CN104361338A - Peat bog information extracting method based on ENVISAT ASAR, Landsat TM and DEM data - Google Patents
Peat bog information extracting method based on ENVISAT ASAR, Landsat TM and DEM data Download PDFInfo
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Abstract
The invention relates to peat bog information extracting methods, in particular to a peat bog information extracting method based on ENVISAT ASAR, Landsat TM and DEM data, and solves the problem that peat bog and other bog types cannot be distinguished by a conventional method. The peat bog information extracting method includes step 1, preprocessing Landsat TM data; step 2, preprocessing ENVISAT ASAR data; step 3, re-sampling the ENVISAT ASAR data; step 4, acquiring an ENVISAT ASAR image; step 5, acquiring gradient data; step 6, extracting back scattering coefficient; step 7, determining optimal polarization mode waveband of the ENVISAT ASAR image; step 8, acquiring a division unit; step 9, extracting feature parameters; step 10, determining optimal classification waveband; step 11, establishing a classification decision-making tree; step 12, generating a soil covering type vector file; step 13, making a peat bog map. The peat bog information extracting method is applied to the field of peat bog information extracting.
Description
Technical Field
The invention relates to an information extraction method, in particular to a peat marsh information extraction method.
Background
Peat bogs are one of the main types of wetlands, and play an important role in maintaining regional ecological balance and sustainable development. In addition, the carbon reserve in peat bogs is huge, which accounts for about 1/3 of global land carbon reservoir, which is equivalent to 75% of carbon content in atmosphere, so peat bogs play a major role in global climate change and ecosystem balance. In recent years, experts and scholars at home and abroad have conducted research on spatial information extraction on wetland types such as herbaceous wetland, forest wetland and seaside mangrove wetland by using different remote sensing image data, and relatively few researches on extraction of peat bog spatial distribution information by using a remote sensing technology are conducted.
The optical remote sensing data has the advantages and characteristics of rich spectral information, high cost performance, easiness in acquisition and simplicity in data processing. However, due to the influence of the vegetation covered on the ground surface, the traditional medium-low resolution optical remote sensing image can be used for distinguishing the wetland from the non-wetland, but the distinguishing of different marsh types is difficult to be completed, and the distinguishing of different peat marsh types is difficult. Compared with optical remote sensing data, the radar remote sensing data has longer wavelength and the characteristics of cloud penetration and fog penetration enable the radar remote sensing data to be free from the limitation of time and weather when monitoring the region where the marsh is widely developed. Moreover, the backscattering of the radar image is sensitive to the dielectric characteristics (soil humidity and vegetation water content) and the geometric characteristics (surface roughness) of the imaging surface, and the penetration capacity of the microwave to the ground object can reflect the information of the ground object close to the ground surface. The low frequency radar bands (P-band and L-band) are better suited for monitoring woodland dominated wetlands, while the high frequency radar band (C-band) is suitable for studying herbaceous and peat bogs.
The object-oriented interpretation method not only takes the spectral information of the ground features into consideration during interpretation, but also takes the geometric features and structural features of the ground features into consideration, and the minimum unit for image interpretation is an object with the same characteristics (such as the characteristics of spectrum, texture, spatial combination relation and the like) and homogeneity. Compared with the traditional remote sensing interpretation method for interpreting the characteristics of a single pixel of an image, the method breaks through the limitation that the traditional remote sensing classification method takes the pixel as a basic classification and processing unit, and can realize high-level remote sensing image classification and target surface feature extraction by taking an object consisting of a plurality of adjacent pixels containing more semantic information as a processing unit. The method is a remote sensing information extraction method based on a cognitive model, is closer to the cognitive process of human beings, and has become one of the main research directions in the field of remote sensing information extraction. The object-oriented interpretation method is developed aiming at various defects of the prior image element-oriented interpretation method. The shortcomings of the object-oriented interpretation method are not mentioned in the current research.
