CN108764188A - A kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method - Google Patents
A kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method Download PDFInfo
- Publication number
- CN108764188A CN108764188A CN201810559473.3A CN201810559473A CN108764188A CN 108764188 A CN108764188 A CN 108764188A CN 201810559473 A CN201810559473 A CN 201810559473A CN 108764188 A CN108764188 A CN 108764188A
- Authority
- CN
- China
- Prior art keywords
- sea
- buckthorn
- area
- index
- remote sensing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/194—Terrestrial scenes using hyperspectral data, i.e. more or other wavelengths than RGB
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Mathematics (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Evolutionary Biology (AREA)
- Remote Sensing (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Computational Biology (AREA)
- Algebra (AREA)
- Astronomy & Astrophysics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
A kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method is to carry out research area's remote sensing image respectively to order and pretreatment；Choose to research area's sea-buckthorn sample；Area's sea-buckthorn spatial distribution data is studied to obtain；Area's fruit per unit area yield is studied to investigate with leaf area index；Calculate the normalized differential vegetation index of sample prescription；Determine research area's sea-buckthorn leaf area index remote sensing appraising model；Determine the regression model between research area's sea-buckthorn fruit per unit area yield and leaf area index；Determine research area's sea-buckthorn fruit yield remote sensing appraising model；Calculate the normalized differential vegetation index of research area sea-buckthorn distributed areas；Study area's sea-buckthorn fruit output remote sensing appraising；This method is using sea-buckthorn leaf area index as intermediary, construct the remote sensing appraising model of monitoring sea-buckthorn fruit per unit area yield, space inversion is carried out to the fructus hippophae yield for studying area using this model, promptly and accurately to estimate that big regional extent sea-buckthorn fruit output provides Accurate Estimation Method, the quick and precisely monitoring of the extensive area sea-buckthorn fruit output based on remote sensing technology is realized.
Description
Technical field
It is especially a kind of fast using satellite remote sensing date the present invention relates to a kind of method of remote sensing appraising sea-buckthorn fruit output
Fast accurate estimation sea-buckthorn fruit output and pattern and dynamic method.
Background technology
Sea-buckthorn be the perennial fallen leaves property shrub of Elaeangnaceae or dungarunga, be it is a kind of with high economy, the ecological value
Plant resources.Contain abundant nutriment and bioactive substance in fructus hippophae, is widely used in food, medicine, light industry, boat
The key areas such as it.Accurate estimation fructus hippophae yield, is of great significance for Efficient Development using sea buckthorn resources.
Traditional sea-buckthorn fruit output obtains generally based on field survey, time-consuming and laborious, and is difficult to large scale development.It is distant
The sea-buckthorn fruit output estimation that develops into of sense technology provides new technological means, and in particular a wide range of quick and precisely estimation provides
It may.Have using spectral technique development fructus hippophae currently, application of the remote sensing technology in terms of sea-buckthorn is concentrated mainly on laboratory
Imitate composition measurement, including sea buckthorn juice flavones content, fructus hippophae Isorhamnetin content, fructus hippophae flavone compound, fructus hippophae
Juice kind Production area recognition.
Carry out the estimation of sea-buckthorn fruit output using remote sensing technology and see relevant report not yet, for scientific and reasonable utilization
Sea buckthorn resources, it is urgent to provide a kind of sea-buckthorn fruit output remote sensing Accurate Estimation Methods.
Invention content
The purpose of the present invention is to provide a kind of accurate remote sensing estimation methods of sea-buckthorn fruit output, to overcome conventional method to estimate
Calculation fructus hippophae yield is time-consuming, laborious, and cannot achieve deficiency that is a wide range of, continuously accurately estimating for a long time.
