CN110309762A - A kind of forestry health assessment system based on air remote sensing - Google Patents
A kind of forestry health assessment system based on air remote sensing Download PDFInfo
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Abstract
A kind of forestry health assessment system based on air remote sensing provided by the invention, using the density of trees, arboreal growth form, three metrics evaluation forestry health status of vegetable layer temperature, distinguish assay Forest Health situation from different angles, three indexs can be used alone, it can also be used so that binomial or three are comprehensive, improve the research evaluation ability to forestry health, by unmanned plane, 3S technology combines, accelerate the innovation speed of evaluation method, the quantitative study of Forest Health is pushed, depth tradeoff recognizes the relationship between each evaluation index, establish more perfect forestry health assessment system.Unmanned plane is designed specifically for forestry health assessment system, data needed for multinomial evaluation index capable of being acquired simultaneously, by system for flight control computer in conjunction with GIS-Geographic Information System, make full use of the Geological And Geomorphological Features and geographic information data of cruise region, cruising, design is scientific and reasonable in path, gives full play to the advantage of unmanned plane forestry health assessment.
Description
Technical field
The present invention relates to a kind of forestry health assessment system, in particular to a kind of forestry health assessment based on air remote sensing
System belongs to unmanned plane forestry health assessment technical field.
Background technique
Forest is the main body of forestry, is the basis of entire national economy sustainable and healthy development, and forestry is that the mankind depend on for existence
Economy, aesthetics, ecology and cultural resource, be the pillar of entire terrestrial ecosystems, play protection and species diversity and keep away
Exempt from the unbalance effect of the ecosystem.But in recent years under the pressure of the pressure of existence, the mankind pass through moving people to other places for land reclamation, lumbering deforestation, mistake
Degree such as herds at the modes, serious damage is caused to the robot control system(RCS) and maintenance function of forest, along with worldwide economy
Development, forest are constantly reduced by over-exploitation, the forest reserves, and Forest Health is faced with unprecedented challenge.The production of forest
Power and the general level of the health are directly related to the social life of global human, and the research of development Forest Health and evaluation have extremely important
Social effect.Forest Health has obtained global attention as a new direction of forestry science and technology, and forest ecosystem is strong
The definition of health, monitoring, assessment and management inquire into and practice it is more and more abundant, Forest Health be increasingly becoming forestry condition evaluation and
The standard and target of forest inventory control.
The assessment of forestry health assessment is the destruction and degeneration that diagnosis human factor and natural cause cause forest ecosystem
Caused forest ecosystem structure disturbance and functional disturbance, be to Forest Ecosystem Productivity level, configuration state,
A kind of quantitative evaluation to many-sided integration capability such as external interference resistance and service function.Current forestry health assessment
Selecting index difference is big, and there is differences to the standard of Forest Health understanding by the researcher in different majors field, to a piece of
The angle of forest observation is different, and the index considered is also different.Appraisement system is imperfect, due to the complicated multiplicity of research object, grinds
Study carefully method and Research scale has differences, there is presently no the Forest health assessment systems of standard.It is difficult to select evaluation appropriate
Method, the method evaluated forest is more, each method all has superiority-inferiority, different surely right using a certain method
Forest is comprehensively evaluated.
The forestry health assessment means of the prior art mainly have Ta Tai lookout, ground tour and satellite remote sensing etc..Control tower
The single control tower limited coverage area of prestige means, need to organize control tower net, therefore higher cost, and larger by the influence of topography, can there is view
Feel blind spot, observation one-sidedness is big；Artificial ground tour efficiency is very low, and sight is easily blocked therefore range of observation is extremely limited, many
Remote forest zone area is big, has inconvenient traffic, and it is very big that difficulty is maked an inspection tour on artificial ground.Current portions forestry health assessment depends on satellite
Remote sensing technology, but since satellite remote sensing has the fixed cycle of operation and the period is longer, and ground resolution is not also high, multiple
Difficulty is bigger under miscellaneous weather condition, and real-time and resolution ratio are poor.Unmanned plane can overcome the shortcomings of aspects above, pass through
Carrying thermal infrared imaging instrument and high clear colorful camera, execution forestry health assessment task, especially small drone light weight,
It is small in size, have the characteristics that acquisition cost is low, operating cost is few, easy to operate, maneuverability, it is strong to be highly suitable for forestry
Health evaluation.
There are many deficiencies for current forestry health assessment: first is that current forestry health assessment indicators are scientific poor, in difference
There is differences for standard understanding of the researcher of professional domain to Forest Health, institute different to the angle observed with a piece of forest
The index of consideration is also different, and appraisement system is imperfect, and due to the complicated multiplicity of research object, research method and Research scale exist
Difference, there is presently no the Forest health assessment systems of standard, it is difficult to select evaluation method appropriate, evaluate forest
Method it is more, all there is superiority-inferiority in each method, surely forest is comprehensively evaluated using a certain method is different,
Lack the quantitative study to Forest Health, recognizes the relationship between each evaluation index, forestry health assessment system without depth tradeoff
It is not scientific perfect enough.Second is that forestry health assessment means mainly have Ta Tai lookout, ground tour and satellite remote sensing etc., means are fallen
Afterwards, higher cost, and it is larger by the influence of topography, there can be vision blind spot, observation one-sidedness is big, and real-time and resolution ratio are poor.
Third is that part is monitored using unmanned plane, but the unmanned plane weight used is big, and the flight time is short, and cruising range is small, generally cannot be complete
At task, or frequent landing and supply, unmanned plane forestry health assessment is needed to implement relatively difficult, monitoring efficiency
Very low, cumbersome difficult, monitoring effect is not fully up to expectations, and the monitoring hardware of UAV flight more falls behind, unmanned plane
Shake serious in flight course, the clearly figure of camera collection image is poor, is more reduction of the accuracy of monitoring and reliable
Property.Fourth is that unmanned plane does not exclusively carry out path planning, not by system for flight control computer in conjunction with GIS-Geographic Information System, lead
It causes unmanned plane fire monitoring path planning blindness big, does not make full use of the Geological And Geomorphological Features and geographical letter of cruise region
Data are ceased, the design of cruise path is old-fashioned and inflexible single, and scientific systematicness is poor, and monitoring specific aim is weak, is unable to give full play unmanned plane
The advantage of forestry monitoring.
Summary of the invention
In view of the deficiencies of the prior art, a kind of forestry health assessment system based on air remote sensing provided by the invention, is adopted
With three density of trees, arboreal growth form, vegetable layer temperature metrics evaluation forestry health status, difference is from different angles
Assay Forest Health situation, three indexs can be used alone, and can also use, improve so that binomial or three are comprehensive
To the research evaluation ability of forestry health, suitable several evaluation methods are combined, unmanned plane, 3S technology are combined, added
The innovation speed of fast evaluation method, has pushed the quantitative study of Forest Health, and depth tradeoff recognizes the pass between each evaluation index
System, establishes more perfect forestry health assessment system.A series of improvement carried out by material to unmanned plane and structure,
The weight for reducing unmanned plane, extends the flight time, expands unmanned plane cruising range, specifically for forestry health assessment
System and design, do not need frequent landing and supply, data needed for multinomial evaluation index capable of being acquired simultaneously, by nobody
Machine flight control system in conjunction with GIS-Geographic Information System, according to the cruise geographical location of monitoring area, size, environmental condition and
The hardware condition of remote sensing aircraft itself, planning forest fire unmanned plane cruise supervised path, path planning both guarantee to monitor matter
Amount and monitoring all standing, it is further contemplated that remote sensing aircraft hardware parameter and flight safety, make full use of the geology and geomorphology of cruise region
Feature and geographic information data, design is scientific and reasonable in cruise path, gives full play to the advantage of unmanned plane forestry health assessment.
To reach the above technical effect, the technical solution adopted in the present invention is as follows:
A kind of forestry health assessment system based on air remote sensing, including remote sensing aircraft and interior industry analysis station, remote sensing fly
Row device includes small drone and high score sensoring, and interior industry analysis station includes microcomputer and ground command system, high score
Sensoring includes holder stabilizer and intelligent console, and intelligent console passes through the lower part phase of holder stabilizer and small drone
It connects, is respectively arranged with thermal infrared imaging instrument and high clear colorful camera on intelligent console, the forestry health based on air remote sensing is commented
Valence system uses the density of trees, three arboreal growth form, vegetable layer temperature metrics evaluation forestry health status, the density of trees
The extraction step of index are as follows:
The density index first step determines density of trees detection zone, and unmanned plane parameter is arranged；
Density index second step, executes task of taking photo by plane, and high clear colorful camera acquires the image information in region of taking photo by plane；
Density index third step, area image of taking photo by plane cut splicing, image preprocessing；
The 4th step of density index, vegetation chromatography combination enhancing wave band difference, SVM supervised classification；
The 5th step of density index draws trees index statistical chart, extracts trees index threshold；
The 6th step of density index, trees cover image and obtain；
The 7th step of density index, density of trees index selection,
The 8th step of density index, the density of trees analyse and evaluate.