Landsat is a series of terrestrial resource satellites launched by the united states space administration (NASA) since 1972. The sensor TM carried by Landsat5 comprises 7 wave bands (0.45-0.53 μm, 0.52-0.60 μm, 0.63-0.69 μm, 0.76-0.90 μm, 1.55-1.75 μm, 10.40-12.50 μm, 2.08-2.35 μm), a track height 705km, a spatial resolution of 30m, and a revisiting period of 16 days. ENVISAT is a giant environment monitoring satellite which emits and rises in 3 months in 2002 by the European space agency, and ASAR (advanced Synthetic Aperture radar) is an advanced Synthetic Aperture radar carried by ENVISAT and has the characteristics of multiple modes, multiple polarizations, large breadth, multiple incidence angles and the like. A Digital Elevation Model (DEM) is a solid ground Model that represents the Elevation of the ground in the form of a set of ordered arrays of values.
The application of high frequency radar band (C band) to the study of herbaceous and peat bogs is the conclusion that the present study has already made, and the study is based on this conclusion that radar images of C band, i.e. ENVI SAT used in the study, were selected when extracting peat bogs. Because only radar images in the C wave band are involved in the research, and radar images in other wave bands are not involved, the problem that the high-frequency radar wave band (C wave band) is suitable for researching herbaceous marsh and peat marsh is not involved.
Disclosure of Invention
The invention aims to solve the problem that the differentiation of peat bogs from other bog types is difficult to complete by using the traditional medium-low resolution optical remote sensing image, and provides a method for extracting peat bog information based on ENVISAT ASAR, Landsat TM and DEM data.
The above-mentioned invention purpose is realized through the following technical scheme:
the method comprises the following steps: preprocessing Landsat TM data;
step two: ENVISAT ASAR preprocessing the data;
step three: resampling the ENVISAT ASAR data preprocessed in the step two, wherein the grid size of the ENVISAT ASAR data after resampling is consistent with that of the Landsat TM data processed in the step one;
step four: selecting a control point on the preprocessed Landsat TM data by utilizing a control point adding function provided by a Georeferencening module of ArcGIS software, and registering the resampled ENVISAT ASAR data according to a control point space to obtain a ENVISAT ASAR image;
step five: gradient extraction is carried out on the DEM data to obtain gradient data;
step six: combining with the land cover type survey sampling points, extracting the backward scattering coefficients of the radar images of different land cover types in different polarization modes from the ENVISAT ASAR images completed in the step four;
step seven: analyzing the difference of radar backscattering coefficients of the peat bogs and other different land cover types under different polarization modes, and determining ENVISAT ASAR image optimal polarization mode wave bands, namely radar image optimal polarization mode wave bands for extracting the peat bogs;
step eight: performing multilayer multi-scale segmentation on the preprocessed Landsat TM data, gradient data and the ENVISATASAR image optimal polarization mode wave band determined in the seventh step to obtain a series of segmentation units;
step nine: extracting characteristic parameters of a series of segmentation units segmented in the step eight; the characteristic parameters comprise the average value of each wave band, a normalized vegetation index, a normalized water body index, TM2+ TM3-TM4-TM5 and color tones;
step ten: determining the optimal classification wave band by using a JM distance method according to the characteristic parameters extracted in the step nine;
step eleven: according to the optimal classification wave band determined in the step ten, establishing a classification decision tree by referring to the land cover type survey sampling points; wherein, the reference soil coverage type survey sample points comprise peat swamp, herbaceous swamp, residential land, transportation land, farmland, forest land and water body soil coverage types;
step twelve: operating a classification decision tree, exporting a land cover type classification result, and producing a land cover type vector file; wherein, the land cover type vector file comprises land cover types of farmlands, forest lands, water bodies, residential traffic lands, herbaceous swamps and peat swamps;
step thirteen: making a peat marsh thematic map according to the land cover type vector file completed in the step twelve; thus completing a peat bog information extraction method based on ENVISAT ASAR, Landsat TM and DEM data.
Effects of the invention
The method distinguishes other marsh types which are easy to be confused with peat information, automatically, quickly and accurately extracts the space distribution information of the peat marsh in a medium-resolution remote sensing image (Landsat TM), thereby realizing the method for automatically extracting thematic mapping from the peat marsh information.
