CN104656098A - Method for inverting remote sensing forest biomass - Google Patents
Method for inverting remote sensing forest biomass Download PDFInfo
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Abstract
The invention discloses a method for inverting remote sensing forest biomass. The method comprises the following steps: on the basis of remote sensing data pretreatment, extracting characteristic variables of a vegetation canopy from a LiDAR point cloud (comprising canopy three-dimensional space information) and multispectrum (comprising spectrum information on the upper surface of the canopy) data respectively; screening the characteristic variables of the LiDAR point cloud and the multispectrum through correlation analysis, and inverting overground and underground biomass by combining the ground actually measured biomass information through a stepwise regression model. Through the adoption of the optimized inverting model of northern subtropical forest biomass, constructed by method, the 'determination coefficient' R<2> of the model can be increase by 3-24%; the forest biomass can be estimated in high precision, and the 'relative root-mean-square error' (rRMSE) can be reduced by 2-10%. The method can be applied to the fields of forestry investigation, forest resource monitoring, forest carbon reserve evaluation, forest ecosystem research and the like, and provides quantitative data support for forest sustainable management and forest resource comprehensive utilization.
Description
Technical field
The present invention relates to monitoring and the non-polluted food base technical field of Pest in Forest, be specifically related to the method for a kind of remote sensing forest biomass inverting.
Background technology
Accurate biomass estimation is significant for forest resource monitoring, forest carbon storage assessment and the research of forest ecosystem.Meanwhile, these information also can provide the Data support of quantification for the comprehensive utilization of forest sustainable management and the forest reserves.Traditional Investigation on biomass method time and effort consuming, and limited " point " upper information can only be obtained, be difficult to applied generalization on large regions; And remote sensing technology can obtain the forest parameters of each yardstick accurately and rapidly, there is good practical value and application prospect.
Landsat series of satellites can obtain forest multispectral (optics) information in medium and large scale.The sensors such as the TM before comparing in (this sensor is mounted on Landsat 8 satellite launched in U.S. spaceflight aviation office (NASA) on February 11st, the 2013) setting at wave band of its up-to-date Landsat 8OLI sensor and the susceptibility to vegetation have a distinct increment.But remote optical sensing is still difficult to penetrate Forest Canopy obtains its vertical stratification information, and easily saturated when obtaining forest biomass information in the region of forest cover degree high (vegetation growth is vigorous).Laser radar (LiDAR, Light Detection And Ranging) be the active remote sensing technology developed rapidly in recent years, its laser pulse launched can penetrate Vegetation canopy and obtain its three-dimensional structure and energy information, and research in the past shows that LiDAR has larger potentiality in the biophysics accurately estimating Different Forest Types and architectural characteristic.
Inverting Forest biont quantifier elimination has in recent years: 1) Zheng etc. 2004 have delivered " Estimating aboveground biomass using Landsat7ETM+data across a managed landscape in northern Wisconsin; USA " on " Remote Sensing ofEnvironment " the 35th volume, and this research is the ground biomass information of University of Wisconsin-Madison pungent state boreal forest by vegetation index invertings such as the NDVI extracted from ETM+ (Landsat 7) image.2) Yang Cunjian etc. 2004 volume deliver " correlation analysis not between the Tropical forests vegetation biomass of cohort and remotely sensed geographic data " at " Acta Phytoecologica Sinica " 28 and utilize the method for the original wave band of TM (Landsat5) image to estimate In Xishuangbanna of Yunnan Tropical forests vegetation biomass.3) Ferster etc. have delivered " AbovegroundLarge Tree Mass Estimation in a Coastal Forest in British Columbia Using Plot-LevelMetrics and Individual Tree Detection from LiDAR " for 2009 on " Canadian Journal of Remote Sensing " the 35th volume, this research by the some cloud level degree extracted from small light spot (diameter: 0.1-2m) LiDAR data and canopy density information, the inverting ground biomass of temperate forests.4) Saatchi etc. deliver " Benchmark Map of Forest Carbon Stocks inTropical Regions across Three Continents " the 12nd phase at " Proceedings of the National Academy of Sciences of the UnitedStates of America " in 2011, this research by extracting the characteristic variable relevant to height of tree information from GLAS (Geoscience LaserAltimeter System) large spot (diameter: 52-90m) LiDAR data, the inverting biomass of hylaea.But, above method only goes to excavate tradition " optics " and LiDAR data from single angle, specificity is lower, and to the excavating depth of characteristic variable more shallow (namely not nested design from multiple angle extraction and screening characteristic variable), inverting can't be carried out to biomass accurately.
