CN113065464A - Forest remote sensing information change probability model - Google Patents
Forest remote sensing information change probability model Download PDFInfo
- Publication number
- CN113065464A CN113065464A CN202110353460.2A CN202110353460A CN113065464A CN 113065464 A CN113065464 A CN 113065464A CN 202110353460 A CN202110353460 A CN 202110353460A CN 113065464 A CN113065464 A CN 113065464A
- Authority
- CN
- China
- Prior art keywords
- gvi
- image
- value
- forest
- images
- 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.)
- Granted
Links
- 230000008859 change Effects 0.000 title claims abstract description 34
- 238000010606 normalization Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 5
- 230000000873 masking effect Effects 0.000 claims abstract description 4
- 238000013507 mapping Methods 0.000 claims description 12
- 230000009466 transformation Effects 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000001131 transforming effect Effects 0.000 claims description 3
- 238000013519 translation Methods 0.000 claims description 3
- 230000015556 catabolic process Effects 0.000 abstract description 5
- 238000006731 degradation reaction Methods 0.000 abstract description 5
- 238000012544 monitoring process Methods 0.000 description 6
- 239000002689 soil Substances 0.000 description 5
- 238000012805 post-processing Methods 0.000 description 4
- 238000001914 filtration Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000005059 dormancy Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 238000002407 reforming Methods 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
Images
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
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a forest remote sensing information change probability model, which is described by using an GVI difference image histogram, wherein the GVI difference image histogram is established by the following steps: firstly, carrying out two-stage TM image accurate registration; establishing a mask image of a forest area; masking the GVI image; obtaining forest region images, and then respectively obtaining GVI images; and if the difference between the phenological periods of the two images is large, performing phenological normalization processing, otherwise, directly calculating the forest change probability, and segmenting the probability image according to a preset threshold value. The forest remote sensing information change probability model can describe the change probability of the forest by using normal distribution. With the implementation of six major projects of forestry in China, the serious forest degradation phenomenon is less and less, the condition that the changed area only occupies a small part in the whole scene image is met, and therefore the GVI difference value is ensured to obey the normal distribution rule.
Description
Technical Field
The invention relates to the technical field of forest remote sensing, in particular to a forest remote sensing information change probability model.
Background
The forest area in the north of China belongs to temperate zone, is clear in four seasons, has large change of phenological condition, and has particularly obvious change of deciduous broad-leaved trees. The growing season of deciduous and broad-leaved trees is the time interval from the beginning of growth to the end of growth to dormancy in a year. Because the growing seasons of forest regions are greatly different, the growing seasons of a specific place can be determined according to plant phenology and remote sensing data. Although the phenological changes are not completely consistent from year to year due to global warming and some incidental factors, we still assume that the phenological period averaged over many years is stable.
Remote sensing data for extracting forest changes must be acquired during the growing season. However, in actual production, even if data obtained in the same growing season is used, imaging in the same phenological period is difficult to guarantee, and data difference of different phenological periods is still large, so that the two are required to be subjected to phenological normalization processing, areas with small changes are suppressed, and areas with large changes are highlighted.
The most direct result of forest changes is a change in the forest Leaf Area Index (LAI), which drops dramatically as the forest degrades. While the LAI cannot directly correspond to a certain band of the multi-band remote sensing image. This is because any one of the bands of the TM remote sensing image is not a function of the reflected radiance of the single pair of vegetation leaves in that band, but is a multivariate function of the band of a complex non-lambertian system of vegetation-soil. The Vegetation Index (VI) of the data of each wave band is a monotonous function of the LAI, and is more stable and reliable than a single wave band value.
The ideal vegetation index should not only reflect the vegetation information sufficiently, but also avoid the influence of the soil background. The general normalized vegetation index (NDVI is low in saturation value (<3) to LAI, although vegetation information can be reflected well under sparse vegetation conditions, the influence of soil background is large.) the greenness coverage index (greenness coverage index) GVI is a vertical vegetation index in a multi-dimensional system, and is a second principal component value in K-T transformation (Thyshat transformation) of a multi-dimensional reflectivity factor of a vegetation-soil system. GVI is characterized in that the dispersion degree of an experimental relation of the LAI is small, the saturated LAI value is high (6-8) and the influence of the soil background is small, so GVI is selected as a description index of forest quality states to be suitable, and a forest remote sensing information change probability model is provided.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a forest remote sensing information change probability model, which is described by using an GVI difference image histogram, wherein the GVI difference image histogram is established by the following steps:
firstly, accurately registering two-stage TM images;
step two, establishing a mask image of the forest area;
thirdly, masking the GVI image;
step four, obtaining forest area images, and then respectively obtaining GVI images;
and fifthly, if the difference between the phenological periods of the two images is large, phenological normalization processing is required, otherwise, forest change probability is directly calculated, and the probability images are segmented according to a preset threshold value.