Radar images and optical images are combined, topographic factors are used as control factors of the peat bog, object-oriented and decision tree remote sensing classification methods are comprehensively utilized to obtain characteristic parameters of objects, the optimal classification wave band is selected through a JM distance method, and therefore decision tree completion classifications are built and a peat bog thematic map is manufactured.
The invention applies the object-oriented and decision tree classification method to the automatic extraction of peat bog information based on Landsat TM data, ENVISAT ASAR images and DEM data, combines independent pixels into a homogeneous object, considers not only spectral characteristics but also textural characteristics and topological characteristics in the object segmentation process, and further establishes a decision tree to gradually obtain peat bog spatial distribution information by selecting an optimal waveband. The precision of the obtained classification result is 93 percent, which is 5 to 8 percent higher than the precision of the existing method for extracting peat bog by using medium-resolution remote sensing images. Meanwhile, the influence of the terrain on the distribution of the peat swamp is considered, so that the classification result has clear geographical significance. The invention overcomes the phenomena of missing and wrong classification caused by only applying optical images in the conventional peat bog information extraction, and simultaneously solves the problems that the classified peat bog spatial information has the phenomena of salt and pepper phenomenon and land flying phenomenon, has no clear geographical significance and the like. The invention has practical significance for fast and automatically extracting the peat bog information by combining the optical image, the radar image and the DEM.
Drawings
FIG. 1 is a flow chart of a peat bog information extraction method based on ENVISAT ASAR, Landsat TM and DEM data according to an embodiment;
fig. 2 is a diagram of a vector file of land cover types for making a special map of peat bog according to an embodiment.
Detailed Description
The first embodiment is as follows: the peat bog information extraction method based on ENVISAT ASAR, Landsat TM and DEM data is specifically prepared according to the following steps:
the method comprises the following steps: preprocessing Landsat TM data;
step two: ENVISAT ASAR preprocessing the data;
step three: resampling ENVISAT ASAR data preprocessed in the step two in ArcGIS, wherein the grid size of the resampled ENVISAT ASAR data is consistent with that of Landsat TM data processed in the step one;
step four: taking the preprocessed Landsat TM data as a reference, contrasting the preprocessed Landsat TM data with the ENVISAT ASAR data resampled in step III in ArcGIS software, selecting a control point on the preprocessed Landsat TM data by utilizing a control point adding function provided by a Georeferencening module of the ArcGIS software, and registering the resampled ENVISAT ASAR data according to the control point space to obtain a ENVISAT ASAR image;
step five: gradient extraction is carried out on DEM data by utilizing an Aspect command in Surface Analysis under a Spatial Analysis module in ArcGIS software to obtain gradient data;
step six: combining with the land cover type survey sampling points, extracting the backward scattering coefficients of the radar images of different land cover types in different polarization modes from the ENVISAT ASAR images completed in the step four in ArcGIS;
step seven: analyzing the difference of radar backscattering coefficients of the peat bogs and other different land cover types under different polarization modes, and determining ENVISAT ASAR image optimal polarization mode wave bands, namely radar image optimal polarization mode wave bands for extracting the peat bogs;
step eight: carrying out multilayer multi-scale segmentation on the preprocessed Landsat TM data, gradient data and the ENVISAT ASAR image optimal polarization mode wave band determined in the seventh step by using eCooginion software to obtain a series of segmentation units, and taking each segmentation unit as an object;
step nine: extracting characteristic parameters of a series of segmentation units segmented in the step eight by using eCogination software; the characteristic parameters comprise the average value of each wave band, normalized vegetation index (NDVI), normalized water body index (NDWI), TM2+ TM3-TM4-TM5, color tone (R: G: B ═ TM5: TM4: TM3) and color tone (R: G: B ═ TM4: TM3: TM 2);
step ten: determining the optimal classification wave band by using a JM Distance (Jeffreys Matusita Distance) method according to the characteristic parameters extracted in the step nine;
step eleven: according to the optimal classification wave band determined in the step ten, establishing a classification decision tree by utilizing See5.0 software according to the land cover type survey sampling points; wherein, the reference soil coverage type survey sample points comprise peat swamp, herbaceous swamp, residential land, transportation land, farmland, forest land, water body and other soil coverage types;
step twelve: operating a classification decision tree in eCoogination software, deriving a land cover type classification result, and producing a land cover type vector file; the system comprises a field coverage type vector file, a data processing system and a data processing system, wherein the field coverage type vector file comprises field coverage types such as farmlands, forest lands, water bodies, residential traffic lands, herb swamps and peat swamps;
step thirteen: under a Layout View mode in ArcGIS software, manufacturing a peat bog thematic map according to the land cover type vector file completed in the step twelve (a schematic diagram of the peat bog thematic map is shown in FIG. 2); thus, a peat bog information extraction method based on ENVISAT ASAR, Landsat TM and DEM data is completed as shown in figure 1.
