CN109344550A - A kind of forest reserves inversion method and system based on domestic high score satellite remote sensing date - Google Patents
A kind of forest reserves inversion method and system based on domestic high score satellite remote sensing date Download PDFInfo
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Abstract
The invention discloses a kind of forest reserves inversion method based on domestic high score satellite remote sensing date, using number table with calculating sample forest reserves measured value；On the basis of carrying out pretreated to remotely-sensed data, the panchromatic textural characteristics of image different windows and multispectral vegetation index feature are extracted；Image feature is subjected to correlation analysis with accumulation measured value respectively, chooses the significant image feature of correlation as the model variable factor；According to terrain data extraction sample terrain factor；Accumulation measured value and image texture feature, spectral signature and terrain factor variable are finally subjected to multivariate statistical regression, construct the forest reserves inverse model based on domestic high score satellite remote sensing date.Also disclose a kind of forest reserves Inversion System based on domestic high score satellite remote sensing date.The present invention has given full play to the advantage of domestic high score satellite remote sensing date inverting forest reserves, further improves the precision of forest reserves inverting.
Description
Technical field
The present invention relates to forest reserves inverting field, specifically a kind of forest based on domestic high score satellite remote sensing date
Accumulation inversion method and system.
Background technique
In recent years, Chinese forest resource monitoring develops to a wide range of, large scale, short cycle direction, and traditional is gloomy
Woods monitoring resource means are time-consuming and laborious based on artificial field investigation, are unfavorable for large scale or obtain on a large scale, have been difficult to fit
Answer the demand of production of forestry and ecological construction under the new situation.Remote sensing technology is in recent years since with monitoring range, very wide, data are adopted
Collect very quick, and can save the cost the advantages that, be widely used in forest composition research and standing forest parameter now
In rapidly extracting, very powerful technical support is provided for the dynamic monitoring of the forest reserves, is provided for forest management and administration
New technological means.
Remote sensing estimation model is the causes described between the observed quantity and earth's surface practical parameter of pixel.Using distant
Feel the research hotspot of image data always in recent years with correlation inverting standing forest parameter stronger between standing forest parameter, major part is all
Achieve preferable research effect.Currently, these research focus primarily upon using the textural characteristics of remotely-sensed data, spectral signature and
Spectrum derives vegetation index isoinversion standing forest parameter.However since the problems such as hypsography, tree crown shade, can seriously affect remote sensing
The various quantitative analyses of image establish standing forest Parameter Inversion Model there are precision and is not high, applicable by characteristics of remote sensing image merely
The problems such as property is not strong.
In recent years high-resolution remote sensing image atmospheric environment, land use, Urban Changes, in terms of answer
With very extensive, " high score series " domestic satellite remote-sensing image development in China is also very rapidly." high-resolution earth observation system
System engineering " is one of " National Program for Medium-to Long-term Scientific and Technological Development (2006-the year two thousand twenty) " important special project, engineering master
It to include near space observation system, Space borne detection system, ground system, aviation measurement system, application system etc..High score No.1
Satellite (hereinafter referred to as GF-1) is starting star of high-resolution earth observation systems national science and technology key special subjects, including 2m it is panchromatic,
The multispectral wide cut image of 8m resolution multi-spectral image and 16m resolution ratio.During No. two satellites (hereinafter referred to as GF-2) of high score are
First spatial discrimination of state's independent development is highest better than the current resolution ratio of civilian optics high score remote sensing satellite and China of 1m
Earth observation optical satellite, spatial resolution are panchromatic image 1m and multispectral image 4m.Domestic high score satellite remote sensing date tool
There is the features such as high spatial resolution, high position precision, high Electrodynamic radiation and rapid attitude maneuver ability, can be the soil in China
Ground utilizes dynamic monitoring, Mineral Resource Survey, urban and rural planning monitoring and evaluation, traffic network planning, forest inventory investigation, desertification
Numerous industries such as monitoring provide technological service and support.It explores based on China's high score satellite remote sensing date inverting forest reserves
New way anticipates for the construction situation of tracking and monitoring Forest Resources Condition and ecological projects related to the forestry industry with highly important practice
Justice.
