CN103400364B - A kind of Forest Resource Change monitoring method - Google Patents
A kind of Forest Resource Change monitoring method Download PDFInfo
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Abstract
The invention discloses a kind of Forest Resource Change monitoring method, comprise step: step 1, the remote sensing image in former and later two periods is carried out to geometric correction; Step 2, the atmosphere apparent reflectance of acquisition remote sensing image; Step 3, carries out Atmospheric Correction and landform correction to the atmosphere apparent reflectance data of remote sensing image; Step 4, obtains the mask data of remote sensing image medium cloud and shade thereof; Step 5, using the mask data of cloud and shade thereof as territory, non-data regions, and the forest index of atmosphere apparent reflectance data acquisition remote sensing image based on remote sensing image; Step 6, the changing value monitoring Forest Resource Change of the forest index based on former and later two period remote sensing. The present invention utilizes remote sensing impact to monitor Forest Resource Change, there is the advantages such as simple, amount of calculation is little, computational accuracy is high, monitoring efficiency is high, height is dynamic, be applicable to the Forest Resource Change monitoring of various landform, there is good universality, can be Forest Resource Change monitoring on a large scale for technical support.
Description
Technical field
The invention belongs to the application of remote sensing technology in field of forestry, particularly a kind of forest based on remote sensing technologyChange in resources monitoring method.
Background technology
Forest is the ecosystem of land maximum, as a kind of renewable resource, forest nature and artificially because ofUnder the acting in conjunction of element, experiencing generation, growth, interference and dead process, forest ecosystem alwaysIn the dynamic process replacing in growth and decline. The variation of the forest reserves is for global ecological environment, biology all the timeThe aspect importants such as diversity, climate change and carbon cycle, have not for maintaining Global Ecological balanceAlternative effect. Therefore, accurately and timely obtain Forest Resource Change information, meaning is important.
Traditional forest inventory investigation is monitored taking ground survey as main, exist that workload is large, labour intensity is large,Cost is high, the cycle is long, efficiency is low and the problem such as effective difference, and investigation precision is not high, is difficult to meet gloomy nowThe needs of woods change in resources monitoring.
Along with the development of Aero-Space remote sensing technology, remote sensing technology with its wide coverage, can repeated accesses etc. spyPoint has shown powerful advantage in Forest Resource Change monitoring, gloomy at the level such as local, regional, nationalIn woods variation monitoring, be widely applied.
A lot of scholars classify and analyze remote sensing variation monitoring technology from different angles. According to whether rightImage is classified, and remote sensing variation monitoring method can be divided into the rear comparison method of image direct comparison method and classification;According to whether quantification of monitoring objective, remote sensing variation monitoring mode can be divided into two kinds of qualitative processing and quantitative Treatment.
Remote sensing variation monitoring is to utilize the remote sensing image in many periods to detect the region changing, and monitoring becomesThe common method in the region of changing has algebraic operation method, changing method, classification monitoring method, visual analyzing prisonSurvey method and object-oriented monitoring method.
Algebraic operation method comprises: image difference method, image ratio method, vegetation index differential technique etc. These generationsNumber operation method is mostly simple, by selecting threshold value to determine the region of variation in image, therefore selects to closeSuitable wave band and threshold value are very crucial. Muchoney etc.[2]It is gloomy that employing image difference method causes forest defoliationThe variation of woods view is studied. Chavez etc.[3]Southwestern United Stares arid and semi-arid regions vegetation is being carried outIn variation monitoring research, find, the error image of red spectral band is than normalized differential vegetation index (NDVI) differential chartPicture more can truly reflect vegetation situation of change. Lyon etc.[4]Compared 3 period MSS data 7 kinds of vegetationThe different-effect of index in forest cover change monitoring, result shows NDVI differential technique effect optimum.
Changing method comprises principal component analysis (PCA), red-tasselled official hat conversion (KT) and card side's conversion (Chisquare)Etc. method. The advantage of above-mentioned changing method is the redundancy that can reduce between data, and change information is changingAfter new images on be enhanced, be convenient to the extraction of region of variation and process again; Shortcoming need to be carried out complexityMatrix operation and threshold value are determined. Sean etc.[5]Landsat data are carried out to red-tasselled official hat and change to monitor the variation of vegetation.Jin etc.[6]Red-tasselled official hat variation humidity and the interference of Normalized Difference Moisture Index detection forest are compared to analysis. CoppinDeng[7]Utilize the comparative analysis of the vegetation index of different time to monitor forest change situation, vegetation index comprises:Brightness, green degree and humidity component, NDVI etc. after KT conversion.
