CN107169467B

CN107169467B - Rare earth mining area land damage and recovery analysis method of multi-source time sequence images

Info

Publication number: CN107169467B
Application number: CN201710379376.1A
Authority: CN
Inventors: 李恒凯; 雷军; 杨柳; 王秀丽
Original assignee: Jiangxi University of Technology
Current assignee: Jiangxi University of Technology
Priority date: 2017-05-25
Filing date: 2017-05-25
Publication date: 2020-01-31
Anticipated expiration: 2037-05-25
Also published as: CN107169467A

Abstract

The invention relates to the technical field of land restoration analysis, in particular to a land damage and restoration analysis method for a rare earth mining area of multi-source time sequence images, which comprises the following steps of S1 obtaining data of a research area, S2 preprocessing data, performing radiation correction, atmospheric correction, geometric correction and image cutting on an obtained original remote sensing image to obtain a remote sensing image of the research area, S3 calculating a vegetation index, S4 constructing a conversion equation, S5 converting NDVI, determining relevant parameters of a time sequence analysis method, and quantitatively analyzing land damage and restoration.

Description

Rare earth mining area land damage and recovery analysis method of multi-source time sequence images

Technical Field

The invention relates to the technical field of land restoration analysis, in particular to a rare earth mining area land damage and restoration analysis method of multi-source time sequence images.

Background

The ionic rare earth ore is firstly discovered in the Jiangxi of China in 1969, has the advantages of distribution, abundant reserves, low radioactivity, complete rare earth distribution, rich medium-heavy rare earth elements and the like, is a rare earth mineral resource which is peculiar in China at present, is a rare ore species in the world, is widely distributed in the Jiangxi, Fujian, Hunan, east, West provinces of the south of China, wherein the Jiangxi provinces have the largest share.

At present, the mining area land damage and recovery analysis method mainly comprises a land damage and recovery evaluation model based on measured data and a land damage and recovery process dynamic monitoring based on remote sensing data, wherein the first methods not only need a large amount of measured data and are time-consuming and labor-consuming, but also need to establish different models aiming at different places, application of the model is difficult to push , the early land damage and recovery process dynamic monitoring is limited by technical methods and expenditure, a few remote sensing images with long intervals and no clouds are mostly adopted, the research method is a multi-time-phase classification comparison method, the method can reflect the distribution situation of the change of the land feature type on the space, but the process of the change of the land feature type is less understood, the remote sensing data sources are continuously abundant, the development of the remote sensing time sequence analysis method provides a great opportunity for improving the understanding of the ecological process of the space-time range of , the time sequence analysis method obtains the change situation of the land feature by the change trend and the rule of the change of the analyzed land feature on the time phase, and has high judgment standard of and high efficiency.

The Chinese patent application CN 105598143A discloses a resource mine restoration method, which comprises the steps of extracting valuable elements from tailing resources in a tailing pond and/or ore resources of a rock stripping area/a dumping site, carrying out deep processing on the tailing resources and/or ore resources without re-extraction value and tailings generated in the valuable element extraction process to obtain a target product if the tailing resources and/or ore resources are suitable for deep processing, filling the target product into a goaf/a subsidence area if the tailing resources and/or ore resources are not suitable for deep processing, carrying out harmless treatment on the land occupied by the rock stripping area/the dumping site and/or an industrial site, and carrying out ecological restoration on the land occupied by the rock stripping area/the dumping site after the harmless treatment and the filled goaf/subsidence area.

Disclosure of Invention

The invention aims to solve the technical problem of efficient rare earth mining area land damage and recovery analysis methods of multi-source time sequence images.

In order to solve the above technical problems, the method for analyzing land damage and recovery of rare earth mining areas of multi-source time sequence images of the present invention comprises the following steps,

step S1: acquiring research area data, namely acquiring an original remote sensing image required by a research area through a data acquisition unit;

step S2: data preprocessing, namely performing radiation correction, atmospheric correction, geometric correction and image cutting on the obtained original remote sensing image to obtain a remote sensing image of a research area;

step S3: calculating a vegetation index, namely calculating the NDVI of the preprocessed image through a vegetation index calculating unit;

step S4: constructing a conversion equation and checking the precision of the conversion equation;

step S5: NVDI conversion, determination of relevant parameters of a time sequence analysis method, and quantitative analysis of land damage and recovery.

And , in step S1, the original remote sensing images comprise Landsat 5TM, Landsat8OLI and HJ-1B CCD remote sensing images with spatial resolutions of 30 m.

, the step S4 specifically includes the following steps,

step S41: constructing a conversion equation, namely constructing a conversion equation between NDVI of Landsat5 and HJ-1B CCD data and Landsat8 and HJ-1B CCD data based on Landsat5 and HJ-1B CCD data, Landsat8 and HJ-1B CCD data in the same period;

step S42: and (3) precision inspection of the conversion equation, converting the NDVI of the HJ-1B CCD data into the NDVI of Landsat5 or Landsat8 with the same time phase based on the obtained conversion equation, comparing the NDVI with the real Landsat5 or Landsat8, and inspecting the precision of the conversion equation by adopting a root mean square error method.