The effect of the embodiment is as follows:
the method distinguishes other marsh types which are easy to be confused with peat information, automatically, quickly and accurately extracts the space distribution information of the peat marsh in a medium-resolution remote sensing image (Landsat TM), thereby realizing the method for automatically extracting thematic mapping from the peat marsh information.
Radar images and optical images are combined, topographic factors are used as control factors of the peat bog, object-oriented and decision tree remote sensing classification methods are comprehensively utilized to obtain characteristic parameters of objects, the optimal classification wave band is selected through a JM distance method, and therefore decision tree completion classifications are built and a peat bog thematic map is manufactured.
The embodiment applies the object-oriented and decision tree classification method to the automatic extraction of the peat bog information based on Landsat TM data, ENVISAT ASAR images and DEM data, combines independent pixels into a homogeneous object, considers not only spectral characteristics but also textural characteristics and topological characteristics in the object segmentation process, and further establishes a decision tree to gradually obtain the peat bog spatial distribution information by selecting an optimal waveband. The precision of the obtained classification result is 93 percent, which is 5 to 8 percent higher than the precision of the existing method for extracting peat bog by using medium-resolution remote sensing images. Meanwhile, the influence of the terrain on the distribution of the peat swamp is considered, so that the classification result has clear geographical significance. The embodiment overcomes the phenomena of missing and wrong classification caused by only applying optical images in the conventional peat bog information extraction, and simultaneously solves the problems that the classified peat bog spatial information has a salt and pepper phenomenon and a land flying phenomenon, has no clear geographic significance and the like. The embodiment has practical significance for quickly and automatically extracting the peat bog information by combining the optical image, the radar image and the DEM.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is: the preprocessing process of the Landsat TM data in the first step is as follows:
(1) determining the track number of Landsat TM data of the peat bogs in the distribution range of the peat bogs, and downloading the Landsat TM data covering the distribution range of the peat bogs according to the track number;
(2) in order to eliminate terrain distortion, performing orthorectification on the Landsat TM data by utilizing DEM data of an area corresponding to the Landsat TM data to obtain the orthorectified Landsat TM data;
(3) in order to eliminate geometric distortion, a ground control point is selected from the ERDAS software by utilizing topographic data, and the Landsat TM data after the direct correction is subjected to geometric fine correction to obtain preprocessed Landsat TM data. Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the present embodiment differs from the first or second embodiment in that: and in the second step, ENVISAT ASAR data are preprocessed:
(1) ENVISAT ASAR fine image Level one data (ENVISAT ASAR APP Level 1B Level data) used in the test are downloaded in the coverage range of the Landsat TM data range (the polarization modes are HH and HV);
(2) carrying out radiometric calibration on ENVISAT ASAR fine image primary data, namely converting DN value of ENVISAT ASAR fine image primary data into backscattering coefficient (unit is dB), and obtaining ENVISAT ASAR data corrected by radiation; the radiometric calibration formula is as follows:
wherein,the backscattering coefficient of the ith row and the jth column of pixels; DNijThe original intensity value of the ith row and the jth column of pixels; thetaijIs the incident angle of the radar wave of the ith row and the jth column of pixels; k is an absolute calibration coefficient;
(3) in order to eliminate terrain distortion, the DEM data of the region corresponding to the ENVISAT ASAR data is utilized to carry out terrain correction on the radiation correction ENVISAT ASAR data by adopting a Range-Doppler imaging algorithm (Range-Doppler);
(4) to remove image noise, an Enhanced Lee filter (window size 3 x 3 pixels) is applied to spatially filter the ENVISAT ASAR data after terrain correction is complete. Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the difference between this embodiment mode and one of the first to third embodiment modes is: and the spatial registration error in the fourth step is controlled within 0.5 pixel. Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode: the difference between this embodiment and one of the first to fourth embodiments is: the optimal polarization mode wave band of the radar image for extracting the peat bogs in the step seven is specifically as follows: the difference between the backscattering coefficients is obviously compared by counting the average value of the backscattering coefficients of different land cover types under the HV and HH polarization modes to be used as the optimal polarization mode wave band. Other steps and parameters are the same as in one of the first to fourth embodiments.
