CN109407113A - A kind of monitoring of woods window change in time and space and quantization method based on airborne laser radar - Google Patents
A kind of monitoring of woods window change in time and space and quantization method based on airborne laser radar Download PDFInfo
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Abstract
The present invention provide it is a kind of based on airborne laser radar woods window change in time and space monitoring and quantization method, comprising the following steps: step 1: the laser radar point cloud data based on multidate, identification woods window, draw woods window edge polar plot simultaneously save woods window vector data collection；Step 2: comparing the woods window vector data collection in two periods, construct the logic mathematical model of newly-generated woods window, stable, extension, atrophy, displacement or closed state；Step 3: the woods window vector data collection in two periods being overlapped, woods window vector polygon Overlap Analysis is carried out, according to the logic mathematical model that the step 2 constructs, constructs the discriminant criterion of woods window vector polygon variable condition.The present invention is based on the draftings that laser radar three dimensional point cloud carries out woods window identification and spatial distribution, it can quickly, accurately draw multiple dimensioned gap distribution polygon, realize the monitoring of woods window spatial distribution, the scientific characterization problems of woods window various change state are solved, it is final to realize the monitoring of woods window change in time and space and quantization.
Description
Technical field
The invention belongs to forestry remote sensing technical fields, and in particular to a kind of woods window change in time and space based on airborne laser radar
Monitoring and quantization method.
Background technique
Woods window is one of the hot issue of recent domestic forest management and administration concern." woods window (canopy gap) " one
Word is initially proposed by British scholar Watt nineteen forty-seven, caused by indicating after one plant or more canopy arbor is dead in forest
Chearance or small location, the space for being new individual invasion, occupying and updating.Forest cycle theory regards forest as spatially different
Matter, " the flowing mosaics " changed on the time, the dynamic change of woods window spatial distribution (size, shape and position) is in certain journey
The heterogeneity of Disturbed habit is enhanced on degree, affect the property of patch in group and its inlays situation, to influence group's knot
Structure and dynamic process, the final structure and function for influencing landscape, therefore the quantization to woods window spatial distribution monitoring and changing condition,
Be the key that comprehensive system recognize forest ecosystem long-term dynamics change procedure, be science carry out forest management and administration base
Plinth.
In the different phase of woods window development, the heterogeneity of space structure and the heterogeneity of function are interweave variation or development
's.The change in time and space such as generation, stabilization (maintenance), extension, atrophy, displacement or the closure of woods window enhance woods window function heterogeneousization,
Selection index system certainly will be played to plant intrusion, seed sprouting, seedling establishment, sapling up-growth.Limit by space data collection technology
System, the heterogeneity that scholar analyzes the research of woods window woods window space structure with being concentrated mainly in single observation period " static state " are right
The influence of species composition, form and the physiology of vegetation, update and growth etc., and " dynamic " monitor woods window change in time and space
It studies less.The monitoring of woods window space structure relies primarily on or field investigation, including the direct method of measurement and photograph estimate method,
Such method is time-consuming and laborious, high labor cost and is affected by extraneous factor；Again due to lack changing pattern quantizating index,
Lead to that the characterization of woods window changes in spatial distribution state is objective, not system, affects the standardization processing of woods window dynamic monitoring.Cause
This, it is necessary to a kind of method or technique is studied, realizes and the space-time dynamic of gap distribution is monitored, the specific woods window that solves generates, is steady
The scientific characterization problems of the variable conditions such as fixed, extension, atrophy, displacement or closure, it is final to realize the monitoring of woods window change in time and space and amount
Change.
Airborne laser radar (Airborne Laser Scanning, ALS) is a kind of emerging active remote sensing technology,
The high-precision vegetation structure information of forest ecosystem, dimensional topography feature can be obtained on multiple space and time scales.ALS is to woods window
High precision monitor has great potential in terms of recognizing forest disturbance regime, updating rule and inverting,
But China still belongs to the starting stage in this respect.Therefore, reinforce the research in terms of the spatial distribution dynamic monitoring of woods window, especially with
Advanced remote sensing technology quantifies woods window Dynamic mode of time and space, helps to improve the theoretical level of woods window research, further
The mechanism that the dynamic law and bio-diversity for disclosing forest cycle maintain has raising forest management and administration level important
Realistic meaning.
Summary of the invention:
It is an object of the present invention to provide a kind of, and quick, objective, accurate measurements woods window space based on airborne laser radar are divided
The quantization method of cloth and its variable condition, in order to make up the technological gap of prior art middle forest window change in time and space monitoring and quantization.