A kind of forestry health assessment system based on air remote sensing, further, in the extraction step of density of trees index,
It determines density of trees detection zone, unmanned plane parameter is arranged according to Density Detection region, according to the ground in Density Detection region
Manage position, size, environmental condition and remote sensing aircraft itself hardware condition, planning unmanned plane cruise detection path, road
Diameter planning should guarantee to detect quality and detect all standing, and need to consider remote sensing aircraft hardware parameter and flight safety, if
Flight is unable to complete, and can be divided into multiple tasks execution；
Execution is taken photo by plane task, and sunny calm weather is selected, and completes forest zone high clear colorful Image Acquisition at noon, nobody
Flying height when machine acquires image is 35 to 60 meters, and flying speed is at the uniform velocity 4 meter per seconds, ship's control and sidelapping
Degree is 80%, acquires the continuous sliceable JPG format HD color image of several width；
It is that the continuous sliceable JPG format HD of several width of unmanned plane acquisition is colored that area image of taking photo by plane, which cuts splicing,
Image carries out image mosaic using Pix4DMapper software, completes image preprocessing, obtains the panorama orthograph in detection forest zone
Picture；
The panorama orthograph picture in forest zone is passed through vegetation color by the wave band difference for enhancing trees and non-trees by band combination
Trees index threshold, vegetation chromatography combination enhancing formula are extracted in spectrum combination enhancing are as follows:
S=2G-B-R
G indicates that green band pixel value, B indicate that blue wave band pixel value, R indicate that red band pixel value, S are tree in formula
The wooden index；
The sample area image object atural object that unmanned plane is shot is divided into two class of trees and non-trees by SVM supervised classification, with trees
For trees index S value with non-trees as abscissa, pixel counts number as ordinate, draws trees and non-trees respectively S
The statistic histogram of value, using the intersection point of trees and non-trees S value histogram under coordinate system as trees and non-tree classfication threshold
Value F, part more than or equal to classification thresholds F are trees pixel, and the part less than classification thresholds F is non-trees pixel, according to point
The density of trees calculation formula that class threshold value F is extracted are as follows:
N in formulaLNumber, N are counted for trees pixelFFor non-trees pixel statistics, M is density of trees value；
Trees pixel point is expressed as black, non-trees pixel is expressed as white to get having arrived by vegetation chromatography group
It closes enhancing treated trees and covers image, according to the trees obtained after density of trees M value and the combination enhancing processing of vegetation chromatography
Image is covered, the density of trees is analysed and evaluated.
A kind of forestry health assessment system based on air remote sensing, further, the acquisition of arboreal growth morphological index walk
Suddenly include:
Growth indexes step 1 determines arboreal growth Morphology observation region, and unmanned plane parameter is arranged；
Growth indexes step 2, executes task of taking photo by plane, and high clear colorful camera obtains forest zone aerial images；
Growth indexes step 3 completes the splicing and pretreatment of aerial images, generates DSM and DOM, and earth's surface DSM is selected to make
For earth's surface datum level, the DSM comprising trees and earth's surface DSM subtract each other to obtain arboreal growth form DSM；
Growth indexes step 4 carries out image preprocessing to DOM, obtains arboreal growth region by form extraction algorithm；
Growth indexes step 5 after the arboreal growth region DOM of extraction carries out geometrical registration, is handled soft by remote sensing image
Part generates exposure mask；
Growth indexes step 6 covers and obtains the arboreal growth on image with obtained exposure mask and arboreal growth form DSM
Form；
Growth indexes step 7, image arboreal growth form and actual form compare and precision evaluation, obtains accuracy
Good arboreal growth appearance model carries out arboreal growth morphological analysis and evaluation.
A kind of forestry health assessment system based on air remote sensing, further, the acquisition of arboreal growth morphological index walk
In rapid, arboreal growth Morphology observation region is determined, unmanned plane parameter, root are arranged according to arboreal growth Morphology observation region
According to the hardware condition of the geographical location of detection zone, size, environmental condition and remote sensing aircraft itself, unmanned plane is planned
Cruise detection path,
Execution is taken photo by plane task, and sunny calm weather is selected, and completes forest zone high clear colorful Image Acquisition, flight at noon
Several width images of 40-60 meters of height shootings are as data source, and ground resolution is 1.25 centimetres, course, sidelapping rate
90%, control point GCP are greater than 5, and spatial resolution is greater than 0.79 centimetre, and empty three errors are missed less than 0.115 pixel, average RMS
Difference is measured less than 0.018 meter using real-time dynamic positioning RTK, for empty three operations and accuracy detection, detects image set
Close positioning accuracy；
Pix4Dmapper obtains camera model automatically when aerial images splice, and is quickly generated using Pix4Dmapper software
Professional accurate DOM and DSM data, it is as follows that Pix4Dmapper handles data flow: first is that importing image and position and posture
System POS data；Second is that importing ground control point GCP file, geometric correction is carried out to image；Third is that being set according to different requirements
Set parameter；Establish fourth is that full automatic treatment carries out data reduction and three-dimensional model, encrypted by full-automatic sky three, obtain DOM and
DSM；
Color space is converted in pretreatment: color space is converted to YCbCr color space model；
OTSU Threshold segmentation in pretreatment: maximum variance between clusters OTSU is according to the gamma characteristic of image, before figure is divided into
Scape and background two parts, for image A (x, y), the segmentation threshold of foreground and background is denoted as B, and the pixel number for belonging to prospect accounts for
The ratio of entire image is denoted as Dj, average gray Hj, the ratio that background pixel points account for entire image is Db, average gray is
Hb, the average gray of entire image is denoted as H, and inter-class variance is denoted as F, then has:
H=DjHj+DbHb
F=Dj(Hj-H)2+Db(Hb-H)2
Above-mentioned two formula of simultaneous can obtain:
F=DjDb(Hj-Hb)2
When inter-class variance F maximum, foreground and background difference is maximum, and segmentation threshold B at this time is optimal threshold；
Form extraction algorithm: assuming that known target is labeled as 1, background dot is labeled as 0, and defining boundary point is 1,8 connections
Neighborhood at least 1 point is labeled as 0, needs to carry out boundary point following processing:
First is that central point is X on the boundary point centered on 8 connection neighborhoods1, clockwise consecutive points are denoted as X2,
X3..., X9.Wherein X2Positioned at central point X1On, firstly, the point that selection is met the requirements:
2≤N(X1)≤6；
S(X1)=1；
X2X4X6=0；
X4X6X8=0；
N(X1) be non-zero consecutive points number, S (X1) it is X2~X9~X2Variation number of the sequence from 0 to 1, by institute
There is the inspection of boundary point, all mark points are all deleted；
Second is that meeting:
2≤N(X1)≤6；
S(X1)=1；
X2X4X8=0；
X2X6X8=0；
On inspection, identification point has been deleted, and recycles above-mentioned two step, defeated until being all marked as deletion without pixel
Result out is the trees form after approach for binary image thinning；
Form extraction algorithm obtains arboreal growth region can be by the skeletal extraction or image thinning realization in OpenCV；
Remote sensing image processing software generates exposure mask: carrying out Morphological scale-space by structural element and obtains the wide tree of 2-6 pixel
The wooden form, obtains more complete trees form region, and the face substitution exposure mask of use production exposure mask is shown；
Extract arboreal growth form: by the exposure mask of generation and arboreal growth form DSM set and, acquisition arboreal growth form refer to
Mark.