To achieve the goals above, the technical measures taken of the present invention are that a kind of sea-buckthorn fruit output remote sensing is accurately estimated
Method, it is characterised in that：The Accurate Estimation Method follows these steps to carry out：
1）Area's remote sensing image is studied to order and pre-process
It is panchromatic with 6 meters of spatial resolution multi light spectrum hands that research 1.5 meters of spatial resolutions of area SPOT-6/7 in October are ordered in programming
Image data merges 1.5 meters of resolution panchromatic images of SPOT-6/7 with 6 meters of resolution multi-spectral images, generates 1.5
Rice resolution multi-spectral fusion evaluation；
2）Choose to research area's sea-buckthorn sample
Binding area site type chooses n（≥30）The sample of a m2, it is ensured that it is uniformly distributed in these samples research area, and
Sea-buckthorn vegetation growing way is uniform in each sample ground, while recording land occupation condition, Community characteristics and plant size, is surveyed by high-precision GPS
Determine and record the geographical coordinate on sample ground；
3）Area's sea-buckthorn spatial distribution data is studied to obtain
According to above-mentioned steps 2）The research area sea-buckthorn sample of selection, to above-mentioned steps 1）1.5 meters of resolution ratio of SPOT-6/7 of generation
Multi-spectral image fusion image interpretation determines research area sea-buckthorn in terms of spectral signature, textural characteristics, shape feature and distribution characteristics
Interpretation mark on 1.5 meters of resolution multi-spectral fusion evaluations of SPOT-6/7；Indicated based on interpretation, using object-oriented point
Class method extracts the Current distribution of research area sea-buckthorn on 1.5 meters of resolution multi-spectral fusion evaluations of SPOT-6/7, generates research area
Sea-buckthorn spatial distribution map；
4）Area's fruit per unit area yield is studied to investigate with leaf area index
In above-mentioned steps 2）The sample of selection ground central area, lays 1 m sample prescription of 5 m × 5, carries out fruit per unit area yield and leaf area
Index is investigated, and is measured by high-precision GPS and is recorded sample prescription latitude and longitude information；Leaf area index is measured using plant canopy point
Analyzer carries out, and measures 5 times, takes respectively in each sample prescription center Fen Dong-west, north-south, northeast-southwest, the orientation of northwest-southeast 4
Leaf area index of its average value as the sample prescription；After leaf area index measures, according to professional standard, the whole in sample prescription is harvested
Sea-buckthorn fruit, scene claim its fresh weight, the fruit per unit area yield of sample prescription are calculated；
5）Calculate the normalized differential vegetation index of sample prescription
According to above-mentioned steps 4）The sample prescription latitude and longitude information of middle determination, in above-mentioned steps 1）1.5 meters points of the SPOT-6/7 of middle generation
It selects the pixel being fully located in sample prescription to correspond to pixel as sample prescription in resolution Multi-spectral image fusion image, extracts the red of corresponding pixel
Wave band and near infrared band reflectivity, calculate the normalized differential vegetation index of two wave band reflectivity combination；
6）Determine research area's sea-buckthorn leaf area index remote sensing appraising model
Using unitary linear analysis to above-mentioned steps 4）The corresponding above-mentioned steps 5 of the sample prescription sea-buckthorn leaf area index of investigation）
In relationship between the normalized differential vegetation index that is calculated carry out regression fit, determine research area's sea-buckthorn leaf area index remote sensing
Appraising model；
7）Determine the regression model between research area's sea-buckthorn fruit per unit area yield and leaf area index
Using unitary linear analysis to above-mentioned steps 4）The corresponding sample prescription fructus hippophae of the sample prescription sea-buckthorn leaf area index of investigation
Relationship between product per unit area yield carries out regression fit, determines the recurrence mould between research area's sea-buckthorn fruit per unit area yield and leaf area index
Type；
8）Determine research area's sea-buckthorn fruit yield remote sensing appraising model
According to above-mentioned steps 6）Determining research area sea-buckthorn leaf area index remote sensing appraising model and above-mentioned steps 7）Determining
Regression model between sea-buckthorn fruit per unit area yield and leaf area index establishes the research area sea-buckthorn fruit based on normalized differential vegetation index
The remote sensing appraising model of per unit area yield；
9）Calculate the normalized differential vegetation index of research area sea-buckthorn distributed areas
In above-mentioned steps 3）On the research area sea-buckthorn spatial distribution map that description method obtains, according to 1.5 meters of resolution ratio of SPOT-6/7
Fusion evaluation multi light spectrum hands reflectivity calculates the research red wave band in area sea-buckthorn distributed areas and near infrared band reflectivity by pixel
The normalization index of combination；
10）Study area's sea-buckthorn fruit output remote sensing appraising
According to above-mentioned steps 9）The red wave band and near infrared band reflectivity for the research area sea-buckthorn distributed areas that description method calculates
The normalized differential vegetation index of combination, and through the above steps 8）Determining research area sea-buckthorn fruit yield remote sensing appraising model, by
Pixel calculates the sea-buckthorn fruit per unit area yield of research area sea-buckthorn distributed areas, further calculates to obtain the sand of research area sea-buckthorn distributed areas
Spine fruit output.