Summary of the invention
Goal of the invention: for the deficiencies in the prior art, the present invention proposes the method for a kind of remote sensing forest biomass inverting, have high specificity, cost low, be easy to features such as applying.
Technical scheme: in order to realize foregoing invention object, the technical solution used in the present invention is:
The method of a kind of remote sensing forest biomass inverting: on the pretreated basis of remotely-sensed data, respectively from the characteristic variable of LiDAR point cloud (comprising canopy three-dimensional spatial information) and multispectral (comprising the spectral information of canopy upper surface) extracting data Vegetation canopy; Screen above LiDAR point cloud and multispectral characteristic variable by correlation analysis, and combined ground actual measurement biomass information is by Gradual regression analysis model inverting ground and underground biomass.
The method of described remote sensing forest biomass inverting, comprises the following steps:
1) OLI Yunnan snub-nosed monkey: the radiation calibration parameter first by OLI sensor carries out radiation calibration to raw video; Original DN value is converted into pixel radiance value; With FLAASH model, atmospheric correction is carried out to image again, thus radiance value is converted into earth's surface actual reflectance; Then carry out geometric accurate correction to image, choose culture point of the same name, adopt quadratic polynomial to correct, correction error controls within 0.1 pixel, and adopts the most contiguous pixel method to carry out resampling.
2) LiDAR data pre-service:
A) noise level is estimated and data smoothing: first raw data is transformed into frequency field, then using the criterion of low magnitude portion higher for frequency as noise level; Then select Gaussian filter smoothing, this is because Gaussian filter is effective smoothed data while, can also keep the trend of original curve to greatest extent;
B) Gauss curve fitting (decomposition) and waveform data points cloud: this hypothesis cumulative based on echo data being multiple Gaussian function, adopts nonlinear least square method to carry out matching to Wave data; Then extract discrete point cloud by local maximal peak detection filter algorithm from the Wave data after process, in each discrete point, have recorded energy and the amplitude information of return signal;
C) generating digital terrain: the normalized object of LiDAR data height eliminates the influence of topography " truly " vegetation height to obtain, usually adopting raw LiDAR data elevation information to deduct Terrain Elevation and obtaining; Therefore, accurately generating digital terrain model (DTM) is the important prerequisite calculating normalization vegetation height; First classifying to extracting discrete point cloud from Wave data, then Kraus filtering process being carried out in order to remove non-ground points to last echo, finally use filtered last echo data and generate digital terrain model by being naturally close to method interpolation;
D) recycle the elevation of DEM to vegetation echo point to be normalized, even if the height value of vegetation point is converted into the height value relative to ground, and by the coordinate in the lower left corner and the upper right corner, every block sample is cut; The normalization cloud data of respective coordinates position, 55 sample ground is extracted finally by GIS analysis tool;
3) OLI characteristic variable is extracted: by carrying out band combination, K-T Transformation, texture information extraction, principal component analysis (PCA) to OLI image, minimal noise is separated conversion and multiple vegetation index converts, extract 5 stack features variablees (referring to table 1), i.e. original single band variable, band combination variable, information enhancement group variable, vegetation index variable and texture information variable; Wherein texture analysis is carried out for the first principal component of principal component analysis (PCA);
4) the LiDAR point cloud characteristic variable extracted: LiDAR point cloud characteristic variable calculates 4 stack features variablees (referring to table 2) based on three-dimensional normalization LiDAR point cloud value, i.e. height variable, height percentile variable, canopy density variables, Canopy cover degrees variable;