A kind of forestry health assessment system based on air remote sensing, further, vegetable layer temperature index obtain the step of
Include:
Temperature obtains step 1, determines forest cover layer temperature sensing area, and setting unmanned plane, which navigates, flies parameter；
Temperature obtains step 2, executes winged task of navigating, and high clear colorful camera obtains forest zone orthography, thermal infrared imaging instrument
Obtain forest zone thermal infrared imagery；
Temperature obtains step 3, orthography and thermal infrared imagery splicing, geometric correction and geometrical registration；
Temperature obtains step 4, the radiation calibration of thermal infrared imagery；
Temperature obtains step 5, extracts forest zone vegetation area in orthography；
Temperature obtains step 6, extracts forest zone vegetable layer temperature on thermal infrared imagery；
Temperature obtains step 7, comparison, analysis and the evaluation of forest zone vegetable layer temperature.
A kind of forestry health assessment system based on air remote sensing, further, the obtaining step of vegetable layer temperature index
In, winged task of navigating is executed, sunny calm weather is selected, forest zone boat is completed at noon and flies job, UAV flight's heat
Infrared thermoviewer and high clear colorful camera, flying height are 50 to 70 meters, and the Duplication of filmed image is 75% to 90%, ground
Space of planes resolution ratio is 1 centimetre to 7.5 centimetres；
Unmanned plane shoots several thermal infrared imageries and orthography, is spliced using Pix4Dmapper, selectes unified
Coordinate system, base area face photo control point to image carry out geometric correction；
Thermal infrared imagery radiation calibration: vegetation near the ground is measured respectively using the accurate temperature measurer in ground and thermal infrared imaging instrument
Layer temperature, completes pre-flight radiation calibration, by correlation analysis, determines the accurate temperature measurer in ground and thermal infrared imaging instrument
Under the influence of not by distance, measured temperature is with uniformity；Utilize the thermal infrared imagery after geometric correction and geometrical registration, benefit
With the accurate temperature measurer in ground, measure respectively the black and white radiation calibration cloth of ground placement, forest zone different location difference trees several
The temperature of point, determines radiation calibration coefficient by correlation analysis, obtains extracting in thermal infrared imagery using radiation calibration coefficient
Vegetable layer temperature；
Forest zone vegetation area is extracted in orthography uses GCanny edge detection algorithm, GCanny edge detection algorithm
Step:
First is that replacing Gaussian Blur using selective surface is fuzzy, it is that 20-40 carrys out smoothed image that its threshold value, which is arranged,；
Second is that looking for the intensity gradient of image, the gradient of each pixel is obtained by Sobel operator in smoothed out image
, Sobel operator has packaged function in OpenCV, selects the Sobel gradient operator of 3*3；
Third is that eliminating side erroneous detection using non-maximum suppression technology, fuzzy boundary is apparent from, each pixel is retained
The maximum of gradient intensity, deletes other values on point；
Fourth is that determining potential boundary using the method for dual threshold, a threshold value upper bound and threshold value lower bound, image are set
In pixel then think necessarily strong boundary if it is greater than the threshold value upper bound, then thinking inevitable less than threshold value lower bound is not boundary,
Between the two be then considered weak boundary, need to be further processed；
Fifth is that tracking boundary using hysteresis techniques, the weak boundary being connected with strong boundary is considered boundary, other weak sides
Boundary is then considered not being boundary.
The forest cover marginal information result of extraction is subjected to binary conversion treatment and generates vector face file, is based on this vector
File, spanning forest vegetation area exposure mask.
Vegetable layer temperature in forest zone is extracted on thermal infrared imagery: the forest cover area mask of generation is covered in thermal infrared imagery
Middle extraction vegetable layer temperature.
A kind of forestry health assessment system based on air remote sensing, further, forestry health assessment utilize air remote sensing
The remote sensing aircraft of forestry health assessment system and interior industry analysis station, pass through the density of trees, arboreal growth form, vegetable layer temperature
Three metrics evaluation forestry health status are spent, three density of trees, arboreal growth form, vegetable layer temperature indexs can be independent
It uses, can also be used so that binomial or three are comprehensive；
Density of trees index using when one optimal value determined according to the type and forest zone geographical situation of forest-tree, according to
The departure degree of the density of trees index and optimal value that measure assesses density of trees situation in conjunction with trees covering image in detail；
Arboreal growth morphological index extracts the limb matrix morphology of forest zone trees, in conjunction with tree families and forest zone geographical location
Determine the growth conditions of trees；
Vegetable layer temperature index using when determine a vegetable layer temperature optima, according to the vegetable layer temperature index measured
With the departure degree of optimal value, arboreal growth situation is assessed in detail in conjunction with the forest zone vegetable layer temperature extracted on thermal infrared imagery.
A kind of forestry health assessment system based on air remote sensing, further, small drone includes rotor assemblies, light
Matter fuselage, buffering steady rest, micro-control unit, rotor assemblies include reinforcing rib, support frame, wing arm, driving motor and rotor
Paddle, rotor assemblies are evenly arranged with several, and rotor is fixed on driving motor and by driving motor supplies power, support
The far-end of frame is provided with driving motor, and the proximal end of support frame is provided with reinforcing rib, and support frame passes through reinforcing rib and lightweight machine
Body is stably connected with.
A kind of forestry health assessment system based on air remote sensing, further, lightweight fuselage includes machine core storehouse, machine core storehouse
Be internally provided with lithium battery, micro-control unit, gyrocontrol instrument, airborne communication device, global positioning satellite instrument, flight control
Device, state of flight acquisition device and electric power controller processed, lithium battery are stably fixed in the detachable storehouse in machine core storehouse, machine
Carrier communication device is arranged in the tail portion in machine core storehouse, and global positioning satellite instrument is arranged in the center top in machine core storehouse, under machine core storehouse
Side is provided with high score sensoring, and machine core storehouse is set as the uniform hollow structure of distribution of weight, and buffering steady rest is fixed on lightweight
The lower end of fuselage, buffering steady rest are symmetrical arranged as a pair, and buffering steady rest includes two flexible links and flexible link lower horizontal
The steady rest of setting, the bottom of steady rest are provided with cushion, and flexible link and steady rest are hollow structure and by light material
It is made.
Compared with the prior art, the advantages of the present invention are as follows:
1. a kind of forestry health assessment system based on air remote sensing provided by the invention is provided with intelligent console, intelligence
Thermal infrared imaging instrument and high clear colorful camera are equipped on holder, using the density of trees, arboreal growth form, vegetable layer temperature
Three metrics evaluation forestry health status, the density of trees, arboreal growth form, vegetable layer temperature are divided from different angles respectively
Analysis evaluation Forest Health situation, three indexs can be used alone, and can also use so that binomial or three are comprehensive, improve pair
The research evaluation ability of forestry health combines suitable several evaluation methods, and unmanned plane, 3S technology are combined, accelerated
The innovation speed of evaluation method has pushed the quantitative study of Forest Health, and depth tradeoff recognizes the relationship between each evaluation index,
Establish more perfect forestry health assessment system.
2. a kind of forestry health assessment system based on air remote sensing provided by the invention refers to using density of trees evaluation
Mark, the density of trees are to reflect the important indicator of land surface arboreal growth dynamic change, are to influence water carbon cycle, substance and energy
The key factor of exchange is measured, also reflects the luxuriant degree of trees photosynthesis area and growth, it being capable of table to a certain extent
The growth conditions and growth tendency for showing trees, are one of forestry health assessment indicators of the invention, and the design of the index can be from
Prominent reaction forestry health status in terms of the density of trees.
3. a kind of forestry health assessment system based on air remote sensing provided by the invention, is commented using arboreal growth form
Valence index, the growthform of trees have largely reacted the growth conditions of trees, and arboreal growth form can be indirect
Forest zone biomass accumulation is reacted, to estimate forestry health status.Traditional arboreal growth method for measuring shape of palaemon adopts artificial observation
With take pictures, speed is slow, heavy workload and accuracy rate are low, is no longer satisfied the needs of arboreal growth form monitoring.Unmanned plane is distant
The series of advantages such as sensing system has low in cost, and image data resolution is high, and flexibility is high, and duty cycle is short, are a wide range of
Accurate, quick, the dynamic monitoring of forest zone arboreal growth morphological index provide important technological means, effectively make up ground investigation
Segmental defect.
4. a kind of forestry health assessment system based on air remote sensing provided by the invention uses vegetable layer temperature and evaluates
Index, vegetable layer temperature are atmosphere and vegetation and soil material and energy exchange as a result, vegetable layer temperature passes through influence trees
Functional period of leaf, transpiration, photosynthetic capacity, chlorophyll content, sucrose synthase and the mechanism of resisting senility of inside influence trees
Health status.Using forest cover layer temperature come monitor trees whether the shadow by adverse environmental factors such as arid, pest and disease damages
It rings, judges the upgrowth situation of trees, it is most important to forestry health assessment.