Summary of the invention
The purpose of the present invention is to provide a kind of forest reserves inversion methods based on domestic high score satellite remote sensing date
And system, to solve the problems mentioned in the above background technology.
To achieve the above object, the invention provides the following technical scheme:
A kind of forest reserves inversion method based on domestic high score satellite remote sensing date, comprising the following steps:
Step a, according to the data calculating sample of research area's ground investigation forest reserves, is calculated sample using number table
Ground forest reserves measured value；
Step b, to domestic high score satellite remote-sensing image carry out Yunnan snub-nosed monkey, respectively include the radiation calibration to image,
Atmospheric correction and ortho-rectification obtain pretreated panchromatic and multi-spectrum remote sensing image；
Step c extracts the panchromatic image textural characteristics variable of domestic high score satellite remote-sensing image different windows size, and step
In rapid a corresponding sample forest reserves measured value carry out correlation analysis, it is special to choose the highest window texture of correlation
Levy the textural characteristics factor of the variable as inverting accumulation；
Step d is extracted in the spectral vegetation indexes variable and step a of domestic high score satellite multispectral image corresponding
Sample forest reserves measured value carry out correlation analysis, choose the significant spectral vegetation indexes variable of correlation as inverting storage
The spectral signature factor of accumulated amount；
Step e extracts the terrain factor on corresponding sample ground according to research area's terrain data；
Step f, using sample forest reserves measured value is as model dependent variable, the domestic panchromatic line of high score satellite remote-sensing image
Characterization factor and the spectral signature factor and terrain factor are managed as model independent variable, forest is constructed using Stepwise Regression Method
Accumulation inverse model, using R2Accuracy test is carried out to optimum prediction model with root-mean-square error；Finally according to inverse model
Estimation research area's forest reserves, makes corresponding accumulation spatial distribution thematic map.
Moreover, in above-mentioned steps c, panchromatic textural characteristics window size includes 3 × 3,5 × 5,7 × 7,9 × 9,11 × 11,
13 × 13,15 × 15, textural characteristics variable include mean value, variance, homogeney, contrast, otherness, comentropy, angular second moment,
Correlation, see Table 1 for details.
1 textural characteristics variable of table and description
| Name variable | Variable description |
| Mean value | Reflect the integral radiation situation of image |
| Variance | Reflect the information content size of image |
| Homogeney | Reflect the measurement of image local gradation uniformity |
| Contrast | Reflect image local grey scale change total amount |
| Otherness | Reflect Image Warping variation |
| Comentropy | Reflect the measurement of information content possessed by image |
| Angular second moment | Reflect image grayscale distributing homogeneity |
| Correlation | Reflect the consistency of image texture |
Moreover, spectral vegetation indexes variable includes brightness index, normalized differential vegetation index, simple ratio in above-mentioned steps d
Vegetation index, red green Ratio index, green red Ratio index, global environment Monitoring Index, soil adjust vegetation index, and see Table 2 for details.
2 spectral vegetation indexes variable of table and description
Moreover, the terrain factor in above-mentioned steps e, including elevation, the gradient and slope aspect, see Table 3 for details.
3 terrain factor variable of table and description
| Name variable | Variable description |
| Elevation | Height value is obtained using terrain data |
| The gradient | Value of slope is obtained using terrain data, is classified as 6 ranks |
| Slope aspect | Slope aspect value is obtained using terrain data, is divided into 9 ranks |
The present invention also accordingly proposes a kind of forest reserves Inversion System based on domestic high score satellite remote sensing date, including
Sample forest reserves obtain module, Yunnan snub-nosed monkey module, image texture characteristic variable choose module, image spectral signature become
Amount chooses module, terrain factor extraction module and forest reserves inverting module, the sample forest reserves to obtain module defeated
Connect forest reserves inverting module out, Yunnan snub-nosed monkey module output connection image texture characteristic variable choose module and
Image spectral signature variable chooses module, and the image texture characteristic variable chooses module, image spectral signature variable chooses mould
Block and terrain factor extraction module output connection forest reserves inverting module.
