CN103760565A - Regional scale forest canopy height remote sensing retrieval method - Google Patents
Regional scale forest canopy height remote sensing retrieval method Download PDFInfo
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- CN103760565A CN103760565A CN201410046762.5A CN201410046762A CN103760565A CN 103760565 A CN103760565 A CN 103760565A CN 201410046762 A CN201410046762 A CN 201410046762A CN 103760565 A CN103760565 A CN 103760565A
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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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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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
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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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
- G01S17/08—Systems determining position data of a target for measuring distance only
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Abstract
The invention discloses a regional scale forest canopy height remote sensing retrieval method. The regional scale forest canopy height remote sensing retrieval method includes the following steps: (1) setting a field sampling plot, and surveying parameters, (2) extracting forest type information based on an object-oriented classification method, (3) carrying out remote sensing estimation on the leaf area index, (4) carrying out remote sensing retrieval on the canopy density, (5) extracting and standardizing laser radar complete-waveform data and corresponding geographic position and elevation information, (6) carrying out Fourier transformation and low-pass filtering on the waveform data, (7) estimating noise of the waveform data, (8) judging the beginning position and the end position of waveform data signals, (9) determining waveform data peak value positions which include the ground echo position, the canopy top position and the centroid position, (10) computing the forest canopy height in a flat area with the gradient smaller than 5 degrees, (11) building a GLAS forest canopy height extracting model under the slopping-field terrain condition, and (12) fusing the laser radar canopy height data with multi-spectral information to carry out regional retrieval.
Description
Technical field
The present invention relates to merge the method that the RS data such as optics and laser radar are carried out the inverting of regional scale Forest Canopy height, belong to the technical field of quantitative remote sensing inverting.
Background technology
Quantitatively obtaining of Forest Vertical structural parameters, as the height of tree, to forest ecosystem function, material and energy exchange, especially forest carbon storage and global carbon research have vital effect.Current, optical remote sensing technology has been widely used in the monitoring of Forest Types, distribution and architectural feature, but it mainly obtains the horizontal information of canopy, and obtaining of vertical information had to significant limitation.New technology take laser radar as representative is owing to having very strong penetration capacity, has unrivaled advantage obtaining aspect Forest Vertical structural parameters.But it is discontinuous that it is spatially sampled, cannot reach seamless coverage, in large scale application, exist equally limitation.Therefore, this method proposes to merge laser radar and Multi-spectral Remote Sensing Data is carried out the inverting of regional scale Forest Canopy height, realizes its seamless estimation.
Summary of the invention
The present invention seeks to cannot Obtaining Accurate regional scale Forest Canopy elevation information for the current single remotely-sensed data source that utilizes, and considers to merge laser radar and multispectral data is realized the inverting of large scale Forest Canopy height.
The present invention is realizing on the basis of large spot laser radar waveform data Processing Algorithm, propose and set up the Forest Canopy height appraising model that can adapt under MODEL OVER COMPLEX TOPOGRAPHY, then merge multispectral information inverting regional scale Forest Canopy height, concrete steps are as follows:
1) should relate to all forest ecosystem types field study sample as far as possible, investigation content mainly comprises geographic position, coenotype, the diameter of a cross-section of a tree trunk 1.3 meters above the ground, the height of tree, canopy density, leaf area index etc., also to consider the geographical location information of ICESat/GLAS laser facula data simultaneously, choose and arrange several circular samples of answering in contrast and carry out on-site inspection, for quantitative remote sensing inverting provides data basis.
Obtaining and Extracting Thematic Information of the multispectral TM data of step 2
The Landsat/TM data that cover study area are obtained in download, carry out successively the processing such as radiant correction, atmospheric correction, ortho-rectification and geometric accurate correction, obtain earth's surface real reflectance.
2) the Forest Types information extraction based on object-oriented classification method.According to modeling needs, forest is divided into coniferous forest, broad-leaf forest and theropencedrymion.
3) leaf area index remote sensing appraising.Based on a series of vegetation indexs of spectral information and generation, select the each Forest Types leaf area index of multiple linear regression and partial least square method estimation area.
4) pixel two sub-models based on vegetation index carry out respectively remote-sensing inversion to coniferous forest, broad-leaf forest and theropencedrymion canopy density.
The Forest Canopy height estimation of step 3 based on ICESat/GLAS complete waveform data
Utilize GLA01 Wave data and the GLA14 land/vegetation altitude information of ICESat/GLAS.The complete waveform data that recorded by GLA01 have reflected the terrain information in corresponding ground laser facula, for the estimation of forest structure parameter; With the corresponding geographic position of Wave data and elevation information by GLA14 record.
5) extraction and the standardization of laser radar complete waveform data, corresponding geographic position and elevation information.