5. a kind of forestry health assessment system based on air remote sensing provided by the invention, by material to unmanned plane and
A series of improvement that structure carries out, reduce the weight of unmanned plane, extend the flight time, expand unmanned plane cruising range,
It is designed specifically for forestry health assessment system, does not need frequent landing and supply, multinomial evaluation can be acquired simultaneously and referred to
Data needed for mark implement more simple, monitoring efficiency raising, operation difficulty reduction, by system for flight control computer and ground
It manages information system to combine, according to geographical location, size, environmental condition and the remote sensing aircraft itself of cruise monitoring area
Hardware condition, planning forest fire unmanned plane cruise supervised path, path planning both guarantee monitor quality and monitoring cover entirely
Lid, it is further contemplated that remote sensing aircraft hardware parameter and flight safety, Path Planning Technique is total to by remote sensing aircraft and interior industry analysis station
It is completed with analytical calculation, judgement is presently in the optimal flight paths of position and all cruises point and takeoff point, makes full use of and patrols
The Geological And Geomorphological Features and geographic information data in navigating area domain, design is scientific and reasonable in cruise path, and it is strong to give full play to unmanned plane forestry
The advantage of health evaluation.
Detailed description of the invention
Fig. 1 is a kind of structural schematic diagram of forestry health assessment system based on air remote sensing provided by the invention.
Fig. 2 is the extraction step figure of density of trees index of the invention.
Fig. 3 is that the combination of vegetation chromatography enhances image before and after treatment in density of trees index selection.
Fig. 4 is the extraction step figure of arboreal growth morphological index of the invention.
Fig. 5 is the extraction step figure of vegetable layer temperature index of the invention.
Description of symbols: 1- rotor assemblies, 2- lightweight fuselage, 3- buffer steady rest, 4- micro-control unit, and 11- reinforces
Muscle, 12- support frame, 13- wing arm, 14- driving motor, 15- rotor, 21- machine core storehouse, 31- flexible link, 32- steady rest,
33- cushion, 50- remote sensing aircraft, 51- small drone, 52- high score sensoring, industry analysis station in 60-, 61- are miniature
Computer, 62- ground command system.
Specific embodiment
With reference to the accompanying drawing, to a kind of technical side of the forestry health assessment system based on air remote sensing provided by the invention
Case is further described, and so that those skilled in the art is better understood the present invention and can be practiced.
One, hardware components
Referring to Fig. 1, a kind of forestry health assessment system based on air remote sensing provided by the invention, including remote sensing aircraft
50 and interior industry analysis station 60, remote sensing aircraft 50 includes small drone 51 and high score sensoring 52, and interior industry analysis station 60 wraps
Include microcomputer 61 and ground command system 62.
1. small drone
Small drone 51 includes rotor assemblies 1, lightweight fuselage 2, buffering steady rest 3, micro-control unit 4, rotor assemblies 1
Including reinforcing rib 11, support frame 12, wing arm 13, driving motor 14 and rotor 15, rotor assemblies 1 are evenly arranged with several
A, rotor 15 is fixed on driving motor 14 and provides power by driving motor 14, and the far-end of support frame 12 is provided with drive
Dynamic motor 14, the proximal end of support frame 12 are provided with reinforcing rib 11, and support frame 12 is stablized by reinforcing rib 11 and lightweight fuselage 2
Connection.
Lightweight fuselage 2 includes machine core storehouse 21, and machine core storehouse 21 is internally provided with lithium battery, micro-control unit 4, gyrocontrol
Instrument, airborne communication device, global positioning satellite instrument, flight control assemblies, state of flight acquisition device and electric power controller,
Lithium battery is stably fixed in the detachable storehouse in machine core storehouse 21, and the tail portion in machine core storehouse 21 is arranged in airborne communication device, and the whole world is defended
The center top in machine core storehouse 21 is arranged in star position indicator, and the lower section in machine core storehouse 21 is provided with high score sensoring, and machine core storehouse 21 is set
It is set to the uniform hollow structure of distribution of weight, can be reduced the weight of small drone entirety, keeps the entirety weight of small drone
Amount balance, is conducive to the heat dissipation of small drone internal electronic component, it is ensured that the flight safety of small drone.
Buffering steady rest 3 is fixed on the lower end of lightweight fuselage 2, and it is right to buffer steady rest 3 for the left and right sides of high score sensoring
Title is set as a pair of, and buffering steady rest 3 includes the steady rest 32 of two flexible links 31 and the setting of 31 lower horizontal of flexible link, surely
The bottom for determining frame 32 is provided with cushion 33, and flexible link 31 and steady rest 32 are hollow structure and are made of light material.
The main function of buffering steady rest 3 is to support small drone, protection high score remote sensing during takeoff and landing
Device, cushion 33 can play preferable antiskid effect, while can slow down impulse force when small drone landing, to small-sized nothing
Man-machine especially high score sensoring plays a protective role.
Lithium battery is high-performance lithium battery, and lighter in weight and reserve of electricity is larger, airborne communication device plays and ground communications,
Flying quality is transmitted to the ground and receives the effect of surface instruction, and 14 revolving speed of driving motor is precisely easy to control, frictional force when operating
Small, low noise, good operation stability, service life is longer, and micro-control unit 4 uses airborne embedded development unit, microcontroller
Unit 4, flight control assemblies and state of flight acquisition device are connected by conducting wire and the flight of co- controlling small drone,
Global positioning satellite instrument obtains the real-time geographical locations information of small drone, and electric power controller monitors lithium battery state simultaneously
Play the role of stable power-supplying.
Small drone small volume and less weight, it is easy to operate, precise and stable hovering may be implemented, maneuverability can also be real
Existing automatic Pilot and cruising flight, operation and maintenance is at low cost, does not need complicated ground handling equipment.Flying distance farther out,
Can low-latitude flying, be particularly suitable for the high-precision image of shooting, undertake the processing work of High-precision image explication de texte, pass through and be
The algorithm and evaluation index of system complete the evaluation task to forestry health.Small drone of the invention can carry height simultaneously
Divide sensoring, device component arranges rationally, and scientific structure design, flight stability is good, and cruise duration is longer, and image obtains
Take quality preferable.
2. high score sensoring
High score sensoring 52 includes holder stabilizer and intelligent console, and intelligent console passes through holder stabilizer and small-sized nothing
Man-machine 51 lower part is connected, and thermal infrared imaging instrument and high clear colorful camera are respectively arranged on intelligent console.
Thermal infrared imaging instrument uses FLIR Vue unmanned plane thermal infrared imager, and FLIR Vue aims at unmanned aerial vehicle design
Thermal infrared imaging instrument, only 90g, size are 57.4mm × 44.4mm × 44.4mm to weight, and resolution ratio reaches 640*512 336*256,
Using simple, compatible mini-USB data line connection mode only needs 5VDC power supply power supply, image quality is clear, and built-in SD card can deposit
The data such as video, picture are stored up, support MavLink communication protocol, 4 channel PWM control the functions such as shooting, storage, focusing, colour table,
USB interface provides power supply and image output, and interface is easy to use, and image setting is of high quality by optimization.
High clear colorful camera use 1 inch of CMOS image sensor, valid pixel 20,000,000,84 ° 8.8 of lens parameters FOV
Mm/24mm, f/2.8-f/11 band auto-focusing, ISO range are 100-12800.
Intelligent console is made of light material, systems stabilisation be pitching, roll, yaw 3- axis, intelligent console up and down and
Level rotates on two directions, has manual operation rotation and certainly cruise rotation both of which.Controllable rotary range pitching be-
90 ° to+30 °, maximum control revolving speed is 90 °/s, and angle jitter amount is ± 0.02 °.
Forestry health assessment system provided by the invention based on air remote sensing, remote sensing aircraft and interior industry analysis station difference
It is responsible for field operation and interior work, 51 flight stability of small drone of remote sensing aircraft, cruise duration are long, high score sensoring
Including thermal infrared imaging instrument and high clear colorful camera, acquisition meets the density of trees, arboreal growth form, vegetable layer temperature respectively
Three indexs calculate desired high clear colorful image and thermal infrared images, and interior industry analysis station includes that microcomputer and ground refer to
System is waved, microcomputer imports image processing software, writes and calculates the density of trees, arboreal growth form, vegetable layer temperature three
Item target procedure, meets the forestry health assessment needs of air remote sensing.