Each module specific features are as follows；
First module, sample forest reserves obtain module, using number table sample is calculated forest reserves survey
Value；
Second module, Yunnan snub-nosed monkey module respectively include the radiation calibration, atmospheric correction and ortho-rectification to image,
Obtain pretreated panchromatic and multi-spectrum remote sensing image；
Third module, image texture characteristic variable choose module, by the panchromatic image textural characteristics of different windows size and
Sample forest reserves forest reserves measured value carries out correlation analysis with obtaining sample corresponding in module, choose correlation
The textural characteristics factor of the highest window textural characteristics variable as inverting accumulation；
4th module, image spectral signature variable choose module, and the spectrum for extracting domestic high score satellite multispectral image is planted
By index variable and sample forest reserves measured value carries out correlation point to forest reserves with obtaining sample corresponding in module
The spectral signature factor of the significant spectral vegetation indexes variable of correlation as inverting accumulation is chosen in analysis；
5th module, terrain factor extraction module extract the terrain factor on corresponding sample ground according to research area's terrain data；
6th module, forest reserves inverting module, using sample forest reserves measured value is domestic as model dependent variable
The panchromatic textural characteristics factor of high score satellite remote-sensing image and the spectral signature factor and terrain factor are used as model independent variable
Stepwise Regression Method constructs forest reserves inverse model, using R2Precision is carried out to optimum prediction model with root-mean-square error
It examines.Research area's forest reserves is finally estimated according to inverse model, produces and generates corresponding accumulation spatial distribution thematic map.
Moreover, in the third module, panchromatic textural characteristics window size includes 3 × 3,5 × 5,7 × 7,9 × 9,11 ×
11,13 × 13,15 × 15, textural characteristics variable includes mean value, variance, homogeney, contrast, otherness, comentropy, angle second order
Square, correlation, see Table 1 for details.
Moreover, spectral vegetation indexes variable includes brightness index BRIGHT, normalized differential vegetation index in the 4th module
NDVI, simple ratio vegetation index SR, red green Ratio index GR, green red Ratio index VI, global environment Monitoring Index GEMI, soil
Earth adjusts vegetation index SAVI, and see Table 2 for details.
Moreover, the terrain factor in the 5th module, including elevation, the gradient and slope aspect, see Table 3 for details.
Compared with prior art, the beneficial effects of the present invention are: it is of the invention based on domestic high score satellite remote sensing date
Forest reserves inversion method and system introduce terrain factor and participate in model construction, overcome orographic factor to model inversion
It influences, in combination with the textural characteristics and spectral signature of image, effectively increases the inversion accuracy of model, realize domestic height
Divide application of the satellite remote sensing date in forest reserves high-precision quantitative inverting, for forest inventory investigation and can monitor, with
And forest management and administration provides data and supports.
Detailed description of the invention
Fig. 1 is the Technology Roadmap of the embodiment of the present invention.
Fig. 2 is the research area GF-2 remote sensing image of the embodiment of the present invention.
Fig. 3 is the research area accumulation spatial distribution map generated using GF-2 remotely-sensed data inverting forest reserves.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Embodiment 1
Embodiment of the invention is to carry out inverting to the forest reserves in research area based on No. two remotely-sensed datas of high score,
Technology path is referring to Fig.1, the specific steps are as follows:
Step a, accumulation calculates with studying area's sample, uses number table calculation formula according to the data of ground investigation, obtains
Sample ground forest reserves (SV) measured value.
Step b, Yunnan snub-nosed monkey pre-process GF-2 remote sensing image (such as Fig. 2), respectively include the radiation to image
Calibration, atmospheric correction and ortho-rectification.
