a region to be researched before and after N years;
FIG. 3 is a schematic structural diagram of a histogram of land use changes for an area to be studied over N years;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 3, the present invention is a method for remote sensing monitoring and analyzing changes in land utilization, comprising the following steps:
SS001, data pre-collection: collecting remote sensing images of the area to be researched before and after N years, simultaneously collecting vector boundary data and auxiliary materials of land utilization statistical data of the area to be researched, and correspondingly processing the auxiliary materials through Arcgis10.2 software, EXCEL software and other software;
SS002, data image radiometric calibration processing: the method comprises the following steps of establishing a quantitative relation between a corresponding radiation brightness value and a digital quantization value by utilizing various standard radiation sources and a radiometric calibration method of a sensor, and carrying out related conversion on the digital quantization value and the radiation brightness value so as to obtain a radiometric calibration graph of a remote sensing image of a region to be researched N years ago and a radiometric calibration graph of a remote sensing image of the region to be researched N years later, wherein the specific formula of the radiometric calibration method is as follows:
Lb＝Gain×DNb+Bias
wherein L isbIs the radiant energy value of the terrain at the top of the atmosphere; DN is the gray value of the image element of the image; gain and Bias are the Bias and Gain of the image, respectively;
after radiometric calibration, the reflectivity of the atmosphere outer layer of the radiometric calibration graph before and after N years is obtained, and compared with the image before calibration, the overall color brightness of the image is reduced, the types of land such as vegetation and urban land are clearer, the color of the denser forest land is darker, and the brightness of the building land in the city is reduced;
SS003, atmospheric correction of radiometric calibration plots: in order to eliminate radiation errors caused by atmospheric scattering, atmospheric correction is needed, the radiometric calibration graph of the remote sensing image of the area to be researched before N years and the radiometric calibration graph of the remote sensing image of the area to be researched after N years obtained in the SS002 step are subjected to atmospheric correction through a radiation transmission model method, after the atmospheric correction, physical models such as surface temperature, reflectivity, radiance and the like of the area to be researched before N years and the area to be researched after N years are obtained, and simultaneously the atmospheric correction image of the area to be researched before N years and the atmospheric correction image of the area to be researched after N years are obtained;
SS004, image mosaic: inlaying the atmospheric correction image of the area to be researched before N years and the atmospheric correction image of the area to be researched after N years obtained in the step of SS003 through ENVI5.3 image processing software, wherein in the inlaying process, the strips of the image have certain influence on inlaying, so that the two images obtained in the step of SS003 are required to be sequentially subjected to strip processing and splicing processing, and finally obtaining a remote sensing image mosaic of the area to be researched before N years and a remote sensing image mosaic of the area to be researched after N years;
SS005, image cropping: vector cutting is carried out on the remote sensing image mosaic image of the area to be researched before N years and the remote sensing image mosaic image of the area to be researched after N years obtained in the SS004 step by utilizing vector boundary data of the area to be researched in the SS001 step through ENVI5.3 image processing software, so that a vector remote sensing image map of the area to be researched before N years and a vector remote sensing image map of the area to be researched after N years are obtained;
SS006, image radiance enhancement: in order to more conveniently and visually interpret the image, the remote sensing image data needs to be subjected to wave band combination, RGB components are synthesized into color images according to different purposes, wherein the reference is needed to be made to the ranges of different wavelengths, such as frequently used true color images, standard false color images and simulated true color images, and the standard false color images of vector remote sensing images of regions to be researched before N years in the SS005 step are synthesized according to Landsat wave bands corresponding to wave band central wavelengths so as to perform color enhancement processing on the images, so that different ground objects are emphasized, and the accuracy of image analysis can be improved through the Landsat wave bands;
SS007, land type classification: setting the types of land features such as paddy fields, dry lands and the like as agricultural lands, forest lands, grasslands, shrubs, urban green belts and the like as vegetation, uniformly setting the water-tight lands as construction lands, setting the residential lands in districts, parks, rural areas and the like as construction lands, and setting the other types of land features as other lands according to the remote sensing image interpretation marks and the remote sensing image features;