Two, software sections
Forestry health assessment system based on air remote sensing uses the density of trees, arboreal growth form, vegetable layer temperature three
Item metrics evaluation forestry health status.
1. the density of trees
The density of trees be reflect land surface arboreal growth dynamic change important indicator, typically refer to trunk, leaf, stem,
Planimetric area percentage of the branch in unit area, be the key that influence water carbon cycle, exchanges of mass and energy because
Son also reflects the luxuriant degree of trees photosynthesis area and growth, can indicate the growth shape of trees to a certain extent
State and growth tendency are one of forestry health assessment indicators of the invention.
The extraction step of density of trees index is as shown in Fig. 2, extraction step are as follows:
The density index first step determines density of trees detection zone, and unmanned plane parameter is arranged；
Density index second step, executes task of taking photo by plane, and high clear colorful camera acquires the image information in region of taking photo by plane；
Density index third step, area image of taking photo by plane cut splicing, image preprocessing；
The 4th step of density index, vegetation chromatography combination enhancing wave band difference, SVM supervised classification；
The 5th step of density index draws trees index statistical chart, extracts trees index threshold；
The 6th step of density index, trees cover image and obtain；
The 7th step of density index, density of trees index selection,
The 8th step of density index, the density of trees analyse and evaluate.
It determines density of trees detection zone, unmanned plane parameter is arranged according to Density Detection region, according to Density Detection
The hardware condition of geographical location, size, environmental condition and the remote sensing aircraft in region itself, planning unmanned plane cruise inspection
Path is surveyed, path planning should guarantee to detect quality and detect all standing, and needs to consider remote sensing aircraft hardware parameter and fly
Row safety can be divided into multiple tasks execution if flight is unable to complete；
Execution is taken photo by plane task, and sunny calm weather is selected, and completes forest zone high clear colorful Image Acquisition at noon, nobody
Flying height when machine acquires image is 35 to 60 meters, and flying speed is at the uniform velocity 4 meter per seconds, ship's control and sidelapping
Degree is 80%, acquires the continuous sliceable JPG format HD color image of several width；
It is that the continuous sliceable JPG format HD of several width of unmanned plane acquisition is colored that area image of taking photo by plane, which cuts splicing,
Image carries out image mosaic using Pix4DMapper software, completes image preprocessing, obtains the panorama orthograph in detection forest zone
Picture；
The wave band difference that trees and non-trees can be enhanced by band combination more easily discriminates trees and non-trees,
The panorama orthograph picture in forest zone is extracted into trees index threshold by the combination enhancing of vegetation chromatography, the combination enhancing of vegetation chromatography is public
Formula are as follows:
S=2G-B-R
G indicates that green band pixel value, B indicate that blue wave band pixel value, R indicate that red band pixel value, S are tree in formula
The wooden index,
The sample area image object atural object that unmanned plane is shot is divided into two class of trees and non-trees by SVM supervised classification, with trees
For trees index S value with non-trees as abscissa, pixel counts number as ordinate, draws trees and non-trees respectively S
The statistic histogram of value, using the intersection point of trees and non-trees S value histogram under coordinate system as trees and non-tree classfication threshold
Value F, part more than or equal to classification thresholds F are trees pixel, and the part less than classification thresholds F is non-trees pixel, according to point
The density of trees calculation formula that class threshold value F is extracted are as follows:
N in formulaLNumber, N are counted for trees pixelFFor non-trees pixel statistics, M is density of trees value；
Trees pixel point is expressed as black, non-trees pixel is expressed as white to get having arrived by vegetation chromatography group
It closes enhancing treated trees and covers image, if Fig. 3 is image and image after handling before the combination enhancing of vegetation chromatography is handled.
Image is covered according to the trees obtained after density of trees M value and the combination enhancing processing of vegetation chromatography, to the density of trees
It analyses and evaluates.
Embodiment one: by taking certain forest zone image of the unmanned plane on the 15th of September in 2018 shooting as an example, in conjunction with field investigation result pair
Image carries out interpretation, selectes 110 typical forest zone numbers on the image according to field interpretation result and 110 relatively independent
Non- trees region, be supported vector machine supervised classification, actual conditions are seen, support vector machines is to trees and non-tree classfication
Effect is preferable, for statistical analysis to the trees index S in forest zone arboreal growth period on the basis of supervised classification result, system
Histogram is counted, in the intersection point of statistic histogram is 35.56 by trees index S trees known to statistic histogram and non-trees pixel,
The trees result extracted using above-mentioned classification thresholds is as shown in the right figure of Fig. 3, and wherein white portion represents non-trees, black part
Divide and represent trees, the density of trees for obtaining the forest zone based on SVM supervised classification is 68.32%.
Augmentation index statistics is combined with vegetation chromatography using supervised classification result by the orthograph picture shot to unmanned plane
The method that histogram combines determining classification thresholds, classification thresholds high stability.Examination woods is carried out using the threshold value that image determines
When the large-scale density of trees in area is extracted, density of trees extraction effect is preferable, analyses and evaluates good reliability to the density of trees,
It is the important indicator of forestry health assessment system.
2. arboreal growth form
It is the most important thing to the growth conditions evaluation of trees in forestry health assessment, and the growthform of trees is in very great Cheng
The growth conditions of trees are reacted on degree, arboreal growth form can indirectly react forest zone biomass accumulation, to estimate woods
Industry health status.Traditional arboreal growth method for measuring shape of palaemon, which is adopted, artificial observation and takes pictures, and speed is slow, heavy workload and accurate
Rate is low, is no longer satisfied the needs of arboreal growth form monitoring.Experiment of UAV remote sensing system has low in cost, image data
The series of advantages such as high resolution, flexibility are high, and duty cycle is short, be a wide range of forest zone arboreal growth morphological index it is accurate,
Quickly, dynamic monitoring provides important technological means, effectively makes up the segmental defect of ground investigation.
Quickly and accurately to grasp forest zone arboreal growth morphological index, being obtained by unmanned plane within the arboreal growth period
Forest zone trees high-definition digital orthography DOM and digital surface model DSM, using form extraction algorithm to the trees area in DOM
Domain extracts, generate exposure mask, with DSM set and, acquisition arboreal growth morphological index, as shown in figure 4, main step includes:
Growth indexes step 1 determines arboreal growth Morphology observation region, and unmanned plane parameter is arranged；
Growth indexes step 2, executes task of taking photo by plane, and high clear colorful camera obtains forest zone aerial images；
Growth indexes step 3 completes the splicing and pretreatment of aerial images, generates DSM and DOM, and earth's surface DSM is selected to make
For earth's surface datum level, the DSM comprising trees and earth's surface DSM subtract each other to obtain arboreal growth form DSM；
Growth indexes step 4 carries out image preprocessing to DOM, obtains arboreal growth region by form extraction algorithm；
Growth indexes step 5 after the arboreal growth region DOM of extraction carries out geometrical registration, is handled soft by remote sensing image
Part generates exposure mask；
Growth indexes step 6 covers and obtains the arboreal growth on image with obtained exposure mask and arboreal growth form DSM
Form；
Growth indexes step 7, image arboreal growth form and actual form compare and precision evaluation, obtains accuracy
Good arboreal growth appearance model carries out arboreal growth morphological analysis and evaluation.
It determines arboreal growth Morphology observation region, unmanned plane parameter is arranged according to arboreal growth Morphology observation region,
According to the hardware condition of the geographical location of detection zone, size, environmental condition and remote sensing aircraft itself, nobody is planned
Machine cruise detection path, path planning should guarantee to detect quality and detect all standing, and need to consider remote sensing aircraft hardware
Parameter and flight safety can be divided into multiple tasks execution if flight is unable to complete；
Execution is taken photo by plane task, and sunny calm weather is selected, and completes forest zone high clear colorful Image Acquisition, flight at noon
Several width images of 40-60 meters of height shootings are as data source, and ground resolution is 1.25 centimetres, course, sidelapping rate
90%, control point GCP are greater than 5, and spatial resolution is greater than 0.79 centimetre, and empty three errors are missed less than 0.115 pixel, average RMS
Difference is measured less than 0.018 meter using real-time dynamic positioning RTK, can be used for empty three operations and accuracy detection, while using this
A little points are to detect image set positioning accuracy；
Pix4Dmapper obtains camera model automatically when aerial images splice, and is quickly generated using Pix4Dmapper software
Professional accurate DOM and DSM data.It is as follows that Pix4Dmapper handles data substantially process: first is that import image and position with
Attitude system POS data；Second is that importing ground control point GCP file, geometric correction is carried out to image；Third is that being wanted according to difference
Seek setting parameter；Fourth is that full automatic treatment carries out data reduction and three-dimensional model is established, is encrypted, obtained by full-automatic sky three
DOM and DSM.