, the step S5 specifically includes the following steps,

step 51, NDVI conversion, namely converting the NDVI of the Landsat8 and HJ-1B CCD data in the time sequence images into the NDsat5 standard based on the obtained conversion equation, and combining the NDVI time sequence images into multispectral images;

step 52: acquiring related parameters of a time sequence analysis method, namely acquiring related parameters of the time sequence analysis method through a data mining method;

step 53: and (3) carrying out quantitative analysis on the land damage and recovery, classifying the land damage and recovery types according to the relevant parameters of the time sequence analysis method to obtain the spatial distribution of the land damage and recovery types, and quantitatively analyzing the land damage and recovery characteristics under the disturbance of rare earth mining.

By adopting the method, the earth surface information of the rare earth mining area can be obtained in real time, quickly and in a large area, compared with the traditional modeling analysis method based on the measured data, the method has higher efficiency, simpler application and easier pushing.

Drawings

The invention is described in further detail with reference to the figures and the detailed description.

Fig. 1 is a flowchart of a land damage and restoration analysis method for kinds of multi-source time-series images in a rare earth mining area according to the present invention.

Fig. 2 is a schematic structural diagram of a rare earth mining area land damage and recovery analysis method of multi-source time sequence images according to the invention.

FIG. 3a is a scattering diagram of Landsat5 and HJ-1B CCD in accordance with the present invention.

FIG. 3B is a scattering diagram of Landsat8 and HJ-1B CCD according to the present invention.

Fig. 3c is a scatter diagram of Landsat5 and simulated images in the present invention.

FIG. 3d is a scattering diagram of Landsat8 and the simulated image according to the present invention.

FIG. 4a is a trace diagram of NDVI for both the temporarily unexplored and non-vegetation covered pel types in the present invention.

FIG. 4b is an NDVI trace plot of post-harvest vegetation-free and low vegetation coverage pixel types in accordance with the present invention.

FIG. 4c is an NDVI locus diagram of the horizontal pixel type before vegetation coverage reaches post-harvest in the invention.

Fig. 5 is a distribution diagram of the land damage and recovery type in the present invention.

Fig. 6a to 6f are high-resolution images corresponding to type 1 in the present invention.

FIGS. 7 a-7 f are high-resolution images corresponding to type 2 of the present invention.

Fig. 8a to 8f are high-resolution images corresponding to type 3 in the present invention.

Fig. 9a to 9f are high-resolution images corresponding to type 4 in the present invention.

Fig. 10a to 10f are high-resolution images corresponding to type 5 in the present invention.

FIG. 11 is a comparison graph of the duration from disturbance of rare earth mining to vegetation restoration in the present invention.

Detailed Description

Referring to fig. 1 and 2, the method for analyzing the land damage and restoration of rare earth mining area by multi-source time-series images according to the present invention comprises the following steps,

step S1: acquiring research area data, namely acquiring an original remote sensing image required by a research area through a data acquisition unit; the image comprises Landsat 5TM, Landsat8OLI and HJ-1B CCD remote sensing images with spatial resolution of 30 m.

Step S2: and (4) data preprocessing, namely performing radiometric calibration on the original image by using the remote sensing data obtained in the step S1 through a radiometric calibration module, performing atmospheric correction by using an atmospheric correction module after the radiometric calibration, correcting the image by using a geometric correction module after the atmospheric correction, and obtaining the remote sensing image of the research area by using an image cutting module after the geometric correction. ENVI used by Landsat5 and Landsat8 data in the Radiometric Calibration module is carried out by a Radiometric Calibration tool, and HJ-1BCCD data is carried out by a plug-in tool; the atmosphere correction module is carried out by using an ENVI (intrinsic safety) FLAASH module; in the geometric correction module, Landsat data is used as a reference, HJ-1B CCD data is used as data to be corrected, and a quadratic polynomial is selected by a correction method to correct the HJ-1BCCD data; and finally, cutting the corrected image through the vector boundary of the research area to obtain the remote sensing image of the research area.

Step S3: and calculating the vegetation index, namely calculating the NDVI of the preprocessed image through a vegetation index calculating unit. NDVI images of the study area were obtained. The vegetation index calculation unit is carried out by an ENVI self-contained Band Math tool, and the calculation formula is as follows:

where rho_NIRAnd ρ_RedRespectively representing the reflectivity of the near infrared wave band and the reflectivity of the red light wave band of the remote sensing image.

Step S4: and (5) constructing a conversion equation and checking the precision of the conversion equation.

Step S41: and constructing a conversion equation, namely constructing the conversion equation between NDVI of Landsat5 and HJ-1B CCD data and Landsat8 and HJ-1B CCD data by adopting a regression analysis method based on Landsat5 and HJ-1B CCD data and Landsat8 and HJ-1B CCD data in the same period.

The calculation formula is as follows:

in the formula X_iAnd Y_iRespectively expressed as HJ-1B CCD data correctionThe NDVI value of the sample, or the NDVI value of Landsat5 or Landsat8, and N is the number of samples.