Classification monitoring method comprises spectrum Space-time Integrated analytic approach, non-supervisory variation monitoring method, mixes variation monitoringMethod and artificial neural network method etc. The advantage of above-mentioned sorting technique can reduce external factor to variation monitoringImpact; Shortcoming is to select the training sample of high-quality and sufficient amount. Forever wait [8] to adopt remote sensing image in moralSorting technique changes and studies Forest Landscape Pattern in Lushuihe Watershed of Jilin Province. Wang Renhua etc. [9] using artificialNeural network model has carried out forest cover sort research to Landsat TM multispectral image, introduces high number of passesAccording to directly classifying together with 3 each multi light spectrum hands as an independent wave band, and obtain better effects.
Visual analyzing monitoring method comprises two steps: the visual interpretation of multidate image and the screen of region of variationCurtain digitlization is a kind of variation monitoring method of man-machine interaction. Shortcoming is that workload is large, efficiency is low, time-consuming,Be difficult to process large area region, and the variation monitoring result that is difficult for upgrading in time. Stone etc.[10]Utilize visual interpretationMethod Brazilian Para area Forest Selective Cutting situation is assessed.
Object-oriented monitoring method, it is that the pixel groups of homogeneity is classified, considered spectral information,The information such as the correlation between shape, texture and object. Platt etc.[11]Utilize OO method pairThe Forest cover change of state of Colorado 1938-1999 is studied, and has obtained the sky of Forest cover changeBetween the information such as distribution.
To sum up, current Forest Resource Change monitoring method is varied, generally has that amount of calculation is large, efficiency is lowEtc. shortcoming.
Pertinent literature is as follows:
[1] Zhao Xianwen, Li Chonggui, Si Lin. the forest inventory investigation new system [J] based on information technology. BeijingForestry University journal, 2002,24(5): 147-155.
[2]MuchoneyDM,HaackBN.Changedetectionformonitoringforestdefoliation[J].PhotogrammetricEngineeringandRemoteSensing,1994,60:1243-1251
[3]ChavezPS,MackinnonDJ.AutomaticdetectionofvegetationchangesinthesouthwesternUnitedStatesusingremotelysensedimages[J].PhotogrammetricEngineeringandRemoteSensing,1998,64:143-150.
[4]LyonJG,YuanD,LunettaRS,etal.Achangedetectionexperimentusingvegetationindices[J].PhotogrammetricEngineeringandRemoteSensing,1998,60:571-583.
[5]SeanPH,WarrenBC,YangZQ,etal.ComparisonofTasseledCap-basedLandsatdatastructuresforuseinforestdisturbancedetection[J].RemoteSensingofEnvironment,2005,97(3):301-310.
[6]JinSM,StevenAS.Comparisonoftimeseriestasseledcapwetnessandthenormalizeddifferencemoistureindexindetectingforestdisturbances[J].RemoteSensingofEnvironment,2005,94(3):364-372.
[7]CoppinPR,BauerME.ProcessingofmullitemporalLandsatTMimagerytooptimizeextractionofforestcoverchangefeatures[J].IEEETransactiononGeoscienceandRemoteSensing,1994,32(4):918-927.
[8] Yu Deyong, Wang Yanyan, Hao Zhanqing, etc. Forest Landscape Pattern in Lushuihe Watershed of Jilin Province changes [J].Resources science, 2005,27 (4): 147-153.
[9] Wang Renhua, Huo Hongtao, You Xianxiang. artificial neural network is in the remote sensing images forest cover classificationApplication [J]. Beijing Forestry University's journal, 2003,25 (4): 1-5.
[10]StoneTA,LefebvreP.UsingmultitemporalsatellitedatatoevaluateselectivelogginginPara,Brazil[J].InternationalJournalofRemoteSensing,1998,19(13):2517-2526.
[11]PlattRV,SchoennagelT.Anobject-orientedapproachtoassessingchangesintreecoverintheColoradofrontrange1938-1999[J].ForestEcologyandManagement,2009,258(7):1342-1349.
Summary of the invention
The problem existing for prior art, the invention provides a kind of based on remote sensing image, simply efficientForest Resource Change monitoring method.