In order to achieve the above object, the present invention adopts the following technical scheme:
A kind of monitoring of woods window change in time and space and quantization method based on airborne laser radar, comprising the following steps:
Step 1: the laser radar point cloud data based on multidate, identification woods window are drawn woods window edge polar plot and are saved
Woods window vector data collection；
The laser radar point cloud data refers to by fixed wing aircraft or the acquisition of UAV flight's laser radar scanner
Remotely-sensed data, the data of each laser point include X, Y, Z coordinate data and echo strength data；Utilize existing remote sensing images
The correct throwing of processing software The Environment for Visualizing Images (ENVI, v5.3) setting point cloud data
Shadow coordinate system, measurement unit are rice.
Step 2: comparing the woods window vector data collection in two periods, building woods window is newly-generated, stable, extension, atrophy, displacement
Or the logic mathematical model of closed state；
Step 3: the woods window vector data collection in two periods is overlapped, woods window vector polygon Overlap Analysis is carried out, according to
The logic mathematical model that the step 2 constructs constructs the discriminant criterion of woods window vector polygon variable condition.
Preferably, in the step 1, if woods window vector data integrates as GnIf the value of some point (x, y) is in woods window
G (x, y) identifies that the model of woods window is as follows:
In formula, G (x, y)=1 indicates that the corresponding position point (x, y) is woods window；G (x, y)=0 indicates that point (x, y) is corresponding
Position be non-woods window；X, y are respectively the abscissa and ordinate of grid (x, y), and CHM (x, y) is the canopy on grid (x, y)
Height value；A is the discrimination threshold of canopy edge average height.Discrimination threshold is determined by empirical value or on the spot sample investigation, is such as sentenced
Other threshold value is 5 meters.
Preferably, in the step 1, CHM is the difference of Malabar Pied Hornbill model and earth's surface elevation model, it may be assumed that CHM (x, y)
=DSM (x, y)-DEM (x, y)；
DSM is Malabar Pied Hornbill model in formula, and DEM is digital elevation model.
Firstly, original point cloud is divided into canopy point cloud and Ground Point using adaptive irregular triangle network filtering method
Cloud.Canopy point cloud is interpolated to DSM using kriging analysis method, Ground Point cloud interpolation is instead generated into DEM apart from weight interpolation method.
The characteristics of characterizing in view of the density and canopy of cloud, setting DSM and DEM have identical raster resolution.
Secondly, carry out difference operation to DSM and DEM using ENVI software, canopy height Raster Data Model CHM is obtained, and
Classified on the basis of this according to the binary map G (x, y) that the height threshold a of woods window edge canopy carries out woods window and Fei Lin window.
Then, woods window recognition result is post-processed, to improve accuracy of identification, using ArcGIS software in G (x, y) two
Morphologic filtering operation is carried out on the basis of value figure, it is final to determine that woods window edge simultaneously carries out vectoring operations to it, draw woods window side
Edge polar plot simultaneously saves data set Gn。
Preferably, the specific steps of the step 2 include:
Step 21: setting first latter two period ti,tjThe woods window vector data collection of acquisition isI < j in formula；I, j > 0；
Step 22: comparing tiThe woods window vector data collection and t in periodjThe woods window vector data collection in period, tiThe woods window in period
Data set and tjPeriod, corresponding woods window data set was compared, in fact it could happen that three kinds of situations: the woods window polygon of two phases does not have
Overlapping, it is completely overlapped with partly overlap.Not being overlapped indicates tiWoods window exists, and tjWoods window disappears (closure)；Or tiWoods window is not
In the presence of, and tjWoods window is newly-generated.The completely overlapped woods window individual for indicating two periods all exists, and woods window maintains stable state not have
There is generation geometry deformation, or upper extension or atrophy in the original location.Partly overlapping phenomenon occurs are due to woods window edge Vegetation renewal
Or the heterogeneity of dead spatial distribution, lead to tjWoods window may be subjected to displacement and (not deform), and atrophy is simultaneously displaced or extends simultaneously position
The dynamic such as shifting.The logic mathematical model of newly-generated woods window, stable, extension, atrophy, displacement or closed state is defined respectively are as follows:
It is newly-generated:
Closure:
Stablize:
Extend non-displacement:
Atrophy non-displacement:
Extension has displacement:
Atrophy has displacement:
Preferably, the specific steps of the discriminant criterion of building woods window temporal and spatial orientation include: in the step 3
Step 31: respectively to ti,tjThe woods window vector polygon in two periods carries out topological structure coding, in formula subscript i or
J indicates the data obtained in i-th or j period, and defines i < j；I, j > 0；
Step 32: by ti,tjThe woods window vector polygon in two periods carries out overlapping operation, the comprehensive t of output figure layeri,tjTwo
The attribute of a period woods window vector data collection, reserve window is in ti,tjThe feature of all polygons in two periods；Figure layer overlapping
Original polygon element is divided into new element in the process, new element combines the attribute of original two datasets, result
It is usually exactly that a polygon is carried out intersection operation by the spatial distribution state of another polygon, to be divided into multiple more
Side shape, while copying the attribute of input object in new object attribute list in attribute assigning process, the purpose for the arrangement is that
The change information of geometrical characteristic (woods window edge) and attributive character (woods window, non-woods window) in woods window spatial distribution is fully retained；
Step 33: the shared segmental arc of the woods window vector polygon after overlapping being encoded, it is more then to calculate single woods window
Side shape attributive character ti,tjIt is superimposed the ratio of area and original forest gap area；
Step 34: constructing woods using the step 32 and the lamination process in step 33, shared segmental arc and area ratio
The discriminant criterion of window variation, monitor different times woods window is newly-generated, stable, extension, atrophy, displacement or closure variable condition
And quantify its variation degree.