Preferably, the mean values of two populations in the invariant in the change of the objective and the climate are respectively assumed to be
And
mean value of the whole image
And the proportions of the two populations in the whole image are respectively f and 1-f, according to the statistical theory, the average value of the normal distribution can be proved to be a one-dimensional variable, and the average value can be linearly combined, so that the following formula can be obtained:
obtained by transforming the formula (1):
for images of different periods, the mean values of the two populations are different, so it is assumed that the mean values of the two populations in the images of the two periods, and the mean value of the whole image are respectively
The formula (2) can be used for obtaining:
comparing formula (3) with formula (4) above, obtaining:
namely, it is
For two determined phenological periods in a certain area
Is determined, therefore
Is invariant independent of the two overall ratios f, although precisely mastered
It is difficult to assume that the conditions are assumed
Is the peak point of the image histogram, and the mean value of the whole image
Can be obtained exactly, so that
When the invariant of GVI images in different phenological periods is defined as the distance ratio T between the peak point and the average point of the GVI image histogram in two periods, the phenological normalization becomes based on the mean value of the whole image
And T histogram reformation of the image, if known for each phenological phase throughout the growing season
And T values, GVI images can be normalized for any two phenological stages throughout the growing season.
Preferably, it is provided that: gray value of peak value x1Mean value of gray scale of
At x1Has a distribution frequency of H (x)1) The distance between the gray value of the peak and the average value of the gray values is
Let the mean of another phenological period GVI image be
Gray value of peak value is x'1Assuming that the variation of the known gray level mean is
The ratio of the gray value of the peak value in the two periods to the gray mean value distance is T, namely:
if the difference of the two general transformation is not considered, only the variation of the mean value of the two-period data is considered, namely
The histogram transformation function for the two-phase GVI image is simply a translation of the slope 1 line OA in the y-direction with an intercept of 1
However, considering the difference between the two populations in the climate change, although the transformation of a single population is also linear mapping relation, the integrated result of the two populations becomes a nonlinear process which cannot be directly expressed by linear mapping, so a nonlinear mapping function must be selected, here, a piecewise linear mapping function is selected, and the process of determining the function is divided into two steps: first, the segmentation points and the slopes of the segments are determined, where the gray value x of the peak is selected1As the x coordinate of the segment point, the y coordinate value of the segment point is equal to the x coordinate of the point plus the distance change Δ x between the peak value and the mean value, i.e. the distance between the point B and the point C is Δ x, thereby determining the slope of the OB and BA line segments, which is obtained by equation (6):
if the change in the overall average value is not considered first, the equation can be transformed into:
then the following results are obtained:
then:
compared with the prior art, the invention has the following beneficial effects: the forest remote sensing information change probability model can describe the change probability of the forest by using normal distribution. With the implementation of six major projects of forestry in China, the serious forest degradation phenomenon is less and less, the condition that the changed area only occupies a small part in the whole scene image is met, and therefore the GVI difference value is ensured to obey the normal distribution rule. We define that the probability of forest change for a certain pixel is the cumulative probability of all pixels being greater than its GVI difference.
Drawings
FIG. 1 is a flow chart of forest change information extraction according to the present invention;
FIG. 2 is a histogram A of an GVI image of different phenological phases according to the invention;
FIG. 3 is a histogram B of an GVI image of different phenological phases according to the invention;
FIG. 4 is a histogram C of an image of different phenological phases GVI according to the invention;
FIG. 5 is a histogram D of an GVI image of different phenological phases according to the invention;
FIG. 6 is a schematic diagram of a histogram reforming function according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-6, the forest remote sensing information change probability model is described by using GVI difference image histogram, and the building of GVI difference image histogram includes the following steps:
firstly, accurately registering two-stage TM images;
step two, establishing a mask image of the forest area;
thirdly, masking the GVI image;
step four, obtaining forest area images, and then respectively obtaining GVI images;
and fifthly, if the difference between the phenological periods of the two images is large, phenological normalization processing is required, otherwise, forest change probability is directly calculated, and the probability images are segmented according to a preset threshold value.