Image enhancement in pretreatment, stretches image, and image is made to cover biggish value interval to improve image
Contrast extracts arboreal growth form convenient for the later period:
Color space is converted in pretreatment, and color space is converted to YCbCr color space model；
OTSU Threshold segmentation in pretreatment, maximum variance between clusters OTSU is according to the gamma characteristic of image, before figure is divided into
Scape and background two parts.For image A (x, y), the segmentation threshold of foreground and background is denoted as B, and the pixel number for belonging to prospect accounts for
The ratio of entire image is denoted as Dj, average gray Hj, the ratio that background pixel points account for entire image is Db, average gray is
Hb, the average gray of entire image is denoted as H, and inter-class variance is denoted as F, then has:
H=DjHj+DbHb
F=Dj(Hj-H)2+Db(Hb-H)2
Above-mentioned two formula of simultaneous can obtain:
F=DjDb(Hj-Jb)2
When inter-class variance F maximum, foreground and background difference is maximum, and segmentation threshold B at this time is optimal threshold.
Form extraction algorithm: assuming that known target is labeled as 1, background dot is labeled as 0, and defining boundary point is 1,8 connections
Neighborhood at least 1 point is labeled as 0, needs to carry out boundary point following processing:
First is that central point is X on the boundary point centered on 8 connection neighborhoods1, clockwise consecutive points are denoted as X2,
X3..., X9.Wherein X2Positioned at central point X1On, firstly, the point that selection is met the requirements:
2≤N(X1)≤6；
S(X1)=1；
X2X4X6=0；
X4X6X8=0；
N(X1) be non-zero consecutive points number, S (X1) it is X2~X9~X2Variation number of the sequence from 0 to 1, by institute
There is the inspection of boundary point, all mark points are all deleted；
Second is that meeting:
2≤N(X1)≤6；
S(X1)=1；
X2X4X8=0；
X2X6X8=0；
On inspection, identification point has been deleted, and recycles above-mentioned two step, defeated until being all marked as deletion without pixel
Result out is the trees form after approach for binary image thinning.
Form extraction algorithm obtains arboreal growth region can be by the skeletal extraction or image thinning realization in OpenCV；
Remote sensing image processing software generates exposure mask: single pixel wide trees form production exposure mask is discontinuous, can lose a large amount of trees
The wooden form area information is carried out Morphological scale-space to it by suitable structural element and obtains the wide trees form of 2-6 pixel,
Available more complete trees form region, since exposure mask, so observing for convenience, cannot be used with DSM Overlapping display
The face substitution exposure mask of production exposure mask is shown.
Extract arboreal growth form: by the exposure mask of generation and arboreal growth form DSM set and, acquisition arboreal growth form refer to
Mark.
3. vegetable layer temperature
Vegetable layer temperature is atmosphere and vegetation and soil material and energy exchange as a result, forest-tree characteristic and trees are raw
Long environment be influence vegetable layer temperature change the main reason for, vegetable layer temperature by influence Tree Blades functional period, transpiration,
Photosynthetic capacity, chlorophyll content, sucrose synthase and the mechanism of resisting senility of inside influence Health of Tree situation.Therefore, may be used
Using forest cover layer temperature come monitor trees whether the influence by adverse environmental factors such as arid, pest and disease damages, judge trees
Upgrowth situation.
Vegetable layer temperature index is obtained, as shown in figure 5, main step includes:
Temperature obtains step 1, determines forest cover layer temperature sensing area, and setting unmanned plane, which navigates, flies parameter；
Temperature obtains step 2, executes winged task of navigating, and high clear colorful camera obtains forest zone orthography, thermal infrared imaging instrument
Obtain forest zone thermal infrared imagery；
Temperature obtains step 3, orthography and thermal infrared imagery splicing, geometric correction and geometrical registration；
Temperature obtains step 4, the radiation calibration of thermal infrared imagery；
Temperature obtains step 5, extracts forest zone vegetation area in orthography；
Temperature obtains step 6, extracts forest zone vegetable layer temperature on thermal infrared imagery；
Temperature obtains step 7, comparison, analysis and the evaluation of forest zone vegetable layer temperature.
Determine forest cover layer temperature sensing area, setting unmanned plane navigate fly parameter, according to the geographical location of detection zone,
The hardware condition of size, environmental condition and remote sensing aircraft itself, planning unmanned plane cruise detection path, path planning
It should guarantee to detect quality and detect all standing, and need to consider remote sensing aircraft hardware parameter and flight safety, if primary fly
Row is unable to complete, and can be divided into multiple tasks execution；
It executes to navigate and flies task, select sunny calm weather, complete forest zone boat at noon and fly job, unmanned plane is taken
Heat-carrying infrared thermoviewer and high clear colorful camera, flying height are 50 to 70 meters, the Duplication of filmed image be 75% to
90%, ground space resolution ratio is 1 centimetre to 7.5 centimetres；
Unmanned plane shoots several thermal infrared imageries and orthography, is spliced using Pix4Dmapper, selectes unified
Coordinate system, base area face photo control point to image carry out geometric correction；
Thermal infrared imagery radiation calibration
For the accuracy and reliability for improving vegetable layer temperature detection, the radiation calibration before and after unmanned plane during flying need to be carried out.
Vegetable layer temperature near the ground is measured respectively using the accurate temperature measurer in ground and thermal infrared imaging instrument, and it is fixed to complete pre-flight radiation
Mark, by correlation analysis, determine the accurate temperature measurer in ground and thermal infrared imaging instrument under the influence of not by distance, measured temperature
It is with uniformity；It is measured respectively using the thermal infrared imagery after geometric correction and geometrical registration using the accurate temperature measurer in ground
The temperature of black and white radiation calibration cloth, forest zone different location difference trees several points that ground is placed, it is true by correlation analysis
Determine radiation calibration coefficient, the vegetable layer temperature extracted in thermal infrared imagery is obtained using radiation calibration coefficient；
Forest zone vegetation area is extracted in orthography uses GCanny edge detection algorithm, GCanny edge detection algorithm
Step:
First is that replacing Gaussian Blur using selective surface is fuzzy, it is that 20-40 carrys out smoothed image that its threshold value, which is arranged, it is therefore an objective to
Noise is removed, selective surface obscures the smoothing method for using band to a hook at the end limbic function, passes through given threshold, conveys center
The pixel that Pixel gray difference is less than the threshold value participates in calculating, and the pixel having big difference with center pixel gray scale difference value is recognized
For with effective information, and it is non-noise, it is not involved in smoothing computation, to retain useful high-frequency signal, margin signal is also being protected
The range stayed.
Second is that looking for the intensity gradient of image, the gradient of each pixel is obtained by Sobel operator in smoothed out image
, Sobel operator has packaged function in OpenCV, selects the Sobel gradient operator of 3*3；
Third is that eliminating side erroneous detection using non-maximum suppression technology, fuzzy boundary is apparent from, each pixel is retained
The maximum of gradient intensity, deletes other values on point；
Fourth is that determining potential boundary using the method for dual threshold, a threshold value upper bound and threshold value lower bound, image are set
In pixel then think necessarily strong boundary if it is greater than the threshold value upper bound, then thinking inevitable less than threshold value lower bound is not boundary,
Between the two be then considered weak boundary, need to be further processed；
Fifth is that tracking boundary using hysteresis techniques, the weak boundary being connected with strong boundary is considered boundary, other weak sides
Boundary is then considered not being boundary.
GCanny edge detection algorithm can better connection marginal information, have for isolated edge and weak edge preferable
Knowledge effect, can more accurately identify the forest cover marginal information of crop.
The forest cover marginal information result of extraction is subjected to binary conversion treatment and generates vector face file, is based on this vector
File, spanning forest vegetation area exposure mask.
Vegetable layer temperature in forest zone is extracted on thermal infrared imagery: the forest cover area mask of generation is covered in thermal infrared imagery
Middle extraction vegetable layer temperature.