In order to solve the problems of the technologies described above, the present invention adopts following technical scheme:
A kind of Forest Resource Change monitoring method, comprises step:
Step 1, carries out geometric correction to the remote sensing image in former and later two periods;
Step 2, carries out radiation calibration to the remote sensing image through geometric correction, and obtains the large gas meter of remote sensing imageSee reflectivity;
Step 3, carries out Atmospheric Correction and landform correction to the atmosphere apparent reflectance data of remote sensing image;
Step 4, the atmosphere apparent reflectance data acquisition remote sensing image medium cloud based on remote sensing image and shade thereofMask data;
Step 5, using the mask data of cloud and shade thereof as territory, non-data regions, and obtain based on step 3 distantThe forest index of the atmosphere apparent reflectance data acquisition remote sensing image of sense image the 2nd, 3,4 wave bands;
Step 6, the changing value monitoring forest reserves of the forest index based on former and later two period remote sensing becomeChange.
Above-mentioned steps 2 further comprises following sub-step:
Step 2.1, to the remote sensing image through geometric correction carry out radiation calibration obtain remote sensing image gray value andQuantitative relationship between radiance value, thus the radiance value of remote sensing image obtained;
Step 2.2, obtains corresponding atmosphere apparent reflectance according to the radiance value of remote sensing image.
In step 3, adopt 6S Atmospheric Correction model to carry out atmosphere to the atmosphere apparent reflectance data of remote sensing imageCorrect.
In step 3, adopt C calibration model to carry out landform correction to the atmosphere apparent reflectance data of remote sensing image.
The forest index of above-mentioned remote sensing imageWherein, ρ2、ρ3And ρ4Be respectivelyThe atmosphere apparent reflectance data of the remote sensing image that step 3 is obtained the 2nd, 3,4 wave bands.
Above-mentioned steps 6 further comprises sub-step:
Step 6.1, arranges forest threshold according to the forest-tree kind of monitored area, arboreal growth situation and experienceValue Threshold, the remote sensing image region that forest index is not less than forest threshold value Threshold is forest districtTerritory, forest index is less than the Wei Fei wood land, remote sensing image region of forest threshold value Threshold;
Step 6.2, the forest index that rear period remote sensing is affected deducts the forest of remote sensing image for the first periodIndex, the forest index variation value of remote sensing impact in period before and after obtaining;
Step 6.3 obtains Forest Resource Change situation according to the forest index variation value of front and back remote sensing in period impact,Be specially: when the forest index variation value of front and back period remote sensing is more than or equal to predetermined threshold value Threshold1Time, area of woods increases; Be less than when the forest index variation value of front and back period remote sensing orEqual predetermined threshold value Threshold2Time, area of woods reduces; Other situations, area of woods does not change;Described threshold value Threshold1And Threshold2Establish according to forest threshold value Threshold and experiencePut.
Compared with prior art, the present invention has the following advantages and beneficial effect:
1, the present invention utilizes remote sensing impact to monitor Forest Resource Change, have simple, amount of calculation is little,The advantages such as computational accuracy is high, monitoring efficiency is high, height is dynamic.
2, the inventive method is applicable to the Forest Resource Change monitoring of various landform, has good universality,Can be Forest Resource Change monitoring on a large scale for technical support.
Brief description of the drawings
Fig. 1 is the Forest Resource Change monitoring schematic flow sheet of the embodiment of the present invention.
Detailed description of the invention
Fig. 1 is the flow chart of a kind of specific embodiments of the inventive method. Below in conjunction with Fig. 1 to the present inventionMethod is described in further detail.
The present invention specifically comprises the steps:
Step 1, carries out geometric correction to the remote sensing image in two periods.
Adopt existing conventional means can complete the geometric correction of remote sensing image, do not repeat at this. This stepObject is the geometric error in order to eliminate or correct remote sensing image, guarantees the remote sensing image of different times the same areaUniformity on locus.
Step 2, carries out radiation calibration to the remote sensing image through geometric correction, and obtains the large gas meter of remote sensing imageSee reflectivity.
Radiation calibration refers to the quantitative relationship of setting up between remote sensing image gray value DN and radiance value. According toThe radiance value of remote sensing image can obtain the atmosphere apparent reflectance of remote sensing image.
This step further comprises following sub-step:
Step 2.1, to the remote sensing image through geometric correction carry out radiation calibration obtain remote sensing image gray value andQuantitative relationship between radiance value, thus the radiance value of remote sensing image obtained.