This example by the publication of inquiry China Resource Satellite Applied Center " domestic land observation satellite year absolute radiation is fixed
The Absolute Radiometric Calibration Coefficients of GF-2 satellite, complete the radiation of data in remote sensing image processing software ENVI5.0 in mark coefficient "
Calibration processing.It recycles the FLAASH atmospheric correction module carried in ENVI5.0 to use and is based on MORTRAN radiative transfer model method
Atmospheric correction is carried out to the GF-2 data for having been subjected to radiation calibration.Finally use ten thousand topographic map of ENVI5.0 remote sensing software combination 1:1
Ortho-rectification is carried out to GF-2 panchromatic image；Again to be reference, Combining with terrain by the GF-2 panchromatic image data of ortho-rectification
Data carry out ortho-rectification to GF-2 multispectral image, obtain pretreated panchromatic and multi-spectrum remote sensing image.
Step c extracts GF-2 panchromatic image image texture feature.Research and utilization remote sensing software ENVI5.0 extracts 3 respectively ×
3, the textural characteristics of 5 × 5,7 × 7,9 × 9,11 × 11,13 × 13,15 × 15 etc. 7 window sizes.Textural characteristics use
8 characteristic values in ENVI5.0 software Co-occurrence Measures module, mean value (Mean), variance (Variance),
Concertedness (Homogeneity), contrast (Contrast), diversity (Dissimilarity), comentropy (Entropy), two
Rank square (SecondMoment) and correlation (Correlation).With sample corresponding in step a forest reserves measured value
SV carries out correlation analysis, finds the correlation of each textural characteristics as the increase of window size gradually changes, and 13 × 13
Reach maximum value at window, therefore the panchromatic image textural characteristics participation model extracted under this research 13 × 13 windows of selection is anti-
It drills.
Step d extracts the spectral vegetation indexes variable of GF-2 multispectral image.Using ENVI5.0 software BandMath mould
Block carries out wave band calculating, including brightness index, difference vegetation index, normalized differential vegetation index, enhancement mode meta file, ratio are planted
Vegetation index is adjusted by index and soil, calculation formula is as follows.It is by sample corresponding in spectral vegetation indexes and step a gloomy
Woods accumulation measured value SV carries out correlation analysis, and discovery vegetation index EVI, RVI, NDVI, DVI is aobvious with accumulation SV correlation
It writes, using this four spectral vegetation indexes as the spectral signature factor of inverting accumulation.
(1) brightness index generated through K-T Transformation: BRIGHT=(B+G+R+NIR)/4；
(2) normalized differential vegetation index: NDVI=(NIR-R)/(NIR+R)；
(3) difference vegetation index: DVI=NIR-R；
(4) enhancement mode meta file: EVI=2.5 × (NIR-R)/(NIR+6 × RED-7.5BLUE+1)；
(5) ratio vegetation index: RVI=NIR/R；
(6) soil adjusts vegetation index: SAVI=(NIR-RED)/(NIR+RED+0.5)；
Step e extracts the terrain factor on sample ground.The topographic map data that this research and utilization studies area 1:1 ten thousand is used
ARCGIS10.2 tool extracts dispersed elevation, the gradient, slope aspect in the size area of sample place, participates in as the terrain variable factor
Model construction.
Step f, using sample forest reserves measured value SV is as model dependent variable, the panchromatic textural characteristics factor of GF-2 image
With the spectral signature factor and terrain factor as model independent variable, Stepwise Regression Method structure is used using SPSS19.0 software
Forest reserves inverse model is built, the accumulation regression model R value based on the building of GF-2 data is 0.907, R2 adjIt is 0.808.
The Estimation of forest parameters value of test samples is calculated according to inverse model, accumulation model prediction mean value is 136.52m3/hm2, actual measurement
Mean value is 131.28m3/hm2, absolute error 3.83%, RMSE 16.48m3/hm2.It can be estimated according to the inverse model and be ground
Study carefully area's forest reserves, obtains corresponding research area accumulation spatial distribution thematic map, such as Fig. 3.
