CN103558599A - Complex heterogeneity forest stand mean height estimating method based on multisource remote sensing data - Google Patents

Complex heterogeneity forest stand mean height estimating method based on multisource remote sensing data Download PDF

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CN103558599A
CN103558599A CN201310556077.2A CN201310556077A CN103558599A CN 103558599 A CN103558599 A CN 103558599A CN 201310556077 A CN201310556077 A CN 201310556077A CN 103558599 A CN103558599 A CN 103558599A
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mean height
forest
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penalty coefficient
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张晓丽
赵明瑶
白金婷
王金兰
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Beijing Forestry University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/882—Radar or analogous systems specially adapted for specific applications for altimeters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9021—SAR image post-processing techniques
    • G01S13/9023—SAR image post-processing techniques combined with interferometric techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
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    • G01S13/9076—Polarimetric features in SAR

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Abstract

The invention discloses a complex heterogeneity forest stand mean height estimating method based on multisource remote sensing data. The complex heterogeneity forest stand mean height estimating method is a multi-data source multi-kind information forest stand mean height estimating technology which integrates the phase and amplitude information of a polarization interference radar, vegetation index information, entropy information reflecting a forest structure, second class investigation information and sample-plot survey information. The spectral information and the structure complexity of a forest are reflected by introducing the vegetation index (NDVI) and the entropy in the information theory in the technology. According to specific forest conditions, different compensation factors are given to different forest stands, and a compensation factor function is established. A coherent phase-amplitude algorithm is improved by using the changed compensation factor after correction to replace a constant compensation factor. Large area, high precision and fast extraction and charting of the forest stand mean height of a complex forest structure in a cloud and rain area are achieved.