5) characteristic variable screening: the LiDAR characteristic variable of extraction and OLI characteristic variable are carried out Pearson ' s correlation analysis with needing the parameter predicted, the absolute value choosing Pearson ' s related coefficient higher than 0.2 characteristic variable as modeling candidate variables; The computing method of Pearson ' s related coefficient are:
In formula, x
ifor certain stand characteristics of ground actual measurement, y
ifor certain LiDAR characteristic variable,
for x
imean value,
for y
imean value;
6) statistical study: ground is surveyed the biomass information that gathers as dependent variable, the characteristic variable that remote sensing technique extracts, as independent variable, sets up multivariate regression model.Use and progressively enter method (stepwise) and the inspection coefficient of determination (R
2) situation of change select the suitable variables entering model; If have independent variable to make statistic F value too small and T inspection do not reach the level of signifiance (P value >0.1), then rejected; F value is comparatively large and T inspection reaches the level of signifiance (P value <0.05) is then entered; Adopt the coefficient of determination (R
2), root-mean-square error (RMSE) and relative root-mean-square error (rRMSE) evaluate the precision of regression model;
In formula, x
ifor certain stand characteristics of ground actual measurement,
for x
imean value,
for certain stand characteristics of model assessment, n is sampling intensity;
From (3) formula, rRMSE be RMSE (root-mean-square error) with
the number percent of (measured value average).
The method of remote sensing forest biomass of the present invention inverting, carrying out on pretreated basis to LiDAR and OLI data, respectively from the characteristic variable an of cloud (comprising canopy three-dimensional spatial information) and multispectral (comprising the spectral information of canopy upper surface) extracting data Vegetation canopy, then on the basis of screening characteristic variable, combined ground actual measurement biomass information passes through Gradual regression analysis model inverting ground and underground biomass, its innovative point and characteristic as follows: 1) fully excavate these two dimensions of spectral information of canopy three-dimensional spatial information and upper surface thereof both the vegetation biophysical properties information that comprises, 2) by correlation analysis, the characteristic variable extracted above is screened, and for final inverse model, thus be beneficial to mechanism explain, method transplanting, 3) because OLI data are free data, therefore the method also saves the cost multiple yardstick promoting Biomass retrieval precision to a great extent.
Beneficial effect: compared with prior art, the present invention is by integrated LiDAR point cloud and OLI multispectral characteristic variable, the vegetation biophysical properties information that both comprise fully is excavated from the spectral information dimension of canopy three-dimensional spatial information and its upper surface, and by correlation analysis, the characteristic variable extracted above is screened, finally determine inverse model.The method is conducive to mechanism explain, method is transplanted; And due to OLI data are free data, therefore the method also saves the cost multiple yardstick promoting Biomass retrieval precision to a great extent.Previous experiments the result shows, the Optimization inversion model (compared with the method based on single multispectral data source) of the north subtropical forest biomass built by the present invention can by model " coefficient of determination " R
2improve 3-24%; And can high precision estimation forest biomass (" relative root-mean-square error " rRMSE is reduced 2-10%).Can be applicable to the field such as research of Forestry Investigation, forest resource monitoring, forest carbon storage assessment and forest ecosystem, and provide the Data support of quantification for the comprehensive utilization of forest sustainable management and the forest reserves.