Preferably, respectively to t in the step 31i,tjThe woods window vector polygon in two periods carries out topological structure coding
When, indicate within the scope of woods window or indicate that already existing woods window, O indicate that woods window range is outer or indicate non-woods window using E.
Preferably, in the step 33, to the shared segmental arc of the woods window vector polygon after overlapping be encoded to BB, BI, IB,
BN, NB, wherein B indicates the boundary of polygon, and I indicates the inside of polygon, and N indicates non-boundary or inside.
Preferably, in the step 33, the specific formula for calculation of area ratio are as follows:
P(EEij)=Area (EEij)/Area(Ei),
P(EOij)=Area (EOij)/Area(Ei),
P(OEij)=Area (OEij)/Area(Ei)；
In formula, P indicates that the ratio of area, Area indicate woods window range or area,
Ei: tiPeriod, the polygon range of single woods window,
EEij: assuming that some woods window tiPeriod exists, tjPeriod, there is also EE indicated ti~tjThe overlapping portion of woods window in period
Point,
OEij: assuming that tiPeriod, some woods window did not occurred, in tjPeriod has occurred, and OE indicates ti~tjDuring woods window change
Change range,
EOij, it is assumed that tiSome woods window of period exists, in tjPeriod has disappeared, and EO indicates ti~tjDuring woods window disappearance
Range.
Preferably, in the step 31, topology is carried out to woods window vector polygon in different times using ArcGIS software
Structured coding.
Technical solution of the present invention has the advantages that
The present invention realizes gap distribution (arrow using airborne laser radar technology and geographical spatial data topology analyzing method
Measure polygon) it fast and accurately monitors and draws, to single woods window, variation characteristic is (such as newly-generated, steady within the different observation phases
The states such as fixed, extension, atrophy, displacement or closure) progress is objective, systematically quantifies, it is precisely supervised to improve forest space structure
The level of survey promotes the scientific characterization of woods window change in time and space state.Present invention enhances woods window spatial distribution dynamic monitoring sides
The research in face quantifies woods window Dynamic mode of time and space especially with advanced remote sensing technology, helps to improve woods window and grind
The standardization studied carefully, the mechanism that the dynamic law and bio-diversity for further disclosing forest cycle maintain, improves forest warp
Seek management level.
(1) woods window spatial distribution state is the basis of woods window research, and the present invention utilizes airborne laser radar technology and geography
Spatial data topology analyzing method realizes that woods window polygon is fast and accurately monitored and drawn, to single woods window in different observations
Variation characteristic (states such as newly-generated, stable, extension, atrophy, displacement or closure) progress is objective in phase, systematically quantifies,
To improve the level that forest space structure precisely monitors, promote the standardization level of woods window spatial variations situation characterization；It solves
The dynamic monitoring problem of woods window spatial distribution and single woods window specifically change journey in landscape scale existing in the prior art
The problems such as precise expression of degree.
(2) it is directed to the monitoring of woods window spatial distribution, the airborne laser radar technology that the present invention uses is a kind of active distant
Sense technology obtains the three dimensional point cloud for accurately reflecting Forest Canopy space structure by laser radar penetration capacity, in this base
The drafting of woods window identification and spatial distribution is carried out on plinth, this method can quickly, accurately draw multiple dimensioned gap distribution polygon, benefit
It is compared with the laser point cloud data of multidate, can accurately reflect the changing condition of woods window spatial distribution；Solves the prior art
Be concentrated mainly on present in on-site inspection method it is time-consuming and laborious, process is tedious, precision is not high, the constraint by natural environment is larger
Etc. problems.
(3) the solution of the present invention can complicated geographic object and phenomenon is simplified and be abstracted into computer be indicated,
Processing and analysis, quantify woods window changes in spatial distribution situation using geographical spatial data topology analyzing method, energy system,
The variation characteristics such as precise expression woods window is newly-generated, stable, extension, atrophy, displacement or closure；It solves at present for woods window space
Changes in distribution situation lacks the quantizating index of complete set, science, and actual mechanical process subjective judgement is more, and human factor is dry
The technical problems such as the precise expression of changing condition are disturbed.