6) Fourier transform and low-pass filtering.Based on Fourier transform, aid in low-pass filtering, eliminate high frequency noise, thereby data are obtained smoothly, carry out waveform fitting simultaneously, its harmonic wave number is determined by formula (1):
ω=2π/T
ω representation unit frequency signal intensity, 2 π=360, T is the duration.
7) noise is estimated.The number of winning the confidence starts front 15 frame data and front last 15 frame data of signal ended calculating noise mean value and standard deviations thereof respectively.
8) signal position judgment at the whole story.On the basis of estimating at noise, the threshold value of determining start signal is that the average of the initial noise of signal adds its 4 times of standard deviations; Corresponding signal ended threshold value is to finish average and its 4 times of standard deviation sums of noise.
9) peak determines.Ground echo position is to start frame by frame sweep backward from signal ended position, and near the peak-peak position searching then judges the spacing of itself and signal ended position again, if be less than laser pulse half-breadth, abandons it, otherwise regards it as ground echo position; Trough place before the canopy tip position number of winning the confidence starting position; Centroid position is called again waveform half energy height position.
10) low relief area (gradient <5 °) Forest Canopy height extracts by the waveform length L between canopy tip position (Canopy_top) and ground echo position (Ground) and determines, Binsize is 0.15m:
L=(Ground-Canopy_top)×Binsize （2）
11) under the condition of hillside fields, owing to only relying on waveform length to be difficult to accurately hold Forest Canopy elevation information, built and merged the multiple linear regression model of waveform length, topographic index and centroid position information, thereby the GLAS Forest Canopy height of realizing under MODEL OVER COMPLEX TOPOGRAPHY extracts.
Step 4 merges laser radar canopy height and multispectral data carries out region inverting
Correlation analysis between maximum Forest Canopy height and original spectrum, each vegetation index, leaf area index and the canopy density of obtaining based on each Forest Types GLAS, consider the impact of orographic factor simultaneously, the feasibility of GLAS Forest Canopy height being carried out to spatial spread based on multispectral data is set up corresponding best remote-sensing inversion model, and the each Forest Types canopy height of regional scale is estimated.
Advantage of the present invention:
The present invention can overcome and utilizes the defect that single remotely-sensed data source cannot Obtaining Accurate regional scale Forest Canopy elevation information, and merges laser radar and multispectral data advantage separately realizes the inverting of large scale Forest Canopy height.
Accompanying drawing explanation
Fig. 1 is field study sample ground distribution plan;
Fig. 2 is forest-floor type map;
Fig. 3 is forest LAI distribution plan;
Fig. 4 is forest canopy density distribution plan;
Fig. 5 is the Wave data after standardization;
Fig. 6 is that cutoff frequency is the smooth effect of 0.125,0.025,0.01 pair of Wave data;
Fig. 7 is primary waves shape parameter schematic diagram;
Fig. 8 is Forest Canopy height distribution plan.
Embodiment
Forest in Changbai Mountain Forest Region is the important forest reserve storehouse of China, is that woodland scenery is preserved the most complete, one of the best original temperate forests ecosystem of growing in the world.Below take the Antu, Jilin Province that is positioned at Conifer Forest at North Slope of Changbai Mountain as example is analyzed:
1) twice field study sample ground distributes as shown in Figure 1.
Obtaining and Extracting Thematic Information of the multispectral TM data of step 2
2) the Forest Types information extraction based on object-oriented classification method, as shown in Figure 2.
3) leaf area index remote sensing appraising.Based on 6 wave band reflectivity of TM remote sensing image and RVI, NDVI, SLAVI, EVI, VII, MSR, NDVIc, BI, 10 vegetation indexs such as GVI, WI, and aid in the terrain informations such as DEM, ASPECT, SLOPE, on the basis of correlation analysis, based on partial least square method, built each Forest Types leaf area index remote-sensing inversion best model, and carry out area extension, as shown in Figure 3:
4) pixel two sub-models based on vegetation index carry out respectively remote-sensing inversion to coniferous forest, broad-leaf forest and theropencedrymion canopy density, result as shown in Figure 4:
The Forest Canopy height estimation of step 3 based on ICESat/GLAS complete waveform data
5) extraction and the standardization of laser radar complete waveform data, corresponding geographic position and elevation information.Be illustrated in figure 5 the Wave data after standardization.
6) Fourier transform and low-pass filtering.
It is the smooth effect of 0.125,0.025,0.01 pair of Wave data that Fig. 6 has contrasted cutoff frequency.Along with reducing of cutoff frequency, Fourier transform matching harmonic wave quantity significantly reduces, although smooth effect is better, but ignored the detailed information of original waveform data, signal position at the whole story is obviously expanded, also there is obvious displacement in peak even, to waveform parameter extraction and waveform length estimation, brings serious deviation.
7) noise is estimated.
8) signal position judgment at the whole story.