Preferably, the mean values of two populations in the invariant in the change of the objective and the climate are respectively assumed to be
And
mean value of the whole image
And the proportions of the two populations in the whole image are respectively f and 1-f, according to the statistical theory, the average value of the normal distribution can be proved to be a one-dimensional variable, and the average value can be linearly combined, so that the following formula can be obtained:
obtained by transforming the formula (1):
for images of different periods, the mean values of the two populations are different, so it is assumed that the mean values of the two populations in the images of the two periods, and the mean value of the whole image are respectively
The formula (2) can be used for obtaining:
comparing formula (3) with formula (4) above, obtaining:
namely, it is
For two determined phenological periods in a certain area
Is determined, therefore
Is invariant independent of the two overall ratios f, although precisely mastered
It is difficult to assume that the conditions are assumed
Is the peak point of the image histogram, and the mean value of the whole image
Can be obtained exactly, so that
When the invariant of GVI images in different phenological periods is defined as the distance ratio T between the peak point and the average point of the GVI image histogram in two periods, the phenological normalization becomes according to the wholeImage mean
And T histogram reformation of the image, if known for each phenological phase throughout the growing season
And T values, GVI images can be normalized for any two phenological stages throughout the growing season.
Setting: gray value of peak value x1Mean value of gray scale of
At x1Has a distribution frequency of H (x)1) The distance between the gray value of the peak and the average value of the gray values is
Let the mean of another phenological period GVI image be
Gray value of peak value x1' assuming that the variation of the mean value of the gray levels is known to be
The ratio of the gray value of the peak value in the two periods to the gray mean value distance is T, namely:
if the difference of the two general transformation is not considered, only the variation of the mean value of the two-period data is considered, namely
The histogram transformation function for the two-phase GVI image is simply a translation of the slope 1 line OA in the y-direction with an intercept of 1
But consider two populationsIn the difference of the climate change, although the transformation of a single population is still linear mapping relation, the integrated result of two population transformations becomes a nonlinear process which cannot be directly expressed by linear mapping, so a nonlinear mapping function must be selected, a piecewise linear mapping function is selected, and the process of determining the function is divided into two steps: first, the segmentation points and the slopes of the segments are determined, where the gray value x of the peak is selected1As the x coordinate of the segment point, the y coordinate value of the segment point is equal to the x coordinate of the point plus the distance change Δ x between the peak value and the mean value, i.e. the distance between the point B and the point C is Δ x, thereby determining the slope of the OB and BA line segments, which is obtained by equation (6):
if the change in the overall average value is not considered first, the equation can be transformed into:
then the following results are obtained:
then:
it should be noted that, considering the influence of comprehensive errors such as scale, data registration, ground survey, etc., we perform precision analysis by using the coincidence of more than 50% of the polygon area of the reference data as correct, that is, when the overlapping area of the known polygon and the monitoring result is less than 50%, it is determined as an erroneous classification. Therefore, the spatial position superposition rate (tables 1 and 2) under various post-processing conditions can be obtained and used as the monitoring precision index.
Table 1 shows the statistics and comparison results based on single pixel
When not post-treated by any kind, the accuracy of degradation monitoring is 81.0%.
Table 2 shows the results of statistics and comparisons after 3-by-3 stencil filtering
When subjected to class post-processing of the 3 x 3 template, the degradation monitoring accuracy was 80.1%.
Table 3 shows the results of statistics and comparisons after filtering with 4-by-4 template
The accuracy of degradation monitoring was 79.6% when post-class treatment with the 4 x 4 template was performed.