Detailed description of the invention
Fig. 1 is canopy laser point cloud and earth's surface laser point cloud schematic diagram；
Fig. 2 is polygon Overlaying analysis schematic diagram；
Fig. 3 is region Malabar Pied Hornbill MODEL C HM schematic diagram to be measured；
Fig. 4 is that CHM sectional view differentiates woods window by threshold value of 5 meters of height；
Fig. 5 is that region woods window to be measured identifies schematic diagram；
Fig. 6 is the woods window edge schematic diagram of " extension " variable condition, a), b) is respectively same woods window t1And t2Period data；
Fig. 7 is the woods window edge schematic diagram of " atrophy " variable condition, a), b) is respectively same woods window t1And t2Period data.
Specific embodiment
The preferred embodiment of the present invention presented below, to help the present invention is further understood.Those skilled in the art answer
It solves, the explanation of the embodiment of the present invention is merely exemplary, the scheme being not meant to limit the present invention.
Embodiment 1:
Woods window change in time and space monitoring and quantization method of this programme based on airborne laser radar, specific implementation are divided into following 3
A step:
Step 1: the laser radar point cloud data based on multidate, identification woods window are drawn woods window edge polar plot and are saved
Woods window vector data collection；
Laser radar point cloud data, which refers to, obtains remote sensing number by fixed wing aircraft or UAV flight's laser radar scanner
According to the data of each laser point include X, Y, Z coordinate data and echo strength data；It is soft using existing remote sensing image processing
The correct projection coordinate of part The Environment for Visualizing Images (ENVI, v5.3) setting point cloud data
System, measurement unit are rice.
Wherein, if woods window vector data integrates as GnIf the value of some point (x, y) is G (x, y) in woods window, woods is identified
The model of window is as follows:
In formula, G (x, y)=1 indicates that the corresponding position point (x, y) is woods window；G (x, y)=0 indicates that point (x, y) is corresponding
Position be non-woods window；X, y are respectively the abscissa and ordinate of grid (x, y), and CHM (x, y) is the canopy on grid (x, y)
Height value；A is the discrimination threshold of canopy edge average height.Discrimination threshold is determined by empirical value or on the spot sample investigation, is such as sentenced
Other threshold value is 5 meters.
CHM is the difference of Malabar Pied Hornbill model and earth's surface elevation model, it may be assumed that
CHM (x, y)=DSM (x, y)-DEM (x, y)；(2)
DSM is Malabar Pied Hornbill model in formula, and DEM is digital elevation model.
Firstly, original point cloud is divided into canopy point cloud and Ground Point using adaptive irregular triangle network filtering method
Cloud, referring to figure 1.Canopy point cloud is interpolated to DSM using kriging analysis method, instead apart from weight interpolation method by Ground Point
Cloud interpolation generates DEM.The characteristics of characterizing in view of the density and canopy of cloud, setting DSM and DEM is differentiated with identical grid
Rate.
Secondly, carry out difference operation to DSM and DEM using ENVI software, canopy height Raster Data Model CHM is obtained, and
Classified on the basis of this according to the binary map G (x, y) that the height threshold a of woods window edge canopy carries out woods window and Fei Lin window.
Then, woods window recognition result is post-processed, to improve accuracy of identification, using ArcGIS software in G (x, y) two
Morphologic filtering operation is carried out on the basis of value figure, it is final to determine that woods window edge simultaneously carries out vectoring operations to it, draw woods window side
Edge polar plot simultaneously saves data set Gn。
Step 2: comparing the woods window vector data collection in two periods, building woods window is newly-generated, stable, extension, atrophy, displacement
Or the logic mathematical model of closed state；Specific steps include:
Step 21: setting first latter two period ti,tjThe woods window vector data collection of acquisition isI < j in formula；I, j > 0；
Step 22: comparing tiThe woods window vector data collection and t in periodjThe woods window vector data collection in period, tiThe woods window in period
Data set and tjPeriod, corresponding woods window data set was compared, in fact it could happen that three kinds of situations: the woods window polygon of two phases does not have
Overlapping, it is completely overlapped with partly overlap.Not being overlapped indicates tiWoods window exists, and tjWoods window disappears (closure)；Or tiWoods window is not
In the presence of, and tjWoods window is newly-generated.The completely overlapped woods window individual for indicating two periods all exists, and woods window maintains stable state not have
There is generation geometry deformation, or upper extension or atrophy in the original location.Partly overlapping phenomenon occurs are due to woods window edge Vegetation renewal
Or the heterogeneity of dead spatial distribution, lead to tjWoods window may be subjected to displacement and (not deform), and atrophy is simultaneously displaced or extends simultaneously position
The dynamic such as shifting.The logic mathematical model of newly-generated woods window, stable, extension, atrophy, displacement or closed state is defined respectively are as follows:
It is newly-generated:
Closure:
Stablize:
Extend non-displacement:
Atrophy non-displacement:
Extension has displacement:
Atrophy has displacement:
Step 3: the woods window vector data collection in two periods is overlapped, woods window vector polygon Overlap Analysis is carried out, according to
The logic mathematical model that the step 2 constructs constructs the discriminant criterion of woods window vector polygon variable condition.