SS008, creation of supervision classification chart: in the remote sensing image, the difference of brightness values or pixel values and the spatial variation in the image represent the difference of different ground features, the multispectral remote sensing automatic classification is to analyze spectral information and spatial information displayed by different ground features in the remote sensing image through a computer, determine characteristic parameters, divide each pixel in the image into different categories according to a certain rule or algorithm, and cluster similar categories, thereby realizing the classification of the remote sensing image;
the common methods for supervision and classification include: the method comprises the following steps of performing supervision and classification by using a maximum likelihood method, a Mahalanobis distance method, a parallel pipeline method, a minimum distance method, an artificial neural network, a decision tree method, a support vector machine method and the like;
the maximum likelihood method classification is a nonlinear classification, which is to classify pixels by probability evaluation of similarity between the pixels to be classified and training samples according to variance and mean of the training samples, and is based on Bayesian criterion with minimum classification error probability, and the algorithm can simultaneously and quantitatively consider more than two wave bands and classes, and is widely applied, in addition, the probability calculation in the method is to assume that the characteristics of each class training area are subject to multivariate normal distribution, and the training data are required to be subject to unimodal distribution, and the function is as follows:
xirepresenting a certain characteristic vector, UkThe average vector, sigma, representing the vector of this typekCovariance matrix, P (w), representing the i-th class vectork) The probability of each class wk appearing in the image, and in most cases P (w) assuming that wk appears with the same probabilityk) 1/C (C indicates category size)
Carrying out supervision and classification on the vector remote sensing image map of the area to be researched before N years and the vector remote sensing image map of the area to be researched after N years after the hyperchromatic processing in the SS006 step by utilizing a maximum likelihood classification method and a land type splitting standard in the SS007 step;
thus obtaining a land use supervision classification map of the area to be researched before N years, a land use supervision classification map of the area to be researched after N years, a pie chart of the supervision classification result of the area to be researched before N years and a pie chart of the supervision classification result of the area to be researched after N years;
SS009, adjusting classification error of supervision classification chart: respectively opening the vector remote sensing image maps of the corresponding year obtained in the step of the land utilization supervision classification map SS005 obtained in the step of the supervision classification post-processing obtained in the step of SS008 in ENVI image processing software, and comparing classification results through connection operation to eliminate land type classification errors caused by light shadows;
if some ground object types are divided wrongly and need to be readjusted manually, the observed errors are as follows: the mountain shadow part is wrongly divided into water or agricultural land, and the water is wrongly divided into agricultural land;
SS010, classification accuracy evaluation: establishing an error matrix of the remote sensing data by methods such as a confusion matrix and a KAPPA coefficient, and calculating various precision indexes to assist in precision evaluation;
the precision analysis is very important in remote sensing data classification, and the precision evaluation work generally needs sampling inspection, and an error matrix is established on the basis to calculate various precision indexes;
SS011, data analysis: performing precision evaluation on the land utilization supervision classification map of the area to be researched before N years and the supervision classification result of the land utilization supervision classification map of the area to be researched after N years, which are obtained in the step of SS008, by using a confusion matrix;
the confusion matrix is composed of n rows and n columns and is used for expressing the precision of the classification result, wherein the confusion matrix can represent the number of categories;
the following table shows the confusion matrix table:
the elements in the confusion matrix are the number of pixels used for classifying various types or the percentage of the number of the pixels to the total number of the pixels, the data on the diagonal line is the number of the pixels representing the correct classification, if the number of the pixels on the main diagonal line is larger, the classification precision is higher, the rightmost column represents the total number of the pixels of each type on the reference image, and the bottommost row represents the total number of the types on the image to be evaluated;
confusion matrix after classification of the region to be studied N years ago as shown in the following table:
the confusion matrix after classification of the region to be studied after N years is shown in the following table:
SS012, change information extraction: analyzing an area matrix and a probability rectangle converted among the land utilization types within N years by using a land transfer matrix method to obtain a land utilization transfer matrix within N years, thereby obtaining a land utilization dynamic change map of a region to be researched within N years;
the land utilization transfer matrix is specifically as follows:
p denotes a land use transfer matrix, PijI-th land utilization type in K period is changed into j-th land utilization type in K +1 periodArea of land use type.