In short, the class post-processing has little influence on the overall monitoring accuracy, but in the actual production management, the appropriate size template should be selected according to the forest stand condition for class post-processing.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. The forest remote sensing information change probability model is described by utilizing an GVI difference image histogram and is characterized in that: the GVI difference image histogram is established by the following steps:
firstly, accurately registering two-stage TM images;
step two, establishing a mask image of the forest area;
thirdly, masking the GVI image;
step four, obtaining forest area images, and then respectively obtaining GVI images;
and fifthly, if the difference between the phenological periods of the two images is large, phenological normalization processing is required, otherwise, forest change probability is directly calculated, and the probability images are segmented according to a preset threshold value.
2. The forest remote sensing information change probability model of claim 1, characterized in that: suppose that the mean values of two populations in the invariant in the change of the physical and the climate are respectively
And
mean value of the whole image
And the proportions of the two populations in the whole image are respectively f and 1-f, according to the statistical theory, the average value of the normal distribution can be proved to be a one-dimensional variable, and the average value can be linearly combined, so that the following formula can be obtained:
obtained by transforming the formula (1):
for images of different periods, the mean values of the two populations are different, so it is assumed that the mean values of the two populations in the images of the two periods, and the mean value of the whole image are respectively
The formula (2) can be used for obtaining:
comparing formula (3) with formula (4) above, obtaining:
namely, it is
For two determined phenological periods in a certain area
Is determined, therefore
Is invariant independent of the two overall ratios f, although precisely mastered
It is difficult to assume that the conditions are assumed
Is the peak point of the image histogram, and the mean value of the whole image
Can be obtained exactly, so that
When the invariant of GVI images in different phenological periods is defined as the distance ratio T between the peak point and the average point of the GVI image histogram in two periods, the phenological normalization becomes based on the mean value of the whole image
And T histogram reformation of the image, if known for each phenological phase throughout the growing season
And T values, GVI images can be normalized for any two phenological stages throughout the growing season.
3. The GVI difference image histogram of claim 1, wherein: setting: gray value of peak value x1Mean value of gray scale of
At x1Has a distribution frequency of H (x)1) The distance between the gray value of the peak and the average value of the gray values is
Let the mean of another phenological period GVI image be
Gray value of peak value is x'1Assuming that the variation of the known gray level mean is
The ratio of the gray value of the peak value in the two periods to the gray mean value distance is T, namely:
if the difference of the two general transformation is not considered, only the variation of the mean value of the two-period data is considered, namely
The histogram transformation function for the two-phase GVI image is simply a translation of the slope 1 line OA in the y-direction with an intercept of 1
However, considering the difference between the two populations in the climate change, although the transformation of a single population is also linear mapping relation, the integrated result of the two populations becomes a nonlinear process which cannot be directly expressed by linear mapping, so a nonlinear mapping function must be selected, here, a piecewise linear mapping function is selected, and the process of determining the function is divided into two steps: first, the segmentation points and the slopes of the segments are determined, where the gray value x of the peak is selected1As the x coordinate of the segment point, the y coordinate value of the segment point is equal to the x coordinate of the point plus the distance change Δ x between the peak value and the mean value, i.e. the distance between the point B and the point C is Δ x, thereby determining the slope of the OB and BA line segments, which is obtained by equation (6):
if the change in the overall average value is not considered first, the equation can be transformed into:
then the following results are obtained:
then:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110353460.2A CN113065464B (en) | 2021-03-31 | 2021-03-31 | Forest remote sensing information change probability model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110353460.2A CN113065464B (en) | 2021-03-31 | 2021-03-31 | Forest remote sensing information change probability model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113065464A true CN113065464A (en) | 2021-07-02 |