And 5: and NDVI conversion, determining relevant parameters of a time sequence analysis method, classifying the land damage and recovery types of the research area according to the relevant parameters to obtain the distribution of different types on the space, and analyzing the land damage and recovery characteristics under the disturbance of rare earth mining.

And 51, NDVI conversion, namely converting the NDVI of the Landsat8 and HJ-1B CCD images in the time sequence image into data similar to multispectral images according to the time sequence by using an ENVI self-contained Layer Stacking tool under the Landsat5 standard through the obtained conversion equation.

Step 52, determining time sequence analysis method parameters, obtaining the change track of the sample point time sequence NDVI through visual observation and comparison, wherein the research area range comprises the following 5 types as shown in fig. 4a-4c, 1) temporarily unexplored vegetation is kept at a higher level in the whole period, 2) pool immersion/heap immersion mining is carried out, vegetation is present before mining, the vegetation is not recovered after mining and is always kept at a lower level, 3) vegetation coverage is not always present or is lower, 4) vegetation coverage level is slowly increased and recovered to the pre-mining level of the similar area at the periphery, 5) vegetation is present before mining, vegetation is recovered after mining, but is still lower than the original level, selecting five types of sample points with certain quantity on the time sequence NDVI image, applying a CART (classification and recovery tree) decision classification method, inputting the collected sample points into RuleGen v1.02 software, and selecting QUTR EST by algorithm to obtain classification thresholds of vegetation and vegetation with little or no vegetation, mined and unexplored vegetation respectively₁＝0.5285,TR₂＝0.3787。

Step 53: and (4) carrying out quantitative analysis on land damage and recovery. According to the obtained related parameter TR of the time sequence analysis method₁,TR₂According to the standard of the table 1, the land damage and recovery types of the research area are divided to obtain a spatial distribution map of the land damage and recovery types and a damage time distribution of the mining area.

TABLE 1 rare earth mining area land damage and recovery type division

MIN in table_iRepresenting the lowest value of the time sequence NDVI in the whole observation period, and reflecting whether the position of the i pixel is disturbed by rare earth mining; MAX_iThe maximum value of the pixel time sequence NDVI in the whole observation period is represented, and the covered peak value of the position where the i pixel is planted for many years is reflected; MAX_{i_pre}Representing the highest value of the time sequence NDVI before disturbance of rare earth mining, and reflecting the peak value covered by planting before mining at the position of the i pixel; MAX_{i_post}The highest value of the time sequence NDVI after disturbance of rare earth mining reflects the peak value of the planted and covered layer at the position where the i pixel is located after mining; r_iExpressing the vegetation recovery level, reflecting whether the vegetation coverage of the pixel area i after the harvest reaches or exceeds the level before the harvest, R_i＝MAX_{i_post}-MAX_{i_pre},R_iMore than or equal to 0 means that the vegetation coverage level after the mining reaches or exceeds the level before the mining; r_i< 0 indicates that the post-harvest vegetation coverage has not reached pre-harvest level.

Taking the rare earth mining area of the mountain of south county of Jiangxi city as an example, the multi-source time sequence images are shown in Table 2, and the interactive comparison image pairs are shown in Table 3.

TABLE 2 time series image date, type and identifier

TABLE 3 Interactive comparative image pairs

As shown in fig. 4a-4c, type 1 is temporarily unexplored, type 2 is post-harvest non-vegetation, type 3 is non-vegetation cover, type 4 is post-harvest vegetation cover reaches pre-harvest level, and type 5 is post-harvest vegetation is recovered by degrees but does not reach pre-harvest level.

Through fig. 5 and fig. 11, the land destruction and restoration process under the disturbance of rare earth mining is analyzed. Combined with the Google Earth high-resolution image, it is found that most of the type 1 is forest lands which are not affected by rare Earth mining, as shown in fig. 6a to 6 f; type 2 is a rare earth mining area and no mine reclamation is performed, as shown in fig. 7 a-7 f; types 3 are mostly farmlands, buildings and the like, as shown in fig. 8 a-8 f; type 4 is mostly returning to the forest, orchard planting area, as shown in fig. 9 a-9 f; type 5 is a rare earth mining area but is subject to mine reclamation as shown in fig. 10 a-10 f.

Although specific embodiments of the present invention have been described above, it will be appreciated by those skilled in the art that these are merely examples and that many variations or modifications may be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims.

Claims

1, kinds of rare earth mining area land damage and recovery analysis method of multi-source time sequence image, which is characterized in that it comprises the following steps,

step S5: NDVI conversion, determining relevant parameters of a time sequence analysis method, and performing quantitative analysis on land damage and recovery;

the original remote sensing images in the step S1 comprise Landsat 5TM, Landsat8OLI and HJ-1B CCD remote sensing images with spatial resolutions of 30 m;

the step S4 specifically includes the following steps,

step S42: the accuracy of the conversion equation is tested, the NDVI of the HJ-1B CCD data is converted into the NDVI of Landsat5 or Landsat8 with the same time phase based on the obtained conversion equation, the NDVI is compared with the real Landsat5 or Landsat8, and the accuracy of the conversion equation is tested by adopting a root mean square error method;

the step S5 specifically includes the following steps,