4. forestry health assessment
Using the remote sensing aircraft and interior industry analysis station of air remote sensing forestry health assessment system, software portion through the invention
The density of trees provided, three arboreal growth form, vegetable layer temperature metrics evaluation forestry health status, the density of trees, tree are provided
Three the wooden growthform, vegetable layer temperature indexs can be used alone, and can also use so that binomial or three are comprehensive.
The density of trees reflects arboreal growth dynamic change and luxuriant degree, according to the type of forest-tree and forest zone geography shape
Condition, when use, determine that an optimal value is covered according to the departure degree of the density of trees index and optimal value that measure in conjunction with trees
Lid image assesses density of trees situation in detail.
Arboreal growth form extracts the limb matrix morphology of forest zone trees, reacts the growth conditions of trees.Trees need to be combined
Type and forest zone geographical location further determine in detail.
Whether vegetable layer temperature monitoring trees are influenced by adverse environmental factors such as arid, pest and disease damages, judge trees
Upgrowth situation.A vegetable layer temperature optima is determined when use, according to the inclined of the vegetable layer temperature index and optimal value measured
From degree, arboreal growth situation is assessed in detail in conjunction with the forest zone vegetable layer temperature extracted on thermal infrared imagery.
Claims (9)
1. a kind of forestry health assessment system based on air remote sensing, it is characterised in that: including remote sensing aircraft (50) and interior industry
Analysis station (60), remote sensing aircraft (50) include small drone (51) and high score sensoring (52), interior industry analysis station (60)
Including microcomputer (61) and ground command system (62), high score sensoring (52) includes holder stabilizer and intelligent cloud
Platform, intelligent console are connected by holder stabilizer with the lower part of small drone (51), and heat is respectively arranged on intelligent console
Infrared thermoviewer and high clear colorful camera, the forestry health assessment system based on air remote sensing use the density of trees, arboreal growth
Three form, vegetable layer temperature metrics evaluation forestry health status, the extraction step of density of trees index are as follows:
The density index first step determines density of trees detection zone, and unmanned plane parameter is arranged；
Density index second step, executes task of taking photo by plane, and high clear colorful camera acquires the image information in region of taking photo by plane；
Density index third step, area image of taking photo by plane cut splicing, image preprocessing；
The 4th step of density index, vegetation chromatography combination enhancing wave band difference, SVM supervised classification；
The 5th step of density index draws trees index statistical chart, extracts trees index threshold；
The 6th step of density index, trees cover image and obtain；
The 7th step of density index, density of trees index selection,
The 8th step of density index, the density of trees analyse and evaluate.
2. a kind of forestry health assessment system based on air remote sensing according to claim 1, it is characterised in that: trees are close
It spends in the extraction step of index, determines density of trees detection zone, unmanned plane parameter, root are arranged according to Density Detection region
According to the hardware condition of the geographical location in Density Detection region, size, environmental condition and remote sensing aircraft itself, nobody is planned
Machine cruise detection path, path planning should guarantee to detect quality and detect all standing, and need to consider remote sensing aircraft hardware
Parameter and flight safety can be divided into multiple tasks execution if flight is unable to complete；
Execution is taken photo by plane task, and sunny calm weather is selected, and completes forest zone high clear colorful Image Acquisition at noon, and unmanned plane is adopted
Flying height when collecting image is 35 to 60 meters, and flying speed is at the uniform velocity 4 meter per seconds, and ship's control and sidelapping degree are equal
It is 80%, acquires the continuous sliceable JPG format HD color image of several width；
It is by the continuous sliceable JPG format HD color image of several width of unmanned plane acquisition that area image of taking photo by plane, which cuts splicing,
Image mosaic is carried out using Pix4DMapper software, completes image preprocessing, obtains the panorama orthograph picture in detection forest zone；
The panorama orthograph picture in forest zone is passed through vegetation chromatography group by the wave band difference for enhancing trees and non-trees by band combination
It closes enhancing and extracts trees index threshold, vegetation chromatography combination enhancing formula are as follows:
S=2G-B-R
G indicates that green band pixel value, B indicate that blue wave band pixel value, R indicate that red band pixel value, S refer to for trees in formula
Number；
The sample area image object atural object that unmanned plane is shot is divided into two class of trees and non-trees by SVM supervised classification, with trees and non-
As abscissa, pixel counts number and is used as ordinate the trees index S value of trees, the respective S value of drafting trees and non-trees
Statistic histogram, using the intersection point of trees and non-trees S value histogram under coordinate system as trees and non-trees classification thresholds F,
Part more than or equal to classification thresholds F is trees pixel, and the part less than classification thresholds F is non-trees pixel, according to classification threshold
The density of trees calculation formula that value F is extracted are as follows:
N in formulaLNumber, N are counted for trees pixelFFor non-trees pixel statistics, M is density of trees value；
Trees pixel point is expressed as black, non-trees pixel is expressed as white to get having arrived by the combination increasing of vegetation chromatography
Treated by force, and trees cover image, are covered according to the trees obtained after density of trees M value and the combination enhancing processing of vegetation chromatography
Image analyses and evaluates the density of trees.
3. a kind of forestry health assessment system based on air remote sensing according to claim 1, it is characterised in that: trees are raw
The obtaining step of long morphological index includes:
Growth indexes step 1 determines arboreal growth Morphology observation region, and unmanned plane parameter is arranged；
Growth indexes step 2, executes task of taking photo by plane, and high clear colorful camera obtains forest zone aerial images；
Growth indexes step 3 completes the splicing and pretreatment of aerial images, generates DSM and DOM, selects earth's surface DSM as ground
Table datum level, the DSM comprising trees and earth's surface DSM subtract each other to obtain arboreal growth form DSM；
Growth indexes step 4 carries out image preprocessing to DOM, obtains arboreal growth region by form extraction algorithm；
Growth indexes step 5, it is raw by remote sensing image processing software after the arboreal growth region DOM of extraction carries out geometrical registration
At exposure mask；
Growth indexes step 6 covers and obtains the arboreal growth shape on image with obtained exposure mask and arboreal growth form DSM
State；
Growth indexes step 7, image arboreal growth form and actual form compare and precision evaluation, show that accuracy is good
Arboreal growth appearance model carries out arboreal growth morphological analysis and evaluation.
4. a kind of forestry health assessment system based on air remote sensing according to claim 3, it is characterised in that: trees are raw
In the obtaining step of long morphological index, arboreal growth Morphology observation region is determined, be arranged according to arboreal growth Morphology observation region
Unmanned plane parameter, according to the hardware of the geographical location of detection zone, size, environmental condition and remote sensing aircraft itself
Condition, planning unmanned plane cruise detection path；
Execution is taken photo by plane task, and sunny calm weather is selected, and completes forest zone high clear colorful Image Acquisition, flying height at noon
Several width images of 40-60 meters of shootings are as data source, and ground resolution is 1.25 centimetres, course, sidelapping rate 90%, control
It makes point GCP and is greater than 5, spatial resolution is greater than 0.79 centimetre, and empty three errors are less than less than 0.115 pixel, average RMS error
It 0.018 meter, is measured using real-time dynamic positioning RTK, for empty three operations and accuracy detection, detects image set positioning accurate
Degree；
Pix4Dmapper obtains camera model automatically when aerial images splice, and quickly generates profession using Pix4Dmapper software
Accurate DOM and DSM data, it is as follows that Pix4Dmapper handles data flow: first is that importing image and position and attitude system
POS data；Second is that importing ground control point GCP file, geometric correction is carried out to image；Join third is that being arranged according to different requirements
Number；Fourth is that full automatic treatment carries out data reduction and three-dimensional model is established, is encrypted by full-automatic sky three, obtain DOM and DSM；
Color space is converted in pretreatment: color space is converted to YCbCr color space model；
OTSU Threshold segmentation in pretreatment: maximum variance between clusters OTSU according to the gamma characteristic of image, by figure be divided into prospect and
Background two parts, for image A (x, y), the segmentation threshold of foreground and background is denoted as B, and the pixel number for belonging to prospect accounts for whole picture
The ratio of image is denoted as Dj, average gray Hj, the ratio that background pixel points account for entire image is Db, average gray Hb, whole
The average gray of width image is denoted as H, and inter-class variance is denoted as F, then has:
H=DjHj+DbHb
F=Dj(Hj-H)2+Db(Hb-H)2
Above-mentioned two formula of simultaneous can obtain:
F=DjDb(Hj-Hb)2
When inter-class variance F maximum, foreground and background difference is maximum, and segmentation threshold B at this time is optimal threshold；
Form extraction algorithm: assuming that known target is labeled as 1, background dot is labeled as 0, and defining boundary point is 1,8 connection neighborhoods
At least 1 point is labeled as 0, needs to carry out boundary point following processing:
First is that central point is X on the boundary point centered on 8 connection neighborhoods1, clockwise consecutive points are denoted as X2,
X3..., X9；Wherein X2Positioned at central point X1On, firstly, the point that selection is met the requirements:
2≤N(X1)≤6；
S(X1)=1；
X2X4X6=0；
X4X6X8=0；
N(X1) be non-zero consecutive points number, S (X1) it is X2~X9~X2Variation number of the sequence from 0 to 1, by all sides
The inspection of boundary's point, all mark points are all deleted；
Second is that meeting:
2≤N(X1)≤6；
S(X1)=1；
X2X4X8=0；
X2X6X8=0；
On inspection, identification point has been deleted, and recycles above-mentioned two step, until being all marked as deletion without pixel, output
It as a result is the trees form after approach for binary image thinning；
Form extraction algorithm obtains arboreal growth region can be by the skeletal extraction or image thinning realization in OpenCV；
Remote sensing image processing software generates exposure mask: carrying out Morphological scale-space by structural element and obtains the wide trees shape of 2-6 pixel
State, obtains more complete trees form region, and the face substitution exposure mask of use production exposure mask is shown；
Extract arboreal growth form: by the exposure mask of generation and arboreal growth form DSM set and, acquisition arboreal growth morphological index.