The sixth specific implementation mode: the difference between this embodiment and one of the first to fifth embodiments is: and step eight, each partition unit consists of spatially adjacent pixels with the homogeneity of 80-100%. Other steps and parameters are the same as those in one of the first to fifth embodiments.
The seventh embodiment: the difference between this embodiment and one of the first to sixth embodiments is: in the ninth step, the normalized vegetation index (NDVI) and the normalized water body index (NDWI) for extracting the characteristic parameters of the series of segmentation units obtained in the eighth step by using eCooginion software are as follows:
wherein, TM2 is the 2 nd band of the Landsat TM sensor, TM3 is the 3 rd band of the Landsat TM sensor, and TM4 is the 4 th band of the Landsat TM sensor. Other steps and parameters are the same as those in one of the first to sixth embodiments.
The specific implementation mode is eight: the present embodiment differs from one of the first to seventh embodiments in that: the calculation formula of the JM Distance method (Jeffreys Matusita Distance) in the step ten is as follows:
wherein i and j represent any two different classification types respectively; bijThe Bhattacharyya distance between the i-class classification type and the j-class classification type; m isiMean vector, m, representing class i class classification typejA mean vector representing class j classification types; ciCovariance matrix representing class i class type, CjA covariance matrix representing class j classification types; according to the JM calculation result, the selected slope value (0-42.43 degrees), the normalized vegetation index (NDVI) (-1), the TM2+ TM3-TM4-TM5 (-200.67-37.77) and the hue (R: G: B ═ TM5: TM4: TM3) (0-1) are determined as the participating classification wave bands. Other steps and parameters are the same as those in one of the first to seventh embodiments.
The following examples were used to demonstrate the beneficial effects of the present invention:
the first embodiment is as follows:
the embodiment of the invention relates to a peat bog information extraction method based on ENVISAT ASAR, Landsat TM and DEM data, which is specifically prepared according to the following steps:
the method comprises the following steps: preprocessing Landsat TM data:
(1) determining the track number of Landsat TM data of the peat bogs in the distribution range of the peat bogs, and downloading Landsat TM data covering the distribution range of the peat bogs according to the track number, wherein the track number is P119R26, and the time is 6 months and 11 days in 2010;
(2) in order to eliminate terrain distortion, performing orthorectification on the Landsat TM data by utilizing DEM data of an area corresponding to the Landsat TM data to obtain the orthorectified Landsat TM data;
(3) in order to eliminate geometric distortion, selecting a ground control point in ERDAS software by using topographic data, and carrying out geometric fine correction on the Landsat TM data after the direct correction to obtain preprocessed Landsat TM data;
step two: ENVISAT ASAR preprocessing the data;
(1) in the coverage range of the Landsat TM data range, ENVISAT ASAR fine image primary data (ENVISAT ASAR APP Level 1B Level data) (polarization modes are HH and HV) used in the test are downloaded for 7 months and 2 days in 2010;
(2) carrying out radiometric calibration on ENVISAT ASAR fine image primary data, namely converting DN value of ENVISAT ASAR fine image primary data into backscattering coefficient (unit is dB), and obtaining ENVISAT ASAR data corrected by radiation; the radiometric calibration formula is as follows:
wherein,the backscattering coefficient of the ith row and the jth column of pixels; DNijThe original intensity value of the ith row and the jth column of pixels; thetaijIs the incident angle of the radar wave of the ith row and the jth column of pixels; k is an absolute calibration coefficient;
(3) in order to eliminate terrain distortion, in NEXT 4C software, the radiation correction ENVISAT ASAR data is corrected in terrain by using a Range-Doppler imaging algorithm (Range-Doppler) by utilizing DEM data of a region corresponding to ENVISAT ASAR data;
(4) in order to eliminate image noise, an Enhanced Lee filter (window size 3 x 3 pixels) is applied to perform spatial filtering processing on ENVISAT ASAR data after terrain correction is completed;
step three: resampling ENVISAT ASAR data preprocessed in the second step in ArcGIS, wherein the grid size of ENVISAT ASAR data after resampling is consistent with that of Landsat TM data processed in the first step, and the grid size is 30m multiplied by 30 m;