Wherein, the specific steps of the discriminant criterion of building woods window temporal and spatial orientation include:
Step 31: using ArcGIS software respectively to ti,tjThe woods window vector polygon in two periods carries out topological structure volume
Yard, the data that subscript i or j expression i-th or j period obtain in formula, and define i < j；I, j > 0 indicates woods window range using E
The interior or already existing woods window of expression, O indicate that woods window range is outer or indicate non-woods window.
Step 32: by ti,tjThe woods window vector polygon in two periods carries out overlapping operation, the comprehensive t of output figure layeri,tjTwo
The attribute of a period woods window vector data collection, reserve window is in ti,tjThe feature of all polygons in two periods.Referring to attached drawing 2
It is shown, original polygon element is divided into new element in figure layer lamination process, new element combines original two datasets
Attribute, result be usually exactly a polygon by another polygon spatial distribution state carry out intersection operation, from
And multiple polygons are divided into, while copying the attribute of input object in new object attribute list in attribute assigning process,
The purpose for the arrangement is that geometrical characteristic (woods window edge) and attributive character (woods window, non-woods window) in woods window spatial distribution is fully retained
Change information；
Step 33: the shared segmental arc of the woods window vector polygon after overlapping being encoded, to the woods window vector after overlapping
The shared segmental arc of polygon is encoded to BB, BI, IB, BN, NB, and wherein B indicates the boundary of polygon, and I indicates the inside of polygon,
N indicates non-boundary or inside；Then single woods window polygon attribute feature t is calculatedi,tjIt is superimposed area and original forest gap area
Ratio, the specific formula for calculation of area ratio are as follows:
P(EEij)=Area (EEij)/Area(Ei),
P(EOij)=Area (EOij)/Area(Ei),
P(OEij)=Area (OEij)/Area(Ei)；
In formula, P indicates that the ratio of area, Area indicate woods window range or area,
Ei: tiPeriod, the polygon range of single woods window,
EEij: assuming that some woods window tiPeriod exists, tjPeriod, there is also EE indicated ti~tjDuring woods window overlapping portion
Point,
OEij: assuming that tiPeriod, some woods window did not occurred, in tjPeriod has occurred, and OE indicates ti~tjDuring woods window change
Change range,
EOij, it is assumed that tiSome woods window of period exists, in tjPeriod has disappeared, and EO indicates ti~tjDuring woods window disappearance
Range.
Step 34: constructing woods using the step 32 and the lamination process in step 33, shared segmental arc and area ratio
The discriminant criterion of window variation, referring to shown in the following table 1, monitoring the newly-generated, stable of woods window, extension, atrophy, displacement or closure etc. become
Change state and quantify its variation degree.
1 woods window dynamic change topological analysis table of table
Illustrate application of the invention below by way of specific example:
Study area's overview:
Research ground is located at Fu Shou mountain forest (28 ° 3 ' 00 " -28 ° 32 ' 30 " N, 113 ° 41 ' 15 "-of Northeast of Hunan
113 ° 45 ' 00 " E), it is located in Luoxiao mountain range and connects Yunshan Mountain offshoot, topography is high in the south and low in the north, more than 1200 rice of mean sea level, and mean inclination is
22-27 degree, are presented the landforms of hills and mountains overlapping.12.1 DEG C of average temperature of the whole year, Nian Zhao 1500 hours, frost-free period 217 days.Mainly
Vegetation pattern is typical Mid-subtropical Evergreen Broadleaved Forests, upper layer trees tree species mainly have China fir, Pinus taiwanesis, Qinggang oak, bitter sweet oak,
Sassafrases, Alnus Trabeculosa, hickory nut and Fagaceae.
Woods window investigation on the spot uses hemisphere face image method, determines gap size according to fish eye lens projection theory；Using angle
Rule method or telescopic height finder measurement woods window edge wood height；Differential GPS or total station survey woods window center height above sea level and margin location
It sets.
Field investigation method:
The sample prescription of 80 30m × 30m is set in permanent sample plot, the position of each sample prescription is determined with differential GPS, each
Hemisphere face woods is obtained with the external fish eye lens of digital camera (wide-angle is 183 °, orthographic projection) at sample prescription center and diagonal line quartile
It is preced with image, image direction is overlapped with magnetic north direction.With Gap Light Analyzer (GLA, V2.0, image processing software) to hat
Layer photo is analyzed and draws out woods window edge.Investigation work selects fine shape respectively summer in 2016 and 2009
It is carried out under condition.40 are used as training sample data in 80 sample prescription data, remaining 40 as verifying sample data.