This sub-step is to carry out respectively for each pixel in remote sensing image. The gray value of the each pixel of remote sensing image and spokePenetrate quantitative relationship between brightness value as follows:
Li＝Gaini*DNi+Biasi(1)
In formula (1):
I represents i wave band of remote sensing image;
LiRepresent the radiance value of i wave band of remote sensing image;
DNiRepresent the gray value of i wave band of remote sensing image;
GainiAnd BiasiRepresent respectively gain and the biasing of i wave band of remote sensing image, can be from remote sensing imageIn header file, obtain.
Step 2.2, obtains corresponding atmosphere apparent reflectance according to the radiance value of remote sensing image.
This sub-step is also to carry out respectively for each pixel in remote sensing image. According to the radiation of the each pixel of remote sensing imageBrightness value calculates corresponding atmosphere apparent reflectance, and formula is as follows:
In formula (2):
I represents i wave band of remote sensing image;
ρiRepresent the atmosphere apparent reflectance of i wave band of remote sensing image;
π is constant, general value 3.1416, unit: surface of sphere (sr);
LiRepresent the radiance value of i wave band of remote sensing image, unit: wm-2·sr-1·μm-1, w tableShow watt, m represents rice, and μ m represents micron;
D is the distance between day ground, unit: astronomical unit;
ESUM is the average solar spectrum irradiancy on atmosphere top, unit: wm-2·μm-1；
θ is the zenith angle of the sun, and ESUM and θ are constant, by the official of US Geological Survey (USGS)Fang Fabu.
Solar distance D can search by the solar distance of searching the issue of official of US Geological Survey (USGS)Table obtains, and also can be obtained by following formula:
D＝1+0.0167*sin(2π(days-93.5/360))(3)
Wherein, days represents to take date of defending sheet at number of days then, for example, and when the obtaining of remote sensing imageBetween be on November 25th, 2009, for the days=304+25=329 of this remote sensing image, wherein, 304Represent total number of days in 1～October.
Step 3, the remote sensing image atmosphere apparent reflectance data that step 2 is obtained are carried out Atmospheric Correction and landformCorrect.
In this concrete enforcement, adopt 6S Atmospheric Correction model to carry out Atmospheric Correction. 6S Atmospheric Correction model is in radiationIn mode, application is comparatively extensive, and this Atmospheric Correction model has been considered the Non Lambert reflector characteristic of target surface and newThe impact etc. of absorption molecular species, adopt good approximate data to calculate atmosphere and aerocolloidal scattering and suctionThe impact of receiving, is applicable to visible ray and near infrared data of multiple angles.
The parameter that adopts 6S Atmospheric Correction model to input mainly contains: (a) geometric parameter, and while comprising observationBetween, solar azimuth, satellite aximuth, solar zenith angle, satellite zenith angle etc.; (b) aerosol model,Comprise aerocolloidal component parameter; If shortage live data, the master pattern that can provide with 6S replaces; (c)Atmospheric model, i.e. the parameter of constituent of atomsphere, comprises steam and gray scale particle etc.; If shortage True Data, alsoThe master pattern that can select 6S model to provide substitutes; (d) aerosol concentration; (e) highly; (f) surveyThe spectral conditions of device etc., Output rusults is the reflectivity after Atmospheric Correction.
Except 6S Atmospheric Correction model, also can adopt ACTOR model, FLAASH model or DOS modelDeng carrying out Atmospheric Correction.
Because object spectrum is subject to landform interference larger, widely different at the object spectrum of sunny side and the back, landform is entangledPositive object is exactly in order to weaken this impact, the spectral information of recovered part. Numerous models of correcting in landformMiddle C calibration model is comparatively pervasive, therefore, adopts C calibration model to through Atmospheric Correction in this concrete enforcementRemote sensing image atmosphere apparent reflectance data are carried out landform correction, specific as follows:
In formula (4)～(5):
LHThe equivalent observation of picture dot in horizontal direction, i.e. atmosphere apparent reflectance after landform correction;
LTThe observation of versant picture dot, i.e. atmosphere apparent reflectance before landform correction;
θ is the zenith angle of the sun, and θ is constant, is issued by official of US Geological Survey (USGS);
α is the angle between picture dot normal and sunshine incident ray, i.e. incidence angle;
S represents the picture dot gradient, and A represents slope aspect, and the gradient and slope aspect data can be by matching with remote sensing imageDem data obtains;
Represent the azimuth of the sun;
C is semiempirical coefficient.