step four: taking the preprocessed Landsat TM data as a reference, contrasting the preprocessed Landsat TM data with the ENVISAT ASAR data resampled in step III in ArcGIS software, selecting a control point on the preprocessed Landsat TM data by utilizing a control point adding function provided by a Georeferencening module of the ArcGIS software, and registering the resampled ENVISAT ASAR data according to the control point space to obtain a ENVISAT ASAR image; the spatial registration error is controlled within 0.5 pixel;
step five: gradient extraction is carried out on DEM data by utilizing an Aspect command in Surface Analysis under a Spatial Analysis module in ArcGIS software to obtain gradient data;
step six: combining with the land cover type survey sampling points, extracting the backward scattering coefficients of the radar images of different land cover types in different polarization modes from the ENVISAT ASAR images completed in the step four in ArcGIS;
step seven: analyzing the difference of radar backscattering coefficients of the peat bogs and other different land cover types under different polarization modes, and determining ENVISAT ASAR image optimal polarization mode wave bands, namely the radar image optimal polarization mode wave bands for extracting the peat bogs:
the average values of the backscattering coefficients of the different land cover types in the HV and HH polarization modes are shown in table 2 by statistics:
TABLE 2
Comparing and finding that the difference between the backscattering coefficients of the peat bogs and other land types in the HV polarization mode is more obvious than that of the peat bogs and other land types in the HH polarization mode, the optimal polarization mode wave band is selected ENVISAT ASAR as the image HV polarization mode wave band. In addition, the backscattering coefficients of the grass swamp and the peat swamp differ by 0.88dB in the HH polarization mode and by 3.44dB in the HV polarization mode, so that the study is suitable for studying the grass swamp;
step eight: carrying out multilayer multi-scale segmentation on the preprocessed Landsat TM data, gradient data and the ENVISAT ASAR image optimal polarization mode wave band determined in the seventh step by using eCooginion software to obtain a series of segmentation units, and taking each segmentation unit as an object; each division unit is composed of pixels which are adjacent in space and have the homogeneity of 80% -100%. The parameter settings showing the multiscale segmentation in the object-oriented classification process are shown in table 1:
| segmentation scale | Color factor | Form factor | Smoothness of the surface | Compactness degree |
| 8 | 0.9 | 0.1 | 0.6 | 0.4 |
Step nine: extracting characteristic parameters of a series of segmentation units segmented in the step eight by using eCogination software; the characteristic parameters comprise average values of all wave bands, normalized vegetation indexes (NDVI), normalized water body indexes (NDWI), TM2+ TM3-TM4-TM5, color tones (R: G: B ═ TM5: TM4: TM3), and color tones (R: G: B ═ TM4: TM3: TM 2);
the normalized vegetation index (NDVI) and the normalized water body index (NDWI) of a series of segmentation units for extracting characteristic parameters are as follows:
wherein, TM2 is the 2 nd band of the Landsat TM sensor, TM3 is the 3 rd band of the Landsat TM sensor, and TM4 is the 4 th band of the Landsat TM sensor;
step ten: determining the optimal classification wave band by using a JM Distance (Jeffreys Matusita Distance) method according to the characteristic parameters extracted in the step nine; the JM Distance method (Jeffreys Matusita Distance) has the following calculation formula:
wherein i and j represent any two different classification types respectively; bijThe Bhattacharyya distance between the i-class classification type and the j-class classification type; m isiRepresenting class i classification typesMean vector, mjA mean vector representing class j classification types; ciCovariance matrix representing class i class type, CjA covariance matrix representing class j classification types; the value range of the JM distance is 0-2.0, and when the value is greater than 1.9, the separability between ground objects is better; according to the JM calculation result, determining selected slope value (0-42.43 degrees), normalized vegetation index (NDVI) (-1), TM2+ TM3-TM4-TM5 (-200.67-37.77) and hue (R: G: B ═ TM5: TM4: TM3) (0-1) as participating classification wave bands.