9) peak determines.
Canopy_top is canopy tip position; Ground is ground echo position; Centroid is centroid position; L is waveform length; Signal
begwith Signal
endbe respectively signal position at the whole story.
10) low relief area (gradient <5 °) Forest Canopy height extracts and is directly obtained by the waveform length L between canopy tip position (Canopy_top) and ground echo position (Ground).For example data, the Forest Canopy height being obtained by formula (2) estimation is 28.35m, and the actual measurement of field sample ground is highly 28.8m, and the ability that visible laser radar obtains Forest Canopy height is still quite high.
11) under the condition of hillside fields, owing to only relying on waveform length to be difficult to accurately hold Forest Canopy elevation information, built and merged the multiple linear regression model of waveform length, topographic index and centroid position information, thereby the GLAS Forest Canopy height of realizing under MODEL OVER COMPLEX TOPOGRAPHY extracts.As shown in table 1.
Each Forest Types RMSE is take multiple linear regression model as good, and between 2.021～2.674, generally speaking theropencedrymion deviation is better than coniferous forest and is better than broad-leaf forest.
The foundation of Forest Canopy height model under the condition of table 1 hillside fields
Step 4 merges laser radar canopy height and multispectral data carries out region inverting
10 vegetation indexs such as the Forest Canopy height extracting based on GLAS data and RVI, the NDVI of 6 wave band reflectivity of TM remote sensing image and generation thereof, SLAVI, EVI, VII, MSR, NDVIc, BI, GVI, WI, and with the correlativity of leaf area index and canopy coverge, consider the impact of orographic factor (height above sea level, the gradient, slope aspect) simultaneously, based on partial least square method, built the best inverse model of each Forest Types, the row space inverting of going forward side by side, result as shown in Figure 8.
In the description of this instructions, the description of reference term " embodiment ", " some embodiment ", " example ", " concrete example " or " some examples " etc. means to be contained at least one embodiment of the present invention or example in conjunction with specific features, structure, material or the feature of this embodiment or example description.In this manual, the schematic statement of above-mentioned term is not necessarily referred to identical embodiment or example.And specific features, structure, material or the feature of description can be with suitable mode combination in any one or more embodiment or example.
Although illustrated and described embodiments of the invention, those having ordinary skill in the art will appreciate that: in the situation that not departing from principle of the present invention and aim, can carry out multiple variation, modification, replacement and modification to these embodiment, scope of the present invention is limited by claim and equivalent thereof.
Claims (4)
1. a regional scale Forest Canopy height remote sensing inversion method, concrete steps are as follows:
Step 1, field sample ground and investigation parameter are set:
1) sample ground investigation content mainly comprises geographic position, coenotype, the diameter of a cross-section of a tree trunk 1.3 meters above the ground, the height of tree, canopy density, leaf area index etc., considers the geographical location information of ICESat/GLAS laser facula data simultaneously;
Step 2, obtain multispectral TM data and extract thematic information:
2) extract the Forest Types information based on object-oriented classification method;
3) remote sensing appraising leaf area index;
4) remote-sensing inversion canopy density;
Step 3, the Forest Canopy height of estimation based on ICESat/GLAS complete waveform data:
5) extract laser radar complete waveform data, corresponding geographic position and elevation information, and by its standardization;
6) described Wave data is carried out to Fourier transform and low-pass filtering;
7) Wave data is carried out to noise estimation;
8) judge Wave data signal position at the whole story;
9) determine Wave data peak, described Wave data peak comprises ground echo position, canopy tip position and centroid position;
10) calculate low relief area Forest Canopy height;
11) build the GLAS Forest Canopy height extraction model adapting under MODEL OVER COMPLEX TOPOGRAPHY;
Step 4 merges laser radar canopy height data and multispectral information is carried out region inverting.
2. a kind of regional scale Forest Canopy height remote sensing inversion method according to claim 1, it is characterized in that, step 2 4) pixel two sub-models of application based on vegetation index carry out respectively remote-sensing inversion to coniferous forest, broad-leaf forest and theropencedrymion canopy density.
3. a kind of regional scale Forest Canopy height remote sensing inversion method according to claim 1, is characterized in that, step 3 6) application Fourier pair waveform carries out low-pass filtering.
4. a kind of regional scale Forest Canopy height remote sensing inversion method according to claim 1, it is characterized in that, step 3 11) build the multiple linear regression model that merges waveform length, topographic index and centroid position information under the condition of hillside fields, thereby the GLAS Forest Canopy height under extraction MODEL OVER COMPLEX TOPOGRAPHY.
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| CN103969645A (en) * | 2014-05-14 | 2014-08-06 | 中国科学院电子学研究所 | Method for measuring tree heights by tomography synthetic aperture radar (SAR) based on compression multi-signal classification (CS-MUSIC) |
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