Accompanying drawing explanation
Fig. 1 is study area true color airphoto and 55 pieces of sample ground distribution plans;
Fig. 2 is the method and technology route map of remote sensing forest biomass inverting;
Fig. 3 is Pearson ' the s related coefficient figure of each characteristic variable and ground biomass;
Fig. 4 is Pearson ' the s related coefficient figure of each characteristic variable and underground biomass;
Fig. 5 is the contrast scatter diagram of OLI model-ground biomass sample ground measured value and model predication value;
Fig. 6 is the contrast scatter diagram of OLI model-underground biomass sample ground measured value and model predication value;
Fig. 7 is the contrast scatter diagram of LiDAR model-ground biomass sample ground measured value and model predication value;
Fig. 8 is the contrast scatter diagram of LiDAR model-underground biomass sample ground measured value and model predication value;
Fig. 9 is the contrast scatter diagram of unified model-ground biomass sample ground measured value and model predication value;
Figure 10 is the contrast scatter diagram of unified model-underground biomass sample ground measured value and model predication value.
Embodiment
Below in conjunction with specific embodiment, the present invention is further illustrated.
Embodiment 1
The method of a kind of remote sensing forest biomass inverting, technology path as shown in Figure 1, the method be integrated LiDAR point cloud and OLI multispectral characteristic variable the method for remote sensing forest biomass inverting, to on the pretreated basis of remotely-sensed data, respectively from the characteristic variable of LiDAR point cloud (comprising canopy three-dimensional spatial information) and multispectral (comprising the spectral information of canopy upper surface) extracting data Vegetation canopy; Screen above LiDAR point cloud and multispectral characteristic variable by correlation analysis, and combined ground actual measurement biomass information is by Gradual regression analysis model inverting ground and underground biomass.Specific as follows:
Study area is positioned at state-run Yu Shan forest farm, Changshu City of Jiangsu Province (120 ° 42 ' 9.4 " E, 31 ° 40 ' 4.1 " N), and belong to subtropics monsoon climate, have a moderate climate, mean annual precipitation 1054 millimeters, area is about 1103hm
2, its sea level elevation is 20-261m.Yu Shan forest farm belongs to north subtropical Secondary Mixed Forest, main forest types is coniferous forest, broad-leaf forest and mixed forest, wherein Main Coniferous Tree Species has masson pine (Pinus massoniana), China fir (Cunninghamialanceolata), wet-land pine tree (Pinus elliottii) and black pine (Pinus thunbergii) etc.; Broad-leaved Trees has Quercus acutissima, and ((Quercus acutissima), sweet gum (Liquidambar formosan) and part Evergreen Broad-leaved Tree Species, as Fagaceae (Fagaceae), canella (Lauraceae) and plant of theaceae (Theaceae).
According to three class investigation historical datas (1995,2012) indexs such as the Forest Types in, age and site index arrange 55 square sample plots (size: 30 × 30m, setup times: 2012 years 7-8 months and in August, 2013) within the scope of study area.According to composition ratio, sample is divided into coniferous forest (n=13), broad-leaf forest (n=16) and mixed forest (n=26) three types.In sample-plot survey process, the diameter of a cross-section of a tree trunk 1.3 meters above the ground is greater than to the tree of 5cm, measure the seeds of Dan Mu, the diameter of a cross-section of a tree trunk 1.3 meters above the ground (measuring with enclosing chi), the height of tree and clear bole height (utilizing Vertex IV laser altimeter to measure) one by one with width of the coming of age (projector distance namely in two principal directions, with tape measuring), be less than 5cm for the diameter of a cross-section of a tree trunk 1.3 meters above the ground and tree that is that die counts, but does not participate in the calculating of biomass.Southwest corner, sample ground coordinate uses differential GPS (Trimble GeoXH6000HandheldGPS units) to measure, and by receiving JSCORS wide area differential signal framing, precision is better than 0.5 meter (Fig. 2).
Gather the relevant forest parameters of sample ground yardstick according to single wooden enquiry data, comprise the unit area ground on sample ground yardstick, underground biomass (thm
-2).Biomass information calculates the biomass of Dan Mu by different rate growth formula (deferring to nearby principle), and gathers the unit area ground biomass (W obtaining every block sample ground
a) and underground biomass (W
b).