Description

A kind of complex heterogeneous mean height estimating and measuring method based on RS data
Technical field
Patent of the present invention relates to a kind of phase place, amplitude information of comprehensive polarization interference radar, the vegetation index information that Landsat8 extracts, the entropy information of reflection forest structure, many information of multi-data source mean height estimation technology of two class investigation and sample ground enquiry data, is especially improved to large region, high precision, the rapid extraction drafting method of cloudy rain area forest mean height.
Background technology
At present, the DEM differential technique of known PolInSAR estimation vegetation height is by polarization information and the effective combination of interference technique are isolated to the phase center of multiple scattering mechanism, then asks phase difference value to resolve acquisition vegetation height the phase center that represents earth's surface and vegetation.In the method, vegetation phase center is always underestimated, there is researchist to propose coherent phase-Amplitude Compensation method, introduce the amplitude information of radar, utilize correlation magnitude algorithm estimation vegetation height, with compensating parameter ε, regulate again the compensation size of relevant amplitude algorithm, improve estimation precision.Wherein, penalty coefficient is relevant with Forest Vertical structure with forest extinction coefficient, and span is 0～0.5, conventionally chooses a fixed value 0.4.But above method only utilizes single radar data source that forest is reduced to a simple R VoG model and has ignored the difference between a large amount of remote optical sensing information, forest complex vertical structure and Different forest stands.General coherent phase-amplitude estimating and measuring method is considered not enough to forest structure complicated state, the heterogeneity of standing forest is ignored, and causes estimated value precision still undesirable, is difficult to be applied to the mean height estimation of complex heterogeneous standing forest.
Summary of the invention
In order to overcome the penalty coefficient of existing coherent phase-Amplitude Compensation method method, immobilize, the problem of ignoring standing forest heterogeneity and Forest Vertical structural complexity, patent of the present invention is created a kind of coherent phase-amplitude method estimation mean height that utilizes the penalty coefficient of variation.This technology reflects respectively spectral information and the structural complexity of forest by the associating entropy of introducing in vegetation index NDVI and information theory.Take the two as the parameter structure penalty coefficient function relevant to forest structure, utilize penalty coefficient function to substitute constant penalty coefficient to improve complex heterogeneous mean height estimation precision.
Patent of the present invention solves the technical scheme that its technical matters adopts: first, calculate the initial value of mean height with coherent phase-amplitude algorithm, the sample ground enquiry data of take is mean height measured value, obtains the corrected value of penalty coefficient by inverse operation.The NDVI of take sample and associating entropy are independent variable, and the penalty coefficient of correction is that dependent variable is carried out respectively multiple linear, linearity, index, logarithm matching, chooses the model construction penalty coefficient function relevant to forest structure that the goodness of fit is large.Then, with Landsat8, extract the NDVI figure of study area, and in conjunction with sample the associating entropy inverting investigated obtain the entropy chart of whole study area.Utilize penalty coefficient function to generate the penalty coefficient figure that corrects rear variation within the scope of full-fledged research district.Finally, with the penalty coefficient changing after correcting, substitute constant penalty coefficient and improve the mean height that coherent phase-amplitude algorithm obtains degree of precision.
The beneficial effect of patent of the present invention is, added abundanter spectral information, and considered that standing forest is heterogeneous when utilizing the high-penetrability estimation vertical height advantage of radar, and estimation result is truer, and estimation precision is more reliable.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, patent of the present invention is further illustrated.
Fig. 1 is the overall technology route map of patent complex heterogeneous standing forest mean stand height estimating and measuring method of the present invention.
Fig. 2 is radar image pre-service and the classification route map of patent complex heterogeneous standing forest mean stand height estimating and measuring method of the present invention.
Fig. 3 is the penalty coefficient ε route map that the generation of patent complex heterogeneous standing forest mean stand height estimating and measuring method of the present invention changes.
Embodiment
(1) pre-processed radar obtains interference image:
In embodiment illustrated in fig. 2, input data source be the study area two width SLC data with coherence, the SAR based on FFT conversion affect autoregistration major-minor image, with nonlinear least square method carry out baseline estimation, phase place is gone on level land, different polarization modes are carried out to complex conjugate and multiply each other and obtain the coherent video between different polarization modes, by circumference phase median filtering method noise reduction process.
(2) forest land and non-forest land classification:
In embodiment illustrated in fig. 2, major-minor image is done respectively to Freeman and decompose, in conjunction with the volume scattering component of the two, utilize threshold value judgement whether to belong to vegetation area.If P volume1+ P volume2> threshold value, differentiates for vegetation area, otherwise is divided into other regions.
(3) traditional coherent phase-Amplitude Compensation algorithm inverting height of tree:
In the embodiment shown in fig. 1, establish
Figure 217677DEST_PATH_IMAGE001