Airborne laser radar data:
t1,t2Laser radar data acquisition time is in June, 2009 and in September, 2016, t respectively1Airborne lidar system
For LMS-Q560, laser beam flying angle is 22.5 ° average, average spot size 50cm, and point cloud density is 2~6/m2。t2It is airborne
Laser scanning system is ALTM2050, and laser beam flying angle is 15 ° average, average spot size 25cm, and point cloud density is 2~10
A/m2.Every Shu Jiguang includes the information such as the coordinate value, height value, intensity value of the first echo and last echo.Laser radar point
Cloud data all use LAS format, and the geographical co-ordinate system using ENVI software set laser point cloud data is CGCS2000, reference
Ellipsoid is WGS84, and projection code name is 38, and laser radar data is with a cloud storage format record (referring to shown in the following table 2).
2 laser point cloud storage format schematic table of table (each laser point corresponds to a line record)
| Air strips number | X-coordinate/m | Y-coordinate/m | Height above sea level/m | Pulse echo intensity |
| 1 | 38476525.641 | 3152896.212 | 864.68 | 2.4 |
| 1 | 38476816.683 | 3153028.504 | 1023.53 | 0.8 |
| 1 | 38476790.225 | 3152909.441 | 1012.42 | 1.6 |
| 1 | 38477068.038 | 3152737.461 | 1018.28 | 1.2 |
| 1 | 38477147.413 | 3152790.378 | 1045.59 | 2.9 |
| … | … | … | … | … |
| … | … | … | … | … |
Change in time and space monitoring and quantizing process are carried out to research area woods window based on the method for the present invention:
(1) the woods window monitoring based on laser radar point cloud data
Using adaptive irregular triangle network filtering method according to height value by t1Airborne laser radar point cloud data divides
For canopy point cloud and Ground Point cloud, and raster interpolation is carried out respectively and generates DSM and DEM, set the raster resolution of DSM and DEM
It is all 2 meters.Grid difference operation is carried out to DSM and DEM using ENVI software, generates the canopy height model of 2 meters of resolution ratio
CHM, referring to shown in attached drawing 3.
The differentiation of woods window is carried out on CHM using woods window identification model (formula 1), sets woods window edge canopy height threshold value a
It is 5 meters, referring to shown in attached drawing 4, on CHM sectional view, vegetation height is woods window less than 5 meters, otherwise is crown canopy (non-woods window).
Using the binary map of ENVI Software Create G (x, y) woods window and Fei Lin window range and morphologic filtering operation is carried out, using " corrosion "
" expansion " algorithm eliminates the small―gap suture (within 2 meters) between forest or small " cavity " or grating image noise in crown canopy, really
Fixed final woods window range carries out " grid conversion vector " operation on this basis, draws and save woods referring to shown in attached drawing 5
Window edge polygon vector data G2009。
t2Airborne laser radar data operating process is such as above-mentioned t1The edge polygon arrow of woods window is drawn and is saved in operation
Measure data G2016。
(2) quantization of the woods window dynamic mode based on spatial analysis
Using the ArcCatalog module creation geographical data bank in ArcGIS software, polygon element collection, selection are created
CGCS2000 geodetic coordinate system, respectively by G2009And G2016Woods window polygon vector data is imported into geographical data bank, creation
The topological structure of vector data, setting XY tolerance are 0.1m.To G2009And G2016Polygon element carries out Overlaying analysis, specifically
Operation is " joint ", according to the logic mathematical model of woods window variation namely formula (3)~(9) and the signal addition of 1 content of table
Topology rule, output factor kind is by the institute of the polygon comprising the geometry union that represents all inputs and all input factor kinds
There is field, topological relation verifying then is carried out to overlapping result, according to spatial analysis as a result, successively selecting to meet the variation of woods window
The result of condition count and drawing result.
(3) interpretation of result
Expert data statistical analysis software Statistical Product and Service Solutions (SPSS,
V19) to t1And t2The woods window edge and field investigation result (verifying sample) of laser radar data monitoring carry out linear fit precision
Analysis, the difference for comparing context of methods and field investigation using paired-samples T-test (paired t-test), referring to shown in the following table 3.
The method of 3 this programme of table and the comparison sheet of field investigation result
X is field investigation value, and y is the method estimated value of this method.
Woods window edge position difference normal distribution conspicuousness Sig. >=0.05 illustrates there is statistics using paired-samples T-test method
Learn meaning.The null hypothesis of paired-samples T-test is the t distribution that the distribution of position difference meets that average value is 0, t1And t2The bilateral of data
Conspicuousness P is both greater than 0.05, and there is no significant differences for the woods window position and field investigation result for illustrating Monitoring by Lidar.From phase
From the point of view of root-mean-square error, the monitoring result error of woods window position is smaller, and higher with the results relevance of field investigation.We
The woods window position and distribution situation precision with higher that method measures, can be used for rapid survey different size, woods of different shapes
Window.
t1The woods window density that airborne laser radar data monitors is 11.67/hm-2, forest gap area mean value is
81.02m2, minimum 4.06m2, maximum 727.43m2, forest gap area integrated distribution is in interquartile-range IQR, the degree of bias of feature distribution
It is 2.542, illustrates that it with positive deviation, that is, is mostly the woods window compared with small area, referring to shown in the following table 4.t2Woods window also has similar
Situation, density are 12.81/hm-2, also based on small area woods window.