5. a kind of forestry health assessment system based on air remote sensing according to claim 1, it is characterised in that: vegetable layer
Temperature index obtain the step of include:
Temperature obtains step 1, determines forest cover layer temperature sensing area, and setting unmanned plane, which navigates, flies parameter；
Temperature obtains step 2, executes winged task of navigating, and high clear colorful camera obtains forest zone orthography, and thermal infrared imaging instrument obtains
Forest zone thermal infrared imagery；
Temperature obtains step 3, orthography and thermal infrared imagery splicing, geometric correction and geometrical registration；
Temperature obtains step 4, the radiation calibration of thermal infrared imagery；
Temperature obtains step 5, extracts forest zone vegetation area in orthography；
Temperature obtains step 6, extracts forest zone vegetable layer temperature on thermal infrared imagery；
Temperature obtains step 7, comparison, analysis and the evaluation of forest zone vegetable layer temperature.
6. a kind of forestry health assessment system based on air remote sensing according to claim 5, it is characterised in that: vegetable layer
In the obtaining step of temperature index, winged task of navigating is executed, sunny calm weather is selected, completed forest zone boat at noon and fly to appoint
Business, UAV flight's thermal infrared imaging instrument and high clear colorful camera, flying height are 50 to 70 meters, and the Duplication of filmed image is
75% to 90%, ground space resolution ratio is 1 centimetre to 7.5 centimetres；
Unmanned plane shoots several thermal infrared imageries and orthography, is spliced using Pix4Dmapper, and unified seat is selected
Mark system, base area face photo control point carry out geometric correction to image；
Thermal infrared imagery radiation calibration: vegetable layer temperature near the ground is measured respectively using the accurate temperature measurer in ground and thermal infrared imaging instrument
Degree, completes pre-flight radiation calibration, by correlation analysis, determine the accurate temperature measurer in ground and thermal infrared imaging instrument not by
Under the influence of distance, measured temperature is with uniformity；Using the thermal infrared imagery after geometric correction and geometrical registration, ground essence is utilized
True temperature measurer measures the black and white radiation calibration cloth of ground placement, the temperature of forest zone different location difference trees several points respectively,
Radiation calibration coefficient is determined by correlation analysis, and the vegetable layer temperature extracted in thermal infrared imagery is obtained using radiation calibration coefficient
Degree；
Forest zone vegetation area is extracted in orthography uses GCanny edge detection algorithm, the step of GCanny edge detection algorithm
It is rapid:
First is that replacing Gaussian Blur using selective surface is fuzzy, it is that 20-40 carrys out smoothed image that its threshold value, which is arranged,；
Second is that looking for the intensity gradient of image, the gradient of each pixel is obtained by Sobel operator in smoothed out image,
Sobel operator has packaged function in OpenCV, selects the Sobel gradient operator of 3*3；
Third is that eliminating side erroneous detection using non-maximum suppression technology, fuzzy boundary is apparent from, is retained on each pixel
The maximum of gradient intensity, deletes other values；
Fourth is that determine potential boundary using the method for dual threshold, a threshold value upper bound and threshold value lower bound are set, in image
Pixel then thinks necessarily strong boundary if it is greater than the threshold value upper bound, and then thinking inevitable less than threshold value lower bound is not boundary, the two
Between be then considered weak boundary, need to be further processed；
Fifth is that tracking boundary using hysteresis techniques, the weak boundary being connected with strong boundary is considered boundary, and other weak boundaries are then
It is considered not being boundary；
The forest cover marginal information result of extraction is subjected to binary conversion treatment and generates vector face file, based on this vector text
Part, spanning forest vegetation area exposure mask；
Vegetable layer temperature in forest zone is extracted on thermal infrared imagery: the forest cover area mask of generation being covered and is mentioned in thermal infrared imagery
Take vegetable layer temperature.
7. a kind of forestry health assessment system based on air remote sensing according to claim 1, it is characterised in that: forestry is strong
Health evaluation utilizes the remote sensing aircraft (50) and interior industry analysis station (60) of air remote sensing forestry health assessment system, close by trees
Three degree, arboreal growth form, vegetable layer temperature metrics evaluation forestry health status, the density of trees, arboreal growth form, vegetation
Layer three index of temperature can be used alone, and can also use so that binomial or three are comprehensive；
Density of trees index using when one optimal value determined according to the type and forest zone geographical situation of forest-tree, according to measuring
Density of trees index and optimal value departure degree, in conjunction with trees covering image assess density of trees situation in detail；
Arboreal growth morphological index extracts the limb matrix morphology of forest zone trees, determines in conjunction with tree families and forest zone geographical location
The growth conditions of trees；
Vegetable layer temperature index using when determine a vegetable layer temperature optima, according to the vegetable layer temperature index that measures with most
The departure degree of the figure of merit assesses arboreal growth situation in conjunction with the forest zone vegetable layer temperature extracted on thermal infrared imagery in detail.
8. a kind of forestry health assessment system based on air remote sensing according to claim 1, it is characterised in that: small-sized nothing
Man-machine (51) include rotor assemblies (1), lightweight fuselage (2), buffering steady rest (3), micro-control unit (4), rotor assemblies (1) packet
Reinforcing rib (11), support frame (12), wing arm (13), driving motor (14) and rotor (15) are included, rotor assemblies (1) are uniformly set
Several are equipped with, rotor (15) is fixed on driving motor (14) and provides power, support frame (12) by driving motor (14)
Far-end be provided with driving motor (14), the proximal end of support frame (12) is provided with reinforcing rib (11), and support frame (12) passes through
Reinforcing rib (11) is stably connected with lightweight fuselage (2).
9. a kind of forestry health assessment system based on air remote sensing according to claim 8, it is characterised in that: lightweight machine
Body (2) includes machine core storehouse (21), and machine core storehouse (21) are internally provided with lithium battery, micro-control unit (4), gyrocontrol instrument, machine
Carrier communication device, global positioning satellite instrument, flight control assemblies, state of flight acquisition device and electric power controller, lithium battery
It is stably fixed in the detachable storehouse in machine core storehouse (21), airborne communication device is arranged in the tail portion of machine core storehouse (21), global satellite
Position indicator setting is provided with high score sensoring, machine core storehouse below the center top of machine core storehouse (21), machine core storehouse (21)
(21) it is set as the uniform hollow structure of distribution of weight, buffering steady rest (3) is fixed on the lower end of lightweight fuselage (2), and buffering is steady
Determine frame (3) to be symmetrical arranged as a pair, buffering steady rest (3) includes two flexible links (31) and the setting of flexible link (31) lower horizontal
Steady rest (32), the bottom of steady rest (32) is provided with cushion (33), and flexible link (31) and steady rest (32) are hollow
It structure and is made of light material.
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