Embodiment 2
When it is implemented, method provided by the present invention can be realized automatically instead based on software systems using modular mode
It drills.Present example also accordingly proposes a kind of forest reserves Inversion System based on domestic high score satellite remote sensing date, including
With lower module:
First module, sample forest reserves obtain module, using number table sample is calculated forest reserves survey
Value；
Second module, Yunnan snub-nosed monkey module respectively include the radiation calibration, atmospheric correction and ortho-rectification to image,
Obtain pretreated panchromatic and multi-spectrum remote sensing image；
Third module, image texture characteristic variable choose module, by the panchromatic image textural characteristics of different windows size and
Sample forest reserves forest reserves measured value carries out correlation analysis with obtaining sample corresponding in module, choose correlation
The textural characteristics factor of the highest window textural characteristics variable as inverting accumulation；
Wherein, panchromatic textural characteristics window size include 3 × 3,5 × 5,7 × 7,9 × 9,11 × 11,13 × 13,15 ×
15, textural characteristics variable includes mean value, variance, homogeney, contrast, otherness, comentropy, angular second moment, correlation；
4th module, image spectral signature variable choose module, and the spectrum for extracting domestic high score satellite multispectral image is planted
By index variable and sample forest reserves measured value carries out correlation point to forest reserves with obtaining sample corresponding in module
The spectral signature factor of the significant spectral vegetation indexes variable of correlation as inverting accumulation is chosen in analysis；
Wherein, spectral vegetation indexes variable include brightness index, it is normalized differential vegetation index, simple ratio vegetation index, red
Green Ratio index, green red Ratio index, global environment Monitoring Index, soil adjust vegetation index；
5th module, terrain factor extraction module extract the terrain factor on corresponding sample ground according to research area's terrain data；
6th module, forest reserves inverting module, using sample forest reserves measured value is domestic as model dependent variable
The panchromatic textural characteristics factor of high score satellite remote-sensing image and the spectral signature factor and terrain factor are used as model independent variable
Stepwise Regression Method constructs forest reserves inverse model, using R2Precision is carried out to optimum prediction model with root-mean-square error
It examines.Research area's forest reserves is finally estimated according to inverse model, makes corresponding accumulation spatial distribution thematic map, is such as schemed
3。
Each module specific implementation can be found in corresponding steps.The multiple tasks of system module have been carried out integrated sum number by the present invention
According to sharing, the complexity of system is reduced, the rapidly and automatically change inverting of forest reserves may be implemented, can be provided for user
Better data supporting.
Forest reserves inversion method and system based on domestic high score satellite remote sensing date of the invention, introduce landform because
Son participates in model construction, influence of the orographic factor to model inversion is overcome, in combination with the textural characteristics and spectrum of image
Feature effectively increases the inversion accuracy of model, and it is high-precision fixed in forest reserves to realize domestic high score satellite remote sensing date
The application in inverting is measured, data can be provided and supported for forest inventory investigation and monitoring and forest management and administration.
The above are merely the preferred embodiment of the present invention, it is noted that for those skilled in the art, not
Under the premise of being detached from present inventive concept, several modifications and improvements can also be made, these also should be considered as protection model of the invention
It encloses, these all will not influence the effect and patent practicability that the present invention is implemented.