Coefficient c adopts with the following method and obtains:
Carry out regression fit by the remote sensing image atmosphere apparent reflectance before landform is corrected and obtain linear functionLT=b+mcos α, obtains c=b/m according to gained linear function.
In addition, adopting Cosine calibration model, SCS calibration model, Minnaert calibration model etc. is also canRow.
Step 4, the remote sensing image atmosphere apparent reflectance data acquisition remote sensing image medium cloud obtaining based on step 3And the mask data of shade.
In order to improve Forest Resource Change monitoring accuracy, guarantee that monitoring result can really react the change of the forest reservesChange, in follow-up treatment step using cloud and shadow mask data thereof as territory, non-data regions.
While extracting cloud and shade thereof, can adopt simple multiband Threshold method, the general wave band adopting is 1,4,7 etc. Cloud shade can extract according to the geometrical relationship of cloud and shade, finally the cloud shade extracting is enteredRow goes the processing such as stigma, corrosion and expansion, to guarantee that nearly all cloud and cloud shade are all detected.
Step 5, using the mask data of cloud and shade thereof as territory, non-data regions, and obtain based on step 3 distantForest index (the Forest of the atmosphere apparent reflectance data acquisition remote sensing image of sense image the 2nd, 3,4 wave bandsIndex, FI), as follows:
In formula (6), ρ2、ρ3And ρ4It is respectively the apparent reflection of atmosphere of remote sensing image the 2nd, 3,4 wave bandsRate data.
Adopt formula (6) can obtain successively the forest index FI of the each pixel of remote sensing image transverse and longitudinal. Forest index is prominentGone out the spectral information of forest, FI is larger for forest index, illustrates that this region is that the probability of forest is also larger, canExtract wood land by setting threshold.
Forest is a kind of vegetation, so can adopt certain vegetation index to distinguish He Fei wood land, wood land.Meanwhile, in remote sensing images, wood land exists between green wave band and near-infrared ripple place and general non-wood landNotable difference, so, in the present invention, adopt being combined in the covering of earth's surface of green wave band and near infrared band outstanding gloomyForest land table covers.
According to trees kind and the arboreal growth situation etc. of monitored area forest, forest threshold value Threshold is set,Threshold value Threshold generally can be arranged between 3～4, and according to forest threshold value Threshold and remote sensing shadowAs forest index, FI obtains the wood land in remote sensing image. Specific as follows:
The atmosphere apparent reflectance that the present invention is based on remote sensing image calculates forest index, can reduce by sensor and drawThe error rising.
Step 6, the forest index FI monitoring Forest Resource Change based on different times remote sensing image.
By the forest index FI of a rear period remote sensingAfterDeduct the forest index FI of remote sensing image for the first periodBeforeObtain forest index variation value, when forest index variation value is more than or equal to threshold value Threshold1, represent gloomyWoods area change; When forest index variation value is less than or equal to threshold value Threshold2, represent that area of woods subtractsFew; When forest index variation value is at Threshold1And Threshold2Between time, represent that area of woods do not send outChanging. Specifically see following formula:
It is all to carry out for pixel that above-mentioned judgement area of woods changes. For example,, to resembling in different times remote sensing imageElement a, before and after it, forest index variation value is not more than threshold value Threshold2, pixel a belongs to Deforestation districtTerritory.
Threshold1And Threshold2Can determine according to the threshold value Threshold setting in step 5. For example,In the time that forest increases, in the light of actual conditions, same pixel must be wood land in rear first phase image, frontIn first phase image, be non-wood land, so Threshold1The forest threshold of two phase remote sensing images before and after must being greater thanValue Threshold's is poor.
In the time of Deforestation, in the light of actual conditions, same pixel must be non-wood land in rear first phase image,In front first phase image, be wood land, so, Threshold2Before and after must being less than, two phase remote sensing images is gloomyWoods threshold value Threshold's is poor.
Threshold value Threshold1And Threshold2Determine, except based on mentioned above principle, also need according to warpTest specifically and arrange. In general, Threshold1Can be at-1～2 values, Threshold2Can be-2～1Between value. Because actual conditions are too complicated, exist the situations such as heterogeneous same spectrum, so, need to carry out partVisual examination, to guarantee the accuracy of monitoring result.
Further illustrate technical scheme of the present invention and beneficial effect below in conjunction with instantiation application.