Wherein, the multi light spectrum hands is blue spectrum wave band, green light spectrum wave band, red spectral wave band and near infrared spectrum wave
Section.
Above-mentioned this method is used as intermediary by sea-buckthorn leaf area index, has excavated sea-buckthorn leaf area index and fructus hippophae per unit area yield
Between internal relation, construct monitoring sea-buckthorn fruit per unit area yield remote sensing appraising model, and application this model to study area sand
Spine fruit output has carried out space inversion, overcomes the limitation of traditional on-site inspection method.This method has been filled up based on distant
The blank for feeling data estimation sea-buckthorn fruit output, promptly and accurately to estimate that it is a kind of new that big regional extent sea-buckthorn fruit output provides
Method realizes the quick and precisely monitoring of the extensive area sea-buckthorn fruit output based on remote sensing technology.
Specific implementation mode
Below by a kind of application case of Shanxi subregion sea-buckthorn fruit output remote sensing estimation method, to the present invention's
Specific implementation mode is described further.
Implement a kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method, the Accurate Estimation Method is husky for Shanxi subregion
Spine fruit output remote sensing appraising, specific remote sensing Accurate Estimation Method follow these steps to carry out：
Step 1: remote sensing image is ordered and pretreatment
It is panchromatic multispectral with 6 meters of spatial resolutions that 1.5 meters of spatial resolutions of Shanxi subregion SPOT-6 in October are ordered in programming
Wave band（Blue, green, red, near-infrared）Image data.To 1.5 meters of resolution panchromatic images of SPOT-6 and 6 meters of resolution multi-spectral shadows
As being merged, 1.5 meters of resolution multi-spectral fusion evaluations are generated.
Step 2: sea-buckthorn sample choose
In conjunction with site type, with choosing 32 samples.It is uniformly distributed in research area with ensuring these samples, and sea-buckthorn is planted in each sample ground
It is uniform by growing way, while recording land occupation condition, Community characteristics, plant size etc., it is measured using high-precision GPS and records sample ground
Geographical coordinate.
Step 3: sea-buckthorn spatial distribution data obtains
The sea-buckthorn sample chosen according to above-mentioned steps two, the 1.5 meters of resolution multi-spectrals of SPOT-6 generated to above-mentioned steps one melt
Image interpretation is closed, from spectral signature, textural characteristics, shape feature, distribution characteristics etc., determines sea-buckthorn in 1.5 meters of SPOT-6
Interpretation mark on resolution multi-spectral fusion evaluation.Indicated based on interpretation, using object oriented classification method in SPOT-6 1.5
The Current distribution that sea-buckthorn is extracted on rice resolution multi-spectral fusion evaluation, generates sea-buckthorn spatial distribution map.
Step 4: sample prescription fruit per unit area yield is investigated with leaf area index
In the sample that above-mentioned steps two are chosen central area, lays 1 sample prescription（5 m × 5 m）Carry out fruit per unit area yield and leaf area
Index is investigated, and is measured using high-precision GPS and is recorded sample prescription latitude and longitude information.Leaf area index, which measures, uses plant canopy
Analyzer carries out, and is measured respectively 5 times in each sample prescription center Fen Dong-west, north-south, northeast-southwest, the orientation of northwest-southeast 4,
Take its average value as the leaf area index of the sample prescription；After leaf area index measures, according to professional standard, harvest complete in sample prescription
Portion's fructus hippophae, scene claim its fresh weight, the fruit per unit area yield of sample prescription are calculated.
Step 5: calculating the normalized differential vegetation index of sample prescription（NDVI）
According to the sample prescription latitude and longitude information determined in above-mentioned steps four, 1.5 meters of resolutions of the SPOT-6 generated in above-mentioned steps one
It selects the pixel being fully located in sample prescription to correspond to pixel as sample prescription in rate Multi-spectral image fusion image, extracts the red wave of corresponding pixel
Section and near infrared band reflectivity, calculate the normalized differential vegetation index of two wave band reflectivity combination（NDVI）.
Step 6: sea-buckthorn leaf area index remote sensing appraising model determines
The corresponding above-mentioned steps five of sample prescription sea-buckthorn leaf area index above-mentioned steps four investigated using unitary linear analysis
In relationship between the normalized differential vegetation index that is calculated carry out regression fit, determine the remote sensing appraising of sea-buckthorn leaf area index
Model.