4 forest gap area (m of table2) essential characteristic statistical form
According to the logic mathematical model of variable condition, to t1-t2The variation of woods window is quantified, and ginseng is shown in Table 5, newly-generated
Woods window is with 5~50m2The overwhelming majority is accounted for, conflicting mode mainly rolls over branch；Due to the lateral update and vertical update of woods window, small grade
Other woods window is easier to be closed completely；Under " stabilization " state, it is greater than 300m2Great Lin window ratio it is maximum, " extension non-displacement "
It is equally based on great Lin window under state, the woods window ratio of remaining rank is not much different, and main cause may be raw in great Lin window
Border deteriorates, and part edge wood generates new confusion area, so that forest gap area becomes larger, referring to shown in attached drawing 6；" atrophy non-displacement "
State 150m2Based on following woods window, reason may be that woods window laterally updates and accounts for leading role, referring to shown in attached drawing 7；" extension has
Displacement " and " atrophy has displacement " two kinds of variable conditions, all ratio, main cause have been the updates of woods window edge wood to woods windows at different levels
With interference and meanwhile act on, eventually led to the changes in spatial distribution of woods window edge.It is dry that above-mentioned change procedure not only demonstrates woods window
The ecological process disturbed, has been completed at the same time the quantization work of various change state, provides accurate prison for forest ecology research
Measured data provides information-based support and skill further to disclose the mechanism that the dynamic law of forest cycle is maintained with bio-diversity
Art guarantee.
5 woods window dynamic change ration statistics table of table
Finally it should be noted that above embodiments are merely to illustrate the technical solution of the application rather than to its protection scope
Limitation, although the application is described in detail referring to above-described embodiment, the those of ordinary skill in the field should
Understand: those skilled in the art read the specific embodiment of application can still be carried out after the application various changes, modification or
Equivalent replacement, but the above change, modification or equivalent replacement, in the application wait authorize or the claim of issued for approval protection model
Within enclosing.
Claims (9)
1. a kind of monitoring of woods window change in time and space and quantization method based on airborne laser radar, which is characterized in that including following step
It is rapid:
Step 1: the laser radar point cloud data based on multidate, identification woods window draw woods window edge polar plot and save woods window
Vector data collection；
Step 2: comparing the woods window vector data collection in two periods, building woods window is newly-generated, stable, extension, atrophy, is displaced or closes
The logic mathematical model of conjunction state；
Step 3: the woods window vector data collection in two periods being overlapped, woods window vector polygon Overlap Analysis is carried out, according to described
The logic mathematical model that step 2 constructs constructs the discriminant criterion of woods window vector polygon variable condition.
2. the monitoring of woods window change in time and space and quantization method according to claim 1 based on airborne laser radar, feature
It is, in the step 1, if woods window vector data integrates as GnIf the value of some point (x, y) is G (x, y) in woods window, know
The model of other woods window is as follows:
In formula, G (x, y)=1 indicates that the corresponding position point (x, y) is woods window；G (x, y)=0 indicates the corresponding position point (x, y)
It is set to non-woods window；X, y are respectively the abscissa and ordinate of grid (x, y), and CHM (x, y) is the canopy height on grid (x, y)
Value；A is the discrimination threshold of canopy edge average height.
3. the monitoring of woods window change in time and space and quantization method according to claim 2 based on airborne laser radar, feature
It is, in the step 1, CHM is the difference of Malabar Pied Hornbill model and earth's surface elevation model, it may be assumed that CHM (x, y)=DSM (x,
Y)-DEM (x, y)；
DSM is Malabar Pied Hornbill model in formula, and DEM is digital elevation model.