Claims (10)
1. a kind of forest reserves inversion method based on domestic high score satellite remote sensing date, which is characterized in that including following step
It is rapid:
Step a, according to the data calculating sample of research area's ground investigation forest reserves, gloomy sample is calculated using number table
Woods accumulation measured value；
Step b carries out Yunnan snub-nosed monkey to domestic high score satellite remote-sensing image, respectively includes the radiation calibration to image, atmosphere
Correction and ortho-rectification, obtain pretreated panchromatic and multi-spectrum remote sensing image；
Step c extracts the panchromatic image textural characteristics variable and step a of domestic high score satellite remote-sensing image different windows size
In corresponding sample forest reserves measured value carry out correlation analysis, choose the highest window textural characteristics variable of correlation
The textural characteristics factor as inverting accumulation；
Step d, with extracting sample corresponding in the spectral vegetation indexes variable and step a of domestic high score satellite multispectral image
Forest reserves measured value carries out correlation analysis, chooses the significant spectral vegetation indexes variable of correlation as inverting accumulation
The spectral signature factor；
Step e extracts the terrain factor on corresponding sample ground according to research area's terrain data；
Step f, using sample for forest reserves measured value as model dependent variable, the domestic panchromatic texture of high score satellite remote-sensing image is special
The factor, the spectral signature factor and terrain factor are levied as model independent variable, management volume is constructed using Stepwise Regression Method
Inverse model is measured, using R2Accuracy test is carried out to optimum prediction model with root-mean-square error, is finally estimated according to inverse model
Study area's forest reserves.
2. the forest reserves inversion method according to claim 1 based on domestic high score satellite remote sensing date, feature
Be, in step c, panchromatic textural characteristics window size include 3 × 3,5 × 5,7 × 7,9 × 9,11 × 11,13 × 13,15 ×
15, textural characteristics variable includes mean value, variance, homogeney, contrast, otherness, comentropy, angular second moment and correlation.
3. the forest reserves inversion method according to claim 2 based on domestic high score satellite remote sensing date, feature
Be, in step d, spectral vegetation indexes variable include brightness index, normalized differential vegetation index, simple ratio vegetation index,
Red green Ratio index, green red Ratio index, global environment Monitoring Index and soil adjust vegetation index.
4. the forest reserves inversion method according to claim 3 based on domestic high score satellite remote sensing date, feature
It is, in step e, terrain factor includes elevation, the gradient and slope aspect.
5. the forest reserves inversion method according to claim 4 based on domestic high score satellite remote sensing date, feature
It is, in step f, estimation models is established by Stepwise Regression Method, is obtained using domestic high score satellite remote-sensing image inverting
Forest reserves is obtained, research area's forest reserves spatial distribution map is generated.
6. a kind of forest reserves Inversion System based on domestic high score satellite remote sensing date, which is characterized in that including sample gloomy
Woods accumulation obtains module, Yunnan snub-nosed monkey module, image texture characteristic variable and chooses module, the selection of image spectral signature variable
Module, terrain factor extraction module and forest reserves inverting module, the sample forest reserves obtain module output connection
Forest reserves inverting module, the Yunnan snub-nosed monkey module output connection image texture characteristic variable choose module and image light
Spectrum signature variable chooses module, and the image texture characteristic variable chooses module, image spectral signature variable chooses module and ground
Shape factor extraction module output connection forest reserves inverting module.
7. the forest reserves Inversion System according to claim 6 based on domestic high score satellite remote sensing date, feature
It is:
The sample forest reserves obtain module using number table forest reserves measured value sample is calculated；
The Yunnan snub-nosed monkey module respectively includes pre-processing the radiation calibration, atmospheric correction and ortho-rectification of image
Panchromatic and multi-spectrum remote sensing image afterwards；
The image texture characteristic variable chooses module for by the panchromatic image textural characteristics of different windows size and sample gloomy
Accumulation measured value carries out correlation analysis to woods accumulation with obtaining sample corresponding in module, chooses the highest window of correlation
The textural characteristics factor of the textural characteristics variable as inverting accumulation；
The image spectral signature variable chooses the spectral vegetation indexes that module is used to extract domestic high score satellite multispectral image
Variable and sample forest reserves accumulation measured value carries out correlation analysis with obtaining sample corresponding in module, choose phase
The spectral signature factor of the significant spectral vegetation indexes variable of closing property as inverting accumulation；
The terrain factor extraction module is used to extract the terrain factor on corresponding sample ground according to research area's terrain data；
The forest reserves inverting module is for using sample for accumulation measured value as model dependent variable, domestic high score satellite to be distant
The panchromatic textural characteristics factor of image and the spectral signature factor and terrain factor are felt as model independent variable, using successive Regression point
Analysis method constructs forest reserves inverse model, using R2Accuracy test is carried out to optimum prediction model with root-mean-square error, finally
Research area's forest reserves is estimated according to inverse model, produces and generates corresponding accumulation spatial distribution thematic map.