represent the mean height that DEM method of difference calculates, the mean height that the relevant amplitude inversion algorithm of representative obtains,
Figure 487302DEST_PATH_IMAGE003
mean height for preliminary estimation.Coherent phase-Amplitude Compensation algorithm can be written as:
Figure 257680DEST_PATH_IMAGE004
.
Expansion obtains,
Figure 507396DEST_PATH_IMAGE005
Wherein,
Figure 492670DEST_PATH_IMAGE006
,
Figure 802428DEST_PATH_IMAGE007
relevant with baseline;
Figure 810836DEST_PATH_IMAGE008
represent volume scattering;
Figure 864242DEST_PATH_IMAGE009
being penalty coefficient, is a constant.
Ground phase place
Figure 704022DEST_PATH_IMAGE010
algorithm for estimating be:
Figure 165539DEST_PATH_IMAGE012
Figure 22636DEST_PATH_IMAGE013
Figure 716923DEST_PATH_IMAGE014
Figure 306167DEST_PATH_IMAGE015
Figure 351484DEST_PATH_IMAGE016
,
Figure 746693DEST_PATH_IMAGE017
represent two polarized states, occlusion body scattering.
Figure 570478DEST_PATH_IMAGE016
,
Figure 837512DEST_PATH_IMAGE017
definite employing phase place optimal theoretical obtain, formula is as follows:
Figure 708516DEST_PATH_IMAGE018
Figure 377394DEST_PATH_IMAGE019
Figure 636337DEST_PATH_IMAGE020
Figure 125088DEST_PATH_IMAGE021
Which is by vegetation deviation criterion, to determine these two polarized states
Figure 813164DEST_PATH_IMAGE016
, which is
Figure 336550DEST_PATH_IMAGE017
.
Figure 500815DEST_PATH_IMAGE008
,
Figure 680123DEST_PATH_IMAGE022
polarization interference coherent coefficient for different scattering mechanism vectors.With said method, calculate preliminary compared with the mean height of rough grade .
(4) in conjunction with sample ground compensation data calculation coefficient corrected value:
In the embodiment shown in fig. 3, in conjunction with the mean height of two class enquiry datas and sample ground data acquisition, with sample ground enquiry data (
Figure 199463DEST_PATH_IMAGE025
for sample ground number), be mean height measured value.Actual measurement sample place is corresponding one by one with the estimated value point on radar data by coordinate,
Figure 849757DEST_PATH_IMAGE026
, again because know sample place position on coherent phase bitmap, so
Figure 692128DEST_PATH_IMAGE027
, all known, utilize inverse operation, obtain
Figure 352096DEST_PATH_IMAGE029
the value correcting.Formula is as follows:
Figure 234601DEST_PATH_IMAGE030
The penalty coefficient corrected value obtaining respectively can form the set of penalty coefficient corrected value n piece sample
Figure 587085DEST_PATH_IMAGE031
.
(5) the penalty coefficient figure changing:
In the embodiment shown in fig. 3, utilize TM image can extract study area any point vegetation index NDVI, and make study area NDVI figure.
Figure 952470DEST_PATH_IMAGE032
Actual measurement sample place is corresponding one by one with the estimated value point on NDVI by coordinate, the NDVI data that obtain sample
Figure 390405DEST_PATH_IMAGE033
.
Associating entropy has represented the uncertainty of the middle-level distribution of forest, has explained structural complexity and the richness of forest.Calculate associating entropy: first the arbor of the arborous layer in 20 * 30m sample ground is carried out to every wooden dipping, investigate and record the seeds of arbor, the diameter of a cross-section of a tree trunk 1.3 meters above the ground, the height of tree, hat width.In the upper left corner, choose the shrub layer sample prescription of 5 * 5m, record the kind name of shrub, highly, hat width and number, in the corner of shrub sample prescription, make the draft subquadrat of 31 * 1m simultaneously, record herbal kind, height and cover degree etc.Utilize above data to calculate associating entropy, obtain the set of sample ground associating entropy
Figure 76601DEST_PATH_IMAGE034
. associating entropy computing formula is as follows:
Figure 221274DEST_PATH_IMAGE035
In above formula
Figure 521675DEST_PATH_IMAGE036
for the species number comprising in forest community,
Figure 446905DEST_PATH_IMAGE037
for the sum of certain species,
Figure 671213DEST_PATH_IMAGE038
for the total strain number in sample ground,
Figure 935972DEST_PATH_IMAGE039
be
Figure 220323DEST_PATH_IMAGE041
kind of species are the
Figure 367271DEST_PATH_IMAGE043
the strain number of layer.
Utilize sample and investigate the associating entropy of calculating and remote optical sensing data to set up model inversion and generate study area entropy chart, can calculate the associating entropy of any point in whole study area.
Because underestimating of vegetation phase center is relevant with Forest Vertical structure with extinction coefficient, and extinction coefficient and leaf area index LAI, NDVI index is relevant.Because NDVI exponential sum LAI exponential dependence is large, NDVI index does not need inverting directly with wave band, to calculate again.So choose the independent variable that NDVI exponential sum associating entropy is estimation penalty coefficient.
Utilize sample to be located in the associating entropy of standing forest
Figure 395270DEST_PATH_IMAGE044
with NDVI value and the penalty coefficient correcting carry out respectively multiple linear, linearity, index, logarithm matching, choose function that fitting effect is good coefficient function by way of compensation,
Figure 617413DEST_PATH_IMAGE047
represent the approximating methods such as multivariate linear model matching, linear model matching, Exponential Model, logarithmic model matching.Take n piece sample is known observation point, with
Figure 121207DEST_PATH_IMAGE048
as priori data collection.After matching, choosing effective is the high model of goodness of fit coefficient by way of compensation, and penalty coefficient becomes the value relevant to forest complex heterogeneous.Utilize function
Figure 157296DEST_PATH_IMAGE049
can, in the hope of the compensation factor value of each pixel in study area, generate the penalty coefficient figure changing within the scope of full-fledged research district.
(6) in the embodiment shown in fig. 1, utilize the penalty coefficient changing after correcting to substitute constant penalty coefficient and improve coherent phase-amplitude algorithm, computing formula is:
Figure 783449DEST_PATH_IMAGE050
The mean height estimated value that utilizes algorithm acquisition degree of precision after improving, improves the precision of complex heterogeneous mean height estimation.