4. the monitoring of woods window change in time and space and quantization method according to claim 1 based on airborne laser radar, feature
It is, the specific steps of the step 2 include:
Step 21: setting first latter two period ti,tjThe woods window vector data collection of acquisition isI < j in formula；I, j > 0；
Step 22: comparing tiThe woods window vector data collection and t in periodjIt is newborn to define woods window respectively for the woods window vector data collection in period
At the logic mathematical model of, stabilization, extension, atrophy, displacement or closed state are as follows:
It is newly-generated:
Closure:
Stablize:
Extend non-displacement:
Atrophy non-displacement:
Extension has displacement:
Atrophy has displacement:
5. the monitoring of woods window change in time and space and quantization side according to any one of claims 1-4 based on airborne laser radar
Method, which is characterized in that the specific steps of the discriminant criterion of building woods window temporal and spatial orientation include: in the step 3
Step 31: respectively to ti,tjThe woods window vector polygon in two periods carries out topological structure coding, subscript i or j table in formula
Show the data that i-th or j period obtains, and defines i < j；I, j > 0；
Step 32: by ti,tjThe woods window vector polygon in two periods carries out overlapping operation, the comprehensive t of output figure layeri,tjAt two
The attribute of phase woods window vector data collection, reserve window is in ti,tjThe feature of all polygons in two periods；
Step 33: the shared segmental arc of the woods window vector polygon after overlapping being encoded, single woods window polygon is then calculated
Attributive character ti,tjIt is superimposed the ratio of area and original forest gap area；
Step 34: being become using the step 32 and the lamination process in step 33, shared segmental arc and area ratio building woods window
The discriminant criterion of change, monitor different times woods window is newly-generated, stable, extension, atrophy, displacement or closure variable condition and
Quantify its variation degree.
6. the monitoring of woods window change in time and space and quantization method according to claim 5 based on airborne laser radar, feature
It is, respectively to t in the step 31i,tjWhen the woods window vector polygon in two periods carries out topological structure coding, using E table
Show within the scope of woods window or indicate that already existing woods window, O indicate that woods window range is outer or indicate non-woods window.
7. the monitoring of woods window change in time and space and quantization method according to claim 6 based on airborne laser radar, feature
It is, in the step 33, BB, BI, IB, BN, NB is encoded to the shared segmental arc of the woods window vector polygon after overlapping, wherein
B indicates the boundary of polygon, and I indicates the inside of polygon, and N indicates non-boundary or inside.
8. the monitoring of woods window change in time and space and quantization method according to claim 7 based on airborne laser radar, feature
It is, in the step 33, the specific formula for calculation of area ratio are as follows:
P(EEij)=Area (EEij)/Area(Ei),
P(EOij)=Area (EOij)/Area(Ei),
P(OEij)=Area (OEij)/Area(Ei)；
In formula, P indicates that the ratio of area, Area indicate woods window range or area,
Ei: tiPeriod, the polygon range of single woods window,
EEij: assuming that some woods window tiPeriod exists, tjPeriod, there is also EE indicated ti~tjDuring woods window lap,
OEij: assuming that tiPeriod, some woods window did not occurred, in tjPeriod has occurred, and OE indicates ti~tjDuring woods window variation model
It encloses,
EOij, it is assumed that tiSome woods window of period exists, in tjPeriod has disappeared, and EO indicates ti~tjDuring woods window disappear model
It encloses.
9. the monitoring of woods window change in time and space and quantization method according to claim 5 based on airborne laser radar, feature
It is, in the step 31, topological structure coding is carried out to woods window vector polygon in different times using ArcGIS software.
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|---|---|---|---|---|
| CN110726998A (en) * | 2019-10-24 | 2020-01-24 | 西安科技大学 | Method for measuring mining subsidence basin in mining area through laser radar scanning |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080260237A1 (en) * | 2004-03-15 | 2008-10-23 | Blom Kartta Oy | Method for Determination of Stand Attributes and a Computer Program for Performing the Method |
| CN104346504A (en) * | 2013-07-24 | 2015-02-11 | 中国科学院沈阳应用生态研究所 | Method for determining forest gap size upper limit |
| CN107479065A (en) * | 2017-07-14 | 2017-12-15 | 中南林业科技大学 | A kind of three-dimensional structure of forest gap method for measurement based on laser radar |
-
2018
- 2018-11-19 CN CN201811378860.3A patent/CN109407113A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080260237A1 (en) * | 2004-03-15 | 2008-10-23 | Blom Kartta Oy | Method for Determination of Stand Attributes and a Computer Program for Performing the Method |
| CN104346504A (en) * | 2013-07-24 | 2015-02-11 | 中国科学院沈阳应用生态研究所 | Method for determining forest gap size upper limit |
| CN107479065A (en) * | 2017-07-14 | 2017-12-15 | 中南林业科技大学 | A kind of three-dimensional structure of forest gap method for measurement based on laser radar |
Non-Patent Citations (1)
| Title |
|---|
| UDAYALAKSHMI VEPAKOMMA ET.AL: "《Spatial contiguity and continuity of canopy gaps in mixed wood boreal forests: persistence, expansion,shrinkage and displacement》", 《JOURNAL OF ECOLOGY 2012》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110726998A (en) * | 2019-10-24 | 2020-01-24 | 西安科技大学 | Method for measuring mining subsidence basin in mining area through laser radar scanning |
| CN110726998B (en) * | 2019-10-24 | 2020-08-07 | 西安科技大学 | Method for measuring mining subsidence basin in mining area through laser radar scanning |
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