The proportion C of the ith land utilization type in the K period to the jth land utilization type in the K +1 period can be calculated through the transfer matrixijAnd a ratio R of the j-th soil utilization type in the K +1 period to the i-th soil utilization type in the K periodijThe formula is as follows:
the land use transfer matrix represents the interconversion relationship of land use types in the same region at different times, and the specific interconversion conditions among land types can be expressed by a two-dimensional table:
the following table shows land use transfer matrices before and after N years:
SS013, land use change analysis: and carrying out data analysis statistics on the land utilization dynamic change diagram of the area to be researched in the period of N years obtained in the SS012, and carrying out area statistics and percentage statistics of various types of land before and after the N years so as to obtain an area comparison diagram of different land utilization types before and after the N years, and obtaining a land utilization change histogram of the area to be researched in the period of N years through statistical analysis of data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (1)
1. A land utilization change remote sensing monitoring and analyzing method is characterized by comprising the following steps:
SS001, data pre-collection: collecting remote sensing images of the area to be researched before and after N years, and simultaneously collecting vector boundary data and land utilization statistical data of the area to be researched;
SS002, data image radiometric calibration processing: utilizing various standard radiation sources and establishing a quantitative relation between the corresponding radiation brightness value and the digital quantization value by a radiometric calibration method of a sensor, thereby obtaining a radiometric calibration graph of a remote sensing image of the to-be-researched area before N years and a radiometric calibration graph of a remote sensing image of the to-be-researched area after N years;
SS003, atmospheric correction of radiometric calibration plots: carrying out atmospheric correction on the radiometric calibration graph of the remote sensing image of the area to be researched before N years and the radiometric calibration graph of the remote sensing image of the area to be researched after N years obtained in the SS002 step by a radiometric transmission model method, and obtaining physical models of surface temperature, reflectivity, radiance and the like of the area to be researched before N years and the area to be researched after N years after atmospheric correction, and obtaining an atmospheric correction image of the area to be researched before N years and an atmospheric correction image of the area to be researched after N years;
SS004, image mosaic: inlaying the atmospheric correction image of the area to be researched before N years and the atmospheric correction image of the area to be researched after N years obtained in the step of SS003 through ENVI5.3 image processing software, and sequentially carrying out strip processing and splicing processing on the two images obtained in the step of SS003 in the inlaying process so as to finally obtain a remote sensing image mosaic of the area to be researched before N years and a remote sensing image mosaic of the area to be researched after N years;
SS005, image cropping: vector cutting is carried out on the remote sensing image mosaic image of the area to be researched before N years and the remote sensing image mosaic image of the area to be researched after N years obtained in the SS004 step by utilizing vector boundary data of the area to be researched in the SS001 step through ENVI5.3 image processing software, so that a vector remote sensing image map of the area to be researched before N years and a vector remote sensing image map of the area to be researched after N years are obtained;
SS006, image radiance enhancement: synthesizing a standard false color image of a vector remote sensing image map of an area to be researched before N years and a standard false color image of a vector remote sensing image map of an area to be researched after N years in the SS005 step according to a Landsat (TM) wave band corresponding to the wave band center wavelength to perform color enhancement processing on the images, so as to emphasize different ground objects;
SS007, land type classification: setting the types of land features such as paddy fields, dry lands and the like as agricultural lands, setting the types of forest lands, grasslands, shrubs, urban green belts and the like as vegetation, uniformly setting the water-impermeable lands as construction lands, setting the types of residential lands in districts, parks, rural areas and the like as construction lands, and setting the types of the residential lands in other types as other lands;
SS008, creation of supervision classification chart: carrying out supervision and classification on the vector remote sensing image map of the area to be researched before N years and the vector remote sensing image map of the area to be researched after N years after the color increasing treatment in the SS006 step by utilizing a maximum likelihood classification method and a land type splitting standard in the SS007 step, thereby obtaining a land utilization supervision classification map of the area to be researched before N years, a land utilization supervision classification map of the area to be researched after N years, a cake-shaped map of a supervision classification result of the area to be researched before N years and a cake-shaped map of a supervision classification result of the area to be researched after N years;
SS009, adjusting classification error of supervision classification chart: respectively opening the vector remote sensing image maps of the corresponding year obtained in the step of the land utilization supervision classification map SS005 obtained in the step of the supervision classification post-processing obtained in the step of SS008 in ENVI image processing software, and comparing classification results through connection operation to eliminate land type classification errors caused by light shadows;
SS010, classification accuracy evaluation: establishing an error matrix of the remote sensing data by methods such as a confusion matrix and a KAPPA coefficient, and calculating various precision indexes to assist in precision evaluation;
SS011, data analysis: performing precision evaluation on the land utilization supervision classification map of the area to be researched before N years and the supervision classification result of the land utilization supervision classification map of the area to be researched after N years, which are obtained in the step of SS008, by using a confusion matrix;
SS012, change information extraction: analyzing an area matrix and a probability rectangle converted among the land utilization types within N years by using a land transfer matrix method to obtain a land utilization transfer matrix within N years, thereby obtaining a land utilization dynamic change map of a region to be researched within N years;
SS013, land use change analysis: and carrying out data analysis statistics on the land utilization dynamic change diagram of the area to be researched in the period of N years obtained in the SS012, and carrying out area statistics and percentage statistics of various types of land before and after the N years so as to obtain an area comparison diagram of different land utilization types before and after the N years, and obtaining a land utilization change histogram of the area to be researched in the period of N years through statistical analysis of data.
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| CN113222005A (en) * | 2021-05-08 | 2021-08-06 | 兰州交通大学 | Automatic updating method for land coverage |
| CN113780057A (en) * | 2021-07-23 | 2021-12-10 | 贵州图智信息技术有限公司 | Idle land identification method and device |
| CN116662466A (en) * | 2023-05-18 | 2023-08-29 | 重庆市规划和自然资源调查监测院 | Land full life cycle maintenance system through big data |
| CN116662466B (en) * | 2023-05-18 | 2023-12-19 | 重庆市规划和自然资源调查监测院 | Land full life cycle maintenance system through big data |
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