8. the forest reserves Inversion System according to claim 7 based on domestic high score satellite remote sensing date, feature
It is, it includes 3 × 3,5 × 5,7 × 7,9 that the image texture characteristic variable, which chooses the panchromatic textural characteristics window size in module,
× 9,11 × 11,13 × 13,15 × 15, textural characteristics variable includes mean value, variance, homogeney, contrast, otherness, information
Entropy, angular second moment and correlation.
9. the forest reserves Inversion System according to claim 8 based on domestic high score satellite remote sensing date, feature
It is, it includes brightness index, normalization vegetation that the image spectral signature variable, which chooses the spectral vegetation indexes variable in module,
Index, simple ratio vegetation index, red green Ratio index, green red Ratio index, global environment Monitoring Index and soil adjustment are planted
By index.
10. the forest reserves Inversion System according to claim 9 based on domestic high score satellite remote sensing date, feature
It is, the terrain factor includes elevation, the gradient and slope aspect.
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| CN110427857A (en) * | 2019-07-26 | 2019-11-08 | 国网湖北省电力有限公司检修公司 | A kind of transmission line of electricity geological disasters analysis method based on Remote Sensing Data Fusion Algorithm |
| CN110807435A (en) * | 2019-11-07 | 2020-02-18 | 航天信德智图（北京）科技有限公司 | Remote sensing forest accumulation monitoring method based on various vegetation indexes |
| CN111062628A (en) * | 2019-12-20 | 2020-04-24 | 上海市园林科学规划研究院 | Forest asset quality grading evaluation method |
| CN111062628B (en) * | 2019-12-20 | 2023-04-18 | 上海市园林科学规划研究院 | Forest asset quality grading evaluation method |
| CN111414891B (en) * | 2020-04-07 | 2023-04-14 | 云南电网有限责任公司昆明供电局 | Power transmission line channel tree height inversion method based on laser radar and optical remote sensing |
| CN111414891A (en) * | 2020-04-07 | 2020-07-14 | 云南电网有限责任公司昆明供电局 | Power transmission line channel tree height inversion method based on laser radar and optical remote sensing |
| CN112861435A (en) * | 2021-02-09 | 2021-05-28 | 深圳大学 | Mangrove forest quality remote sensing retrieval method and intelligent terminal |
| CN112861435B (en) * | 2021-02-09 | 2023-07-18 | 深圳大学 | Mangrove quality remote sensing inversion method and intelligent terminal |
| CN113156394A (en) * | 2021-03-31 | 2021-07-23 | 国家林业和草原局华东调查规划设计院 | Forest resource monitoring method and device based on laser radar and storage medium |
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| CN113221445A (en) * | 2021-04-21 | 2021-08-06 | 山东师范大学 | Method and system for estimating soil salinity by using joint characteristics of remote sensing images |
| CN113221445B (en) * | 2021-04-21 | 2023-01-17 | 山东师范大学 | Method and system for estimating soil salinity by using joint characteristics of remote sensing images |
| CN113870425A (en) * | 2021-09-03 | 2021-12-31 | 中林信达（北京）科技信息有限责任公司 | Forest accumulation space mapping method based on random forest and multi-source remote sensing technology |
| CN114694036A (en) * | 2022-03-18 | 2022-07-01 | 南京农业大学 | High-altitude area crop classification and identification method based on high-resolution images and machine learning |
| CN114648705A (en) * | 2022-03-28 | 2022-06-21 | 王大成 | Carbon sink monitoring system and method based on satellite remote sensing |
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