OLI Yunnan snub-nosed monkey.Study the 2-7 wave band in the Landsat 8OLI image (bar reel number 119/38) in employing on July 19th, 2013, image spatial resolution is 30m, and radiometric resolution is 12bit, and spectral range covers 11 wave bands.First the radiation calibration parameter by OLI sensor carries out radiation calibration to raw video.Original DN value is converted into pixel radiance value.With FLAASH model, atmospheric correction (Atmospheric models: Tropical are carried out to image again; Aerosol model: Urban; The gasoloid method of inversion: 2-Band (K-T); Initial visibility: 40; Spectral response function: ldcm_oli.sli), thus radiance value is converted into earth's surface actual reflectance.Then carry out geometric accurate correction to image, choose 40 culture points of the same name, adopt quadratic polynomial to correct, correction error controls within 0.1 pixel, and adopts the most contiguous pixel method to carry out resampling.
LiDAR data pre-service.1) noise level is estimated and data smoothing: first raw data is transformed into frequency field, then using the criterion of low magnitude portion higher for frequency as noise level.Then select Gaussian filter smoothing, this is because Gaussian filter is effective smoothed data while, can also keep the trend of original curve to greatest extent.2) Gauss curve fitting (decomposition) and waveform data points cloud: this hypothesis cumulative based on echo data being multiple Gaussian function, adopts nonlinear least square method to carry out matching to Wave data.Then extract discrete point cloud by local maximal peak detection filter algorithm from the Wave data after process, in each discrete point, have recorded energy and the amplitude information of return signal.3) generating digital terrain: the normalized object of LiDAR data height eliminates the influence of topography " truly " vegetation height to obtain, usually adopting raw LiDAR data elevation information to deduct Terrain Elevation and obtaining.Therefore, accurately generating digital terrain model (DTM) is the important prerequisite calculating normalization vegetation height.First classifying to extracting discrete point cloud from Wave data, then Kraus filtering process being carried out in order to remove non-ground points to last echo, finally use filtered last echo data and generate digital terrain model by being naturally close to method interpolation.The elevation of recycling DEM to vegetation echo point is normalized, even if the height value of vegetation point is converted into the height value relative to ground, and is cut every block sample by the coordinate in the lower left corner and the upper right corner.The normalization cloud data of respective coordinates position, 55 sample ground is extracted finally by GIS analysis tool.
By carrying out band combination, K-T Transformation, texture information extraction, principal component analysis (PCA) to OLI image, minimal noise is separated conversion and multiple vegetation index converts, extract 5 groups of (totally 53) characteristic variables, 6 original single band variablees, 10 band combination variablees, 10 information enhancement group variablees, 18 vegetation index variablees and 9 texture information variablees wherein texture analysis carry out for the first principal component of principal component analysis (PCA), window size is 3 × 3, and delay distance is 1 pixel.The implication of 53 variablees and computing formula (see table 1).LiDAR characteristic variable calculates 4 groups of (34) characteristic variables based on three-dimensional normalization LiDAR point cloud value: 10 height variable, 13 height percentile variablees, 10 canopy density variables, 1 Canopy cover degrees variable.The implication of 34 variablees and computing formula (see table 2).
Table 1 OLI characteristic variable and description
Totally 87 (34 LiDAR characteristic variables and 53 the OLI characteristic variables) variablees extracted are carried out Pearson correlation analysis with needing the parameter predicted, the absolute value choosing Pearson ' s related coefficient higher than 0.2 characteristic variable as modeling candidate variables.The computing method of Pearson ' s related coefficient are:
In formula, x
ifor certain stand characteristics of ground actual measurement, y
ifor certain LiDAR characteristic variable,
for x
imean value,
for y
imean value;
Research have selected 34 candidate variables, these candidate variables and with need the absolute value of Pearson ' the s related coefficient of parameter predicted as shown in Figure 3 and Figure 4.Pearson ' the s related coefficient of these 34 variablees and biomass is higher and relation remarkable, illustrates there is good linear relationship between them.Therefore using these 34 characteristic variables as final modeling candidate variables.