Step 7: regression model determines between sea-buckthorn fruit per unit area yield and leaf area index
The corresponding sample prescription fructus hippophae of sample prescription sea-buckthorn leaf area index above-mentioned steps four investigated using unitary linear analysis
Relationship between per unit area yield carries out regression fit, determines the regression model between fructus hippophae per unit area yield and leaf area index.
Step 8: sea-buckthorn fruit yield remote sensing appraising model determines
The sea-buckthorn for grinding sea-buckthorn leaf area index remote sensing appraising model and the determination of above-mentioned steps seven determined according to above-mentioned steps six
Regression model between fruit per unit area yield and leaf area index is established and is based on normalized differential vegetation index（NDVI）Fructus hippophae per unit area yield it is distant
Feel appraising model.
Step 9: calculating the normalized differential vegetation index of sea-buckthorn distributed areas（NDVI）
On the sea-buckthorn spatial distribution map that above-mentioned steps three describe that method obtains, according to 1.5 meters of resolution ratio fusion evaluations of SPOT-6
Multi light spectrum hands reflectivity calculates the normalization that the red wave band in sea-buckthorn distributed areas is combined near infrared band reflectivity by pixel and refers to
Number.
Step 10: sea-buckthorn fruit output remote sensing appraising
The red wave band that the sea-buckthorn distributed areas of method calculating are described according to above-mentioned steps nine is combined near infrared band reflectivity
Normalized differential vegetation index, and the eight fructus hippophae yield remote sensing appraising model determined through the above steps, sea-buckthorn point is calculated by pixel
The sea-buckthorn fruit per unit area yield in cloth region, further calculates the sea-buckthorn fruit output for obtaining sea-buckthorn distributed areas.
Claims (2)
1. a kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method, it is characterised in that：The Accurate Estimation Method is by following step
Suddenly it carries out：
1）Area's remote sensing image is studied to order and pre-process
It is panchromatic with 6 meters of spatial resolution multi light spectrum hands that research 1.5 meters of spatial resolutions of area SPOT-6/7 in October are ordered in programming
Image data merges 1.5 meters of resolution panchromatic images of SPOT-6/7 with 6 meters of resolution multi-spectral images, generates 1.5
Rice resolution multi-spectral fusion evaluation；
2）Choose to research area's sea-buckthorn sample
Binding area site type chooses n（≥30）The sample of a m2, it is ensured that it is uniformly distributed in these samples research area, and
Sea-buckthorn vegetation growing way is uniform in each sample ground, while recording land occupation condition, Community characteristics and plant size, is surveyed by high-precision GPS
Determine and record the geographical coordinate on sample ground；
3）Area's sea-buckthorn spatial distribution data is studied to obtain
According to above-mentioned steps 2）The research area sea-buckthorn sample of selection, to above-mentioned steps 1）1.5 meters of resolution ratio of SPOT-6/7 of generation
Multi-spectral image fusion image interpretation determines research area sea-buckthorn in terms of spectral signature, textural characteristics, shape feature and distribution characteristics
Interpretation mark on 1.5 meters of resolution multi-spectral fusion evaluations of SPOT-6/7；Indicated based on interpretation, using object-oriented point
Class method extracts the Current distribution of research area sea-buckthorn on 1.5 meters of resolution multi-spectral fusion evaluations of SPOT-6/7, generates research area
Sea-buckthorn spatial distribution map；
4）Area's fruit per unit area yield is studied to investigate with leaf area index
In above-mentioned steps 2）The sample of selection ground central area, lays 1 m sample prescription of 5 m × 5, carries out fruit per unit area yield and leaf area
Index is investigated, and is measured by high-precision GPS and is recorded sample prescription latitude and longitude information；Leaf area index is measured using plant canopy point
Analyzer carries out, and measures 5 times, takes respectively in each sample prescription center Fen Dong-west, north-south, northeast-southwest, the orientation of northwest-southeast 4
Leaf area index of its average value as the sample prescription；After leaf area index measures, according to professional standard, the whole in sample prescription is harvested
Sea-buckthorn fruit, scene claim its fresh weight, the fruit per unit area yield of sample prescription are calculated；