Step eleven: according to the optimal classification wave band determined in the step ten, establishing a classification decision tree by utilizing See5.0 software according to the land cover type survey sampling points; wherein, the reference soil coverage type survey sample points comprise peat swamp, herbaceous swamp, residential land, transportation land, farmland, forest land, water body and other soil coverage types;
step twelve: operating a classification decision tree on eCoogination software, deriving a land cover type classification result, and producing a land cover type vector file, wherein the peat marsh extraction precision is 93%; the system comprises a field coverage type vector file, a data processing system and a data processing system, wherein the field coverage type vector file comprises field coverage types such as farmlands, forest lands, water bodies, residential traffic lands, herb swamps and peat swamps;
step thirteen: under a Layout View mode in ArcGIS software, manufacturing a peat bog thematic map according to the land cover type vector file completed in the step twelve (a schematic diagram of the peat bog thematic map is shown in FIG. 2); thus completing a peat bog information extraction method based on ENVISAT ASAR, Landsat TM and DEM data.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.
Claims (8)
1. A peat bog information extraction method based on ENVISAT ASAR, Landsat TM and DEM data is characterized in that:
the method comprises the following steps: preprocessing Landsat TM data;
step two: ENVISAT ASAR preprocessing the data;
step three: resampling the ENVISAT ASAR data preprocessed in the step two, wherein the grid size of the ENVISAT ASAR data after resampling is consistent with that of the Landsat TM data processed in the step one;
step four: selecting a control point on the preprocessed Landsat TM data by utilizing a control point adding function provided by a Georeferencening module of ArcGIS software, and registering the resampled ENVISAT ASAR data according to a control point space to obtain a ENVISAT ASAR image;
step five: gradient extraction is carried out on the DEM data to obtain gradient data;
step six: combining with the land cover type survey sampling points, extracting the backward scattering coefficients of the radar images of different land cover types in different polarization modes from the ENVISAT ASAR images completed in the step four;
step seven: analyzing the difference of radar backscattering coefficients of the peat bogs and other different land cover types under different polarization modes, and determining ENVISAT ASAR image optimal polarization mode wave bands, namely radar image optimal polarization mode wave bands for extracting the peat bogs;
step eight: performing multilayer multi-scale segmentation on the preprocessed Landsat TM data, gradient data and the ENVISATASAR image optimal polarization mode wave band determined in the seventh step to obtain a series of segmentation units;
step nine: extracting characteristic parameters of a series of segmentation units segmented in the step eight; the characteristic parameters comprise the average value of each wave band, a normalized vegetation index, a normalized water body index, TM2+ TM3-TM4-TM5 and color tones;
step ten: determining the optimal classification wave band by using a JM distance method according to the characteristic parameters extracted in the step nine;
step eleven: according to the optimal classification wave band determined in the step ten, establishing a classification decision tree by referring to the land cover type survey sampling points; wherein, the reference soil coverage type survey sample points comprise peat swamp, herbaceous swamp, residential land, transportation land, farmland, forest land and water body soil coverage types;
step twelve: operating a classification decision tree, exporting a land cover type classification result, and producing a land cover type vector file; wherein, the land cover type vector file comprises land cover types of farmlands, forest lands, water bodies, residential traffic lands, herbaceous swamps and peat swamps;
step thirteen: making a peat marsh thematic map according to the land cover type vector file completed in the step twelve; thus completing a peat bog information extraction method based on ENVISAT ASAR, Landsat TM and DEM data.