Table 2 LiDAR characteristic variable and description
Ground is surveyed the biomass information that gathers as dependent variable, the characteristic variable that remote sensing technique extracts, as independent variable, sets up multivariate regression model.Use and progressively enter method (stepwise) and the inspection coefficient of determination (R
2) situation of change select the suitable variables entering model.If have independent variable to make statistic F value too small and T inspection do not reach the level of signifiance (P value >0.1), then rejected; F value is comparatively large and T inspection reaches the level of signifiance (P value <0.05) is then entered.Adopt the coefficient of determination (R
2), root-mean-square error (RMSE) and relative root-mean-square error (rRMSE) evaluate the precision of regression model;
In formula, x
ifor certain stand characteristics of ground actual measurement,
for x
imean value,
for certain stand characteristics of model assessment, n is sampling intensity;
From (3) formula, rRMSE be RMSE (root-mean-square error) with
the number percent of (measured value average).
First utilize respectively OLI characteristic variable and LiDAR characteristic variable to build OLI biomass estimation model (being called for short OLI model) and LiDAR biomass estimation model (being called for short LiDAR model), then build comprehensive organism amount appraising model (abbreviation unified model) based on this two category features variable.When building this three kinds of models, analyze all in two kinds of situation, be first to all samples without the statistical study distinguished, then sample is divided into coniferous forest, broad-leaf forest and mixed forest by composition and analyzes respectively.For each model accuracy evaluation of Different Forest Types as table 3.The loose point of contrast based on Different Forest Types ground and underground biomass sample ground measured value and model (OLI model, LiDAR model and unified model) predicted value is shown in Fig. 5-10.
Table 3 is for the model accuracy evaluation table of Different Forest Types
Wherein, R
2for the model coefficient of determination; RMSE is root-mean-square error; RRMSE is relative root-mean-square error.
Above result shows that the inversion result of integrated model is better than single features variate model (that is, OLI model and LiDAR model), and its models fitting effect promoting is: R
2improve 3-24%; Estimation precision is promoted to: rRMSE reduces 2-10%.
Claims (6)
1. a method for remote sensing forest biomass inverting, is characterized in that, comprises the following steps:
1) OLI Yunnan snub-nosed monkey: the radiation calibration parameter first by OLI sensor carries out radiation calibration to raw video; Original DN value is converted into pixel radiance value; With FLAASH model, atmospheric correction is carried out to image again, thus radiance value is converted into earth's surface actual reflectance; Then carry out geometric accurate correction to image, choose culture point of the same name, adopt quadratic polynomial to correct, correction error controls within 0.1 pixel, and adopts the most contiguous pixel method to carry out resampling;
2) LiDAR data pre-service: first carry out noise level estimation and data smoothing, then Gauss curve fitting and waveform data points cloud, generate digital terrain, and the elevation of recycling DEM to vegetation echo point is normalized;
3) OLI characteristic variable is extracted: by carrying out band combination, K-T Transformation, texture information extraction, principal component analysis (PCA) to OLI image, minimal noise is separated conversion and multiple vegetation index converts, extract 5 stack features variablees, be respectively original single band variable, band combination variable, information enhancement group variable, vegetation index variable and texture information variable; Wherein texture analysis is carried out for the first principal component of principal component analysis (PCA);
4) the LiDAR point cloud characteristic variable extracted: LiDAR point cloud characteristic variable is the 4 stack features variablees calculated based on three-dimensional normalization LiDAR point cloud value, is respectively height variable, height percentile variable, canopy density variables, Canopy cover degrees variable;
5) characteristic variable screening: the LiDAR characteristic variable of extraction and OLI characteristic variable are carried out Pearson ' s correlation analysis with needing the parameter predicted, the absolute value choosing Pearson ' s related coefficient higher than 0.2 characteristic variable as modeling candidate variables; The computing method of Pearson ' s related coefficient are:
In formula, x
ifor certain stand characteristics of ground actual measurement, y
ifor certain LiDAR characteristic variable,
for x
imean value,
for y
imean value;
6) statistical study: ground is surveyed the biomass information that gathers as dependent variable, the characteristic variable that remote sensing technique extracts, as independent variable, sets up multivariate regression model; Use and progressively enter method stepwise and inspection coefficient of determination R
2situation of change select the suitable variables entering model; If have independent variable to make statistic F value too small and T inspection do not reach the level of signifiance, then rejected; F value is comparatively large and T inspection reaches the level of signifiance is then entered; Adopt coefficient of determination R
2, root-mean-square error RMSE and relative root-mean-square error rRMSE evaluate the precision of regression model;
In formula, x
ifor certain stand characteristics of ground actual measurement,
for x
imean value,
for certain stand characteristics of model assessment, n is sampling intensity;
2. the method for remote sensing forest biomass according to claim 1 inverting, it is characterized in that: step 2) in, noise level is estimated and data smoothing is: first raw data is transformed into frequency field, then using the criterion of low magnitude portion higher for frequency as noise level; Then select Gaussian filter smoothing.