5）Calculate the normalized differential vegetation index of sample prescription
According to above-mentioned steps 4）The sample prescription latitude and longitude information of middle determination, in above-mentioned steps 1）1.5 meters points of the SPOT-6/7 of middle generation
It selects the pixel being fully located in sample prescription to correspond to pixel as sample prescription in resolution Multi-spectral image fusion image, extracts the red of corresponding pixel
Wave band and near infrared band reflectivity, calculate the normalized differential vegetation index of two wave band reflectivity combination；
6）Determine research area's sea-buckthorn leaf area index remote sensing appraising model
Using unitary linear analysis to above-mentioned steps 4）The corresponding above-mentioned steps 5 of the sample prescription sea-buckthorn leaf area index of investigation）
In relationship between the normalized differential vegetation index that is calculated carry out regression fit, determine research area's sea-buckthorn leaf area index remote sensing
Appraising model；
7）Determine the regression model between research area's sea-buckthorn fruit per unit area yield and leaf area index
Using unitary linear analysis to above-mentioned steps 4）The corresponding sample prescription fructus hippophae of the sample prescription sea-buckthorn leaf area index of investigation
Relationship between product per unit area yield carries out regression fit, determines the recurrence mould between research area's sea-buckthorn fruit per unit area yield and leaf area index
Type；
8）Determine research area's sea-buckthorn fruit yield remote sensing appraising model
According to above-mentioned steps 6）Determining research area sea-buckthorn leaf area index remote sensing appraising model and above-mentioned steps 7）Determining
Regression model between sea-buckthorn fruit per unit area yield and leaf area index establishes the research area sea-buckthorn fruit based on normalized differential vegetation index
The remote sensing appraising model of per unit area yield；
9）Calculate the normalized differential vegetation index of research area sea-buckthorn distributed areas
In above-mentioned steps 3）On the research area sea-buckthorn spatial distribution map that description method obtains, according to 1.5 meters of resolution ratio of SPOT-6/7
Fusion evaluation multi light spectrum hands reflectivity calculates the research red wave band in area sea-buckthorn distributed areas and near infrared band reflectivity by pixel
The normalization index of combination；
10）Study area's sea-buckthorn fruit output remote sensing appraising
According to above-mentioned steps 9）The red wave band and near infrared band reflectivity for the research area sea-buckthorn distributed areas that description method calculates
The normalized differential vegetation index of combination, and through the above steps 8）Determining research area sea-buckthorn fruit yield remote sensing appraising model, by
Pixel calculates the sea-buckthorn fruit per unit area yield of research area sea-buckthorn distributed areas, further calculates to obtain the sand of research area sea-buckthorn distributed areas
Spine fruit output.
2. sea-buckthorn fruit output remote sensing Accurate Estimation Method as described in claim 1, it is characterised in that：The multi light spectrum hands
It is blue spectrum wave band, green light spectrum wave band, red spectral wave band and near infrared spectrum wave band.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810559473.3A CN108764188A (en) | 2018-06-02 | 2018-06-02 | A kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810559473.3A CN108764188A (en) | 2018-06-02 | 2018-06-02 | A kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108764188A true CN108764188A (en) | 2018-11-06 |
Family
ID=64002199
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810559473.3A Pending CN108764188A (en) | 2018-06-02 | 2018-06-02 | A kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108764188A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110188955A (en) * | 2019-05-31 | 2019-08-30 | 西南大学 | A kind of large scale autumn grain crops yield evaluation method based on chlorophyll fluorescence remote sensing |
| CN110736710A (en) * | 2019-11-07 | 2020-01-31 | 航天信德智图（北京）科技有限公司 | corn yield evaluation method based on NDVI time sequence |
| CN113449984A (en) * | 2021-06-25 | 2021-09-28 | 中国水利水电科学研究院 | Reed resource quantity evaluation method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107145728A (en) * | 2017-04-27 | 2017-09-08 | 太原理工大学 | A kind of desert steppe non-acoustic detection withered grass weight remote sensing estimation method |
| CN107527364A (en) * | 2017-07-25 | 2017-12-29 | 浙江大学 | A kind of seaweed growing area monitoring method based on remote sensing images and lace curtaining information |