Claims (7)

1. the complex heterogeneous mean height estimating and measuring method based on RS data, effective in isolating the phase center of earth's surface and vegetation by polarization information and interference technique, utilize the two phase differential to resolve mean height, introduce amplitude information compensation and underestimate the low valuation of the height causing because of vegetation phase center, it is characterized in that: the vegetation index information of extracting in conjunction with Landsat8, the combination entropy value information of reflection forest structure, the information architecture penalty coefficient function of two class investigation and sample ground enquiry data, utilize the penalty coefficient changing to substitute constant penalty coefficient and improve coherent phase-amplitude algorithm.
2. the complex heterogeneous mean height estimating and measuring method based on RS data according to claim 1, is characterized in that: with coherent phase-amplitude algorithm
Figure 628140DEST_PATH_IMAGE001
obtain the initial valuation of mean height
Figure 545280DEST_PATH_IMAGE002
, from two class enquiry datas and sample ground data acquisition mean height
Figure 325017DEST_PATH_IMAGE003
as actual value, by sample place
Figure 376019DEST_PATH_IMAGE004
by coordinate setting, on radar coherent phase bitmap, can obtain sample place
Figure 190391DEST_PATH_IMAGE004
estimated value
Figure 12854DEST_PATH_IMAGE005
.
3. the complex heterogeneous mean height estimating and measuring method based on RS data according to claim 1, is characterized in that: true mean height is corresponding with initial valuation,
Figure 279887DEST_PATH_IMAGE006
, by formula
Figure 9945DEST_PATH_IMAGE001
carry out inverse operation, be compensated coefficient
Figure 678824DEST_PATH_IMAGE007
the value correcting, formula is,
Figure 672188DEST_PATH_IMAGE008
，
Can obtain n block compensation coefficient corrected value n piece sample, form the set of penalty coefficient corrected value
Figure 426517DEST_PATH_IMAGE009
.
4. the complex heterogeneous mean height estimating and measuring method based on RS data according to claim 1, is characterized in that: utilize associating entropy
Figure 570054DEST_PATH_IMAGE010
description makes the structural complexity of the forest that vegetation phase center underestimates, utilizes NDVI index to describe the extinction coefficient that vegetation phase center is underestimated, and chooses the independent variable that NDVI exponential sum associating entropy is estimation penalty coefficient.
5. the complex heterogeneous mean height estimating and measuring method based on RS data according to claim 1, is characterized in that: utilize sample to be located in the associating entropy of standing forest
Figure 93439DEST_PATH_IMAGE010
with NDVI value and the penalty coefficient correcting
Figure 523283DEST_PATH_IMAGE009
carry out respectively multiple linear, linearity, index, logarithm matching, choose function that fitting effect is good coefficient function by way of compensation,
Figure 764909DEST_PATH_IMAGE011
。
6. the complex heterogeneous mean height estimating and measuring method based on RS data according to claim 1, it is characterized in that: utilize the penalty coefficient function of setting up, the region-wide associating entropy that Landsat8 inverting obtains, the region-wide NDVI value extracting, the penalty coefficient figure of the variation in generation full-fledged research district.
7. the complex heterogeneous mean height estimating and measuring method based on RS data according to claim 1, is characterized in that: with the penalty coefficient changing after correcting, substitutes constant penalty coefficient and improves coherent phase-amplitude algorithm,
Figure 571191DEST_PATH_IMAGE012
, utilize and improve the mean height estimated value that rear algorithm obtains degree of precision, improve the precision of complex heterogeneous mean height estimation.
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CN110321402A (en) * 2019-07-29 2019-10-11 新疆林业科学院现代林业研究所 A kind of prediction technique of mountain area high forest potential distribution
CN111352109A (en) * 2020-01-19 2020-06-30 中南大学 Vegetation height inversion method and device based on two-scene SAR (synthetic aperture radar) image
CN113205475A (en) * 2020-01-16 2021-08-03 吉林大学 Forest height inversion method based on multi-source satellite remote sensing data
CN113945927A (en) * 2021-09-17 2022-01-18 西南林业大学 Forest canopy height inversion method through volume scattering optimization
CN113945926A (en) * 2021-09-17 2022-01-18 西南林业大学 Forest canopy height inversion method improved through underestimation compensation
CN117452432A (en) * 2023-12-21 2024-01-26 西南林业大学 Forest canopy height estimation method based on forest penetration compensation

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CN105373694B (en) * 2015-08-31 2017-12-29 北京师范大学 The fusion calculation method of MODIS broadbands emissivity and GLASS broadband emissivity
CN105373694A (en) * 2015-08-31 2016-03-02 北京师范大学 Fusion calculation method of MODIS broadband emissivity and GLASS broadband emissivity
CN110321402A (en) * 2019-07-29 2019-10-11 新疆林业科学院现代林业研究所 A kind of prediction technique of mountain area high forest potential distribution
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CN113205475B (en) * 2020-01-16 2022-07-12 吉林大学 Forest height inversion method based on multi-source satellite remote sensing data
CN113205475A (en) * 2020-01-16 2021-08-03 吉林大学 Forest height inversion method based on multi-source satellite remote sensing data
CN111352109A (en) * 2020-01-19 2020-06-30 中南大学 Vegetation height inversion method and device based on two-scene SAR (synthetic aperture radar) image
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CN113945927B (en) * 2021-09-17 2022-09-06 西南林业大学 Forest canopy height inversion method through volume scattering optimization
CN113945926A (en) * 2021-09-17 2022-01-18 西南林业大学 Forest canopy height inversion method improved through underestimation compensation
CN117452432A (en) * 2023-12-21 2024-01-26 西南林业大学 Forest canopy height estimation method based on forest penetration compensation
CN117452432B (en) * 2023-12-21 2024-03-15 西南林业大学 Forest canopy height estimation method based on forest penetration compensation

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