2. The method for extracting peat bog information based on ENVISAT ASAR Landsat TM and DEM data as claimed in claim 1, wherein: the preprocessing process of the Landsat TM data in the first step is as follows:
(1) determining the track number of Landsat TM data of the peat bogs in the distribution range of the peat bogs, and downloading the Landsat TM data covering the distribution range of the peat bogs according to the track number;
(2) performing orthorectification on the Landsat TM data by using DEM data of an area corresponding to the Landsat TM data to obtain the orthorectified Landsat TM data;
(3) and selecting a ground control point in the ERDAS software by utilizing the topographic data, and performing geometric fine correction on the directly corrected Landsat TM data to obtain preprocessed Landsat TM data.
3. The method for extracting peat bog information based on ENVISAT ASAR Landsat TM and DEM data as claimed in claim 1, wherein: and in the second step, ENVISAT ASAR data are preprocessed:
(1) downloading ENVISAT ASAR fine image primary data within the coverage range of the Landsat TM data range;
(2) carrying out radiometric calibration on ENVISAT ASAR fine image primary data, namely converting DN value of ENVISAT ASAR fine image primary data into backscattering coefficient to obtain ENVISAT ASAR data of radiation correction; the radiometric calibration formula is as follows:
wherein,the backscattering coefficient of the ith row and the jth column of pixels; DNijThe original intensity value of the ith row and the jth column of pixels; thetaijIs the incident angle of the radar wave of the ith row and the jth column of pixels; k is an absolute calibration coefficient;
(3) performing terrain correction on radiation correction ENVISAT ASAR data by utilizing DEM data of a region corresponding to ENVISAT ASAR data and adopting a range-Doppler imaging algorithm;
(4) and applying an Enhanced Lee filter to perform spatial filtering processing on ENVISAT ASAR data after the terrain correction is completed.
4. The method for extracting peat bog information based on ENVISAT ASAR Landsat TM and DEM data as claimed in claim 1, wherein: and the spatial registration error in the fourth step is controlled within 0.5 pixel.
5. The method for extracting peat bog information based on ENVISAT ASAR Landsat TM and DEM data as claimed in claim 1, wherein: the optimal polarization mode wave band of the radar image for extracting the peat bogs in the step seven is specifically as follows: the difference between the backscattering coefficients is obviously compared by counting the average value of the backscattering coefficients of different land cover types under the HV and HH polarization modes to be used as the optimal polarization mode wave band.
6. The method for extracting peat bog information based on ENVISAT ASAR Landsat TM and DEM data as claimed in claim 1, wherein: and step eight, each partition unit consists of spatially adjacent pixels with the homogeneity of 80-100%.
7. The method for extracting peat bog information based on ENVISAT ASAR Landsat TM and DEM data as claimed in claim 1, wherein: in the ninth step, the normalized vegetation index NDVI and the normalized water body index NDWI for extracting the characteristic parameters of the series of segmentation units obtained in the eighth step are as follows:
wherein, TM2 is the 2 nd band of the Landsat TM sensor, TM3 is the 3 rd band of the Landsat TM sensor, and TM4 is the 4 th band of the Landsat TM sensor.
8. The method for extracting peat bog information based on ENVISAT ASAR Landsat TM and DEM data as claimed in claim 1, wherein: in the step ten, a calculation formula of the JM distance method is as follows:
wherein i and j represent any two different classification types respectively; bijThe Bhattacharyya distance between the i-class classification type and the j-class classification type; m isiMean vector, m, representing class i class classification typejA mean vector representing class j classification types; ciCovariance matrix representing class i class type, CjA covariance matrix representing the class j classification type.
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