| CN107831168A (en) * | 2017-10-25 | 2018-03-23 | 吉林省林业科学研究院 | The method that remote sensing technology measures paddy field shelter-forest protection effect |
-
2018
- 2018-06-02 CN CN201810559473.3A patent/CN108764188A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107145728A (en) * | 2017-04-27 | 2017-09-08 | 太原理工大学 | A kind of desert steppe non-acoustic detection withered grass weight remote sensing estimation method |
| CN107527364A (en) * | 2017-07-25 | 2017-12-29 | 浙江大学 | A kind of seaweed growing area monitoring method based on remote sensing images and lace curtaining information |
| CN107831168A (en) * | 2017-10-25 | 2018-03-23 | 吉林省林业科学研究院 | The method that remote sensing technology measures paddy field shelter-forest protection effect |
Non-Patent Citations (2)
| Title |
|---|
| 何亚娟等: "基于SPOT遥感数据的甘蔗叶面积指数反演和产量估算", 《农业机械学报》 * |
| 李章成等: "基于SPOT5 影像分析植被指数与水稻叶面积指数和产量的相关性", 《江苏农业科学》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110188955A (en) * | 2019-05-31 | 2019-08-30 | 西南大学 | A kind of large scale autumn grain crops yield evaluation method based on chlorophyll fluorescence remote sensing |
| CN110188955B (en) * | 2019-05-31 | 2023-05-12 | 西南大学 | Chlorophyll fluorescence remote sensing-based large-scale autumn grain yield estimation method |
| CN110736710A (en) * | 2019-11-07 | 2020-01-31 | 航天信德智图（北京）科技有限公司 | corn yield evaluation method based on NDVI time sequence |
| CN110736710B (en) * | 2019-11-07 | 2022-12-09 | 航天信德智图（北京）科技有限公司 | NDVI time sequence-based corn yield evaluation method |
| CN113449984A (en) * | 2021-06-25 | 2021-09-28 | 中国水利水电科学研究院 | Reed resource quantity evaluation method |
| CN113449984B (en) * | 2021-06-25 | 2023-12-29 | 中国水利水电科学研究院 | Reed resource quantity assessment method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111598019B (en) | Crop type and planting mode identification method based on multi-source remote sensing data | |
| Hall et al. | Optical remote sensing applications in viticulture‐a review | |
| CN105740759B (en) | Semilate rice information decision tree classification approach based on feature extraction in multi-temporal data | |
| CN108764255B (en) | Method for extracting winter wheat planting information | |
| CN102393238B (en) | Grassland dry matter mass remote sensing estimating method | |
| CN111368736B (en) | Rice refined estimation method based on SAR and optical remote sensing data | |
| Kundu et al. | Monitoring desertification risk through climate change and human interference using remote sensing and GIS techniques | |
| CN106092915B (en) | A kind of meadowbrook plant and withered plant weight ratio remote sensing estimation method | |
| CN104614321B (en) | A kind of crop growing state method of real-time based on spectrum picture | |
| CN112287892B (en) | Arbor biomass measurement and calculation method based on unmanned aerial vehicle hyperspectral and machine learning algorithm | |
| CN110927082A (en) | Winter wheat yield prediction method based on unmanned aerial vehicle imaging hyperspectral remote sensing | |
| CN108764188A (en) | A kind of sea-buckthorn fruit output remote sensing Accurate Estimation Method | |
| CN109754127A (en) | Rice grain amylose content estimating and measuring method based on unmanned plane imaging EO-1 hyperion | |
| Shahabfar et al. | Agricultural drought monitoring in semi-arid and arid areas using MODIS data | |
| CN109427091A (en) | A kind of Biomass Models based on high-spectrum remote-sensing and photogrammetric technology grind construction method | |
| CN107328741A (en) | Vegetative coverage and object element C improved methods in soil erosion equation | |
| CN109903234A (en) | A kind of city thermal landscape quantitative description and Analysis On Multi-scale Features analysis method | |
| CN107632967A (en) | A kind of meadow grass yield evaluation method | |
| Dong et al. | Using RapidEye imagery to identify within-field variability of crop growth and yield in Ontario, Canada | |
| Mokarram et al. | RELATIONSHIP BETWEEN LAND COVER AND VEGETATION INDICES. CASE STUDY: EGHLID PLAIN, FARS PROVINCE, IRAN. | |
| Anna | Detection of vegetation degradation on Swedish mountainous heaths at an early stage by image interpretation | |
| Wittamperuma et al. | Remote-sensing-based biophysical models for estimating LAI of irrigated crops in Murry darling basin | |
| CN112052720B (en) | High-space-time normalization vegetation index NDVI fusion model based on histogram clustering | |
| Tiwari et al. | In-season crop-area mapping for wheat and rice in Afghanistan and Bangladesh | |
| CN115019205B (en) | Rape flowering phase SPAD and LAI estimation method based on unmanned aerial vehicle multispectral image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181106 |