Choose a certain region, Daxing'an Mountainrange (ranks number: 124,25), the remote sensing image in the first period is 2005The Landsat image that obtain on August 25, in, the remote sensing image in the second period is to obtain on August 12nd, 2009The Landsat image of getting, the resolution ratio of the remote sensing image in above-mentioned two periods is 30 meters. First to above-mentionedThe remote sensing image in two periods carries out pretreatment, comprising: geometric correction, radiation calibration, Atmospheric Correction,Shape correction and cloud and shadow Detection processing thereof, can meet data precision required for the present invention to obtain.
Threshold in the present embodiment1Value is 0.8, Threshold2Value is-0.7, according to Threshold1WithThreshold2Value, adopt the inventive method to monitor the forest change situation in this region, monitoring result is:The region that forest increases concentrates on (50 ° of 33 ' N, 118 ° of 23 ' E), (50 ° of 51 ' N, 119 ° 36' E) and (50 ° of 49 ' N, 119 ° of 34 ' E) region; The region of Deforestation concentrate on (51 ° 2 'N, 119 ° of 2 ' E), (50 ° of 48 ' N, 117 ° of 56 ' E) and (50 ° of 36 ' N, 117 ° of 42 ' E)Region, through visual differentiation, testing result of the present invention is substantially accurate.
Claims (5)
1. a Forest Resource Change monitoring method, is characterized in that, comprises step:
Step 1, carries out geometric correction to the remote sensing image in former and later two periods;
Step 2, carries out radiation calibration to the remote sensing image through geometric correction, and obtains the large gas meter of remote sensing imageSee reflectivity;
Step 3, carries out Atmospheric Correction and landform correction to the atmosphere apparent reflectance data of remote sensing image;
Step 4, the atmosphere apparent reflectance data acquisition remote sensing image medium cloud based on remote sensing image and shade thereofMask data;
Step 5, using the mask data of cloud and shade thereof as territory, non-data regions, and obtain based on step 3 distantThe forest index of the atmosphere apparent reflectance data acquisition remote sensing image of sense image the 2nd, 3,4 wave bandsWherein, ρ2、ρ3And ρ4Be respectively the remote sensing image the 2nd that obtains of step 3,3, the atmosphere apparent reflectance data of 4 wave bands;
Step 6, the changing value monitoring forest reserves of the forest index based on former and later two period remote sensing becomeChange.
2. Forest Resource Change monitoring method as claimed in claim 1, is characterized in that:
Step 2 further comprises following sub-step:
Step 2.1, to the remote sensing image through geometric correction carry out radiation calibration obtain remote sensing image gray value andQuantitative relationship between radiance value, thus the radiance value of remote sensing image obtained;
Step 2.2, obtains corresponding atmosphere apparent reflectance according to the radiance value of remote sensing image.
3. Forest Resource Change monitoring method as claimed in claim 1, is characterized in that:
In step 3, adopt 6S Atmospheric Correction model to carry out atmosphere to the atmosphere apparent reflectance data of remote sensing imageCorrect.
4. Forest Resource Change monitoring method as claimed in claim 1, is characterized in that:
In step 3, adopt C calibration model to carry out landform correction to the atmosphere apparent reflectance data of remote sensing image.
5. Forest Resource Change monitoring method as claimed in claim 1, is characterized in that:
Step 6 further comprises sub-step:
Step 6.1, arranges forest threshold according to the forest-tree kind of monitored area, arboreal growth situation and experienceValue Threshold, the remote sensing image region that forest index is not less than forest threshold value Threshold is wood land,Forest index is less than the Wei Fei wood land, remote sensing image region of forest threshold value Threshold;
Step 6.2, the forest index that rear period remote sensing is affected deducts the forest of remote sensing image for the first periodIndex, the forest index variation value of remote sensing impact in period before and after obtaining;
Step 6.3 obtains Forest Resource Change situation according to the forest index variation value of front and back remote sensing in period impact,Be specially: when the forest index variation value of front and back period remote sensing is more than or equal to predetermined threshold value Threshold1Time, area of woods increases; When the forest index variation value of front and back period remote sensing is less than or equal to predetermined threshold valueThreshold2Time, area of woods reduces; Other situations, area of woods does not change; Described threshold valueThreshold1And Threshold2Arrange according to forest threshold value Threshold and experience.
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| CN110645961B (en) * | 2019-09-27 | 2021-07-30 | 佛山科学技术学院 | Forest resource dynamic change detection method based on remote sensing and NDVI |
| CN112131953A (en) * | 2020-08-26 | 2020-12-25 | 航天信德智图（北京）科技有限公司 | Rapid forest change detection method research based on high-resolution second image |
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