3. the method for remote sensing forest biomass according to claim 1 inverting, is characterized in that: step 2) in, Gauss curve fitting and waveform data points cloud turn to: adopt nonlinear least square method to carry out matching to Wave data; Then extract discrete point cloud by local maximal peak detection filter algorithm from the Wave data after process, in each discrete point, have recorded energy and the amplitude information of return signal.
4. the method for remote sensing forest biomass according to claim 1 inverting, it is characterized in that: step 2) in, generation digital terrain is: first classify to extracting discrete point cloud from Wave data, then Kraus filtering process is carried out in order to remove non-ground points to last echo, finally use filtered last echo data and generate digital terrain model by being naturally close to method interpolation.
5. the method for remote sensing forest biomass according to claim 1 inverting, it is characterized in that: step 2) in, utilize the elevation of DEM to vegetation echo point be normalized for: make the height value of vegetation point be converted into height value relative to ground, and by the coordinate in the lower left corner and the upper right corner, every block sample cut; The normalization cloud data of respective coordinates position, 55 sample ground is extracted finally by GIS analysis tool.
6. the method for remote sensing forest biomass according to claim 1 inverting, is characterized in that: step 6) in, it is that P value >0.1, T check that to reach the level of signifiance be P value <0.05 that T inspection does not reach the level of signifiance.
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| CN115561773A (en) * | 2022-12-02 | 2023-01-03 | 武汉大学 | Forest carbon reserve inversion method based on ICESat-2 satellite-borne LiDAR data and multispectral data |
| CN117313959A (en) * | 2023-11-28 | 2023-12-29 | 吉林省林业科学研究院(吉林省林业生物防治中心站) | Forestry carbon sink monitoring method and system based on big data |
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-
2015
- 2015-02-03 CN CN201510056042.1A patent/CN104656098B/en active Active
Non-Patent Citations (7)
| Title |
|---|
| LIN CAO ET AL.: "Using Small-Footprint Discrete and Full-Waveform Airborne LiDAR Metrics to Estimate Total Biomass and Biomass Components in Subtropical Forests", 《REMOTE SENSING》 * |
| 宁亮亮等: "基于纹理信息的Landsat-8影像植被分类初探", 《中南林业科技大学学报》 * |
| 张凤霖等: "Landsat7 ETM+与Landsat8 OLI 植被和非植被定量研究", 《山西建筑》 * |
| 张颖等: "基于Landsat-8影像森林植被信息计算机自动提取研究", 《中国农学通报》 * |
| 曹林等: "基于机载小光斑LiDAR技术的亚热带森林参数信息优化提取", 《北京林业大学学报》 * |
| 曹林等: "基于机载小光斑LiDAR数据插值的亚热带森林丘陵地形的误差分析", 《南京林业大学学报(自然科学版）》 * |
| 曹林等: "激光雷达技术估测森林生物量的研究现状及展望", 《南京林业大学学报（自然科学版）》 * |
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