| CN113065464B CN113065464B (en) | 2024-02-02 |
Family
ID=76565239
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110353460.2A Active CN113065464B (en) | 2021-03-31 | 2021-03-31 | Forest remote sensing information change probability model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113065464B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104851087A (en) * | 2015-04-17 | 2015-08-19 | 华中农业大学 | Multi-scale forest dynamic change monitoring method |
| CN105046188A (en) * | 2015-04-13 | 2015-11-11 | 中南林业科技大学 | MODIS mixed pixels decomposition forest information extraction method |
| CN105718936A (en) * | 2016-02-02 | 2016-06-29 | 福州大学 | Forest dynamic change mode automatic extraction method |
| CN110135322A (en) * | 2019-05-09 | 2019-08-16 | 航天恒星科技有限公司 | A kind of time series forest change monitoring method based on IFI |
-
2021
- 2021-03-31 CN CN202110353460.2A patent/CN113065464B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046188A (en) * | 2015-04-13 | 2015-11-11 | 中南林业科技大学 | MODIS mixed pixels decomposition forest information extraction method |
| CN104851087A (en) * | 2015-04-17 | 2015-08-19 | 华中农业大学 | Multi-scale forest dynamic change monitoring method |
| CN105718936A (en) * | 2016-02-02 | 2016-06-29 | 福州大学 | Forest dynamic change mode automatic extraction method |
| CN110135322A (en) * | 2019-05-09 | 2019-08-16 | 航天恒星科技有限公司 | A kind of time series forest change monitoring method based on IFI |
Non-Patent Citations (5)
| Title |
|---|
| DI LIU 等: "MONITORING THE DYNAMICS OF FOREST AREA IN ANSHAN BASED ON TM IMAGES", 《IEEE》 * |
| 刘谦 等: "东南亚国家森林资源年度遥感监测设计", 《林业资源管理》, no. 3 * |
| 张煜星 等: "全国森林蓄积生物量模型建立和碳变化研究", 《中国科学: 生命科学》, vol. 51, no. 2 * |
| 张煜星 等: "森林面积多阶遥感监测方法", 《林业科学》, vol. 53, no. 7 * |
| 谢馨娴 等: "森林变化检测方法比较", 《四川林业科技》, vol. 39, no. 3 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113065464B (en) | 2024-02-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107292310B (en) | Visual positioning and automatic reading method for circular pointer type dial plate | |
| CN108428220B (en) | Automatic geometric correction method for ocean island reef area of remote sensing image of geostationary orbit satellite sequence | |
| CN107358260B (en) | Multispectral image classification method based on surface wave CNN | |
| CN107564017B (en) | Method for detecting and segmenting urban high-resolution remote sensing image shadow | |
| CN104574347A (en) | On-orbit satellite image geometric positioning accuracy evaluation method on basis of multi-source remote sensing data | |
| CN107688777B (en) | Urban green land extraction method for collaborative multi-source remote sensing image | |
| WO2019049324A1 (en) | Image processing device, image processing method and storage medium | |
| US11151377B2 (en) | Cloud detection method based on landsat 8 snow-containing image | |
| CN103150330A (en) | Local object and block matching-based scanning certificate image search method | |
| CN113409332B (en) | Building plane segmentation method based on three-dimensional point cloud | |
| CN105117701A (en) | Corn crop row skeleton extraction method based on largest square principle | |
| CN113963260A (en) | Extraction method and device for winter wheat planting area and computer equipment | |
| CN104077769A (en) | Error matching point pair removing algorithm in image registration | |
| CN117649451A (en) | Visual positioning method for positioning edge finder wafer | |
| CN116758049A (en) | Urban flood three-dimensional monitoring method based on active and passive satellite remote sensing | |
| CN110472697B (en) | Water body identification method and device based on iterative classification | |
| CN110310263B (en) | SAR image residential area detection method based on significance analysis and background prior | |
| CN114742849A (en) | Leveling instrument distance measuring method based on image enhancement | |
| CN106324708B (en) | Digitizing solution, the device of rainfall record drawing | |
| CN113065464A (en) | Forest remote sensing information change probability model | |
| CN104881670A (en) | Rapid target extraction method used for SAR azimuth estimation | |
| CN116977851A (en) | Abandoned tillage recognition method and device based on change detection | |
| CN116129258A (en) | Citrus land parcel identification method and system based on canopy density and multisource remote sensing image | |
| CN103366376A (en) | Image characteristic extraction method based on neighborhood scale changes | |
| CN114419489A (en) | Training method and device for feature extraction network, terminal equipment and medium |
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 | ||
| CB02 | Change of applicant information |
Address after: 100714 No. 18 Pingli East Street, Dongcheng District, Beijing Applicant after: Forestry and grassland investigation and Planning Institute of the State Forestry and grassland administration Address before: 100714 No. 18 Pingli East Street, Dongcheng District, Beijing Applicant before: INVESTIGATION PLANNING AND DESIGN INSTITUTE OF THE NATIONAL FORESTRY AND GRASSLAND BUILDING |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |